*Article* **Sustainability Trade-Offs in Media Coverage of Poverty Alleviation: A Content-Based Spatiotemporal Analysis in China's Provinces**

**Yuting Sun 1,\* and Shu-Nung Yao 2,\***


**Abstract:** Poverty alleviation has always been fundamental for China to achieve the goal of creating a moderately prosperous society. This study conducted a content-based spatiotemporal analysis of media coverage, regression analysis of panel data, and text mining to examine how China's Targeted Poverty Alleviation (TPA) Strategy is characterised by online mainstream media platform. A total of 10,857 articles related to TPA in 31 specific provinces of mainland China were collected and analysed by Natural Language Processing (NLP) analysis. The results of this study indicated that spatiotemporal distribution of TPA coverage was consistent with the typical logic of the Chinese government in policy implementation based on spatial and social marginalisation. Media attention on TPA is influenced by economic, environmental, and community sustainability indicators, reflecting the sustainability trade-offs in TPA-related media coverage. The keywords embedded in media coverage indicated that agricultural product promotion in extremely impoverished areas and the experiences of economically developed agricultural areas were essential for poverty eradication. Keywords emphasise top-down administrative-led poverty governance for extremely impoverished areas and local autonomy for relatively impoverished areas. This study provides perspectives for antipoverty governance and media empowerment in the postpoverty era in China.

**Keywords:** content-based analysis; media coverage; spatiotemporal distribution; sustainability trade-offs; Targeted Poverty Alleviation Strategy

### **1. Introduction**

Poverty may involve a lack of opportunities, empowerment, or security, malnutrition, or poor health [1]. Definitions of poverty are diverse. Traditional approach views poverty as either low utility or shortfall in primary goods, resources, or income; Capability approach proposed views poverty as the deprivation of basic capabilities from realising their full potential [2–4]. Absolute poverty describes a condition characterised by severe deprivation of basic human needs, which depends not only on income but also on access to services [5]. Relative poverty as a standard is measured in terms of the society in which an individual lives and which therefore differs between countries and over time. Marginalisation is a root cause of poverty in many cases and is synonymous with poverty [6–8]. Poverty is unevenly distributed from the spatial dimension of marginalisation. In rural areas, especially those with a poor ecological environment, a remote geographical location, and inadequate public services and facilities, diverse forms of poverty are evident [9,10]. The social dimension of marginalisation emphasises the importance of the marginalisation process for marginalised groups [11–13]. As a marginalised group, low-income individuals in disadvantaged villages face difficulties participating in various aspects of life.

Both developing and developed countries today are experiencing poverty in different forms and to varying degrees. Much of the research on poverty in developed countries tends to predominantly focus on urban poverty and relative poverty [14], while studies

**Citation:** Sun, Y.; Yao, S.-N. Sustainability Trade-Offs in Media Coverage of Poverty Alleviation: A Content-Based Spatiotemporal Analysis in China's Provinces. *Sustainability* **2022**, *14*, 10058. https://doi.org/10.3390/su141610058

Academic Editor: Aaron K. Hoshide

Received: 29 June 2022 Accepted: 12 August 2022 Published: 14 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

on poverty in developing countries are mostly restricted to rural poverty and extreme poverty [15]. This is because the majority of the population of developing countries live in the rural areas, and they have less access to the socio-economic and infrastructural facilities than their urban counterparts [16]. Poverty governance is essentially a multifaceted process of using political power, exercising political authority, mobilizing political resources, running political institutions, and gaining political legitimacy [17]. In developed countries, poverty reduction is regarded as a poverty governance model. Developing countries, due to potentially weak political and administrative areas of governance hardly achieved sustainable rapid growth for a long time in reducing poverty [18–20]. China has a large poor population and has contributed to most of the world poverty reduction [21]. It is essential to examine poverty governance in China due to its diversity.

Poverty reduction related to human prosperity is one goal of sustainable development [22]. Sustainable development is a balance between economic, social, and ecological goals [23]. Previous evidence shows that, over the past two centuries, economic development has resulted in a sharp decrease in absolute poverty worldwide [24]. Poverty alleviation faces trade-offs among economic, environmental, and community/social sustainability. Especially in China, due to the long-standing urban-rural dual system, rural multidimensional poverty contributed 80% of the national multidimensional poverty. Poverty mainly occurs in rural areas with a very large peasant population of rural hukou [25]. To eliminate rural poverty and realise rural revitalisation, poverty reduction must be adapted to the social and environmental conditions in different regions of China, and the social process of poverty alleviation must be adjusted to and embedded in local areas. Consequently, it is essential to address poverty alleviation trade-offs among sustainability dimensions and ensure the balanced and full development between urban and rural areas.

Poverty alleviation is a vital goal for China to create a moderately prosperous society. Since the 1980s, the Chinese government has been committed to poverty reduction [26]. In 2013, the Targeted Poverty Alleviation (TPA) Strategy was set forth by Chinese President Xi Jinping as a departure from previous poverty alleviation strategies; he sought to move from the transfusion type of poverty alleviation to haematopoietic poverty alleviation [26]. Since then, poverty alleviation measures have been applied with the aim of establishing of a long-term system for reducing poverty. After eight years of continual effort, China finally eradicated absolute poverty in 2020 [27].

Mass media is considered as a major force against poverty [28]. Some research has examined the media role influencing policymakers to set the agenda for poverty alleviation programs and the effect of the influence of media coverage of poverty alleviation programmes on the people [29]. With the development of Internet technologies and horizontal and vertical strategic advancements in the field of media integration, diversified-level mainstream news sources gathered in online media platforms. Understanding TPA-related spatial distributions and temporal trends and identifying the logic of online media related to TPA are paramount for sustainable long-term poverty eradication. Therefore, a discussion of online mainstream media coverage in this crucial stage of poverty alleviation is essential, and this discussion is expected to inspire future media coverage strategies in the postpoverty era.

Previous research employed text mining approach to examine policy documents of the poverty reduction strategy. Smith-Carrier and Lawlor [30] used corpus linguistics and critical discourse analysis to study a poverty reduction strategy implemented in Ontario, Canada. Some researchers examined specific TPA programs of China such as projects on power generation [31–33] from policy documents and e-commerce poverty alleviation from social media platform [34]. These studies mainly focused on policy texts and few studies have adopted text mining to examine how China's TPA Strategy is characterised by online media. Furthermore, when considering the amount of media coverage and meaning of media content as assessments of media attention, few studies have focused on sustainability trade-offs of media attention on TPA and how China's TPA Strategy is being characterised by the media.

Based on above research gaps, in this study, content analysis was initially applied to investigate the spatial and temporal distribution of TPA-related coverage. Then, according to economic, environmental, and social sustainability, a panel data econometrics regression model with the above three dimensions of sustainability and sum of TPA-related coverage on mainland China's provinces from 2017 to 2020 was established to explore the sustainability trade-offs in TPA-related media attention. Finally, a text mining approach was employed to explore keywords in the media coverage, and the mechanism through which TPA is represented by the media. The research questions investigated in this study are described as follows: (1) What dynamic patterns of temporal and spatial distributions are represented in TPA-related coverage? (2) What indicators of sustainability dimensions affect TPA-related coverage? (3) How are dynamic patterns of temporal and spatial distributions of keywords embedded in media storytelling related to TPA? The results of this study can contribute to a deeper understanding of antipoverty governance in rural China through media empowerment.

#### **2. Literature Review**

#### *2.1. Marginality and Poverty*

Marginality is primarily defined and described using two major conceptual frameworks, namely spatial and societal dimensions [35]. The spatial dimension of marginality is primarily based on physical location and distance from centres of development; such locations lie at the edge of or are poorly integrated into a system [36,37]. This concept is used to gain insights into the influence of physical locations and distance on the livelihoods of individuals and groups. The societal dimension focuses on understanding the underlying causes of exclusion, inequality, social injustice, and the spatial segregation of people by demography, religion, culture, social structure, economics, and politics. Such factors are related to access to resources by individuals and groups [36–42].

The concept of marginality, which is multidimensional and multidisciplinary, generally integrates geographical or physical locations with sociocultural, political, and economic spheres where disadvantaged people struggle to gain access (societal and spatial) to resources and fully participate in social life [35,37,43]. Marginality is a social construct, and social and political forces are regarded as the core determinants of marginality [44]. Marginalisation is a social structure and the result of conscious actions by social actors. When marginalisation becomes a part of rules, it constitutes the condition and medium of individual actions in social structures [45].

Marginalisation is a root cause of poverty in many cases and is synonymous with poverty [6–8]. The main reasons for such difficulties lie in the internal cohesion and local mechanism of marginalised areas. Villagers may reject outsiders and lack trust in foreign poverty alleviation personnel and technology. Moreover, villages lack effective integration mechanisms for different stakeholders. The aforementioned phenomena affect the development and effects of poverty alleviation governance in villages.

Demarginalisation is a process with low and high marginality on two ends of a continuum [46]. Although the development of technical tools helps people in spatially marginalised areas to improve their lives gradually, social difficulties persist [47]. Demarginalisation efforts must be adapted to the environmental and social conditions of different regions, and the social process embedded in local areas must be accordingly adjusted. Poverty alleviation is a poverty governance model in developed countries. Some countries (e.g., Turkey, German, Greece, etc.) established extensive participation and negotiation mechanisms with stakeholders [21]. Japan paid attention to youth education and participation in sustainable development knowledge, as well as promoting increased awareness of, commitment to, and ownership of the agenda [22,48]. Good governance associated with the buzzwords of "participation" and "empowerment" provides a sense of purpose and hope for equality of opportunity in the fast-moving world of development policy [49]. As a consequence, a sustainable poverty alleviation strategy focuses on improving the lives of the poor and vulnerable through strengthening the social dimension of demarginalisation [50].

#### *2.2. News Media's Role in Poverty Alleviation and Discussing Poverty-Related News*

News media are regarded as crucial components in the fight against poverty. They call to action with strong involvement from civil society to policies and programmes, thus setting national agendas and ultimately motivating the implementation of policy-making processes [51]. Therefore, policy debates through media can help democratise policies and increase awareness of poverty alleviation policies, ultimately strengthening the advocacy and demand for improved poverty alleviation programmes. Reports on TPA reports are vital for the promotion of TPA activities. Thus, how media portray the fight against poverty has a considerable effect on the public's and policymakers' expectations of poverty alleviation processes and eventually determines whether and to what extent stakeholders and social actors participate in poverty alleviation.

In an English-language context, previous studies [52–55] centred on news framing of poverty coverage in mass media. This type of coverage mostly focuses on the "consumption of the poor suffering" from the perspective of "the other" [52] rather than on the plight of low-income individuals [53]. Poverty coverage by mass media is mostly attributed at the societal level rather than the personal level; thus, the audience may form the belief that governments should take responsibility to alleviate poverty [54,55]. Although some researchers have employed content analysis to examine policy texts focusing on certain TPA projects in China, such as policy texts on photovoltaic power [32,33], few studies have focused on the media coverage of TPA. Given the importance of rural and national representations for fighting poverty in mainland China, the main aim of this study was to determine if and how online mainstream media give voice to or represent the values of rural Chinese people in discussions of poverty alleviation strategies in mainland China.

#### *2.3. TPA policy and Regional Profiles in a Chinese Context*

China's national poverty reduction programmes have experienced a three-stage evolution: from region-focused targeting (*quyu miaozhun*) during 1986–2000 to village-focused targeting (*zhengcun tuijin*) during 2001–2010 and then to household-focused targeting (*jingzhun daohu*) during 2011–2020 [25]. The TPA Strategy was on the agenda of China's 13th Five-Year Plan for Economic and Social Development [26]. Essentially, TPA involves accurately identifying low-income individuals, accurately allocating poverty alleviation funds, accurately formulating poverty alleviation measures, accurately implementing poverty alleviation strategies in appropriate areas, and accurately assessing poverty alleviation results. The aim of TPA is to alter poverty alleviation methods from being material centric to being region and household centric [56]. Furthermore, TPA policy focused on economic, environmental, and social sustainability. In 2015, ten projects of the TPA were issued including vocational education and training, helping cadres' residency in impoverished villages, microfinance, ex situ poverty alleviation relocation (ESPAR), e-commerce, tourism, photovoltaic power generation, papyrifera planting, entrepreneurship training of rich leaders, and leading enterprises driving poverty alleviation [26]. In addition, to strengthen the community-level poverty alleviation capacity, China's governments at various levels have dispatched human resources to poverty-stricken villages. These targeted poverty alleviation measures are calling for the establishment of a long-term system to better carry out efforts.

As to TPA implemented regions, in 2012, the State Council Leading Group Office of Poverty Alleviation and Development in China identified impoverished counties for poverty alleviation and development. In total, 832 counties across 22 provinces were identified as national-level impoverished counties according to the criteria of farmers' per capita net income and the size of the poor population. The aforementioned poverty-stricken areas are located in ecologically vulnerable zones with poor living conditions, frequent natural disasters, defective economic foundations, poor infrastructure, and insufficient

public services [57]. Before extreme poverty was eradicated by the end of 2020, people experiencing poverty were primarily concentrated in designated poverty-stricken areas, especially in the most impoverished areas of minority provinces, autonomous regions, and remote regions in western China, including several prefectures in Tibet, Xinjiang, Gansu, Sichuan, and Yunnan [57]. After eight years of continual effort, on 23 November 2020, China announced that it had eliminated absolute poverty nationwide by uplifting all of its citizens beyond the absolute poverty line of 2300 RMB per year (2010 constant prices) set in 2012, or less than a dollar per day poverty line [25,27]. In total, 832 impoverished counties, 128,000 impoverished villages, and 98.99 million impoverished people have been lifted out of poverty [27].

#### **3. Research Methodology and Data**

#### *3.1. Content Analysis and Natural Language Processing*

Conventional content analysis can provide an integrative perspective of a text and its related context for researchers to understand social phenomena in a subjective yet scientific manner [58,59]. Content analysis involves the thematic categorisation of words to reveal the content and context of the language used [60]. The frequency of appearance of thematically categorised words can provide an objective means of gauging the salience of certain concepts in a corpus [61]. In this study, content analysis was conducted to examine media trends as well as identify and analyse observable semantic data pertaining to TPA-related articles.

Natural language processing (NLP) involves an automatic analysis of human language and aims to address complexity and multiple connotations. In text mining, NLP is employed to understand data as though a human coder is reading the relevant text [62]. NLP enables the identification of relevant information within a text from a large corpus, which can assist researchers in making large data sets manageable and enhancing the trustworthiness of analysis results [63]. The text mining techniques used in the current study were correspondence analysis and Word2Vector analysis.

Content analysis combined with NLP may help extract meaning from data and enhance the inferences that researchers can make from a given text [63]. Currently, this method has been widely used in web data mining, search engines, geopolitical events, sentiment analysis, and social media content [64–66]. This present study combined content analysis and NLP in qualitative data analysis to provide a deeper understanding of texts.

#### *3.2. Data Collection and Related Analysis*

#### 3.2.1. Media Attention: Poverty Alleviation Coverage

The present study's research sample was obtained from NTV (www.ntv.cn, accessed on 1 January 2021), which is a mainstream media convergence platform including statecontrolled and local news sources concerned with three issues, namely agriculture, rural areas, and farmers, distinguished from social media, bloggers, or independent journalists. The term 'fupin' (poverty alleviation) was used to search for articles about poverty alleviation. The concept of TPA was proposed in 2013, and absolute poverty was eradicated in China in 2020. All included articles were published between July 2017 and December 2020. The article content and related news information were collected using the R crawling package (rvest and httr) to extract textual information [67]. Data collection involved the retrieval of 10,857 articles and related information, including article headings, URLs, publication dates, news sources, and news content. Advanced parsing techniques were used to remove redundant segments (e.g., pictures and short videos) of data that might have biased the results [68]. Furthermore, ethical issues for data mining were considered in relation to individual privacy [69]; hence, neither personal data nor behavioural data were revealed in this study.

3.2.2. Indicators of Sustainability Dimensions Affecting Media Attention

Previous studies indicated that specific indicators quantitatively assess the economic, environmental, and social sustainability [70–73]. Indicators used in economic sustainability assessments includes profitability [70], farm revenues and household income [71], crop yield [72] as well as several inputs and outputs such as farm productivity and technical efficiency [72,73]. Environmental indicators include pesticide use, greenhouse gas emissions, biodiversity, water pollution, soil quality, and land conservation [73,74]. The social dimension is associated with the broader society indicators, such as vitality of rural areas and contribution to local residents [73]. In this study, indicators were selected and constructed from related studies. In addition, researchers also took data accessibility and openness into consideration. Thus, the indicators chosen in this study were those similar or relevant with sustainability dimensions which can be found in the National Bureau of Statistics of China.

Specifically speaking, first, "agricultural gross domestic product (GDP)" represents agricultural productivity; "effective irrigated area" reflects technical efficiency; and "per capita disposable income of rural residents" represents rural household income. These three variables were employed to assess economic sustainability. Second, "pollution control investment" indicates pollution treatment and "affected area of crop" indicates disaster risk resistance capacity. These two variables were used to evaluate environmental dimension. Third, "rural doctors and medical workers" reveals the input of rural community healthcare resources; and "numbers of receiving social relief in rural areas" reveals equity and poverty alleviation efficiency of rural communities. These two variables were selected to measure social dimensions. The list of the variables for the years 2017–2020 are shown in Table 1. Then a panel data approach was selected for the analysis, and annual data for 31 provinces in mainland China were taken from the National Statistical Yearbook 2021 Indicators. For 31 provinces and 4 years, there were 124 observations, which was a suitable number to proceed with quantitative data analysis.


**Table 1.** Sustainability indicators used as variables in regression analysis.

#### *3.3. Category Building of Media Coverage and Intercoder Reliability*

Numerous key steps in content analysis are required to enable valid and reliable inferences to be derived from data [63]. Each coverage should be coded with a region based on which place of TPA experience it reported. Through observation before coding, researchers found place names such as XX province, XX city, XX town and XX township, etc., mostly occurred in news title and news content. As news title and news source are short, region coding of each coverage was easier to identify. Thus, a dictionary of geographical names was firstly constructed. Furthermore, news sources were mainly named by the form of "place + newspaper/media/broadcasting . . . ", and local media always reported local TPA experience. Thus, a local media dictionary was then constructed to detect regions of news sources. Specifically speaking, in the dictionary of geographical names, 31 provincial-level administrative regions in mainland China comprised the largest geographical unit in mainland China. This unit included the geographical names of 22 provinces, 5 autonomous regions, and 4 municipalities directly under the control of the central government. The names of prefecture-level cities were provided in the second unit; district and county names were provided in the third unit; and town names were provided in the fourth and smallest unit. Village names were not included in the dictionary. In the local media dictionary, names of local media were included in the fifth and as a unit to identify the region each coverage belongs to through detecting news sources. If coverage can't be identified from news title and news source, content of coverage can be as judgment. For news content that focuses nationwide TPA experience but not related to a specific place, researchers manually classified it as nationwide/others. Finally, researchers established a table presenting the classification of regional categories. The categorisation scheme applied in this study involved manual coding and a data-driven approach (Tables 2 and 3).


**Table 2.** Categorisation scheme and 2019 agricultural land use in Chinese provinces.

**Note:** Agricultural land includes cultivated land, orchards, forestland, pasture, and other agricultural land [75,76].


**Table 3.** Coding scheme: take Gansu and Jiangsu as two coding examples.

**Note:** As several County-level administrative units in one Prefecture-level city in each Chinese province, we take place names highlighted in bold and italics as sub-leveled examples.

News articles were coded by two doctoral students in mainland China who had sufficient understanding of China's TPA context and were familiar with content analysis approaches. Two coders were trained using a random sample of 20% of all coded articles. When different codes were applied, the two coders held discussions and selected the most suitable code. First, a reliability pretest involving Holsti's formula [77] indicated an average agreement of 0.87 among the classification of regional categories. Subsequently, Krippendorff's alpha [78] yielded an average reliability value of 0.93 among regional categories; this value is considered acceptable.

#### *3.4. Relevant Analyses and Research Process*

First, spatial distribution of media coverage as a proportion of total coverage was calculated by province units from 2017 to 2020. R packages such as Remap, baidumap, and ggplot2 facilitated data extraction and output visualisation. Second, the temporal distribution of media coverage related to different provinces was determined for each month from 2017 to 2020. Term frequency (TF) refers to how often a term appears in the corpus, and inverse document frequency (IDF) decreases the weight of commonly used words and increases the weight of words that are less commonly used in a corpus. The term frequency-inverse document frequency (TF-IDF) statistic is used to measure how important a word is to a document in a corpus [79,80].

An analysis based on TFs can determine whether a data set can capture differences in language over various years. Such an analysis involves determining which words are more or less likely to be used during a certain period by using the log odds ratio; then, the results are assessed as a descriptive base. The number of times each word is used over 2 years is counted, and the log odds ratio for each word is then calculated [80,81]. The formula for the log odds ratio is as follows:

$$\text{Log odds ratio} = \ln\left(\frac{\frac{n+1}{\text{total}+1}}{\left[\frac{n+1}{\text{total}+1}\right] \text{later year}}\right) \tag{1}$$

where n is the number of times that a given word is used in each period, and total indicates the total number of words in each period.

In terms of panel data, the analysis first formulates the generalized functional model in the sense of [82] as media coverage being a function of Agriculture GDP, effective irrigated area, rural residents' income, pollution control investment, affected area of crop, rural doctors and medical workers, and numbers of receiving social relief in rural areas. The model is stated in the following equation:

Media coverage(it) = Intercept(it) + GDP(it) + Effective area(it) + Income(it) + pollution invement(it) + affected area(it) + healthcare resources(it) + social relief(it) (2)

Two indices are included in this equation, t for the time series and i for the crosssections. The dependent variable of this model is media coverage in logarithmic terms. Except for the variable of rural doctors and medical workers, other independent variables are included in logarithmic (log) terms to decrease multicollinearity. In order to avoid pseudo regression, the ADF–Fisher test of the panel unit root test is conducted, and results indicates that all variables were stationary, as the *p*-values were less than 0.05. Then, multicollinearity test and heteroscedasticity test are separately conducted to examine the OLS model. Regarding the multicollinearity test, all variance inflation factor (VIF) values corresponding to the independent variables are below 10, and we could conclude that the model does not have collinearity problems. Regarding the heteroscedasticity test, *p*-value is 0.31 > 0.05 in White test. Thus, the errors have equal variance across the range of the dependent variable and the OLS regression analysis is efficient.

After these preliminary considerations, the approach proposes a panel fixed effects regression (FE), a panel random effects regression (RE), and a panel pool (POOL) regression to be compared for selection. A rule of thumb for the Hausman test indicates that a *p*-value (probability) smaller than 0.05 would indicate the selection of the fixed effects regression model, whereas the opposite would indicate the selection of the random effects regression model. F-test indicates that a *p* < 0.05 would achieve the selection of the fixed effects regression model, whereas the opposite would select the pool effects regression model; BP-test indicates that *p* < 0.05 would select the fixed effects regression model, whereas the opposite would choose the pool effects regression model. In this case, the pool effects regression model should be chosen according to the results of Hausman test, F-test, and BP-test (Table 4).

