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Article

The Carbon Emission Intensity of Industrial Land in China: Spatiotemporal Characteristics and Driving Factors

by
Liangen Zeng
1,
Chengming Li
2,*,
Zhongqi Liang
1,
Xuhai Zhao
3,4,
Haoyu Hu
1,
Xiao Wang
5,
Dandan Yuan
1,
Zhao Yu
6,
Tingzhang Yang
7,
Jingming Lu
8,
Qi Huang
9 and
Fuyao Qu
10
1
College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
2
School of Economics, Minzu University of China, Beijing 100871, China
3
School of Economics and Management, Tsinghua University, Beijing 100871, China
4
Guangxi Rig Rural Revitalization Private Fund Management Co., Ltd., Nanning 530023, China
5
Guanghua School of Management, Peking University, Beijing 100871, China
6
Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
7
College of Business and Economics, Australian National University, Canberra, ACT 2600, Australia
8
School of Agricultural Economics and Rural Development, Renmin University of China, Beijing 100872, China
9
School of Software and Microelectronics, Peking University, Beijing 100871, China
10
School of Economics, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Land 2022, 11(8), 1156; https://doi.org/10.3390/land11081156
Submission received: 14 June 2022 / Revised: 14 July 2022 / Accepted: 19 July 2022 / Published: 26 July 2022
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Abstract

:
CO2 emission reduction has become a consensus all around the world. This paper investigates the spatiotemporal characteristics of industrial land carbon emission intensity (ILCEI) in China by spatial autocorrelation analysis, and applies the spatial Durbin model to reveal the influence of driving factors on ILCEI. The results indicate the following: (1) national ILCEI first shows a downward and then an upward trend during the period and presents a low pattern in both Eastern and Northeastern regions and a high pattern in the Northwestern region. (2) From a global perspective, ILCEI shows significant spatial agglomeration characteristics; from a local perspective, ILCEI is dominated by H-H and L-L agglomeration types, showing that spatial heterogeneity and spatial dependence are apparent in ILCEI. (3) ILCEI is significantly negatively affected by both R & D personnel and foreign-trade dependence, while urban population density notably has positive impacts on ILCEI. This paper is a beneficial policy practice for harmonizing the contradiction between industrial land expansion and carbon discharge.

1. Introduction

Climate change caused by CO2 emissions is seriously threatening the ecological environment and human survival [1]. Faced with this alarming fact, China, as the second-largest economy and the greatest CO2 emitter in the world [2], has announced that it will make considerable efforts to achieve a CO2 emissions peak by 2030 and achieve CO2 neutrality by 2060 in 2020 [3]. Over the past 40 years, China’s industry has boomed. From 1978 to 2019, the added value of the industrial sector rose to RMB 31.19 trillion from RMB 162.14 billion, and it accounted for 31.7% of the GDP in 2019 [4]. However, it also consumed a lot of energy and resources and discharged a great amount of CO2. In 2019, the industrial sector in China consumed 66.2% of energy consumption [5], 19.7% of urban construction land [6], and 83.9% of fossil energy CO2 emission distribution [7]. Clearly, the industrial sector is the key economic sector of energy and resource conservation and reducing emissions [8].
Carbon emission intensity is a concept of reflecting carbon emission efficiency in the process of economic development [9]. By viewing the related literature [8,10,11,12,13,14], we can observe that the main method for measuring the carbon emission intensity of the industrial sector is to calculate the ratio value of industrial GDP to CO2 emissions, representing the CO2 emissions caused by the industrial GDP per unit. The literature has not studied the industrial CO2 emission intensity from land production factors. As previously mentioned, the industrial sector of China occupies a large amount of land. So, what about the CO2 emissions caused by industrial land area per unit in China? In taking the approach of the low-carbon industrial land use model, it is first necessary to understand the CO2 emissions of industrial land, and then research and evaluate the CO2 emissions caused by industrial land area per unit, before determining whether it has adapted to the stage of industrial land expansion. Therefore, this paper proposes the concept of industrial land carbon emission intensity (ILCEI), which is a key measure of the sustainable use of industrial land because it is equivalent to the ratio of industrial land area to CO2 emissions; the lower ILCEI, the higher the quality of industrial land use, that is, it emits less carbon.
The main contribution of this paper is as follows: this is the first time that ILCEI has been studied. As per the definition, this study measures the ILCEI of China’s 30 provinces from 2014 to 2019, revealing its regional disparity and spatiotemporal characteristics. Secondly, this study applies the Moran’s I index to thoroughly analyze the spatial dependence of ILCEI and uses the SDM to explore the main driving factors of ILCEI. The research results provide a scientific basis for the government to harmonize the contradiction between industrial land expansion and carbon discharge, and will be helpful to improve the sustainable use of regional industrial land.
The remainder of this paper is as follows: Section 2 introduces the study’s data and methodology. The regional disparity and spatiotemporal characteristics of ILCEI in China are introduced in Section 3. Section 4 empirically studies the relationship between ILCEI and its influencing factors. The conclusions, policy guidance, and future research directions are proposed in Section 5. Figure 1 shows the flowchart of the empirical research of ILCEI in China.

