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China put forward that it would strive to achieve the carbon peaking in 2030 and carbon neutrality goals in 2060, which needed realizing the synergistic effect of pollution reduction and CO 2 reduction. CO 2 and PM 2.5 from coal consumption were "homogenous and synchronous" and they had significant synergistic effect. The synergistic reduction of PM 2.5 from coal consumption could have impacts on public health. The Logarithmic Mean Divisia Index (LMDI) was adopted to decompose the PM 2.5 from coal consumption in China from 2012 to 2017 based on data availability and quantified the synergy. PM 2.5 emissions from coal consumption were forecasted in 2030, 2035, 2050 and 2060, and its health effects of PM 2.5 were estimated. The main conclusions were: (1) the synergy of CO 2 and PM 2.5 was the largest contribution factor of PM 2.5 from coal consumption. (2) PM 2.5 from coal consumption in 2060 would achieve the WHO air quality guidelines. (3) The most health effects economic loss from PM 2.5 generated by coal consumption in China was in 2014. The health effects economic loss would have been decreased to negative by 2050. We should reinforce the synergistic effect of CO 2 and PM 2.5 , improve energy efficiency and optimize energy structure to achieve PM 2.5 pollution control and reduce health effects economic loss., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The emissions reduction of CO 2 and air pollutants are the main task in China. The two have the same roots and they interact with each other. However, CO 2 and air pollutants are quite different in space, so it is of great practical significance to explore the spatial differences of their synergy. As PM 2.5 and O 3 are more concerned at present, thus, this paper examined the decoupling of CO 2 , PM 2.5 and O 3 from GDP in China's 296 cities using the latest available data from 2015 to 2016. And the spatial differences of synergy among CO 2 , PM 2.5 and O 3 were quantitatively analyzed by using spatial autocorrelation analysis and geographically weighted regression model. The results showed that: (1) The cities achieving the three synergy emissions reduction were mainly in the southeast of China. (2) Only 26 cities had achieved the strong decoupling of CO 2 , PM 2.5 and O 3 from GDP. (3) The synergy characteristics between CO 2 and PM 2.5 , CO 2 and O 3 were different. This paper put forward the policies according to the conclusions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Due to the fast growth of China's economy, urban atmospheric pollution has become a serious problem affecting the public's physical and mental health. The '2 + 26' cities, as the Jing-Jin-Ji atmospheric pollution transmission channel, has attracted widespread concern. There were several previous studies on the economic loss of public health caused by PM 2.5 pollution in '2 + 26' cities. To assess the economic loss caused by PM 2.5 on human health in '2 + 26' cities, this paper used the exposure-response model, the health effect loss model and willingness to pay method to obtain the economic loss from PM 2.5 pollution with the latest available data in 2020. It was concluded that, in 2020, the economic loss of '2 + 26' cities from PM 2.5 was spatially distributed low in the east and high in the west. In addition, it was larger in the southern and northern part, which was smaller in the middle of the region. Based on the conclusions, policy recommendations were put forward.

The increasing uncertainty related to disaster risk under climate change brings about new challenges for sustainable urban management. The emergence of the urban resilience concept can improve the ability and extent to which cities can absorb and resolve risks, providing insight into the sustainable development of cities and regions. Yet, to date, the impact of climate change on regional urban resilience is not well understood. This paper measures the changes in urban resilience of the Beijing-Tianjin-Hebei (BTH) region from 1998 to 2019, and then explores the contribution of climate influencing factors such as temperature, precipitation and wind speed to urban resilience using econometric models. Results demonstrate the following: (1) Urban resilience shows a large spatial heterogeneity in the BTH region. Overall, Beijing and Tianjin have better and more stable resilience than Hebei Province. (2) Regarding the static impact of climate change on urban resilience, a 1 unit increase in Ln temperature and Ln precipitation will respectively increase Ln resilience by 1.01 units and 0.54 units, indicating that it has a significant positive impact on urban resilience. Each 1 unit increase in Ln wind speed will decrease resilience by 1.65 units, representing a significant negative effect. (3) Regarding the dynamic impact of climate change on urban resilience, a positive 1 unit impact of climatic factors indicates that an increase in temperature will first increase and then decrease urban resilience, and an increase in precipitation and wind speed will initially support improvement in urban resilience. Based on these findings, this article offers policy recommendations to improve urban resilience., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interests regarding the publication of the article., (Copyright © 2022. Published by Elsevier B.V.)