**Table 4.** Related tests for panel data regression analysis.


#### *3.5. Data Processing*

Several text mining techniques were used to ensure the accuracy of the analysis results. First, the Jieba system was used [83] for the segmentation of Chinese words and sentences in all articles. Second, a simplified Chinese stop words corpus was imported. This corpus excluded high-frequency numbers and letters used in texts in such a manner that would prevent the meaningful interpretation of results. Third, compound words and specific expressions were detected and combined. Fourth, alternative spellings were accounted for, and names with two and more words were combined into one word so that they would not be counted separately. After the aforementioned processing steps, additional stop words such as the time expressions year and hour, directions such as under and above, and other similar terms were manually excluded according to a common interpretation of the Mandarin language [69]. After data cleaning, 6,162,276 words were used for further text analysis. Statistics analysis in this study was conducted in R [83].

#### **4. Results and Discussion 4. Results and Discussion**

*3.5. Data Processing*

#### *4.1. Spatiotemporal Distribution of Media Coverage on Poverty Alleviation 4.1. Spatiotemporal Distribution of Media Coverage on Poverty Alleviation*

*Sustainability* **2022**, *14*, 10058 10 of 27

**Table 4.** Related tests for panel data regression analysis.

Figure 1 shows the spatial distribution of media coverage on TPA as a proportion of total coverage during 2017–2020. In this figure, a darker hue represents a higher media focus on poverty alleviation. Media attention on poverty alleviation is represented by obvious spatial clusters distributed in 31 provinces and cities in China. Figure 1 shows the spatial distribution of media coverage on TPA as a proportion of total coverage during 2017–2020. In this figure, a darker hue represents a higher media focus on poverty alleviation. Media attention on poverty alleviation is represented by ob‐ vious spatial clusters distributed in 31 provinces and cities in China.

**Test Purpose Value Result** Hausman test FE vs. RE χ²(7) = 17.073, *p* = 0.017 FE F‐test FE vs. POOL *F*(30,86) = 1.302, *p* = 0.173 POOL

Breusch‐Pagan test (BP‐test) RE vs. POOL *χ*²(1) = 0.640, *p* = 0.212 POOL

Several text mining techniques were used to ensure the accuracy of the analysis re‐ sults. First, the Jieba system was used [83] for the segmentation of Chinese words and sentences in all articles. Second, a simplified Chinese stop words corpus was imported. This corpus excluded high‐frequency numbers and letters used in texts in such a manner that would prevent the meaningful interpretation of results. Third, compound words and specific expressions were detected and combined. Fourth, alternative spellings were ac‐ counted for, and names with two and more words were combined into one word so that they would not be counted separately. After the aforementioned processing steps, addi‐ tional stop words such as the time expressions year and hour, directions such as under and above, and other similar terms were manually excluded according to a common in‐ terpretation of the Mandarin language [69]. After data cleaning, 6,162,276 words were used for further text analysis. Statistics analysis in this study was conducted in R [83].

**Figure 1.** Spatial distribution of media coverage on TPA as a proportion of total coverage in a given year during 2017–2020. **Figure 1.** Spatial distribution of media coverage on TPA as a proportion of total coverage in a given year during 2017–2020.

Media attention was mainly distributed on the central, northwest, and southwest regions of China and neighbouring provinces. Substantial differences existed between eastern and western regions, and attention was higher in inland areas than in coastal areas. Studies have divided the total land area of mainland China into an eastern part and a western part by using the Hu Huanyong Line, with 96% of the population of China living in the eastern part, which covers 36% of the total land area of China [84]. Rural low-income individuals have been unevenly distributed in China over the past decade, with 16.4% of them living in northwestern regions and 83.6% of them living in southeastern regions [85,86]. This result indicated that provinces closer to or crossing the Hu Huanyong Line, such as Shaanxi, Henan, and Gansu, had high media coverage; provinces far away from the Hu Huanyong Line, such as the eastern provinces of Xinjiang and Tibet, had the lowest media coverage. This finding is consistent with mainland China's poverty distribution.

In terms of dynamic spatial patterns, media coverage of certain regions expanded yearly. Such coverage concentrated on certain provinces in 2017 and then expanded to cover most regions in China by 2020. In line with the yearly progress on poverty alleviation in China, media focus on regional poverty alleviation has undergone a marked shift from impoverished regions to the non-impoverished and those emerging out of poverty and from regional targeted coverage to widespread national coverage.

With regard to the media coverage of specific regions, Henan, which is a province located in central China, received the most coverage each year. The northwestern province Gansu and southwestern provinces such as Guangxi, Sichuan, Guizhou, and Yunnan also received substantial media attention from 2017 to 2020. Media attention on northeast

China declined from 2017 to 2020. Similarly, Shanxi and Inner Mongolia, as central impoverished provinces with 36 and 31 impoverished counties, respectively, received little attention over a long period. In eastern China, Jiangxi and Anhui, which are relatively impoverished compared with other provinces this region, attracted media attention, which increased yearly. *Sustainability* **2022**, *14*, 10058 12 of 27 Bureau of Statistics of China [87]. Furthermore, agriculture is a crucial contributor to pov‐ erty reduction [88,89]. As a large agricultural province and a major source of high‐quality

The results indicate that poverty is dynamic, complex, and multidimensional, and it can have different characteristics in different geographical regions. A lack of natural endowments, poor geographic conditions, and a fragile ecological environment place areas at a major competitive disadvantage [87]. These factors are the main drivers of China's persistent poverty and are reflected in media coverage on poverty alleviation. agricultural products in China, the value that Henan adds to this primary industry is sec‐ ond only to that added by Shandong and Sichuan [87]. According to the average value of "Agricultural gross domestic product (AGDP)" from 2017 to 2020 in Figure. 2, provinces with high agricultural GDP generally received high media attention, while the "Per Capita Disposable Income of rural residents (PCDI)" in these high agricultural GDP provinces is low. As agricultural‐dominated provinces with low levels of economic development are

#### *4.2. Monthly Distribution of News Coverage of Poverty Alleviation* the focus of TPA policies, this result also reveals that media attention on poverty issues is

Figure 2 presents the monthly distribution of provinces' news coverage. In terms of temporal distribution, the second halves of the years 2017, 2018, and 2020 are represented by darker hues than are their first halves, which indicates that media attention was higher in the second halves of these years. Since 17 October 2014 has been designated as National Poverty Alleviation Day; thus, media coverage on poverty alleviation increased every October. TPA activities in the first half of the year were examined at the end of the year. Furthermore, the highest number of poverty-alleviation-related coverage was nationwide, which indicated that the trends of media coverage for TPA closely follow political affairs and important sessions of the Communist Party of China (CPC), such as the 19th National Congress in October 2017, the Third Plenary Session of the 19th Central Committee in January 2018, the Political Bureau Meeting of the CPC Central Committee in July 2018, and the two sessions held in May 2020. After the top-down policy deployment of TPA in late 2017 and 2018, each province had to transform national policy documents into concrete actions for implementation in 2019. When progress in alleviating COVID-19 outbreaks was achieved in March 2020, media attention shifted to the struggle to end poverty. The aforementioned findings indicate that the temporal trends of media coverage were in line with China's TPA mechanism. related to those agricultural provinces with low economic development. Media attention focused first on areas of extreme poverty in the central and western regions of China. Attention was then given to Gansu, Guizhou, Guangxi, Shaanxi, Hebei, Yunnan, and Sichuan. Apart from Hebei and Shaanxi, the other five provinces are cur‐ rently among the seven provinces with the most poverty in China and include national‐ level impoverished counties. The aforementioned results indicate that impoverished re‐ gions and those with and large agrarian populations are factors attracting media attention to TPA. Xinjiang and Ningxia are provinces that have yet to eradicate poverty but lack media attention. Media attention on Ningxia was biased in the first three months of 2020. From 2017 to 2020, provinces and cities such as Shanghai, Beijing, Tianjin, Jiangsu, Zhejiang, Tibet, Guangdong, and Liaoning lacked media coverage for several months. Ti‐ bet was the first area to eradicate poverty among areas with national‐level poverty in De‐ cember 2019. The remaining seven cities and provinces are located in developed areas along the eastern coast [57]. Therefore, media coverage was in line with areas' poverty level and the time at which poverty was eradicated. Particular focus was given to areas that had not eradicated poverty. The more difficult was poverty alleviation, and the longer it took, the more media coverage an area received. Provinces with relatively low poverty levels in eastern China exhibited the lowest media coverage among all the regions.

**Figure 2.** 2017–2020 Heatmap of the monthly distribution of TPA coverage in different provinces**.** Notes: AGDP is the short of "Agricultural gross domestic product (GDP)", the value of AGDP is the average value from 2017 to 2020; PCDI is the short of "Per Capita Disposable Income of Rural resi‐ dents", the value of PCDI in Figure 2 is the average value from 2017 to 2020 [87]. **Figure 2.** 2017–2020 Heatmap of the monthly distribution of TPA coverage in different provinces. Notes: AGDP is the short of "Agricultural gross domestic product (GDP)", the value of AGDP is the average value from 2017 to 2020; PCDI is the short of "Per Capita Disposable Income of Rural residents", the value of PCDI in Figure 2 is the average value from 2017 to 2020 [87].

Of all the provinces, Henan received the most media attention on TPA because Henan is located in the area of three mountains and one beach and has the largest rural population in China, which reached 45.11 million people in 2020 according to the National Bureau of Statistics of China [87]. Furthermore, agriculture is a crucial contributor to poverty reduction [88,89]. As a large agricultural province and a major source of high-quality agricultural products in China, the value that Henan adds to this primary industry is second only to that added by Shandong and Sichuan [87]. According to the average value of "Agricultural gross domestic product (AGDP)" from 2017 to 2020 in Figure 2, provinces with high agricultural GDP generally received high media attention, while the "Per Capita Disposable Income of rural residents (PCDI)" in these high agricultural GDP provinces is low. As agricultural-dominated provinces with low levels of economic development are the focus of TPA policies, this result also reveals that media attention on poverty issues is related to those agricultural provinces with low economic development.

Media attention focused first on areas of extreme poverty in the central and western regions of China. Attention was then given to Gansu, Guizhou, Guangxi, Shaanxi, Hebei, Yunnan, and Sichuan. Apart from Hebei and Shaanxi, the other five provinces are currently among the seven provinces with the most poverty in China and include national-level impoverished counties. The aforementioned results indicate that impoverished regions and those with and large agrarian populations are factors attracting media attention to TPA. Xinjiang and Ningxia are provinces that have yet to eradicate poverty but lack media attention. Media attention on Ningxia was biased in the first three months of 2020.

From 2017 to 2020, provinces and cities such as Shanghai, Beijing, Tianjin, Jiangsu, Zhejiang, Tibet, Guangdong, and Liaoning lacked media coverage for several months. Tibet was the first area to eradicate poverty among areas with national-level poverty in December 2019. The remaining seven cities and provinces are located in developed areas along the eastern coast [57]. Therefore, media coverage was in line with areas' poverty level and the time at which poverty was eradicated. Particular focus was given to areas that had not eradicated poverty. The more difficult was poverty alleviation, and the longer it took, the more media coverage an area received. Provinces with relatively low poverty levels in eastern China exhibited the lowest media coverage among all the regions.

#### *4.3. Indicators Affecting News Coverage of Poverty Alleviation*

Through looking into the results of the pool effects regression model as shown in Table 5, all seven variables significant positively or negatively influenced media coverage which indicates that media attention is related to three dimensions of sustainability. As to the absolute values of the co-efficient among independent variables, TPA-related coverage mainly focused on rural economics sustainability, followed by rural community sustainability, and the least on environmental indicators.

Specifically regarding economic sustainability, "agricultural gross domestic product (GDP)" positively influenced media coverage where a 1% increase in agricultural GDP results in a 0.339% increase in media coverage. The relation between per capita disposable income of rural residents and media coverage was negative, indicating that an increase of 1% in the disposable income of rural residents decreased media coverage by 0.890%. Furthermore, the "effective irrigated area" also negatively influenced media coverage where a 1% increase in "effective irrigated area" decreased media coverage by 0.417%. These results reflected that high media attention on TPA was related to those agricultural provinces with low income and low agricultural irrigation technology.

In terms of environmental sustainability, "pollution treatment" negatively influences media coverage where a 1% increase in pollution results in a 0.167% drop in media coverage. High capital input for pollution treatment is associated with those highly developed areas with serious industrial pollution or dominated by industrialization. "Affected area of crop" positively influences media coverage where a 1% increase in affected crop area boosts media coverage by 0.117%. Combined with our result on economic sustainability, TPA-related

coverage focused more on agriculture than industry, especially those with weak resistance to weather agricultural risk.


**Table 5.** Pool effect regression for sustainability indicators influencing media coverage (2017–2020).

**Notes:** \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001.

Regarding social and community sustainability, "rural doctors and medical workers" positively influenced media coverage and ten thousand medical workers increase rural community media coverage by 0.037%. Social relief played a positive role in media coverage. For every 1% change in the number of those receiving social relief in rural areas, the media coverage positively changed by 0.293%. These results indicated that the role of media in public welfare with reporting is more associated with the input of rural community resources and solving social inequity.

#### *4.4. Analysis of Annual Differences in Keywords Used in Media Reports*

Yearly comparisons of six groups based on keywords' log odds ratios were conducted, and the top 25 keywords are presented in Figure 3. The larger the absolute value of the log odds ratio, the better the keyword can represent the prominent feature of this category. Figure 3 indicated that among the common keywords of the two years, the greater the absolute value of log ratio, the more important it can represent in that year. The closer the log ratio is to 0, the more it represents that the prominence of the word in the two years is basically the same.

Keywords in media coverage between 2017 and 2019 were different, and keywords in 2020 were also different from those in the previous three years. The keywords in 2017 were mainly related to state organs and major national conferences, such as the Ministry of Agriculture, the CPC, the Agricultural Fair, the Central Committee of the CPC, and the 18th and 19th National Congresses of the CPC. The number of keywords related to state organs used in 2017 was 1–3 times higher than that used in 2018, 5–10 times higher than that used in 2019, and over 10 times higher than that used in 2020. The number of keywords related to major national conferences used in 2017 was 1–3 times higher than that used in 2018 and 2.5–4 times higher than that used in 2020. The number of keywords related to major national conferences used in 2017 was not markedly different to that used in 2019. Policy-oriented keywords appeared frequently in 2017, and the coverage of such keywords decreased year on year. Furthermore, compared with the

keywords used in 2018, 2019, and 2020, the keywords used in 2017 focused more on the names of agricultural products, planting, and breeding and animal husbandry. These keywords were combined with keywords such as "exhibition" and "agricultural fair", which indicated that media attention focused on TPA policies and the promotion of TPA-related agricultural products. *Sustainability* **2022**, *14*, 10058 15 of 27

**Figure 3.** 2017–2020 Pairwise comparisons of keyword log ratios in different years. **Figure 3.** 2017–2020 Pairwise comparisons of keyword log ratios in different years.

Keywords related to poverty alleviation actions, such as "audit", "supervisory com‐ mittee", "accountability", and "punishment" were more prominent in 2020 than in 2019. Similarly, keywords such as "corruption", "accountability", "punishment", "violation", "inspection", "discipline", and "director" were more salient in 2018 than in 2020. The aforementioned results indicated that political elites often abused their powerfor personal gain and violated the law, and such activities affected poverty alleviation work. Media attention in 2018 mainly focused on reflections on TPA mechanisms the exposing of re‐ lated problems. Keywords related to natural ecological environment, such as "natural forest", "pro‐ tected land", "environmental sanitation", and "Tibetan area", appeared more in 2019 than in 2018, which indicated the increased focus on the fragility of the natural environment. When combined with media coverage of contiguous areas with substantial poverty, such as the "Three Regions" and "Three Prefectures", which were used in the previous com‐ parison involving the year 2017, media coverage in 2019 was concentrated on the spatial dimensions of ecological fragility and poverty alleviation in marginal areas such as "Ti‐ betan areas" and "minority areas". Compared with the keywords in 2020, those in 2019 were more related to agricultural modernisation and professional management (e.g., "mechanisation," "management right," "property right system," "operation," and "Alibaba e‐commerce"). The combina‐ In 2018, keywords related to vulnerable groups, such as "children", "staying behind", and "disabled individuals" were similar to those in 2017. Keyword similarities were also noted in relation to volunteering (e.g., "teachers", "teams", and "rural departments") and eradicating poverty (e.g., "fight well", "inspect", "engagement", and "workable"). Negative words such as "corruption" appeared in media articles, which indicated that some problems could not be ignored in terms of poverty alleviation. Keywords in 2019 were related to impoverished areas (e.g., "three districts" and "three states"), vulnerable groups (e.g., "the disabled" and "women"), and those assisting with poverty alleviation ("rural department", "team", "committee members", and "teachers"). Moreover, some keywords were related to new digital technologies and tools that can help low-income individuals in agricultural settings (e.g., "harvest of tea garden" and "mushrooms") and improve welfare in human settlements. In 2020, which was the final year in the fight for poverty alleviation, the COVID-19 pandemic became an obstacle to achieving poverty alleviation goals. Words related to the pandemic were the most prominent that year, which reflected the urgency of pandemic alleviation (e.g., "bring it under control"). Keywords such as "revitalisation" and "human settlement" were less prominent; keywords related to the top-down decisions and policies related to economic activities, such as "resumption of work", "resumption of production", and "resumption of normal life", were more prominent. Moreover, phrases such as "decisive battle" and "supervision of war" reflected the determination to persevere in the "war" against the COVID-19 pandemic in China.

tion of the aforementioned keywords with the word "digital" highlighted that compared with the media coverage in 2017, that in 2019 was more focused on alleviating poverty among marginalised groups in impoverished areas by using technology and modern pov‐ erty alleviation approaches. A trend of large‐scale and specialised poverty alleviation measures was identified. In addition to theoretical, organisational, and institutional inno‐ vation [72], technological innovation is another means of poverty alleviation with Chinese characteristics. In contrast to the media coverage in 2018 and 2019, that in 2020 focused on challenges Keywords related to poverty alleviation actions, such as "audit", "supervisory committee", "accountability", and "punishment" were more prominent in 2020 than in 2019. Similarly, keywords such as "corruption", "accountability", "punishment", "violation", "inspection", "discipline", and "director" were more salient in 2018 than in 2020. The aforementioned results indicated that political elites often abused their power for personal gain and violated the law, and such activities affected poverty alleviation work. Media attention in 2018 mainly focused on reflections on TPA mechanisms the exposing of related problems.

to TPA work related to the COVID‐19 pandemic, as indicated by the usage of keywords such as "unsalable," "migrant workers," and "unemployment." The keywords used in 2020 were related to TPA actions, such as "curb the spread of the disease," "resumption Keywords related to natural ecological environment, such as "natural forest", "protected land", "environmental sanitation", and "Tibetan area", appeared more in 2019 than in 2018, which indicated the increased focus on the fragility of the natural environment.

When combined with media coverage of contiguous areas with substantial poverty, such as the "Three Regions" and "Three Prefectures", which were used in the previous comparison involving the year 2017, media coverage in 2019 was concentrated on the spatial dimensions of ecological fragility and poverty alleviation in marginal areas such as "Tibetan areas" and "minority areas".

Compared with the keywords in 2020, those in 2019 were more related to agricultural modernisation and professional management (e.g., "mechanisation", "management right", "property right system", "operation", and "Alibaba e-commerce"). The combination of the aforementioned keywords with the word "digital" highlighted that compared with the media coverage in 2017, that in 2019 was more focused on alleviating poverty among marginalised groups in impoverished areas by using technology and modern poverty alleviation approaches. A trend of large-scale and specialised poverty alleviation measures was identified. In addition to theoretical, organisational, and institutional innovation [72], technological innovation is another means of poverty alleviation with Chinese characteristics.

In contrast to the media coverage in 2018 and 2019, that in 2020 focused on challenges to TPA work related to the COVID-19 pandemic, as indicated by the usage of keywords such as "unsalable", "migrant workers", and "unemployment". The keywords used in 2020 were related to TPA actions, such as "curb the spread of the disease", "resumption of work", "early warning", "water supply", "emergency", and "support farmers and agriculture", and achievement, such as "overcome". Furthermore, discourse on encouragementbased emotions revealed the destiny of communities sparing no effort and uniting social forces to overcome difficulties. Media empowerment is a means of poverty alleviation; it can involve maintaining social stability during the COVID-19 pandemic, stimulating the endogenous self-empowerment of low-income individuals, and promoting changes in cognition and action.

#### *4.5. Regional Differences in Media Coverage Keywords*

Term frequency-inverse document frequency (TF-IDF) measures how relevant a word to a certain category of documents, and it is widely used to extract the most representative words as features for text classification. Figures 4 and 5 display the TF-IDF results by region. The greater TF-IDF, the more representative the keyword is to distinguish the regional categories. Figure 4 shows that in each regional category, keywords with greater TF-IDF values are more representative of the region-based TPA coverage. Most of the top 20 keywords for regional poverty alleviation coverage in Figure 4 were nouns; they were related to the main crops or breeding organisms that are suitable for the local climate, soil, and natural conditions. Moreover, keywords related to the local landscapes, buildings, and customs of southwestern minority areas were salient. Agricultural contacts, business opportunities, and local industries for TPA was promoted in eastern China according to locational advantages. The Maritime Silk Road aided local poverty alleviation efforts. Poverty alleviation through business development was highlighted by the media, especially in relation to local primary industries such as arable and pastoral farming. Media coverage also focused on poverty alleviation in the central and western regions of mainland China as well as developing countries worldwide.

**Figure 4.** Term frequency‐inverse document frequency (TF‐IDF) results by region during 2017–2020**. Figure 4.** Term frequency-inverse document frequency (TF-IDF) results by region during 2017–2020.

