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Photocatalysis as a green and efficient pollutant degradation technology, displaying great potential in environmental purification. By one step hydrothermal method in this study, flower-like Bi 2 WO 6 with a large specific surface area was obtained, and the preparation of biomass carbon modified Bi 2 WO 6 with sorghum stalks was also proposed to construct a series (BWO/x%C). The composites were effectively characterized in terms of several aspects of structure and properties. With the dyes rhodamine B and diclofenac as typical organic pollutants, the photocatalytic activity of the proposed photocatalysts was evaluated. Under visible light irradiation, the removal of rhodamine B by BWO/4%C reached 97.27% within 50 min and that of diclofenac reached 96.19% within 120 min, which were significantly favorable to single-phase Bi 2 WO 6. The superiority of this system BWO/4%C is mainly due to its large specific surface area (33.51 m2/g), which provides additional reaction sites for contaminants. The UV–Vis DRS findings indicated that the modified biomass C reduced the band gap of Bi 2 WO 6 and improved the availability of Bi 2 WO 6 to visible light, and the separation efficiency of the electron and hole pairs of Bi 2 WO 6 was improved due to the strong electron transfer ability of biomass C. The analyzing results of free radical trapping experiments confirmed that h+ and •O 2 - are the main active substances involved in the photodegradation reaction. The results of five cycle experiments exhibited that the degradation rate of rhodamine B still reached higher than 90%, confirming the good stability of the prepared material. Finally, a feasible degradation mechanism was proposed for the degradation of rhodamine B and diclofenac. It offers new ideas for the development of composite nano-photocatalytic materials targeting difficult-to-degrade organic pollutants. [ABSTRACT FROM AUTHOR]

• Water quality induced water scarcity is incorporated. • Quantify local and virtual water scarcity risks for YRB through MRIO. • Pollution reduces the resilience of supply chains in the YRB to water scarcity. Water scarcity poses economic risks and affects the quality of national and regional development. The risk of local water scarcity can be conveyed to downstream economies through interregional trade for potential economic losses. However, most previous water scarcity studies focused on the availability of adequate freshwater supplies, ignoring the economic losses associated with inadequate water quality. With the aggravation of water pollution, water scarcity caused by inadequate water quality is exacerbated. This study incorporates water quality and quantity into local water scarcity risk assessments, revealing the virtual water scarcity risks and transmission pathways of the cities and sectors in the Yellow River Basin (YRB) in China. The results show that the number of cities with severe water stress increases from 13 to 53 when water quality is considered. Virtual water scarcity risks transfer from upstream cities to downstream cities in Henan and Shandong Provinces, and high-risk cities in Henan can be identified by considering water quality. Key transmission pathways for intersectoral transfer are identified. This study validates the significance of considering water quality factors in water scarcity risk assessments and provides a perspective for policy decisions to mitigate water scarcity and thus ensure water security in the YRB. [ABSTRACT FROM AUTHOR]

The Strategies of Reviving the Old Industrial Bases provide opportunities for low-carbon transition in Northeast China, which is one of the earliest regions to industrialize and the largest rustbelt in China, but study on the impacts of its socioeconomic factors on CO 2 emissions is still in short, though it is essential for guiding the pathways to achieve low-carbon socioeconomic transition. We adopted the structural decomposition analysis (SDA) to identify the main contributors to emissions increase in Heilongjiang province during 2002–2012, which is the heartland of Northeast revitalization. The results show that the increase in CO 2 emissions was mainly driven by growth in per-capita final demand, which generated 203.8 Mt (153.6%) upstream CO 2 emissions between 2002 and 2012. Changes in production structure and final demand structure had smaller impacts on CO 2 emissions increase (36.1 Mt and 27.0 Mt). However, the positive influences were largely overwhelmed by change in emission intensity, which avoided 135.4 Mt (−102%) CO 2 emissions. Therefore, appropriate measures related to energy structure optimization and efficiency improvement should be implemented. Especially, increasing the proportion of wind, solar and biomass energy in Heilongjiang, where renewable energy is abundant, would reduce the CO 2 emissions significantly. In addition, domestic export took the lead position in driving the CO 2 emissions in Heilongjiang, accounting for 37.6%–43.1% annual emissions between 2002 and 2012. Thus, some financial instrument, such as tax relief for less carbon intensive exports could be adopted to prompt upstream suppliers to decarbonize their production processes. Unlabelled Image • Structural decomposition analysis (SDA) is used to identify the main contributors of CO 2 emissions increase during 2002-2012 in Heilongjiang province. • Growth in per-capita final demand was main driver for CO 2 emissions Increase. • The positive influences were largely overwhelmed by change in emission intensity, which avoided 135.4 Mt (-102%) CO 2 emissions. • Domestic export took the lead position in driving the CO2 emissions in Heilongjiang, accounting for 37.6%-43.1% annual emissions between 2002-2012. [ABSTRACT FROM AUTHOR]

As the capital economic circle, Jing-Jin-Ji faces a serious shortage of land resource and a deterioration of the ecological environment owing to economic development. Alleviating ecological pressure has become a focus issue in the region. Extant studies have mainly focused on the ecological carrying capacity of individual regions, while it is lacking for the research on the ecological dependency relationship between the various regions. In this article, the authors analyze the ecological footprint transfer between Jing-Jin-Ji and China's other provinces by using multi-regional input–output method. According to the results, the metal/non-metallic mineral and agriculture industries in Jing-Jin-Ji exported a large amount of ecological footprint to the eastern coastal areas. However, the ecological footprint outflow of Jing-Jin-Ji can be attributed to Hebei province, with Beijing and Tianjin showing some net inflow. In interior Jing-Jin-Ji, Hebei has transferred large ecological footprint to Beijing and Tianjin, but Hebei has not achieved the equivalent economic benefits. Thus, Jing-Jin-Ji should further increase its dependence on ecological resources in other provinces; Hebei should especially reduce metal and agriculture product exports. Meanwhile, an ecological compensation system should be established to use the funds provided by Beijing and Tianjin to support the transformation of economic growth mode in Hebei. [ABSTRACT FROM AUTHOR]