At present, the concentration of PM 2.5 in the Fenwei Plain has become the second highest in China, ranking second to the Beijing-Tianjin-Hebei region. At the same time, China began to monitor and control the PM 2.5 concentration in the Fenwei Plain, but the data indicated that the concentration of PM 2.5 in the Fenwei Plain rose instead of falling. PM 2.5 concentrations in 6 cities from the Fenwei Plain ranked highest among the 20 cities in 2018 in China, and PM 2.5 pollution can cause some health economic loss. Based on this background, the exposure-response model is used to estimate the impact of PM 2.5 pollution on the health economic loss in the Fenwei Plain, and PM 2.5 concentration in 2020, 2025, and 2030 is also predicted based on the setting of three scenarios: baseline scenario, emission reduction scenario, and enhanced emission reduction scenario. Then, according to the estimated results, the paper provides suggestions for reducing public health loss in the Fenwei Plain in the future.

China's energy structure is based on coal resource and it accounts for main proportion in the primary energy consumption. Coal consumption produces PM 2.5 pollution, which seriously affects public health. Considering that there are few studies on the effect PM 2.5 pollution produced by coal consumption, this paper uses the Poisson Regression model to estimate the impacts on public health and the economic loss of PM 2.5 pollution produced by coal consumption using the data in 2015. Based on these results, the paper also predicts the impacts on public health effect and its economic loss caused by PM 2.5 pollution from coal consumption under the baseline scenario and total coal consumption control scenario in 2020 and 2030. Finally, based on the research conclusions, suggestions are proposed to reduce the public health economic loss from PM 2.5 pollution caused by coal consumption., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The Central Plains Urban Agglomeration is an important growth pole of China's economy, but the rapid economic growth is accompanied by serious air pollution problems. In this paper, the latest available PM 2.5 monitoring data in 2015-2017 and exposure-response relationship model are used to quantitatively analyze the health effect damages due to PM 2.5 pollution. The way of disease cost, adjusted human capital, and willingness to pay were adopted to estimate the economic loss of health effect damages. The results show that health effect damages of PM 2.5 pollution in 2015-2017 were 11.9251 million, 11.4292 million, and 11.1012 million, respectively, accounting for 7.41%, 7.05%, and 6.94% of the total population of this area. The health effect economic loss was 97.398 billion RMB, 93.516 billion RMB, and 94.485 billion RMB, accounting for 1.73%, 1.53%, and 1.41% of the GDP. Chronic bronchitis and premature death due to PM 2.5 are the main sources of health effect economic loss. Elderly people and infants are vulnerable groups of PM 2.5 pollution. Affected by economic growth level, population density, and economic structure, Heze, Zhengzhou, Handan, and Liaocheng were greatly affected by PM 2.5 pollution, and their health effect damages was larger. The health effect damages and health effect economic loss due to PM 2.5 pollution in this area show a downward trend, indicating that air pollution reduction measures have played a positive role. However, the whole effect is still large. According to the results, this paper puts forward the policies.

