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Photochemical air quality models (AQMs) are a vital tool for atmospheric pollution research and have been widely used in various applications, such as air quality prediction and evaluation of pollution control strategies. Before using these models for further studies, it is essential to thoroughly evaluate their reliability and accuracy. While previous guidelines and benchmarks have primarily focused on fine particulate matter (PM 2.5) and ozone (O 3), there is still a lack of benchmarks for evaluating the model performance on primary criteria pollutants such as sulfur dioxide (SO 2), nitrogen dioxide (NO 2), carbon monoxide (CO), and particulate matter with aerodynamic diameter less than or equal to 10 μm (PM 10). The use of air quality models in China has increased significantly in the past decades. However, there is still a lack of standardized benchmarks for the performance evaluation of these models. Building upon our previous work on PM 2.5 and its chemical species, we propose a set of benchmarks for evaluating the performance of the aforementioned four air pollutants. Initially, we identified a total of 475 papers published during 2007–2019 that utilized at least one of the five commonly used AQMs in China. From these papers, we selected 164 articles that provided model performance evaluation (MPE) results of the four primary air pollutants. The three most frequently used Model Performance Evaluation (MPE) metrics were selected to analyse the impact of different model configurations on the reported statistics, including modelling region, season, and emission inventory. Lastly, three commonly used statistical indicators, including normalized mean bias (NMB), normalized mean error (NME), and correlation coefficient (R), were proposed for the validation of simulated NO 2 , SO 2 , CO, and PM 10. Two sets of benchmarks are given, including the "goal" and "criteria". The "goal" represents the best range of performance that a model can be expected to achieve, and the "criteria" represents performance that the majority of studies have achieved. We recommend R values above 0.50, 0.35, 0.45, and 0.40 for NO 2 , SO 2 , PM 10 , and CO, respectively, in order to meet the "criteria" benchmark. If the "goal" benchmark is to be achieved, the corresponding R values are 0.60, 0.55, 0.60, and 0.60. The "goal" benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are within ±20%, ±25%, ±20%, and ±25%, respectively; while the "goal" benchmarks of NME for the four pollutants are less than 40%, 45%, 45%, and 60%, respectively. These benchmarks supplement our previous benchmarks for PM 2.5 and its components and provide a more comprehensive guideline for the air quality modelling community in China. • A set of benchmarks for evaluation of the NO 2 , SO 2 , CO and PM 10 simulation over China are proposed. • Three commonly used statistical indicators (NMB, NME, and R) were proposed for the validation of the four pollutants. • The goal benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are <20%, <25%, <20% and <25%, respectively. [ABSTRACT FROM AUTHOR]

• Urban green spaces have direct and indirect effects on improving residents' health. • The health benefits of urban green spaces exhibit regional heterogeneity. • Economic development weakens the health benefits of urban green spaces. • Environmental regulations can enhance the health benefits of urban green spaces. Urban green spaces have long been cherished as the "lungs of the city", it can not only improve air quality and enhance residents' health. The impact of urban green spaces on air pollution and residents' health has attracted great attention. In this paper, we select 31 provinces in China as research samples and use ArcGIS software to draw spatial distribution maps of urban green spaces, air pollution, and residents' health in China, characterizing their spatiotemporal changes in order to explore whether there is a correlation among the three. On this basis, this paper utilizes panel data of Chinese provincial-level from 2010 to 2020 and conducts empirical tests through a two-way fixed effect model and a threshold regression model. The research findings are as follows: (1) Urban green spaces not only bring environmental benefits, but also health benefits, and the health benefits are greater than the environmental benefits. (2) There is a health risk of air pollution in northern China, and the health benefits of urban green spaces are not significant. Conversely, there is no health risk of air pollution in southern China, and the health benefits of urban green spaces are significant. (3) Although environmental regulations can enhance the health benefits of urban green spaces, the role of environmental regulations in enhancing the health benefits of urban green spaces may be further weakened as the economy develops. [ABSTRACT FROM AUTHOR]

• Examining the problem of capital allocation efficiency in China from the perspective of air pollution. • Constructing a new indicator of capital allocation efficiency, measured using actual capital productivity over potential capital productivity. • Constructing a mechanism for air pollution to affect capital allocation efficiency in terms of innovation activities and human capital accumulation channels. • The effect of air pollution on capital allocation efficiency and its heterogeneity are empirically analyzed. As China seeks to transition to a high-quality economic development model, improving its capital allocation efficiency serves as a means to revitalize the capital stock and facilitate a shift from an investment-driven growth approach to an innovation-driven one. By exploring the relationship between air pollution and capital allocation efficiency, new avenues for promoting high-quality economic development can be identified. This paper develops a theoretical analysis framework that posits the mechanisms by which air pollution affect capital allocation efficiency through innovation activities and human capital channels. Based on the data from prefecture-level cities in China from 2003 to 2019, this paper introduces an innovative method to evaluate capital allocation efficiency. Further, using a panel two-way fixed-effects model to examine the impact of air pollution on capital allocation efficiency and its mechanisms. The findings show that ① the intensification of air pollution reduces capital allocation efficiency and the above findings pass the robustness tests. ② The results of heterogeneity analysis show that there is significant heterogeneity in the effect of air pollution on capital allocation efficiency. The intensification of the air pollution significantly inhibits capital allocation efficiency in inland areas, small and medium-scale cities, after 2013, and in areas with high foreign economic dependence. ③ The results of mechanism analysis shows that the intensification of the air pollution will inhibit capital allocation efficiency by suppressing innovation activities and human capital accumulation channels. [ABSTRACT FROM AUTHOR]

The increase in air pollution has posed numerous new challenges for human society, making the exploration of an effective method for predicting air pollutant concentrations highly significant. The current research faces several primary challenges: the neglect of non-Euclidean characteristics of site distribution on data and the strong spatiotemporal dependencies in the dispersion process of pollutants. To address these issues, this paper constructs a spatiotemporal hybrid prediction model – the MGAtt-LSTM method – for predicting PM 2.5 concentrations, which employs the dynamic multi-graph attention module (MGAtt) to tackle spatial dependencies and Long Short-Term Memory networks (LSTM) to address temporal dependencies. Additionally, extensive experiments are conducted by using historical air pollutant monitoring data and meteorological data from the Beijing-Tianjin-Hebei region. The results demonstrate that the proposed MGAtt-LSTM model achieved superior performance in concentration prediction compared to existing benchmark models. • The neural network consists of multi-graph attention network and LSTM network. • Air pollution data in North China and meteorological data are used to forecast. • The uneven distribution of pollutant sites is considered to predict PM 2.5 concentrations precisely. • The use of multi-graph attention networks addresses the issue of traditional GCN methods relying on fixed graph structures. [ABSTRACT FROM AUTHOR]

Air pollution presents a serious public health risk globally, particularly in fast industrializing countries such as China. Effectiveness of local air pollution control measures is influenced by external forces such as atmospheric emission transport and interprovincial trade. However, there is lack of research on comparison of these two indicators and local indicators (e.g. per capita income and emissions) determines the responsibility allocation in pollution control. In this paper, cross-provincial air pollution is stimulated via atmospheric transport (atmospheric linkage indicator) based on Weather Research and Forecasting/Comprehensive Air Quality Model with Extensions (WRF/CAMx) model. The air pollutants embodied in interprovincial trade (trade linkage indicator) was studied by using the latest multi-regional input-output (MRIO) model. This study revealed the significant influence of atmospheric transport on neighboring provinces. However, transport of trade-induced embodied emissions affects both neighboring areas and larger areas. According to aggregation analysis of these two indicators, in most provinces of China, direction of atmospheric transport contradicts to that of the trade-induced transport. These findings are used to analyze the rationality of regional differences in environmental tax rate in the new environmental protection tax law of China. By comparing the local and interregional linkage indicators, we found the current environment tax for air pollutants was mainly affected by per capita income whereas other environment and linkage indicators show little correlation. Therefore, we integrated environment and linkage indicators to propose adjusted provincial environment tax rates plan for air pollutants as a cost sharing mechanism. The results of this paper provide novel perspective for central government to adjust fairness environmental responsibility between provinces of China by tax policy. Image 1 • Cross-provincial air pollution via atmospheric transport was simulated by WRF/CAMx. • Interprovincial air pollution embodied in trade was traced using MRIO model. • The influence range via trade is greater than that of atmospheric transport. • The environmental tax rate is advised to be adjusted base on linkage indicators. [ABSTRACT FROM AUTHOR]

