Search

Although global total fossil fuel CO 2 emissions continued to grow between 2005 and 2016, many developed countries had experienced a decline in their emissions in the past decade, which significantly slowing down the growth of global emissions. Understanding the determinants of emissions trends in economies with continuously decreasing emissions is key to gauging whether efforts to decarbonize heavy industry and energy systems is working. This study selected a "peak-and-decline" panel of 18 economies to carry out a decomposition analysis on CO 2 emissions with six contributing factors using an extended STIRPAT model and estimated the future trend of their CO 2 emissions from both historical and prospective perspectives. The results show that CO 2 emissions and the six determining factors to be stationary after the first difference and to maintain a cointegrated relationship. The two-way fixed effect modeling analysis showed that the transition from fossil fuels to renewable energy and variations in energy intensity and fossil CO 2 intensity were the primary factors contributed to the decrease in CO 2 emissions. The Granger causality test reveals that there exists a bi-directional long-run causal link between the renewable energy consumption share and CO 2 emissions, and an uni-directional short-run causal association from industrial structure, GDP per capita and energy intensity to CO 2 emissions. Moreover, the forecasting results indicated that changes in the renewable energy share and fossil CO 2 intensity will be the two primary factors for the decline inCO 2 emissions in the next thirty years, while the contribution from the industrial structure, economic growth, and fossil energy intensity are rather limited. These findings highlight the importance of improved energy efficiency and the use of renewable energy in reducing CO 2 emissions in the eighteen developed countries. • Drivers of declining CO 2 emissions are analyzed from a historical and prospective analysis. • RES, FEI and FCI can explain the decrease in CO 2 emissions. • A bi-directional long-run causal link exists between CO 2 emissions and RES. • RES and FCI will be the two primary factors which will increasingly effect on CO 2 emissions. • The effect of population growth is forecasted to gradually increase subsequently. [ABSTRACT FROM AUTHOR]

Efforts to avoid the acceleration of global warming have tended to focus on countries with high CO 2 emissions levels and large populations, with a high level of economic development or industrialization. African countries, which often do not conform to such criteria, are more vulnerable to climate change due to their dependence on climate-sensitive industries and their limited infrastructure and technological capacity to cope with its impacts. The long-term economic growth rates projected for Africa's rapid development period will, further, make Africa a potential emission hotspot in the near future. Here, for the first time, we built an energy-related emissions inventory for 19 African countries for 2010–2019, which addresses emissions from 47 economic sectors and 5 energy types, making it the most comprehensive of its kind. The degree of decoupling of economy and emissions, and drivers of CO 2 emission changes are also examined. Most African countries experienced rapid growth in CO 2 emissions, with an average annual growth rate of 5.5% for fossil fuel-related CO 2 emissions and 6.0% for unsustainable biomass-related CO 2 emissions. Only two countries, South Africa and Tanzania, have achieved a strong decoupling of economic growth from CO 2 emissions. Economic and population are the most important drivers of emissions, while energy intensity has been identified as a key factor in mitigating CO 2 emissions, especially for those countries that have reached strong or weak decoupling. The findings from this study provide essential insights that could guide the development of low-carbon policies and strategies in Africa. • We provide energy-related emission inventories for 19 African countries from 2010 to 2019. • African countries experienced rapid growth in CO2 emissions. • Two countries achieved strong decoupling of GDP from CO2 emissions. • Economic and population growth are the most important drivers of emissions. [ABSTRACT FROM AUTHOR]