Abstract China is under great pressure to reduce CO 2 emissions (CEs). Meanwhile, China's economy is more reliant on domestic consumption (particularly household consumption) than on exports. So, to achieve large-scale CEs' reduction for China, there is urgent need to study the CO 2 emissions from household consumption (CEs HC). Based on the environmentally extended Multiregional Input-Output model, this paper not only calculates both the direct CO 2 emissions from household consumption (DCEs-CH) and the indirect CO 2 emissions from household consumption （ICEs CH）at the provincial level in China, but also splits ICEs CH into domestic and foreign CEs. The findings of this paper are as follows: (1) between 2002 and 2012, CEs HC in China increased significantly, by 2.27 times, from 1306.17 Mt in 2002 to 2971.01 Mt in 2012. The CO 2 emissions from urban household consumption (CEs UHC) in China accounted for 75% of the total CEs HC in 2012. (2) The per capita CEs HC in China increased from 1.02 t/person in 2002 to 2.19 t/person in 2012. There was a prominent disparity in the total CEs and per capital CEs HC among the provinces in China. The per capita CEs HC were larger in provinces with a higher level of urbanization and higher per capita income. (3) The large-scale interprovincial transfer of ICEs caused by household consumption mainly occurred either within the Yangtze River Delta, the Pearl River Delta, and the Jing-Jin-Ji region or among these three regions. In addition, to service the household consumption, the energy-abundant provinces transferred a considerable amount of CEs to eastern coastal provinces. To achieve CEs' reduction, consumption-based strategies, such as lifestyle changes, should be required in parallel with strategies to reduce emission intensities on the producer side in China. Urban households should take more responsibility for the reduction of CEs in China. The interprovincial "Carbon leakage" should be taken into account when making policies for CEs' reduction in China. [ABSTRACT FROM AUTHOR]

The cities' area accounts for only 2% of the world's surface area, but the cities' population accounts for 50% of the total population and produces more than 80% of the total CO 2 emissions. So the cities have a key position in solving the global challenge of climate change. In order to estimate the impacts of city size change and industrial structure change on CO 2 emissions, based on the background, the data availability of the CO 2 emissions per person, the economic scale, the size of land use, the industrial concentration degree and the industrial structure change in 50 cities in different sizes (the population size between 0.5 million and 1 million, 1–2 million, 2–4 million and more than 4 million) from 2005 to 2014, the paper studies the impact of the city size change and the industrial structure change on CO 2 emissions. The results show that the increase in the sizes of cities can bring in the rise of CO 2 emissions and the impacts on CO 2 emissions in different city sizes are significant. Meanwhile, both industrial agglomeration and industrial structure change have a significant role in the CO 2 emissions reduction. The paper finds out that (1) the medium-sized cities produce relatively fewer CO 2 emissions than the smaller cities and the bigger cities. As smaller cities are not conducive to save land and also can't play the externalities of industry agglomeration, leading to the reduction in energy efficiency. Bigger cities may produce all kinds of city diseases. The medium-sized cities with the population of 1 million and 2 million can have relatively higher energy efficiency and fewer city diseases, which may produce lower CO 2 emissions. (2) economic growth can increase CO 2 emissions.(3) the industrial structure change has effects on CO 2 emissions, and CO 2 emissions from the secondary industry are the largest in the three industries. So, the government should reasonably give priority development of medium-sized cities with the population between 1 million and 2 million, and adjust energy consumption structure and the industrial structure to give priority to the tertiary industry to reduce CO 2 emissions in China's cities. [ABSTRACT FROM AUTHOR]

To mitigate climate change, many studies have been conducted to identify the determinants of CO 2 emissions. However, a consensus has not been reached yet on the issue because past work has often not considered the unobserved individual heterogeneity across countries. Therefore, this study revisits the environment-energy-growth nexus by employing a panel quantile regression to incorporate the effects of renewable energy consumption and technological innovation within the research background of global 30 countries over the period 1980–2014. The advantage of this method is considering the distributional heterogeneity to provide a detailed description of linkage between the CO 2 emissions and driving factors at different emissions levels. The results show that the effects of determinants on CO 2 emissions are heterogeneous. For high-emissions countries, the function of renewable energy consumption is limited in reducing CO 2 emissions due to the smaller proportion of renewable energy use. Moreover, technological innovation greatly affects countries with relatively higher CO 2 emissions. Therefore, one option is to financially support and apply technological innovations to generate renewable energy at lower costs as well as increase energy efficiency. Moreover, transforming the economic growth mode is helpful to transfer from non-renewable to renewable sources of energy to meet energy demand. [ABSTRACT FROM AUTHOR]

An ecological footprint (EF) is a vital indicator to measure the sustainable development of a region. Analysis of the driving factors of EFs can facilitate the sustainable development of the population, resources and environment. As the capital city of China, Beijing has a unique municipal economic structure and faces greater urban ecosystem challenges than other cities. This paper used an input-output model to calculate an EF on the basis of the original decomposition analysis model. We consider the population effect, the economic effect, the industrial structure effect, and the footprint intensity to explore the factors driving the changes in the EF of 39 sectors in Beijing. In addition, 9 screened sectors with more significant effects on the change in the EF were analyzed. The results indicated that (1) among the 39 sectors, the crucial factors that drive EF growth are the economic effect, the population effect, and footprint intensity, with contribution rates to total EF of 59.4%, 31.0%, and 7.7%, respectively, while the effect of the industrial structure tends to be 0; and (2) among the 9 screened industries, the population effect and the economic effect promote the growth of the EF of 9 sectors. The reduction in the EF of the primary industry sector is due to the impact of industrial structure effect and footprint intensity. The four effects all promote an increase of the EF of the transportation, warehousing and postal services. These results can be used to propose suggestions for the future development of sectors in Beijing. [ABSTRACT FROM AUTHOR]