Aerosol types have crucial and distinguished impacts on climate effects and cause the difference of air pollution. However, direct retrieval of aerosol type by satellite remote sensing is difficult, and the satellite products of aerosol type are seriously lacking. At present, the identification of aerosol type is mainly based on ground-based observation data, but the coverage area of ground-based stations is limited, and large-scale spatial continuous monitoring cannot be carried out. The existing aerosol type identification methods based on satellite data need to combine multiple satellites data (e.g. Moderate-resolution Imaging Spectroradiometer (MODIS), Ozone Monitoring Instrument (OMI), etc.) which leads to a low temporal resolution. The research about aerosol type identification only based on geostationary satellite data is lacking. Therefore, aerosol type identification based on the multi-parameter threshold method and the neural network method are proposed and compared in this paper. The neural network jointly using spectral and temporal information was trained and tested by the measurements from 26 sites of Aerosol Robotic Network (AERONET) and Sun-Sky Radiometer Observation Network (SONET). For the overall validation, the aerosol type identification based on the neural network is better than that based on the multi-parameter threshold method, and the accuracy reaches 71.44%. Atmospheric environment monitoring and quantitative remote sensing can be supported by this study. • Aerosol type, neural network, geostationary satellite [ABSTRACT FROM AUTHOR]

• The principle of difference information and the principle of minimum information are discussed. • The proposed method can deal with the environment problem in other regions. • The unequal adjacent accumulation multivariate grey model is used to forecast the public concern about air pollution. In grey theory, the principle of difference information holds that difference is information. To mine the difference information between data, the unequal accumulation is proposed in this paper. The unequal accumulation reduces the loss of difference information and improves the forecasting performance of the grey model. Based on the unequal accumulation, the unequal adjacent discrete multivariable grey model is proposed. According to the comparison with other models, the proposed model has good forecast performance and algorithm efficiency. To further prove the practical significance of the model, it is used to predict the public concern about air pollution in three cities of China. The results show that the public concern about air pollution in cities with different air pollution conditions is quite different. Environmental protection departments could pay attention to the public concern about air pollution to better control air pollution. [ABSTRACT FROM AUTHOR]

Faced with complex air pollution and traffic congestion, the road area ratio has gradually gained great importance in society and government. It is the ratio introduced from the field of road design and road engineering, but to study the efficiency of city planning and transportation recently. However, few studies have been conducted to study the relationship between urban congestion and pollution in the view of improving road area ratio. This paper investigated the effect of increasing the relative capacity of urban roads, i.e., road area ratio, on urban air pollution using panel data from cities in China from 2006 to 2018. In addition, the road area ratio can be further decomposed to explore the inner logic of pollution from congestion. Thus, we also explored whether the insufficient network density or width caused the pollution from congestion. We found that an increasing urban road area ratio significantly reduced air pollution, regardless of whether or not the impact of surrounding cities is included. Moreover, only an increased road density can increase traffic efficiency and reduce congestion emissions, which should be the priority of future traffic construction. The effect of increasing road width is not significant. • The increase in the urban road area ratio decreases air pollution. • There exists the "neighbor effect" of pollution in urban cities. • Decomposing the road area ratio factor to road width and road network density. • Only the increased road network density reduces traffic emissions. [ABSTRACT FROM AUTHOR]

This paper primarily studies the air quality in China's cities during the urbanization stage. Using a cross-sectional data of 282 cities, we discuss the influencing factors and the existence of the Kuznets Curve for three air pollutants based on the EKC (Environmental Kuznets Curve) theory and the BMA (Bayesian Model Average) method. The results show that the concentration of SO 2 and PM 10 presents the characteristics of an inverse-U shape. The urbanization process has a significant and negative effect on air pollutant concentration, which means that cities with higher urbanization rate tends to have lower air pollutant concentration. Population density, possession of civil motor vehicles, the proportion of secondary industry, and annual average temperature are the main influencing factors of air pollution. Based on these results, this paper suggests that China should speed up the “new” type urbanization process, constantly optimize the industrial structure, and promote the harmonious development of the economy and environment. [ABSTRACT FROM AUTHOR]

Most empirical researches in behavioral finance have found that the air pollution can affect the stock market in the developed world, while a few works show some different results in China. This paper deeply discusses the reasons for the difference of air pollution effect, and attributes it to the intensity of public environmental awareness. This paper selects some important environmental events which can enhance the public environmental awareness as samples, to investigate the influence of public environmental awareness on the air pollution effect. The results firstly show that the air pollution effect in Chinese stock market is not significant during a long-term sample period. Secondly, the empirical findings support that these events have significant impacts on the air pollution effect in Chinese stock market. Finally, this paper provides direct evidence on that the air pollution effect has a significant negative change after these events, which is consistent with the expected impact of the air pollution effect's mechanism. This research extends the behavioral financial issue of air pollution effect and provides the empirical evidence for the public environmental awareness's obstacle to the air pollution effect. [ABSTRACT FROM AUTHOR]

Due to the ever-increasing quantity of produced and sold automobiles in China, many side effects such as resource depletion, energy shortages and air pollution have emerged. Remanufacturing, as the highest form of the recycling economy, has been promoted by the Chinese government as a major measure to save resources and energy. The remanufactured products, however, get very low market recognition from Chinese consumers. Thus, understanding how to cultivate consumers potential behavior of purchasing remanufactured products is the first step and also very important for developing the remanufacturing industry. This paper, focusing on heavy truck engines due to their short life expectancy and repair frequency, aims to identify the factors influencing remanufactured heavy truck engine (RHTE) potential purchase and analyze the relationship among those factors and potential purchasing behavior. Based on 301 questionnaires from Chinese heavy truck drivers, this paper develops a conceptual model to hypothesize how the influencing factors affect RHTE potential purchasing behavior. Hierarchical regression results show that individual factors (including product knowledge, product trust and environmental values) mediate the relationship between environmental factors (including product attribute and advertisement) and RHTE purchasing intention. Contextual factors (including incentive policy and purchasing convenience) moderate the relationship between RHTE purchasing intention and potential purchasing behavior. Our results indicate that individual factors are essential in cultivating RHTE purchasing intention and environmental factors are helpful for improving individual factors. Contextual factors can motivate the transformation from RHTE purchasing intention to potential purchasing behavior. [ABSTRACT FROM AUTHOR]

Under the background of climate change, the haze days in China have increased significantly, which seriously hinders the sustainable development of society and arouses wide attention from the public. However, the researches on the effect of air pollution on the stock yield of heavy pollution enterprise in key control cities are quite limited, This paper collects the Air Quality Index (AQI) from key control cities above prefecture level in China as well as the stock yield data of these cities’ heavy pollution enterprises in December from 2014 to 2016 and through multi-discontinuities regression model, testes the effect of air pollution on the stock yield of heavy pollution enterprise. The results show that: (1) Heavy air pollution (AQI = 300) has negative influence on the stock yield. (2) There exists time effect of air pollution on stock yield. Since the year 2014, heavy air pollution has exerted significantly negative influence on the stock yield in that 2013 may be the “first year” when the Chinese society began to take air pollution seriously. This paper gives a brief discussion on the cause of it and suggests that severe air pollution should be strictly controlled. Only by facing air pollution seriously, can we eliminate air pollution with collective wisdom and concerted efforts and achieve the sustainable development of city. Being the first study to look into the effect of air pollution on stock yield in key control cities in China, this paper provides empirical reference for government supervision departments, stock investors as well as enterprises. [ABSTRACT FROM AUTHOR]

