The Empirical Analysis of Environmental Regulation's Spatial Spillover Effects on Green Technology Innovation in China

Green technology innovation is one of the driving forces of industrial structure upgrading. This innovation is thought to be related to environmental regulation. The study uses panel data for 30 Chinese provinces and cities from 2009 to 2020 and presents a comprehensive research-based explanation of how environmental regulations impact green innovation. This study employs the spatial Durbin model to analyze the spillover effect of the region. The results show that the total impact of environmental regulations is 0.223%, of which the direct effect is 0.099%. This impact includes the effects of both formal and informal environmental regulation. It indicates that ecological regulations significantly enhance green technology innovation. Furthermore, the spatial spillover effect is significantly positive at the 1% level with a coefficient of 0.124. Such spillover effects represent a learning effect of regional environmental regulation. Based on the results, the study suggests a few policy measures based on the detailed outcomes.

Does Agricultural Credit Input Promote Agricultural Green Total Factor Productivity? Evidence from Spatial Panel Data of 30 Provinces in China

Improving agricultural green total factor productivity is crucial to promoting high-quality agricultural development. This paper selects the panel data of 30 provinces in China from 2009 to 2020 and uses the super-efficiency SBM model with undesirable outputs to measure the agricultural green total factor productivity of all regions in China. On this basis, this paper uses the panel data fixed-effect model and spatial Durbin model to empirically discuss the impact of agricultural credit input on agricultural green total factor productivity and its spatial spillover effect. The main conclusions are as follows: First, from 2009 to 2020, the average values of agricultural green total factor productivity in national, eastern, central, and western regions are 0.8909, 0.9977, 0.9231, and 0.8068, respectively, and the agricultural green total factor productivity needs to be further improved. Second, the agricultural green total factor productivity presents a significant and positive spatial correlation, and the spatial distribution of agricultural green total factor productivity is not random and irregular. Third, agricultural credit input can significantly promote agricultural green total factor productivity in the local region, but it hinders the improvement of agricultural green total factor productivity in the adjacent regions. Fourth, the impact of agricultural credit input on the agricultural green total factor productivity and its spillover effect has a significant regional heterogeneity. This paper believes that paying attention to the spatial spillover effect of agricultural total factor productivity, optimizing the structure and scale of agricultural credit input, and formulating reasonable agricultural credit policies can improve agricultural green total factor productivity.

Booster or Killer? Research on Undertaking Transferred Industries and Residents' Well-Being Improvements

Inter-regional industrial transfers would change the economic, societal, and ecological environment of the undertaking area profoundly. Some experts have recognized the ecological and environmental problems caused by industrial transfers. However, there are few studies on whether undertaking an industrial transfer will ultimately improve the well-being of residents. There is a strong application value for exploring this issue under the domestic cycle in China. This paper uses the shift-share analysis method to measure China's inter-provincial industrial transfer from 2004 to 2019. According to the subjective and objective indicators, the article measures the level of residents' well-being. A spatial econometric model is used to empirically test the impact of undertaking transferred industries on residents' well-being and its mechanism. The results show that: 1. There is a significant spatial positive correlation between the well-being of residents at the national level. The empirical results also indicated significant spatial correlations at the level of the three major economic belts in the east, central, west, and northeast; 2. From the perspective of China as a whole, the inter-regional industrial transfer improved the well-being of the residents significantly, but the indirect negative effect reduced the total effect; 3. From the regional perspective, undertaking a transferred industry could significantly improve the well-being of residents in the central and eastern regions. However, in the northeast and western regions, it showed a serious negative effect. We should enhance the orderly transfer of industries deeply, considering the ecological and environmental capacities of the undertaking area fully and strictly limiting the inter-regional transfer of polluting industries. Only in this way could the government improve the well-being of residents in the industrial transfer-out areas and undertake areas effectively.