Energy consumption has promoted a continuous development for economy and society, but it has also brought serious environmental pollution problems which endanger public health. In order to focus on the impact of PM 2.5 emissions from future energy consumption on human health better, this paper estimated PM 2.5 emissions caused by energy consumption in Beijing in 2020,2025 and 2030 by setting the baseline scenario, emission reduction scenario, intensified emission reduction scenario and the data of the base year 2015, which will be 162.6 thousand tons, 195.9 thousand tons and 235.9 thousand tons in the baseline scenario, 143.1 thousand tons, 151.8 thousand tons and 160.9 thousand tons under the emission reduction scenario, and 132.6 thousand tons, 130.2 thousand tons, 127.9 thousand tons under the intensified emission reduction scenario. Finally the paper measured the number of different health effect ends caused by PM 2.5 emissions and the economic loss. The results showed that there was a rapid increase, a mild increase and a moderate decrease in energy consumption and PM 2.5 emissions in Beijing under the three scenarios from 2015 to 2030. Besides, under the same scenario and the same concentration level baseline, the number of the internal medicine (over the age of 15) was the largest, and the economic loss caused by total death rate was the biggest. This article provides suggestions to reduce PM 2.5 emissions and public health effect ends for Beijing in the future. [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]

Accurate prediction of the ecological footprint (EF), an effective indicator for measuring urban sustainable development, enables better protection of urban ecosystems and alleviates discrepancies in urban development, resource utilization, and environmental protection. In contrast to previous research using general methods, we introduce the support vector machine (SVM) method. A novel model with improved prediction accuracy based on SVM is proposed, and we deploy this method to predict the EF of Beijing between 2016 and 2020. First, we calculate the EF of Beijing between 1996 and 2015 and screen out the 6 dominant indicators of EF changes using partial least squares (PLS). Second, based on 2014 and 2015 EF data, we compare the prediction accuracy of the back propagation neural network (BPNN) with the SVM using the 6 indicators as inputs and EF as the output, which then allows us to predict the year 2020 EF in Beijing. The results demonstrate that (1) the relative error rates between the prediction value and the actual value using the two models are 2% and 1% in 2014 and 3% and 0.53% in 2015, respectively, and the fact that the standard deviation of the SVM approaches zero demonstrates its higher prediction accuracy and stability compared to the BPNN; and (2) the EF of Beijing almost doubled to 8984 ten thousand acres from 1996 to 2015 and is predicted to increase to up to 14,206 ten thousand acres by 2020. Based on our prediction model, we provide science-based suggestions for the future development of Beijing. [ABSTRACT FROM AUTHOR]

This study used panel data for 28 provinces and municipalities in China from 2003 to 2014 to investigate the relationship between land urbanization and landslides by building panel models for a national sample and subsamples from the three regions of China and studied the problems of landslide prevention measures based on the relationship. The results showed that 1) at the national level, the percentage of built-up area and road density are respectively negative and positive for landslides. 2) At the regional level, the improvement of landslide prevention measures with increasing economic development only appears in built-up areas. The percentage of built-up areas increases the number of landslides in the western region and decreases the number in the central and eastern regions; the degree of decrease in the eastern region is larger than in the central region. Road density increases the number of landslides in each region, and the degree increases gradually from the west to the east. 3) The effect of landslide prevention funding is not obvious. Although the amount of landslide prevention funds decreases the number of landslides at the national level, the degree of increase is too small. Except in the central region, the amount of landslide prevention funding did not decrease the number of landslides effectively in the western and eastern regions. We propose a series of policy implications based on these test results that may help to improve landslide prevention measures. [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]

Under conditions of policy incentives and energy transition, an investment is considered to become green if greater investment would support a reduction in the proportion of total energy consumption involving coal use. However, few empirical studies have attempted to investigate green investment in China at the sector level. Using panel data for 5 sectors in China between 2003 and 2012, we tested the relationships between investment and different factors, including carbon dioxide (CO 2 ) emission reduction policies and energy structure, by building panel models for the national sample. The results demonstrated that investment did not become green and mainly relied on gross domestic product (GDP) during the 2003–2012 period in China. Although investments in the industry sector and the commerce and services sector were weakened by the CO 2 emission regulations, Chinese climate policies had no effect on investment in the agriculture sector or the construction sector and had a positive effect on investment in the transportation sector. Moreover, the proportion of total energy consumption involving coal use had no effect on investment in the construction sector or the commerce and services sector but had a negative effect on investments in the agriculture sector and the transportation sector. Investment in the industry sector showed strong viscosity to low-price coal consumption. Finally, we propose a series of policy implications based on the test results, which may guide green investment. [ABSTRACT FROM AUTHOR]

The power industry produces nearly 40% of China’s carbon emission, thus, this sector should be regarded as priority for carbon emission reduction. Identifying the unevenness of regional development may be crucial for increasing the carbon emission efficiency of power plants. This work evaluates the carbon emission efficiency using the Undesirable-SBM (slacks-based measure) model and data from China’s power industry in 30 provinces from 2003 to 2014. Moreover, the global Malmquist index, which consists of efficiency changes (ECs) and technical changes (TCs), is used to determine the driving factors of these changes. Finally, a spatial autocorrelation analysis that is based on Moran’s index is performed to confirm the non-equilibrium spatial distribution of the carbon emission efficiency for the power industry. The main findings are as follows: (1) compared to economically underdeveloped provinces, the wealthy eastern coastal provinces exhibit higher carbon emission efficiency; (2) the positive effects of TCs on the efficiency changes are stronger, moreover, the provinces with lower efficiency are more likely to achieve greater improvements; and (3) significant spatial correlations exist among the power sectors of the 30 provinces in terms of carbon emission efficiency; the eastern regions have relatively high efficiency and tend to have a positive spillover effect on the neighboring provinces. Therefore, technological cooperation between various regions is beneficial to ameliorate carbon emission efficiency. Finally, policy implications are provided to address such spatial discrepancies. [ABSTRACT FROM AUTHOR]

