Research on the Optimization Path of Green Development in Shaanxi Province Based on the OECD’s Perspective

Abstract

The concept of green development proposed by the OECD has effectively promoted the coordinated development of economic growth, resource conservation, and environmental protection. In addition, it has provided effective ways and means of improving the quality of global social and economic development. Therefore, this study constructed the green development evaluation index system of Shaanxi Province based on the OECD green development concept, used the AHP and the entropy weight method for weighting, and measured the green development level of ten urban areas in Shaanxi Province from 2016 to 2021. Based on the score of the green development level, the LSE model, ESDA model, and kernel density model were introduced to decompose the optimization path of green development in Shaanxi Province from the three dimensions of contributing factors, spatial distribution, and temporal evolution. The specific research conclusions are: first, from 2016 to 2021, the green development score of Shaanxi Province was between 0.92 and 1.79, and the level of green development showed an overall upward trend, but the two-level differentiation trend between regions was serious. Second, the green development levels of each region in Shaanxi Province were three-factor type I, type II, four-factor type, and five-factor type. The influencing factors of each region showed different characteristics. Third, the level of green development among various regions in Shaanxi Province did not show spatial aggregation, and the development level was significantly different. The regional characteristics show that Xi’an is obviously superior to other regions and has not played a leading role. Fourth, over time, the situation of two-level differentiation in the ten city-level administrative districts of Shaanxi Province has eased, but the development situation is still very severe. The above research conclusions provide a reference and scientific basis for Shaanxi Province to formulate economic development policies and solve the problem associated with coordinated, sustainable development of the regional society-economy-environment.

1. Introduction

Since the reform and opening up, China has gone through 45 years of economic and social development, with its GDP growing from CNY 0.37 trillion in 1978 to CNY 121.02 trillion in 2021 [1]. China has grown from a poor to an upper-middle-income country and has made a historic leap from its citizens having only adequate food and clothing to many now leading a prosperous life [2]. While remarkable achievements have been made regarding China’s economy, the ecological and environmental conditions of heavy pollution, great losses, and high risks still exist. Taking the “Hu Huanyong Line” as an example, 43% of the land in southeast China is inhabited by approximately 94% of the country’s population [3]. The terrain is dominated by plains, water networks, low hills, and karst plateau landforms, and the ecological environment is under great pressure. However, in the western part of inland China, the geographical environment is dominated by grasslands, the Gobi Desert, oases, and snowy plateaus, where water resources are scarce, wind and sand activities are intense, land desertification is serious, and ecosystems are very fragile [4]. Shaanxi Province is located in the eastern part of Northwestern China. Shaanxi Province consists of three geographic units from north to south: the northern Shaanxi Plateau, the Guanzhong Plain, and the Qinling Mountains. Shaanxi currently has ten prefecture-level cities, with Xi’an as its capital. In 2022, the province’s GDP reached CNY 3277.268 billion, accounting for approximately 2.7% of the country’s total [5]. As an important participating unit in the economic development strategy of Western China, Shaanxi has made great achievements in terms of social and economic development in recent years, while problems associated with the ecological environment in the province have become increasingly serious. From 2011 to 2020, the total water resources in Shaanxi Province dropped from 60.443 billion cubic meters to 41.962 billion cubic meters, a reduction of nearly 30.6%; the province’s arable land resource area dropped from 3.988 million hectares to 2.931 million hectares, a reduction of 26.5% [6,7]. In addition to the substantial consumption of natural resources, the process of industrialization and urbanization has further aggravated the pollution of the ecological environment. The production of industrial solid waste in the province has increased by 54.4397 million tons, an increase of 77.93%; in terms of the province’s urban sewage discharge and domestic garbage removal, the volume also increased by 763 million tons and 1.6201 million tons, representing 107.34% and 41.76%, respectively, seriously affecting the quality of life of residents [6,7]. Overall, the social and economic development of Shaanxi Province presents a phenomenon of “high pollution and high governance”. The province is affected by natural conditions, brain drain, and industrial structural imbalances, resulting in insufficient governance and weak development. The resource-based cities in some areas relying on natural resources for their development have also led to the continuous reduction in natural resources and serious damage to the ecological environment, reducing the competitiveness of the entire region’s social and economic development. The imbalance in industrial structure, insufficient resource utilization, differences in the ability to deal with environmental pollution, and the unevenness of investment in environmental protection presented in the development between different regions have directly or indirectly led to the inability of Shaanxi Province to promote sustainable development immediately and effectively. Therefore, promoting green development is not only a key link to promoting the effective growth of the regional social economy but also an inevitable trend to solve various environmental problems brought about by the economic development of Shaanxi Province.