The impacts of socioeconomic development on urban land expansion in China vary across space and time; however, comprehensive investigation of this issue remains scarce in the existing literature. This study used a geographically and temporally weighted regression model (GTWR) to examine the spatiotemporally heterogeneous impacts of socioeconomic factors on urban land expansion in China using a newly available annual urban land-use dataset from 2000 to 2015. We found that although the eastern region has maintained its leading role (53.79%) in terms of urban expansion, the share of the central (20.34%) and western (16.13%) regions is gradually increasing. Cities with a higher administrative status tended to expand more rapidly; however, increasingly expansion has also taken place in the prefecture-level cities in recent years. We further found that Gross domestic product (GDP) growth, population density, and capital investment positively contributed to the expansion, although the directions and strengths of association between these factors and urban expansion varied across space and time. Industrial structure and foreign direct investment (FDI) showed a similar variation change trend, with the number of cities evidencing a negative relationship rapidly expanding and increasingly being seen not just in northwest China but also in the southeast during the study period. We also found that the correlation between public finance expenditure and urban expansion presented significant north-south differentiation. It is worth noting that governmental behavior plays a significant role in driving urban land expansion. Our empirical study confirmed the spatiotemporal heterogeneous effects of socioeconomic factors on urban expansion in China, providing useful insights for city governments and urban planners. Unlabelled Image • Spatiotemporally heterogeneous impacts of socioeconomic factors on urban land expansion in China are estimated. • A geographically and temporally weighted regression model with a newly available annual urban land-use dataset is used. • Directions and strengths of association between factors and urban land expansion varied across geographical space and time. • Governmental behavior plays a significant role in driving urban land expansion. • Links between public finance expenditure and urban land expansion presented significant north-south differentiation. [ABSTRACT FROM AUTHOR]

To tackle climate change, Chinese government has applied an array of mitigating measures to reduce CO2 emissions. During the 11th Five-year Plan, Chinese government set the emission reduction target policy to reduce energy consumption per unit of gross domestic product (GDP) by 20%. This paper attempts to estimate the effect of this emission reduction target policy on industrial performance from complexity perspective. The result shows more complex industrial structure is related to less coal consumption. In general, emission reduction target policy has a negative effect on the probability of branching into new industries and lower the productivity and profitability of a given industry. However, this negative effect is weaker for more complex industries. Only for industries with very high complexity, emission reduction target policy can improve the performance of an industry rather than exerting a negative effect. Our finding not only helps to design a more effective policy to achieve industrial development strategy, but also provides a potential way to achieve economic growth while reduce the emission of greenhouse gases at the same time. • We investigate the impact of Emission reduction target policy(ERT) on industrial performance from complexity perspective. • We apply the method of reflection to calculate the Economic Complexity of provinces and Industry Complexity of industries. • More complex industrial structure contributes to the coal consumption reduction. • ERT has negative effects on the probability of branching into new industries, lowers the productivity and profitability of a given industry. • ERT has weaker negative effects for more complex industries. [ABSTRACT FROM AUTHOR]

Timely and accurate delineation of global urban land is fundamental to the understanding of global environmental changes. However, most of the contemporary global urban land maps have coarse resolutions and are available for one or two years only. In this study, we developed the multi-temporal global urban land maps based on Landsat images for the 1990–2010 period with a five-year interval (‘Urban land’ in these maps refers to ‘impervious surface’, i.e., artificial cover and structures such as pavement, concrete, brick, stone and other man-made impenetrable cover types). We proposed the method of Normalized Urban Areas Composite Index (NUACI) and utilized the Google Earth Engine to facilitate the global urban land classifications from an extensive number of Landsat images. The global level's overall accuracy, producer's accuracy and user's accuracy for our mapping results are 0.81–0.84, 0.50–0.60 and 0.49–0.61, respectively. The Kappa values are 0.43–0.50 at the global level, and ~0.33 (in China) and ~0.42 (in the U.S.) at the country level. By analyzing the presented dataset, we found that the world's urban land area had increased from 450.97 ± 1.18 thousand km 2 in 1990 to 747.05 ± 1.50 thousand km 2 in 2010, reaching a global coverage of 0.63%. China, the United States and India together (14% of the world's terrestrial area in total) contributed almost 43% of the total increase of global urban land area. A free download link for these data is attached at the end of this paper. [ABSTRACT FROM AUTHOR]