2. Methodology

2.1. Research Area

According to the availability and validity of the data, this paper selected the data of 30 provinces in China from 2014 to 2019 to study the spatiotemporal characteristics and driving factors of ILCEI. Based on the geographical location and economic development level, the National Bureau of Statistics of China has divided 30 provinces into four regions: East, Central, West, and Northeast (see Figure 2).

2.1.1. Dependent Variables

In this empirical research, ILCEI is defined as the dependent variable, unit: t/sq. The CO2 emissions data of the industrial sector were obtained from China’s carbon emission database (https://www.ceads.net/data/province/energy_inventory/ Accessed on: 11 July 2022) [7]. The industrial land data were obtained from China’s Urban Construction Statistical Yearbook (2015–2019) [15]. The data are presented in Table 1. The estimation method of ILCEI is Equation (1):
ILCEI = The   CO 2   emissions   of   industrial   sec tor The   area   of   industrial   land

2.1.2. Theoretical Mechanism

ILCEI depends on both CO2 emissions and industrial land and is affected by other factors (see Table 2). Referring to the literature, the driving factors of ILCEI include R & D personnel (RDP), Technology market (TM), governmental intervention (GI), foreign-trade dependence (FTD), foreign direct investment (FDI), and urban population density (UPD). The original data were obtained from China’s Statistical Yearbook (2015–2020) [16] and the National Bureau of Statistics of China (2022) [4].

The Mechanism of Technological Progress

R & D personnel investment is a key factor in technological progress. Developing and encouraging a large number of R & D personnel to work in scientific and technological activities is necessary for developing technologies and equipment for energy savings and CO2 emission reductions [17]; therefore, R & D personnel were selected as the most important independent variable. An increase in the technology market will further promote technology diffusion and the technology spillover effect [18], which benefits energy savings and CO2 emission reductions. Therefore, the technology market is a factor affecting ILCEI.
Hypothesis 1.
Technological progress inhibits the ILCEI in China.

The Mechanism of Government Intervention

Economic markets under moderate government intervention can adjust the allocation of resources and correct market failures [19]. However, when the government excessively intervenes in the economic market and guides state-owned industrial enterprises to over-invest in fixed assets in the industrial sectors, it increases industrial CO2 emissions. Therefore, this paper selected government intervention as one of the most important variables.
Hypothesis 2.
Government intervention influences the ILCEI in China.

The Mechanism of Opening-Up

Raising the opening-up level can introduce advanced production technology and management experience [20,21,22], which is conducive to CO2 emission reductions and improves the utilization efficiency of industrial land. The dependence on foreign trade and foreign direct investment (FDI) are used to measure the opening-up level. However, according to the pollution havens hypothesis, the introduction of outdated industry from abroad and the development of export-oriented resource-intensive industries may increase CO2 emissions. Therefore, in this paper, the foreign-trade dependence and foreign direct investment were selected as the two independent variables.
Hypothesis 3.
Raising the opening-up level influences the ILCEI in China.

The Mechanism of Urban Population Density

Urban population agglomeration can cause the accumulation of innovative elements, and promotes technological advancement and industrial upgrading. However, it may also result in the growth in consumer demands for industrial products and environmental pressure. Therefore, in this research, the independent variables included urban population density.
Hypothesis 4.
Urban population density influences the ILCEI in China.

2.2. Methods

2.2.1. Spatial Autocorrelation Analysis

Spatial autocorrelation can describe the degree of correlation between observed values in a geographic space [23], which can be divided into global and local spatial autocorrelations. This paper applied the global Moran’s I and local Moran’s I indices to make the conduct autocorrelation analysis.
The global Moran’s I index can test the degree of spatial correlation of the region as a whole, which can be calculated as follows:
Global   Moran ′ I = ∑ i = 1 N ∑ j = 1 N W i , j ILCEI i , t − ILCEI ¯ t ILCEI j , t − ILCEI ¯ t 1 N ∑ i = 1 N ILCEI i , t − ILCEI ¯ t 2 ∑ i = 1 N ∑ j = 1 N W i , j ,
where n is the number of provinces, t stands for the year, and I denotes the province. ILCEI ¯ t is the mean value of the ILCEI in 30 provinces of China in t year. The value range of the global Moran’s I index is from −1 to 1. When the value > 0, it represents a positive spatial autocorrelation. If the value < 0, it indicates a negative spatial autocorrelation. When the value = 0, it means that ILCEI is randomly distributed. W is the spatial weight matrix. According to the first law of geography, the closer the distance, the greater the impact on each other [24]. In this paper, the geographical distance weight matrix was structured to measure the spatial correlation of ILCEI, which takes the geographic distance between provinces into consideration. The specific form of the Wi,j is the following:
W i , j = 1 / d ij 2 i ≠ j 0 i = j ,
where d ij 2 is the reciprocal square of the distance between the geographical centers of provinces i and j. The local Moran’s I index can reflect the agglomeration in local space. The calculation formula can be as shown in Equation (4) as follows:
Local   Moran ′ I = N ILCEI i , t − ILCEI ¯ t ∑ j = 1 N W i , j ILCEI j , t − ILCEI ¯ t ∑ i = 1 N ILCEI i , t − ILCEI ¯ t 2
The definition of all variables in Equation (4) is the same as in Equations (1) and (2). The local Moran’s I index can be shown by the Moran scatter plot (MSP) map and the local indicator for spatial autocorrelation (LISA) map.
The MSP map consists of four quadrants. The first quadrant is the high–high (H–H) agglomeration area and represents provinces of high ILCEIs surrounded by locations with high ILCEIs. The third quadrant is the low–low (L–L) agglomeration area, which is low ILCEI provinces surrounded by other provinces with low ILCEIs. Therefore, the above two quadrants show positive spatial autocorrelations. Finally, the second and fourth quadrants are low–high (L–H) and high–low (H–H) agglomeration areas, respectively, reflecting that the provinces and their adjacent provinces have opposite ILCEI attributes and show negative spatial autocorrelations.
The LISA map can exhibit the significance of each spatial unit, but the MSP map cannot. The LISA map also consists of the above four agglomeration areas: H–H, L–L, L–H, and H–L agglomeration areas, and their definitions are consistent with the MSP map.