Currently, China has gradually become an important participant and contributor to global environmental governance. We should exert effort to promote green, circular, and low-carbon development, reverse the adverse trend in the ecological environment from the source, and create good working and living environments for our people. We should integrate the concept of green development into all aspects of economic, political, cultural, and social construction and make due contributions to the global response to climate change, environmental challenges, and biodiversity destruction through practical actions [8]. As an international cooperation organization, the Organization for Economic Cooperation and Development (OECD) has provided many forward-looking, scientific, and rational guiding concepts for the social and economic development of countries around the world. In particular, this includes the concept of green development, which expands the solution of resource and environmental sustainability issues from the field of ecological environment to the fields of human development and socio-economic development. It has been shaped into a global consensus and gradually accepted and adopted by countries all over the world [9]. At the same time, the Chinese government has clearly stated in several documents that it is necessary to “promote green development and promote harmonious coexistence between man and nature”, which is the only way to build a beautiful China in the new era and is also an inherent requirement of promoting Chinese-style modernization [10]. Although the OECD and the Chinese government have interpreted coordinated development of environmental protection and economic development at global and regional levels from different perspectives based on the concept of green development, they are highly compatible in terms of the idea, goal, and tenet. Firstly, the green development concept proposed by the OECD expounds on a new approach to economic development, which recognizes the necessity of promoting environmental protection and sustainable development while increasing economic growth. This is in line with the concept of “maintaining harmony between humanity and nature when planning our development” proposed by the Chinese government. Secondly, as an international cooperation organization, the OECD has brought together the commonalities of development of most countries and regions. While proposing the guiding concept of green development, it also proposes goals and plans for the global governance of green development. The green development strategy proposed by the Chinese government not only conforms to the development path of Chinese-style modernization but also shows that China, as an important participant in promoting global economic development, has provided new exploration and practice for the economic and social development of the world while solving its development problems. Finally, the Chinese government has incorporated the concept of green development into the national economic and social development strategy, emphasizing that China’s purpose of promoting green development is not only to improve its development path but also to benefit people around the world. This is highly in line with the OECD’s tenet of providing green development to countries around the world. To this end, how China should deeply participate in global environmental governance and enhance its influence in the global environmental governance system has become a question that the academic community and all sectors of society should consider and solve.

Through the review of the relevant literature, Chinese and foreign scholars have conducted extensive research on green development. Zheng [11], Guo [12], Yu [13], and Hu [14] have conducted an in-depth analysis of the forward-looking concepts, methods, policies, and impacts of green development abroad by reviewing the research progress of the green development evaluation index system. Cao [15], Hu [16], and Shang [17] have conducted research on the connotation, function, and mechanism of green development from the perspective of economic growth. Lyu [18] and Wang [19] have introduced the research idea of high-quality development to explore the possible contradictory relationship between green development and economic growth. Taking the concept of sustainable development as the starting point, Yang [20], Yang [21], Qi [22], Zhu [23], and Andrea Molocchi [24] have combined green development with the concepts of a circular economy, low-carbon economy, and ecological economy, and have conducted in-depth analysis and practice on the scientific model and practice method of green development. Additionally, Enrico Bergamini [25] and Du [26] have emphasized the synergies between low-carbon and green development. From the perspective of sociological research, by using methods such as system dynamics models, Tong [27], Wang [28], Miguel-Angel Galindo-Martín [29], Xuan [30], and Yang [31] have discussed the potential impact of promoting green development on regional economic, social, and environmental systems, starting from social structure, smart cities, and urbanization process. Zhao [32], Zhang [33], and Sun [34] have drawn lessons from the experience of international green development research and have evaluated and compared the green development level of different regions in China. Based on systematically combing through the internal mechanism of the coordinated development of the digital economy and green development, Jiang [35], Hu [36], and Aleksy Kwilinski [37] have analyzed the balance of the coordinated development of the digital economy and green development from the dimensions of the characteristics of temporal and spatial differentiation, dynamic evolution, and convergence. Beata Gavurova [38] and other scholars have analyzed the characteristics of economic growth under the green development of OECD countries using a multivariate analytical approach. Busra Agan and Mehmet Balcilar [39] have explored the driving effects of climate change adaptation and green technology diffusion on economic growth in the process of green development. Linking the indicators of green development and economic growth with the concept of sustainable development, Mieczysław Adamowicz [40] has comprehensively examined the indicators of green development constructed by specialized agencies, including the United Nations, UNEP, OECD, the European Union, and the World Bank. Through analyzing the differences that exist in promoting a green development strategy and action plan, which may be required by various national institutions, economic systems, and development levels, Zhen [41] has analyzed the international experience in green development policy and its implementation, in particular the experience of OECD countries. Some commonly recognized experiences and successful cases provide a certain reference and guidance to begin the implementation of green development in the western region of China. Li [42] has produced a systematic introduction to the OECD green total factor productivity accounting framework and main conclusions and discussed the importance of building a green total factor productivity accounting system with Chinese characteristics in achieving high-quality development in China. Based on the green total factor productivity under the OECD green development strategy framework, Sun [43] and other scholars have empirically tested the impact of technology market development on green total factor productivity and inspected the level of green total factor productivity of the Yangtze River Economic Belt.