Figure 5 shows that in each regional category, keywords of attributes with greater TF-IDF values can better represent the emotional characteristics of the region-based TPA coverage. As indicated in Figure 5, the top 20 keywords related to characteristics and attributes were extracted; they included adjectives, adverbs, verb, adverbs, idioms, phrases, and other modifiers. First, the number of negative words was small, and they were related to descriptions of spatial marginalisation based on geographic disadvantages. Specifically, the words "drought", "barren", "fragile", and "soil erosion" mainly appeared in media coverage of north-eastern areas, north China, and north-western and southwestern regions. Words and phrases such as "live at the mercy of the elements", "make use of local resources", "formidable project", and hardship were prominent in media coverage of north-west and south-west regions, which reflected the harsh environmental conditions in western areas and the hardship of people's lives there. By contrast, numerous words and phrases with rich and diverse meanings, such as "smooth", "prosperous", "rich", "clear", "roomy", "beautiful", "make a difference", "blooming everywhere", "vigorous", "dense" and "progress", indicated that some residents in certain regions had a better

life than did those in other regions. These positive adjectives are in contrast to words associated with geographical disadvantages, which indicated that impoverished areas had been improved through TPA projects. Some positive and inspiring words and phrases, such as "heart to heart", "concerted efforts", "pragmatic", and "constant dropping wears away the stone", were also common in the media coverage of various regions. These words and phrases from positive local stories shared by local media are related to initiative and enthusiasm and emphasise the collectivist spirit of solidarity, reflecting a form of discourse empowerment. *Sustainability* **2022**, *14*, 10058 19 of 27

Some areas lack natural endowments and are characterised by poorliving conditions, giving residents a substantial competitive disadvantage [57,85]. However, the marginali‐ sation of spatial dimensions can also inspire people to pursue subjective initiatives of pov‐ erty alleviation. The core keywords in poverty‐alleviation‐related media coverage were "increasing people's confidence" and "enriching their knowledge," which reflected the collective consensus and wish for a better life after poverty alleviation. "Weak" and "lax" were words used to describe the inaction of local party organisa‐ Some areas lack natural endowments and are characterised by poor living conditions, giving residents a substantial competitive disadvantage [57,85]. However, the marginalisation of spatial dimensions can also inspire people to pursue subjective initiatives of poverty alleviation. The core keywords in poverty-alleviation-related media coverage were "increasing people's confidence" and "enriching their knowledge", which reflected the collective consensus and wish for a better life after poverty alleviation.

tions. These words were common in poverty alleviation coverage related to north, south, and east China. Words and phrases related to work style, such as "mendacious," "incor‐ ruptibility," and "evil forces," were concentrated in central and southern China, which reflected the shortcomings of bureaucratic systems in achieving local poverty alleviation

highlighted in the media coverage of the aforementioned regions. The aforementioned

"Weak" and "lax" were words used to describe the inaction of local party organisations. These words were common in poverty alleviation coverage related to north, south, and east China. Words and phrases related to work style, such as "mendacious", "incorruptibility", and "evil forces", were concentrated in central and southern China, which reflected the shortcomings of bureaucratic systems in achieving local poverty alleviation in these regions. Furthermore, "door to door", "focus", "emphasis", and "refined" were highlighted in the media coverage of the aforementioned regions. The aforementioned results indicated that problems existed in the local grassroot bureaus and targeted nature of TPA work. *Sustainability* **2022**, *14*, 10058 20 of 27 results indicated that problems existed in the local grassroot bureaus and targeted nature of TPA work. In general, in north and west China, poverty alleviation activities were mainly fo‐

In general, in north and west China, poverty alleviation activities were mainly focused on the poverty caused by spatial marginalisation. In central, east, and south China, poverty alleviation work was mainly conducted to overcome the poverty caused by human factors, such as the ineffectiveness of local bureaucrats. Nationwide/other media coverage emphasised that state power can solve problems caused by inappropriate local governance. cused on the poverty caused by spatial marginalisation. In central, east, and south China, poverty alleviation work was mainly conducted to overcome the poverty caused by hu‐ man factors, such as the ineffectiveness of local bureaucrats. Nationwide/other media cov‐ erage emphasised that state power can solve problems caused by inappropriate local gov‐ ernance.

#### *4.6. Differences in Keywords Used in Various Regions 4.6. Differences in Keywords Used in Various Regions*

Figure 6 shows that among the common keywords of the two regions, the greater the absolute value of log odds ratio, the more important the keyword can represent in that region. The closer the log ratio is to 0, the more it represents that the importance of the keyword in both two regions is basically the same. As depicted in Figure 6, compared with the relatively impoverished areas of the nine eastern provinces, higher media coverage, a higher frequency of certain keywords, and more diverse word types were observed for extremely impoverished areas in the 22 central and western provinces. Keywords related to ESPAR, such as "ethnic groups", "highways", and "autonomous regions", were the most prominent keywords in the 22 central and western provinces. The aforementioned results indicate that media coverage keywords in extremely impoverished areas were based on geographic marginalisation. "Farmhouse touring", "tourism poverty alleviation", "model zone", and words related to crops and livestock (e.g., "oil tea", "pepper", "walnut", "beef cattle", "prickly ash", "pig", and "kiwi fruit") as well as e-commerce poverty alleviation and infrastructure construction (e.g., "[road] hardening", "sanitation", and "green water") indicate that a modern poverty alleviation model was embedded in the extremely impoverished areas. Furthermore, the beneficial effects of poverty alleviation were highlighted through quantifiers and phrases such as "10,000 tonnes", "output value", "net income", and "more than 10,000 yuan" as well as words related to low-income groups' interests such as "10,000 households", "average household", and "net income". Figure 6 shows that among the common keywords of the two regions, the greater the absolute value of log odds ratio, the more important the keyword can represent in that region. The closer the log ratio is to 0, the more it represents that the importance of the keyword in both two regions is basically the same. As depicted in Figure 6, compared with the relatively impoverished areas of the nine eastern provinces, higher media cover‐ age, a higher frequency of certain keywords, and more diverse word types were observed for extremely impoverished areas in the 22 central and western provinces. Keywords re‐ lated to ESPAR, such as "ethnic groups," "highways," and "autonomous regions," were the most prominent keywords in the 22 central and western provinces. The aforemen‐ tioned results indicate that media coverage keywords in extremely impoverished areas were based on geographic marginalisation. "Farmhouse touring," "tourism poverty alle‐ viation," "model zone," and words related to crops and livestock (e.g., "oil tea," "pepper," "walnut," "beef cattle," "prickly ash," "pig," and "kiwi fruit") as well as e‐commerce pov‐ erty alleviation and infrastructure construction (e.g., "[road] hardening," "sanitation," and "green water") indicate that a modern poverty alleviation model was embedded in the extremely impoverished areas. Furthermore, the beneficial effects of poverty allevia‐ tion were highlighted through quantifiers and phrases such as "10,000 tonnes," "output value," "net income," and "more than 10,000 yuan" as well as words related to low‐in‐ come groups' interests such as "10,000 households," "average household," and "net in‐ come."

**Figure 6.** Log ratios of keywords by region during 2017–2020**. Figure 6.** Log ratios of keywords by region during 2017–2020.

Terms related to arable farming were more widely embedded in the media coverage related to poverty alleviation in areas with extreme poverty than in the nationwide coverage or media coverage in other regions. Words related to agricultural crops and related products were more prominent in areas with absolute poverty (e.g., "edible fungi", "oil tea", "pepper", "walnut", "fruit", "prickly ash", "grape", "kiwi fruit", and "mushroom") than in other areas. Words and phrases related to leisure and amusement, such as "picking", "farmhouse touring", "bed & breakfast", "orchard", and "tea garden", were connected with people's livelihood (i.e., "enriching the people"), which indicates that for areas with extreme poverty, poverty alleviation emphasised the revitalisation and activation of rural life. The promotion of administrative forces (words such as "county party committee") was the main driving force of poverty alleviation in extremely impoverished areas. Finally, similar to the keywords related to the media coverage of relatively impoverished areas, words related to profit, achievements, and the interests of low-income households highlighted the effectiveness of poverty-alleviation-related governance.

Lower media coverage and fewer types of keywords were noted for the relatively impoverished areas than for the extremely impoverished areas; however, the meanings of the keywords used in the media coverage of the relatively impoverished areas are somewhat flourish. First, in terms of geographical locations and the effects of climatic and environmental factors (e.g., water, soil, and precipitation), the extremely impoverished provinces in the central and western regions were suited to arable farming and animal husbandry. The provinces located in eastern coastal areas were more suited to the development of the aquaculture industry. Second, some words and phrases such as "support", "counterpart support", "cooperation", "pairing", "hand in hand", "co-construct", and "blood transfusion" (to give aid and support) revealed poverty alleviation to be a common social mission involving solidarity that shaped the collective consensus of communities. In contrast with the action of getting rid of poverty in extremely impoverished areas, the poverty alleviation mechanisms in relatively impoverished areas involved prosperity of industry specialisation and urbanisation (e.g., see words and phrases such as "Academy of Agricultural Sciences", "special commissioners", "Neighbour", and "below the basic living allowance").

In contrast to the nationwide coverage or media coverage in other regions, the media coverage in relatively impoverished areas included keywords related to leisure-oriented arable agricultural activities such as "orchards" and "tea gardens". Media attention was concentrated on the south-eastern areas of China, especially Guangdong and Fujian, which indicated that the media paid more attention to the TPA practices of these areas with developed agricultural industries than it did to those of other areas. Media coverage of relatively impoverished areas was related to local practices and assigned developed areas the social responsibility to pursue the values of "sharing" and "great harmony".

Different from media coverage of extremely impoverished areas, international poverty alleviation strategies and domestic industrial poverty alleviation modes, such as manufacture and finance were highlighted in nationwide/other coverage. Examples of words and phrases used in nationwide/other media coverage are "powerful country", "giant", "developing countries", "whole party", "all nations" and "running a country", and examples of words and phrases related to international commerce are "tax reduction", "fee reduction", "exemption", "value-added tax", "import", "open to the outside world", and "equality". Moreover, words related to bureaucracies such as "Ministry of Civil Affairs", "Ministry and Party Group" and "Ministry of Education" as well as words related to officials" statuses and positions such as "Liu Yongfu" and "Director-general" indicated that poverty alleviation measures at the international and national level were dominated by bureaucracies with a top-down approach.

Compared with media coverage of relatively impoverished areas, state-dominated coverage and coverage of extremely impoverished areas were highlighted more in nationwide and other media coverage. By contrast, local autonomy was more important than administration-led activities in relatively impoverished areas. Nationwide/other media coverage paid attention to international assistance, especially among developing countries,

and the coverage of extreme poverty in China was also emphasised. The aforementioned results are consistent with the concept of an "environmentally bundled economic interest", which indicates that China's local governments fulfil the central government's new political mission and satisfy the demand for local economic development [90].

#### **5. Conclusions, Contributions, and Limitations**

This study employed regression analysis of panel data and content analysis with a text mining approach to investigate the spatiotemporal distribution and influence factors of Targeted Poverty Alleviation (TPA)-related coverage, and keywords embedded in media coverage. The results of this study revealed that, first, the media coverage was higher in central inland areas than in southeast coastal areas. Each year, more attention was given to relatively impoverished areas than to extremely impoverished areas in nationwide/other media coverage. Second, regarding temporal characteristics, media coverage on poverty alleviation was high in 2017 and 2018, low and scattered in 2019, and high in 2020. The monthly distribution of media coverage was characterised by a midyear peak and a peak at the end of the year. These patterns conform to the schedule of important political events, such as committee meetings. Third, the temporal distribution of media keywords demonstrated that policy propaganda was highlighted in 2017, diversified group characteristics and problem exposure of local TPA practice were emphasised in 2018, digital and modernised practices were prominent in 2019, and positive actions for addressing the poverty caused by the COVID-19 pandemic were highlighted in 2020.

For the purpose of poverty alleviation, there are several trade-offs between economic, environmental, and community/social sustainability. Factors influencing TPA-related coverage are significantly related to key economic, environmental, and community sustainability indicators we evaluated. Specifically, high media attention on TPA is related to those agricultural provinces with low income, low agricultural technology, and weak risk resistance capacity in agriculture rather than those highly developed areas dominated by industrialisation. Furthermore, media as a social power of poverty alleviation tends to be associated more with the input of community resources and addressing social inequity.

Keywords describing extremely impoverished areas emphasised spatial poverty, the welfare of low-income households, the revitalisation of rural areas, and the effectiveness of poverty governance. Keywords related to relatively impoverished areas focused on local autonomy in developing diversified industries, collective responsibility, and the concept of sharing and harmony in rural China. China's local governments developed an approach based on "environmentally bundled economic interests" that simultaneously fulfils the central government's new political mission and local economic development. Finally, the positive discourse in media coverage indicated that TPA stakeholders pursued subjective initiatives. This result is consistent with the finding of Tsai and Liao [91] that poverty alleviation has been promoted with substantial enthusiasm. Consequently, media coverage was consistent with China's poverty alleviation mechanism. The implementation of TPA policies from top to bottom can be promoted effectively, and campaign-style enforcement can be achieved.

Previous policy following a "pollute first, clean up later" linked to the idea of progressive economic development, which led poverty alleviation only focused on agricultural intensification and progressive economics development but with limited understanding of the impacts on ecological dynamics [92]. Environmental degradation leads to lower agricultural outputs and rural incomes [93]. For example, rice yields declined due to increasing fertilizer application rates [94]. In addition, losses of cultivated land transferred to other land uses due to urbanization in some regions [92]. However, compared with 69.10% of agricultural land use composition in 2008 [75], statistics in 2019 is 72.49%; especially cultivated land had increased from 12.80% to 13.30% [76]. Consequently, combined with the results of media attention and media discourse in this study, environmentally bundled economic interests and social participation of current Targeted Poverty Alleviation creates trade-offs and synergetic relationships, which made implementation of TPA effective.

This study contributes to the poverty governance literature through various means. First, in terms of theory, this study not only extends the importance of the marginality theory of poverty governance but also provides a theoretical marginality-based perspective for understanding poverty governance in media settings. Second, this study is the only one to use text mining to examine the representations of Chinese TPA in online media for providing an integrative perspective of TPA media representation. Third, the results of this study are vital for understanding poverty governance in different regions through dynamic patterns of media coverage and keywords related to TPA, especially in regions where poverty alleviation projects and social capital are seldom combined. The results of this study can provide insight for local governments to assess local poverty scenarios and for the central government in terms of allocating resources to poverty alleviation.

Regarding practical implications, this study can inspire new strategies for alleviating poverty in various regions. First, for extremely impoverished areas, this study revealed that the top-down administrative-led approach was used to overcome spatial poverty; after poverty is eradicated, market-oriented operations and full local autonomy should be learned from eastern areas to establish a long-term mechanism for poverty alleviation. Second, the growth of China's agricultural industry is particularly crucial for poverty alleviation. This industry is four times as effective in reducing poverty as secondary and tertiary industries are [85,95]. In this study, the spatial distribution of media attention was focused on large agricultural provinces with large agrarian populations; these provinces are major bases for high-quality agricultural products in China. The keywords embedded in media coverage indicated that throughout all the study years, agricultural product promotion in extremely impoverished areas and the experiences of economically developed agricultural areas were essential for poverty eradication. As technical efficiency is one assessment of agricultural economic sustainability, accordingly, poverty alleviation projects of the agricultural industry should be combined with modernised technological innovations to achieve poverty alleviation with Chinese characteristics.

This study provides strategies related to antipoverty governance for the media in the postpoverty era. First, for extremely impoverished areas, the media should pay more attention to the process of local TPA by comparing regional differences in media ecology in areas with similar natural endowments or geographical proximity to establish a long-term mechanism for the media's representation of TPA approaches. Second, news media is considered a major force against poverty, keywords in media coverage indicated discourse empowerment with the dual function of increasing people's confidence and enriching their knowledge. This coverage focused on local stories on getting rid of poverty and becoming rich. Thus, media discourse should focus on providing detailed microlevel perspectives on local TPA cases instead of brief descriptions. Positive local stories should be shared by local media as a form of discourse empowerment to inspire low-income households to pursue self-empowerment. Third, media coverage should improve public awareness of TPA sustainability and promote an optimal mechanism established for the agricultural sector and the rural community of impoverished areas.

Inevitably, several limitations hindered this study. First, the term frequency-inverse document frequency (TF-IDF) and log odds ratio were used in this study. These parameters focus on keywords embedded in articles but not on topics. In future research, latent Dirichlet allocation, which is a topic modelling algorithm for extracting topics present in sets of patent documents, will be adopted [96]. Subsequently, the trends of different topics represented in media coverage will be explored. Second, although NTV (www.ntv.cn, accessed on 1 January 2021) as a convergence platform integrates mainstream news sources and news from state-controlled media and local media, the expressions of individuals such as bloggers and opinion leaders are lacking. Prospective studies could enable deeper interpretations than the current format affords by including diverse media voices and conducting different analyses. Finally, the TPA approach was proposed in November 2013; however, news articles in this study covered only the three years before China eradicated

absolute poverty in November 2020. Thus, NTV does not provide sufficient information on the TPA in China over time.

**Author Contributions:** Conceptualisation, Y.S.; methodology, Y.S.; software, Y.S.; validation, Y.S.; formal analysis, Y.S.; investigation, Y.S.; resources, Y.S. and S.-N.Y.; data curation, Y.S.; writing original draft preparation, Y.S. and S.-N.Y.; writing—review and editing, Y.S. and S.-N.Y.; visualisation, Y.S.; project administration, Y.S.; funding acquisition, S.-N.Y. and Y.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Ministry of Science and Technology, Taiwan (grant number 109-2221-E-305-011-MY2), Fundamental Research Funds for the Central Universities, Hohai University, China (grant number B220201066), and University System of Taipei Joint Research Program (grant number USTP-NTPU-TMU-111-02).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding authors.

**Acknowledgments:** Special thanks to Aaron K. Hoshide for his contribution in highly promoting the review process of the earlier version of this article. The authors would also like to thank the anonymous reviewers for their constructive comments.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Structural Evolution and Sustainability of Agricultural Trade between China and Countries along the "Belt and Road"**

**Lunzheng Zhou and Guangji Tong \***

College of Economics and Management, Northeast Forestry University, Harbin 150040, China; 1611886506@nefu.edu.cn

**\*** Correspondence: tonggj63@nefu.edu.cn; Tel.: +86-135-0361-0923

**Abstract:** Enhancing trade in agricultural products between China and countries along the "Belt and Road" (B&R) will help strengthen China's food security and promote global, sustainable economic development. Based on the agricultural trade data between China and B&R countries from 2001 to 2019, we used the TII index, the HHI index, and the social network analysis method to calculate the trade structure of agricultural products between China and B&R countries, in terms of plane structure and spatial network structure, and analyzed the influencing factors of their spatial network structure. The results show that China's agricultural trade with B&R countries is highly concentrated in terms of regions and types, the import trade is decentralized, while the export trade is concentrated, and the regions with high trade intensity are mainly concentrated in the countries in close proximity. China's agricultural trade network with B&R countries has become increasingly close, and China has a significant presence in trade networks. The trade network shows four major segments, and the internal and external trade of each segment has become increasingly close. Water resources, geographical location, transportation, trade agreements, and trade structure are the main influencing factors in the trade network between China and B&R countries. Our findings provide useful insights for informed decision-making in the development of international agricultural sustainable cooperation strategies.

**Keywords:** "Belt and Road"; agriculture products; trade structure; social networks; QAP

#### **1. Introduction**

Food security is the foundation of national development. Under the rigid constraints of natural resources, the establishment of the carbon peaking and carbon neutrality goals has brought new challenges to China's domestic agricultural production. The global spread of COVID-19 [1], the Russia–Ukraine conflict, and climate change have exacerbated agricultural supply risks. How to ensure the sustainable trade of Chinese and global agricultural products in the surging world economic tide has become a major issue that both domestic and foreign practitioners and academics must reconsider.

Currently, the impact of world agricultural trade on global food security has risen from 9% to 17%, and the food exports of many developed agricultural countries have made up for the food shortages of most countries [2]. Integrated planning and the full utilization of both domestic and international markets and resources to enable the sustainable production and consumption of global agricultural products is the only choice for ensuring food security in China and globally [3]. In recent years, the No. 1 document of the Central Government has repeatedly mentioned the strengthening of international cooperation in agriculture in B&R countries, expanding diversified import channels, and expanding the exports of superior agricultural products. In 2022, the No. 1 central document emphasized the optimization of the agricultural product trade layout, and the diversification strategy of agricultural product import. At present, the focus of China's agricultural imports has shifted to B&R countries [4], but to achieve coordinated and sustainable development, fundamentally speaking, this still depends on whether the structure of agricultural trade between China

**Citation:** Zhou, L.; Tong, G. Structural Evolution and Sustainability of Agricultural Trade between China and Countries along the "Belt and Road". *Sustainability* **2022**, *14*, 9512. https://doi.org/ 10.3390/su14159512

Academic Editor: Aaron K. Hoshide

Received: 27 June 2022 Accepted: 30 July 2022 Published: 3 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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and the B&R countries is reasonable. In particular, the structuring of the agricultural trade relationship to be based on comparative advantage, and the empirical investigation of related issues, are of great significance for ensuring the effective supply of agricultural products in China and the world.

The current research on agricultural trade between China and B&R countries has yielded many results, but it still needs to be further expanded and improved. We examined the literature and found that in terms of cooperation objects, this mostly involves the analysis of China and some of the countries or regions along the B&R; this lacks a holistic analysis of the region, which mainly includes China and ASEAN [5], China and Central Asia [6,7], China and South Asia [8], China and the "21st Century Maritime Silk Road" [9,10], China and Russia [11,12], China and Central and Eastern Europe [13,14], China and Southeast Asia [15], China and the countries along the Silk Road Economic Belt [16–18], China and Africa [19], and China and Ukraine [20].

In terms of trade categories, they are mostly focused on a certain type of agricultural trade, and they lack an overall exploration of agricultural trade. This mainly includes dairy products [21], corn [22], aquatic products [23,24], apples [25], grains [26–29], and fruits [30]. In terms of analyzing data, many studies only explore the trade network structure under cross-sectional data at certain time points, which cannot reflect the dynamic evolution of trade in a specific time context. Wei (2019) analyzed the structure, association characteristics, and strategy choices of agricultural trade networks between China and B&R countries, using cross-sectional data in 2017 [31]. Zhan (2019) analyzed the competitiveness and complementarity of agricultural trade networks of B&R countries in 2007 and 2015 [32]. Su (2019) analyzed the structure and cooperation trends of agricultural trade networks between China and B&R countries in 2012 and 2016 [33].

Although some scholars have explored the structure of agricultural trade between China and B&R countries from a holistic perspective over the past two years, most of them have adopted an index approach to explore the structure of flat trade and to analyze its influencing factors. For example, Yang et al. (2021) analyzed the evolution of agricultural trade characteristics between China and B&R countries [34]. Although all of the agricultural products of the B&R countries were studied, the countries and agricultural products within the B&R countries were not discussed separately, and all the countries and agricultural products along B&R countries were treated as a single whole, without discussing the internal structure reflecting the trade. Liu et al. (2021) explored the countries of China's agricultural trade with B&R countries in 2018, using descriptive analysis, but they took a static trade situation of one year as the sample of the study, they did not analyze the evolution of trade dynamically from the time series; they only used a descriptive analysis of plane structure, and they did not use index and spatial analysis methods [35]. Sun (2021) used the index method to investigate the intra-industry trade of agricultural products and its influencing factors between China and B&R countries, which included all B&R countries and all types of agricultural products, but they only explored the flat structure. While the definition of agricultural products in Sun's paper was based on the World Trade Organization (WTO) Agricultural Agreement + Fishery Products, this paper uses the United Nations International Trade Standards Classification, with its different focuses [36].

This paper explores the structural evolution of agricultural trade between China and B&R countries, from both a planar and a spatial perspective. The study differs from the existing literature in four ways. First, instead of limiting the scope to certain countries or certain agricultural products, all countries and agricultural products along the B&R countries are used as samples to classify and compare the agricultural trade relations between China and B&R countries, which is conducive to grasping the current situation of trade cooperation in terms of levels and varieties within the overall sample, and exploring the directions for sustainable cooperation. Second, the study is not limited to a single point in time; the data chain covers four time points, namely, China's accession to the WTO, the global financial crisis, the introduction of the "Belt and Road" initiative, and the latest data; it explores the dynamic evolution of trade relations between China and B&R countries.

It is useful for China and B&R countries to forecast the future direction of sustainable cooperation based on past bilateral trade dynamics in the current situation of increased world uncertainty. Third, the literature has used descriptive analysis, index analysis, and social network analysis separately, but the comprehensive planar analysis can highlight the accuracy of trade data and the continuity of the time series, while the spatial analysis can better reflect the relationship between countries, between countries and small groups, and between countries and the whole. Combining the advantages of the two analysis methods, we choose to adopt the dual perspective of planar and spatial research. Fourth, drawing on the research techniques of Li (2017) [37], the competition index, CS, was improved and the competitive advantage index, CN, was constructed, which provides a methodological improvement for measuring the spatial analysis of multinational cooperation network development and is conducive to providing analytical tools for scholars studying trade cooperation.