Do Land Use Structure Changes Impact Regional Carbon Emissions? A Spatial Econometric Study in Sichuan Basin, China

Human activities are closely related to carbon emissions and the mechanism of land-use structure change on carbon emissions is unclear. In this study, 143 counties in the Sichuan Basin of China were used as sample units, and the land use structure of each sample unit in the Sichuan Basin was measured by applying the information entropy theory, analyzing the spatial and temporal evolutionary characteristics and the influencing relationships of land use structure and carbon emissions in the Sichuan Basin, by spatial econometric analysis of panel data on carbon emissions and information entropy of land use structure over five time periods from 2000 to 2018. The results indicate that: the carbon emission intensity and information entropy of land use in the Sichuan basin are increasing over the years, and the cross-sectional data reflect inconsistent spatial distribution characteristics, with greater changes around large cities; both carbon emissions and land use structure are spatially auto-correlated, the information entropy of land use positively affects carbon emission intensity; carbon emissions have positive spillover effects, and changes in land use structure have no obvious regional impact on surrounding areas; there may be potential threshold areas for the impact of land-use structure change on carbon emissions. This study has certain reference value for land use planning and carbon emission reduction policies.

Would Financial Development Help China Achieve Carbon Peak Emissions?

China has committed to reaching carbon peak before 2030. To realize the carbon peak goal, financial development plays an essential role in developing a green economy. Based on the panel data of 30 provinces in China from 2006 to 2019, this paper explores the impact of financial development on carbon intensity both theoretically and empirically. A financial development index system is constructed and computed using the entropy method. A spatial lag panel data model is employed to empirically test the interaction effect of financial development on carbon intensity. Moreover, the mediating effects of industrial upgrading and technological innovation are further investigated. The results show that: first, carbon intensity generates strong spatial spillover effects between provinces in China. Second, financial development significantly reduces carbon intensity, and is most pronounced in central China, followed by western and eastern China. Third, industrial upgrading and technological innovation are important channels to assist financial development in cutting down carbon intensity, and both produce positive spatial spillover effects. These findings suggest that inter-regional cooperation and coordination on financial development, industrial upgrading, and technological innovation are conducive to achieving low-carbon development targets. This research not only has practical significance to China, but also provides global reference value to other countries.

Air Pollution Increased the Demand for Gym Sports under COVID-19: Evidence from Beijing, China

Air pollution may change people's gym sports behavior. To test this claim, first, we used big data crawler technology and ordinary least square (OLS) models to investigate the effect of air pollution on people' gym visits in Beijing, China, especially under the COVID-19 pandemic of 2019-2020, and the results showed that a one-standard-deviation increase in PM2.5 concentration (fine particulate matter with diameters equal to or smaller than 2.5 μm) derived from the land use regression model (LUR) was positively associated with a 0.119 and a 0.171 standard-deviation increase in gym visits without or with consideration of the COVID-19 variable, respectively. Second, using spatial autocorrelation analysis and a series of spatial econometric models, we provided consistent evidence that the gym industry of Beijing had a strong spatial dependence, and PM2.5 and its spatial spillover effect had a positive impact on the demand for gym sports. Such a phenomenon offers us a new perspective that gym sports can be developed into an essential activity for the public due to this avoidance behavior regarding COVID-19 virus contact and pollution exposure.

[Temporal and spatial evolution of regional cultural landscape under the influence of construction land expansion: A case study of Hanjiang delta in Guangdong, China.]

Cultural landscapes are the products of combination of natural and human factors with constant change in response to human behavior. Exploring the impacts of construction land expansion on cultural landscapes is the key to understand the formal connotations and value characteristics of human activities interfering with cultural landscapes. In this study, we first classified the cultural landscape of the Hanjiang Delta and then used the landscape index to quantitatively describe the spatial and temporal evolution characteristics of the cultural landscape from 1980 to 2018. Finally, we analyzed the spatial effect between construction land expansion and the cultural landscape with a spatial panel econometric model. The results showed that a total of seven cultural landscape types were identified at the regional level. From 1980 to 2000, the cultural landscape pattern in the study area changed substantially, with increasing fragmentation, deepening irregularity, and increasing diversity. The proportion of regional construction land increased from 14.8% to 29.9%. The year 2000 was the cut-off point for the rate of construction land expansion, and the chronological characteristics of cultural landscape change coincided with it. There was a spatial dependency between the expansion of construction land and the change of cultural landscape. With the expansion of construction land, the sprawl town landscape in sand dike became the dominant type, and the paddy scattered historical villages, the wetland agglomeration town landscape, and the paddy wetland landscape in net river lowland faced extinction. Construction land expansion affected the local landscape pattern and had spatial spillover effects on neighboring areas. For a particular landscape type, the expansion of construction land led to a general increase in the degree of patch integration and an enhanced landscape agglomeration effect. For different types, this led to a decrease in inter-landscape sprawl, an increase in patch irregularity, and enhanced fragmentation. This study could provide a reference for the human history inheritance and ecological pattern optimization in the Hanjiang Delta.