The transportation sector is one of the major driving forces of carbon emissions. Identifying the factors that affect CO 2 emissions from the transportation sector is important to build a low-carbon city. Most existing research focuses on the total effect of factors on CO 2 emissions while the indirect influence is also the driving force of CO 2 emissions. Additionally, identifying the causal relationship between variables is helpful to study the mutual acting mechanism. Therefore, this paper uses the path analysis model to estimate the direct, indirect and total influences of driving factors on transportation CO 2 emissions in Beijing and investigate the causality relationships between variables. The results show that reducing energy intensity and transportation intensity are the key factors in controlling the increase of transportation-related CO 2 emissions. Population has the greatest positive impact on CO 2 emissions because an increasing population is leading to growth in energy consumption and the number of motor vehicles. However, population could indirectly affect the energy intensity and transportation intensity to reduce carbon emissions. Moreover, motor vehicles increase CO 2 emissions due to the growth in private car population and its low energy efficiency. And, the change in the economic growth pattern somewhat inhibits the growth rate of CO 2 emissions by reducing the energy intensity and transportation intensity indirectly. To further suppress the growth of transportation carbon emissions, the following steps should be taken: appropriately improve the quality of population, control the scale of motor vehicles, develop and promote clean energy, and reduce traffic energy intensity and transportation intensity. [ABSTRACT FROM AUTHOR]

Urban resilience refers to the resilience of a city after external shocks and its ability to prevent, respond and recover from extreme disasters. In 2015, the United Nations Sustainable Development Goals (UN-SDGs) made urban resilience one of the global sustainable development goals. Additional information regarding urban resilience can have a positive effect on the transformation toward a more sustainable city. For China, the development and research of urban resilience is still in its infancy, and there is insufficient experience in implementing key tasks and project management. This research studies 30 of China's provincial capital cities, constructs an evaluation framework and system for urban resilience, measures the resilience of Chinese capital cities from 1998 to 2017, and explores their temporal and spatial evolution based on econometric models. The results show that (1) the gap in the level of resilience between provincial capital cities have been increasing over time; (2) the gap in the level of provincial capital cities in Northeast, Southwest, Northwest and Yangtze River Delta regions in China show significant "convergence"; and (3) cities located in economically developed areas such as the Beijing-Tianjin-Hebei region, the Yangtze River Delta and the Pearl River Delta, namely, Beijing, Shanghai, Hangzhou and Guangzhou, exhibit higher resilience levels. In contrast, Lanzhou and Yinchuan in the northwest and Harbin and Changchun in the northeast show lower levels of resilience. Finally, relevant policy recommendations are proposed at the national, regional and city levels. • An urban resilience (UR) evaluation framework and quantitative indicator systems are proposed. • In terms of temporal evolution, the UR gap of 30 provincial cities gradually increased between 1998 and 2017. • In terms of space, more provincial capital cities show a significant positive spatial correlation. • China has formed the representative capital cities of Beijing, Shanghai, Hangzhou and Guangzhou with a high level of UR. • Bilateral or multilateral exchange mechanisms for urban resilience should be established.". [ABSTRACT FROM AUTHOR]

• Information entropy method and principal component analysis is applied. • Indicators' eigenvalues are three-dimensionally displayed. • WRCC in Hubei Province from 2008 to 2020 has been unstable in various years. • The impact of farmland irrigation on WRCC is greater than that of the other factors. • Economically developed areas in the province have higher WRCC. Scientific evaluation of water resources carrying capacity (WRCC) is the basis for implementing water resources conservation measures. Hubei Province, with the longest runoff mileage of the Yangtze River, the reservoir area of the Three Gorges Project and the core water source area of the South-to-North Water Diversion Middle Line Project, bears great responsibility for ecology conservation in China. How to evaluate WRCC in Hubei Province has become significant. This study created a WRCC evaluation method based on information entropy method, principal component analysis and Spearman's rank correlation coefficient to assess the WRCC in Hubei Province from 2008 to 2020. The available indicators were collected and summarized from the aspects of economy, population, resources and environment. By three-dimensionally displaying different indicators' eigenvalues of principal component analysis, the major indicators can be screened. Subsequently, information entropy method was applied to estimate WRCC in Hubei Province and Spearman's rank correlation was examined between WRCC and relevant factors. The results indicated that the WRCC in Hubei Province had been unstable in the range of 0.065 to 0.088 with the annual growth rate of 0.096%. It had a few peaks in year 2010, 2012, 2014, 2015 and 2016 while it decreased dramatically in year 2009, 2011, 2013, 2018 and 2020. Specifically, agricultural water use is more influential than industrial water use. The annual decline at 1.865% in agricultural water use is much smaller than the annual decline at 10.471% in industrial water use. Economically developed areas in the province have higher WRCC while economically backward areas have weaker WRCC. [ABSTRACT FROM AUTHOR]

In the process of rapid development and urbanization in Beijing, identifying the potential factors of carbon emissions in the transportation sector is an important prerequisite to controlling carbon emissions. Based on the expanded Kaya identity, we built a multivariate generalized Fisher index (GFI) decomposition model to measure the influence of the energy structure, energy intensity, output value of per unit traffic turnover, transportation intensity, economic growth and population size on carbon emissions from 1995 to 2012 in the transportation sector of Beijing. Compared to most methods used in previous studies, the GFI model possesses the advantage of eliminating decomposition residuals, which enables it to display better decomposition characteristics (Ang et al., 2004). The results show: (i) The primary positive drivers of carbon emissions in the transportation sector include the economic growth, energy intensity and population size. The cumulative contribution of economic growth to transportation carbon emissions reaches 334.5%. (ii) The negative drivers are the transportation intensity and energy structure, while the transportation intensity is the main factor that restrains transportation carbon emissions. The energy structure displays a certain inhibition effect, but its inhibition is not obvious. (iii) The contribution rate of the output value of per unit traffic turnover on transportation carbon emissions appears as a flat “M”. To suppress the growth of carbon emissions in transportation further, the government of Beijing should take the measures of promoting the development of new energy vehicles, limiting private vehicles’ increase and promoting public transportation, evacuating non-core functions of Beijing and continuingly controlling population size. [ABSTRACT FROM AUTHOR]

