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.

China has become the largest coal consumer and important emitter of trace metals in the world. A multiple-year inventory of atmospheric copper (Cu) and zinc (Zn) emissions from coal combustion in 30 provinces of China and 4 economic sectors (power plant, industry sector, residential sector, and others) for the period of 1995–2014 has been calculated. The results indicated that the total emissions of Cu and Zn increased from 5137.70 t and 11484.16 t in 1995–7099.24 t and 14536.61 t in 2014, at an annual average growth rate of 1.90% and 1.33%, respectively. The industrial sector ranked as the leading source, followed by power plants, the residential use, and other sectors. The emissions of Cu and Zn were predominantly concentrated in the northern and eastern regions of China due to the enormous consumption of coal by the industrial and the power sectors. The emissions of Cu and Zn were closely associated with mortality and life expectancy (LE) on the basis of multiple regression analysis. Spatial econometric models suggested that Cu and Zn emissions displayed significantly positive relevance with mortality, while they exhibited negative correlation with LE. The influence of the Cu emission peaked in the north of China for both mortality and LE, while the impacts of the Zn emission on mortality and LE reached a maximum value in Xinjiang Province. The results of the grey prediction model suggested that the Cu emission would decrease to 5424.73 t, whereas the Zn emissions could reach 17402.13 t in 2020. Analysis of more specific data are imperative in order to estimate the emissions of both metals, to assess their human health effects, and then to adopt effective measures to prevent environmental pollution.

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.

Within the context of coordination level between state and central government, we develop an econometric model to estimate the association between income and ambient air pollution, considering the societal preferences jointly influenced by the citizens and the government. We obtain empirical evidence supporting our hypothesis that state level coalition government can effectively improve quality of environment by means of reducing ambient air pollution level. This impact can be increased or decreased based on the societal preferences of the citizens, based on the area of inhabitance and irrespective of the choice of pollutants.

Coastal regions are characterized by intense human activity and climatic pressures, often intensified by competing interests in the use of marine waters. To assess the effect of public spending on the regional economy, an econometric model is here proposed. Not only are the regional investment and the climatic risks included in the model, but also variables related to the anthropogenic pressure, such as population, economic activities and waste production. Feedback effects of economic and demographic expansion on the pollution of coastal areas are also considered. It is found that dangerous waste increases with growing shipping and transportation activities and with growing population density in non-touristic coastal areas. On the other hand, the amount of non-dangerous wastes increases with marine mining, defense and offshore energy production activities. However, lower waste production occurs in areas where aquaculture and touristic industry are more exploited, and accompanied by increasing regional investment in waste disposal.

This research investigates the spillover effect of the US interest rate and oil prices on renewable energy utilization in Turkey. By employing a novel bootstrap autoregressive distributed lag approach on annual data from 1985 to 2016, the empirical findings and discussions represent the first contribution to the energy economics literature. The findings of this research confirm that the US interest rate has a significant spillover effect on the use of renewable energy in Turkey through the channels of income and local interest rate. Due to limited foreign exchange reserves, high foreign debt, low international reserves, and devaluation of the local currency, the Turkish economy is highly intertwined with the US economy through international investment and trade. All these factors reinforce the spillover influence of the US interest rate on energy consumption in Turkey. Moreover, this study affirms that the price of oil has a negative impact on renewable energy use through the real income channel. In order for Turkey to realize its investments in renewable energy resources more reliably and sustainably, the study suggests that policymakers should revise the current economic growth model by making it more resilient to external shocks such as the US interest rate, exchange rate, and oil prices.

Haze pollution poses a serious threat to residents’ health. In this study, a spatial econometric model of environmental health was established to investigate the direction, intensity, and spatial–temporal heterogeneity of the impact of haze pollution and its spillover effects on public health in 26 cities of the Yangtze River Delta urban agglomerations from 2005 to 2018. The study found that (1) PM₂.₅ pollution and public health level all show the characteristic of positive spatial correlation and spatial clustering. (2) Haze pollution is the main influencing factor of residents’ public health level, with significant negative effects and obvious spillover effects. The urbanization rate, the number of health technicians, and the green area per capita have significant positive impacts on public health. (3) The spatial and temporal heterogeneity of the impact of haze pollution and other factors on public health is obvious. The negative correlation between PM₂.₅ pollution and public health in eastern cities is higher than that in other cities. Both urbanization rate and green area per capita have a greater positive impact on public health in the northeast of the Yangtze River Delta region. The improvement effect of the number of health technicians on the public health is stronger in the cities of Anhui Province. The research results of this paper provide certain support for the city governments to formulate targeted policies.