What Cause Large Spatiotemporal Differences in Carbon Intensity of Energy-Intensive Industries in China? Evidence from Provincial Data during 2000-2019

China has been reported as the world's largest carbon emitter, facing a tough challenge to meet its carbon peaking goal by 2030. Reducing the carbon intensity of energy-intensive industries (EIICI) is a significant starting point for China to achieve its emission reduction targets. To decompose the overall target into regions, understanding the spatiotemporal differences and drivers of carbon intensity is a solid basis for the scientific formulation of differentiated regional emission reduction policies. In this study, the spatiotemporal differences of EIICI are described using the panel data of 30 provinces in China from 2000 to 2019, and a spatial econometric model is further adopted to analyze its drivers. As indicated by the results: (1) from 2000 to 2019, China's EIICI tended to be reduced continuously, and the spatial differences at the provincial and regional levels expanded continuously, thus revealing the coexistence of "high in the west and low in the east" and "high in the north and low in the south" spatial patterns. (2) There is a significant spatial autocorrelation in the EIICI, characterized by high and high agglomeration and low and low agglomeration types. Moreover, the spatial spillover effects are denoted by a 1% change in the local EIICI, and the adjacent areas will change by 0.484% in the same direction. (3) Technological innovation, energy structure, and industrial agglomeration have direct and indirect effects, thus affecting the local EIICI and the adjacent areas through spatial spillover effects. Economic levels and firm sizes only negatively affect the local EIICI. Environmental regulation merely has a positive effect on adjacent areas. However, the effect of urbanization level on EIICI has not been verified, and the effect of urbanization level on the EIICI has not been verified. The results presented in this study show a scientific insight into the reduction of EIICI in China. Furthermore, policymakers should formulate differentiated abatement policies based on dominant drivers, spatial effects, and regional differences, instead of implementing similar policies in all provinces.

COVID-19: Analysis of Factors Affecting the Economy of Hunan Province Based on the Spatial Econometric Model

The COVID-19 pandemic has spread across the country negatively impacting on the economy. This paper uses the panel data of 14 prefecture-level cities from 2015 to 2020 in Hunan to determine the factors and effects of economic downturns based on the spatial econometric model. We calculate the Moran index, so-called the Moran's I, to analyse the impact of each factor on the economy. The results show that the spatial correlation of the cities around Chang-Zhu-Tan is high, and the economic growth of the entire province can be influenced by these cities. These cities should adopt strategies to improve the economy, such as reducing the tax revenues, improving the local financial revenues, and reducing the ineffective educational input. These results can also be helpful for policymakers, who will attempt to retransform the Hunan economy during the post-COVID era.

[Spatial Heterogeneity of PM2.5 Concentration in Response to Land Use/Cover Conversion in the Yangtze River Delta Region]

The sustainable management direction of PM_2.5 concentrations in the Yangtze River Delta region remains unclear due to regional spatial effects. This study combined the random forest model, spatial econometric model, and multi-scale geographically weighted regression model (MGWR) to explore the multi-scale spatial response of PM_2.5 concentration to land use/cover conversion. The results show that:① PM_2.5 concentrations in the Yangtze River Delta region from 2000 to 2018 showed four types of spatial-temporal patterns of spatially continuous aggregation, with strong regional synchronous changes. ② The relative influence of land conversion on PM_2.5 concentrations showed a complex performance, and the source-sink effect of cultivated land and forest land was obvious. Neighborhood analysis indicated that the effect of surrounding aggregated land use conversion was generally more significant than that of single cells on PM_2.5 concentration change, and the spatial effect was obvious. ③ PM_2.5 concentration changes were mostly significantly negatively correlated with forest land and grassland conversion types and significantly positively correlated with conversion types between cropland, construction land, and water bodies. The importance ranking of the random forest model and correlation coefficient intensity indicated that the conversion between cropland-cropland (29.65%; 0.650), forest land-forest land (26.98%; 0.726), construction land-cropland (22.57%; 0.519), cropland-forestland (17.84%; 0.602), and cropland-construction land (16.34%; 0.424) contributed more to the variation in PM_2.5 concentration. The spatial Durbin model revealed a significant spatial dependence of the change in PM_2.5 concentration and a strong spatial spillover effect. ④ The MGWR model revealed the scale effects and non-stationary characteristics of the spatial relationships between different land use conversions acting on PM_2.5 concentration change, and its spatial relationship showed strong differences in transfer types. However, the multi-models revealed that different land conversions drove the PM_2.5 concentration change in different ways, so it is necessary to formulate targeted joint management strategies in a categorical and hierarchical manner.