#### **2. Materials and Methods**

#### *2.1. Research Methods*

To quantify the planar and spatial network structure of agricultural trade between China and B&R countries, we used a combination of descriptive statistics and the index method to quantify the planar structure [38–40]. The trade index was selected as the trade intensity index and the export concentration index. We used the social network analysis method [29,31,36,37,41,42] to quantify the spatial network structure.

#### 2.1.1. Planar Structure Quantification Method

1. Trade Intensity Index (TII). TII index was proposed by Brown (1949) [43] and was later improved and refined by Kojima (1964) [44]. TII index measures the ratio of a country's exports to a trading partner country, to that country's total exports to that trading partner country's total imports, as a share of total world imports; this is often used in inter-country trade interdependence analysis, with the following formula.

$$\text{TII}\_{\text{ab}} = \frac{\chi\_{\text{ab}} / \chi\_{\text{a}}}{\text{M}\_{\text{b}} / \text{M}\_{\text{W}}} \tag{1}$$

In the formula, a, b, w represent country a, b, and the world market, respectively, TIIab represents the trade combination degree of country a and b, Xab represents the export volume of country a to country b, X<sup>a</sup> represents the total export volume of country a, M<sup>b</sup> represents the total import volume of country b, and M<sup>w</sup> represents the total import volume of country. When TIIab > 1, this indicates that a and b have a close trade relationship. When TIIab < 1, it indicates that the trade relationship between a and b is loose.

2. Export Concentration Index (HHI). HHI index, also known as the Hirschman index, is used to measure the degree of concentration of a country or region in terms of the types of products exported [45]. The formula is as follows.

$$\text{HHH} = \sqrt{\sum\_{\mathbf{k}=1}^{n} (\mathbf{X}\_{\mathbf{k}} / \mathbf{X})^2} \tag{2}$$

In the formula, X<sup>k</sup> is the export volume of k products of the country, and X is its total export volume. The value range of HHI is <sup>h</sup> √ 1 n , 1i . The smaller the value is, the more fragmented the country's export product structure is; likewise, the larger the value, the more concentrated the export product structure.

#### 2.1.2. Quantification Method of Spatial Structure

1. Origin of social network approach. The application of social network analysis in economics is mainly inspired by the sociologist Granovetter (1985) [46], who argues that the key to many high-transaction-cost behaviors in the real economy that are still traded through the market is that both buyers and sellers are embedded in a long-term network of business relationships, i.e., both buyers and sellers are unwilling to lose the trust relationship they have built up in mutual transactions, and the whole system. The whole system is constantly adaptive through mutual coordination and information exchange. This means that the real economic system has the essential characteristics of a social network. After that, social network analysis has gradually received the attention of economists and has been widely used in many fields such as industrial economics, finance, and international trade. With the development of economic globalization, the close economic ties between countries make global trade relations an organic whole, and the growing international trade is becoming the key to shape the global economic and political landscape. The adoption of social network analysis method to study the characteristic laws of international trade system has become an emerging research direction.

2. Standard construction of social network methods. The social network analysis method regards trading countries as points, and the resulting trade relations as connecting lines, and analyzes the structural characteristics of trade networks according to the connections between nodes in the network. The two–two relationship conditions are different and can be constructed into different trade networks; according to the import–export and competitive advantage of a two–two relationship, two different trade relationship networks can be constructed, so as to reflect the trade prospects between trading countries more comprehensively, where the two–two import-export relationship is reflected by the bilateral trade volume. For the competitive advantage relationship, based on the method of Li Jing and Chen Ni et al. (2017) [29], the trade competition index is improved into the trade competition difference index based on the comparative advantage theory, which is the competitive advantage index. The original trade competition index CS formula is as follows.

$$\text{CS} = 1 - \frac{1}{2} \sum |\mathbf{a\_i^n} - \mathbf{a\_j^n}| \tag{3}$$

where i and j represent countries, n represents industries, a n i represents the comparative advantage of industry n in country i, and a n j represents the comparative advantage of industry n in country j. The closer the comparative advantage of two countries, the smaller 1 <sup>2</sup> ∑ |a n <sup>i</sup> − a n j | becomes, and the larger the CS. The improvement in this paper is to consider industry n as a single industry, and the comparative advantage is specified as the NRCA index, as in Equation (4).

$$\text{CN} = 1 - \frac{1}{2} |\text{NRCA}\_{\text{i}}^{\text{n}} - \text{NRCA}\_{\text{j}}^{\text{n}}| \tag{4}$$

$$\text{RCA}\_{\text{i}\text{j}} = \frac{\chi\_{\text{i}\_{\text{j}}} / \chi\_{\text{t}\text{j}}}{\chi\_{\text{i}\_{\text{W}}} / \chi\_{\text{tw}}} \tag{5}$$

$$\text{NRCA}\_{\text{ij}} = \frac{\text{RCA}\_{\text{ij}} - 1}{\text{RCA}\_{\text{ij}} + 1} \tag{6}$$

where: Xi<sup>j</sup> represents the export value of i products of Country j, Xtj represents the export volume of products of Country j, Xi<sup>w</sup> represents the export value of i products in the world, and Xtw stands for the world export of goods.

According to Formulas (5) and (6), when RCAij = ±∞, NRCAij = 1; when RCAij = 0, NRCAij <sup>=</sup> <sup>−</sup>1, and NRCAij <sup>∈</sup> [−1, 1], it can be deduced that <sup>−</sup><sup>2</sup> <sup>≤</sup> NRCA<sup>n</sup> <sup>i</sup> <sup>−</sup> NRCA<sup>n</sup> <sup>j</sup> ≤ 2. We obtain <sup>0</sup> <sup>≤</sup> <sup>1</sup> <sup>−</sup> <sup>1</sup> 2 <sup>|</sup>NRCA<sup>n</sup> <sup>i</sup> <sup>−</sup> NRCA<sup>n</sup> j | ≤ 1, and CN ∈ [0, 1]. Therefore, if NRCA is taken as an independent variable, the domain of CN index is [−1,1] and the range is [0,1]. According to the theory of comparative advantage put forward by Ricardo, international trade is based on the relative difference in production technology and the resulting relative cost of production. Every country should concentrate on producing and exporting products with "comparative advantage" and importing products with "comparative disadvantage". The NRCA index represents the trade competitiveness index of a country. One country exports products with relative advantages, while the other exports products with relative disadvantages. The absolute value of the NRCA index is the convergence point of the interests of the trade between the two countries, namely, the competitive advantage. According to Formula (4), if the set <sup>|</sup>NRCA<sup>n</sup> <sup>i</sup> <sup>−</sup> NRCA<sup>n</sup> j | ≥ A, there are comparative advantages for trade between the two countries, so that <sup>1</sup> <sup>−</sup> <sup>1</sup> 2 <sup>|</sup>NRCA<sup>n</sup> <sup>i</sup> <sup>−</sup> NRCA<sup>n</sup> j | ≤ <sup>1</sup> <sup>−</sup> <sup>1</sup> <sup>2</sup>A, launch CN <sup>≤</sup> <sup>1</sup> <sup>−</sup> <sup>1</sup> <sup>2</sup>A, <sup>|</sup>NRCA<sup>n</sup> <sup>i</sup> <sup>−</sup> NRCA<sup>n</sup> j | ≥ <sup>A</sup> set up, which sets up CN <sup>≤</sup> <sup>1</sup> <sup>−</sup> <sup>1</sup> <sup>2</sup>A, and the two countries have the competitive advantage.

3. Analysis of the density of trade networks (Dn). Dn Trade network density reflects the sparseness of trade relationships between countries.

$$\mathbf{Dn} = \mathbf{L} / \left[ \mathbf{N} \times (\mathbf{N} - \mathbf{1}) \right] \tag{7}$$

In the formula, N is the number of countries in the trade network, where the number of countries in that trade network that meet the criteria is L. The value range of Dn is [0, 1].

The larger the Dn, the greater the number of important trade relationships in the network and the higher the trade density.

4. Analysis of the centrality of trade networks. De is the relative degree centrality, which measures a country's position and role in the overall network. NC is the relative degree centrality index, which measures the centrality of the entire network.

$$\mathbf{De} = \mathbf{n} / (\mathbf{N} - \mathbf{1}) \tag{8}$$

$$\text{NC} = \sum\_{i=1}^{\text{M}} \left( \text{Max}(\text{De}) - \text{De}\_{i} \right) / \left( \text{M} - \text{2} \right) \tag{9}$$

In the formula, n denotes the number of countries in network trade with which a country has significant trade relations, N denotes the maximum possible number of directly connected countries, and M denotes the number of countries in the trade network. The value range of De is [0, 1]. The larger the De, the more central a country is in the network, the more "influence" it has in the network, and the more it can influence other countries. The value range of NC is [0, 1]. The larger the NC, the greater the degree to which the network is built around a point or points in the network, and the more concentrated the trade.

5. Block model analysis. Block model analysis is a network location analysis model proposed by White et al. (1976) [47]. According to the block model theory, using the CONCOR method in Ucinet 6, a trade network can be divided into several plates to reveal the trade relations between the inside and the outside of the plates, revealing the roles and functions of each economic segment and its member countries in international trade. In this paper, referring to the classification method of Li Jing et al. (2017) [37], the economic plates are classified into four major categories. One is the internal type, if the plate has many internal relationships and few or no external relationships; two is the outward type, if the plate has few or no internal relationships and many external relationships; three is the eclectic type, if there are many internal relationships and also many external relationships; and four is the isolated type, if there are few or no both external and internal relationships.

#### 2.1.3. Analysis Methods of Spatial Network Influence Factors

After analyzing the characteristics of the spatial network of agricultural trade, it is necessary to analyze what factors affect the spatial network of agricultural trade between China and B&R countries. In order to avoid the problems of multicollinearity and spurious regression in social network analysis, the study combines QAP correlation analysis and QAP regression analysis. These analyses were based on the research methods of Liu (2007) [48], Li (2014) [49], and Ma (2016) [42]. The framework for analyzing the occurrence of agricultural trade in a country in this paper consists of three parts: agricultural production—transportation of agricultural products—intercountry trade. In the agricultural production stage, the main factors affecting agricultural production are arable land, water, and seeds. The main influencing factors in the transportation stage are distance and means of transportation, and the main influencing factors in the trade stage are economic

distance between two countries, cultural differences, trade structure, and trade agreements. Combining the above analysis and referring to the existing research results, 10 indicators are selected to characterize the corresponding influencing factors [31,50–55].

1. Agricultural resource endowment: We use the absolute value matrix of the difference in per capita water resources (PCWR), the absolute value matrix of the difference in per capita arable land area (PCLA), and the absolute value matrix of the difference in the share of investment in scientific research in each country (SCI), to represent the effects of water, arable land, and seeds, respectively.

2. Agricultural product transportation: We consider whether the two countries are bordering each other to indicate the trade distance. If the two countries are bordering, it will be recorded as 1, otherwise it will be 0, for constructing the distance matrix (DIS). Agricultural products belong to the large volume of low-value goods; the two countries trade in order to save costs, and generally use railroad or waterway transportation. We adopt the absolute value of the difference between the railroad length of each country matrix (TRA), indicating the convenience of transportation in each country.

3. Trading between two countries: The economic distance between countries will affect agricultural trade. We combine two ways of representing economic distance in the existing literature, namely, the absolute value of the difference between the total economic output value of each country (DGDP), and the economic distance matrix (DE) of two countries. The formula for calculating the economic distance between two countries in the DE matrix is: DEij = (PGDPi−PGDPj) 2 GDPi∗GDP<sup>j</sup> , where DEij denotes the economic distance between country i and country j, and PGDP and GDP are the GDP per capita and GDP, respectively. The trade agreement facilitates international trade between the two countries and is recorded as 1 if both countries are members of the trade agreement; otherwise, it is 0. The trade agreement matrix (TA) is constructed. As the cultural factor, language is the tool of communication between two countries. If an official language is used in both countries, it is recorded as 1; otherwise, it is 0. The cultural matrix (CUL) is constructed. For trade structure, this paper uses the share of an agricultural export in a country's total trade to represent the trade structure and to construct the trade structure matrix (IS). F is used to represent the trade network of US \$100 million between China and B&R countries in 2019, and then the model is constructed as follows:

$$\mathbf{F} = \mathbf{f}(\text{PCWR, PCLA, SCL, Dis, TRA, DGDP, DE, TA, CUL, IS}) \tag{10}$$

In the formula, the GDP, population, scientific research expenditure ratio, water resources, arable land resources, and railroad length of each country are obtained from the World Bank database, trade agreements and official languages are obtained from the official websites of each country, and geographical distances are obtained from Google Maps.

#### *2.2. Description of Study Subjects and Data*

#### 2.2.1. Definition of the Research Area

Since the "Belt and Road" is an open international economic cooperation region, the academic community has not precisely defined the distribution range. This paper refers to the definition methods of scholars [22,26], and in view of the availability of trade data, the B&R countries are divided into six regions and 60 countries. The specific regions are: <sup>1</sup> Mongolia and Russia; <sup>2</sup> Central Asia, including Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan; <sup>3</sup> Southeast Asia, including Vietnam, Laos, Cambodia, Thailand, Malaysia, Singapore, Indonesia, Brunei, the Philippines, Myanmar, and Timor-Leste; <sup>4</sup> South Asia, including India, Pakistan, Bangladesh, Afghanistan, and Nepal; <sup>5</sup> Western Asia and the Middle East, including Turkey, Iran, Syria, Iraq, Saudi Arabia, Qatar, Bahrain, Kuwait, Lebanon, Oman, Yemen, Jordan, Israel, Palestine, Armenia, Georgia, Azerbaijan, and Egypt; <sup>6</sup> Central and Eastern Europe, including Poland, the Czech Republic, Slovakia, Hungary, Slovenia, Croatia, Romania, Bulgaria, Serbia, Montenegro, Bosnia and Herzegovina, Albania, Estonia, Lithuania, Latvia, and Ukraine.

#### 2.2.2. Agricultural Product Scoping and Data Sources

According to the United Nations Standard International Trade Classification (SITC Rev.3), the definition of agricultural products includes four categories and 22 chapters of agricultural products. The four categories of agricultural products are 0, 1, 2, and 4. Category 0 is food and live things, including 10 chapters; category 1 is beverages and tobacco, including two chapters of agricultural products; category 2 is non-edible raw materials (except fuel), including seven chapters of agricultural products, except 27 and 28; and category 4 is animal and vegetable oils, and fats and waxes, including three chapters of agricultural products. In order to study the change of trade structure after China's accession to the WTO, this paper selects the data related to China's agricultural trade with B&R countries from 2001 to 2019 for analysis, and the data are obtained from the UN COMTRADE database.

#### **3. Results**

#### *3.1. Planar Structure Analysis*

#### 3.1.1. Trade Type Structure

According to Table 1, the import and export of agricultural products categories between China and B&R countries are highly concentrated. Agricultural products are classified using SITC into 22 chapters, of which the first 10 chapters account for more than 80% of the total proportion of trade, so that more than 80% of the total trade is concentrated in 45% of the agricultural product categories. Among them, from the time series, the share of China's agricultural products' import categories showed a decentralized trend, decreasing from 94.94% in 2009 to 90.37% in 2019, while China's agricultural products export categories showed a relatively concentrated trend, increasing from 82.54% in 2001 to 90.02% in 2019. In terms of specific types of agricultural products, China's agricultural imports to B&R countries are more evenly concentrated into five categories: 02 (dairy products and poultry eggs), 23 (crude rubber), 05 (vegetables and fruits), 42 (fixed vegetable fats and oils), and 03 (fish, crustaceans, mollusks and aquatic invertebrates, and their products), while China's exports to B&R countries are highly concentrated into two categories of agricultural products: 05 (vegetables and fruits) and 03 (fish, crustaceans, mollusks, and aquatic invertebrates and their products), with five products accounting for approximately 45% of the total in recent years.


**Table 1.** Trade structure of specific types of agricultural products.

3.1.2. Trade Region Structure

According to Table 2, China's agricultural trade with B&R countries is highly concentrated, with imports tending to be decentralized, and exports tending to be concentrated. In terms of the top 10 import and export trade shares overall, China's agricultural imports from B&R countries fell from 93.78% in 2001 to 89.68% in 2019, indicating that the effect of China's diversified import strategy has emerged, while China's agricultural exports to B&R countries rose from 66.51% in 2001 to 78.76% in 2019, and have been concentrated overall. Specifically, from the top 10 countries in import and export trade, 8 of them rank in the top 10 countries for both import and export, namely, Thailand, Indonesia, Russia, Vietnam, Malaysia, India, the Philippines, and Myanmar, indicating that China has close trade ties in agricultural products with B&R countries, for both importing and exporting. From the perspective of individual import and export trade countries, the ranking of China's agricultural trade with B&R countries has almost always tended to stabilize, with Thailand, Indonesia, and Russia holding the top three in the import ranking, and the top three in the export ranking being Vietnam, Thailand, and Malaysia.

**Table 2.** Regional distribution structure of agricultural trade products.


#### 3.1.3. Trade Intensity Structure

In order to analyze the structure of agricultural trade intensity between China and B&R countries as a whole, this paper divides B&R countries into six regions. As shown in Table 3, trade intensity is greater in regions where China is close to B&R countries, such as Mongolia and Russia, Southeast Asia, and Central Asia, which border China and are ranked in the top three in terms of trade intensity. From the general trend, China's trade intensity with B&R countries tends to disperse, among which China's trade intensity with Mongolia and Russia tends to weaken, and its trade intensity with Central Asia, Southeast Asia, and South Asia increases, while the trade intensity of B&R countries with China tends to strengthen slightly, but remains stable overall. From the comparison of trade intensity between China to B&R countries and B&R countries to China, the overall trade intensity of China to B&R countries is higher than the trade intensity of B&R countries to China, indicating that China is more dependent on B&R countries, especially neighboring countries, while B&R countries are less dependent on China's trade.

#### 3.1.4. Trade Concentration Structure

According to Figure 1, the concentration of China's import trade to B&R countries has a tendency to decrease, while the concentration of the export trade has a tendency to increase. In terms of agricultural trade types, the concentration of agricultural import types from China to B&R countries tends to decline, from 0.39 in 2001 to 0.33 in 2019, while the concentration of export types from China to B&R countries tends to rise, from 0.35 in 2001 to 0.49 in 2019. In terms of agricultural trade regions, the regional concentration of China's imports to B&R countries tends to decline in recent years, from 0.73 in 2017 to Western Asia

Central and

0.67 in 2019, while the regional concentration of China's exports to B&R countries tends to increase, from 0.55 in 2008 to 0.70 in 2019. From the value of concentration, the regional concentration curve is always above the category concentration, and the trade concentration between China and the countries (regions) along the B&R is higher than the category trade concentration. and Middle East 0.33 0.65 0.48 0.70 0.65 0.70 Western Asia and Middle East 0.18 0.10 0.10 0.20 0.20 0.33 Eastern Europe 0.03 0.04 0.03 0.03 0.03 0.03 Central and Eastern Europe 0.17 0.05 0.16 0.30 0.30 0.44 3.1.4. Trade Concentration Structure


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**China's Agricultural Trade Intensity with B&R Countries B&R Countries Agricultural Trade Intensity with China Region 2001 2009 2013 2017 2018 2019 Region 2001 2009 2013 2017 2018 2019**  Mongolia 73.95 141.87 104.38 125.07 97.01 95.84 Mongolia 9.10 5.03 2.56 3.86 3.55 3.40 Central Asia 1.18 2.02 1.66 2.65 2.33 2.85 Central Asia 1.80 1.48 1.72 1.51 1.43 1.48

South Asia 0.93 1.30 1.00 1.09 1.06 1.25 South Asia 0.44 0.89 0.84 0.52 0.59 0.97

**Table 3.** Structure of agricultural product trade intensity.

**Table 3.** Structure of agricultural product trade intensity. According to Figure 1, the concentration of China's import trade to B&R countries

**Figure 1.** Product categories and regional concentration of agricultural trade. **Figure 1.** Product categories and regional concentration of agricultural trade.

#### *3.2. Spatial Network Structure Analysis*

*3.2. Spatial Network Structure Analysis*  In order to reflect the closeness of the trade relations, in this paper, referring to the method of Li Jing et al. (2017) [37], the import–export relationship is divided into US \$10 million and US \$100 million categories, and the existence of significant trade relations is judged if the trade volume between the two countries meets the classification criteria. If the CN index is less than 0.7 and 0.8, a significant competitive advantage relationship is indicated. In order to reflect the evolution of the agricultural trade network between China In order to reflect the closeness of the trade relations, in this paper, referring to the method of Li Jing et al. (2017) [37], the import–export relationship is divided into US \$10 million and US \$100 million categories, and the existence of significant trade relations is judged if the trade volume between the two countries meets the classification criteria. If the CN index is less than 0.7 and 0.8, a significant competitive advantage relationship is indicated. In order to reflect the evolution of the agricultural trade network between China and B&R countries from 2001 to 2019 based on the cross-sectional analysis of the trade network analysis as a single year, considering the financial crisis in 2008 and the "Belt and Road" initiative proposed by China for the first time in 2013, and other important nodes due to the delayed impact of the financial crisis, which generally only appeared in 2009, we selected four different years, 2001, 2009, 2013, and 2019, as representatives, and constructed 16 trade networks according to the time dimension and the degree of trade relations (Figures 2–17).

tions (Figures 2–17).

tions (Figures 2–17).

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**Figure 2.** Relative degree centrality network of the trade volume of US \$10 million in 2001.

and B&R countries from 2001 to 2019 based on the cross-sectional analysis of the trade network analysis as a single year, considering the financial crisis in 2008 and the "Belt and Road" initiative proposed by China for the first time in 2013, and other important nodes due to the delayed impact of the financial crisis, which generally only appeared in 2009, we selected four different years, 2001, 2009, 2013, and 2019, as representatives, and con-

and B&R countries from 2001 to 2019 based on the cross-sectional analysis of the trade network analysis as a single year, considering the financial crisis in 2008 and the "Belt and Road" initiative proposed by China for the first time in 2013, and other important nodes due to the delayed impact of the financial crisis, which generally only appeared in 2009, we selected four different years, 2001, 2009, 2013, and 2019, as representatives, and constructed 16 trade networks according to the time dimension and the degree of trade rela-

and B&R countries from 2001 to 2019 based on the cross-sectional analysis of the trade network analysis as a single year, considering the financial crisis in 2008 and the "Belt and Road" initiative proposed by China for the first time in 2013, and other important nodes due to the delayed impact of the financial crisis, which generally only appeared in 2009, we selected four different years, 2001, 2009, 2013, and 2019, as representatives, and constructed 16 trade networks according to the time dimension and the degree of trade rela-

**Figure 3.** Relative degree centrality network of the trade volume of US \$10 million in 2009.

**Figure 4.** Relative degree centrality network of the trade volume of US \$10 million in 2013.

**Figure 5.** Relative degree centrality network of the trade volume of US \$10 million in 2019. **Figure 5.** Relative degree centrality network of the trade volume of US \$10 million in 2019.

**Figure 6.** Relative degree centrality network of the trade volume of US \$100 million in 2001.

**Figure 7.** Relative degree centrality network of the trade volume of US \$100 million in 2009.

**Figure 8.** Relative degree centrality network of the trade volume of US \$100 million in 2013.

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**Figure 5.** Relative degree centrality network of the trade volume of US \$10 million in 2019.

