The determination of the spatiotemporal patterns and driving factors of PM₂.₅ is of great interest to the atmospheric and climate science community, who aim to understand and better control the atmospheric linkage indicators. However, most previous studies have been conducted on pollution-sensitive cities, and there is a lack of large-scale and long-term systematic analyses. In this study, we investigated the spatiotemporal evolution of PM₂.₅ and its influencing factors by using an exploratory spatiotemporal data analysis (ESTDA) technique and spatial econometric model based on remote sensing imagery inversion data of the Yangtze River Economic Belt (YREB), China, between 2000 and 2016. The results showed that 1) the annual value of PM₂.₅ was in the range of 23.49–37.67 μg/m³ with an inverted U-shaped change trend, and the PM₂.₅ distribution presented distinct spatial heterogeneity; 2) there was a strong local spatial dependence and dynamic PM₂.₅ growth process, and the spatial agglomeration of PM₂.₅ exhibited higher path-dependence and spatial locking characteristics; and 3) the endogenous interaction effect of PM₂.₅ was significant, where each 1% increase in the neighbouring PM₂.₅ levels caused the local PM₂.₅ to increase by at least 0.4%. Natural and anthropogenic factors directly and indirectly influenced the PM₂.₅ levels. Our results provide spatial decision references for coordinated trans-regional air pollution governance as well as support for further studies which can inform sustainable development strategies in the YREB.

Quantitating the health effects of employment history in factories, especially polluting ones, is essential for understanding the benefits or losses of industrialization in rural areas. Using a traced subset of nationwide panel data from 2005 covering five provinces, 101 villages, and 2026 households (collected recently in 2016) and the econometric models, this study estimated the effect of factory employment history on workers' health. The results showed that: the absolute number of factory workers increased from 1998 to 2015, and the proportion of factory workers was 7.68% in 2015; the absolute number and the proportion of farmers decreased from 63.84% in 1998 to 29.06% in 2015. Given that all the respondents live in rural areas, the HlthPlace (the first place the individual went to for their last illness in 2015) was selected as the main dependent variable of interest, and Hlthexp (Healthcare expenditure per person at last illness in 2015) and self-reported health were used as auxiliary dependent variables. The findings revealed that, after controlling the characteristics of individual, household, hospital and area, a one year increase of factory employment history corresponded to a 0.035 level increase in the probability of people choosing high-level hospital (p < 0.01) and a 237.61 yuan increase in healthcare expenditure (p < 0.1). The results also showed the adverse effect of self-reported health on factory employment history (p < 0.01). In addition, the relationship between the farming history and health was evaluated, and the econometric results showed that compared with factory employment history, farming history had opposite impacts on health (p < 0.01). Finally, the robustness check showed that the empirical results were reliable and that the initial results were robust. Generally, this study revealed the effect of overall factory employment on health, which is a useful research supplement to the studies on the health effects of specific pollution exposure.

This study attempts to construct an econometric model using China’s natural disaster losses and macro-industry development data from 1980 to 2017 to explore the macroeconomic fluctuations caused by natural disasters. The structural vector autoregressive (SVAR) and the seemingly unrelated regression (SUR) models are employed in estimating the impact of natural disasters on China’s macroeconomy and how the disasters specifically affect the three sectors of the economy: primary, secondary, and tertiary. This study concludes that even though natural disasters in China do not significantly affect the overall real GDP, they have adverse impacts on the production in the primary industry, causing a sudden reduction in the means of production in the market and directly affecting various industries, but the impact on the secondary and tertiary industries is weak. This study also shows that the effect of natural disasters on the primary sector reduced significantly following industry restructuring after China’s accession to the World Trade Organization (WTO). The impact of natural disasters on the primary industry could be reduced by adjusting the industrial structure to deal with macroeconomic shocks caused by natural disasters in order to promote macroeconomic stability of both regional and national economies. Finally, national aid policy should focus on the primary industry since that sector is significantly affected by natural disasters shocks.