Spatio-Temporal Variation of Health Production Efficiency Considering Environmental Pollution in China Based on Modified EBM and Spatial Econometric Model

In order to strengthen the construction of China's health industry and improve the health of the people, based on the data of 31 provinces and cities in China from 2009 to 2019, the improved EBM model is used to measure the health production efficiency of each region, and Moran index is used to study the Spatio-temporal variation of health production efficiency of each province. Finally, the spatial econometric model is applied to study the influencing factors of the Spatio-temporal variation of health production efficiency. The results show that generally speaking, the average efficiency of 31 provinces and cities is above 0.7, and the average efficiency of some regions is above 1. From the perspective of time variation, the average efficiency value in the eastern region and the middle region increases from 0.816 to 0.882 and from 0.851 to 0.861, respectively. However, the average efficiency value in the western region and northeast region decreases from 0.861 to 0.83 and from 0.864 to 0.805, respectively. From the perspective of spatial distribution, HH agglomeration and LL agglomeration exist in most regions. By comparing Moran scatter plots in 2009 and 2019, it is found that the quadrants of most regions remain unchanged, and LL agglomeration is the main agglomeration type in local space. There is a significant spatial dependence among different regions. From the perspective of spatial empirical results, Pgdp, Med, and Pd have a positive effect on health production efficiency. The direct effect and indirect effect of Pgdp, Med, and Gov all pass the significance test of 1%, indicating that there are spatial spillover effects of the three indicators. Each region should reasonably deal with the spillover effect of surrounding regions, vigorously develop economic activities, carry out cooperation with surrounding regions and apply demonstration effect to accelerate the development of overall health production.

Finding flaws in the spatial distribution of health workforce and its influential factors: An empirical analysis based on Chinese provincial panel data, 2010-2019

Background

The maldistributions of the health workforce showed great inconsistency when singly measured by population quantity or geographic area in China. Meanwhile, earlier studies mainly employed traditional econometric approaches to investigate determinants for the health workforce, which ignored spillover effects of influential factors on neighboring regions. Therefore, we aimed to analyze health workforce allocation in China from demographic and geographic perspectives simultaneously and then explore the spatial pattern and determinants for health workforce allocation taking account of the spillover effect.

Methods

The health resource density index (HRDI) equals the geometric mean of health resources per 1,000 persons and per square kilometer. First, the HRDI of licensed physicians (HRDI_P) and registered nurses (HRDI_N) was calculated for descriptive analysis. Then, global and local Moran's I indices were employed to explore the spatial features and aggregation clusters of the health workforce. Finally, four types of independent variables were selected: supportive resources (bed density and government health expenditure), healthcare need (proportion of the elderly population), socioeconomic factors (urbanization rate and GDP per capita), and sociocultural factors (education expenditure per pupil and park green area per capita), and then the spatial panel econometric model was used to assess direct associations and intra-region spillover effects between independent variables and HRDI_P and HRDI_N.

Results

Global Moran's I index of HRDI_P and HRDI_N increased from 0.2136 (P = 0.0070) to 0.2316 (P = 0.0050), and from 0.1645 (P = 0.0120) to 0.2022 (P = 0.0080), respectively. Local Moran's I suggested spatial aggregation clusters of HRDI_P and HRDI_N. For HRDI_P, bed density, government health expenditure, and GDP had significantly positive associations with local HRDI_P, while the proportion of the elderly population and education expenditure showed opposite spillover effects. More precisely, a 1% increase in the proportion of the elderly population would lead to a 0.4098% increase in HRDI_P of neighboring provinces, while a 1% increase in education expenditure leads to a 0.2688% decline in neighboring HRDI_P. For HRDI_N, the urbanization rate, bed density, and government health expenditure exerted significantly positive impacted local HRDI_N. In addition, the spillover effect was more evident in the urbanization rate, with a 1% increase in the urbanization rate relating to 0.9080% growth of HRDI_N of surrounding provinces. Negative spillover effects of education expenditure, government health expenditure, and elderly proportion were observed in neighboring HRDI_N.