Global land-use and land-cover change (LUCC) data are crucial for modeling a wide range of environmental conditions. So far, access to high-resolution LUCC products at a global scale for public use is difficult because of data and technical issues. This article presents a Future Land-Use Simulation (FLUS) system to simulate global LUCC in relation to human–environment interactions, which is built and verified by using remote sensing data. IMAGE has been widely used in environmental studies despite its relatively coarse spatial resolution of 30 arc-min, which is about 55 km at the equator. Recently, an improved model has been developed to simulate global LUCC with a 5-min resolution (about 10 km at the equator). We found that even the 10-km resolution, however, still produced major distortions in land-use patterns, leading urban land areas to be underestimated by 19.77 percent at the global scale and global land change relating to urban growth to be underestimated by 60 to 97 percent, compared with the 1-km resolution model proposed through this article. These distortions occurred because a large percentage of small areas of urban land was merged into other land-use classes. During land-use change simulation, a majority of small urban clusters were also lost using the IMAGE product. Responding to these deficiencies, the 1-km FLUS product developed in this study is able to provide the spatial detail necessary to identify spatial heterogeneous land-use patterns at a global scale. We argue that this new global land-use product has strong potential in radically reducing uncertainty in global environmental modeling. [ABSTRACT FROM PUBLISHER]

While promoting environmental efficiency is widely perceived as one of the panaceas for solving environmental issues in China, little effort has been made to identify whether environmental efficiency improvement helps to reduce carbon emission intensity (CEI). Therefore, the work presented in this study remedies the inadequacies in the previous literature by investigating the linkages between environmental efficiency and CEI, by explicitly considering its spatiotemporal heterogeneous effect and dynamic measurement. We first measure the environmental efficiency (EE) and its growth (environmental total factor productivity) at prefecture level in China during 2003–2016 by incorporating a recently modified epsilon-based data envelopment analysis technique in the global Malmquist–Luenberger index. A spatial panel regression approach is then applied to reveal the impact mechanism of EE and its growth on the CEI. The empirical results confirm that promoting EE and environmental technology efficiency can help mitigate the urban CEI. However, the impact of economic scale efficiency on the CEI reduction has not been verified, indicating great room for CEI mitigation by optimizing the urban production scale and reducing production redundancy. Evidence from the spatiotemporal econometric analysis shows a spatially heterogeneous effect of EE on CEI, and the strength of influence tends to decrease over time. The findings also reveal that enhancing EE matters in reducing CEI for southern and western cities. Finally, several control variables have negative direct and indirect (spillover effect) effects on CEI reduction, including urbanization, industrial structure, and foreign direct investment. Our findings suggest that strengthening the synergistic effect between environmental pollution governance and carbon emission control by enhancing EE to promote low-carbon development in Chinese cities. [Display omitted] • An extended epsilon-based measure and global Malmquist–Luenberger index is proposed to measure environmental efficiency and its growth. • Enhancing environmental efficiency has positive effects on CEI reduction in China. • The technological progress change and pure technical efficiency change help to reduce CEI. • The positive impact of environmental efficiency on CEI mitigation is gradually weakening. • The impact of explanatory variables as well as their spatial spillovers on CEI are geographical heterogeneity. [ABSTRACT FROM AUTHOR]

We investigated the factors influencing the progression of the pandemic from a global perspective by using the Geodetector and Correlation methods and explored the pandemic response policies and effects in different countries. The results yielded three notable findings. First, empirical results show the COVID-19 pandemic is influenced by various factors, including demographic and economic parameters, international travelers, urbanization ratio, urban population, etc. Among them, the correlation between urban population and confirmed cases is strongest. Cities become the key factor affecting the COVID-19 pandemic, with high urbanization levels and population mobility increases the risk of large-scale outbreaks. Second, among control measures, School-closures, International-travel-restrictions, and Public-gathering-restriction have the best control effect on the epidemic. In addition, the combination of different types of control measures is more effective in controlling the outbreak, especially for Public-gathering-restrictions ∩ School-closures, International-travel-restrictions ∩ Workplace-closures, Public-transport-restrictions ∩ International-travel-restrictions. Third, implementing appropriate control measures in the first month of an outbreak played a critical role in future pandemic trends. Since there are few local cases in this period and the control measures have an obvious effect. • Analysis the evolution and control of COVID-19 pandemic from global perspective. • The pandemic has been most severe in countries with high urbanization proportion, large urban populations. • The pandemic evolution is influenced by population, economic, urban, mobility characteristics and policy control factors. • School-closures, International-travel-restrictions, and Public-gathering-restriction have the best control effect. • The combination of different types of control measures is more effective in controlling the outbreak. • Corresponding control measures in the first month of the outbreak plays an important role in future trends of the pandemic. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between resource dependence and technology diversification with a territorial perspective, considering heterogeneity of different technologies, based on the patent application data of 641 categories in 341 cities in China. Using a linear probability model with fixed effects, the result shows that regional natural resources dependence is negatively related to the probability of diversifying into new technologies and the growth of a technology, providing evidence of "resource curse". However, the probability of diversifying into less related and more complex technology spaces is less likely to be hindered by reliance on natural resources. Cities with higher complex knowledge structure are inclined to develop new technologies, and also experience a less negative effect of resource dependence on the probability of entering new technology space. Moreover, for technologies of different position in a city, reliance on natural resource also affects their growth distinctively. This study provides significant policy implications for the development of resource-dependent cities and the transformation of old industrial regions. Governments can manage existing resources to make a strategy and develop technical fields that have higher complexity or lower relatedness, which is vital to convert resource curse to resource blessing, thereby improving the sustainability and productivity of resource use. • Natural resources dependence hinders cities' diversifying new technologies. • Natural resources dependence impedes growth of technologies in cities. • Less related technologies are less negatively impacted by reliance on natural resources. • Cities with knowledge structure of higher complexity experience a less negative effect of resource dependence. • Natural resources dependence has different influence on technologies of different position in a city. [ABSTRACT FROM AUTHOR]