Jing-Jin-Ji is the largest and most dynamic economic region in northern China, and its air pollution has attracted much public attention. Scientific evaluation of health losses caused by air pollution can provide decision-making basis for formulation and improvement of pollution reduction policies in the Jing-Jin-Ji region. This paper estimated the adverse effects of particulate matter pollution on health in the Jing-Jin-Ji region in 2016 by using logarithmic linear exposure-response function, and monetized the health effects by adjusting human capital method and disease cost method. The results show non-ignorable health hazards and economic impacts caused by atmospheric particulate pollution. The economic losses relevant to health hazards by PM2.5 in the Jing-Jin-Ji region are 122.40 billion yuan, and those relevant to PM10 are 118.34 billion yuan, accounting for 1.62% and 1.56% of the region's GDP, respectively. Similar evaluations previously conducted in other countries yielded figures within the same order of magnitude. Considering the difference in economic losses per unit among disease types, the economic losses caused by air pollution in the Jing-Jin-Ji region mainly come from premature deaths. Infants and elderly people are the main victims of particulate matter. Affected by population, pollutant concentration, industrial structure, and other factors, the economic losses of particulate matter pollution in Beijing, Tianjin, Shijiazhuang, Tangshan, and Baoding are large. In order to reduce health hazards and economic impacts caused by particulate matter pollution, this paper put forward to guide the urban population diversion, reduce the outgoing frequency of susceptible groups such as infants and the elderly in haze weather, adopt high-efficiency particulate matter air purifier indoors, and develop public transportation to reduce motor vehicle exhaust emissions. In Tianjin and Hebei, promoting cleaner production in industries such as steel and cement and reducing coal use in the power industry are also suggested.

Based on the China's 1997, 2002, 2007, and 2012 multiregional input-output model, this study calculates China's provincial CO 2 emissions from investment demand and interprovincial transfer of CO 2 emissions caused by investment demand. The findings of this study are as follows: (1) From 1997 to 2012, the CO 2 emissions from China's investment demand have seen rapid growth-the CO 2 emissions from investment demand has increased by 4.52 times, and the per capita CO 2 emissions caused by investment demand has increased by 4.13 times. Investment demand is an important driver of growth of China's CO 2 emissions. The proportion of CO 2 emissions from investment demand in CO 2 emissions from China's three final demands rose from 37.72% in 1997 to 50.68% in 2012. (2) The CO 2 emissions from investment demand are relatively large in provinces which have large-scale industries. Affected by investment-driven economic growth, CO 2 emissions from investment demand in central, western, and northeastern provinces have increased more rapidly. (3) Large amounts of CO 2 are emitted in the less-developed central and western provinces to meet the investment demand of the developed eastern provinces. As China's economy enters the "new normal," economic growth is shifting from investment-driven to consumption-driven, and the growth of CO 2 emissions from investment demand will slow down.

Investments, especially fixed asset investments, greatly affect carbon dioxide (CO 2 ) emissions. When investments are concentrated in regions with high CO 2 emissions and high fossil energy consumption, the CO 2 emission reduction targets in these areas are difficult to reach in the short term, and the cost of CO 2 emission abatement is high. The current CO 2 emission regulations focus on existing production activities and consumption behaviors. However, whether an investment, which may affect CO 2 emissions in the long term, is effectively inhibited by CO 2 emission regulations has not been investigated in previous studies. Using panel data from 30 provinces in China between 2003 and 2012, we tested whether the amount of provincial investment was constrained or promoted by the provincial CO 2 emission regulations by creating a panel model with provincial samples. The results revealed that CO 2 emission regulations did not inhibit the growth of an investment, but they stimulated investments to varying degrees in different provinces. A relatively positive result is that provinces with stronger CO 2 emission regulations exhibited a relatively small contribution to investment promotion, while provinces with weaker CO 2 reduction policies demonstrated a relatively large contribution to investment growth. We also found that investment was correlated with the growth rate of the gross domestic product (GDP) in the northeastern and western provinces. Finally, we proposed policy implications based on the utilization of policy tools from the perspectives of investment, energy structure, and local protectionism.