• The causal impact of green space on air pollution remains understudied. • Here, the Normalized Difference Vegetation Index (NDVI) was used to assess green space level in each county of China. • Two-stage least squares estimation (2SLS) and multiplex IV was employed to verify the robustness. • The increased greenspace significantly decreases the PM 2.5 concentration and other air pollutants. • Green spaces show a more pronounced air pollution reduction effect in southern or higher administrative-level cities. Can green vegetation absorb air pollutants and control regional air pollution? The existing research has not reached a consistent conclusion on this issue. Using the multi-level data in China, this paper provides empirical evidence of the causal impact of greenspace on PM 2.5 through the abundant fixed effect and controls. Besides, this paper applied the soil humidity, Normalized Difference Vegetation Index (NDVI) last month, NDVI in the same month last year, and NDVI prefecture-level city average as the instrumental variables, respectively, to test the robustness of the estimation. The results show that increased greenspace significantly decreases the PM 2.5 concentration and other air pollutants. In further analysis, we found that the decreasing effect turns significant when NDVI exceeds 0.3. In addition, there are regional heterogeneities in the impact of greenspace on air pollution. Greenspaces show a more pronounced air pollution reduction effect in southern or higher administrative-level cities. This paper suggests that increasing greenspace is an economical and effective way to achieve co-management of multiple air pollutants. [ABSTRACT FROM AUTHOR]

Knowledge of vertical distribution of air pollutants is important for understanding the mechanisms underlying haze pollution. In this paper, we characterize the vertical structure of air pollutants based on ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements in the wintertime in Hefei, East China. Both NO 2 and aerosol show exponentially decreasing profile with high concentrations concentrated close to the surface, suggesting the major emissions are at ground level. SO 2 profiles extend to slightly higher altitudes than NO 2 , which might be due to the SO 2 emissions from elevated point sources (e.g., power plants). Elevated HCHO was observed in the middle layer during noon time, probably indicating that the larger effect of the photochemical formation of atmospheric HCHO rather than direct HCHO emissions. Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) models were used to evaluate the potential sources of Hefei's air pollution in the lower, middle and upper boundary layer. The results revealed that the major regional source of NO 2 and aerosol were located in the northern and eastern part of Anhui and the transport mainly occured in the lower layer. Transported SO 2 from the northwestern regions in the middle layer made great contribution to SO 2 in Hefei. HCHO was mostly produced locally while the transport of HCHO from potential source areas was negligible. To further explore the potential source contributions affecting haze pollution, PSCF and CWT analysis for heavy haze episodes suggested that the potential source areas for aerosol were almost located in the northern region, the northwestern region, and the YRD region. Our findings provide a more comprehensive understanding for regional transport through the study on potential sources at different altitudes, which are useful for designing collaborative air pollution control strategies on a regional scale. • Vertical distribution of air pollutants is investigated to identify potential sources at different altitudes during winter. • Both NO 2 and aerosol show exponentially decreasing profile with high concentrations concentrated close to the surface. • SO 2 and HCHO profile typically extended to higher altitudes than NO 2 and aerosol did during winter. • The main potential sources for aerosol were located in the northern region, the northwestern region, and the YRD region. • This study is useful for designing collaborative air pollution control strategies on a regional scale. [ABSTRACT FROM AUTHOR]

In contrast to the surge of attention to China's business registration reform per se , the environmental externalities of the reform have been somewhat neglected by mainstream academics. Adopting the difference-in-differences method alongside propensity-score matching, and utilizing the panel data of 283 cities in China from 2000 to 2013, this paper investigates the impacts of the deregulation reform on PM2.5 concentrations. The results indicate that the business registration reform has contributed to PM2.5 air pollution, with above-average impacts in central areas, but insignificant effects in western China. Furthermore, the treatment effects are stronger in cities with larger entry of small and medium enterprises and greater pressure to achieve targets for local GDP growth. Aiming to draw public attention to the problems arising from the reform, our study advocates necessary environmental regulations on business registration, which lends support to the theoretical view of "public interest" rather than "public choice". • Deregulation reform of business registration increases PM2.5 concentrations in China. • Treatment effects of deregulation reform on PM2.5 air pollution vary widely among regions. • More SME entries and higher targets for GDP growth are associated with stronger effects of deregulation reform on PM2.5. • PM2.5 air pollution should be considered during the deregulation reform in China and other developing economies. [ABSTRACT FROM AUTHOR]

From 1990 to 2017, the number of vehicles in China increased sharply from 5 million to 310 million, and vehicle emissions became one of the major sources of air pollution. Among the main sources of PM 2.5 in Beijing, the contributions from vehicle sources increased from 5.6% (2000) to 31.1% (2012) to 45% (2017). In 2010, the CO, hydrocarbon (HC), NO x , and particulate matter (PM) emissions of yellow-label vehicles (YLVs) accounted for 46.1%, 47.9%, 63.1% and 86.1% of the total emissions from on-road vehicles. In recent years, China has been implementing a YLV scrappage subsidy policy to control air pollution, and this policy has cost several hundred billion yuan nationwide. However, China has not evaluated the costs and benefits of the policy. Many people question whether the YLV scrappage subsidy policy is worthwhile, but all the stakeholders involved complicate the process of accurately evaluating the true value of the policy. In this study, we evaluated and compared the costs and benefits of the policy in the Beijing-Tianjin-Hebei (BTH) region based on 1.362 million YLVs from 2008 to 2015. Because of the implementation of the policy, the PM 2.5 , PM 10 , NO x , CO and HC emissions in the BTH region decreased by 16.46, 18.12, 174.44, 669.11, and 91.64 kt, respectively, in 2008–2015. The results show that the policy played an important role in reducing pollutant emissions and improving environmental quality in the BTH region. The costs, benefits, and net benefits of the policy were 13.69, 34.03, and 20.34 billion yuan , respectively. The cost-benefit ratio in the BTH region was 1:2.49; at the city scale, Beijing ranked first (1:7.27), and the cost-benefit ratios of Tianjin and Hebei Province were 1:2.06 and 1:1.14, respectively. Of the 13 cities in the BTH region, Beijing had the highest net benefits, amounting to 16.43 billion yuan , while Handan City has the lowest net benefits, amounting to −0.58 billion yuan. The results may provide scientific support for the promotion of older vehicle and diesel vehicle scrappage subsidy policies, the development of cost-benefit analysis (CBA), and effective environmental decision-making in China. • Based on net number of scrappage YLVs, net benefits of the policy are calculated. • This paper estimates costs and benefits of the policy based on 1.27 million YLVs. • The costs, benefits, and net benefits are 13.69, 34.03, and 20.34 billion yuan. • The results help China promote vehicle scrappage subsidy policy on a large scale. • The results promote the development of the cost-benefit analysis in China. [ABSTRACT FROM AUTHOR]

Exposure to air pollutants has become a major global health threat and is a dominant contributor to increasing respiratory mortality. Accurate projections of respiratory mortality are crucial to mitigating the adverse influences of air pollution on public health and guiding long-term policy implementation. However, the forecasting models' accuracy is difficult to guarantee because seasonal factors perturb the raw series of air pollutants and respiratory mortality. This paper proposes an enhanced seasonal and self-adaptive multivariate grey convolution model to solve this dilemma. The innovations of the proposed model reside in the following areas: firstly, a seasonal operator vector of multidimensional data is initially built to digitize the seasonality of air pollutants and related mortality. Secondly, a multivariate grey convolution model with an adaptive time-lag cumulative optimization item is put forward. The weight coefficients of each driving variable at different periods are intended to adapt dynamically. Thirdly, the self-adaptive time-delay coefficient is determined by the Particle Swarm Algorithm, enhancing the accuracy of predictions. Theoretically, the generalizability and adaptability of the newly presented model are well-validated. Moreover, its practical applicability and higher forecasting performance are further verified in estimating the respiratory mortality attributable to the comprehensive effects of air pollutants in China. [ABSTRACT FROM AUTHOR]

Regional collaborative governance has become one of the most significant trends in environmental protection due to its importance in suppressing pollution transfer. This paper employs panel data from 285 cities in China during 2003–2019 and a multi-period difference-in-differences (DID) method to evaluate the influence of regional collaborative governance on air pollution. Results show that regional collaborative governance significantly reduces air pollution. Heterogeneity assessment suggests that the inhibiting impact of regional collaborative governance on air pollution is long-term and increases temporally, whereas the spillover effect of regional collaborative governance is only significant in a specific range and decays spatially. In addition, the mechanism analysis reveals that regional collaborative governance reduces air pollution by increasing environmental regulation intensity, improving environmental governance efficiency, and promoting environmental protection technology. This study provides useful policy insights for policymakers seeking green and sustainable development to curb air pollution and improve environmental quality. • The impact of regional collaborative governance on air pollution is examined. • We employ a multi-period difference-in-differences approach. • Regional collaboration significantly reduces air pollution emissions. • Local effects increase temporally whereas spillover effects decay spatially. • The strongest inhibition comes from promoting environmental protection technology. [ABSTRACT FROM AUTHOR]