**Figure 5.** Relative degree centrality network of the trade volume of US \$10 million in 2019.

**Figure 5.** Relative degree centrality network of the trade volume of US \$10 million in 2019.

**Figure 7.** Relative degree centrality network of the trade volume of US \$100 million in 2009. **Figure 7.** Relative degree centrality network of the trade volume of US \$100 million in 2009.

**Figure 7.** Relative degree centrality network of the trade volume of US \$100 million in 2009.

**Figure 8.** Relative degree centrality network of the trade volume of US \$100 million in 2013. **Figure 8.** Relative degree centrality network of the trade volume of US \$100 million in 2013.

**Figure 9.** Relative degree centrality network of the trade volume of US \$100 million in 2019. **Figure 9.** Relative degree centrality network of the trade volume of US \$100 million in 2019.

**Figure 10.** The 2001 competition index CN = 0.7 relative degree centrality network.

**Figure 11.** The 2009 competition index CN = 0.7 relative degree centrality network.

**Figure 12.** The 2013 competition index CN = 0.7 relative degree centrality network.

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**Figure 10.** The 2001 competition index CN = 0.7 relative degree centrality network. **Figure 10.** The 2001 competition index CN = 0.7 relative degree centrality network. **Figure 10.** The 2001 competition index CN = 0.7 relative degree centrality network.

**Figure 9.** Relative degree centrality network of the trade volume of US \$100 million in 2019.

**Figure 9.** Relative degree centrality network of the trade volume of US \$100 million in 2019.

**Figure 9.** Relative degree centrality network of the trade volume of US \$100 million in 2019.

**Figure 11.** The 2009 competition index CN = 0.7 relative degree centrality network. **Figure 11.** The 2009 competition index CN = 0.7 relative degree centrality network. **Figure 11.** The 2009 competition index CN = 0.7 relative degree centrality network.

**Figure 12. Figure 12.** The 2013 competition index CN = 0.7 relative degree centrality network. The 2013 competition index CN = 0.7 relative degree centrality network.

**Figure 13.** The 2019 competition index CN = 0.7 relative degree centrality network. **Figure 13.** The 2019 competition index CN = 0.7 relative degree centrality network.

**Figure 14.** The 2001 competition index CN = 0.8 relative degree centrality network.

**Figure 15.** The 2009 competition index CN = 0.8 relative degree centrality network.

**Figure 16.** The 2013 competition index CN = 0.8 relative degree centrality network.

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**Figure 14.** The 2001 competition index CN = 0.8 relative degree centrality network. **Figure 14.** The 2001 competition index CN = 0.8 relative degree centrality network. **Figure 14.** The 2001 competition index CN = 0.8 relative degree centrality network.

**Figure 13.** The 2019 competition index CN = 0.7 relative degree centrality network.

**Figure 13.** The 2019 competition index CN = 0.7 relative degree centrality network.

**Figure 13.** The 2019 competition index CN = 0.7 relative degree centrality network.

**Figure 15.** The 2009 competition index CN = 0.8 relative degree centrality network.

**Figure 16.** The 2013 competition index CN = 0.8 relative degree centrality network. **Figure 16.** The 2013 competition index CN = 0.8 relative degree centrality network.

**Figure 17.** The 2019 competition index CN = 0.8 relative degree centrality network. 100 0.1421 520 0.7 0.3339 1222 **Figure 17.** The 2019 competition index CN = 0.8 relative degree centrality network.

criterion relationship gradually increases, and trade tends to be concentrated.

**Table 4.** Network density of 16 trade relations of China with B&R countries.

**Year Trade Volume CN Index Network Den-**

2001 10 0.1785 653

2009 10 0.2809 1028

2013 10 0.3251 1190

\$10 million criterion ranged from 0.1785 to 0.3251 over the four years of the study sample, tending to increase overall and decreasing slightly in recent years. Dn in the US \$100 million criterion increases from 0.0404 to 0.1505 year by year, and Dn in 2019 is 3.52 times that of 2001, indicating a rapid increase in trade relations reaching the US \$100 million criterion. In general, China's agricultural trade relations with B&R countries achieve faster growth, but the US \$10 million criterion relationship declines slightly, the US \$100 million

With the CN = 0.7 criterion, the four-year Dn ranged from 0.3221 to 0.3509, with growth rates of 11.21%, −6.78%, and 5.09% for the four time points, indicating that the development space of agricultural trade between China and B&R countries has experienced the process of "rise–fall–rise". In 2013, the "Belt and Road" initiative injected new impetus to the agricultural trade of B&R countries. With the CN=0.8 criterion, Dn ranged from 0.4975 to 0.5426, with growth rates of 7.32%, -5.175%, and 7.17% for the four time points, indicating that, when lowering the competitive advantage criterion, the development space of the agricultural trade of the B&R countries is also in the trend of "rise–fall– rise". Comparing the CN = 0.7 and CN = 0.8 criteria, it is found that the "Belt and Road" initiative provides development opportunities for countries with different levels of competitive advantage. Compared with the CN = 0.7 criterion, Dn in 2019 is the largest under the CN = 0.8 criterion, indicating that the countries with weaker comparative advantages in agricultural trade have achieved the best development in history after the introduction

**sity (Dn) Significant (US \$ Million)** 

100 0.0404 148 0.7 0.3221 1179 0.8 0.4975 1821

100 0.1121 410 0.7 0.3582 1311 0.8 0.5339 1954

3.2.1. Analysis of the Density of Trade Networks

of the "Belt and Road" initiative.

#### 3.2.1. Analysis of the Density of Trade Networks

Combining Figures 2–17 and Table 4 in terms of the network density, Dn for the US \$10 million criterion ranged from 0.1785 to 0.3251 over the four years of the study sample, tending to increase overall and decreasing slightly in recent years. Dn in the US \$100 million criterion increases from 0.0404 to 0.1505 year by year, and Dn in 2019 is 3.52 times that of 2001, indicating a rapid increase in trade relations reaching the US \$100 million criterion. In general, China's agricultural trade relations with B&R countries achieve faster growth, but the US \$10 million criterion relationship declines slightly, the US \$100 million criterion relationship gradually increases, and trade tends to be concentrated.


**Table 4.** Network density of 16 trade relations of China with B&R countries.

With the CN = 0.7 criterion, the four-year Dn ranged from 0.3221 to 0.3509, with growth rates of 11.21%, −6.78%, and 5.09% for the four time points, indicating that the development space of agricultural trade between China and B&R countries has experienced the process of "rise–fall–rise". In 2013, the "Belt and Road" initiative injected new impetus to the agricultural trade of B&R countries. With the CN = 0.8 criterion, Dn ranged from 0.4975 to 0.5426, with growth rates of 7.32%, −5.175%, and 7.17% for the four time points, indicating that, when lowering the competitive advantage criterion, the development space of the agricultural trade of the B&R countries is also in the trend of "rise–fall–rise". Comparing the CN = 0.7 and CN = 0.8 criteria, it is found that the "Belt and Road" initiative provides development opportunities for countries with different levels of competitive advantage. Compared with the CN = 0.7 criterion, Dn in 2019 is the largest under the CN = 0.8 criterion, indicating that the countries with weaker comparative advantages in agricultural trade have achieved the best development in history after the introduction of the "Belt and Road" initiative.

#### 3.2.2. Analysis of the Centrality of Trade Networks

Combining Figures 2–17 and Table 5, in terms of the De values, China, Russia, India, Turkey, Thailand, and Malaysia occupy the main centrality positions in the standard condition of trade volume, and they are the most influential countries in the agricultural trade network between China and B&R countries. Over the four years of the study sample, under the criterion of US \$10 million, China's centrality rankings are 2, 1, 1, and 1, respectively, indicating that under the criterion of small and medium trade, China has the highest centrality and the greatest influence among B&R countries. Under the US \$100 million criterion, China's De rankings over the four years are 2, 2, 2, and 2, respectively, indicating

that among B&R countries, under the large trade volume criterion, China's influence is firmly in second place, after Russia. Under the criterion of CN = 0.7, China's De rankings over the four years are 31, 11, 10, and 15, respectively, indicating that, under the condition of greater competitive advantage, China's agricultural trade development space is generally improving. Under the criterion of CN = 0.8, China's De ranking over the four years are 29, 10, 11, and 11, respectively, which indicates that China's trade development space is steadily increasing and staying stable under the smaller trade competitive advantage. The criterion of CN = 0.7 and CN = 0.8 indicates that China's trade development space with B&R countries is huge.


**Table 5.** Network centricity of trade relations of China with B&R countries.

According to Table 5, from the relative degree centrality index, under the US \$10 million criterion, the NC index between China and B&R countries grew from 40% to 59.66% in the four years of the simulation, with an overall growth rate of 49.15% and annual growth rates of 37.78%, 7.24%, and 0.95%, indicating that the centrality of agricultural trade between China and B&R countries tends to be concentrated, and that the countries have closer trade relations, but the centrality tends to slow down. Under the US \$100 million criterion, the NC index between China and B&R countries grew from 18.16% to 48.16% in the four years of the simulation and annual growth rates of 120.87%, 13.31%, and 5.96%.

Compared with the US \$10 million criterion, the US \$100 million criterion centered on a smaller base but a faster growth rate, proving that China has relatively fewer countries, with a trade volume exceeding US \$100 million along the B&R countries, but this closeness

is growing faster. Under the criterion of CN = 0.7, the NC index between China and B&R countries ranged from 47.20% to 58.28% in the four years of the simulation and annual growth rates of −13.47%, 5.40%, and 17.15%, indicating that after the financial crisis, the central tendency of China and B&R countries with large competitive advantages has increased, and the trade exchanges tended to be closer. Under the criterion of CN = 0.8, the NC index between China and B&R countries ranged from 37.63% to 41.78% in the four years of the simulation and annual growth rates of 19.21%, 11.93%, and 0.81%, indicating that the trade center potential of China and B&R countries with smaller competitive advantages in the "Belt and Road" initiative tended to disperse, and the countries with room for trade development were more widely distributed.

#### 3.2.3. Analysis of the Trade Block Model

According to the analysis in Table 6, the first plate is an internal type plate. The second plate is an internal type plate. Third plate is an isolated plate and the fourth plate is an internal plate. According to the above analysis, it can be concluded that in the early stage of China's accession to WTO, the agricultural trade between China and B&R countries is in an internal block or isolated state; a "small circle" trade. Similarly, the first, second, third, and fourth plates in 2009 are, respectively, the internal plate, isolated plate, isolated plate, and isolated plate. In 2013, the first, second, third, and fourth plates are the simultaneous plate, the internal plate, the simultaneous plate, and the internal plate, respectively. In 2019, the first, second, third, and fourth plates are the simultaneous plate, internal plate, internal plate, and internal plate, respectively.

**Table 6.** Trade volume (US\$ 100 million) in 2001, 2009, 2013, and 2019 between China and B&R country plates (%).


Due to space limitations, only the country distributions for 2001 and 2019 are listed. According to the CONCOR method analysis results, in 2001, the countries in the first plate are China, nine countries in Southeast Asia, five countries in South Asia, and four countries in Western Asia and the Middle East. The countries in the second plate are Russia, two countries in Central Asia, three countries in Western Asia and the Middle East, and seven countries in central and Eastern Europe. The countries in the third plate are Mongolia, three countries in Central Asia, two countries in Southeast Asia, three countries in South Asia, seven countries in Western Asia and the Middle East, and six countries in central and Eastern Europe. The countries in the fourth plate are four countries in Western Asia and the Middle East, and three countries in Central and Eastern Europe. In 2019, the countries in the first plate are China, Russia, 10 countries in Southeast Asia, 3 countries in South Asia, and 2 countries in Western Asia and the Middle East. The countries in the second plate are four countries in South Asia, seven countries in Western Asia and the Middle East, and one country in Central and Eastern Europe. The countries in the third plate are Mongolia, four countries in Central Asia, five countries in Western Asia and the Middle East, and six countries in Central and Eastern Europe. The countries in the fourth plate are one in Southeast Asia, one in South Asia, four in Western Asia and the Middle East, and nine in Central and Eastern Europe. China always belonged to the first plate, and most of the

countries in the first plate belonged to China's neighboring countries. The trade density of the first plate continued to increase from 2001 to 2019, rising from 0.216 to 0.591, indicating that the trade of countries in the first plate became increasingly close.

According to the analysis in Table 7, the first, second, third, and fourth plates in 2001 were, respectively, the export-oriented plate, dual-oriented plate, dual-oriented plate, and the dual-oriented plate. In 2009, they were the export-oriented plate, take into account plate, take into account plate, and the take into account plate. In 2013, they were the export-oriented plate, multi-faceted plate, multi-faceted plate, and the internal plate. In 2019, they were the simultaneous plate, simultaneous plate, simultaneous plate, and the simultaneous plate. Due to space limitations, only the country distributions in 2001 and 2019 are listed. Countries belonging to the first plate in 2001, included China, Russia, one in Southeast Asia, four in West Asia and the Middle East, and three in Central and Eastern Europe. The countries belonging to the second plate numbered three in Southeast Asia and eight in West Asia and the Middle East. The countries belonging to the third plate were Mongolia, and two in Central Asia, five in Southeast Asia, five in South Asia, three West Asia and the Middle East, and four in Central and Eastern Europe. Finally, the countries in the fourth plate numbered three in Central Asia, two in Southeast Asia, three in South Asia, three in West Asia and the Middle East, and nine in Central and Eastern Europe. The countries belonging to the first segment in 2019 were China, one in Southeast Asia, one in South Asia, eight in West Asia and the Middle East, and two in Central and Eastern Europe. The countries in the second segment were Mongolia and Russia, three in Central Asia, four in Southeast Asia, two in South Asia, one in West Asia and the Middle East, and five in Central and Eastern Europe. The countries in the third segment cinluded two in Central Asia, one in Southeast Asia, one in South Asia, two in West Asia and Middle East, and seven in Central and Eastern Europe. The countries in the fourth segment included four in Southeast Asia, four in South Asia, seven in West Asia and the Middle East, and four in Central and Eastern Europe. From 2001 to 2019, China was in the first plate, which changed from an export-oriented plate in 2001 to a balanced plate in 2019, indicating that the internal and external trade space of the plate gradually increased, and that the agricultural trade between China and B&R countries has broad prospects. From the perspective of the overall trade sector, the second, third, and fourth trade sectors had a large space for trade development. Although the fourth sector was transformed into an internal sector in 2013, and the foreign trade was not close enough, after the "Belt and Road" Initiative was proposed, the four sectors were all balanced sectors in 2019. This shows that the trade potential of agricultural products between China and B&R countries is huge.



#### *3.3. Analysis of Spatial Network Influencing Factors*

#### 3.3.1. QAP Correlation Analysis

Using the QAP correlation analysis method and Ucinet 6 software, 5000 random permutations are selected to obtain the results of correlation analysis between China's agricultural trade network F and each influencing factor with B&R countries in 2019, where P represents the probability that the random correlation coefficient is greater or less than the actual value. This is shown in Table 8. The agricultural trade network F is not significantly correlated with the per capita arable land resource matrix (PCLA), scientific research expenditure share (SCI), and economic distance (DE), and is significantly correlated with the per capita water resource matrix (PCWR), geographic location matrix (DIS), transportation (TRA), total economic output (DGDP), trade agreement matrix (TA), culture matrix (CUL), and trade structure matrix (IS). We tentatively determine that the geographical distance, transportation, GDP difference, trade agreements, cultural differences, and trade structure differences between the two countries are the main factors affecting the agricultural trade network between China and B&R countries.

**Table 8.** QAP correlation analysis results of F and influencing factors of China with B&R countries.


#### 3.3.2. QAP Regression Analysis

Based on the correlation analysis results in Table 9, we included the matrices of PCWR, DIS, TRA, DGDP, TA, CUL, and IS, which were significantly correlated with F, in the QAP regression analysis, and set the number of random permutations to 5000. The results are shown in Table 9. As seen from the table, (1) PCWR is significantly correlated with the trade network between China and B&R countries, with a regression coefficient of −0.000002, and a negative regression coefficient, indicating that the smaller the difference in water resources, the closer the trade in agricultural products. (2) The correlation of DIS to China's trade network with B&R countries is significant, with a regression coefficient of 0.360666, indicating that countries with closer geographical locations are more likely to trade agricultural products, which also verifies the fact that China has close agricultural trade with neighboring countries such as Russia, Thailand, and Vietnam. (3) The correlation of TRA with the trade network between China and B&R countries is significant, with a regression coefficient of 0.053962, indicating that the more convenient the domestic and international transportation, the greater the possibility of agricultural trade. (4) DGDP is not significantly correlated with China's trade network with B&R countries, indicating that although GDP is correlated with agricultural trade between countries, countries with a large difference in GDP between the two countries do not necessarily have close agricultural trade. (5) TA is significantly correlated with the trade network between China and B&R countries, indicating that a trade agreement between two or more countries can facilitate bilateral or multilateral trade exchanges. (6) The correlation of CUL with the trade network between China and B&R countries is significant, indicating that the two countries have

the same or similar culture, which is conducive to the economic and trade exchanges between the two countries and the expansion of bilateral agricultural trade. (7) IS is significantly correlated with the trade network between China and B&R countries, with a negative coefficient of −0.000983, indicating that the smaller the gap in the agricultural trade structure, the closer the bilateral trade between the countries.


**Table 9.** QAP regression analysis results of China with B&R countries.

Note: \*\*\*, \*\* indicate that the coefficients passed the significance tests of 1%, 5%.

#### **4. Discussion**

#### *4.1. Comparison to Prior Studies*

The main objective of our paper is to explore the factors influencing the evolution and trade space of agricultural trade between China and B&R countries, from a planar and spatial perspective. Our results confirm that China's agricultural trade with B&R countries has become increasingly close and highly concentrated, with high intensity areas being mainly concentrated in closer or neighboring countries, and that the trade development space shows a "rise-fall-rise " trend, with China remaining at the "power" core of the trade network, and with the centralization of trade in the B&R countries tending to be concentrated. The agricultural trade network between China and B&R countries can be divided into four major segments, with increasingly close internal and external trade in each segment. The trade network linkages are mainly influenced by water resources, geographic location, railroad convenience, trade agreements, and trade structure. The findings of this paper are consistent with previous studies on agricultural trade between China and B&R countries. For example, Yu (2016) found that China's total bilateral trade with eight South Asian countries has quadrupled, and that China's total agricultural imports from South Asia are greater than its total agricultural exports, with a widening deficit [8]. Zhan (2018) pointed out that the network density of agricultural export relations, competitive relations, and complementary relations among B&R countries is increasing day by day [32]. Su (2019) argued that the density of spatially linked networks of agricultural trade in China and B&R countries is high, and that China is at the center of this spatially linked network [33]. As expected, the empirical results of this paper show that China's agricultural trade with B&R countries is getting closer and closer, and that China is gradually becoming the center of agricultural trade. Our findings are almost consistent with those of the aforementioned scholars.

However, our study is somewhat different from previous studies, from the following perspectives. In analyzing the comprehensiveness of agricultural products and time chains, we have used all the total agricultural products and the time of WTO accession as samples, which can provide China with an overall perspective and ability to grasp the dynamics of agricultural cooperation in B&R countries; this is conducive to making comprehensive and sustainable decisions. For example, Li (2018) takes aquatic products as a sample and concludes that China is located in the middle and high end of the regional value chain of B&R countries, and has the ability to dominate the regional value chain [23], which is similar to the conclusion reached in this paper. However, the conclusion of this paper

has a greater generalization of agricultural products and is more conducive to national agricultural sector decision-making.

Second, we use a combination of planar- and spatial-shaped analysis, taking into account the quantity, specific agricultural product dynamics, and spatial national agricultural trade dynamics, each focusing on the other and complementing each other. For example, both Chen (2019) and Su (2019) analyzed the relationship model of food and agricultural trade networks between China and B&R countries from a spatial perspective [26,33], which can reflect that the density of food trade networks among B&R countries is increasing. However, they did not compare with the flat volume structure reference, and could not produce accurate figures.

Third, we improved the competitive index CS and constructed the competitive advantage index CN in the research technique of Li et al. (2017) [37], which provides a method for scholars to measure the spatial relationship. For example, Wei (2018) refers to Li Jing's (2017) method of screening nodes, and uses the total agricultural import and export trade of more than US \$100 million, and unilateral agricultural imports of more than US \$10 million as the criteria for trade flows, without further constructing the network model with the criteria of comparative advantage to derive new trade structure information [31].

Finally, regarding measurements of the influencing factors of spatial association relationships, for example, previous studies found that Wei's (2018) "proximity effect", FTAs, differences in consumer population base, and differences in total economic size all enhance the association relationships of agricultural trade between countries [31]. However, we found that trade linkages are influenced by water resources, geographic location, railroad accessibility, trade agreements, and trade structure. There were also both overlapping elements and new elements that can provide a reference for China to select sustainable cooperation partners.

#### *4.2. Sustainability Implications*

First, the types of agricultural products traded and the import and export areas between China and B&R countries are highly concentrated. China has close trade with countries that are geographically close, so China has to maintain friendly relations with neighboring countries, such as Russia, India, Vietnam, Thailand and other large agricultural countries, and actively sign trade agreements. It can not only save China's trade costs and improve its own agricultural products supply security capacity, but also promote the differentiated division of labor in agriculture between China and B&R countries, improve labor productivity, increase the export of agricultural products with comparative advantages, and achieve sustainable mutual benefits.

Second, large agricultural countries such as China, Russia, and India occupy a dominant position, and the trade network can be divided into four major segments, with increasingly close trade within and outside each segment. Therefore, China should stabilize production and trade with large agricultural countries such as Russia and India to reduce the overall trade risk and enhance sustainable agricultural production and trade. China should also actively use its dominant position as a large agricultural trading country to guide other strong agricultural trade countries to play a greater role in emphasizing their own advantages, supporting countries with weaker trade by providing agricultural production factors, and promoting sustainable cooperation among countries.

Last, the relationship between China and the agricultural trade network of B&R countries is mainly influenced by water resources, geographical location, railroad convenience, trade agreements and trade structure. Therefore, China can, based on the conditions of natural resources, public facilities, and trade agreements of the B&R countries, reduce unnecessary waste of agricultural resources and the environment in the context of the global goal of achieving carbon peaking and carbon neutrality. China should also strengthen infrastructure development and natural resource advantages of neighboring countries to complement each other, reduce trade costs and increase agricultural productivity, and

improve environmentally friendly and sustainable production between countries. Additionally, China should develop an agricultural production policy within China that suits its own resource environment and trade structure, so that both domestic and international agricultural exports and imports can take advantage of their comparative advantages and form a sustainable domestic production and international trade relationship.

#### *4.3. Limitations and Future Research Directions*

There are also some shortcomings to our research. First, when discussing the classification of specific agricultural products, this paper only explores in the plane structure, and it hardly shows the spatial structure. Therefore, although the current situation of the cooperation of specific classified agricultural products with B&R countries can be understood from the perspective of a single Chinese country, the analysis of the agricultural products classified in the spatial scope of B&R countries is lacking. The results of our study need to be supplemented and improved again by subsequent studies. Second, when discussing the factors influencing the spatial structure of trade, we only list 10 indicators, due to the length and the availability of the data, and there are other important influencing factors to be explored to further improve the indicator system for promoting bilateral trade. Third, we assumed only two significant indicators, namely the US \$10 million and US \$100 million markers, since other data were not available for the countries and years we analyzed. Fourth, we constructed indicators of comparative competitive advantage in trade without constructing indicators of trade complementarity. The conclusions drawn can only reflect a situation of competitive advantage in trade.