Jing-Jin-Ji region (i.e., Beijing, Tianjin, and Hebei) is China's key development region, but it is also the leading and most serious air pollution region in China. High fossil fuel consumption is the major source of both carbon dioxide (CO 2 ) emissions and air pollutants. Therefore, it is important to reveal the source of CO 2 emissions to control the air pollution in the Jing-Jin-Ji region. In this study, an interregional input-output model was applied to quantitatively estimate the embodied CO 2 transfer between Jing-Jin-Ji region and other region in China using China's interregional input-output data in 2010. The results indicated that there was a significant difference in the production-based CO 2 emissions in China, and furthermore, the Jing-Jin-Ji region and its surrounding regions were the main regions of the production-based CO 2 emissions in China. Hebei Province exported a large amount of embodied CO 2 to meet the investment, consumption, and export demands of Beijing and Tianjin. The Jing-Jin-Ji regions exported a great deal of embodied CO 2 to the coastal provinces of southeast China and imported it from neighboring provinces.

The Central Plains Urban Agglomeration is an important growth pole of China's economy, but the rapid economic growth is accompanied by serious air pollution problems. In this paper, the latest available PM2.5 monitoring data in 2015–2017 and exposure-response relationship model are used to quantitatively analyze the health effect damages due to PM2.5 pollution. The way of disease cost, adjusted human capital, and willingness to pay were adopted to estimate the economic loss of health effect damages. The results show that health effect damages of PM2.5 pollution in 2015–2017 were 11.9251 million, 11.4292 million, and 11.1012 million, respectively, accounting for 7.41%, 7.05%, and 6.94% of the total population of this area. The health effect economic loss was 97.398 billion RMB, 93.516 billion RMB, and 94.485 billion RMB, accounting for 1.73%, 1.53%, and 1.41% of the GDP. Chronic bronchitis and premature death due to PM2.5 are the main sources of health effect economic loss. Elderly people and infants are vulnerable groups of PM2.5 pollution. Affected by economic growth level, population density, and economic structure, Heze, Zhengzhou, Handan, and Liaocheng were greatly affected by PM2.5 pollution, and their health effect damages was larger. The health effect damages and health effect economic loss due to PM2.5 pollution in this area show a downward trend, indicating that air pollution reduction measures have played a positive role. However, the whole effect is still large. According to the results, this paper puts forward the policies. [ABSTRACT FROM AUTHOR]

Atmospheric haze pollution has become a global concern because of its severe effects on human health and the environment. The Beijing-Tianjin-Hebei urban agglomeration is located in northern China, and its haze is the most serious in China. The high concentration of PM2.5 is the main cause of haze pollution, and thus investigating the temporal and spatial characteristics of PM2.5 is important for understanding the mechanisms underlying PM2.5 pollution and for preventing haze. In this study, the PM2.5 concentration status in 13 cities from the Beijing-Tianjin-Hebei region was statistically analyzed from January 2016 to November 2016, and the spatial variation of PM2.5 was explored via spatial autocorrelation analysis. The research yielded three overall results. (1) The distribution of PM2.5 concentrations in this area varied greatly during the study period. The concentrations increased from late autumn to early winter, and the spatial range expanded from southeast to northwest. In contrast, the PM2.5 concentration decreased rapidly from late winter to early spring, and the spatial range narrowed from northwest to southeast. (2) The spatial dependence degree, by season from high to low, was in the order winter, autumn, spring, summer. Winter (from December to February of the subsequent year) and summer (from June to August) were, respectively, the highest and lowest seasons with regard to the spatial homogeneity of PM2.5 concentrations. (3) The PM2.5 concentration in the Beijing-Tianjin-Hebei region has significant spatial spillovers. Overall, cities far from Bohai Bay, such as Shijiazhuang and Hengshui, demonstrated a high-high concentration of PM2.5 pollution, while coastal cities, such as Chengde and Qinhuangdao, showed a low-low concentration. [ABSTRACT FROM AUTHOR]