2.2.2. Spatial Durbin Model

Spatial econometric models can consider spatial heterogeneity and relevance, whereas traditional econometric models ignore this. If the dependent variable exhibits the characteristics of spatial autocorrelation, the spatial econometric models should be applied instead of traditional econometric models when empirically analyzing the driving factors of the dependent variable. The spatial econometric models include the spatial lag model (SLM), spatial error (SEM) model, and spatial Durbin model (SDM). The SLM only takes the spatial lag factor of explained variables into account, and SEM only considers the spatial correlation of the error term [25,26,27], respectively. The SDM is based on both the SLM and SEM and considers both the explained variable and the spatial lag factor of the explanatory variable [28,29]. Therefore, this study applied the SDM to empirically analyze the driving factors of ILCEI. The specific form of the SDM is as follows:
Y = ρWY + βX + θWX + ε,
where Y represents the dependent variable, X indicates the independent variable, ρ stands for spatial lag coefficients, and W is the geographical distance weight matrix. β and θ are the regression coefficients to be estimated. ε is the random error term.

3. Characteristics’ Analyses

3.1. National Characteristics of ILCEI

China’s ILCEI first showed a downward and then an upward trend during the period during 2003–2017, with the lowest point in 2017 (see Table 3 and Figure 3). The industrial CO2 emissions have decreased significantly from 2014 to 2016 because the idea of ecological civilization was put forward after the 18th National Congress of the Communist Party of China [30]. The industrial sector has always been the largest CO2 emissions sector in China. In 2012, industrial CO2 emissions accounted for 86% of all industries (CCED, 2012). Therefore, the industrial sector is the priority sector for CO2 emission reductions. Since 2012, China has strictly implemented a series of emission-reduction measures, which has effectively restrained ILCEI. However, the national ILCEI rebounded significantly after 2016. To develop the economy, many Central and Western provinces sped up the development of the industrial economy. However, the industrial structure and technology accumulation are considerably lower in the Eastern provinces, which is not conducive to restraining their ILCEI.

3.2. Regional Characteristics of ILCEI

The ILCEI generally presents the spatial characteristics of being low in both Eastern and Northeast regions and high in the Northwestern region. During the research period, Eastern, Western, and Northeastern regions first showed a downward and then an upward trend, and the Central regions fluctuated downward (Table 4 and Figure 4). Under the background of carbon peaks and carbon neutralization, many inland provinces of China face the double pressure of developing industries and controlling CO2 emissions.
Western ILCEI is significantly higher than other regions. Although its ILCEI decreased from 1.357 t/sq. m in 2014 to 1.227 t/sq. m in 2017, it rose to 1.325 t/sq. m in 2019. To date, the Western region is accelerating industrialization. Many high-emission industries have been introduced to Western regions, which has caused the ILCEI to increase to some degree. Eastern and Northeast regions have the lowest levels of ILCEI among the four regions.
To date, the Eastern region has accelerated industrial transformation and upgrading, and its industries follow a green and low-carbon path. This is closely related to the high technical level and mature management concept [31,32]. Although the ILCEI level in the Northeastern region is low, the region has a high level of CO2 emissions per unit of GDP. Northeastern China has long been a base of heavy industry and has a relatively large scale of CO2 emissions; the region has a low ILCEI level because of its large industrial land area.
The ILCEI level in the Central region is lower than that of the Western regions, but higher than that of the Eastern and Northeastern regions. The ILCEI showed a trend of fluctuating downward. The regional ILCEI decreased from 0.997 t/sq. m in 2014 to 0.907 t/sq. m in 2019. The Central region is adjacent to Eastern coastal areas, which is conducive to its introduction of advanced production technology [19]. With national policy support and the region attaching great importance to the green development of industrial sectors, the regional ILCEI has improved during the period studied.