Coal remains the world's most important energy resource and it reached the highest share of global primary energy consumption (29.9%) in 2012, which was the highest level since 1970 according to the statistics of the British Petroleum public limited company. As to the relationship between coal consumption and economic growth, many studies have been done and there have not been consistent results gained yet in previous research. To further explore the relationship and look for more reliable support for policy making in different countries, with coal price as a third variable, the panel data model using a common source of data from 2000 through 2010 is applied in this paper in the tests of unit root, panel cointegration and Granger causality among the six main coal consumption countries, namely China, the United States of America, India, Germany, Russia and Japan. The tests show: (1) Bidirectional causal relationships between coal consumption and economic growth exist in Germany, Russia and Japan. (2) Only a unidirectional causality from economic growth to coal consumption exists in China. (3) There are no causal relationships between coal consumption and economic growth in USA and India. These coincident results with previous research further indicate that each country should form their own coal consuming policies according to their own situations. [ABSTRACT FROM AUTHOR]

It is not clear whether there are differences in the risk factors for nonsuicidal self-injury (NSSI) between children and early adolescents. Clarifying this question is crucial for identifying actionable prevention strategies for NSSI in these two age groups. The study, comprising 8611 children and early adolescents (4409 (51.2%) children, 4202 (48.8%) early adolescents), was based on the baseline data of the Chengdu Positive Child Development (CPCD) in China. NSSI behaviours, emotional and behavioural problems and family environment were assessed and obtained via self-reports and parent reports. Overall, 2520 (29.26%) participants reported having ever engaged in NSSI. There was a higher lifetime NSSI rate in males than in females during childhood, contrasting with higher NSSI rates in females than in males during early adolescence. Furthermore, NSSI shared similar risk factors, including major family conflict and poor relationships with caregivers, in both age groups. Specifically, in children, the risk of NSSI increased along with thought and attention problems (OR, 95% CI: 1.194, 1.106–1.288 and 1.114, 1.028–1.207, respectively), whereas in early adolescents, it increased with anxiety and depressive problems (OR, 95% CI: 1.259, 1.116–1.422). The findings suggested the need for difference in preventive strategies for NSSI in the two age groups. It may be more efficacious to screen for NSSI in children with thought and attention problems and in early adolescents with anxiety and depressive problems. • This study found that the lifetime prevalence of NSSI in children and early adolescents was 29.26%. • The NSSI group had a significantly longer screen media use time than the non-self-harm group. • The poor family environment was associated with NSSI in both children and early adolescents. • In children, the risk of NSSI increased with thought and attention problems. • In early adolescents, the risk of NSSI increased with anxiety and depressive problems. [ABSTRACT FROM AUTHOR]

Abstract: Concerning food security, China has launched non-grain fuel ethanol projects with potential land. However, there are concerns and facts, such as feedstock price rise, regarding its implications on quantity of food supply and food price. The study aims to better understand the impacts of expanding non-grain fuel ethanol on food price, supply and consumption using a CGE (computable general equilibrium) model. The investigation is divided into two scenarios, no supply of potential land and supply of potential land. The results show that: an increase in the fuel ethanol production raises food prices under both scenarios; and food supply and consumption can be ensured when there is a supply of potential land. Also, the simulated results predict adequate and quality potential land supply is one of the most important aspects to ensure food security in China. In addition, financial and trade policy implications are proposed. [Copyright &y& Elsevier]

As an important grain production base in China, the Yellow River basin (YRB) plays an important role in China's economic development and ecological security. However, with increasing agricultural water environmental problems and deteriorating water quality, the agricultural water situation in the YRB is grim. Under the background of comprehensively promoting the high-quality development of the YRB, improving agricultural water efficiency can reduce the constraint effect of insufficient water resources on agriculture and improve the water ecological environment, which will help achieve coordinated social, economic and environmental development. In this study, agricultural water efficiency of nine provinces in the YRB from 2008 to 2017 was measured by the super-efficient slack-based measured Data Envelopment Analysis (SBM-DEA) method with unexpected outputs, spatial autocorrelation analysis and the Malmquist index, and the key influencing factors were identified by the spatial Tobit regression model. The results showed that the agricultural water efficiency of nine provinces in the YRB was increasing, with large differences among provinces and little spatial correlation, presenting a spatial distribution with the lower reaches higher than the middle reaches and the middle reaches higher than the upper reaches; the change in the Malmquist index of agricultural water showed an increasing trend, which was mainly determined by the technical progress. Additionally, the economic development level and water resource endowment had positive effects on agricultural water efficiency, while government expenditure and urbanization level had significant negative correlation with agricultural water efficiency. [ABSTRACT FROM AUTHOR]

• Studying on dynamic ecological footprint (EF) in Jing-Jin-Ji from 1996 to 2020. • Six impact factors for driving EF is decomposed. • The EF in Jing-Jin-Ji will increase of 10% from 2015 to 2020. The ecological footprint (EF) describes the complex relationship between the eco-environment and economic development. EF dynamics can better reflect the appropriation of natural resources in various countries or regions compared with previous studies. This paper identified six dominant factors including population, per capita GDP, three major industrial added values, and energy consumption using grey correlation model for the EF changes of the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 1996 to 2015. Then, we predicted the EF of the Jing-Jin-Ji region from 2016 to 2020 based on support vector machine model. (1) Since 1996, the EF and per capita EF of the overall Jing-Jin-Ji region have increased, of which those of Hebei Province and Tianjin increased while Beijing decreased. (2) Energy consumption dominated the EF of Jing-Jin-Ji region. The population of Beijing had a high correlation coefficient of 0.735 with the local EF, and the degree of correlation between the EF and per capita GDP was 0.812 in Hebei Province. In Tianjin, the added value of the tertiary industry was closely correlated with its EF, with a correlation coefficient of 0.741. (3) The EF of the Jing-Jin-Ji region will reach 778.30 million hectares by 2020, an increase of 10% compared with the value in 2015. Finally, suggestions for the development of industrial structure and energy consumption are listed for the sustainable development of Jing-Jin-Ji region. [ABSTRACT FROM AUTHOR]