Jing-Jin-Ji region (i.e., Beijing, Tianjin, and Hebei) is China’s key development region, but it is also the leading and most serious air pollution region in China. High fossil fuel consumption is the major source of both carbon dioxide (CO2) emissions and air pollutants. Therefore, it is important to reveal the source of CO2 emissions to control the air pollution in the Jing-Jin-Ji region. In this study, an interregional input-output model was applied to quantitatively estimate the embodied CO2 transfer between Jing-Jin-Ji region and other region in China using China’s interregional input-output data in 2010. The results indicated that there was a significant difference in the production-based CO2 emissions in China, and furthermore, the Jing-Jin-Ji region and its surrounding regions were the main regions of the production-based CO2 emissions in China. Hebei Province exported a large amount of embodied CO2 to meet the investment, consumption, and export demands of Beijing and Tianjin. The Jing-Jin-Ji regions exported a great deal of embodied CO2 to the coastal provinces of southeast China and imported it from neighboring provinces. [ABSTRACT FROM AUTHOR]

PM 2.5 is the fine particle matter with the size smaller than 2.5 μm, and it is considered to be one of the atmospheric pollutants whose effects are the greatest on the public health. In recent years, the effects of PM 2.5 in Beijing are getting more and more public attention. Based on this situation, using the exposure-response relationship model, the health loss assessment model and the annual average concentration data of PM 2.5 from 2014 to 2015, this paper quantified the public health effect losses of PM 2.5 and estimated the economic loss utilizing the willingness to pay in Beijing. The results demonstrated that in the four different concentrations baseline levels and the three different categories, the health economic loss caused by PM 2.5 pollution was 4.83–6.63 billion yuan in 2014 and 4.32–6.32 billion yuan in 2015 in Beijing. And, the loss of the total death, cardiovascular disease death and respiratory system disease death accounted for the major loss in all kinds of health effect loss. From the results, it could also be seen that the number of people damaged by PM 2.5 and the economic loss were falling from 2014 to 2015, which showed the worsening trend of air quality began to reverse in Beijing. It had played a positive role for Beijing to continue to control atmospheric pollution. However, because the annual concentration of PM 2.5 in Beijing is still higher than four annual mean concentration baseline levels, it still exists a larger threat to the health effects. [ABSTRACT FROM AUTHOR]

This study applied the interprovincial input-output model to calculate the carbon footprint of 30 provinces and the interprovincial transfer of embodied CO emissions in China. The results indicate that there are significant differences in the carbon footprints and the per capita carbon footprints of these provinces. The provinces with large carbon footprints are the economically developed provinces, while those with high per capita carbon footprints are the provinces that include central cities, such as Beijing and Shanghai, and the areas with abundant resources and heavy industry bases. The spatial transfer of embodied CO emissions in China occurs as follows: from economically developed provinces to provinces with rich resources and intensive heavy industries, from southern provinces to northern provinces, and from coastal provinces to inland provinces. To meet the demand of the coastal, southern, and economically developed provinces, the inland, northern provinces have assumed responsibility for most CO emissions. Thus, the domain decomposition of the responsibility for the reduction of CO emissions in China must take into account the spatial transfer of embodied CO emissions. The responsibility of producers and consumers should be integrated when establishing the indexes for the reduction of CO emissions in the various provinces of China. To design a comprehensive burden-sharing scheme for CO emissions, especially with respect to the carbon tax, the consumer principle needs to be considered; coastal provinces such as Jiangsu, Zhejiang, and Guangdong and southern economically developed provinces are obliged to assume greater responsibility for the reduction of CO emissions. [ABSTRACT FROM AUTHOR]