3.3. Provincial Characteristics of ILCEI

Comparing Table 1 and Table 3, it is clear that when a province has high industrial CO2 emissions, its ILCEI is not always high. For example, in 2019, the industrial CO2 emissions in Shandong ranked second out of 30 provinces, but the ILCEI in Shandong ranked 16 of 30 provinces. According to the annual average value of ILCEI from 2014 to 2019 (Figure 5 and Figure 6), the ILCEI of 30 provinces in China can be divided into the following categories.
The ILCEI high-level provinces are Hebei, Shanxi, Inner Mongolia, Qinghai, and Ningxia. These provinces are mainly in Northeastern or North China. The average annual ILCEI values of these five provinces are more than 2.6 t/sq. m. Hebei is the province with not only the highest ILCEI in China, but also the highest industrial CO2 emission level. Hebei province is still at a stage of rapid industrialization and its production structure is still developing and following a high-carbon pathway [33]. In recent years, Hebei has undertaken a number of high-emission industries from Beijing. That may be the reason that the provincial ILCEI stays at a high level. Industrial CO2 emissions in Qinghai are low. However, Qinghai is located in Western China, and its industrial structures are primarily traditional labor-intensive and resource-intensive industries [34], and its technology accumulation is weak, leading to a high ILCEI level.
The ILCEI mid-level provinces are Henan, Hainan, Shaanxi, Guizhou, Yunnan, and Xinjiang. The annual average ILCEI in these six provinces is between 1.192 and 1.860 t/sq. m. Although the industrial CO2 emissions in Xinjiang and Henan are at the same level, the annual average ILCEI during the study period was 1.860 and 1.192 t/sq. m, respectively. Henan is a province with high industrial CO2 emissions; in recent years, Henan has made great progress in industrial CO2 emission reductions, and the provincial emission of industrial CO2 has been decreased from 412.9 million tons in 2014 to 381.5 million tons in 2019. The decreased margin is 7.6%, which means the ILCEI of Henan is lower than that of Xinjiang; as the province is in a period of accelerated development of industrialization, this is not easy for Henan. In comparison with other Eastern provinces, Hainan has a relatively high level of ILCEI. The over scale of industrial CO2 emissions in Hainan is the lowest in China, and the industrial land area is the second-to-last in China; as a famous tourism center in China, Hainan has a low level of industrialization [35].
The ILCEI low-level provinces are Beijing, Shanghai, Guangdong, Hubei, Chongqing, Zhejiang, Sichuan, Tianjin, Heilongjiang, Jilin, Liaoning, Jiangsu, Shandong, Jiangxi, Fujian, Anhui, Gansu, Hunan, and Guangxi. The annual average ILCEI of these 19 provinces is less than t/sq. m. These provinces are mainly located in Eastern, Central, and Northeastern China, and only a few provinces (Guangxi, Sichuan, and Gansu) are located in Western China. In fact, Beijing and Shanghai not only have a low level of ILCEI, but also a low level of the over scale of industrial CO2 emissions. Although Liaoning, Jiangsu, Shandong, and Guangdong have a high scale of industrial CO2 emissions, their ILCEI is low, probably because these four provinces are developed provinces in China, were opened to the outside world early, and have a high level of production technology. Gansu has a low level of ILCEI in the Western region; the fast-growing industrial land is the important factor in this respect. During the study period, the area of industrial land in Gansu increased rapidly; compared with 2014, the area of industrial land increased by 54.4%, and its growth rate greatly exceeded the growth rate of industrial CO2 emissions.

3.4. Spatial Autocorrelation Analysis of ILCEI

In this study, the spatial autocorrelation intensity of the ILCEI of 30 provinces in China from 2014 to 2019 was analyzed by both the global and local Moran’s I indices. As observed in Table 5, the global Moran’s I index of the ILCEI is positive at a significance level of 1% during the study time, meaning that the ILCEI has a positive spatial effect. In other words, the ILCEI in a region is affected by ILCEIs in the adjoining provinces. Regions with similar ILCEIs show the trend of geographic agglomeration. As shown in Table 5, the global Moran’s I index during the period fluctuates up and down from 0.217 to 0.264. The global Moran’s I index reached its lowest point in 2017 and its highest value in 2018, meaning that the agglomeration effect was weakest in 2017 and strongest in 2018.
Then, this study tested for the local spatial autocorrelation of ILCEI by the Moran scatter plot (MSP) map based on the local Moran’s I index (Figure 7). The results represent that the ILCEI of China is dominated by H–H and L–L types, suggesting that the ILCEI was both spatially heterogeneous and spatially clustered [36]. From 2014 to 2019, the number of H–H, H–L, and L–H types of provinces decreased, while that of L–L types of provinces increased. Thus, many provinces have reduced their ILCEIs, implying that China has developed well in the transformation of an industrial growth mode and the technology of emission reductions in recent years.
MSP maps fail to exhibit the significance level of the researched provinces. The LISA map can address this shortcoming. Figure 8 shows that Shanxi, Inner Mongolia, and Ningxia are in the H–H agglomeration area and have passed the 5% significance level test during the study period, and Shannxi and Qinhai in 2014, Xinjiang in 2015 to 2017, and Hebei in 2018 and 2019 stay in the H–H agglomeration area (p < 0.05). The other provinces fail to arrive at the significance level. Therefore, China’s LISA has had an H–H effect area centered on Shanxi, Inner Mongolia, and Ningxia for a long time.
The above analysis shows that the ILCEI’s value is highly polarized. High-ILCEI provinces are mainly in the Northern region, while low-ILCEI regions are mainly in the Central and Northeastern regions. Such areas have similar spatial dependencies and agglomeration characteristics, so this paper used a spatial econometric model to analyze the effect factors on these ILCEIs.