There are three future research directions. First, our research here mainly explores the agricultural trade structure and its influencing factors between China and B&R countries from a plane and spatial perspective. However, with the establishment of AFTA, CEFTA, and RCEP, the policies between countries are very different, and the global dual carbon initiative goals are included in the influence of the factors. These factors enrich the system of indicators affecting sustainable trade structure and help to quantify the effect of regional cooperation and the reference direction of future sustainable cooperation in the face of COVID-19, the Russia–Ukraine conflict, and climate change. Second, the spatial structure of this paper only explores the overall agricultural trade structure from different markers, so we can take global bulk agricultural products, such as soybean, wheat, corn, and rice, as the research objects, and explore the agricultural trade structure between China and B&R countries, which is conducive to exploring the targets of trade-led sustainable cooperation from the perspective of specific agricultural products. Third, future research can construct trade complementarity indexes. International trade not only has the theory of comparative advantage, but complementarity is also one of the important theories for promoting the development of international trade, and so it is beneficial to expand the criteria of trade cooperation and explore the trade network space from multiple perspectives to provide a rich reference for international agricultural sustainable cooperation.

#### **5. Conclusions**

From the perspective of plane and space, this paper analyzes the plane structure and the spatial network structure, and the influencing factors of agricultural trade between China and the B&R countries from 2001 to 2019. This paper takes all agricultural products and all countries along the B&R as research samples, classifies countries and products, and uses plane and spatial perspectives to conclude that China's agricultural product trade with B&R countries tends to be decentralized in terms of import types and regions, and concentrated in terms of export types and regions, the regions with high trade intensity are mainly concentrated in close proximity or in neighboring countries, and that trade relations are getting closer. China has always had a greater influence in the trade network, and the trade centrality of the B&R countries tends to be concentrated. China's agricultural trade network with the B&R countries can be divided into four major segments, with increasingly close internal and external trade in each segment. The results of this paper are beneficial for China and B&R countries to provide a basis for making decisions on trade cooperation from the perspective of agriculture as a whole, to promote global agricultural cooperation, and to facilitate global agricultural cooperation, the flow of global agricultural factors, world food production, and the establishment of a reference method to study global trade structure.

**Author Contributions:** Conceptualization: L.Z. and G.T.; methodology: L.Z. and G.T.; software: L.Z.; validation: L.Z. and G.T.; formal analysis: L.Z.; investigation: L.Z.; data curation: L.Z. and G.T.; writing—original draft preparation: L.Z.; writing—review and editing: L.Z. and G.T.; visualization: L.Z. and G.T.; funding acquisition: L.Z. and G.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** The research was funded by Heilongjiang Provincial Philosophy and Social Science Office, grant number 18JLD310.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** The authors would like to thank the anonymous reviewers for their constructive comments and valuable suggestions on this article.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Xiangdong Guo 1,\*, Pei Lung <sup>2</sup> , Jianli Sui <sup>3</sup> , Ruiping Zhang <sup>1</sup> and Chao Wang <sup>1</sup>**


**Abstract:** Due to the weak nature of agricultural production, governments usually adopt supportive policies to protect food security. To discern the growth of agriculture from 2001 to 2018 under China's agricultural support policies, we use the nonlinear MS(M)-AR(p) model to distinguish China's agricultural economic cycle into three growth regimes—rapid, medium, and low—and analyze the probability of shifts and maintenance among the different regimes. We further calculated the average duration of each regime. Moreover, we calculated the growth regime transfers for specific times. In this study, we find that China's agricultural economy has maintained a relatively consistent growth trend with the support of China's proactive agricultural policies. However, China's agricultural economy tends to maintain a low-growth status in the long-term. Finally, we make policy recommendations for agricultural development based on our findings that continue existing agricultural policies and strengthen support for agriculture, forestry, and animal husbandry.

**Keywords:** agricultural development; agricultural economic cycle; agricultural policies

#### **1. Introduction**

Agriculture not only affects the macroeconomics of a country but is also associated with the food security and employment issues of a country, particularly for developing countries. With the majority of countries in the world still in the developing stage and a very high proportion of the population still underdeveloped, development remains a central topic in the world economy. As early as 1946, economists Burns and Mitchell emphasized that economic growth can be effectively guided only by a thorough analysis of the mechanisms of change inherent in economic growth [1].

The agricultural surplus theory considers a highly developed agricultural economy as the fundamental condition for macroeconomic development. For this reason, the Chinese government introduced beneficial agricultural policies every year since 2004 to support agricultural development. China's economy has been increasing with a double digit high growth rate in the past few decades. The growth rate has only slowed down slightly in recent years, but it is still one of the fastest growing economies in the world. Within this historical context, what are the dynamics that drive China's agricultural economic growth? In this paper, we investigate the true underlying dynamics of China's agricultural economy during this period of time, and summarize its experience to incentivize further growth of China's agricultural economy.

The available literature focuses mainly on the factors influencing the growth of agriculture. Even though agriculture may grow rapidly in the short term, long-term growth is constrained by factors such as overconsumption of natural resources and environmental pollution [2]. The role of water use in driving agricultural growth in different regions of China was based on a panel vector autoregressive model [3]. Infrastructure development has a catalytic effect on China's agricultural GDP [4]. Agricultural production and

**Citation:** Guo, X.; Lung, P.; Sui, J.; Zhang, R.; Wang, C. Agricultural Support Policies and China's Cyclical Evolutionary Path of Agricultural Economic Growth. *Sustainability* **2021**, *13*, 6134. https://doi.org/10.3390/ su13116134

Academic Editor: Aaron K. Hoshide

Received: 14 April 2021 Accepted: 27 May 2021 Published: 29 May 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

development can also be influenced by other factors. For example, in Nigeria rainfall, currency exchange rate, and food exports are the most important factors driving agricultural output. Food importation, diversion of funds for agriculture, and low penetration of agricultural technology were identified as the major constraints to agricultural development in Nigeria [5].

Other studies reach the same results, in which increasing agricultural research and development expenditures will support agricultural economic growth [6–9]. However, increasing fiscal spending on agriculture, while promoting agricultural growth, can also impact the quality of agroecosystems [10]. Soil and water conservation has a significant impact on the per capita income of rural households in China [11].

To achieve sustainable long-term growth in agriculture, we need to strengthen reforms and innovation in the rural economic system [12]. In the long run, a land system that is compatible with the country's macroeconomic condition has a positive contribution to China's agricultural economic growth [13,14]. Studies in Commonwealth of Independent States (CIS) countries have shown that policy factors such as land reform contribute, to some extent, to the growth of agricultural production [15]. Studies in the EU countries show that the average farm income is already close to the average non-farm income, thanks to the Common Agricultural Policy (CAP) support [16]. Agricultural policies are important for poverty reduction and agricultural development [17–22]. On the contrary, distorted agricultural policies can hinder its agricultural development [23]. Effective monetary and fiscal policies can boost agricultural growth over time [24–26].

The studies above focus on the different factors that are related to agricultural growth, but few scholars have analyzed the historical trajectory of agricultural growth in depth. As a reflection of China's economic growth, agricultural growth has shown up-and-down cycles over the past 20 years. This is despite China's high rate of macroeconomic growth and strong support from the government's pro-agricultural policies.

Recent studies on agricultural economic cycles in Spain, Cuba, and the United Kingdom identified the causative factors driving these cycles. Studies in the Spanish regional economy show that the agricultural production cycle is constrained by the natural environment and ecological conditions, meanwhile, at the same time, benefitted from rapid economic and social development and globalization [27]. The history of agricultural development in Cuba indicates that, according to the theory of the adaptive renewal cycle, the process of agricultural development is nonlinear and is divided into four stages: Growth, maturation, collapse, and transformation [28]. Studies on the United Kingdom agriculture shows that agriculture sustainably reinforces cultural management and ecosystems and will affect cultural service assets in a broad sense [29].

Since 1952, China's agricultural economic cycle has fluctuated several times, with three classical economic cycles and three growth cycles. The agricultural economies have achieved growth and development in the midst of cyclical fluctuations, and responded to the economic policies and institutional reforms in different economic periods [30]. Changes in China's agricultural policies are the main cause of agricultural fluctuations. Institutional factors are important causes of the cyclical fluctuations of the agricultural economy [31]. The pricing mechanism and land system can promote the change of the agricultural economy to a high-growth state [32].

The magnitude of fluctuations in China's agricultural economic cycle has declined significantly and China's agricultural development has gradually stabilized from the 1980s. Overall, China's agricultural economic cycle has a high frequency of fluctuations with small magnitude [33]. China's agricultural economy is characterized by significant inertia during low-growth rates. When the agricultural growth rate is relatively high, its risk of shocks is higher, as well. When the growth rate is relatively low, its uncertainty is relatively low [34]. Agricultural economic cycles have certain spatial correlations and will amplify agricultural economic fluctuations through cyclical spatial spillover, forming cyclical synergistic effects [35]. Technological and institutional innovations in China should emphasize more on sustainable development which considers the relationship between agriculture

and the environment, rather than setting inconsistent and sometimes incompatible policy goals from different perspectives [36].

Agricultural policies implemented by the Chinese government have raised farmers' income levels and contributed to long-term food security goals. However, such policies have also led to a price gap between domestic and international markets for agricultural products leading to a sharp increase in agricultural imports and the accumulation of large stocks [37]. Previous experience in developing agriculture through institutional reform, technological change, market reform, and investment in agriculture remains the key to future success in ensuring food security and sustainable agriculture growth in China [38]. The true family farm of moderate "small and precise" scale, which has emerged quite widely in China over the past 30 years, can chart a more sustainable way forward for Chinese agriculture [39].

Agricultural economic growth should not come at the cost of damage to natural resources and pollution of the ecological environment, but should focus on the coordination and balance between the short- and long-term [2]. Environmentally friendly technological innovation is a long-term driving force for both the development and sustainable growth of agricultural economies. On the whole, every 1% increase in environmentally friendly agricultural technology innovation causes a 0.375% increase in agricultural economic growth, while every 1% increase in the extent of environmentally friendly technology diffusion causes a 0.542% increase in agricultural economic growth [40].

Attention should be paid to the phenomenon of decreasing ecological land use in the agro-pastoral zone, and the land use structure should be adjusted to provide good ecological conditions for the sustainable development of the agricultural economy in the agro-pastoral zone [41]. Soil and water conservation can contribute to agricultural economic growth and rural poverty reduction in China. Soil quality and capital inputs are now more important than farmland size and agricultural labor in poverty reduction and economic growth. Governments and farmers need to prioritize investments in soil and water conservation to boost the agricultural economy and reduce rural poverty [11]. As the use of linear measures does not allow for effective measurement of the characteristics of economic cycles, such as those found in agriculture, scholars have proposed various nonlinear econometric models to characterize the variability of economic cycles in detail, such as the Smoothed Migration Autoregressive (STAR) model, the Markov transfer (MS) model, and Threshold Autoregressive (TAR) models [42–44]. In recent years, researchers have measured the economic cycles of South Africa, Brazil, Turkey, etc. using Markov regime transfer models [45–47].

On this basis, in the existing studies on the fluctuation dynamics of the Chinese economic cycle, scholars have quantitatively measured the economic cycle based on different forms of Markov transfer (MS) models to analyze the dynamic characteristics of the economic cycle when it varies across regimes [48–50]. Sui Jianli and Song Diandian first used a Markovian regime shift model to study the Chinese agricultural economic cycle, but he chose a two-regimes Markovian shift model that could only distinguish the Chinese agricultural economic cycle into a high-growth and a low-growth regime [51].

This paper follows the previous research path but differs in that the time domain of this paper is placed in the specific context of the Chinese government's annual agricultural policies to support agricultural development from the early 21st century. Our research focuses on China's agricultural economic development under the support of agricultural policies. In this paper, we construct a Markov transfer model with three regions to analyze the agricultural development in China since 2001 and use it to initially assess the effects of agricultural policies during this period.

In addition, a nonlinear MS (M)-Autoregressive Process (AR) (p) model with "mean form" and "intercept form" was created, and the growth rate of the overall agricultural product is included in our study. The nonlinear model with the "mean form" focuses on capturing the trajectory of the mean levels as they shift across time, while the nonlinear model with the "intercept form" is able to track the dynamic path of time series means since they smoothly transition over time as they shift by regime. The model is able to more accurately and sharply capture the cyclical fluctuations in the mean of each variable's time series data. Drawing on the ideas of Krolzig, this paper provides a quantitative demonstration of the growth dynamics in the context of policy support for China's agricultural economy [52]. The specific contribution of this paper to the scientific literature on agricultural development is to empirically highlight the importance of agricultural policy for agricultural development.

The Markov transfer model has the advantage of being able to accurately distinguish the dynamic changes between different variables, which is different from other models. Therefore, the Markov transfer model is used in this paper to study the growth of China's agricultural economy. In order to accurately understand the cyclical evolutionary path of the agricultural economy, this paper takes not only the total agricultural output as the object of study, but also the total output of agriculture, forestry, fishery, and animal husbandry sub-sectors of Chinese agriculture.

#### **2. Methods and Materials**

#### *2.1. Nonlinear MS (M)-AR (p) Model Construction and Model Estimation*

In this paper, we model the growth rates of China's agriculture, forestry, livestock, fisheries, and overall agriculture, respectively, and its regime shifts. The growth rate is measured as *y<sup>t</sup>* and the regime shift in the growth rate is measured as *s<sup>t</sup>* . We consider a linear p-order Autoregressive Process (AR) as the starting point for the nonlinear model:

$$y\_t = v + A\_1 y\_{t-1} + \dots + A\_p y\_{t-p} + u\_t \tag{1}$$

where *v* is chosen as the intercept term. In addition, this paper requires the necessary assumptions regarding the smoothness of the *y<sup>t</sup>* time series. In the equation 1 − *A*1*L* − *A*2*L* <sup>2</sup> − · · · − *<sup>A</sup>p<sup>L</sup> <sup>p</sup>* = 0 of the lag operator *L*, we assumed that the characteristic roots of the equation are located outside the unit circle. We also assumed that the error term *u<sup>t</sup>* of Equation (1) follows the standard normal distribution, i.e., *u<sup>t</sup>* ∼ *N ID*(0, Σ). Based on these assumptions, the model form presented in Equation (1) is the "intercept form" of the classical AR (*p*) model. The "mean value form" of the linear p-order AR (*p*) model is presented as follows:

$$y\_t - \mu = A\_1(y\_{t-1} - \mu) + \dots + A\_p(y\_{t-p} - \mu) + u\_t \tag{2}$$

In this paper, *µ* is defined as the mean of the time series *y<sup>t</sup>* for each variable. We can clearly see that the linear AR (*p*) models in "intercept form" and "mean form" constructed in the previous section have limitations in detailing the possible *y<sup>t</sup>* nonlinear features in the time series and cannot successfully capture the "structural mutations" embedded in the time series. In light of this, we explored in depth the "structural mutation" phenomenon in China.

This paper follows the approach of Hamilton and Krolzig [43,52] to add random *s<sup>t</sup>* variables to the time series *y<sup>t</sup>* to deeply explore the potential nonlinear "Markovian shifts" in China's agricultural economic growth process, where M different regimes *s<sup>t</sup>* can be characterized, *s<sup>t</sup>* ∈ {1, . . . , *M*}. By introducing *s<sup>t</sup>* into the time series data generation process, we are able to more accurately examine the dynamic changes in the nonlinear AR (*p*) model. At the same time, this paper further assumes *s<sup>t</sup>* that it is possible for the Markov process to be followed to traverse all *M* regimes, based on which, the specific transfer matrix can be expressed in the following form:

$$P = \begin{bmatrix} P\_{11} & P\_{12} & \cdots & P\_{1M} \\ P\_{21} & P\_{22} & \cdots & P\_{2M} \\ \vdots & \vdots & \ddots & \vdots \\ P\_{M1} & P\_{M2} & \cdots & P\_{MM} \end{bmatrix} \tag{3}$$

In Equation (3), *pij* = Pr(*st*+<sup>1</sup> = *j s<sup>t</sup>* = *i*), Σ *M j*=1 *pij* = 1, ∀*i*, *j* ∈ {1, . . . , *M*}.

In the following sections of this paper, we construct nonlinear MS (*M*)-AR (*p*) models with reference to the "mean-form" linear AR (*p*) models. The MSM (*M*)-AR (*p*) model containing the variable parameter function *µ*(*st*) can be constructed by introducing the regime state variable *s<sup>t</sup>* into the mean *µ* shown in Equation (2).

$$y\_t - \mu(s\_t) = A\_1[y\_{t-1} - \mu(s\_{t-1})] + \dots + A\_p[y\_{t-p} - \mu(s\_{t-p})] + u\_t, \quad u\_t \sim NID(0, \Sigma) \tag{4}$$

It is known that

$$\mu(s\_t) = \begin{cases} \mu\_{1\prime} & s\_t = 1 \\ \vdots & \vdots \\ \mu\_{M\prime} & s\_t = M \end{cases} \tag{5}$$

The variable-parameter functions *A*1(*st*), . . . , *Ap*(*st*), Σ(*st*), and, *v*(*st*) have very similar expressions to *µ*(*st*) as defined above and, therefore, will not be repeated in the following section.

The average duration *D*[*st*(*i*)] with regime variables *s<sup>t</sup>* using the following formula:

$$D[\mathbf{s}\_t(i)] = E[\mathbf{s}\_t = i] = \frac{1}{1 - p\_{ii}}, i = 1, 2, 3 \tag{6}$$

The unique approach of this paper is to create a nonlinear MS (M)-AR (p) model that includes both "mean form" and "intercept form." The nonlinear MS (M)-AR (p) model is further explored using the Expectation Maximization (EM) algorithm and the Maximum Likelihood (ML) technique [43,52].

When we use these nonlinear MS (M)-AR (p) models for economic analysis, we need to first verify the stationarity of the variable data. In this paper, we will use ADF (Augmented Dickey–Fuller) test, PP (Phillips–Perron) test, and KPSS (Kwiatkowski–Phillips–Schmidt– Shin) test to check the stability of the variable data. In addition, for the multiple nonlinear models created in this paper, it is necessary to calculate the AIC (Akaike Information Criterion), HQ (Hannan Quinn), and SC (Schwarz Criterion) values under different model settings according to the AIC information criterion, HQ information criterion, and SC information criterion to analyze the reliability and validity of the model.

#### *2.2. Data Selection for China's Agricultural Economic Growth*

Based on the quarterly data of China's gross product of agriculture, forestry, livestock, fishery, and overall gross agricultural product from Q1 2001 to Q1 2018, this paper further calculated the quarterly data of the growth rate of each variable to examine the cyclical dynamic change process of China's agricultural economic growth in detail. The growth rate is calculated by the year-on-year method and takes into account price inflation. The data in this paper were obtained from the China Economy Internet (CEI) data (http://db.cei.cn, accessed on 24 May 2021) and the China Statistical Yearbook.

To further explore the dynamic paths of the growth rates of China's gross product of agriculture, forestry, livestock, fishery, and overall agricultural product over time, this paper uses the H-P filtering technique [52,53] to capture the *trend component* and the *volatility component* of the time series of each variable to provide a clearer picture of the dynamic evolution of the aggregate value of each variable within the time domain under study. Specifically, the "trend component" can clearly depict the trend state and the change process of each variable time series over a long period of time. The "volatility component" can more carefully depict the fluctuation magnitude and uncertainty of each variable time series data in different economic periods. The "volatility component" can provide a more detailed picture of the volatility and uncertainty of the time series of each variable in different economic periods.

#### **3. Results 3. Results**  *3.1. Growth Rate Dynamic Trajectory Analysis*

#### *3.1. Growth Rate Dynamic Trajectory Analysis* In this paper, we first depict the time fluctuation paths of the growth rates of the

variable in different economic periods.

*Sustainability* **2021**, *13*, 6134 6 of 29

In this paper, we first depict the time fluctuation paths of the growth rates of the gross product value in China's agriculture, forestry, livestock, fishery, and agriculture industries. We can see that, on the one hand, the growth rates of the gross product value of China's agriculture, livestock, fishery, and agriculture time series have generally similar time dynamic trajectories, while the growth rates of China's forestry industry alone show relatively different trend changes (Figures 1–5). On the whole, the fluctuations and oscillations of the time series of the growth rates of the gross product value of China's agriculture, forestry, fishery, and agriculture are relatively small, while the fluctuations of the growth rate of the gross product value of China's livestock are more drastic—showing steep fluctuations with steep increases and decreases. gross product value in China's agriculture, forestry, livestock, fishery, and agriculture industries. We can see that, on the one hand, the growth rates of the gross product value of China's agriculture, livestock, fishery, and agriculture time series have generally similar time dynamic trajectories, while the growth rates of China's forestry industry alone show relatively different trend changes (Figures 1–5). On the whole, the fluctuations and oscillations of the time series of the growth rates of the gross product value of China's agriculture, forestry, fishery, and agriculture are relatively small, while the fluctuations of the growth rate of the gross product value of China's livestock are more drastic—showing steep fluctuations with steep increases and decreases.

paper uses the H-P filtering technique [52,53] to capture the *trend component* and the *volatility component* of the time series of each variable to provide a clearer picture of the dynamic evolution of the aggregate value of each variable within the time domain under study. Specifically, the "trend component" can clearly depict the trend state and the change process of each variable time series over a long period of time. The "volatility component" can more carefully depict the fluctuation magnitude and uncertainty of each variable time series data in different economic periods. The "volatility component" can provide a more detailed picture of the volatility and uncertainty of the time series of each

**Figure 1.** Time series of the growth rate of the gross agriculture product. **Figure 1.** Time series of the growth rate of the gross agriculture product.

The "trend component" of the growth rate of China's gross agriculture product shows that China's agriculture started to achieve significant growth in the early 21st century and showed an increasing trend year by year, reaching its highest "peak" roughly around 2010 and declining from 2010–2018 (Figure 1). In addition, looking at the "fluctuating components" of the growth rate of China's gross agriculture product depicted in Figure 1, the growth rate of China's gross agriculture product has been characterized by weak fluctuations since 2010, with more significant fluctuations clustering in the sample interval before 2010. However, in recent years, the volatility of the growth rate of China's The "trend component" of the growth rate of China's gross agriculture product shows that China's agriculture started to achieve significant growth in the early 21st century and showed an increasing trend year by year, reaching its highest "peak" roughly around 2010 and declining from 2010–2018 (Figure 1). In addition, looking at the "fluctuating components" of the growth rate of China's gross agriculture product depicted in Figure 1, the growth rate of China's gross agriculture product has been characterized by weak fluctuations since 2010, with more significant fluctuations clustering in the sample interval before 2010. However, in recent years, the volatility of the growth rate of China's gross agriculture product has increased.

gross agriculture product has increased. The "trend component" of the growth rate of China's gross forestry product as depicted shows that China's gross forestry product has generally grown steadily over the time horizon selected for this paper, rising year by year at the beginning of the 2000s and peaking in 2003 (Figure 2). Subsequently, the growth rate of the gross forestry product moves down from the "peak" at a very slow pace. In the rest of the sample period, the fluctuation of the growth rate of China's gross forestry product is small, and, especially in recent years, the fluctuation of the growth rate of China's gross forestry product is extremely weak.