Although the above research results have enriched the relevant content of green development, there are still several points worthy of further research. Firstly, the empirical investigation of green development mainly focuses on logical reasoning and quantitative analysis. However, scholars have not yet given sufficient attention to the issues of causal relationships in time and corresponding relationships in geographical space, and there is a lack of corresponding research. Systematically researching the spatiotemporal relationship of green development can provide a theoretical basis and practical interpretation for scientifically and effectively responding to regional development and economic structural imbalances brought about by development transformation. Secondly, according to the previous literature, there have been relatively few studies on green development from an international perspective combined with the domestic development status. The analysis of the internal structure and development trend in green development is mostly based on domestic research paradigms, lacking international perspectives. Although the multi-dimensional characteristics of green development can be initially outlined, the new paradigms and new methods of green development research have not been paid enough attention to yet. Finally, as a mature and objective evaluation system for regional development, the concept of green development proposed by the OECD has the ability to measure the socio-economic development status of provincial-level administrative units in China. It can explore the differences between the development level of provincial-level administrative units and the international dimension in promoting green development in China. As a province with a high level of economic and social development in western China, Shaanxi is also the core area and important node of the “New Silk Road Economic Belt” cooperation initiative [44]. In recent years, the economic level of Shaanxi Province has shown a strong “catch-up” trend, constantly narrowing the gap with other provinces. However, at the same time, weak links such as natural resource depletion and ecological and environmental degradation have also emerged in the process of social development, and the problems of soil erosion and land desertification in the province are still severe. To this end, measuring the green development level of Shaanxi Province from the perspective of the OECD can provide practical experience for other regions to compare with international standards. Experiential cases, through data analysis and verification, have a certain reference and guiding role for the in-depth promotion of green development in China. In addition, they provide a new theoretical perspective for Shaanxi Province to integrate deeply into the co-construction of the “New Silk Road Economic Belt” initiative. Therefore, this article is based on the OECD concept of green development, constructing an evaluation framework for green development in Shaanxi Province through measurement and analysis in order to provide reasonable and appropriate reference cases for provincial administrative units in China [45,46,47].

To sum up, the possible marginal contributions of this study are as follows: first, this study has attempted to break through the previous research ideas of relative isolation of resources, economy, and environment and systematically consider the whole process and multi-dimensional interactions between resources, the environment, and the economy based on the OECD green development concept. In addition, it aims to construct a framework for evaluating the level of green development in Shaanxi Province under the dimensions of “Socio-Economic-Environmental Pressure-Quality of Life-Government Support-Resource Endowment” and analyze its measurement [48]. Second, in terms of research method, the green development of Shaanxi Province was comprehensively evaluated based on the OECD evaluation method. The existing research methods, to a certain extent, neglect the dynamic comparability of the measurement results or fail to obtain reasonable comprehensive evaluation indicators, which do not facilitate in-depth analysis and comparison of the measurement results. As an objective method applicable to the dynamic evaluation of panel data, the LSE model evaluation method makes up for this deficiency, which fits the research purpose of this study. Based on the green development level score, this study introduces the LSE model, ESDA model, and kernel density model and has conducted research on the decomposition of optimal paths in three dimensions including contributing factors, spatial distribution, and temporal evolution, in an attempt to explore the implementation paths consistent with the green development of Shaanxi’s economy in the new era. Third, in terms of research content, the structural characteristics of green development have been comprehensively demonstrated by distribution dynamics, spatial differences, and diagnosis of obstacle factors. In combination with the current development reality, this study took the existing indicator system as an important reference and put forward the necessity and urgency of analyzing the green development level of Shaanxi Province and the scientific transformation path based on the perspective of the OECD.

2. Indicator System and Source of Data

2.1. Construction of a Green Development Indicator System

The OECD has a well-established system of indicators that can objectively quantify and evaluate regional green development. The indicators not only reflect the socio-economic development of the region but also the basic conditions of the local natural resources and geographic environment [12,49]. Based on existing research, following the principles of indicator construction such as comprehensiveness, representativeness, and timeliness, the green development evaluation indicator system constructed in this study combined the local conditions of Shaanxi and integrated “socio-economic”, “environmental pressure”, “quality of life”, “government support”, “resource endowment” as the five dimensions. Furthermore, several evaluation indicators were set within each dimension. Based on the green development evaluation indicator system, an AHP was applied to weigh the five dimension systems to reflect the objective situation of the contradictory relationship between the local economy, resources, and environment (

Table 1. Shaanxi Province green development indicator system.