• Provincial CO 2 emissions of China are investigated from different perspectives. • Embodied emission flows driven by final demands and primary inputs are quantified. • Income-based emissions are relatively higher in energy resource-abundant province. • Tertiary industries are the major contributors to China's income-based emissions. Emission accounting can help to identify main CO 2 emitters and inform emission mitigation policymaking. Previous studies have proved that the application of different accounting principles results in different emission levels, thus bring different policy implications, while the emissions enabled by primary inputs (or income-based emission) have been overlooked in studies for carbon mitigation in China. Understanding the role of primary inputs in CO 2 emissions is a prerequisite to create efficient supply-side mitigation policies. Here, we conduct a quantitative study of China's provincial production-, consumption-, and income-based CO 2 emissions in a unified multi-regional input-output analysis framework. The results are compared from the three perspectives for 30 provinces in China to help the government identify the main policy targets from production, demand, and supply sides. We found that 64% and 35% of China's emissions are transferred among provinces driven by final demands and primary inputs, respectively. Mitigation policies in heavily industrialized provinces, such as Hebei, Liaoning, and Henan, where the production-based emissions are higher than the consumption- and income-based emissions, should be focused on production side. Similarly, policies in eastern coastal developed provinces and resource-abundant provinces should be focused on demand- and supply-side, respectively. Moreover, we found that tertiary industries, which previous studies generally regard as low-carbon industries, are the major contributors to China's income-based CO 2 emissions with a total of 2026 Mt or 31% of China's total income-based CO 2 emissions. Thus, expanding tertiary industries without reducing their industrial linkages to carbon-intensive industries is not conducive to China's emission reduction. [ABSTRACT FROM AUTHOR]

China's climate and environmental problems remained prominent. Energy transition was the key to improve climate and pollution, and it was at a critical period. Previous studies mostly focused on the impact of climate policies on the reduction of air pollutants and the environmental health impacts of individual sector or pathway policies. Thus, focusing on the synergistic abatement of CO 2 and air pollutants and health effects of energy transition in carbon neutrality was significant for building beautiful and healthy China. LEAP-HEA model was constructed to project energy demand, CO 2 and air pollutants, and assess energy transition's environmental health effects from 2020 to 2060. LMDI model was adopted to decompose the abatement contribution. Synergistic assessment index was used to estimate the coordinated abatement effects. Cost-effectiveness of energy transition and regional heterogeneity of PM 2.5 toxicity were not considered. The results indicated: (1) energy demand would decrease and structure would be optimized significantly under deep energy transition scenario. (2) Deep energy transition could promote earlier realization of carbon neutrality. It would contribute significantly to CO 2 and air pollutants abatement with strong synergistic effects. (3) Energy transition would reduce health risks with large economic benefits. Recommendations were proposed based on the conclusions. The framework of LEAP-HEA model. [Display omitted] • The synergistic abatement and health effect of energy transition were explored. • The models of LEAP, LMDI, exposure-response and the data of 2020 were used. • Energy transition would have significant synergistic abatement and health effect. • The paper has significance for promoting carbon neutrality and healthy China. [ABSTRACT FROM AUTHOR]

The chemical industry is one of the fundamental industries of economic development. Coordinating the relationship between economic growth and carbon emissions (EGCE) is crucial in realizing the carbon neutrality target. The Tapio model and decomposition models are used to assess the degree of decoupling within the Chinese chemical industry and identify pivotal factors impeding progress. The energy efficiency decomposition model explores the crux of failure to achieve strong decoupling. This framework further decomposed the energy intensity to uncover how technical efficiency, technological progress, and input substitution changes affect energy intensity and CO 2 emission. The paper found that the EGCE of the chemical industry were stable in a weak decoupling mode from 2009 to 2019. The reliance on technological progress to reduce energy intensity and emissions is insufficient (accounting for only 18.2%), far below the cumulative contribution from economic growth (accounting for 57.76%), which has become the crux of the strong decoupling failure. Technological advancements drive energy substitution for labour, resulting in 244.41 Mt of cumulative carbon emissions. The improper factor input allocation hinders the desired reduction in energy intensity and carbon emissions. A series of policy implications are proposed based on the insights derived from the decomposition analysis to foster sustainable practices in the chemical industry. [Display omitted] • This study focuses on the decoupling in chemical industry. • The decoupling between economic growth and carbon emissions (EGCE) is explored. • The reasons for the failure of strong decoupling from technical perspective are tested. • The decoupling between EGCE of chemical industry was in a weak decoupling mode. • Technological progress accelerated the substitution of energy for labour. [ABSTRACT FROM AUTHOR]

Carbon emissions in China have attracted increasing world attention with rapid urbanization of this country. It is critical for the government to identify the key factors causing these emissions and take controlling measures. Consistent results have not been achieved yet although some research has been conducted on the factors leading to emissions. Meanwhile, there is still considerable room to improve the methods of previous research. Index decomposition analysis (IDA) is the main method for quantifying the impact of different factors on carbon emissions. At present, the widely used forms of IDA are primarily the Laspeyres and the Divisia index methods. Compared with the Laspeyres and the majority of the Divisia index methods, the generalized Fisher index (GFI) decomposition method can eliminate the residuals and has better factor decomposition characteristics. This paper chooses Beijing as a typical example and analyzes the factors causing carbon emissions. Based on the extended Kaya identity, we built a multivariate generalized Fisher index decomposition model to measure the impacts of economic growth, population size, energy intensity and energy structure on energy-related carbon emissions from 1995 to 2012 in Beijing. The results show that the sustained growth of economic output in Beijing was the leading factor in carbon emissions. Population size had a stimulating effect on the growth of carbon emissions during this period; the pulling effect increased after 2003 and then decreased slightly after 2011 with a cumulative effect of 165.4%. Energy intensity was the primary factor restraining carbon emissions, and the inhibition effect increased yearly. The continuous optimization of the energy structure had no obvious inhibitory effect on carbon emissions. To control carbon emissions, Beijing should continue to adjust the mode of economic development and appropriately control the population size while improving energy efficiency. [ABSTRACT FROM AUTHOR]