Previous studies have often focused on the direct benefits of environmental regulations on health. This paper quantifies the impact of environmental regulations on the cost of prevention for urban residents. The purpose of this study is to examine how environmental regulations affect urban commercial health insurance purchases. Air pollution monitoring stations in China capture exogenous air quality changes to overcome potential endogenous problems. Evidence shows that an increase in the operating days of air pollution monitoring stations will reduce the purchase of commercial health insurance in the city. The mechanism is that the operation of air pollution monitoring stations is conducive to improving local air quality and alleviating residents' health prevention needs. Heterogeneity analysis shows that the impact of air pollution monitoring station operation on the cost of prevention is less effective in more developed cities. Finally, we demonstrate that the number of claims for commercial health insurance dropped with the operation of air pollution monitoring stations. The results show that the benefits of improved air pollution do not only include the direct benefits of increased health, but also include the potential benefits of reduced prevention costs. Ultimately, the results further indicate the importance of air pollution control. [ABSTRACT FROM AUTHOR]

With the rapid economic growth, people's need for environment and atmospheric qualities is getting higher. The Chinese government is taking active measures to deal with it, and the scientific evaluation of atmospheric emission efficiency is necessary for atmospheric environment management. Concerning this reason, this paper uses the non-radial DEA model and the PS (Phillips and Sul) convergence test to study the atmospheric emission efficiency and spatial convergence characteristics in China and analyzes the changing trends and transfer path of atmospheric emission efficiency from a dynamic perspective. We conclude that (1) the overall level of atmospheric emission efficiency in China is low, and further improvement can be done; (2) there is a positive relationship between atmospheric emission efficiency and regional economic development; (3) there are obvious characteristics of unbalanced distribution and regional distribution of atmospheric emission efficiency; (4) the transfer path of atmospheric emission efficiency shows a decreasing trend year by year, which indicates that China's atmospheric environment is gradually being improved. These findings show some directions for the atmospheric environment management in China and can help the cities to deal with atmospheric pollution management issues. [ABSTRACT FROM AUTHOR]

China's economic growth is accompanied by air pollution, which has harmed public health. Former scholars have explored the influencing factors of public health from different perspectives with different methods, among which family income and environmental pollution are two important factors. The paper explored the combined effects of economic level and air pollution on public health at the micro and macro level using the multivariate ordered Logit model and panel data regression model. A comparative analysis was also explored between China's eastern and western regions. The results demonstrated that: (1) Both at the micro and macro level, economic level was positively correlated with public health, while air pollution was negatively correlated with it. (2) The improvement of the economic level made public health more susceptible to air pollution at the micro level and at the macro level. (3) The higher the economic level, the lower the effect of economic growth in reducing the adverse effects of air pollution on public health. This paper proposed that sustainable economic growth should be pursued, the emissions of air pollutants should be reduced, regional development should be balanced and the development gap between China's eastern and western regions should be narrowed. [ABSTRACT FROM AUTHOR]

Transportation infrastructure plays a fundamental role in reducing pollution emissions to avert global environmental degradation. However, the mechanisms behind these effects remain unclear. This paper aims to provide empirical evidence by investigating the effects of roads on the intensity of air pollutant emissions. Using a city-level data set for the period 2003–2015, we find that roads help to reduce the intensity of pollution emissions in China and induce greener growth performance. Specifically, we test two possible mechanisms through which roads improve environmental performance, and the results suggest that roads have a more positive and significant impact on air pollution reduction when an eco-industrial park is located in a nearby city. There is also evidence that roads can spur green innovation and hence can contribute to regional green performance. • We use city level data to examine transportation infrastructure's effect on air pollution in China. • We propose two possible mechanisms to confirm the effect of transportation infrastructure. • Transportation infrastructure plays an important role in reduction of air pollution emission. • The implications of policy in emerging economies are discussed. [ABSTRACT FROM AUTHOR]

Air pollution in the form of particulate matter (PM) is becoming one of the greatest current threats to human health on a global scale. This paper firstly presents a Bayesian space–time hierarch piecewise regression model (BSTHPRM) which can self-adaptively detect the transitions of local trends, accounting for spatial correlations. The spatiotemporal trends of the approximately anthropogenic PM 2.5 removed natural dust (PM 2.5_No Dust) concentrations and the corresponding population's PM 2.5_No Dust exposure (PPM 2.5 E) in the global continent from 1998 to 2016 were investigated by the presented BSTHPRM. The total areas of the high and higher PM 2.5_No Dust -polluted regions, whose spatial relative magnitude of PM 2.5_NoDust pollution to the global continental overall level was between 1.89 and 14.68, accounted for about 13.4% of the global land area, and the corresponding exposed populations accounted for 56.0% of the global total population. The spatial heterogeneity of the global PM 2.5_NoDust pollution increased generally from 1998 to 2016. The areas of hot, warm, and cold spots with increasing trends of PM 2.5_NoDust concentration initially contracted and then later expanded. The local trends of the global continental PM 2.5_NoDust concentrations and PPM 2.5 E can be parted into three changing stages, early, medium, and later stages, using the BSTHPRM. The area proportions of the regions experiencing a decreasing trend of PM 2.5_NoDust concentrations and PPM 2.5 E were greater in the medium stage than in the early and later stages. The local trends of PM 2.5_NoDust concentration and PPM 2.5 E in the two higher PM 2.5_NoDust polluted areas, northern India and eastern and southern China, increased in the early stage and then decreased in the medium stage. In the later stage (recent years), northern India displayed a stronger increasing trend; nevertheless, the follow-up decreasing trend still occurred in eastern and southern China. In the first two stages, more than half of the areas in Europe experienced a decreasing trend of PM 2.5_NoDust concentration and PPM 2.5 E; later, more than half of areas in Europe exhibited increasing trends in the later stage. North America and South America experienced a similar local trend of PPM 2.5 E to Europe. The PPM 2.5 E trend in Africa generally increased during the study period. • First presentation of a Bayesian space-time hierarchy piecewise regression model detecting self-adaptively transitions. • Firstly applies a developed Bayesian space-time model in investigating the global population's exposure to PM 2.5. • Deeply analyses the spatiotemporal trends of the global continental PM 2.5 pollution removed natural dust. • Details the spatiotemporal trends of the global population's exposure to anthropogenic PM 2.5. [ABSTRACT FROM AUTHOR]

Abstract: China is currently facing the threat of serious air pollution and has adopted a series of governance policies. However, the fact that PM 2.5 indicator in some regions often reached far higher than the official threshold since 2015 has reflected that China's current Air Quality Index system for measuring and evaluating air quality is still not perfect. Therefore, this paper extended the current air quality indicator system officially used by China. Using improved Entropy-weighted Factor Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Method, we calculated the air quality scores of 26 cities in China's most economically developed region - the Yangtze River Delta region based on their daily average concentration numbers of main air pollutants from February 2015 to January 2018. Based on the air quality calculation result, we further studied the effectiveness of air pollution control policies of this region by using the Box-Jenkins Model with fuzzy strategy parameter adjustment. Our findings are: the pollution control policy is more effective to Shanghai, Jiangsu and Zhejiang. The air quality score of Shanghai has improved by 51.07% compared with its level before the policy. Meanwhile, the air quality improvement in Zhejiang province has shown the characteristics of "Campaign-style Governance" right before the G20 Summit, especially that in Hangzhou, the host city of the Summit. On the other hand, maybe due to late start of air pollution control programs, the cities of Anhui province haven't shown obvious improvements in air quality, and some cities have even seen deterioration in air quality scores during our study period. Graphical abstract Image 1 Highlights • We extended the current air quality indicator system officially used by China. • We used the Entropy-weighted Factor Method and TOPSIS Method for assessing. • We studied the control policies by Box-Jenkins Model with fuzzy strategy parameter. • Results and corresponding reasons were analyzed according to provinces and cities. • Policy recommendations for air pollution control in YRD were proposed. [ABSTRACT FROM AUTHOR]