Conclusion

There were substantial spatial disparities in health workforce distribution in China; moreover, the health workforce showed positive spatial agglomeration with a strengthening tendency in the last decade. In addition, supportive resources, healthcare needs, and socioeconomic and sociocultural factors would affect the health labor configuration not only in a given province but also in its nearby provinces.

Spatial Heterogeneity Influences of Environmental Control and Informal Regulation on Air Pollutant Emissions in China

High air pollutant emissions in China have become serious environmental issues threatening public health. While spatial heterogeneity plays an important role in environmental regulation in China, it is necessary to analyze the spatial heterogeneity influences of air pollution control policies and informal environmental regulation on air pollutant emissions in China. Based on the quantification of air pollution control policies, this paper incorporates the central government's policy formulation and local government's policy implementation into the intensity of air pollution control policy. This paper uses the panel data of China's 30 provinces to examine the spatial impact of air pollution control policy and informal environmental regulation on air pollutant emissions. The results show that (a) air pollutant emissions represented by soot and dust emission intensity has a significant positive spatial spillover effect; (b) air pollution control policy and informal environmental regulation play significant inhibitory roles in air pollutant emissions; (c) informal environmental regulation has a negative moderating effect on the negative relationship between air pollution control policy and air pollutant emissions. Other implications for environmental management have also been discussed.

Spatial Characteristics and Factor Analysis of Pollution Emission from Heavy-Duty Diesel Trucks in the Beijing-Tianjin-Hebei Region, China

Heavy-duty diesel trucks (HDDTs) contribute significantly to NO_X and particulate matter (PM) pollution. Although existing studies have emphasized that HDDTs play a dominant role in vehicular pollution, the spatial distribution pattern of HDDT emissions and their related socioeconomic factors are unclear. To fill this research gap, this study investigates the spatial distribution pattern and spatial autocorrelation characteristics of NO_X, PM, and SO₂ emissions from HDDTs in 200 districts and counties of the Beijing-Tianjin-Hebei (BTH) region. We used the spatial lag model to calculate the significances and directions of the pollutants from HDDTs and their related socioeconomic factors, namely, per capita GDP, population density, urbanization rate, and proportions of secondary and tertiary industries. Then, the geographical detector technique was applied to quantify the strengths of the significant socioeconomic factors of HDDT emissions. The results show that (1) NO_X, PM, and SO₂ pollutants emitted by HDDTs in the BTH region have spatial heterogeneity, i.e., low in the north and high in the east and south. (2) The pollutants from HDDTs in the BTH region have significant spatial autocorrelation characteristics. The spatial dependence effect was obvious; for every 1% increase in the HDDT emissions in the surrounding districts and counties, the local HDDT emissions increased by 0.39%. (3) Related factors analysis showed that the proportion of tertiary industries had a significant negative correlation, whereas the proportion of secondary industries and urbanization rate had significant positive correlations with HDDT emissions. Population density and per capita GDP did not pass the significance test. (4) The order of effect intensities of the significant socioeconomic factors was proportion of tertiary industry > proportion of secondary industry > urbanization rate. This study guides scientific decision making for pollution control of HDDTs in the BTH region.

Land Use Eco-Efficiency and Its Convergence Characteristics Under the Constraint of Carbon Emissions in China

By defining the connotation of land use eco-efficiency, land use eco-efficiency from 2003 to 2015 was calculated on the basis of the mixed directional distance function, and its spatial convergence analyzed using a spatial econometric model. Results showed that (1) the land use eco-efficiency in most regions of China was relatively ineffective—only Guangdong and Guangxi were relatively effective—and the spatial distribution of efficiency levels in each region was polarized. (2) Sigma and beta convergences were observed in land use eco-efficiency in China, and land use eco-efficiency in each province had an influence on the other. (3) The convergence rate of the eastern region was the same as that of the national region (0.164). The convergence rates of the central, western, and northeast regions were 0.181, 0.183, and 0.189, respectively, which were all higher than the national convergence rate. (4) Scientific and technological strength and industrial structure significantly promoted the improvement of land use eco-efficiency and steady development of land use in China.