The significant economic development witnessed in China in recent decades has been accompanied by the increasing expansion of urban areas. Although a growing literature has analyzed the characteristics and driving forces of urban land expansion, less attention has been paid to examining the different expansion determinants driving fine-scale urban land use (residential land, administration and public services land, commercial land, and industrial land) change. This paper aims to identify the differences of multi-mechanisms driving fine-scale urban land use expansion based on big data and machine learning, in the Huizhou downtown area in 2000–2015. The Random Forest (RF) algorithm is used to identify the natural, transportation, location, social, and POI factors driving land expansion by considering different urban land-use categories. Our RF estimations showed that enormous differences existed between various urban land-use types in terms of the role they played in this expansion and their relation to potential determinants, during the different urban development stages studied. Transportation, location, and the distribution of actual land use were found to exert a greater influence on urban land expansion than other factors. All the findings above provide detailed spatiotemporal knowledge and targeted information that can aid in understanding fine-scale urban land use dynamics. In this way, sound planning strategies for different fine-scale land uses can be formulated more scientifically. The strength of association between these factors and urban land expansion differed greatly depending on the different land-use types involved as well as the urban development stage that it occurred within. These results cast a new light on the importance of investigating the potential driving forces in the expansion of different urban land-use types. • The determinants of fine-scale urban land expansion are explored based on big data and machine learning. • Natural, transportation, location, social, and POI factors are considered. • Random Forest method is used considering the period 2000–2015. • The strength of association between factors and urban land expansion differed greatly across land use types. • Transportation, location, and the distribution of actual land use exert a greater influence. [ABSTRACT FROM AUTHOR]

China is currently the world's largest energy consumer and, at the same time, a huge trading power. With the increasing complexity of production processes along global value chains (GVCs), it is of great significance to study the impact of GVC participation on energy intensity. By using production length to measure GVC participation, this study first calculates China's manufacturing industry's total average production length of GVC activities and its three segments: length of pure domestic production, length of traditional trade production, and production length of GVC activities. Next, this study explains the influence mechanisms of GVC participation on energy intensity, proposes three research hypotheses, and conducts econometric analyses to examine the influence of production length and its three segments on energy intensity for a sample of China's manufacturing sector from 2000 to 2014. The results indicate that the total average production length of GVC activities significantly affects energy intensity and presents an inverted U-shaped, non-linear relationship wherein China has passed the critical point. The interactions between the three segments of production length and energy intensity also present an inverted U-shaped, non-linear relationship, where the impacts of the pure domestic segment and the "traditional" trade related segment on energy intensity have passed the critical point, while that of the segment related to GVC production has not yet crossed the critical point. It is suggested that further promoting China's manufacturing industry towards the mid-to-high end of the GVCs and breaking through its captive and "squeezed" position in the GVCs could significantly contribute toward curtailing energy intensity. • Global value chains (GVC)'s production processes have become increasingly complex. • GVC participation greatly impacts China's energy intensity. • Total production length has an inverted U-shaped non-linear relationship with energy intensity. • The production length of the GVC segment has not crossed the critical point. [ABSTRACT FROM AUTHOR]