Dimensions	Variables	Unit	References
Socio- Economic	Urbanization Rate (−)	%	[12,49]
	Urban-Rural Income Ratio (−)	%	[12,36,50]
	Economic Growth Rate (+)	%	[51]
	GDP Per Capita (+)	Yuan	[12,51]
	Unemployment Rate (−)	%	[51]
Environmental Pressure	Scale amount of chemical fertilizer application (−)	Ton	[11,50]
	Days with air quality better than class 2 (+)	Days	[11]
	Amount of domestic garbage removal (+)	Ton	[11,12,49]
	Direct economic loss from disasters (−)	Yuan	[12,49,50]
Quality of Life	Amount of public toilets (+)	Number	[50]
	Amount of natural gas use (+)	m3	[50,51]
	Per capita daily domestic water consumption (+)	Liter	[11,12,36]
	Consumer price index (−)	%	[50,51]
Government Support	Soil erosion control area (+)	Hectare	[12,50]
	Expenditure on Science and Technology (+)	Yuan	[12,49,51]
	Expenditure on environmental protection (+)	Yuan	[11,12,36]
	Area of afforestation (+)	Hectare	[11,36,50]
	Financial self-sufficiency rate (+)	%	[12,51]
Resource Endowment	Cultivated land area (+)	Hectare	[11,12,49]
	Precipitation (+)	mm	[49]
	Grain output per unit area (+)	kg/ha	[49,50]
	Annual sunshine (+)	Hour	[49]

).

2.2. Source of Data

This study took ten prefecture-level cities in Shaanxi Province as the research object, and 2016–2021 was the research interval. Relevant data were obtained from “China Statistical Yearbook”, “China Environmental Statistical Yearbook”, “Shaanxi Provincial Statistical Yearbook”, and statistical yearbooks of various cities, along with other relevant information. Among the ten prefecture-level administrative units in Shaanxi Province selected in this study, the indicators are inconsistent, and the data are incomplete before 2016 in most cities. Therefore, this study selects data from 2016 to 2021 to evaluate and analyze the green economic development of Shaanxi Province. The missing data were extrapolated by the interpolation method to compensate for the missing data; finally, the panel data of ten prefectures and cities for five years were obtained. As there were different units in the selected sample data, in order to avoid deviations in the analysis results due to inconsistent units, all raw data were standardized for handling to eliminate the influence of the dimension [50].

3. Measurement of the Level of Green Development in Shaanxi Province and Analysis of the Optimization Path

3.1. Analysis of the Measurement of the Level of Green Development in Shaanxi Province

3.1.1. Weighting Method

To avoid the imbalance caused by the differences in various indicator units, this study used a combination of subjective and objective methods to assign weights to all indicators, where the subjective weights were determined using the hierarchical analysis method (AHP), and the objective weights were determined using the entropy value method (EVM) [52,53]. In order to achieve a uniform subjective and objective weighting of the indicators, the integrated weighting combined the two weighting methods and ensured that the sum of the internal weights of each dimension was 1.

The subjective weight vector determined by the AHP is:

$$\omega ={\left({\omega }_1,{\omega }_2,{\omega }_3\cdots {\omega }_m\right)}^T$$

(1)

The objective weight vector determined by the EVM is:

$$\mu ={\left({\mu }_1,{\mu }_2,{\mu }_3\cdots {\mu }_n\right)}^T$$

(2)

The comprehensive weight of each indicator is:

$$\omega ={\left({\omega }_1,{\omega }_2,{\omega }_3\cdots {\omega }_n\right)}^T$$

(3)

The standardized decision matrix is:

$$Z={\left(z_{ij}\right)}_n\times m^o$$

(4)

3.1.2. Analysis of the Measurement of the Level of Green Development in Shaanxi Province

Firstly, the comprehensive weights of each indicator were calculated by objective (Appendix A) and subjective weighting methods. In addition, each indicator of Shaanxi Province’s green development evaluation system was weighted and summed to obtain the comprehensive evaluation scores of each sub-system of the province’s ten prefecture-level administrative units (

Table 2. Weight scores of green development indicators for 10 prefecture-level cities in Shaanxi Province.

Dimensions	Variables	AHP	EVM	Integrated
Socio-Economic	Urbanization rate (−)	0.021	0.206	0.113
	Urban–rural income ratio (−)	0.019	0.203	0.111
	Economic growth rate (+)	0.050	0.191	0.120
	GDP per capita (+)	0.037	0.173	0.105
	Unemployment rate (−)	0.046	0.176	0.111
Environmental Pressure	Scale amount of chemical fertilizer application (−)	0.037	0.279	0.158
	Days with air quality better than class 2 (+)	0.058	0.304	0.181
	Amount of domestic garbage removal (+)	0.017	0.151	0.084
	Direct economic loss from disasters (−)	0.026	0.292	0.159
Quality of Life	Amount of public toilets (+)	0.011	0.189	0.100
	Amount of natural gas use (+)	0.027	0.171	0.099
	Per capita daily domestic water consumption (+)	0.027	0.319	0.173
	Consumer price index (−)	0.008	0.318	0.163
Government Support	Soil erosion control area (+)	0.083	0.193	0.138
	Expenditure on Science and Technology (+)	0.076	0.078	0.077
	Expenditure on environmental protection (+)	0.133	0.275	0.204
	Area of afforestation (+)	0.050	0.216	0.133
	Financial self-sufficiency rate (+)	0.061	0.275	0.168
Resource Endowment	Cultivated land area (+)	0.071	0.229	0.150
	Precipitation (+)	0.066	0.218	0.142
	Grain output per unit area (+)	0.050	0.310	0.180
	Annual sunshine (+)	0.027	0.237	0.132

).