Abstract The severe health damage and massive health costs associated with industrial air pollution have attracted global concern. A spatial lag model with decomposed direct and indirect spatial effects was used to estimate the impact of industrial air pollution on health care expenditures in Chinese provinces from 2002 to 2014. The control variables of health reform, GDP per capita, urbanization, aging, education level, and hospital supplies were considered. Moran's I index was applied to investigate the spatial agglomeration of health care expenditures. The spatial lag model with fixed effects provided the best fit to measure the effect of industrial air pollution on health care expenditures. The global Moran's I of provincial health care expenditures increased from 0.3988 in 2002 to 0.4554 in 2014. The estimated spatial lag coefficient of provincial health care expenditures was 0.188. The estimated direct and indirect effects of industrial air pollution on provincial health care expenditures were 0.032 and 0.0072, respectively. The direct effect of health reform on health care expenditures was −0.006. The results indicate that the strengthening spatial agglomeration of provincial health care expenditures is caused by the joint effect of industrial air pollution, health reform, and other socioeconomic factors. Industrial air pollution increases health care expenditures in the local and neighboring provinces. Most importantly, the effect of industrial air pollution outweighs the reduction effect of health reform on health care expenditures. The final remarks of this paper suggest three main measures to reduce industrial air pollution and its health costs. [ABSTRACT FROM AUTHOR]

Abstract Air pollution is widely believed to cause healthy risks and influence nations' economic dimensions. This paper investigates the impact of air pollution on housing prices in China's 286 prefectural-level cities during 2005–2013. To address the endogeneity problem of PM 2.5 concentrations in our regression, we propose an instrument variable strategy based on ventilation coefficient. The estimation results suggest that a 10% increase in PM 2.5 concentrations causes a 2.4% reduction in local housing prices. Air pollution also hinders the progress of urbanization, limits cities' human capital formation and changes people's expectations of housing prices that we interpret as three potential mechanisms behind our main findings. [ABSTRACT FROM AUTHOR]

Abstract Urban air pollution has become a major concern in China. In the heavily populated and developed Beijing-Tianjin-Hebei (BTH) region, vehicle emissions are major pollution contributors. This paper aims to establish 2014 vehicle emission inventories for five major air pollutants in the BTH region: CO, HC, NO x , PM 10 and PM 2.5 and carry out an emission control policy scenario analysis based on the newly developed inventories. Results showed that total emissions of CO, HC, NO x , PM 10 and PM 2.5 from vehicles in the BTH region were 1622.4 kt, 187.5 kt, 544.7 kt, 23.5 kt, and 26.1 kt, respectively. Based on the emission inventory, high-resolution emission distribution maps of the study area were created at a scale of 1 km × 1 km by incorporating local traffic flow data. The CO, HC and NO x emission levels were higher in the southeast, while the PM 10 and PM 2.5 levels were relatively evenly distributed throughout the BTH region. Based on the emission inventories, four policy scenarios were set to simulate the emission changes, and results showed that eliminating old vehicles and upgrading oil quality could reduce emission efficiently. A policy of eliminating old vehicles was selected to calculate the economic cost. Results showed that the emission reduction efficiencies of unit cost decreased from Hebei, Tianjin to Beijing. The results of this study could help policy makers identify pollution sources and design appropriate control measures. Highlights • We calculated vehicle emission factors in the Beijing-Tianjin-Hebei region. • We generated high spatial resolution emission maps based on traffic flow data. • We set various policy scenarios to evaluate vehicle pollution control measures. • We analyzed governmental and social costs of typical policy scenario. [ABSTRACT FROM AUTHOR]

Abstract Air pollution and its effects on public health have received considerable attention in China. This paper extends discussion of the relationship between pollution and health by focusing on the effects of certain socioeconomic factors from an equality perspective. Hierarchical linear regression models are used to analyze the effects of environmental pollution on the health of residents and explore the inherent mechanisms through which environmental and economic factors contribute to increases in health inequality. The main results indicate that pollution poses significant risks to health. Individual income and life satisfaction are found to be significantly positively related to health and this relationship is intensified by the effects of environmental pollution. The results show that health inequality is prevalent throughout China and is more severe in rural areas. The damage to health caused by pollution further increases the levels of health inequality to varying degrees in groups with different income levels. Pollution also increases the impact of income inequality on health inequality. Overall, the results to some extent validate the concept of an "environment-health-poverty trap." [ABSTRACT FROM AUTHOR]

Abstract Over the last three decades, rapid industrialization in China has generated an unprecedentedly high level of air pollution and associated health problems. Given that China accounts for one-fifth of the world population and suffers from severe air pollution, a comprehensive review of the indicators accounting for the health costs in relation to air pollution will benefit evidence-based and health-related environmental policy-making. This paper reviews the conventional static and the new dynamic approach adopted for air pollution-related health cost accounting in China and analyzes the difference between the two in estimating GDP loss. The advantages of adopting the dynamic approach for health cost accounting in China, with conditions guaranteeing its optimal performance are highlighted. Guidelines on how one can identify an appropriate approach for health cost accounting in China are put forward. Further, we outline and compare the globally-applicable and China-specific indicators adopted by different accounting methodologies, with their pros and cons being discussed. A comprehensive account of the available databases and methodologies for health cost accounting in China are outlined. Future directions to guide health cost accounting in China are provided. Our work provides valuable insights into future health cost accounting research in China. Our study has strengthen the view that the dynamic approach is comparatively more preferred than the static approach for health cost accounting in China, if more data is available to train the dynamic models and improve the robustness of the parameters employed. In addition, future dynamic model should address the socio-economic impacts, including benefits or losses of air pollution polices, to provide a more robust policy picture. Our work has laid the key principles and guidelines for selecting proper econometric approaches and parameters. We have also identified a proper estimation method for the Value of Life in China, and proposed the integration of engineering approaches, such as the use of deep learning and big data analysis for health cost accounting at the fine-grained level (city-district or sub-regional level). Our work has also identified the gap for more accurate health cost accounting at the fine-grained level in China, which will subsequently affect the quality of health-related air pollution policy decision-making at such levels, and the health-related quality of life of the citizens in China. Highlights • The dynamic health cost accounting a preferred approach in China • Future dynamic model should address parameter robustness and socio-economic impacts. • Guidelines for selecting proper econometric approaches and parameters stated • Estimation methods for Values of Life in China and integration of methods proposed • Accurate health cost accounting at fine spatial resolutions needed [ABSTRACT FROM AUTHOR]

A strategy of controlling individual pollutants without considering synergistic effects and ancillary benefit/cost has been followed in the course of air pollution control in China. This policy orientation could lead to divided and costly technology pathways. In this paper, the coal-fired power sector is used as a representative case to investigate the technology schemes and assess their cost effectiveness, so as to provide empirical evidence of technology synergies' impacts on cost-effectiveness and to shed light on the future directions for pollution control strategies. The results indicate that more pollution damage caused by particulate matter and mercury, could be avoided at less cost if the proposed technology schemes can be used more instead of the current mainstream scheme in this sector at the beginning of air pollution regulation. It implies that more detailed evaluation of technology schemes is needed to better control multiple pollutants and alleviate various negative health endpoint effects. The transformation of strategy will rely on a combination of technological developments and policy changes. The lessons learned from coal-fired power sector can contribute to policy-making across sectors within the industry. [ABSTRACT FROM AUTHOR]

Highlights • An energy-related PM 2.5 footprint inventory framework for households is constructed. • PM 2.5 footprint is decreasing from urban households (high income) to rural households (low income). • Sectors related to food, residence and health care are the main contributors of PM 2.5 footprint. Abstract Particulate matter 2.5 (PM 2.5 ) as a major hazardous constituent is strongly associated with household energy consumption. In this paper, we investigate the PM 2.5 footprint of household energy consumption in Beijing based on input–output analysis. An inventory of primary and secondary household energy consumption is developed to quantify the direct PM 2.5 emissions. The household PM 2.5 footprint is then traced through goods or services that ultimately consumed by households to unveil the indirect PM 2.5 emissions triggered by economic activities. PM 2.5 fingerprint is also proposed to describe the characteristic of household PM 2.5 footprint. Results show that PM 2.5 footprint of Beijing households in 2010 is 7831.36 kt, of which 92.61% is contributed by urban households. The source of direct PM 2.5 emissions in urban area is diversified, which is composed of coal (42.07%), heat and electricity (32.83%), gasoline (21.29%), natural gas (3.04%) and liquefied petroleum gas (0.77%), while in rural area, coal (98.09%) plays a dominant role. The indirect PM 2.5 accounts for 99.96% of the total footprint in urban area, about one third of which is contributed by sectors of “Food Processing and Production”, “Healthcare and Social Security”, and “Farming, Forestry, Animal Husbandry and Fishery”. The disparity between urban and rural households PM 2.5 footprints is further evaluated with income levels. The PM 2.5 footprint from living expenditures of urban households is found to be nearly twice as much as that of rural households. Such inventory of PM 2.5 footprint and examination of drivers for PM 2.5 emissions may be essential for urban pollution mitigation policy. [ABSTRACT FROM AUTHOR]

