Convergence Analysis of Cross-Province Human Well-Being in China: A Spatiotemporal Perspective

China's economy has been experiencing a new development mode that emphasizes an environmentally friendly green economy and high living standards. The concept of human well-being has become increasingly prominent in recent years to replace GDP per capita as an important indicator for evaluating happiness. In the context of the green economy, it is of great significance to incorporate environmental indicators for evaluating human well-being. To this end, this paper constructs a new human well-being evaluation indicator system including environmental sustainability, and then evaluates the well-being levels of 30 provinces in China from 2011 to 2020 using a comprehensive evaluation method. Then, various statistical methods and visualization methods are used to deeply analyze the spatiotemporal changes in the well-being scores of Chinese provinces during the sample period. Finally, the spatial convergence model was used to verify if cross-province well-being scores would converge to a common steady state. The findings are as follows. (1) The scores of the environmental sustainability subsystem greatly vary from province to province. This is because the local governments have attached great importance to the construction of green ecological civilization in recent years, thus increasing the investment in protecting the ecological environment. (2) From temporal dimensions, overall human well-being scores of 30 provinces slightly increased year after year. In geography, eastern provinces have the highest human well-being scores, followed by northeast, northwest, and southwest provinces. (3) In terms of the scores of the four subsystems, we find that nearly all provinces have their advantages and disadvantages. (4) From the results of the spatial convergence models, both absolute and conditional β convergence have been verified, indicating that the human well-being of all provinces will converge to the common steady state in the future.

Agricultural Eco-Efficiency Response and Its Influencing Factors from the Perspective of Rural Population Outflowing: A Case Study in Qinan County, China

Agriculture is the source of human clothing and food, but it also brings negative externalities to the environment. The outflow of the rural population is one of the factors for changes in the characteristics of the rural population. Farmers' decisions on agricultural production can affect agricultural ecological efficiency. Therefore, it is necessary to study the relationship between the two in rural development. Taking Qin'an County in the Loess Hilly Region of central Gansu, China, as an example, this paper analyzed the demographic characteristics and the evolution characteristics of agricultural eco-efficiency under the background of rural population outflowing, and the impact of the former on the latter, based on the panel statistical data of 17 villages and towns from 2001 to 2020. The results show that (1) From 2001 to 2020, the non-agricultural level of Qin'an County's labor force showed an upward fluctuation trend. The level of aging was relatively stable, and the per capita disposable income was significantly increased. (2) From 2001 to 2020, the agricultural eco-efficiency of Qin'an County showed a wavy change, but there were some towns and villages that have not been effectively developed. The regional differences are significantly different. (3) The non-agriculturalization level of the labor force promotes agricultural eco-efficiency through the direct effect rather than the space spillover effect. The positive effect of aging on agricultural eco-efficiency was mainly reflected through direct effect rather than spatial spillover effect. Per capita, disposable income has a significant positive spatial spillover effect on agricultural eco-efficiency. Finally, this paper provides a scientific reference for promoting the improvement of agricultural eco-efficiency and sustainable development. This is of great theoretical and practical significance for the realization of rural revitalization.

Spatial Human Development Index in China: Measurement and Interpretation Based on Bayesian Estimation

The development of urban agglomerations dominated by the service industry is an important driving force for further sustainable economic growth of China. Spatial analysis marked by population density and regional integration is an essential perspective for studying the human development index (HDI) in China. Based on Bayesian estimation, this paper examines the influence of a spatial factor on HDI by using a spatial hierarchical factor model within the framework of Sen Capability Approach theory, overcoming the neglect of spatial factors and their equal weight in traditional measurement of HDI. On this basis, the HDI including the spatial factor was measured based on the panel data from 2000 to 2018. The results reveal that (1) provinces with high population densities and regional integration have higher rankings and low uncertainties of HDI, which can be attributed to the improvement of education weights; (2) HDI has a certain spatial spillover effect, and the spatial association increases year by year; (3) robust test by using nighttime lighting as an alternative indicator of GDP supports that the spatial correlation is positively related to HDI ranking. The policy recommendations of this paper are to remove the obstacles for cross-regional population mobility and adjust the direction and structure of public expenditure.

How to Achieve Carbon Neutrality: From the Perspective of Innovative City Pilot Policy in China

The innovative city pilot policy is a new engine to accelerate the social development of China, which is an important support feature for realizing sustainable economic development. Using the city pilot policy issued by the Chinese government in 2008 as a quasi-natural experiment and the method of multi-period difference-in-differences (DID) model, we explore the effect of the policy on regional carbon emission efficiency. The research shows that the innovative city pilot policy could lead a significant promotion of the carbon emission efficiency of cities, which shows the characteristics of dynamic sustainability, that is, the policy effect continues to increase over time. Mechanism analysis reveals that the innovative city pilot policy mainly drives the improvement of urban carbon emission efficiency through improving the green technology innovation level of pilot cities, promoting the upgrading of regional industrial structure and increasing government investment in science and technology. In addition, the innovative city pilot policy has a spatial spillover effect on urban carbon emission efficiency, that is, the innovative city pilot policy not only promotes the local carbon emission efficiency, but also improves the carbon efficiency of neighboring areas.