Secondly, the total score of green development of the ten prefecture-level administrative regions in Shaanxi Province was obtained by weighting and summing the scores of the sub-systems within the system. Finally, the obtained evaluation results can basically reflect the actual situation of the green development scores of each prefecture-level administrative region in Shaanxi Province from 2016 to 2021. The smaller the green development value, the worse the economic situation of the region; the larger the green development value, the better the economic situation of the region. Through calculation, it was concluded that the green development values of the ten prefecture-level administrative regions in Shaanxi Province ranged from 0.92 to 1.79 in 2016–2021. Overall, the province’s green development level was broadly able to maintain a stage-by-stage upward trend, with the green development of each region showing an upward and downward year-on-year fluctuation with a steady upward trend (

Table 3. Green development score for 10 prefecture-level cities in Shaanxi Province (2016–2021).

Regions	Score
	2016	2017	2018	2019	2020	2021
Xi’an	1.63	1.59	1.65	1.76	1.79	1.71
Tongchuan	0.92	0.94	0.94	0.97	0.99	1.07
Baoji	1.17	1.16	1.26	1.26	1.23	1.21
Xianyang	1.02	1.05	1.08	1.16	1.09	1.18
Weinan	0.94	0.98	1.04	1.10	1.20	1.15
Yan’an	1.38	1.36	1.47	1.44	1.47	1.36
Hanzhong	1.15	1.18	1.27	1.28	1.23	1.14
Yulin	1.39	1.45	1.44	1.66	1.57	1.51
Ankang	1.16	1.21	1.24	1.29	1.23	1.05
Shangluo	1.05	1.02	1.11	1.07	1.06	0.96

).

Overall, the phenomenon of differentiation in the level of green development is mainly due to the different levels of economic development and geographical advantages of each region. Specifically, the level of economic development, rationalization of industrial structure, integrated development of urban and rural, living standards of the population, and openness of the Guanzhong region are better than those of northern and Southern Shaanxi, which has intensified the massive transfer of talents to the Guanzhong region. However, the overall level of development in the Southern Shaanxi region is not optimistic, with a weak economic base, fragile ecological environment, unbalanced industrial structure, and lagging scientific and technological innovation; at the same time, during the overall development period in the Northern Shaanxi region, a large number of problems such as resource consumption, excessive waste discharge, and serious environmental pollution have occurred, which have seriously affected the improvement of the level of green development.

3.2. Analysis of Temporal Evolution

3.2.1. Kernel Density Estimation

The main logic of KDE is to describe the distribution pattern of a random variable by estimating the probability density of the random variable with a continuous density profile. According to the different forms of expression, the kernel function can usually be divided into Gaussian kernel, triangular kernel, quadratic kernel, and other types. Different types of kernel function shape have little impact on the accuracy of the estimation results; that is, you can choose any available kernel function as an important tool to study the unbalanced spatial distribution by estimating the probability density of the variable, using the density curve to describe the distribution of the variable, reflecting the distribution position, shape, and extension of the variable and other information. The kernel density function is less dependent on the model and has strong robustness [54].

The KDE function is expressed as:

$$f\left(x\right)=\frac{1}{nh}{\sum }_{i=1}^{n}K\left(\frac{x_i-\overline{x}}{h}\right)$$

(5)

3.2.2. Analysis of Temporal Evolution

Based on the results, we observed the temporal evolution of green development in ten prefecture-level cities in Shaanxi Province, including the first year (2016), the middle year (2019), and the end year (2021). Based on the calculation results, the kernel density distribution of green development was derived to determine the overall development trend in green development in ten prefecture-level cities in Shaanxi Province. The results of the study are as follows:

Firstly, Figure 1 reflects the evolution of green development in the ten prefecture-level cities in Shaanxi Province over the sample inspection period. Overall, the starting value on the left side of the kernel density curve changes moderately and evenly, while the value on the right side keeps shifting to the right, and the peak continues to decrease, with its interval increasing. This indicates that the overall level of green development is increasing within the sample inspection period, but the gap between regions is also increasing.

Secondly, the state distribution of the function shows that the level of green development does not show a simple single-peaked pattern. In 2019, the curve is craggy and has a somewhat multi-peaked distribution, with the main peak corresponding to a much higher kernel density value than the other peaks. It indicates a low level of green development and a slight polarization in the region [36]. In the bimodal distribution in 2021, the first bimodal peak is smaller, and the second bimodal peak is larger, indicating that green development is polarized in China as a whole. This is mainly due to the intensification of resource-environment demand conflicts during a period of rapid economic development, with different rates of green development between regions [55].