Urban air pollution generated from straw open burning after the harvest seasons in China has been one of the significant problems interfering with the city's proper functioning. This paper applied contingent valuation in a face to face survey to assess the individual willingness of urban residents in Henan, China to pay for corn straw burning ban to policy makers. Such assessments are important for policy makers to determine the investment and policy instruments for regulating environmental impacts of straw open burning. To investigate the determinants of the stated willingness to pay (WtP) a sample of 1890 urban residents in Henan, China was selected. The study used Tobit model. About 62% of respondents stated that they were willing to pay. The expected WtP was about 76.72 Chinese Yuan (RMB) per person per year for the total respondents and 142.70 RMB per person per year for observations with positive WtP bids. Aggregate value was between 3.41 and 3.90 billion RMB indicating that corn straw burning ban is of considerable economic value to Henan. Results from the study show that the experience of environmental protection, expenditure on protecting health from air pollution, influence of straw burning on respondent's health, life, work, household income, education, job place, and family size were significant variables explaining WtP. The results of the study will be useful for policy makers when making up their mind how much funds should be invested and what kinds of policy instruments could be suitable for banning straw burning. [ABSTRACT FROM AUTHOR]

Beijing, the capital of China, has experienced rapid industrialization, urbanization and motorization in recent decades. Consequently, air pollution in Beijing, especially fine particulate matter (PM 2.5 ) pollution, has gradually become a severe environmental issue, due to the continuing growth in energy consumption and the resulting multiple pollutant emissions. In response to the increasingly serious PM 2.5 pollution, Beijing's government implemented a series of policies, measures and regulations on air pollution prevention and control and took some concrete actions to improve air quality. In this paper, firstly, we summarize China's ambient air quality standards, China's policies and regulations on air pollution prevention and control. Secondly, we illustrate historical evolution and current status of air pollution in Beijing. Finally, we introduce control measures and actions in Beijing and its surrounding areas. The paper aims to help environmental scientists and policy makers around the world understand the past and current air pollution in Beijing and control strategies and actions taken by Beijing's government. [ABSTRACT FROM AUTHOR]

Air pollution is a major health hazard in China and a serious focus of governmental concern. To reduce the levels of air pollution will take a strategic realignment of priorities for all of the major players responsible for monitoring and implementing environmental controls to air emissions. Gaining and maintaining compliance of on-the-ground company directors and managers is essential to adequate pollution control, but to date little is known about how managers of heavily polluting companies view pro-environmental measures in their company operation. This paper identifies common characteristics of managers in heavy polluting industries, and measures the impact these characteristics have on corporate environmental behavior based on the survey data that was collected in 2013 from sixty energy companies in China's Shaanxi Province. Personal characteristics of these managers include age, gender, place of birth, level of education, average monthly household income, department, position, environmental awareness, and environmental behavior. Empirical results show that 33.5% of managers that belong to companies with high corporate environmental behavior scores also have the highest level of environmental awareness and environmental behavior. Managers' environmental behavior, awareness and education are significantly positively correlated to corporate environmental behavior. The research also discovered that managers' seniority is positively correlated with corporate environmental behavior, which indicates that senior managers have higher environmental awareness and knowledge. The paper concluded that distinct differences between senior management types related to their core underlying values and these differences matter both for environmental protection and training. Understanding how company directors and managers view their roles in relationship to company environmental protection will require a nuanced approach that rejects a simple uni-dimensional conceptualization of company managers' relationship to environmental implementation. By extension, a “single-service” educational approach will also need to be rejected since one message aimed at one set of values will not tap into the range of environmental motivations and values currently held by those in management positions and therefore will be less effective than training approaches that are targeted at specific types of managers. [ABSTRACT FROM AUTHOR]

Both meteorological changes and emission control have significant impacts on PM 2.5 concentration and regional transport contribution, where it is important to clarify the relationship among them for air pollution control. In this paper, the WRF-CAMx coupled with a set of simulation scenarios was employed for decomposing the contributions of meteorological conditions, emission, and regional transport considering transport matrix to PM 2.5 concentration in the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions during 2013–2017. The results show that the local contributions occupied the primary position in PM 2.5 concentrations in the BTH and YRD regions, reaching 57.9% and 58.1% in 2017, respectively. In terms of regional transports, they were mainly from Shandong Peninsula (SP) and Fen Nutrient Plain (FNP) for BTH, while Central triangle Region (CTR) and SP for YRD. Analysis of typical representative months suggested that in January, the concentration of local contribution reached peak. In addition, the results also show that when the intensity of emission reduction increased, the proportion of local contribution increased significantly while the local absolute contribution concentration decreased correspondingly. The meteorological conditions had little impact on transport matrix dominated by local contribution, whereas, the more favorable the meteorological diffusion conditions, the lower the contribution ratio of the out-of-region transport and the lower the contribution concentration. Moreover, the variation of meteorological conditions was more significant to the transport contribution outside the region. These findings can be helpful for formulating reasonable strategies of regional joint prevention and control and providing a valuable preference for other areas of China. [Display omitted] • The transport matrix of PM 2.5 was established using the WRF-CAMx model. • Emission reduction dominated the total decrease in PM 2.5. • Local emissions contributed 57.9% and 58.1% of PM 2.5 in BTH and YRD. • Meteorological conditions had a significant impact on the regional transport. [ABSTRACT FROM AUTHOR]

Previous studies analysing the effects of air pollution on work efficiency did not pay much attention to indoor impacts. This paper attempts to fill this gap by using technical data from Chinese Basketball Association (CBA) players. For the first time, we present personal work efficiency using a comprehensive evaluation index, the player efficiency rating (PER). The information is then combined with daily air quality data. We find that air pollution reduces work productivity: for every 10% increase in the air quality index (AQI), the average PER decreases by 0.4221, and the effects vary by air pollutant. Heterogeneity analysis shows that at the level of competition venues, the negative impact of air pollution on work efficiency can be weakened at home, while the rising temperature in the competition city can amplify the negative impact of air pollution on work efficiency. At the level of individual players, older players and foreign players are less affected by air pollution. Moreover, we find that air pollution affects work efficiency by reducing total work output and work accuracy. Our research constructs a comprehensive evaluation index for work efficiency, enriches research on the influence of air pollution on work efficiency, and provides a policy basis for identifying groups that are sensitive to air pollution, which might help with air pollution control policy making. [ABSTRACT FROM AUTHOR]

The PM 2.5 is the culprit of air pollution, and it leads to respiratory system disease when the fine particles are inhaled. Therefore, it is increasingly significant to develop an effective model for PM 2.5 forecasting and warnings that informs people to foresee the air quality. People can reduce outdoor activities and take preventive measures if they know the air quality is bad ahead of time. In addition, reliable forecasting results can remind the relevant departments to control and reduce pollutants discharge. According to our knowledge, the current hybrid forecasting techniques of PM 2.5 do not take the meteorological factors into consideration. Actually, meteorological factors affect the concentrations of air pollution, but it is unclear whether meteorological factors are helpful for improving the PM 2.5 forecasting results or not. This paper proposes a hybrid model called CEEMD-PSOGSA-SVR-GRNN, based on complementary ensemble empirical mode decomposition (CEEMD), particle swarm optimization and gravitational search algorithm (PSOGSA), support vector regression (SVR), generalized regression neural network (GRNN) and grey correlation analysis (GCA), for the daily PM 2.5 concentrations forecasting. The main steps of proposed model are described as follows: the original PM 2.5 data decomposition with CEEMD, optimal SVR selection with PSOGCA, meteorological factors selection with GCA, residual revision by GRNN and forecasting results analysis. Three cities (Chongqing, Harbin and Jinan) in China with different characteristics of climate, terrain and pollution sources are selected to verify the effectiveness of proposed model, and CEEMD-PSOGSA-SVR*, EEMD-PSOGSA-SVR, PSOGSA-SVR, CEEMD-PSO-SVR, CEEMD-GSA-SVR, CEEMD-GWO-SVR are considered to be compared models. The experimental results show that the hybrid CEEMD-PSOGSA-SVR-GRNN model outperforms other six compared models. Therefore, the proposed CEEMD-PSOGSA-SVR-GRNN model can be used to develop air quality forecasting and warnings. [ABSTRACT FROM AUTHOR]