China's swift socioeconomic development has led to extremely severe ambient PM 2.5 levels, the associated negative health outcomes of which include premature death. However, a comprehensive explanation of the socioeconomic mechanism contributing to PM 2.5 -related premature deaths has not yet to be fully elucidated through long-term spatial panel data. Here, we employed a global exposure mortality model (GEMM) and the system generalized method of moments (Sys-GMM) to examine the primary determinants contributing to premature deaths in Chinese provinces from 2000 to 2021. We found that in the research period, premature deaths in China increased by 46 %, reaching 1.87 million, a figure that decreased somewhat after the COVID-19 outbreak. 62 thousand premature deaths were avoided in 2020 and 2021 compared to 2019, primarily due to the decline in PM 2.5 concentrations. Premature deaths have increased across all provinces, particularly in North China, and a discernible spatial agglomeration effect was observed, highlighting effects on nearby provinces. The findings also underscored the significance of determinants such as urbanization, import and export trade, and energy consumption in exacerbating premature deaths, while energy intensity exerted a mitigating influence. Importantly, a U-shaped relationship between premature deaths and economic development was unveiled for the first time, implying the need for vigilance regarding potential health impact deterioration and the implementation of countermeasures as the per capita GDP increases in China. Our findings deserve attention from policymakers as they shed fresh insights into atmospheric control and Health China action., Competing Interests: Declaration of competing interest No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described is original research that has not been published previously. The manuscript has also not been submitted to another journal for the consideration of publication. All the authors listed have approved the enclosed manuscript., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Efforts to stabilize the global climate change while also continuing human development depend upon "decoupling" economic growth from fossil fuel CO 2 emissions. However, evaluations of such decoupling have typically relied on production-based emissions, which do not account for emissions embodied in international trade. Yet international trade can greatly change emissions accounting and reshape the decoupling between emissions and economic growth. Here, we evaluate decoupling of economic growth from different accounts of emissions in each of the 159 countries and analyze the drivers of decoupling. We find that between 1995 and 2015, although 29 countries exhibited strong decoupling of territorial emissions (growing economies and decreasing emissions), only 19 countries achieved economic growth while their consumption-based emissions decreased. Most developed countries have achieved decoupling of emissions related to domestic goods and services, but have not achieved decoupling of emissions related to imported goods and services. The U-test confirms that the domestic component of consumption-based emissions exhibits a stronger decoupling trend from gross domestic product (GDP) growth than consumption-based emissions, and emissions from imports continue to rise with GDP per capita without a corresponding decline, providing a statistical validation of the decoupling analysis. Moreover, in the countries where economic growth and consumption-based emissions are most decoupled, a key driver is decreasing emissions intensity due to technological progress─and especially reductions in the intensity of imported goods and services. Our results reveal the importance of assessing decoupling using consumption-based emissions; successful decoupling may require international cooperation and coordinated mitigation efforts of trading partners.

Reducing urban energy consumption is a crucial step towards achieving sustainable urban development. Urban energy plays a fundamental role in urban development, and while previous studies have examined the relationship between population size and energy conservation, the impact of increasing population density on per capita energy consumption (PCEC) remains unclear. To achieve urban energy conservation in China, it is vital to comprehend this significant relationship. This study constructs a spatial regression model to examine the relationship between population density and PCEC using 9 years of balanced panel data from 276 cities to fill a gap in the literature. The results of spatial autocorrelation indicate a significant negative relationship and heterogeneity between population density and PCEC. The results of spatial regression show that for every 1% increase in population density, there is a subsequent increase in PCEC of 0.074%. Our findings suggest that lower PCEC correlation is associated with higher urban population density. This study can be a reference for policymakers seeking new energy conservation strategies for urban development., Competing Interests: 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., (© 2024 The Authors.)