3.3. Analysis of Spatial Distribution

3.3.1. Spatial Autocorrelation

Spatial autocorrelation captures the spatial relevance and variability of the sample areas over the sample period [56]. At a certain level of significance, when Moran’s I is significantly positive, this indicates that areas that tend to be at the poles of imbalance show a significant concentration pattern in space. The closer the Moran’s I value is to 1, the smaller the overall spatial variability. When Moran’s I is significantly negative, the more significant the spatial variability in the areas that tend to be at the poles of imbalance. The closer the Moran’s I value is to −1, the greater the overall spatial variability. When the global Moran’s I value is zero, then there is generally no correlation from a spatial perspective.

The function is expressed as:

$$\mathrm{M}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{n}\mathrm{’}\mathrm{s} \mathrm{I}=\frac{\sum _{i=1}^n\sum _{j\ne i}^n{W}_{ij}{z}_i{z}_j}{{\sigma }^2\sum _{i=1}^n\sum _{j\ne i}^n{W}_{ij}}=\frac{n\sum _{i=1}^n\sum _{j\ne i}^n{W}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{\sum _{i=1}^n\sum _{j\ne i}^n{W}_{ij}\sum _{i=1}^n{\left(x_i-\overline{x}\right)}^2}$$

(6)

3.3.2. Analysis of Global Spatial Autocorrelation

As is shown in

Table 4. Global autocorrelation of green development in Shaanxi Province.

Year	p	Z	Moran’s I
2016	0.369	−0.891	−0.269
2017	0.478	−0.709	−0.238
2018	0.476	−0.712	−0.239
2019	0.485	−0.698	−0.236
2020	0.178	−1.343	−0.347
2021	0.235	−1.187	−0.317

, the global “Moran’s I” of the green development of ten prefecture-level cities in Shaanxi Province from 2016 to 2021 is in the range of [−0.347–−0.236], and they are all negative values. This indicates that the overall level of green development is generally not high. At the same time, the green development in these areas failed the significance test in the “p” of the “Moran’s I”, indicating that there is no obvious autocorrelation in the spatial distribution of the green development in Shaanxi Province and no spatial aggregation has been formed.

3.3.3. Analysis of Local Spatial Autocorrelation

“Geoda” was used to measure the local “Moran’s I” of the green development of ten prefecture-level cities in Shaanxi Province from 2016 to 2021. It can be seen from Figure 2 that only Xi’an has the H-L type from 2016 to 2021, and there are no H-H and L-L agglomerations in other regions. Specifically, the level of green development in Xi’an is relatively high, while the development level of surrounding areas is low, and the spatial difference between the two is relatively large.

3.4. Analysis of Contributing Factors

3.4.1. LSE Method

For the LSE (Least Square Error) method, as the sample size of a set of data increases, the variance first becomes larger and then smaller to obtain the smallest sample size in the variance value, so it can reflect the regional situation more realistically [57]. As far as the green development studied in this paper is concerned, it belongs to the construction of the indicator system. The LSE model is suitable for extracting the key factors from the indicator system in order to determine the driving factors of this paper. In this study, the LSE model was used to spatially sort and analyze the types of contribution to green development in ten prefecture-level cities in Shaanxi.

Its formula is as follows:

$$S^2=\frac{1}{n}{\sum }_{i=1}^n{\left(X_i-\overline{X}\right)}^2$$

(7)

3.4.2. Analysis of Contributing Factors

By using the LSE method to analyze the OECD model, we identified the main types of green development in Shaanxi as three-factor type I, type II, four-factor type, and five-factor type according to the driving factors and causal mechanisms.

The three-factor type is divided into two categories, and the contributing factors are not quite the same. The three-factor type I areas are mainly contributed to by “environmental pressure”, “quality of life”, and “government support”, including Baoji, Hanzhong, and Ankang. Among them, Baoji belongs to an area with a moderate degree of green development, while Hanzhong and Ankang belong to areas with a weaker degree of green development. In areas with a moderate degree of green development, the government attaches great importance to sustainable development so that the areas have a relatively high resource utilization efficiency, strong environmental protection capabilities, and relatively complete living environment and sanitation conditions [15,28].

The three-factor type II area was dominated by the contributing factors of “environmental pressure”, “government support”, and “resource endowment”, encompassing Tongchuan only. On the one hand, agricultural production in this area has reduced the use of pesticides, fertilizers, and plastic films, reducing the pressure on environmental protection, so the contribution to the environment is relatively high. On the other hand, in order to expand the advantages of developing secondary and tertiary industries in this region, the government policy support for the development of environmental protection technologies and the construction of green cities has been strengthened. However, the low quality of life of rural residents has become a shortcoming of development [58]. Therefore, improving the development of rural modernization will help to enhance the region’s score and improve the level of rural green development.