Correlation between urbanization and environmental pollution is a major focus of study in geography, environmental science, and economics. Particulate matter is the primary pollutant of air pollution and made up of heavy metal, organic carbon and aromatic hydrocarbon and complicated chemicals. PM 2.5 are fine particulate matter with diameters that are less than 2.5 μm. The aim of this study on the relationship between urbanization and PM 2.5 concentration is to achieve a win-win situation of both economic development and environmental protection, which is of great significance to sustainable development in China. This paper uses PM 2.5 remote sensing data and statistical yearbook data from 2000 to 2011 to build four panel data models within the urbanization-PM 2.5 concentration Environmental Kuznets Curve (EKC) framework. The goal is to find out the correlations between PM 2.5 concentration and economic urbanization, population urbanization, and space urbanization. Furthermore, scenario simulations are set to predict when China will reach inflection point and achieve its target concentration. Results show that the relationship between economic urbanization and PM 2.5 concentration is an inverted N-shaped or inverted U-shaped curve. Most cities in East China have reached the second inflection point of inverted-N curve to step into the win-win stage while many cities in Middle China still need 10–15 years to arrive at the inflection point of the inverted-U curve. Therefore, China is under great pressure to prevent PM 2.5 pollution and pursue more targeted PM 2.5 -reduction policies for air quality improvement. [ABSTRACT FROM AUTHOR]

Severe outdoor haze pollution has brought about associated public health impacts for China in recent years. Haze pollution therefore has become one of the most serious environmental problems in China. To alleviate this, the government issued its National Air Pollution Control NACAP (NAPCAP) in 2013 containing pollution reduction targets for 2017 and beyond. However, there are doubts whether these targets can be achieved. This paper examines this issue by modeling the statistical relationship between PM 2.5 concentration and air pollutant emissions (SO 2 , NO x , etc.), together with wind and neighboring transfer impacts at the city level, to identify ways of calculating intended maximum emission levels in the Beijing-Tianjin-Hebei area. The results indicate that local air pollutant emissions, lagged PM 2.5 concentrations, wind speed, and PM 2.5 concentrations in adjacent areas have a significant impact on PM 2.5 concentrations in the Beijing-Tianjin-Hebei area. The 2017 haze concentration control targets could not be achieved through the current NAPCAP plan, with PM 2.5 concentration levels in many locations being predicted as between two and three times those targeted. However, the 2017 concentration target can be fulfilled given the pollutant reduction rates of NAPCAP plus Enhanced Measures of Air Pollution Control for Beijing-Tianjin-Hebei region (EMAPC) under the same wind conditions as 2016. There are large deviations between the simulation and monitor values of PM 2.5 concentration when it is less windy, and the simulation value of most cities are generally lower than the monitor values. The unsuccessful implementation of pollutant reduction measures and inaccuracy of the monitor data are two possible reasons to explain the relatively large deviations involved. Finally, some preliminary institutional causes of haze pollution and solutions for its control in the Beijing-Tianjin-Hebei area are discussed. [ABSTRACT FROM AUTHOR]

With the rapid economic development of China, the increasing emission of industrial waste gas as well as its poor control efficiency have caused serious air pollution issues and put threats to human health and sustainable development. Under the current development model, China's industrial sectors still remain the dominant economic component. Based on existing academic research in China and abroad, as well as with the help of the Data Envelopment Analysis (DEA) model, this paper adopted 39 industrial sectors of China as Decision Making Units (DMU) and their input-output data from 2003 to 2014 to calculate and evaluate the TFE of waste gas control in different industries of China. By designing an original MATLAB algorithm for optimization solutions of multi-variable constrained nonlinear functions, we found that there were 24 industries (61.5% of total industries) whose waste gas control efficiency at period end was lower than that at period beginning. Although the tobacco industry kept the optimal level, the major sectors with the biggest industrial waste gas emission all had poor waste gas control TFE. Based on the results, we further explained possible reasons for the difference in TFE across different industries from 2003 to 2014, and provided policy recommendations accordingly. [ABSTRACT FROM AUTHOR]

The increasingly high frequency of heavy air pollution in most regions of China signals the urgent need for the transition to an environmentally friendly production performance by socioeconomic sectors for the sake of people's health and sustainable development. Focusing on CO 2 and major air pollutants, this paper presents a comprehensive environmental efficiency index based on evaluating the environmental efficiency of major socioeconomic sectors, including agriculture, power, industry, residential and transportation, at the province level in China in 2010 based on a slack-based measure DEA model with non-separable bad output and weights determined by the coefficient of variation method. In terms of the environment, 5, 16, 6, 7 and 4 provinces operated along the production frontier for the agricultural, power, industrial, residential and transportation sectors, respectively, in China in 2010, whereas Shanxi, Heilongjiang, Ningxia, Hubei and Yunnan showed lowest efficiency correspondingly. The comprehensive environmental efficiency index varied from 0.3863 to 0.9261 for 30 provinces in China, with a nationwide average of 0.6383 in 2010; Shanghai ranked at the top, and Shanxi was last. Regional disparities in environmental efficiency were identified. A more detailed inefficiency decomposition and benchmarking analysis provided insight for understanding the source of comprehensive environmental inefficiency and, more specifically, the reduction potential for CO 2 and air pollutants. Some specific research and policy implications were uncovered from this work. [ABSTRACT FROM AUTHOR]

The present paper has used a comprehensive approach to study atmosphere pollution sources including the study of vertical distribution characteristics, the epicenters of occurrence and transport of atmospheric aerosol in North-West China under intensive dust storm registered in all cities of the region in April 2014. To achieve this goal, the remote sensing data using Moderate Resolution Imaging Spectroradiometer satellite (MODIS) as well as model-simulated data, were used, which facilitate tracking the sources, routes, and spatial extent of dust storms. The results of the study have shown strong territory pollution with aerosol during sandstorm. According to ground-based air quality monitoring stations data, concentrations of PM 10 and PM 2.5 exceeded 400 μg/m 3 and 150 μg/m 3 , respectively, the ratio PM 2.5 /PM 10 being within the range of 0.123–0.661. According to MODIS/Terra Collection 6 Level-2 aerosol products data and the Deep Blue algorithm data, the aerosol optical depth (AOD) at 550 nm in the pollution epicenter was within 0.75–1. The vertical distribution of aerosols indicates that the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) 532 nm total attenuates backscatter coefficient ranges from 0.01 to 0.0001 km −1  × sr −1 with the distribution of the main types of aerosols in the troposphere of the region within 0–12.5 km, where the most severe aerosol contamination is observed in the lower troposphere (at 3–6 km). According to satellite sounding and model-simulated data, the sources of pollution are the deserted regions of Northern and Northwestern China. [ABSTRACT FROM AUTHOR]

China has become one of the most heavily polluted countries in the world with China's air pollution attracting claims from some international media of its spread to neighboring countries such as Korea and Japan, in order to bring to the concern of the international community. PM 2.5 (fine particles) pollution is one of the main sources of China's air pollution. The detrimental effect PM 2.5 pollution poses on health of residents and its hindrances to transportation has attracted the attention of many scholars who have conducted a wide range of investigations on PM 2.5 pollution. However, in spite of the avalanche of research on PM 2.5 pollution in China, majority of the existing studies in terms of methodology has usually investigated air pollution using the averaging method. In fact, the data distribution of socio-economic variables is often non-normal, with the tail having hidden important information. In order to overcome the shortcomings of existing research, this paper uses a quantile regression approach to explore the main driving forces of the difference in PM 2.5 pollution under high, medium and low emission levels. The results show that the effect of economic growth on PM 2.5 pollution in the upper 90th quantile provinces is the highest in all the quantile provinces due to the differences in fixed–asset investment and export trade. The impacts of energy efficiency in the 75th–90th and upper 90th quantile provinces are stronger than those in the lower 10th, 10th–25th, 25th–50th, and 50th–75th quantile provinces because of a big differences in research and development (R&D) funding and R&D personnel investment. The case of industrialization was similar on account of the differences in the industrial and building sectors. However, the empirical evidence showed that influences of urbanization in the 25th–50th and 50th–75th quantile provinces were lower than those in the other quantile provinces owing to the differences in motor vehicle and real estate industry. Thus, the heterogeneous effects of these driving forces on the different quantile provinces should be taken into consideration when discussing the mitigation of PM 2.5 pollution in China. [ABSTRACT FROM AUTHOR]