4. Regression Analysis

4.1. The Wald and LR Tests

According to the above analysis, ILCEI is characterized by a significant spatial correlation and dependence. Therefore, the study applied the spatial econometric model to empirically test the driving factors of ILCEI. Firstly, the Wald and likelihood ratio (LR) tests were used to judge whether the SDM can degenerate into SLM or SEM. As shown in Table 6, the test statistics of Wald-lag, LR-lag, Wald-error, and LR-error are all at the 1% level of significance, regardless of the fixed effects or random, indicating that the SDM is the more appropriate spatial econometric model for this regression model.

4.2. VIF and Co-Integration Tests

Before proceeding to the SDM estimation, the variance inflation factor (VIF) test was performed to avoid multicollinearity among variables. As displayed in Table 7, all the VIF values of the independent variables are lower than eight, indicating that multicollinearity does not exist in the panel data. To prevent pseudo-regression, the paper tested the stationarity of the variables by the LLC, Fisher-ADF, and PP-ADF methods. As shown in Table 8, all variables are second-order stable. A Kao co-integration test was then performed, and the value of the t-statistic (3.720802) passed the test of significance (1%). Thus, there was a co-integration relationships between ILCEI and its driving factors for the research period.

4.3. Discussions of Results

Conducting the Hausman test to observe whether the SDM uses random or fixed effects is essential. The test shows that the fixed effects should be used. As shown in Equation (5), the explicit equation for the SDM with fixed effects is the following:
ILCEIi,t = ρW*ILCEIi,t + β1RDPi,t + β2TMi,t + β3GIi,t + β4FTDi,t + β5FDIi,t + β6UPDi,t
+ θ1W*RDPi,t + θ2W*TMi,t + θ3W*GIi,t + θ4W*FTDi,t + θ5W*FDIi,t +
θ6W*UPDi,t + εi,t      εi,t ~ N (0, σ2i,t In),
where RDP, TM, GI, FTD, FDI, and UPD express R & D personnel, the technology market, governmental intervention, foreign-trade dependence, foreign direct investment, and urban population density, respectively. Table 9 displays the estimation results of spatial fixed effects, time fixed effects, and spatial and time fixed effects. From the value of log-likelihood from Table 9, the spatial and time fixed effects (177.517) had a better ft than the other two effects (171.077 and −84.369). Therefore, it is more reasonable to apply the SDM with spatial and time fixed effects to the empirical analysis.
As shown in Table 7, the R & D personnel, foreign-trade dependence, and urban population density have notable impacts on ILCEIs. The ILCEI was negatively affected by the R & D personnel, which is consistent with the expectations. During the study period, the number of R & D personnel in industrial enterprises have increased rapidly from 2.6415 to 3.1518 million [4]. China has always attached great importance to the cultivation of engineering talents. Compared with the United States, UK, and the European Union, China has a high number of engineering graduates. In 2019, the engineering graduates in China accounted for 38% of the world’s total [37], and a large number of engineering graduates worked in industrial enterprises, which has favorably driven a decline in China’s ILCEIs. Secondly, foreign-trade dependence has notably negative effects on ILCEIs. After China’s entry into WTO in 2001, the economy in China has developed rapidly and the process of integrating into the global economy has been sped up significantly; all industries in China are facing fiercer market competition [38]. For promoting domestic and international competitive power, many industrial enterprises in China work very hard to promote product environmental quality and service level, and the China government has also taken measures to create and provide a healthy external environment for the growth of enterprises, which has negative impacts on ILCEIs. However, unlike R & D personnel and foreign-trade dependence, ILCEI was positively affected by urban population density, which means the urban population agglomeration effects do not offset the negative effects of CO2 emissions enough.
The regression coefficients of the technology market and foreign direct investment were negative, and that of governmental intervention was positive, but they had no significant effect (p > 5%), meaning these factors could not significantly influence ILCEIs. The transactions of the technology market have swiftly grown to RMB 2239.839 billion in 2019 from RMB 1769.42 billion in 2014. However, the proportion of the transaction amount of the technical market to global GDP was nearly 3.9% in 2018 [39], whereas China’s was only 1.9%. Therefore, there was plenty of room for growth in China’s technology market. The protection of intellectual property rights and encouragement of the technology intermediary are important factors in promoting the technology market. As for governmental intervention, China needs to optimize the fiscal expenditure structure to guide the green development of industries. Regarding foreign direct investment, China needs to increase environmental protection requirements for foreign investment and encourage foreign capitals to invest in green and high-tech industries.