The Impact of Intermediate Goods Imports on Energy Efficiency: Empirical Evidence from Chinese Cities

Improving energy efficiency is a critical way to solve energy shortage and environmental problems and achieve the goal of "double carbon". As China expands imports and integrates into global value chains, can import trade improve energy efficiency? This topic is extremely important for solving current energy problems and promoting sustainable economic development. Based on panel data of prefecture-level cities in China, this paper uses the Super-SBM model to measure the total factor energy efficiency of cities and investigates the impact of intermediate goods imports on energy efficiency with fixed effects models and instrumental variable method (IV). The study finds that: (1) intermediate goods imports contribute to the increase of urban energy efficiency, and the mechanism test indicates that intermediate goods imports affect energy efficiency through the technology spillover effect and intermediate goods type diversification effect. (2) According to the heterogeneity analysis, the effect of intermediate goods imports on energy efficiency is more evident in eastern China and cities with low topographic relief, medium population scale, and high absorption capability. (3) Analysis of the spatial spillover effect with the SDM model shows that importing intermediate goods promotes energy efficiency in local cities and radiates energy efficiency improvement in neighboring cities.

Analysis of the Lockdown Effects on the Economy, Environment, and COVID-19 Spread: Lesson Learnt from a Global Pandemic in 2020

Lockdown policies have been implemented to reduce COVID-19 transmission worldwide. However, the shutdown of activities has resulted in large economic losses, and it has been widely reported that lockdown measures have resulted in improved air quality. Therefore, many previous studies have attempted to investigate the impacts of the COVID-19-induced lockdowns on the economy, environment, and COVID-19 spread. Nevertheless, the heterogeneity among countries worldwide in the economic, environmental, and public health aspects and the spatial effects of decomposition have not been well investigated in the existing related literature. In this study, based on the cross-sectional data of 158 countries in 2020 and the proposed nonlinear simultaneous spatial econometric models, we investigate the nonlinear and spatial impacts of the COVID-19-induced lockdowns on the economy, environment, and COVID-19 spread. The findings show that lockdowns have had statistically significant negative economic impacts and beneficial environmental consequences but no effect on COVID-19 spread. Noteworthily, this study also found the length of lockdown periods to affect the three domains of interest differently, with a piece of empirical evidence that the imposition of lockdowns for more than 31 days a year could result in economic impairments but contribute to environmental improvements. Lockdowns were shown to have substantially reduced PM2.5 not only in the countries that imposed the measures but also indirectly in the neighboring countries as a spatial spillover effect.

Spatiotemporal Differences and Spatial Spillovers of China's Green Manufacturing under Environmental Regulation

Faced with the real demand of manufacturing industry to achieve the goal of green and high-quality development, exploring spatiotemporal heterogeneity and the spatial spillover effect of green manufacturing efficiency under environmental regulation can help reveal the path and mechanism of green development in the manufacturing industry. By using the SBM-DEM model to measure green manufacturing efficiency at the urban scale in China, exploratory spatial analysis is used to characterize the spatiotemporal differentiation of urban green manufacturing efficiency from 2003 to 2018. With the help of the spatial Durbin model, the impact of environmental regulation on green manufacturing efficiency and the spatial spillover effect are demonstrated. The results show that: (1) The green manufacturing efficiency of cities has developed in a gradual and balanced manner in time series, and the degree of equalization is stronger in the eastern coast than in the western inland; (2) Urban green manufacturing efficiency patterns are misaligned with economic scale patterns, indicating that green manufacturing is not traditionally dominated by economic factor inputs; (3) The practice of Chinese cities has proved that environmental regulation can significantly inhibit the development of green manufacturing efficiency in local cities. The crowding-out effect and optimization effect of environmental regulation on other external factors indirectly affect green development. By comparing different spatial weight matrices, it is shown that the economic relationship between cities can offset the inhibition of environmental regulation; (4) Although environmental regulation under spatial interaction would have significantly contributed to the green manufacturing efficiency of neighboring cities, this contribution effect is insignificant and weak due to the economic interactions between cities. Empirical research provides a theoretical foundation for the development of green manufacturing from the standpoint of environmental regulation, allowing green development research in manufacturing to move further.

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.