Agriculture is one of humankind's most significant sources of biomass; it also places tremendous pressure on ecosystems through its increasing demand for agricultural products. However, few studies have assessed human pressures on ecosystems from agricultural production and consumption based on a whole-supply-chain perspective. Based on the concept of human appropriation of net primary productivity (HANPP), we evaluate trends of agricultural HANPP embodied in consumption from a global perspective and trace the pressure from agriculture production that is exerted on the environment using an environmentally extended multiregional input-output (MRIO) model. The results show that agricultural HANPP embodied in consumption accounted for over two-thirds of total HANPP but brought about less than 7% of global value added. India, Brazil, and China were found to have the highest level of agricultural HANPP embodied in consumption. Agricultural net exporters were found to usually be low- and lower-income countries, while net importers are found to be high-income countries. According to the driving factor analysis, high-income and low-income countries should cooperate by adjusting consumption patterns and sharing agricultural technology to alleviate the pressure from agricultural production. Our study highlights the importance of agrarian expertise sharing and the need to develop sustainable and green agricultural production.

To mitigate the challenges pertaining to coating damage and processing defects arising from the utilization of ultrafast laser drilling for microhole creation in thermal barrier coatings (TBCs), thereby exerting substantial influence on the long-term durability of these microholes, the investigation proposes a comprehensive methodology. It encompasses the design of a two-factor four-level full factorial experiment and the execution of experimental research on picosecond laser drilling of TBC microholes. By meticulously analyzing the morphology of the microholes and the coating interface, the damage mechanisms associated with picosecond laser drilling of TBC microholes, as well as the influence of laser process parameters on coating damage, are revealed. The findings reveal that the optimal microhole entrance quality and the lowest roughness along the hole perimeter are attained at a laser power of 12 W and a scanning speed of 320 mm/s. Moreover, at a laser power of 30 W and a scanning speed of 320 mm/s, the minimal crack length on the blunt angle side of the hole and the highest machining quality are observed.

Rapid urbanization has tremendously changed the global landscape with profound impacts on our society. Nighttime light (NTL) data can provide valuable information about human activities and socioeconomic conditions thus has become an effective proxy to measure urban development. By using NTL-derived urban measures from 1992 to 2018, we analyzed the spatiotemporal patterns of global urban development from country to region to city scales, which presented a distinct North-South divergence characterized by the rising and declining patterns. A global North-South division line was identified to partition the globe into the Line-North and the Line-South geographically, which accorded with the socioeconomic difference from the aspects of urban population and economy. This line may keep a certain degree of stability deriving from the trends of population and economic information but also bears uncertainties in the long term., Competing Interests: The authors declare no competing interests., (© 2021 The Author(s).)