In recent years, haze pollution has frequently shrouded most regions of northern and eastern China. Air pollution has drawn increasing attention at home and abroad. However, the potential negative impacts of environmental pollution on economic development have long been ignored. Considering the possible effect of economic growth on environmental pollution, the conventional ordinary least squares (OLS) estimation may suffer from endogeneity biases caused by possible bilateral causality. In this paper, using city-level panel data for the period between 2013 and 2015, the influence of PM 2.5 concentrations on per capita GDP is estimated through a carefully designed simultaneous equations model for the first time. To control for fixed effects, a series of time dummies and region dummies are also introduced. The estimation results indicate that haze pollution indeed has a significantly negative impact on economic development. On average, as of 2015, when other conditions are equal, an increase of 5 μg/m 3 in PM 2.5 concentrations may cause a decrease of approximately 2500 yuan in GDP per capita. In addition, the results suggest that sustainable economic growth may help reduce PM 2.5 concentrations, which in turn benefits economic development. [ABSTRACT FROM AUTHOR]

Both air pollutant and carbon emissions are serious problems in China, particularly the economic and pollution intensive urban agglomerations. Therefore, this paper selected the most severely polluted Jing-Jin-Ji region (Beijing, Tianjin and Hebei urban clusters) to study the potential of mitigating both carbon and air pollutant emission. The Computable General Equilibrium (CGE) model was applied and six scenarios were constructed by considering policies of environmental tax and renewable energy. Business-as-usual (BaU) scenario assumed no tax and fixed renewable energy to 2002 level, whereas other scenarios assumed that intensified renewable energy (RE) and tax on sulfur dioxide (SO 2 ) (TAX1 and TAX2) were implemented either alone or combined. The results indicated that both RE and SO 2 tax could contribute to regional low-carbon development and air pollutant reduction, and the reduction is about 10–40% depending on scenarios. However, Renewable energy is always more effective in reducing CO 2 and air pollutant emissions than TAX1 but less effective than TAX2 for Hebei. From the sectoral perspective, power sector would exhibit both the largest emissions and reduction potentials in all three Cities, accounting for about 21%, 31% and 28% of total CO 2 emissions respectively for Beijing, Tianjin and Hebei, in 2020. Considering different policies, it is more effective for Hebei to apply renewable energy to reduce its power sector emissions in the long term, and tax policy is more effective for emission reduction from other sectors in Tianjin. Further analysis on the economic impact of mitigation policy revealed that Tianjin and Beijing would suffer relatively greater negative impacts than Hebei in terms of loss in total GDP, while developing renewable energy could help to reduce such negative impacts. It is suggested that the policymakers need to take into accounts the regional economic situation and natural resource potential when they formulate low-carbon policies. [ABSTRACT FROM AUTHOR]

Since June 2014, international crude oil prices have fallen sharply; because of the new refined oil pricing mechanism, China's domestic refined oil prices fell with the international oil prices. During the period from November 28, 2014, to January 12, 2015, the China's domestic refined oil consumption tax had been increased for three times. Then the lowest price of refined oil was set to protect refined oil prices from further declining. Through financial means used to control the prices of refined oil, can the goals of oil consumption reduction and harmful emissions mitigation be realised? Is it reasonable to use such a way adjusting refined oil consumption tax? In this paper, we construct an econometric model, investigate the elasticity of China's gasoline demand, and then make a further research on the effect of carbon dioxide (CO 2 ) emission reduction. We also study the rationality of the way adjusting the consumption tax. We propose some policy suggestions based on the research in the context of the frequent fluctuations in international oil prices and the rising dependence on international crude oil in China. According to our study, compared with the developed countries (e.g., the United States), China's gasoline demand elasticity is not high; however, a certain extent, tax adjustment can still guide consumers towards saving energy, so as to achieve the purpose of controlling gasoline consumption and reducing CO 2 emissions. The frequent tax adjustment reveals the irrationality of the refined oil pricing mechanism and the taxes policy. China has entered the 13th five development term. More reform measures should be proposed to build the crude oil market and improve the taxes policy. [ABSTRACT FROM AUTHOR]

China has been suffering from severe PM 2.5 pollution in recent years. Significant effort has been devoted to analyzing the chemical characteristics and emission sources of PM 2.5 pollution. However, because air pollution is a complex problem with a broad scope, macro-level analysis is also required to systematically address this issue. In this paper, we analyze the spatial-temporal features of PM 2.5 pollution and discover sequential patterns in cities located in three important zones in China, namely, the Bohai Sea, Yangtze River Delta, and Pearl River Delta regions. The sequential patterns reveal hidden associative relationships that could provide evidence to support united policy-making in different areas. Our results also reveal significant heterogeneities among the underlying relationships, while certain homogeneities do exist in some seasons in the three regions. Moreover, the relations to the monsoon systems and Huai River policy are further discussed to draw policy implications. [ABSTRACT FROM AUTHOR]

During the last two decades of rapid economic growth, China's environmental pollution has become increasingly serious, and the corresponding health damage has become a significant problem that has attracted domestic and foreign attention. As China pursues sustainable economic growth, the issues of environmental degradation and public health should be well addressed. Using sulfur dioxide emissions, wastewater emissions, and soot and dust emissions as indicators of environmental quality, this study investigates the comprehensive dynamic relationship between environmental quality, economic development and public health in China for the first time. To control for potential endogeneity, this paper utilizes a carefully designed simultaneous equation model (SEM) that is composed of three equations that describe the relationships among economic development, environmental quality and public health. Using panel data from 30 Chinese provinces for the period from y2002 to y2014, the model verified the negative effect of environmental pollution on public health. Moreover, economic and social factors may also affect public health. For instance, real GDP per capita has a significant negative impact on perinatal mortality rates, and education and medical conditions also contribute significantly to promoting economic growth and improving the level of public health. [ABSTRACT FROM AUTHOR]

At present, China's carbon dioxide (CO 2 ) emissions ranked first in the world. Moreover, the manufacturing industry is the biggest contributor to CO 2 emissions. Most of the existing studies use the average estimation method to investigate the main drivers of CO 2 emissions in this industry. However, the data distribution of economic variables is often not normal distribution, and the tail of the data hidden important information. In order to provide a realistic basis for emission reduction in this industry, this paper applies the quantile regression approach to investigate the driving forces of CO 2 emissions under high, medium and low emission levels. The empirical results indicate that the effect of economic growth on CO 2 emissions in the upper 90th and 75th–90th quantile provinces are higher than those in the other quantile provinces. The influence of energy intensity in the 25th–50th quantile provinces is lower than those in the other percentile provinces. The impact of urbanization in the upper 90th quantile provinces is the strongest in all the quantile provinces. However, the influence intensity of energy structure in the lower 10th quantile provinces is the highest in all the quantile provinces. Therefore, policymakers should focus on the heterogeneous effects of driving forces on CO 2 emissions in different quantiles during the process of CO 2 emission reduction. [ABSTRACT FROM AUTHOR]

In this paper, the impact of the “Carbon Neutral Growth from 2020” (CNG2020) strategy on airline efficiency is analyzed based on the predicted data of 29 international airlines during 2021–2023. The data is predicted through neural network. Following the principle of CNG2020 strategy, we calculate the emission limit for each airline. Two models, Network Range Adjusted Measure with natural disposability and Network Range Adjusted Measure with managerial disposability, are proposed to discuss the efficiency change when the CNG2020 strategy is considered. The following findings are gotten: 1. Under natural disposability, the indices related with undesirable output have a larger role in deciding benchmarking airline. 2. For most of the 29 airlines, CNG2020 strategy has little influence on their efficiency. [ABSTRACT FROM AUTHOR]