The four factors of “socio-economic”, “environmental pressure”, “quality of life”, and “government support” dominated Xianyang, Weinan, and Yan’an. The governments of these areas attach great importance to green development, vigorously developing science and technology, education, and improving the quality of life of residents by supporting substantial financial investment. On the one hand, affected by the economic development strategy of the Guanzhong region, governments at all levels have formed a model that gives priority to the development of secondary and tertiary industries. This development model not only increases the pressure on the use of ecological resources in rural areas but also causes agricultural production to become a shortcoming of development [32,59]. On the other hand, the product structure in this region is single, the added value is not high, and the market competitiveness is low. This requires actively introducing some leading enterprises in line with the situation of the region under the condition of limited economic development to promote the increase in product added value in order to earn extra points for green development.

The five-factor type areas include Xi’an, Yulin, and Shangluo. Among these, Xi’an and Yulin are the areas with the best green development in Shaanxi Province. In these areas, the local economic conditions are good, the financial self-sufficiency and the level of urbanization are relatively high, and the government has invested a lot of financial resources in social development in terms of education, science and technology, ecology, and the environment. Shangluo is located in the hinterland of the Qinling Mountains, with contains rich resources and a suitable climate. However, due to the constraints of topography, its economic development is still dominated by traditional agriculture. The social and economic development is relatively lagging, and the green economic development can barely maintain the average level of the province. In this case, local governments and enterprises should actively implement a governance model tailored to the local conditions and develop a resource-saving and environmentally friendly regional economy of mutual benefit between urban and rural areas [23,38,42].

4. Conclusions and Suggestions

4.1. Conclusions

In this paper, we conducted a preliminary study of the evaluation methods of green development in ten cities of Shaanxi Province. First, the evaluation index system of green development of Shaanxi Province was constructed by adjusting some indicators in combination with the actual development situation of Shaanxi. The results show that the green development values of the ten prefecture-level administrative regions in Shaanxi Province ranged from 0.92 to 1.79 in 2016–2021. The overall level fluctuated year by year, but there was a steady increase, indicating that the green development of Shaanxi Province was significantly improved. Second, in this paper, the temporal evolution trend and spatial clustering analysis of green development of ten cities in Shaanxi Province from 2016 to 2021 were carried out. The results showed that although the overall level of green development in Shaanxi Province has been increasing year by year, and the phenomenon of polarization has improved, the development process still faces many challenges.

4.2. Suggestions

Based on the summary of the status quo of green development in Shaanxi Province and the analysis of the subjective and objective factors affecting green development, this paper proposes that the next reform direction and focus of green development in Shaanxi Province should be on the following four aspects.

4.2.1. Increasing Investment in Basic Research and Improving Green Innovation Capabilities

Increasing investment in basic research is the only way to improve the ability of industrial innovation and transformation in various fields [60]. Under the current wave of scientific and technological revolution in the world, promoting innovation is an effective way of enhancing product competitiveness and industrial influence. Taking Shaanxi Province as an example, first of all, we must make full use of the industrial advantages of colleges, universities, and scientific research institutions in the Guanzhong region to promote investment in basic research and improve the original innovation capabilities of the province. The number of colleges, universities, and scientific research institutions in Shaanxi Province is sufficient and diverse. This is necessary to give full play to the concentrated advantages of innovation capabilities, gathering the power of the entire society to contribute to the high-quality development of Shaanxi Province and green development in order to concentrate all efforts to do a good job in the transformation of Shaanxi’s industrial structure. Secondly, the development and popularization of green technology can help improve resource utilization efficiency and reduce pollution emissions [30,31,32,61]; hence, the development and application technology of new energy should be used to support the transformation of the resource-based cities in the Northern Shaanxi region. In transforming the energy-dependent industrial structure, more attention should be paid to building a dual-evaluation standard system for the region’s green innovation technological achievements and industrial economic benefits. Thirdly, it is necessary to promote the construction of a high-level green industrial structure in the Southern Shaanxi region with ecological balance technologies, such as water resource development and protection technologies centered on environmental technology. At the same time, the targeted introduction of top-level technical talents that adapt to the development model and reform requirements of the Southern Shaanxi region is required.

4.2.2. Improving Green Performance and Cultivating New Momentum for High-Quality Development

First of all, we must realize the synergistic effect of technological innovation and technology introduction and vigorously develop new green production technologies, including carbon capture technology and pollutant treatment technology. This is in order to provide good technical support for the green economic development of Shaanxi Province. Secondly, it is necessary to introduce high-level scientific and technological talents guided by high-quality development policies. We can attract scientific and technological talents from all walks of life by setting up green technology innovation funds and patent awards and applying new green technologies to agricultural and industrial production in Shaanxi Province. Thirdly, reducing the difference in the level of green development between different regions in the province through the popularization of new technologies is necessary. This can be achieved by using the technological progress in the Guanzhong region to drive technological innovation in the northern and Southern Shaanxi areas and then improving the impetus for the economic high-quality development of the province as a whole.