Technological progress is widely considered to play an important role in reducing air pollution. While growing literature has explored the effects of technological progress on environmental quality, fewer studies have considered the varied effects exerted by different technological progress paths on PM 2.5 concentrations. This paper explored the relationship between two different kinds of technological progress (indigenous innovation and technology diffusion) and PM 2.5 concentrations. Indigenous innovation was in this study considered to be composed by research and development investment (R&D) and import technology (IM); technology diffusion was represented by foreign direction investment (FDI) and export learning effect represent (EX). A panel data model was employed in order to explore the varied impact of these different technological progress paths on PM 2.5 concentrations, using data for 48 cities located in China's three most developed urban agglomerations (Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta), for the period 2000-2015. The results reveal that without control variables, FDI had a significant negative impact on PM 2.5 levels in Beijing-Tianjin-Hebei, and EX a significant positive impact. FDI, R&D, and EX were found to positively correlate with PM 2.5 levels in the Yangtze River Delta. In Pearl River Delta, R&D presented a significant negative relationship. The findings of this study provide decision makers and industry managers with a scientific basis from which to approach the task of mitigating PM 2.5 concentrations through technological progress., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The existence of Pollution Haven Hypothesis (PHH) is still being hot debated due to its importance in environmental and industrial policymaking. However, research concerning the PHH in relation to local areas and regions within country borders is limited. Therefore, this research focused on the relationship between the migration of pollution-intensive industries (PIIs) and environmental efficiency (EE) at the prefecture level throughout China's Guangdong Province from 2001 to 2014. Firstly, this research found that many PIIs migrated from the core industrial region of the Pearl River Delta (PRD) to peripheral Non-Pearl River Delta (NPRD) areas after 2006. A Data Envelopment Analysis (DEA) model was used to evaluate industrial EE of a range of different cities. Then, analysis using PMG/ARDL regression model shows that industrial EE maintained a negative long run equilibrium relationship with PII migration in the NPRD region where PIIs have moved out, but had little correlation with PII migration in the PRD where PIIs have moved in. The results show pollution transfer caused by migration of PIIs from the PRD to the NPRD region in Guangdong Province that supports the PHH. The NPRD had become a pollution haven for PIIs in the PRD. This study proposes that policymakers should build a series of environmental regulatory policies and industrial developing policies to avoid the creation of pollution havens in the developing area in China, instead of simply pollution emission control policies., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Whilst numerous studies have explored the spatial patterns and underlying causes of PM 2.5 , little attention has been paid to the spatial heterogeneity of the factors affecting PM 2.5 . In this study, a geographically weighted regression (GWR) model was used to explore the strength and direction of nexus between various factors and PM 2.5 in Chinese cities. A comprehensive interpretive framework was established, composed of 18 determinants spanning the three categories of natural conditions, socioeconomic factors, and city features. Our results indicate that PM 2.5 concentration levels were spatially heterogeneous and markedly higher in cities in eastern China than in cities in the west of the country. Based on the results of GWR, significant spatial heterogeneity was identified in both the direction and strength of the determinants at the local scale. Among all of the natural variables, elevation was found to be statistically significant with its effects on PM 2.5 in 95.60% of the cities and it correlated negatively with PM 2.5 in 99.63% cities, with its effect gradually weakening from the eastern to the western parts of China. The variable of built-up areas emerged as the strongest variable amongst the socioeconomic variables studied; it maintained a positive significant relationship in cities located in the Pearl River Delta and surrounding areas, while in other cities it exhibited a negative relationship to PM 2.5 . The highest coefficients were located in cities in northeast China. As the strongest variable amongst the six landscape factors, patch density maintained a positive relationship in part of cities. While in cities in the northeast regions, patch density exhibited a negative relationship with PM 2.5 , revealing that increasing urban fragmentation was conducive to PM 2.5 reductions in those regions. These empirical results provide a basis for the formulation of targeted and differentiated air quality improvement measures in the task of regional PM 2.5 governances., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The aim of this paper is to estimate the effects of natural conditions and anthropogenic factors on PM 2.5 concentrations, taking into consideration differences in the income levels, and thus the development stages, of the cities studied. To achieve this goal, a balanced dataset of 287 Chinese cities was divided into different income-based panels for the period 1998-2015. The empirical estimation results indicated that meteorological conditions exerted varied effects on PM 2.5 concentrations across different income-based panels. The results show that the coefficients of temperature were positive and significant in all panels, with the exception of upper-middle-income cities. Whilst wind speed and precipitation were found to be conducive to reducing PM 2.5 concentrations, no such significant correlation was found in relation to relative humidity (except in high-income cities). In terms of the anthropogenic factors addressed in the study, we found an inverted U-shaped relationship between economic development and PM 2.5 concentrations, confirming the Environmental Kuznets Curve hypothesis. In addition, the industrial structure and road density were observed to exert significant positive impacts on PM 2.5 concentrations. The empirical analysis of the effects of FDI on PM 2.5 concentrations indicate that FDI aggravated PM 2.5 pollutions in the total cities and lower-middle-income cities panels, supporting the Pollution Haven Hypothesis. The empirical results for population density suggested that it does not significantly influence PM 2.5 concentrations. Moreover, we found that built-up area exerts mixed effects on PM 2.5 concentrations. These results cast a new light on the issue of PM 2.5 pollution for government policy makers tasked with formulating measures to mitigate the concentration of such pollutants, encouraging that consideration be given to the differences between cities with different income levels., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Air pollution contributes significantly to premature death in China. However, only a limited number of studies have identified the potential determinants of population exposure to PM 2.5 from a socioeconomic perspective. This paper analyses the socioeconomic determinants of population exposure at the city level in China. We first estimated population exposure to PM 2.5 by integrating high resolution spatial distribution maps of PM 2.5 concentrations and population density, using data for 2013. Then, geographically weighted regression (GWR) modeling was undertaken to explore the strength and direction of relationships between the selected socioeconomic factors and population exposure. The results indicate that approximately 75% of the population of China lived in an area where PM 2.5 concentrations were over 35 μg/m 3 in 2013. From the GWR models, we found that the percentages for cities that showed a statistically significant relationship (p < 0.05) between population exposure and each of the six factors were: urbanization, 91.92%; industry share, 91.58%; construction level, 88.55%; urban expansion, 73.40%; income disparity, 64.98%; and private vehicles, 27.27%. The R-squared value for the six factors in the multivariable GWR model was 0.88, and all cities demonstrated a statistically significant relationship. More importantly, the association between the six factors and population exposure was found to be spatially heterogeneous at the local geographic level. Consideration of these six drivers of population exposure can help policy makers and epidemiologists to evaluate and reduce population exposure risks., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