The process of tourism economic development is accompanied by the consumption of energy and environment. It is of a big significance to measure the level of tourism economic development and regional eco-efficiency correctly to clarify the relationship between them, as it contributes to realizing the high-quality development of the tourism economy and the construction of “beautiful China”. On the basis of the panel data of China’s 30 provinces and cities from 2002 to 2016, the paper intends to evaluate the regional eco-efficiency and tourism economic development level by using the super-efficiency DEA model and the grey entropy weight method, and then construct spatial panel econometric model which is based on the previous data to deeply discuss the influence of tourism economy development on regional ecological efficiency and its spatial effect. The research shows that (1) regional ecological efficiency has significant spatial dependence and spatial aggregation characteristics. With the passing of time, this kind of positive spatial autocorrelation is gradually strengthened. (2) In the long-term development, tourism economic development and regional ecological efficiency show a more obvious “Kuznets curve” effect. (3) The “U”-curve relationship between urbanization, environmental regulation, and regional eco-efficiency was confirmed. (4) In the process of tourism economic transformation and development, industrial pollution control, environmental regulation, technological level, urbanization, and investment openness are the main factors that affect the improvement of ecological efficiency in the local region. (5) Tourism economic development and urbanization levels have different spatial spillover effects in different periods, while investment openness has obvious positive spillover effects.

To investigate the effects of regulation on environmental pollution under Chinese-style fiscal decentralization, this research analyzes annual data over the period 2003 to 2017 covering 30 provinces in China with the spatial economic model. The empirical results show significant spatial agglomeration effects on the emissions of wastewater, sulfur dioxide, and solid waste. Environmental regulation helps reduce discharge of wastewater and solid waste, but does not help reduce the emission of sulfur dioxide; because there is significantly positive externality in treating pollutants with high fluidity, cost is larger than revenue for local governments. The relationship between fiscal decentralization and pollutants shapes an inverted U-shaped curve. We finally offer some implications in accordance with our empirical finding, such as the intensity of environmental regulation should be suitable for economic development, different measures should be taken based on the fluidity of pollutants, and a new evaluation system should be established.

Facing increasingly serious environmental problems, technological innovation has become the key for industrial enterprises to coordinate energy conservation and emission reduction constraints and achieve steady growth of the industrial economy. Considering the impact of energy consumption and environmental pollution on the technological innovation efficiency of industrial enterprises, this paper incorporates industrial energy consumption, pollution control, and wastewater and exhaust emissions into the technical inefficiency equation. Based on the panel data of industrial enterprises in 30 provinces and autonomous regions in China from 2009 to 2016, the stochastic frontier analysis (SFA) model is used to study the effect of energy consumption and environmental pollution on technological innovation efficiency of industrial enterprises. The research results show that reducing energy consumption and increasing pollution treatment investment both have a significant driving effect on the improvement of industrial enterprises’ technological innovation efficiency. Industrial wastewater and exhaust emissions have the opposite effect; unreasonable input mode of pollution control and personnel allocation have hindered the improvement of industrial enterprises’ technological innovation efficiency. The average annual trend of technological innovation efficiency in industrial enterprises shows a curve of first rising, then falling, and rising again. The average values of Chongqing, Zhejiang, and Hunan rank in the top three, and the average values of Qinghai, Heilongjiang, and Inner Mongolia rank the bottom three. The average values of other provinces are higher than 0.9, and the difference is small. A suitable incentive mechanism should be established for industrial enterprises to save energy and reduce emissions and strengthen pollution control, improve the training program for environmental protection technical personnel, and provide important support for improving the green competitiveness of industrial enterprises.