In order to cope with climate change, environmental pollution, resource depletion and so on, energy transformation (ET) has become an urgent task for China's development. Due to the great difference in the progress of ET in different regions, it is urgent to analyze the fairness of ET. At the same time, promoting the transformation of energy equity is conducive to promoting economic diversification and achieving a healthy society. Thus, the dynamic panel data model was used to analyze the impact of ET on economy and health from 2000 to 2020 and its fairness among 31 provinces in China. The correlation coefficient between R and GDP and between R and health index was 21.331 and 35.166. The correlation coefficient between energy intensity (EI) and GDP was −3.844. It could be seen that ET had significantly promoted the improvement of economy and health. The fairness study showed that the economic equity values of ET were between 0.8-1.5 and 0.7–2 in the central and eastern regions, respectively. ET promoted economic fairness. The health equity of energy transformation in the eastern region was bigger. • The impact of energy transition on economy and health was discussed. • The fairness of energy transition was assessed. • The data of 31 provinces in the country from 2000 to 2020 were used. [ABSTRACT FROM AUTHOR]

Abstract As the largest energy consumer and CO 2 -emitting country, China is committed to achieving a low-carbon economy (LCE). This study seeks to understand the spatial evolution of China's LCE provinces and determine which sectors could promote the formation of LCE provinces. Multiregional input-output (MRIO) analysis is applied to filter the LCE provinces and the sectoral structure changes behind the LCE in China from 2002 to 2012. The result shows that approximately 30% of the provinces (i.e., Tianjin, Zhejiang, Jiangsu and Chongqing) become LCE provinces faster than other provinces from 2002 to 2012, and the location of the LCE provinces gradually shifts from coastal to inland regions after 2007. Some sectors (i.e., nonmetal mining, chemical industry and nonmetal manufacturing) gradually become LCE sectors from 2002 to 2012, and these sectors promote the formation and development of LCE provinces. On this basis, this study proposes policy implications regarding the benchmarking of sectors and a sectoral structure that can promote the formation of LCE provinces. Graphical abstract Unlabelled Image Highlights • MRIO tables from 2002 to 2012 are used to study the low-carbon spatial evolution. • REIC and RCIC are constructed to select low-carbon economy provinces. • Low-carbon economy provinces gradually shift from coastal to inland regions. • Sectoral restructuring helps achieve regional low-carbon economy transition. [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]

Water shortages and the uneven distribution of water resources restrict China’s sustainable development. The concepts of virtual water and water footprints provide a new approach to alleviate regional shortages of Chinese water resources by the inter-provincial allocation of commercial water resources. In this study, an interregional input-output model was applied to quantitatively estimate the water footprint of each province in China and to quantify the inter-provincial transfer of virtual water. The results indicated that there was considerable diversity in the water footprints of the various provinces. Provinces with larger populations and greater GDP had larger water footprints, and developed regions had higher proportions of external water footprints. From the perspective of final demand, local consumption was the main factor driving the water footprints of these provinces. From the perspective of sectoral structure, the agricultural water footprint had a larger proportion in these provinces. The transfer of virtual water in China did not occur from regions with abundant water resources to those suffering from water shortages, but it generally occurred from west to east, from inland to coastal areas, and from underdeveloped to developed regions. Many water-deficient regions also had large net virtual water exports. Water shortages in China will be alleviated by the enhancement of industrial water-use efficiency in water-deficient regions, the transfer of water-intensive industries to regions with abundant water resources, and the development of tertiary industries with low water consumption. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2) emissions are currently a hot topic of global concern. It is of great significance for reducing CO 2 emissions to fully understand the transfer pattern of CO 2 emissions among industries and the key factors affecting CO 2 emissions. This paper uses the structural path analysis model to explore deeply the main paths of inter-industry transfer of CO 2 emissions in China from 2002 to 2017 and applies the structural path decomposition model to analyze the main factors affecting CO 2 emissions in specific paths from the perspectives of CO 2 emission intensity, intermediate product input structure, final demand structure, per capita final demand, and population size. The results show that: (1) China's CO 2 emissions increased from 3500.41 million tons (Mt) in 2002 to 9475.66Mt in 2017, with an average annual growth rate of 6.86%. The growth rate of China's CO 2 emissions slowed down after 2012. (2) Non-metallic mineral industry\electricity industry\metal products industry→(intermediate sector)→investment demand and electricity industry→(intermediate sector)→consumption demand are two types of key paths that affect China's CO 2 emissions, and these paths remain basically unchanged during the study period. (3) The CO 2 emission intensity effect is the main factor in restraining the growth of emissions, and the per capita final demand effect and intermediate product structure effect are the main promoting factors. The effect of driving factors on different industrial paths is different, and the offsetting effect of the driving factor in different paths may lead to the insignificant effect of this factor in the overall decomposition. To effectively reduce CO 2 emissions, China should focus on specific industrial paths and implement upstream and downstream comprehensive governance to achieve a low-carbon industrial chain throughout the whole process. Unlabelled Image • China's CO 2 emissions have slowed down since the start of the new normal. • The key sectors and industrial paths that affect China's CO 2 emissions are identified. • The crucial industrial paths for generating CO 2 emissions remained basically unchanged during 2002–2017. • The effect of driving factors of path decomposition and the overall decomposition are quite different. [ABSTRACT FROM AUTHOR]