China has received increased international criticism in recent years in relation to its air pollution levels, both in terms of the transmission of pollutants across international borders and the attendant adverse health effects being witnessed. Whilst existing research has examined the factors influencing ambient air pollutant concentrations, previous studies have failed to adequately explore the determinants of such concentrations from either a source or diffusion perspective. This study addressed both source (specifically, anthropogenic emissions) and diffusion (namely, meteorological conditions) indicators, in order to detect their respective impacts on the spatial variations seen in the distribution of air pollution. Spatial panel data for 113 major cities in China was processed using a range of global regression models-the ordinary least square model, the spatial lag model, and the spatial error model-as well as a local, geographic weighted regression (GWR) model. Results from the study suggest that in 2014, average SO 2 concentrations exceeded China's first-level target. The most polluted cities were found to be predominantly located in northern China, while less polluted cities were located in southern China. Global regression results indicated that precipitation exerts a significant effect on SO 2 reduction (p<0.001) and that a regional increase of 1mm in precipitation can reduce SO 2 concentrations by 0.026μg/m 3 . Both emission and temperature factors were found to aggravate SO 2 concentrations, although no such significant correlation was found in relation to wind speed. GWR results suggest that the association between SO 2 and its factors varied over space. Increased emissions were found to be able to produce more pollution in the northwest than in other parts of the country. Higher wind speeds and temperatures in northwestern areas were shown to reinforce SO 2 pollution, while in southern regions, they had the opposite effect. Further, increased precipitation was found to exert a greater inhibitory effect on SO 2 pollution in the country's northeast than that in other areas. Our findings could provide a detailed reference for formulating regionally specific emission reduction policies in China., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Rapid economic growth, industrialization, and urbanization in China have led to extremely severe air pollution that causes increasing negative effects on human health, visibility, and climate change. However, the influence mechanisms of these anthropogenic factors on fine particulate matter (PM 2.5 ) concentrations are poorly understood. In this study, we combined panel data and econometric methods to investigate the main anthropogenic factors that contribute to increasing PM 2.5 concentrations in China at the prefecture level from 1999 to 2011. The results showed that PM 2.5 concentrations and three anthropogenic factors were cointegrated. The panel Fully Modified Least Squares and panel Granger causality test results indicated that economic growth, industrialization, and urbanization increased PM 2.5 concentrations in the long run. The results implied that if China persists in its current development pattern, economic growth, industrialization and urbanization will inevitably lead to increased PM 2.5 emissions in the long term. Industrialization was the principal factor that affected PM 2.5 concentrations for the total panel, the industry-oriented panel and the service-oriented panel. PM 2.5 concentrations can be reduced at the cost of short-term economic growth and industrialization. However, reducing the urbanization level is not an efficient way to decrease PM 2.5 pollutions in the short term. The findings also suggest that a rapid reduction of PM 2.5 concentrations relying solely on adjusting these anthropogenic factors is difficult in a short-term for the heavily PM 2.5 -polluted panel. Moreover, the Chinese government will have to seek much broader policies that favor a decoupling of these coupling relationships.