4.2.3. Strengthening the Publicity of the Concept of Green Development and Improving the Supporting Policies of the Industrial Structure

Firstly, strengthening the publicity and education of green development in the province and improving the awareness of green production and the life of urban and rural residents is vital. On the one hand, the innovation of industrial technology and the socialization process of the concept of green development all depend on the promotion and popularization among people with higher levels of education. Urban residents generally have a higher level of education [43]. Therefore, it is necessary to make full use of the city’s convenient and fast publicity channels that have a wide audience in order to vigorously promote the dissemination of green development concepts among urban residents. On the other hand, compared with urban residents, rural residents have a weaker awareness of green development. In terms of publicity and education, it is necessary to fully consider the differences in rural development status and cultural customs between the Northern Shaanxi region, the Guanzhong region, and the Southern Shaanxi region and carry out the green transformation of rural production and life according to local conditions.

Secondly, this can be achieved by increasing the proportion of finance in related fields and improving the tax system for green industries. Given the resource-dependent development model in the Northern Shaanxi region, tax incentives are given to the resource-mining industry to implement a green and sustainable mining model, and a tax increase system is implemented for individual high-polluting industries. At the same time, limiting the amount of sewage discharged by enterprises and promoting the use of clean energy and emission treatment technologies should be considered. As for the Guanzhong region, the supervision mechanism of agricultural production should be strengthened, and financial subsidies and tax incentives should be provided to enterprises and individuals implementing green production. This will ensure that the Guanzhong region becomes a leading demonstration area and a policy practice base for green economic development in the province. Finally, the level of economic development in the Southern Shaanxi region is relatively backward, and it needs the support of provincial finance and the cooperation of local policies to improve the level of green development effectively. At the same time, providing tax benefits will also help attract foreign scientific and technological talents. This will subsequently provide a good economic foundation and sufficient development momentum for the improvement of the green development level in the Southern Shaanxi region [62,63].

4.2.4. Squarely Facing the Differences in Development Status and Promoting the Joint Development of Different Regions

When formulating a macro strategy for high-quality development across the province, it is necessary to fully consider regional differences in green development [51]. Firstly, it is necessary to strengthen inter-regional exchanges and cooperation, make full use of and give full play to the spatial spillover effect of the high-level green development in the Guanzhong region, and promote the radiation effect of the Guanzhong region to the Northern Shaanxi and Southern Shaanxi areas. The development differences in different regions should not be simply regarded as the absolute relationship between the ups and downs of each other, and the overall layout of the province’s social and economic development should be grasped with the strategic thinking of “‘development first’ promoting ‘development later’”. Secondly, it is necessary to adopt a development strategy tailored to local conditions, rationally regulate the distribution of resources in the province, and prevent the problem of unbalanced regional development from further expanding. The level of green development in the Guanzhong region is usually in a leading position, and it also has an advantage in terms of the allocation of resources across the province. However, it is also doomed by the fact that the Guanzhong region needs to undertake more tough tasks and fault-tolerant risks. However, the level of green development in the Northern Shaanxi and Southern Shaanxi areas is relatively lagging. The primary task should be to learn from the successful governance experience and policy models of the Guanzhong region, striving to achieve the simultaneous rise of relatively backward areas under the banner of the province’s high-quality development.

5. Discussion

In this study, preliminary research was conducted on the evaluation methods of economic green development in ten regions of Shaanxi Province. There are several issues that require further research.

Firstly, in the context of promoting Chinese-style modernization and promoting high-quality development in Shaanxi Province, further research on the green development of Shaanxi Province’s economy is required to explore the significance of its economic transformation and policy optimization path. Current research on the green development of Shaanxi Province’s economy is based on administrative divisions, and the division regarding the economic perspective has not yet been uniformly defined.

Secondly, this study’s conclusions are based on municipal panel data from 2016 to 2021. The scale of the study will be narrowed in the next step, and the units will be refined to districts and counties. Internal differences and evolutionary trends in the level of green development of regional economies can be further analyzed more comprehensively by using urban or rural panel data over longer periods.

Thirdly, there is a need to test the robustness and reliability of existing analytical methods in the future by using other indicators, evaluation criteria, and methods. For example, in the indicator system for green development of Shaanxi Province’s economy, indicators such as the import and export volume of resources, the share of foreign trade in GDP, and the net amount of foreign direct investment can be added to characterize the degree of external dependence in the process of economic development. Due to the limitation of data availability in the study area, the study only selected the five dimensions of socio-economic, environmental pressure, resource endowment, quality of life, and government support as elements to measure the green development of the economy. Future research is yet to be added.

Finally, the “data-driven” nature of the LSE approach is largely due to the lack of description of the statistical data. Considering the spatial effects, Exploratory Spatial Data Analysis (ESDA) identifies spatial data analysis methods that can further explain regional differences in the level of economic green development.