Spatial Pattern and Spillover of Abatement Effect of Chinese Environmental Protection Tax Law on PM2.5 Pollution

Particulate matter (PM2.5) pollution is a threat to public health, and environmental taxation is an important regulatory mode controlling PM2.5 pollution. In 2018, China implemented the Environmental Protection Tax Law (EPTL) targeting PM2.5 pollution. Based on in-situ monitoring and emission inventory data, a Bayesian hierarchical spatiotemporal model combining a two-period trends difference method was employed to measure the abatement effects of China’s EPTL on PM2.5 pollution (AEEPTLPM). On this basis, a spatial spillover index (SSI) of the AEEPTLPM is proposed. Applying this index, we calculated the spatial spillover characteristics of the AEEPTLPM in mainland China at a provincial scale in 2018–2019. The results show that the EPTL has had significant abatement effects on both in-situ-monitored PM2.5 concentrations and local total industrial PM2.5 emissions. Additionally, the two types of AEEPTLPM display distinct spatial heterogeneity. A correlation between the AEEPTLPM and the degree of PM2.5 pollution was observed; areas with serious PM2.5 pollution have higher AEEPTLPM levels, and vice versa. The SSI indicates that the AEEPTLPM exhibits significant spatial spillover characteristics, and spatial heterogeneity is also present.

Does China’s green economic recovery generate a spatial convergence trend: an explanation using agglomeration effects and fiscal instruments

China’s urbanization process has entered a period of rapid development, and cities have become key to driving regional economic development. This paper uses data from 286 cities in China in the period 2005–2018 to construct an urban economic growth quality index system and examine the influence of spatial factors on the convergence trend of China’s urban economic growth quality. It is found that there is a β absolute convergence trend of economic growth quality in Chinese cities across the whole country. After controlling for the initial conditions of individual economies, spatial factors strengthen the spatial convergence trend of urban economic growth quality and significantly increase the corresponding convergence rate. Among the areas studied, the western region has the fastest convergence rate, followed by the central and eastern regions, and the convergence rates of both the central and western regions are higher than the national average. Agglomeration economies and fiscal policy tools are important for the promotion of the urban economic growth quality. The agglomeration of productive service industries significantly improves the spatial convergence rate of urban economic growth quality. This effect is mainly due to the spatial spillover of industrial agglomeration. The expansion of government fiscal expenditure also contributes to the spatial convergence trend of urban economic growth quality. Local economic growth quality is also affected by government fiscal expenditure in neighboring cities.

Spatial-Temporal Pattern and Evolution Trend of the Cultivated Land Use Eco-Efficiency in the National Pilot Zone for Ecological Conservation in China

The cultivated land use eco-efficiency (CLUE) is an important indicator to evaluate ecological civilization construction in China. Research on the spatial-temporal pattern and evolution trend of the CLUE can help to assess the level of ecological civilization construction and reveal associated demonstration and driving effects on surrounding areas. Based on the perspective of the CLUE, this paper obtains cultivated land use data pertaining to National Pilot Zones for Ecological Conservation in China and neighboring provinces from 2008 to 2018. In this study, the SBM-undesirable, Moran's I, and Markov chain models are adopted to quantitatively measure and analyze the CLUE and its temporal and spatial patterns and evolution trend. The research results indicate that the CLUE in the whole study area exhibited the characteristics of one growth, two stable, and two decline stages, with a positive spatial autocorrelation that increased year by year, and a spatial spillover effect was observed. Geographical spatial patterns and spatial spillover effects played a major role in the evolution of the CLUE, and there occurred a higher probability of improvement in the vicinity of cities with high CLUE values. In the future, practical construction experience should be disseminated at the provincial level, and policies and measures should be formulated according to local conditions. In addition, a linkage model between prefecture-level cities should be developed at the municipal level to fully manifest the positive spatial spillover effect. Moreover, we should thoroughly evaluate the risk associated with CLUE transition from high to low levels and establish a low-level early warning mechanism.

Exploring geographic variation in US mortality rates using a spatial Durbin approach

Previous studies focused on identifying the determinants of mortality in US counties have examined the relationships between mortality and explanatory covariates within a county only, and have ignored the well-documented spatial dependence of mortality. We challenge earlier literature by arguing that the mortality rate of a certain county may also be associated with the features of its neighboring counties beyond its own features. Drawing from both the spillover (i.e., same direction effect) and social relativity (i.e., opposite direction effect) perspectives, our spatial Durbin modeling results indicate that both theoretical perspectives provide valuable frameworks to guide the modeling of mortality variation in US counties. Our empirical findings support that mortality rate of a certain county is associated with the features of its neighbors beyond its own features. Specifically, we found support for the spillover perspective in which the percentage of the Hispanic population, concentrated disadvantage, and the social capital of a specific county are negatively associated with the mortality rate in the specific county and also in neighboring counties. On the other hand, the following covariates fit the social relativity process: health insurance coverage, percentage of non-Hispanic other races, and income inequality. Their direction of the associations with mortality in the specific county is opposite to that of the relationships with mortality in neighboring counties. Methodologically, spatial Durbin modeling addresses the shortcomings of traditional analytic approaches used in ecological mortality research such as ordinary least squares, spatial error, and spatial lag regression. Our results produce new insights drawn from unbiased estimates.