*Sustainability* **2021**, *13*, 6134 7 of 29

**Figure 2.** Time series of growth rate of the gross forestry product. **Figure 2.** Time series of growth rate of the gross forestry product. tremely weak.

**Figure 3.** Time series of growth rate of the gross livestock product. **Figure 3.** Time series of growth rate of the gross livestock product.

The "trend component" shown in Figure 3 shows that the growth rate of China's gross livestock product changes slowly and appears to hover between "peaks" and "troughs" several times. The growth rate of the gross livestock product in general gradually shifts downward, particularly in recent years. Industry livestock growth rates are The "trend component" shown in Figure 3 shows that the growth rate of China's gross livestock product changes slowly and appears to hover between "peaks" and "troughs" several times. The growth rate of the gross livestock product in general gradually shifts downward, particularly in recent years. Industry livestock growth rates are lower than the initial levels during the early 21st century.

**Figure 3.** Time series of growth rate of the gross livestock product. The "trend component" shown in Figure 3 shows that the growth rate of China's gross livestock product changes slowly and appears to hover between "peaks" and "troughs" several times. The growth rate of the gross livestock product in general gradually shifts downward, particularly in recent years. Industry livestock growth rates are lower than the initial levels during the early 21st century. lower than the initial levels during the early 21st century. At the same time, the "fluctuation component" in Figure 3 reflects that the time series of the growth rate of China's gross livestock product contains significant fluctuation clustering characteristics, and shows higher fluctuation than the growth rate of gross product At the same time, the "fluctuation component" in Figure 3 reflects that the time series of the growth rate of China's gross livestock product contains significant fluctuation clustering characteristics, and shows higher fluctuation than the growth rate of gross product of agriculture, forestry, and fishery industries. In other words, there is relatively more volatility and uncertainty in the time series of the growth rate of China's gross product value of livestock. However, it is also clear that the volatility of the growth rate of China's gross livestock product has significantly decreased in the recent years. In recent years, the Chinese government has implemented policies to curb the development of animal husbandry in order to protect the environment, resulting in negative growth in the animal husbandry industry.

At the same time, the "fluctuation component" in Figure 3 reflects that the time series of the growth rate of China's gross livestock product contains significant fluctuation clustering characteristics, and shows higher fluctuation than the growth rate of gross product

**Figure 4.** Time series of growth rate of the gross fishery product. **Figure 4.** Time series of growth rate of the gross fishery product.

husbandry industry.

The dynamic trajectory of the growth rate of China's gross fishery product over time is shown in Figure 4. It can be seen from the "trend component" of the figure that, during the initial period in 2001, the growth rate of gross fishery product was at a low level. Subsequently, the growth rate of the gross fishery product shows a "cyclical-like" pattern of gradually climbing from a lower growth level to a higher "peak" level, and then slowly falling back to a lower "trough" level. In recent years, the growth rate of China's gross fishery product has been moving downward. In addition, the "volatility component" of the time series of the gross fishery product growth rate shows that during the global financial crisis from 2007 to 2010, China's gross fishery product growth rate exhibited a highly volatile clustering characteristic. However, in recent years, the volatility of China's The dynamic trajectory of the growth rate of China's gross fishery product over time is shown in Figure 4. It can be seen from the "trend component" of the figure that, during the initial period in 2001, the growth rate of gross fishery product was at a low level. Subsequently, the growth rate of the gross fishery product shows a "cyclical-like" pattern of gradually climbing from a lower growth level to a higher "peak" level, and then slowly falling back to a lower "trough" level. In recent years, the growth rate of China's gross fishery product has been moving downward. In addition, the "volatility component" of the time series of the gross fishery product growth rate shows that during the global financial crisis from 2007 to 2010, China's gross fishery product growth rate exhibited a highly volatile clustering characteristic. However, in recent years, the volatility of China's gross fishery product growth rate has significantly weakened.

of agriculture, forestry, and fishery industries. In other words, there is relatively more volatility and uncertainty in the time series of the growth rate of China's gross product value of livestock. However, it is also clear that the volatility of the growth rate of China's gross livestock product has significantly decreased in the recent years. In recent years, the Chinese government has implemented policies to curb the development of animal husbandry in order to protect the environment, resulting in negative growth in the animal

gross fishery product growth rate has significantly weakened. The growth rate of China's gross agricultural product for all four agricultural sectors aggregated together is depicted in Figure 5. Since 2001, this aggregated growth rate has shown a gradual increase in development momentum and, after reaching the highest "peak" in succession, has been declining. In addition, looking at the "fluctuating component" of the growth rate of China's agricultural product depicted in Figure 5, we can see that, since 2001, China's gross agricultural product has shown more obvious fluctuations and a certain clustering of fluctuations. During the subsequent period of 2012–2018, the volatility of the growth rate of China's gross agricultural product decreased. The growth rate of China's gross agricultural product for all four agricultural sectors aggregated together is depicted in Figure 5. Since 2001, this aggregated growth rate has shown a gradual increase in development momentum and, after reaching the highest "peak" in succession, has been declining. In addition, looking at the "fluctuating component" of the growth rate of China's agricultural product depicted in Figure 5, we can see that, since 2001, China's gross agricultural product has shown more obvious fluctuations and a certain clustering of fluctuations. During the subsequent period of 2012–2018, the volatility of the growth rate of China's gross agricultural product decreased. *Sustainability* **2021**, *13*, 6134 9 of 29

> ment of the long-term dynamic changes in China's agricultural economy. The next part of this paper is based on a nonlinear MS (M)-AR (p) model with time series data on the growth rates of China's gross product of agriculture, forestry, livestock, fishery, and agriculture. The results show that the growth rates of China's gross product of agriculture, forestry, livestock, fishery, and overall agriculture were stationary at the 5% significance level, while the growth rates of all variables were first-order single integers at the 1% sig-

> The study shows that the AIC, HQ, and SC values of the time series of the growth rate of China's gross agriculture product were the smallest when the model was set to the MSM (3)-AR (3) form. The AIC, HQ, and SC values of the time series of the growth rate of China's gross forestry product were the smallest when the model was set to the MSM (3)- AR (1) form. The AIC, HQ, and SC values of the time series of the growth rate of China's livestock, fishery, and China's agricultural output are all minimized when the model is set in the form of MSM (3)—AR (4). Thus, it is reasonable and reliable to use the MSM (3)—AR (p) model constructed in this paper to investigate the dynamic evolution of China's agricultural economy in terms of the growth region system and its changing dy-

> The parameter estimation results of the MSM (M)-AR (p) model calculated by different variables are presented in Tables 1 and 2, respectively. The results of the t-statistical test indicate that all values were significant at the 1% or 5% level, except for the growth rate of the total fishery output in the zone system 1 ( 1 *<sup>t</sup> s* = ). This indicates that the model

> of China's agriculture, forestry, livestock, fishery, and agricultural industries are all con-

growth rates of each variable are considered as the low-growth regime, medium-growth regime, and rapid-growth regime for regime 1, regime 2, and regime 3, respectively.

μμμ

estimates of the growth rates of the gross product

<sup>123</sup> < < (Tables 1 and 2). Therefore, the

μ

**Figure 5.** Time series of growth rate of the gross agricultural product. **Figure 5.** Time series of growth rate of the gross agricultural product.

nificance level.

namics.

we chose is appropriate. The mean

sistent with the parameter constraints

*3.2. The Parameter Estimation of the MSM (M)-AR (p) Model* 

#### *3.2. The Parameter Estimation of the MSM (M)-AR (p) Model*

The growth rates of China's gross product of agriculture, forestry, livestock, fishery, and agriculture, depicted in Figures 1–5, can be used to make a preliminary visual judgment of the long-term dynamic changes in China's agricultural economy. The next part of this paper is based on a nonlinear MS (M)-AR (p) model with time series data on the growth rates of China's gross product of agriculture, forestry, livestock, fishery, and agriculture. The results show that the growth rates of China's gross product of agriculture, forestry, livestock, fishery, and overall agriculture were stationary at the 5% significance level, while the growth rates of all variables were first-order single integers at the 1% significance level.

The study shows that the AIC, HQ, and SC values of the time series of the growth rate of China's gross agriculture product were the smallest when the model was set to the MSM (3)-AR (3) form. The AIC, HQ, and SC values of the time series of the growth rate of China's gross forestry product were the smallest when the model was set to the MSM (3)-AR (1) form. The AIC, HQ, and SC values of the time series of the growth rate of China's livestock, fishery, and China's agricultural output are all minimized when the model is set in the form of MSM (3)-AR (4). Thus, it is reasonable and reliable to use the MSM (3)-AR (p) model constructed in this paper to investigate the dynamic evolution of China's agricultural economy in terms of the growth region system and its changing dynamics.

The parameter estimation results of the MSM (M)-AR (p) model calculated by different variables are presented in Tables 1 and 2, respectively. The results of the t-statistical test indicate that all values were significant at the 1% or 5% level, except for the growth rate of the total fishery output in the zone system 1 (*s<sup>t</sup>* = 1). This indicates that the model we chose is appropriate. The mean *µ* estimates of the growth rates of the gross product of China's agriculture, forestry, livestock, fishery, and agricultural industries are all consistent with the parameter constraints *µ*<sup>1</sup> < *µ*<sup>2</sup> < *µ*<sup>3</sup> (Tables 1 and 2). Therefore, the growth rates of each variable are considered as the low-growth regime, medium-growth regime, and rapid-growth regime for regime 1, regime 2, and regime 3, respectively.


Note: "\*\*\*", "\*\*", and "\*" indicate significance at the 1%, 5%, and 10% levels, respectively.



**Table 2.** *Cont.*


Note: "\*\*\*" and "\*\*" indicate significance at the 1% and 5% levels, respectively.

#### *3.3. The Dynamic Shift Probabilities of China's Agricultural Growth Regime*

Based on the time series data of the growth rates of total output value of Chinese agriculture, forestry, livestock, fishery and overall agricultural in China, we calculated the dynamic shift probabilities of China's agricultural economic growth regimes using Equation (3). The results are presented in Tables 3–7, respectively. We can see that the probability of staying at a low-growth rate for the agriculture sector is 0.9133 (*p*<sup>11</sup> = 0.9133), and the probability of shifting to the medium or fast growth range is very low, corresponding to A and B, which respectively, indicates that the agriculture sector is likely to maintain a low-growth state (Table 3). The probability of staying in the medium-growth range for agriculture is only 0.0548, while the probability of shifting to the low-growth range is as high as 0.9452 (*p*<sup>21</sup> = 0.9452), and there is almost no possibility of shifting to the high-growth range. This reinforces the high probability of maintaining a low-growth rate for the agriculture sector.

The probability of maintaining a high-growth range for the agriculture is relatively high at 0.5588 (*p*<sup>33</sup> = 0.5588), but the probability of falling back to a medium-growth state is also relatively high (0.4421). Since the probability of agriculture maintaining the medium-growth state is very low and the probability of shifting to the low-growth state is very high 0.9452 (*p*<sup>21</sup> = 0.9452), the probability of falling back from the fast-growth state to the medium-growth state through the medium-growth state to the low-growth state is also very high. Again, the result shows that the probability of maintaining the low-growth rate in the agriculture sector is high.

Overall, the probability of staying at the low-growth state for the agriculture sector is very high, while the probability of shifting to the medium or fast rate range is very low. Moreover, the probability of falling from a medium to a low-speed state and of falling from a fast state to a low-speed state through a medium-speed state is very high. Therefore, agriculture in China clearly tends to maintain a low-growth rate.


**Table 3.** Transfer probability matrix of the regime for the growth rate of the gross agriculture product.

The probability of forestry maintaining the low and medium speed states are both very high at 0.8937 (*p*<sup>11</sup> = 0.8937) and 0.9774 (*p*<sup>22</sup> = 0.9774), respectively, and the probability of shifting to other states is very small (Table 4). The probability of maintaining forestry in the fast-growth state is close to zero, while the probability of shifting to the low and medium speed states is very high, 0.6109 (*p*<sup>31</sup> = 0.6109) and 0.3891 (*p*<sup>32</sup> = 0.3891), respectively. This indicates that forestry will likely maintain a low to medium-growth state and has the highest probability of staying in the medium-growth state.


**Table 4.** Transfer probability matrix of the regime of the growth rate of the gross forestry product.

The probability of maintaining a low-growth state in the livestock sector is very high 0.9063 (*p*<sup>11</sup> = 0.9063), while the probability of shifting to either the medium or high growth range is low 0.0937 (*p*<sup>12</sup> = 0.0937) and close to zero, respectively (Table 5). Therefore, it is relatively easy for the livestock industry to maintain a low-growth range. At the same time, the probability that the livestock sector maintains a fast-growth state is relatively high 0.6609 (*p*<sup>33</sup> = 0.6609), but the probability of maintaining a medium-growth range is very low.

**Table 5.** Transfer probability matrix of the regime of the growth rate of the gross livestock production.


The probability of shifting China's total fishery output from the low-growth range to the medium-growth range and the fast-growth range is *p*<sup>12</sup> = 0.5515 and *p*<sup>13</sup> = 0.4464, respectively. The probability of maintaining a particular growth rate is highest sustaining fast-growth (*p*<sup>33</sup> = 0.8026) followed by keeping at medium (*p*<sup>22</sup> = 0.7345) and low (*p*<sup>11</sup> = 0.0020) growth (Table 6). It can be seen that the probability of maintaining the total fishery output value in the low-growth range is very low, and it is easy to climb from the low-growth state to the medium- and high-growth state. There is a high probability of maintaining the medium-growth state and high-growth state, and it is easy to maintain the medium- and high-growth state, so China's fishery industry has a very good developmental trend.

The probability of transferring China's total fishery output value from the mediumgrowth regime back to the low-growth regime is *p*<sup>21</sup> = 0.2646, and the probability of transferring from the fast-growth regime to the medium-growth regime is *p*<sup>32</sup> = 0.1974. The probability of shifting from the "fast-growth regime" to the "medium-growth regime" is close to zero. Therefore, the probability of shifting from a higher growth state to a lower growth state is low, which indicates a more stable development trend of fisheries.

**Table 6.** Transfer probability matrix of regimes for the growth rate of the gross fishery product.


From the results of the transfer probability matrix of China's gross agricultural product, the maintaining probabilities of the low-, medium-, and rapid-growth regime of the gross agricultural product are *p*<sup>11</sup> = 0.8425, *p*<sup>22</sup> = 0.5909, and *p*<sup>33</sup> = 0.8020, respectively (Table 7). This indicates that China's gross agricultural product does not easily change its growth

status when it is in different regimes, and it has certain inertia characteristics. When China's gross agricultural product is in the low-growth regime, it does not easily change to a higher growth rate, but when the level of agricultural development increases significantly, the gross agricultural product easily stays in the medium-growth regime and the rapid-growth regime and does not decline significantly.

It is clear that the probability of gross agricultural product climbing from the lowgrowth regime to the medium-growth regime is low (*p*<sup>12</sup> = 0.1575), and essentially does little to possibly jump from the low-growth regime to the rapid-growth regime, while the probability of transferring the gross agricultural product from the medium-growth regime to the rapid-growth regime is relatively high (*p*<sup>23</sup> = 0.3999). Thus, when the gross agricultural product is in the low-growth regime, it is difficult to achieve a significant increase due to the limitation of resources and technical facilities. When the gross agricultural product is in the medium-growth regime, the existing capital and technology advantages can be fully utilized to achieve a smooth transition from the medium-growth regime to the rapid-growth regime. The rise from the medium-growth regime to the rapid-growth regime is smooth.

The probability of change of China's gross agricultural product falling back from the medium-growth regime is *p*<sup>21</sup> = 0.0092, while the probability of change from the fastgrowth regime to the low-growth regime is relatively low (*p*<sup>31</sup> = 0.1980). At the same time, the probability of change of gross agricultural product falling back from the rapid-growth regime to the medium-growth regime is close to zero. Thus, it is clear that the probability of a decline in gross agricultural product from the medium-growth regime is not high, and the gross agricultural product has a relatively stable development trend. In addition, when the gross agricultural product is in the rapid-growth regime, it does not decrease to the medium-growth regime. Therefore, the national strategy of modernizing agricultural development has achieved significant results during the sample period.

The overall improvement of China's agricultural production conditions and mechanization level in recent years has laid a good foundation for the development of agricultural facilities. Coupled with the Chinese government's rational planning and allocation of resources and active financial support policies, China's agricultural production modernization level has maintained steady and rapid growth. China's existing agricultural policies have achieved positive results.


**Table 7.** Transfer probability matrix of growth rate regime of China's gross agricultural product.

#### *3.4. Estimated Average Duration of Each Regime of China's Agricultural Economy*

We calculated the average durations of the growth rates of China's agriculture, forestry, livestock, fisheries, and overall agriculture for each growth rate (Table 8). Combining the maintenance probabilities given in Tables 3–7, and considering the average duration presented in Table 8, we further examined the dynamic characteristics of the variables in different regimes. The probability that China's gross agriculture product is in the lowgrowth regime is *p*<sup>11</sup> = 0.9133 maintained with an average duration of 11.54 quarters. This indicates that the lower level of development of the agriculture is more inclined to maintain a low rate of growth and will not make a significant leap to a state of rapid growth.

At the same time, the probability of maintaining a fast-growth state for the gross agriculture product is *p*<sup>33</sup> = 0.5588, and 2.27 quarters of average duration. The gross agriculture product can be stabilized in the rapid-growth regime with a moderate probability of maintenance in the short-term. When the gross agriculture product is in the

medium-growth regime, the maintenance probability is *p*<sup>22</sup> = 0.0548 with an average duration of 1.06 quarters. The probability and average duration of sustaining agriculture in the medium-growth regime are low. Overall, China's agriculture is most likely to remain in the low-growth regime and is the least likely to be in the medium-growth regime among all regimes.

The probability that China's gross forestry product is in the low-growth regime is *p*<sup>11</sup> = 0.8937 with an average duration of 9.41 quarters and has the inertia characteristic of maintaining a low-growth rate. The probability of maintaining the medium-growth rate of forestry product is higher *p*<sup>22</sup> = 0.9774 with an average duration of 44.19 quarters, and the gross forestry product tends to maintain the medium-growth regime. The probability of maintaining the forestry product in the rapid-growth regime is close to zero with an average duration of 1.00 quarter. On balance, the gross forestry product tends to maintain a medium-growth rate over a longer time horizon, with a possible shift to a low-growth regime but a difficult shift to a rapid-growth regime.

The probabilities of maintaining medium- and fast-growth in the gross livestock product are *p*<sup>22</sup> = 0.0066 and *p*<sup>33</sup> = 0.6609 with 1.01 and 2.95 quarters of average duration, respectively. The probability that the gross product of livestock is in the low-growth regime is the highest among all regimes (*p*<sup>11</sup> = 0.9063) and has an average duration of 10.67 quarters. Overall, the gross product of the livestock sector is more likely to remain in the low-growth regime, the least likely to remain in the medium-growth regime, and moderately likely to be in the rapid-growth regime.

The probability of maintaining low growth in China's gross fishery product was the lowest among all regimes (*p*<sup>11</sup> = 0.0020) and only showed an average duration of 1.00 quarter. The probabilities of maintaining medium- and rapid-growth rates were *p*<sup>22</sup> = 0.7345 and *p*<sup>33</sup> = 0.8026, with 3.77 and 5.07 quarters average duration, respectively. This indicates that the gross fishery product is more likely to achieve medium- and highgrowth rates and has the highest probability of being in a fast-growth state and the lowest probability of being in a low-growth state.


**Table 8.** Estimated average duration of each regime (quarters).

The average duration of the gross agricultural product in the low-growth regime was relatively long, with an average duration of 6.35 quarters, while the average duration in the rapid-growth regime was also relatively long (Table 8). However, the average duration in the medium-growth regime was relatively short, at 2.44 quarters. At the same time, the probability that China's gross agricultural product will remain in the low-growth regime and rapid-growth regime was high (*p*<sup>11</sup> = 0.8425, *p*<sup>33</sup> = 0.8020), and the probability of medium-growth was lower (*p*<sup>22</sup> = 0.5909). This indicates that China's gross agricultural product is more likely to be in the low-growth regime and rapid-growth regime, and less likely to be in the medium-growth regime.

#### *3.5. Filter Probability and Smoothing Probability*

In the next section, the dynamic paths of the time series trajectories of the growth rates of each variable are clarified by depicting the smoothed probabilities of the gross

product value of China's agriculture, forestry, livestock, fishery, and overall agriculture industries in each growth regime. In the smoothed probabilities of the low-growth regime (probability of *s<sup>t</sup>* = 1:Pr(*s<sup>t</sup>* = 1 *<sup>ξ</sup>t*|*<sup>t</sup>* ) > 0.5), medium-growth regime (probability of: *s<sup>t</sup>* = 2:Pr(*s<sup>t</sup>* = 2 *<sup>ξ</sup>t*|*<sup>t</sup>* ) > 0.5), and rapid-growth regime (probability of *s<sup>t</sup>* = 3: Pr(*s<sup>t</sup>* = 3 *<sup>ξ</sup>t*|*<sup>t</sup>* ) > 0.5), *<sup>ξ</sup>t*|*<sup>t</sup>* refers to all information sets based on past *t* periods. *Sustainability* **2021**, *13*, 6134 15 of 29 Overall, we found that China's gross agriculture product generally fluctuated between the rapid-growth regime and the medium-growth regime but was able to maintain

The growth rates of China's gross product of agriculture, forestry, livestock, fishery, and overall agricultural industries changed frequently and dynamically among various regimes during the development of China's agricultural economic growth, and the dynamic paths of the growth rates of each variable in each regime will be further detailed in this paper. Within the sample interval studied, the gross agriculture product was in the rapidgrowth regime during (a) Q2 to Q4 2004, (b) Q1 2006, (c) Q4 2007 to Q1 2008, and (d) Q1 to Q4 2010 (Figure 6). Looking back at history, it is easy to see that these particular timeframes correspond to the launch of relevant policies in China (Appendix A, Table A1). For example in 2004, the first "Document No. 1" of the century was issued, which introduced the "three subsidies" policy, namely "direct subsidies for farmers, subsidies for good seeds, and subsidies for the purchase of agricultural machinery, and minimum purchase price for rice." a relatively stable growth rate when it was in the low-growth regime. However, when in the low-growth regime, the growth rate remained relatively stable and had a more consistent smoothing probability. In the time domain selected in this paper, the gross agriculture product in the low-growth regime basically coincided with the occurrence of natural disasters, such as floods and snowstorms. This judgment is consistent with the phenomena revealed in Tables 3 and 8. That is, it is difficult for China's gross agriculture product to remain in the rapid-growth regime or medium-growth regime in general, but it tends to stay in the low-growth regime. Unlike prior literature, the model in this paper captures the rapid-growth regime of Chinese agriculture around 2006 and 2008, suggesting that the model in this paper is more accurate in modeling real fluctuations in economic development.