5. Conclusions

Based on the panel data of 30 provinces of China from 2014 to 2019, this paper used the spatial autocorrelation analysis and spatial Durbin model to reach the following conclusions. First, during industrial development, the ILCEIs of the whole country and Eastern, Western, and Northeastern regions first showed a downward and then an upward trend and that of Central regions fluctuated downward. Second, ILTEI presented a low pattern in both Eastern and Northeast regions and a high pattern in the Northwestern region. Thirdly, China’s inter-provincial ILTEI presents an agglomeration effect in space. The agglomeration effect was weakest in 2017 and strongest in 2018. Fourth, the local ILCEI had a positive effect on the ILCEI of neighboring areas through the space spillover effect. Fifth, the degree of R & D personnel and foreign-trade dependence could, to a certain extent, have a notably negative impact on ILCEIs, whereas urban population density had notably positive effects on ILCEIs.
Based on the above research results, this paper puts forward the following policy suggestions. (1) The government should strengthen the administration of industrial land use and can consider using the ILCEI as a crucial index for measuring the emission-reduction performance. (2) The government should establish a cross-provincial cooperation mechanism to reduce industrial CO2 emissions. Eastern provinces should increase technical assistance to Western provinces in strengthening their techniques of industrial energy saving and CO2 emission reductions in manufacturing. (3) Policy support of green and environmentally friendly trade should be stepped up regarding service trade and new and high-tech product trade. (4) The government should optimize and upgrade the industrial structure and use more energy-saving equipment for industrial production to reduce CO2 emissions. (5) The government should take a new path to industrialization and increase investment in basic and vocational education to improve the urban population synthesis quality.
However, future research on ILCEIs still has the prospect of expansibility. Although we consider the relevant driving factors of ILCEI as many as possible to obtain more reliable and comprehensive results, there were some driving factors, such as financial subsidies, preferential taxation, and other policy factors, which we could not discuss owing to the unavailability of some data. Therefore, future research could further explore the more relevant influencing factors. As for the research area, we can also thoroughly study the characteristics and influencing factors of ILCEIs in other developing countries, such as India and Vietnam, which will contribute to their sustainable development. In the end, using mathematical forecasting methods and making predictions for industrial CO2 emissions and land area for the coming period provides a scientific basis for policymakers.

