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.

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.

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.

Digital finance as a new technology-driven business model shortens the distance between borrowers and lenders. Economic research finds that digital finance promotes economic efficiency by reducing transaction costs, information asymmetry, and inequality. Digital finance is an energy-intensive industry; therefore, increased efficiency in the industry should yield environmental benefits. We examine the externality of digital finance on air pollution. By analyzing data on digital financial inclusion and fine particulate matter concentration in China, we demonstrate using a dynamic panel data model that the development of digital finance damages the environment. However, after incorporating a threshold effect into a kink model, we determine that digital finance reduces pollution when its development exceeds a certain level. The results suggest that a high level of digital finance development not only increases economic growth but also improves air quality; this result provides novel insight into the relationship between economic growth and the environment.

This current study seeks to investigate the policy implication of Turkey’s recent energy policies on its sustainable development. This study uses Turkey’s country-specific data and series of 1974 to 2018 for effective investigation and justification of the findings of this study with emphasis on both short-run and long-run implications. Three models were fitted to achieve study objectives to accommodate both environmental sustainability and economic impacts. Ecological footprint was considered better measure and used as proxy for the environment related model. In summary, with environment models, the selected series (per capita GDP, industrialization, agriculture, coal as a single energy use, and mixed energy use) except per capita GDP² were found positively and significantly related to ecological footprint in both short run and long run which translates to poor performance of Turkey’s environment. Also, using economic growth model, the selected series (industrialization, energy use, and agriculture) were all confirmed positively and significantly related to the economic growth (per capita GDP). Additionally, environmental Kuznets curve (EKC) was established for Turkey’s environment and economic performance. Furthermore, using Granger causality as robust check to these findings, a nexus was found among the series confirming the validity of the cointegration (short- and long-run policies) estimations and results. In congruence with literature and hypotheses, the results from cointegration estimation shows that the twin polices may be good to the economic performance but will spark off adverse effect on environment.

Facing the growing problem of carbon emission pollution, the scientific and reasonable division of environmental management power between governments is the premise and institutional foundation for realizing China’s carbon emission reduction target in 2030. In this article, we directly assess the degree of environmental decentralization according to the allocation of environmental managers among different levels of government. By incorporating fiscal decentralization indicators, the provincial panel data and dynamic spatial econometric model are used to empirically test the impact of environmental decentralization on carbon emissions from a spatial perspective. The results show that (1) China’s provincial carbon emissions have significant inertia dependence and spatial path dependence. The increase (decrease) of provincial carbon emissions will lead to the increase (decrease) of carbon emissions in neighboring regions. (2) At the national level, environmental decentralization, environmental administrative decentralization, and environmental monitoring decentralization significantly reduce China’s carbon emissions, while environmental supervision decentralization and fiscal decentralization significantly increase carbon emissions. Similarly, the interaction of environmental decentralization and its decomposition indicators and fiscal decentralization also significantly promotes carbon emissions, and the impact is related to the types of environmental management decentralization. (3) The carbon emission effects of environmental decentralization in different regions are heterogeneous. The inhibition effect of environmental decentralization, environmental administrative decentralization, and environmental monitoring decentralization on carbon emissions in the western region is significantly greater than that in the eastern and central regions, but the inhibitory effect of the interaction of environmental decentralization and its decomposition index and fiscal decentralization on carbon emissions in the eastern region was significantly stronger than that in the central and western regions. The above results provide theoretical support for China to construct a differentiated carbon emission environmental management system from two aspects of regional differences and environmental management power categories.

The River Basin Model which is a man-machine simulation model, used primarily to replicate the interactions taking place, within a real or hypothetical area, between the local water system and the full range of economic, social, and governmental activities of that area. It is a water resource model representing supply of, demand for, and quality of water, but it is also a labor market model, a land use and assessment model, and several more; it is a model of an entire regional system with water a subsystem realistically interacting with all the other major subsystems; the output from the operating programs of the computer package illustrate the impact that the water system has on such phenomena as housing selection, employment, and government budgetary activity. Model users are given control over all the resources of the local area being represented. | The River Basin Model is a man-machine model that can be use to represent in a suggestive fashion the interactions that take place within a real or hypothetical regional area between the local water system and the economic, social, and governmental sectors of that area. The computer portions of the model are a synthesis of several hundred sub-programs that deal with such regional phenomena as migration, housing selection, water supply, water quality, physical deterioration, employment, transportation, leisure time allocation, public school allocations, shopping patterns, and terminal use. The human portions of the model allow its users to make decisions that deal with population and economic growth, water pollution abatement, recycling of water, salaries, rents, prices, land transfers, leisure time allocations, voting, boycotts, property assessment, tax rates, budget appropriations, school operation, highway operation, public construction, utility service, municipal service, water service, recreation availability, zoning, and many more. Through the computer and human portions of the model, the holistic workings of a regional river basin area may be represented for purposes of training decision-makers, simulating the aggregate impacts of alternative decisions, and performing research on the regional system itself. When used in a gaming format, the economic decision-makers represent major corporations that allocate financial resources, operate existing businesses, and exercise the economic power associated with the control of economic assets. Social decision-makers represent population groups in one of three socio-economic classes who reside in different parts of the regional area. Government decision-makers represent local government departments and elected officials who provide either a departmental service or exercise budgetary power. The River Basin Model in its present form is not usable as a predictive device. Rather, its primary function is to replicate the dynamic and interactive decision-making environment that faces persons from all interest groups who are concerned with doing something about water pollution control and the quality of the regional environment.

In China, industrial pollution is a prominent source of environmental pollution, and the important goal of sustainable development is to reduce industrial pollutant emissions, while ensuring economic stability. Special fiscal revenue (SFR) is the direct channel of government environmental regulation and the main source of environmental protection investment; it is of great significance to analyze the influence of special revenue on industrial pollution for industrial sustainable development. Therefore, this paper uses panel data from 30 provinces in China from 2005 to 2018 to empirically analyze the impact of SFR on the comprehensive industrial pollution index (CIP) and the spatial spillover effect by combining traditional regression and spatial econometric model. The empirical results show that: (1) The increase in SFR will significantly reduce the level of regional CIP emissions, and it verifies the role of SFR as a channel of government environmental regulation and a major source of special funds for environmental protection. (2) SFR exists a significant negative spatial spillover effect. For every 1% increase in SFR in surrounding provinces, the local CIP will decrease by about 0.448%, reflecting SFR has a stronger inhibition effect on CIP for surrounding areas. (3) According to the analysis of different periods, the main impact of SFR on CIP is after the new round of reforms in 2012. The sources and expenditure channels of SFR are more transparent, indicating that the institutional policies have a significant effect on emission reduction. (4) The analysis of individual heterogeneity finds that the increase of SFR in economically underdeveloped areas has a stronger inhibitory effect on CIP, and the space for technological progress in economically developed areas is small, so it is difficult to inhibit CIP in a short period of time. In addition, the instrumental variable model and robustness test support the above conclusions.