In China, national environmental regulations have customarily found themselves to be inhibited by local government’s ostensible obedience. This research investigates how local officials, motivated and constrained by political competition, dedicate themselves to the environment and interact with each other regarding environmental regulation implementation and actual regulatory performance. Based on a spatial econometric model using data from 30 provinces from 2000 to 2016, the empirical results document the spatial dependence of environmental regulatory enforcement among provinces of similar economic levels and reveal that since 2007, there has been a performance-oriented peer competition for SO₂ emission reduction but no similar competition for CO₂ emission reduction. The findings indicate a transformation of the regulatory behavior of local governments from a race-to-the-bottom to strategic imitation and provide institutional insight into the spatial attributes of environmental enforcement under the impact of the political regime in China.

Along with the development of urbanization and informationization, an increasing attention has been attracted to CO₂ emissions of China’s transportation sector and its influencing factors. Such researches mainly utilize single indicator or two indicators to represent technology process. This research aims to verify the influence of technology-environmental innovation indicator system on CO₂ emissions of China’s transportation sector by decoupling elasticity and econometric model. We firstly recognize the decoupling status of CO₂ emissions of China’s transportation sector from social economic development and aggregate China’s 30 provinces into two groups according to the varied decoupling status, namely expansive coupling and weak decoupling groups. Then, we develop a relatively comprehensive technology-environmental innovation indicator system to measure technology process. Finally, the multi-region comparison of emission drivers is studied among overall China and the two groups. The result shows that the decoupling elasticity of China’s transportation has experienced an evolution process trending to desired development status and all the provinces have experienced expansive coupling and weak decoupling from 2001 to 2016, except Qinghai. Innovation performance indicators exert most important influence on the CO₂ emissions of transportation sector. Finally, the influences of technology-environmental innovation indicators are similar across groups with different magnitude, suggesting that common but differentiated strategies should be provided when mitigating CO₂ emissions with technology process. Graphical abstract

The status of technical efficiency (TE) of environmental protection enterprises is crucial to the sustainable economic development. Based on the micro-survey data of China’s environmental protection enterprises from 2003 to 2013, through a systematic calculation and comparison about TE level under stochastic frontier analysis, this article investigated the distribution characteristics and heterogeneous sources of them comprehensively and found that first, there are wide-ranging technical efficiency differences among sub-sectors, ownership, and regions within China’s environmental protection industry, and this type of heterogeneity was significantly interfered by the institution and policy environment. Second, there is obvious scale economy effect and no scope economy effect in the TE distribution of China’s environmental protection enterprises, and their TE level has a positive response to management improvement and competition enhancement, but has a negative feedback on heavy asset expansion and debt-driven growth mode. Third, the overall TE levels of non-state-owned enterprises are higher than that of state-owned enterprises; the overall TE levels of enterprises located in the eastern provinces are higher than those of enterprises located in the central and western provinces. Fourth, reducing tax burdens of environmental protection enterprises is more effective to promote their TE level than providing governmental subsidies directly. Therefore, to promote the quality of the development for China’s environmental protection industry, it is necessary to emphasize the market mechanism. Based on the market power, we should accelerate the industry integration, cultivate the market demand, and promote market competition. Furthermore, the government should also need to design a targeted support system and differentiated policy arrangements for the development of environmental protection enterprises.

Green growth in manufacturing is critical to the sustainable development of manufacturing, and environmental regulations can help ensure green growth. The impact of environmental regulations on China’s manufacturing industry sectors is investigated to further green development in manufacturing. Using panel data for manufacturing industry sectors from 2008 to 2015, the Malmquist-Luenberger index model is employed to calculate green growth efficiency and an econometric model is constructed to measure the impact of environmental regulations on green growth. By using the system generalized method of moments (system GMM) model and other panel estimation models to generate regression results, it is found that environmental regulation exhibits a U-shaped nonlinear influence on green growth; as the intensity of environmental regulations increases, there is an initial inhibiting effect followed a positive impact on green growth in the manufacturing industry. Once environmental regulation intensity reaches a certain level, it mainly promotes green growth through technological progress. Further findings include the following: impacts of environmental regulation on green growth are heterogeneous across industries, and effects (e.g. U-shaped impacts) are most significant among high-energy industries, high-pollution industries, and medium-pollution industries.