In China, cities are the basic units for implementing CO 2 abatement policies. However, few studies have comprehensively explored the spatial characteristics of CO 2 emissions (CEs) and their influencing factors at the city level from different perspectives. After collecting spatial data from 280 Chinese prefecture-level cities for 2005, 2012, and 2015, this work firstly uncovered the overall and local spatial characteristics of CEs by adopting spatial autocorrelation analysis. Then, five influencing factors, including the total resident population (POP), per capita GDP (PCGDP), energy intensity (EI), the proportion of secondary industry (SI), and climate factor–heating degree days (HDD), were examined using global and local regression models. The analyses revealed that (1) CEs presented spatial agglomeration features from global and local perspectives, indicating spatial association between neighboring cities; and (2) POP, PCGDP, EI, and HDD had statistically significant spatial correlations with CEs, and their effect sizes were as follows: PCGDP > POP > EI > HDD. More importantly, the impacts of these influencing factors on CEs varied across cities, exhibiting obvious spatial heterogeneity. According to these findings, local governments should strengthen coordination and cooperation with their surrounding cities to promote regional synergistic action on emission reduction. In addition, policymakers should also design differentiated abatement policies based on regional characteristics and differences instead of applying similar policies to all cities. Unlabelled Image • The spatial distribution of China's city-level CO 2 emissions was mapped. • The spatial aggregation features of CO 2 emissions were revealed at the city level. • Factors affecting city-level CO 2 emissions were examined both globally and locally. • The impacts of different influencing factors on CO 2 emissions varied across cities. • The impact of heating degree days was statistically significant on CO 2 emissions. [ABSTRACT FROM AUTHOR]

Once, the fast-growing economy has dependence on resources and environment, especially in Central Plains Urban Agglomeration (CPUA). Assessing the relationship between economic growth and resources and environment can be helpful in planning future region development. As there were fewer researches on the decoupling analysis in CPUA, therefore, according to the decoupling index designed by Tapio, this paper connected the resources and the environment to describe the comprehensive decoupling state of economic growth and resources environment as a whole with the latest available data in 2004–2015. The results showed that: (1) The change of environmental decoupling index had a greater impact on the comprehensive decoupling index. Economic growth has been less dependent on resources consumption and environment pollution since 2011, and the relationship between economic growth and resources environment reached strong decoupling in 2015. (2) The decoupling state was towards the direction of strong decoupling in Luoyang, Pingdingshan, Jiaozuo, Xuchang, Nanyang, and Xinyang. The economic growth was less dependent on resources consumption and the environment pollution. (3) Economic growth depended strongly on resources consumption and environment pollution in Changzhi, Jincheng, Heze, and Anyang. They had not yet achieved the strong decoupling state among economic growth, resources and the environment. Thus, the policy implementations were put forward to realize strong decoupling in CPUA. Unlabelled Image • The decoupling of economic growth and resources environment was analyzed in CPUA. • Tapio decoupling model was used to analyze the decoupling in CPUA in 2004–2015. • There was the weak decoupling from 2004 to 2010. • The decoupling states were unstable from 2011 to 2014. • There was the strong decoupling in 2015. [ABSTRACT FROM AUTHOR]

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. Unlabelled Image • Poisson Regression model is used to estimate the economic loss. • Health economic loss caused by PM 2.5 from coal consumption is estimated in 2015. • Health economic loss change is assessed in 2020 and 2030. [ABSTRACT FROM AUTHOR]

• Indirect CO 2 emissions from Chinese households showed a growing trend. • Growth of Chinese households' indirect CO 2 emissions has slowed down since entering the new normal. • Per capita consumption is the main driver of CO 2 emissions growth. • CO 2 emissions from Chinese households are mainly concentrated in several sectors. • China's per capita CO 2 emissions are far below the world average. Controlling CO 2 emissions (CEs) is an important measure to mitigate global climate change. In recent years, the research on household consumption and its environmental impact has become a research hotspot in the field of sustainable development. Taking 2000–2014 as the research period, this paper studies the indirect CO 2 emissions of household consumption (ICEs-HC) in China by using the Multi-region Input-Output model. Then the structural decomposition analysis method is used to analyze the driving factors of ICEs-HC. The results show that: (1) During the study period, ICEs-HC in China showed an increasing trend. The total ICEs-HC increased by 1.90 times, and the per capita ICEs-HC increased by 1.76 times. (2) ICEs-HC in China are concentrated mainly in Commercial and Public Services (CPS), Electricity, Gas, Steam and Air Conditioning Supply (EGSA), and Manufacture of Food and Tobacco (MFT), which accounted for 26.63%, 17.69% and 13.52%, respectively, of the total emissions in 2014. (3) China has been in the position of net outflow of ICEs-HC. (4) The growth of per capita household consumption is the main factor promoting the growth of ICEs-HC in China, and the reduction of carbon intensity in various countries is the main factor in restraining ICEs-HC in China. This study shows that ICEs-HC in China are likely to rise, and the government should not only constantly improve the level of household consumption, but also actively adjust the industrial structure and optimize the consumption structure to alleviate CEs effectively. [ABSTRACT FROM AUTHOR]

Chinese government proposed that "reducing pollution while reducing carbon" should be promoted for synergistic emission reduction. It is crucial to evaluate of particulate matter (PM 2.5) and carbon dioxide (CO 2) synergistic emission reduction level and explore its driving factors in China. Using the coupled coordination model, this study evaluated the synergistic reduction emission levels of PM 2.5 and CO 2 and studied their spatial agglomeration in China's provinces from 2000 to 2018. The spatiotemporal changes of seven driving factors for synergistic emission reduction were studied using geographic and time weighted regression model. (1) The synergistic emission reduction level showed spatiotemporal differences in China. The level presented a trend of first decreasing and then increasing from 2000 to 2018. It was the lowest in the northern coastal economic zone and the highest in the southern coastal economic zone. It had strong spatial aggregation. The High-High aggregation regions were mainly distributed in the northwest and southwest economic zones. There was only one Low-Low aggregation region. It distributed in the northern coastal economic zone. (2) The energy consumption structure played an important role in promoting synergistic emission reduction, while the industrial structure was a key driving factor that hindered synergistic emission reduction. • The synergistic emission reduction level showed spatiotemporal differences in China. • The synergistic emission reduction level had strong spatial aggregation. • The energy consumption structure and industrial structure were key driving factors. [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]