Author Contributions

L.Z.: conceptualization, methodology, software, validation, formal analysis, investigation, resources. C.L.: data curation, writing—original draft preparation, writing—review and editing. Z.L.: visualization, supervision, project administration. X.Z.: funding acquisition. H.H.: investigation, resources. X.W.: software, validation. D.Y.: data curation. Z.Y.: validation, formal analysis. T.Y.: funding acquisition. J.L.: writing—review and editing. Q.H.: writing—review and editing. F.Q.: writing—review and editing. 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.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data were obtained from China’s Statistical Yearbook (2015–2020), National Bureau of Statistics of China (2022), and China’s carbon emission database (https://www.ceads.net/data/province/energy_inventory/, accessed on 12 July 2022), and China’s Urban Construction Statistical Yearbook (2015–2019).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Flowchart of the empirical research of ILCEI in China.
Figure 1. Flowchart of the empirical research of ILCEI in China.
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Figure 2. The schematic diagram of four economic zones of China.
Figure 2. The schematic diagram of four economic zones of China.
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Figure 3. The trend of ILCEI in mainland China (except Tibet) from 2014 to 2019.
Figure 3. The trend of ILCEI in mainland China (except Tibet) from 2014 to 2019.
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Figure 4. The trend of ILCEI in four regions during 2014–2019.
Figure 4. The trend of ILCEI in four regions during 2014–2019.
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Figure 5. Average ILCEI values in 31 Chinese provinces (2009–2017).
Figure 5. Average ILCEI values in 31 Chinese provinces (2009–2017).
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Figure 6. The LISA of ILCEI in 30 provinces from 2014 to 2019.
Figure 6. The LISA of ILCEI in 30 provinces from 2014 to 2019.
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Figure 7. The Moran’s I scatterplot maps of ILCEIs in 30 provinces from 2014 to 2019.
Figure 7. The Moran’s I scatterplot maps of ILCEIs in 30 provinces from 2014 to 2019.
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Figure 8. The LISA maps of ILCEI in 30 provinces from 2014 to 2019.
Figure 8. The LISA maps of ILCEI in 30 provinces from 2014 to 2019.
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Table
Table 1. The data of industrial CO2 emissions and land area of 30 provinces in China from 2014 to 2019.
Table 1. The data of industrial CO2 emissions and land area of 30 provinces in China from 2014 to 2019.
RegionsIndustrial CO2 Emissions (Million Tons)Industrial Land Area (Square Kilometers)
201420152016201720182019201420152016201720182019
Beijing40.038.836.831.836.535.3240.0263.6263.3263.1263.1263.1
Tianjing129.8125.1117.7112.0125.9129.9185.9208.9231.2242.5222.4237.7
Hebei681.8655.7664.7647.9842.1870.4249.8279.5314.3274.0262.7257.6
Shanghai118.3117.1111.2109.1115.2115.4733.1733.1555.8550.6547.5537.7
Jiangsu639.8692.2656.2666.4689.7723.9923.6973.81009.41064.81054.8964.5
Zhejiang310.1306.2302.3312.2321.4317.0610.7562.3565.7588.5608.8678.5
Fujian213.3199.7180.6194.3224.2238.8246.9247.5237.2276.0277.7289.2
Shandong699.1730.0737.0705.1807.2842.5934.61044.1997.11025.01069.31113.4
Guangdong395.4325.1385.8408.7431.7451.61121.51298.31375.81488.01364.41554.6
Hainan30.531.628.630.531.430.920.125.622.317.017.417.8
Eastern3258.13221.53220.93218.03625.23755.75266.25636.75572.15789.45688.15914.0
Shanxi426.7390.9400.0436.9497.6523.5181.7176.6174.7143.3105.8168.6
Anhui306.9305.1315.0319.7348.0356.8341.6349.0357.0370.7396.7437.5
Jiangxi177.0182.4185.4193.3200.8205.1207.5220.5240.1277.5272.5286.6
Henan472.9450.8450.7428.6412.7381.5347.8370.7378.4386.4344.7352.8
Hubei232.2227.9230.9242.8242.7262.4549.9443.6460.4573.8611.5624.6
Hunan200.1200.3212.1225.1215.9218.1192.8198.1202.0239.7267.0276.4
Central1815.81757.31794.01846.41917.71947.31821.31758.41812.61991.51998.22146.5
Inner Mongolia505.7514.9532.4581.2674.5744.8170.6164.4165.3161.4147.7147.3
Guangxi179.4238.9178.9188.9202.1218.8174.6199.8204.1220.5208.5206.5
Chongqing126.8128.2119.1121.1127.3120.3218.6236.0246.8244.6252.6262.6
Sichuan276.7257.1234.9231.6218.9235.5428.1410.6441.8450.8480.1482.4
Guizhou166.9163.5175.2177.3182.7191.8109.7122.2122.9155.2161.0162.3
Yunnan152.7132.5135.4149.3164.0135.5104.4106.7111.2122.6126.6126.7
Shaanxi237.0239.0230.9227.8240.1260.1118.2129.5132.4146.0149.6174.6
Gansu139.6132.9124.8122.8137.1138.3129.8122.6129.7171.4173.3200.5
Qinghai39.341.546.441.339.639.212.413.113.213.213.314.5
Ningxia135.5133.3129.3167.4185.2205.535.438.641.142.643.045.4
Xinjiang293.3302.7321.7352.7373.1410.3158.0170.5168.5196.1194.6215.5
Western2253.12284.62229.02361.42544.52700.21659.81713.91777.01924.31950.32038.3
Liaoning416.2399.1383.2404.3446.6459.7570.5565.0711.7719.3719.2707.1
Jilin188.5169.9163.9169.2170.0177.7269.0260.6274.7286.8296.0300.4
Heilongjiang208.0192.7194.7196.7189.9222.7334.3350.6357.5357.7360.4357.9
Northeastern812.7761.7741.8770.1806.4860.11173.71176.21344.01363.81375.61365.5
China8139.78025.27985.78195.98893.89263.49921.110,28510,50611,06911,01211,464
Table
Table 2. Influencing factors.
Table 2. Influencing factors.
Explanatory VariableVariables’ Definitions and UnitsPre-Judgment
R & D personnel (RDP)R & D personnel of industrial enterprises above designated size (person)Negative
Technology market (TM)The proportion of the total value of technical market to GDP (%)Negative
Governmental intervention (GI)The proportion of the financial expenditure to GDP (%)Unknown
Foreign trade dependence (FTD)The proportion of the foreign trade to GDP (%)Unknown
Foreign direct investment (FDI)The proportion of the foreign direct Investment to GDP (%)Unknown