**Figure 6.** Filter probability and smoothing probability of gross agriculture product located in different growth regimes. Note: The green arrows mark the times when important policies were in-**Figure 6.** Filter probability and smoothing probability of gross agriculture product located in different growth regimes. Note: The green arrows mark the times when important policies were introduced.

troduced. We can see from Figure 7 that in the early part of this century, China's agricultural development, including forestry, was greatly affected by the *soft landing* of China's macroeconomics. In the case of forestry, the gross forestry product showed more dramatic fluctuations at the beginning of the 20th century, with more frequent cyclical fluctuations. Coupled with the sudden impact of natural disasters on forestry development, the process of change in gross forestry product showed a direct fall from the rapid-growth regime to the low-growth regime and was less often in the medium-growth regime. In 2006, China completely abolished all agricultural taxes except the tobacco tax nationwide. In 2009, the Chinese government implemented the property rights policy of giving farmers fuller and more secure rights to contracted land management, and indicated that the existing land contract relationship would remain stable and unchanged for a long time. This injected confidence and vitality into the agricultural economy. In 2010, the government clearly proposed to give full play to the effective allocation of resources to promote agricultural development, and to improve the efficiency of agricultural development by improving the allocation of resource factors.

Under a series of effective policy measures, China's agriculture has achieved remarkable results and has been in the rapid-growth regime for a long period of time. However, China's gross agriculture product still fell into the medium-growth regime during Q1 2005, Q2 2006, and Q2 2008. It was in the low-growth rate during the periods from (a) Q4 2001 to Q1 2004, (b) Q2 to Q4 2005, (c) Q3 2006 to Q3 2007, (d) Q3 2008 to Q4 2009, and (e) Q1 2011 to Q1 2018.

Overall, we found that China's gross agriculture product generally fluctuated between the rapid-growth regime and the medium-growth regime but was able to maintain a relatively stable growth rate when it was in the low-growth regime. However, when in the low-growth regime, the growth rate remained relatively stable and had a more consistent smoothing probability. In the time domain selected in this paper, the gross agriculture product in the low-growth regime basically coincided with the occurrence of natural disasters, such as floods and snowstorms.

This judgment is consistent with the phenomena revealed in Tables 3 and 8. That is, it is difficult for China's gross agriculture product to remain in the rapid-growth regime or medium-growth regime in general, but it tends to stay in the low-growth regime. Unlike prior literature, the model in this paper captures the rapid-growth regime of Chinese agriculture around 2006 and 2008, suggesting that the model in this paper is more accurate in modeling real fluctuations in economic development.

We can see from Figure 7 that in the early part of this century, China's agricultural development, including forestry, was greatly affected by the *soft landing* of China's macroeconomics. In the case of forestry, the gross forestry product showed more dramatic fluctuations at the beginning of the 20th century, with more frequent cyclical fluctuations. Coupled with the sudden impact of natural disasters on forestry development, the process of change in gross forestry product showed a direct fall from the rapid-growth regime to the low-growth regime and was less often in the medium-growth regime. *Sustainability* **2021**, *13*, 6134 16 of 29

**Figure 7.** Filter probability and smoothing probability of gross forestry product located in different growth regimes. Note: The green arrows mark the times when important policies were intro-**Figure 7.** Filter probability and smoothing probability of gross forestry product located in different growth regimes. Note: The green arrows mark the times when important policies were introduced.

duced. During the periods from Q2 2001 to Q4 2001 and Q2 2002 to Q4 2002, China's gross forestry product was in the low-growth regime, and, during Q1 2002 and Q1 2003, China's gross forestry product was in the rapid-growth regime. In 2002, the Chinese government promulgated the *Regulations on Returning Farmland to Forestry* to promote the development of forestry, which solved the inherent problems in forestry development while expanding the area of forestland and laid the policy foundation for sustainable development of for-During the periods from Q2 2001 to Q4 2001 and Q2 2002 to Q4 2002, China's gross forestry product was in the low-growth regime, and, during Q1 2002 and Q1 2003, China's gross forestry product was in the rapid-growth regime. In 2002, the Chinese government promulgated the *Regulations on Returning Farmland to Forestry* to promote the development of forestry, which solved the inherent problems in forestry development while expanding the area of forestland and laid the policy foundation for sustainable development of forestry (Appendix A, Table A1).

estry (Appendix A, Table A1). In 2003, the Chinese government further promulgated the *Decision on Accelerating the Development of Forestry*, which provided concrete measures for forestry development by adjusting the structure of forestry industry and strengthening the construction of forestry bases (refer to the attachment for details). This series of policies and measures strongly promoted the development of forestry and the process of forestry modernization. The In 2003, the Chinese government further promulgated the *Decision on Accelerating the Development of Forestry*, which provided concrete measures for forestry development by adjusting the structure of forestry industry and strengthening the construction of forestry bases (refer to the attachment for details). This series of policies and measures strongly promoted the development of forestry and the process of forestry modernization. The gross forestry product entered the medium-growth regime from Q2 2003 to Q3 2014.

gross forestry product entered the medium-growth regime from Q2 2003 to Q3 2014. However, China's gross forestry product was in the low-growth regime from Q4 2014 to Q1 2018. From the smoothed probability time dynamics trajectory shown in Figure 7, However, China's gross forestry product was in the low-growth regime from Q4 2014 to Q1 2018. From the smoothed probability time dynamics trajectory shown in Figure 7, whether in the low-growth regime, medium-growth regime or rapid-growth regime, the

whether in the low-growth regime, medium-growth regime or rapid-growth regime, the smoothed probability of China's gross forestry product in different regimes were all close

At the beginning of this century, the gross livestock product, which has a certain scale but is still immature in general, was basically in the low-growth regime due to the influence of the soft landing of China's economy (Figure 8). Several factors caused China's grow livestock product to drop from a rapid-growth to a low-growth regime. In 2004, the No. 1 Document encouraged the continuous improvement of feed, technology, equipment, and other inputs. In addition, there was increasing industry maturity in the pro-

In 2006, under the influence of the policy of abolishing agricultural tax and benefiting farmers, China's gross livestock product stepped into the rapid-growth regime again, fluctuating back and forth until falling back to a low-growth regime in 2009. In 2011, stimulated by the policy of accelerating the construction of water resources and infrastructure, it again entered the rapid-growth regime. From 2001 to 2012, there was an interaction between the rapid-growth regime and the low-growth regime, with a long-term low-growth regime after 2012. Unlike previous studies, our model sensitively captures the rapid-

product was more effective.

cessing of both dairy and meat products.

smoothed probability of China's gross forestry product in different regimes were all close to the 1.00 level. This shows that the risk prevention and control of China's gross forestry product was more effective.

At the beginning of this century, the gross livestock product, which has a certain scale but is still immature in general, was basically in the low-growth regime due to the influence of the soft landing of China's economy (Figure 8). Several factors caused China's grow livestock product to drop from a rapid-growth to a low-growth regime. In 2004, the No. 1 Document encouraged the continuous improvement of feed, technology, equipment, and other inputs. In addition, there was increasing industry maturity in the processing of both dairy and meat products. *Sustainability* **2021**, *13*, 6134 17 of 29 growth regime of China's livestock industry from (a) 2003–2005, (b) 2007–2008, (c) 2008– 2009, and (d) 2010–2012, indicating that our model can more accurately simulate agricultural economic development.

**Figure 8.** Filter probability and smoothing probability when the gross livestock product is in different growth regimes. Note: The green arrows mark the times when important policies were introduced. **Figure 8.** Filter probability and smoothing probability when the gross livestock product is in different growth regimes. Note: The green arrows mark the times when important policies were introduced.

China's fisheries industry was also affected by the *soft landing* of China's economy and was developing at a low rate in the early 2000s. However, fisheries entered a rapidgrowth phase starting in 2004 under the influence of the first Document No. 1 (Figure 9). In 2008, the Chinese government introduced policies to strengthen the safety of fishery production, improve regulatory efforts, strengthen fishery production measures, and optimize departmental cooperation to ensure safe fishery production, while establishing a long-term mechanism to maintain sustainable fishery development (Appendix A, Table A1). This series of comprehensive requirements as well as specific initiatives have greatly In 2006, under the influence of the policy of abolishing agricultural tax and benefiting farmers, China's gross livestock product stepped into the rapid-growth regime again, fluctuating back and forth until falling back to a low-growth regime in 2009. In 2011, stimulated by the policy of accelerating the construction of water resources and infrastructure, it again entered the rapid-growth regime. From 2001 to 2012, there was an interaction between the rapid-growth regime and the low-growth regime, with a long-term low-growth regime after 2012. Unlike previous studies, our model sensitively captures the rapid-growth regime of China's livestock industry from (a) 2003–2005, (b) 2007–2008, (c) 2008–2009, and (d) 2010–2012, indicating that our model can more accurately simulate agricultural economic development.

improved the pattern of fishery production, and under the new management mechanism, China's gross fishery production climbed to the medium-growth regime during the periods from Q3 2008 to Q1 2009 and from Q2 2010 to Q4 2012, while China's gross fishery production was in the rapid-growth regime during the periods from Q3 2008 to Q1 2009 and from Q2 2010 to Q4 2012. In general, China's fishery industry has been developing at a medium to high speed for a long time and has achieved a very good development trend. The possibility of China's fishery product moving into a low-growth regime still requires China's fisheries industry was also affected by the *soft landing* of China's economy and was developing at a low rate in the early 2000s. However, fisheries entered a rapid-growth phase starting in 2004 under the influence of the first Document No. 1 (Figure 9). In 2008, the Chinese government introduced policies to strengthen the safety of fishery production, improve regulatory efforts, strengthen fishery production measures, and optimize departmental cooperation to ensure safe fishery production, while establishing a long-term mechanism to maintain sustainable fishery development (Appendix A, Table A1).

special attention. Finally, we can see the basic overview of China's gross agricultural product in the low-growth regime, medium-growth regime, and rapid-growth regime. Specifically, China's agricultural economy was in the rapid-growth regime for 24 quarters in stages throughout the sample period (Figure 10). Looking back at history, during the period when China's gross agricultural product was in rapid growth, the Chinese government had major policies benefiting agriculture (Appendix A, Table A1). This series of comprehensive requirements as well as specific initiatives have greatly improved the pattern of fishery production, and under the new management mechanism, China's gross fishery production climbed to the medium-growth regime during the periods from Q3 2008 to Q1 2009 and from Q2 2010 to Q4 2012, while China's gross fishery production was in the rapid-growth regime during the periods from Q3 2008 to Q1 2009 and from Q2 2010 to Q4 2012. In general, China's fishery industry has been developing at a medium to high speed for a long time and has achieved a very good development trend. The possibility of China's fishery product moving into a low-growth regime still requires special attention.

*Sustainability* **2021**, *13*, 6134 18 of 29

**Figure 9.** Filter probability and smoothing probability when the gross fishery product is in different growth regimes. Note: The green arrows mark the times when important policies were intro-**Figure 9.** Filter probability and smoothing probability when the gross fishery product is in different growth regimes. Note: The green arrows mark the times when important policies were introduced.

duced. Finally, we can see the basic overview of China's gross agricultural product in the lowgrowth regime, medium-growth regime, and rapid-growth regime. Specifically, China's agricultural economy was in the rapid-growth regime for 24 quarters in stages throughout the sample period (Figure 10). Looking back at history, during the period when China's gross agricultural product was in rapid growth, the Chinese government had major policies benefiting agriculture (Appendix A, Table A1). **Figure 9.** Filter probability and smoothing probability when the gross fishery product is in different growth regimes. Note: The green arrows mark the times when important policies were introduced.

In 2004, the Chinese government issued the first No. 1 Document, which introduced direct subsidies for farmers, subsidies for good seeds and agricultural machinery, and subsidies for the purchase of agricultural machinery, and a minimum purchase price for rice, which greatly motivated farmers. In 2006, the agricultural tax regulations were abol-**Figure 10.** Filter probability and smoothing probability when the overall agricultural product is in different growth regimes. Note: The green arrows mark the times when important policies were introduced. **Figure 10.** Filter probability and smoothing probability when the overall agricultural product is in different growth regimes. Note: The green arrows mark the times when important policies were introduced.

ished. The agricultural tax regulations, which had been implemented for nearly 50 years in New China, became historical records, and the system of taxing farmers by the area of land, which had lasted for 2600 years, was retired from the historical precedence. The burden of farmers nationwide was reduced by CNY 133.5 billion per year, and the per capita burden was reduced by about CNY 140. After 2009, the Chinese government implemented the property rights policy of giving farmers fuller and more secure rights to In 2004, the Chinese government issued the first No. 1 Document, which introduced direct subsidies for farmers, subsidies for good seeds and agricultural machinery, and subsidies for the purchase of agricultural machinery, and a minimum purchase price for rice, which greatly motivated farmers. In 2006, the agricultural tax regulations were abolished. The agricultural tax regulations, which had been implemented for nearly 50 years In 2004, the Chinese government issued the first No. 1 Document, which introduced direct subsidies for farmers, subsidies for good seeds and agricultural machinery, and subsidies for the purchase of agricultural machinery, and a minimum purchase price for rice, which greatly motivated farmers. In 2006, the agricultural tax regulations were abolished. The agricultural tax regulations, which had been implemented for nearly 50 years in New

contracted land management and indicated that the existing land contracting relationship would remain stable and unchanged for a long time. In 2010, the Chinese government

in New China, became historical records, and the system of taxing farmers by the area of

implemented the property rights policy of giving farmers fuller and more secure rights to contracted land management and indicated that the existing land contracting relationship would remain stable and unchanged for a long time. In 2010, the Chinese government

land, which had lasted for 2600 years, was retired from the historical precedence.

China, became historical records, and the system of taxing farmers by the area of land, which had lasted for 2600 years, was retired from the historical precedence.

The burden of farmers nationwide was reduced by CNY 133.5 billion per year, and the per capita burden was reduced by about CNY 140. After 2009, the Chinese government implemented the property rights policy of giving farmers fuller and more secure rights to contracted land management and indicated that the existing land contracting relationship would remain stable and unchanged for a long time. In 2010, the Chinese government made it clear that it was committed to giving full play to the role of efficient allocation of resources in promoting agricultural development, and to achieving increased efficiency in agricultural development by improving the allocation of resource factors. This injected confidence and vitality into the agricultural economy, and China's agricultural economy once again entered a high-speed growth regime. As the above-mentioned policy dividend receded, China's total agricultural output value shifts to a low-growth regime after a brief transition to a medium-growth regime.

Overall, China's gross agricultural product generally fluctuated frequently between the rapid-growth regime and the low-growth regime. The medium-growth regime occurred over shorter time periods. After 2012, except for 2016 when it was in the medium-highgrowth range for a short time, China's gross agricultural product was in the low-growth regime for a long time and was able to maintain relative stability (Tables 7 and 8).

The Chinese government has introduced beneficial agricultural policies every year since 2004. However, the growth of China's agricultural economy fluctuated between fast and low growth. After 2012, despite active fiscal policy, and, except for one quarter of fast growth in 2016, it has been in a low growth trend for a long time. This fully illustrates the vulnerability of agricultural economic growth and indicates that the government should pay attention to agricultural development in the long-term and increase policy support.

Comparing Figures 6–10 with Figures 1–5, we can see that the changes in the total output value of the agriculture, forestry, livestock, fishery, and overall agriculture industries show some similarities. Specifically, the time range of the "fast-growth regime" for each industry shown in Figures 5–8 corresponds to the period when the "fluctuation component" is relatively strong as shown in Figures 1–5, and the time range of the low-growth regime for each industry shown in Figures 6–10 corresponds to the period when the "fluctuation component" is relatively calm as shown in Figures 1–5. This implies that the risk of shocks is higher when the growth rate of the total output of each industry was relatively high in the agriculture, forestry, livestock, fishery, and overall agriculture industries and less uncertain when the growth rate of the total output of each industry was relatively low.

#### **4. Discussion**

#### *4.1. Contrast to Prior Studies*

Since China is still a developing country, the level of development of China's rural areas still lags behind that of the developed world, and agricultural production is still the main source of income for many Chinese farmers. Development remains a major issue for the Chinese government now and in the future. Therefore, in order to better understand the implications of our results, we need to compare and contrast our results to previous studies.

First, based on the above-mentioned empirical findings, we found that China's agricultural economy maintained a relatively good development with the support of the benefit agriculture policy during the sample period, i.e., the probability of maintaining China's agricultural economy in the fast-growth regime was *p*<sup>33</sup> = 0.8020 and had an average duration of 5.05 quarters, which is only slightly lower than the average duration of 6.65 quarters in the low-growth regime. This indicates that China's agricultural economy developed relatively well during periods following the agricultural support policies, which is consistent with prior studies [13–15].

Second, we found that the agricultural economy tended to maintain a low-growth rate, with the highest maintenance probability of *p*<sup>11</sup> = 0.8425 and the longest average duration of 6.35 quarters, which is consistent with the results of another study [34], demonstrating

that China's agricultural economy is not easy to move into the expansion phase of its economic cycle. Our results are also consistent with research showing that maintaining rapid long-term growth in the agricultural economy is not easy due to factors such as excessive consumption of natural resources and environmental pollution [2].

A possible reason for this phenomenon is that the law of diminishing marginal returns is particularly evident in traditional agriculture due to natural conditions. Since the amount of resources invested in agriculture, such as land, is fixed, increasing the labor force will not increase the agricultural output significantly, resulting in slow agricultural growth. This pattern is particularly evident in developing countries where technological progress in agriculture is slow. Another possible reason is that since the demand for agricultural products typically lacks elasticity, an increase in the supply of agricultural products will cause the prices of agricultural products to fall, and hence an increase in production will not lead to an increase in income, which will, in turn, lead to a slow growth in agricultural output.

Third, based on the results of our empirical tests, we distinguished three growth statuses of China's agricultural economy—low, medium, and rapid—and we show their specific transfer paths in Figures 6–10. Specifically, China's agricultural economy was in the rapid-growth regime for 24 quarters in stages throughout the sample period. These rapid- growth states are clearly synchronized with the timing of important agricultural policies. Our analysis suggests that the possible reasons for the shifts to the rapid-growth regime are the introduction of major pro-agricultural policies by the Chinese government.

For example, in 2004, the Chinese government issued the first "Document No. 1" in the time period under examination, which introduced "direct subsidies for farmers, subsidies for good seeds and agricultural machinery purchases, and a minimum purchase price for rice," that greatly motivated farmers to produce more agricultural products. China's total agricultural output value also entered a high-growth zone. In 2006, when the agricultural tax regulations were abolished, China changed from an agricultural tax-raising country to an agricultural subsidy country, and the total agricultural output value entered a high-growth zone at the same time. In 2009, the Chinese government implemented the property rights policy of "granting farmers more complete and more secure rights to contracted land management and the existing land contract relationship should remain stable and unchanged for a long time."

From 2009 to 2010, a series of major initiatives for the benefit of farmers were proposed, including improving the policy system for the benefit of farmers, focusing on promoting the allocation of resources to rural areas, promoting the transformation of agricultural development, and improving the level of modern agricultural equipment. It also includes accelerating the improvement of rural people's livelihood, narrowing the gap between the development of urban and rural public utilities, coordinating the reform of urban and rural areas, and enhancing the vitality of agricultural and rural development. This was intended to enhance the vitality of agricultural and rural development, to promote urbanization actively and steadily, with the development of small and medium-sized cities and small towns as the focus, and to deepen the reform of the household registration system, etc. This has injected confidence, capital, technology, and vitality into China's agricultural economy, which has once again stepped into a high-speed growth regime. The above results support the findings of Qiao et al. [12], Jin and Deininger [13], Deininger et al. [14], and Xi et al. [32] that demonstrated agricultural growth is influenced by policies and institutions.

In addition, our research results show that China's agriculture, forestry, and animal husbandry tended to grow at a low rate. It was in the low-growth regime for a long time after 2011. The recent low-growth rate of the Chinese agricultural economy has been previously documented [34]. Policymakers should pay attention to this issue and continue to rely on agricultural support policies to avoid food shortages in China as well as adverse macroeconomic outcomes.

In this paper, the causal explanation of the correlation between agricultural economic cycles and government support policies is based on the common sense judgment of temporal synchronization and is not the result of empirical studies constructed using specialized

models, such as Data Envelopment Analysis (DEA), Common Agricultural Policy Regionalized Impact (CAPRI), Propensity Score Matching (PSM), and other modeling methods. Hence, the generalizability of our results may be limited. Using specialized policy efficiency assessment models to evaluate the efficiency of agricultural support policies in China may be a fruitful, future research direction.

#### *4.2. Policy Recommendations*

Since China is still a developing country. The level of development of China's rural areas still lags behind that of the developed world, and agricultural production is still the main source of income for many Chinese farmers. Development remains a major issue for the Chinese government now and for the foreseeable future. Therefore, based on the results of our study, we propose the following policy recommendations.

First, there is a large body of theoretical and empirical research that shows a strong correlation between agricultural policy and agricultural development. China's current agricultural policies have achieved relatively good results. Therefore the current agricultural policy in the form of the annual Central Government Document No. 1 should be continued.

Secondly, agriculture, forestry, animal husbandry, and fisheries each have their own industry characteristics. Past research results show that the impacts of policies are rather limited with the exception of fisheries. Therefore, the formulation of agricultural policies in line with the development characteristics and stages of each industry may achieve better policy results.

Finally, China is a vast country with a wide range of regional development levels, and the geographical characteristics of agricultural development vary. Therefore policymakers should consider this situation and delegate policy-making authority to provincial governments or lower institutions. This is especially true for autonomous regions, autonomous prefectures, and even autonomous counties that are less developed. These institutions may be able to develop policies in their own regions that are more in line with the level of local agricultural development. Financial and policy supports from the central government could be channeled to allow for more local control if policy makers can be convinced that this improves productivity. Thus, it may be possible to promote the development of Chinese agriculture better than the current grand unified agricultural policy.

Because this study is not an assessment of the effectiveness of current agricultural policies in China, the above policy recommendations may be somewhat biased. Our next research aims to specifically assess the policy efficiency of China's current agricultural policies. Therefore it may be possible to make more nuanced policy recommendations that are more in line with China's agricultural development after completing this additional research.

#### **5. Conclusions**

Based on the above findings and discussion, it can be concluded that China's agricultural economy has achieved relatively good development in the context of the country's rapid macroeconomic development and agricultural policies, especially the fishery industry, which has been able to maintain medium to high-growth rates. However, in the long-term, China's agricultural economy tends to maintain a low-growth rate. Since 2011, China's agriculture, forestry, and livestock industries have mostly maintained low-growth rates. In order to maintain agricultural development in China, the Chinese government should continue its current agricultural support policies, especially increasing support for the agriculture, forestry, and livestock industries. Future research should focus on using specialized policy assessment models to evaluate the efficiency of agricultural support policies in China.

**Author Contributions:** Conceptualization, X.G., J.S., R.Z., and C.W.; methodology, X.G., J.S., and P.L.; formal analysis, X.G., J.S., and C.W.; writing—original draft preparation, X.G. and J.S.; writing reviewing and editing, X.G., P.L., and R.Z.; supervision, P.L. and C.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable for studies not involving humans or animals.

**Informed Consent Statement:** Not applicable for studies not involving humans.

**Data Availability Statement:** The data presented in this study are available in article.

**Acknowledgments:** We gratefully acknowledge Yu Zhu for his comments and suggestions for improvement, and Yudong Chen for checking the paper. Similarly, we also thank Qiusheng Zhang, Li Li, Changsheng Gao, Zisheng Fang, Xinsheng Deng, Qing Chang, Dehong Liu, and Zizheng Wu for their advice and help with the paper. Finally, we are particularly grateful to Aaron K. Hoshide for his editorial comments and to the two anonymous reviewers for their review comments.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

**Table A1.** Summary of the Chinese government's main agricultural support policies from 2002–2018.




are protected. The contracted land plots are identified, registered, and certified.




#### **References**