Air deterioration caused by pollution has harmed public health. The existing studies on the economic loss caused by a variety of air pollutants in multiple cities are lacking. To understand the effect of different pollutants on public health and to provide the basis of the environmental governance for governments, based on the dose-response relation and the willingness to pay, this paper used the latest available data of the inhalable particulate matter (PM) and sulphur dioxide (SO) from January 2015 to June 2015 in 74 cities by establishing the lowest and the highest limit scenarios. The results show that (1) in the lowest and highest limit scenario, the health-related economic loss caused by PM and SO represented 1.63 and 2.32 % of the GDP, respectively; (2) For a single city, in the lowest and the highest limit scenarios, the highest economic loss of the public health effect caused by PM and SO was observed in Chongqing; the highest economic loss of the public health effect per capita occurred in Hebei Baoding. The highest proportion of the health-related economic loss accounting for GDP was found in Hebei Xingtai. The main reason is that the terrain conditions are not conducive to the spread of air pollutants in Chongqing, Baoding and Xingtai, and the three cities are typical heavy industrial cities that are based on coal resources. Therefore, this paper proposes to improve the energy structure, use the advanced production process, reasonably control the urban population growth, and adopt the emissions trading system in order to reduce the economic loss caused by the effects of air pollution on public health. [ABSTRACT FROM AUTHOR]

Commodity trade between regions implies a large amount of energy transfer. As an important economic growth pole of China, the Jing-Jin-Ji area (Beijing-Tianjin-Hebei) is also one of the areas with the largest energy consumption in China. Moreover, the primary energy consumer goods in this area are fossil fuels, such as coal. This has led to serious air pollution in the area. Therefore, the reduction of energy consumption under the premise of maintaining sustained economic growth is an important task of the Jing-Jin-Ji area. In this study, an interprovincial input-output model was applied to quantitatively estimate the embodied energy transfer between Jing-Jin-Ji area and other provinces in China. The results indicated that the Metal and nonmetal mineral processing industry and the Electrical, gas and water industry in the Jing-Jin-Ji area exported a large amount of embodied energy to the Yangtze River Delta and the Pearl River Delta. However, the embodied energy export of the Jing-Jin-Ji area mainly exported by Hebei province. Beijing and Tianjin even have some net import of embodied energy. The embodied energy transfer between Tianjin, Hebei and other provinces was mainly driven by investment, while the main media of embodied energy transfer between Beijing and other provinces was consumption. Therefore, we suggest that the Jing-Jin-Ji area should further increase the degree of dependence on other provinces' energy-intensive products and reduce the export of energy-intensive products. In addition, there should be difference in the energy and industrial policies among Beijing, Tianjin and Hebei, and the problems of high energy consumption and high proportion of heavy industry in Hebei should be first resolved. [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]

During the development of the Middle Yellow River Urban Agglomerations (Central Plains Urban Agglomeration (CPUA), Guanzhong Plains Urban Agglomeration (GPUA), Jinzhong Urban Agglomeration (JZUA)), "urban disease" was prominent, and the concentrations of PM 2.5 in many cities in the region were ultra-high, resulting in serious air pollution. Taking into account the hysteresis of PM 2.5 pollution, from the point of view of socioeconomic, this paper uses the geographically and temporally weighted regression (GTWR) model and the latest available data of PM 2.5 concentration and socio-economic factors of the urban agglomeration in the Middle Yellow River from 2015 to 2018 to explore the spatiotemporal heterogeneity of PM 2.5 concentrations and the driving factors. The results indicate that: (1) The spatiotemporal distribution profiles of PM 2.5 concentrations in urban agglomerations in the middle Yellow River have certain regularities. It reveals a U-shaped pattern in each year, and the seasonal changes show the spring and winter are high and summer and autumn are low. (2) PM 2.5 in the Middle Yellow River Urban Agglomerations has obvious spatial agglomeration characteristics. (3) There are obvious spatiotemporal heterogeneity in the change of the intensity and direction of each driving factor. Based on the above analysis, three suggestions were put forward: (1) Increase the proportion of new energy in heating. (2) Establish regional joint prevention and control and long-term incentive mechanisms. (3) Different urban agglomerations should formulate differentiated PM 2.5 emission reduction strategies. [ABSTRACT FROM AUTHOR]