Urban population density (UPD)Population density of urban area (person/sq.km)Unknown
Table
Table 3. The values of ILCEI for 30 provinces in China from 2014 to 2019 (t/sq. m).
Table 3. The values of ILCEI for 30 provinces in China from 2014 to 2019 (t/sq. m).
Regions201420152016201720182019Mean
Beijing0.1670.1470.140.1210.1390.1340.141
Tianjing0.6980.5990.5090.4620.5660.5460.563
Hebei2.7292.3462.1152.3653.2063.382.690
Shanghai0.1610.1600.2000.1980.2100.2150.191
Jiangsu0.6930.7110.650.6260.6540.7510.681
Zhejiang0.5080.5450.5340.5310.5280.4670.519
Fujian0.8640.8070.7610.7040.8070.8260.795
Shandong0.7480.6990.7390.6880.7550.7570.731
Guangdong0.3530.2500.2800.2750.3160.2910.294
Hainan1.5181.2321.2831.7971.8081.7421.563
Eastern0.6190.5720.5780.5560.6370.6350.600
Shanxi2.3482.2132.2903.0484.7013.1052.951
Anhui0.8980.8740.8820.8620.8770.8160.868
Jiangxi0.8530.8270.7720.6960.7370.7160.767
Henan1.3601.2161.1911.1091.1971.0811.192
Hubei0.4220.5140.5010.4230.3970.4200.446
Hunan1.0381.0111.050.9390.8090.7890.939
Central0.9970.9990.990.9270.960.9070.963
Inner Mongolia2.9643.1333.223.6014.5655.0553.756
Guangxi1.0271.1950.8770.8570.9691.0600.998
Chongqing0.5800.5430.4830.4950.5040.4580.511
Sichuan0.6460.6260.5320.5140.4560.4880.544
Guizhou1.5211.3381.4261.1421.1341.1821.291
Yunnan1.4631.2421.2181.2181.2951.0691.251
Shaanxi2.0061.8461.7441.5611.6051.4901.709
Gansu1.0751.0840.9620.7170.7910.6900.887
Qinghai3.1633.1573.5223.1252.9912.7033.110
Ningxia3.8273.4573.1493.934.3064.5243.866
Xinjiang1.8571.7761.911.7991.9171.9051.861
Western1.3571.3331.2541.2271.3051.3251.300
Liaoning0.7300.7060.5380.5620.6210.6500.635
Jilin0.7010.6520.5970.5900.5740.5920.618
Heilongjiang0.6220.5500.5450.550.5270.6220.569
Northeastern0.6920.6480.5520.5650.5860.6300.612
China0.6190.5720.5780.5560.6370.6350.600
Table
Table 4. The values of ILCEI for 30 provinces in China from 2014 to 2019 (t/sq. m).
Table 4. The values of ILCEI for 30 provinces in China from 2014 to 2019 (t/sq. m).
Regions201420152016201720182019Mean
Eastern0.6190.5720.5780.5560.6370.6350.600
Central0.9970.9990.990.9270.9600.9070.963
Western1.3571.3331.2541.2271.3051.3251.300
Northeastern0.6920.6480.5520.5650.5860.6300.612
China0.6190.5720.5780.5560.6370.6350.600
Table
Table 5. Value of Global Moran’s I index of provincial ILCEI in China (2014–2019).
Table 5. Value of Global Moran’s I index of provincial ILCEI in China (2014–2019).
YearGlobal Moran’s IZ-Scorep-Value
20140.246 ***2.9990.003
20150.251 ***3.0560.002
20160.229 ***2.8210.005
20170.217 ***2.7040.007
20180.264 ***3.2350.001
20190.222 ***2.8370.005
*** represents significance at the 1% level.
Table
Table 6. The regression results of likelihood ratio and Wald tests.
Table 6. The regression results of likelihood ratio and Wald tests.
Fixed EffectsRandom Effects
Wald test spatial lag45.52 ***36.00 ***
LR test spatial lag42.29 ***33.29 ***
Wald test spatial error44.58 ***31.38 ***
LR test spatial error62.06 ***29.38 ***
*** represents significance at the 1% level.
Table
Table 7. The VIF test.
Table 7. The VIF test.
RDPTMGIFTDFDIUPDMean VIF
VIF7.321.297.511.621.651.113.42
1/VIF0.1370.7750.1330.6170.6060.901
Table
Table 8. The results of the unit root test.
Table 8. The results of the unit root test.
LLCFisher-ADFPP-ADF
InILCEI−4.71747 ***55.180967.7630
InRDP−9.39487 ***76.7227 *113.816 ***
InTM−5.82977 ***90.5035 ***126.535 ***
InGI−0.9648538.217533.9923
InFTD3.04179 ***27.025226.5892
InFDI−14.7583 ***49.106252.2592
InUPD−13.4544 ***79.2340 **98.7717 ***
ΔInILCEI−17.5301 ***99.2307 ***113.333 ***
ΔInRDP−15.4006 ***92.2719 ***106.558 ***
ΔInTM−17.8624 ***−6.97951 ***129.987 ***
ΔInGI−5.95718 ***62.694265.4803
ΔInFTD−17.3686 ***65.223968.0295
ΔInFDI−25.8630 ***120.086 ***140.144 ***
ΔInUPD−54.8441 ***109.315 ***130.428 ***
ΔΔInILCEI−25.2679 ***146.034 ***163.233 ***
ΔΔInRDP−55.7402 ***145.721 ***165.895 ***
ΔΔInTM−736.844 ***142.570 ***165.242 ***
ΔΔInGI−8.63430 ***79.0983 **90.9410 ***
ΔΔInFTD−133.564 ***−133.564 ***123.301 ***
ΔΔInFDI−38.0943 ***171.338 ***192.268 ***
ΔΔInUPD−41.3728 ***188.040 ***207.668 ***
***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.
Table
Table 9. The regression results of SDM.
Table 9. The regression results of SDM.
Spatial Fixed EffectsTime Fixed EffectsSpatial and Time Fixed Effects
InRDP−0.111 **−0.093−0.097 **
InTM−0.002−0.223 ***−0.007
InGI0.187−0.465 *0.163
InFTD−0.067−0.559 ***−0.081 ***
InFDI−0.002−0.065−0.026
InUPD0.201 ***−0.0810.248 ***
W*InRDP−0.0610.146−0.079
W*InTM0.076 **0.315 ***0.159 ***
W*InGI0.536 **0.2240.994 **
W*InFTD0.549 ***0.2740.381 **
W*InFDI0.0227−0.246 **−0.003
W*InUPD0.620 ***−0.861 ***0.880 ***
Spatial rho0.327 ***0.366 ***0.341 **
Variance sigma2_e0.009 ***0.145 ***0.008 ***
R-squared0.1750.7300.198
Log-likelihood171.077−84.369177.517
***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.
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Zeng, L.; Li, C.; Liang, Z.; Zhao, X.; Hu, H.; Wang, X.; Yuan, D.; Yu, Z.; Yang, T.; Lu, J.; et al. The Carbon Emission Intensity of Industrial Land in China: Spatiotemporal Characteristics and Driving Factors. Land 2022, 11, 1156. https://doi.org/10.3390/land11081156

AMA Style

Zeng L, Li C, Liang Z, Zhao X, Hu H, Wang X, Yuan D, Yu Z, Yang T, Lu J, et al. The Carbon Emission Intensity of Industrial Land in China: Spatiotemporal Characteristics and Driving Factors. Land. 2022; 11(8):1156. https://doi.org/10.3390/land11081156

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Zeng, Liangen, Chengming Li, Zhongqi Liang, Xuhai Zhao, Haoyu Hu, Xiao Wang, Dandan Yuan, Zhao Yu, Tingzhang Yang, Jingming Lu, and et al. 2022. "The Carbon Emission Intensity of Industrial Land in China: Spatiotemporal Characteristics and Driving Factors" Land 11, no. 8: 1156. https://doi.org/10.3390/land11081156

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