The coupling coordination of the logistics industry and manufacturing industry is conducive to the sustainable development of logistics and manufacturing and the stability of sustainable supply chain. The logistics and manufacturing industries are not only the basic industries that support social development, but also the industries with high carbon emissions. This paper constructs the measurement system of coupling coordinating ecological efficiency of two industries based on carbon emission constraints and finds out the driving factors affecting ecological efficiency, which is of great significance to the low-carbon coordinated development of the two industries in the future. Firstly, this paper classifies the carbon emissions from the logistics industry and manufacturing industry as undesirable outputs and evaluates the ecological efficiency of the logistics industry (LEE) and manufacturing industry (MEE) in the three urban agglomerations from 2006 to 2019 by using the unexpected slacks-based measure (SBM) model. Secondly, the coupling coordination method is used to analyze the coupling coordination scheduling of industrial ecological efficiency (MLCC). Finally, the spatial econometric model is used to analyze the driving factors of the MLCC. The results show that during the study period, the coupling coordination of the three urban agglomerations continued to grow, the Pearl River Delta coupling coordination is the highest, the Yangtze River Delta coupling coordination grew the fastest, and the Beijing–Tianjin–Hebei coupling coordination grew slightly slower. The development during the 13th Five-Year Plan period is obviously faster than that during the 11th and 12th Five-Year Plan. The empirical analysis results of spatial econometrics show that the driving factors have an impact on the coupling coordination degree of the three urban agglomerations, but the significance of each factor is different. The driving factors have significant spatial heterogeneity. The three urban agglomerations should formulate low-carbon industry development policies in line with local development according to the actual situation of each region and local conditions.

The essence of carbon trading is to optimize the allocation of factor resources by using price leverage. However, the effect of the carbon trading market might be seriously affected by carbon trading price distortion (CTPD), which means the deviation of the actual trading price from the carbon marginal abatement cost (MAC). This paper proposes a novel analytical framework to evaluate the impacts of CTPD and explore corresponding correction strategies. Specifically, the impacts of CTPD are analyzed theoretically, and then CTPD in China’s carbon trading pilot markets during 2013–2017 is evaluated by the Stochastic Frontier Analysis. Last, the panel data vector autoregression model and panel data vector error correction model are used to explore correction strategies. Our analysis reveals several interesting insights. First, our results show that CTPD negatively impacts the economic benefits, environmental benefits, and policy acceptance of the carbon trading market. Second, China’s carbon trading market suffers from not only CTPD but also persistent negative price distortions in energy, labor, and capital factors, although CTPD is the most serious issue. Third, the energy price and macroeconomic environment have positive impacts on the carbon trading price. Last, a greater of any factor price distortion would lead to an increase in the carbon MAC. Based on these results, several correction strategies are recommended.

To examine the asymmetric effects of militarization on economic growth and environmental degradation, this empirical research analyzes annual data of Pakistan and India over the period 1985–2018 using the NARDL econometric model. The empirical results show significant positive militarization effects on economic growth, while non-militarization also shows positive effects on the economic growth in Pakistan and India. Estimation showed that a 1% increase in militarization (non-militarization) led to 8.818% (3.849%) increase in GDP growth, whereas a 1% increase in militarization (non-militarization) decreased carbon emissions by − 1.034% (− 0.225%) in the long run in Pakistan, while militarization has also decreased the carbon emissions − 0.337% in India in the long run. The relationship between militarization and economic growth has an asymmetry in Pakistan and India in the short and long run, while asymmetry also exists between militarization and CO₂ in Pakistan and India in the short and long run. Our findings offer significant policy implications for promoting economic growth and environmental quality in Pakistan and India.