Chinese cities show different trend toward carbon peak

Understanding the disparities in carbon emission trend among cities is critical for achieving carbon peak goal. However, the status and trends of carbon peaking and reduction in various city types are still unclear. Therefore, this study classified 315 Chinese cities according to their economic and industrial structure by SOM-K-means, aiming to evaluate the trends and dynamic drivers of carbon peaking progress in different city types. The findings reveal a decline in carbon emissions in 110 cities (34.9 %) since 2020. Notably, all city types show potential for carbon reduction and achieving carbon peaking. Specifically, resource-based cities and high-end service cities have the most effect on reducing emissions, with 48.4 % and 42.1 % of the cities declining in carbon emissions. Energy-based and heavy industrial cities face heightened pressure to reduce carbon emissions. Additionally, in high-end service cities, energy efficiency and investment intensity contribute to emission reduction, while industrial structure adjustment decrease carbon emissions in resource-based cities. Furthermore, enhancing energy efficiency effects and R&D intensity are effective ways to significantly reduce carbon emissions in heavy industrial cities. We conclude that differentiating carbon reduction pathways for different cities should constitute be a breakthrough in achieving the goal of carbon peaking. These insights provide recommendations for cities that have yet to reach their carbon peak for both China and other developing countries.

Health impact assessment of the surface water pollution in China

China suffers from severe surface water pollution. Health impact assessment could provide a novel and quantifiable metric for the health burden attributed to surface water pollution. This study establishes a health impact assessment method for surface water pollution based on classic frameworks, integrating the multi-pollutant city water quality index (CWQI), informative epidemiological findings, and benchmark public health information. A relative risk level assignment approach is proposed based on the CWQI, innovatively addressing the challenge in surface water-human exposure risk assessment. A case study assesses the surface water pollution-related health impact in 336 Chinese cities. The results show (1) between 2015 and 2022, total health impact decreased from 3980.42 thousand disability-adjusted life years (DALYs) (95 % Confidence Interval: 3242.67-4339.29) to 3260.10 thousand DALYs (95 % CI: 2475.88-3641.35), measured by total cancer. (2) The annual average health impacts of oesophageal, stomach, colorectal, gallbladder, and pancreatic cancers added up to 2621.20 thousand DALYs (95 % CI: 2095.58-3091.10), revealing the significant health impact of surface water pollution on digestive cancer. (3) In 2022, health impacts in the Beijing-Tianjin-Hebei and surroundings, the Yangtze River Delta, and the middle reaches of the Yangtze River added up to 1893.06 thousand DALYs (95 % CI: 1471.82-2097.88), showing a regional aggregating trend. (4) Surface water pollution control has been the primary driving factor to health impact improvement, contributing -3.49 % to the health impact change from 2015 to 2022. It is the first city-level health impact map for China's surface water pollution. The methods and findings will support the water management policymaking in China and other countries suffering from water pollution.

A DEA game cross-efficiency based improved method for measuring urban carbon emission efficiency in China

An accurate evaluation of carbon emission efficiency (CEE) at the city level can provide guidelines for understanding low carbon performance, which is crucial to achieving dual carbon targets. Existing CEE studies focused on national, industrial, and provincial scales while neglecting the city level and failing to consider competing relationships among decision-making units in their measurement models. To fill these gaps, this paper introduces the data envelopment analysis game cross-efficiency model (DEA-GCE) to measure urban CEE performance and compares it with the traditional Super-SBM model using the data from 283 Chinese cities between 2006 and 2019. The results show that (1) the DEA-GCE method provided more intensive and stable results. (2) Overall CEE of Chinese cities declined slightly amidst fluctuations during this period. (3) CEE in cities exhibits spatial clustering characteristics. CEE performance in Northeast China has improved, while CEE in Northwest China continues to lag behind. This study introduced an innovative method for calculating urban CEE and conducted an empirical study of 283 Chinese cities, which has implications for formulation of emission reduction policies.

Preference of consumers for higher-grade energy-saving appliances in hierarchical Chinese cities

Promotion of energy-saving household appliances (ESHAs) potentially contributes to optimizing both the total quantity and efficiency of household energy consumption. Differences in urban consumers' preference for higher-grade ESHAs as well as its influencing factors in cities with hierarchical socioeconomic levels remain elusive. Targeting 55 Chinese cities pertaining to three levels of socioeconomic development, we distribute questionnaires designed to cover both demographic and consciousness factors. By combining Contingent Valuation Method and multiple linear regression, the extra willingness to pay (WTP) for Grade-1/2 appliances compared with Grade-3 appliances is measured, and the influence factors on the WTP as well as consumers with highest WTP are identified. The extra WTP for Grade-1 appliances in First-, Second- and Third-level cities is 44.1%, 42.3% and 32.7%, respectively. The influences of age, household income, having children or not and monthly electricity bill parallel the socioeconomic level, while gender and schooling affect differently across socioeconomic levels. Consumers in less developed cities focus more on their affordability for the ESHAs, and in more developed cities have better environmental consciousness. Subsidies for consumers, such as those having master degree or above in First-level and Second-level cities, and having children in Third-level cities will increase their WTP. The findings provide insights for policy interventions aimed at boosting the purchase behavior for ESHAs according to local conditions for control of both household energy consumption and carbon emissions.

Governing public health emergencies during the coronavirus disease outbreak: Lessons from four Chinese cities in the first wave

The ongoing coronavirus disease (COVID-19) pandemic has had a far-reaching impact on urban living, prompting emergency preparedness and response from public health governance at multiple levels. The Chinese government has adopted a series of policy measures to control infectious disease, for which cities are the key spatial units. This research traces and reports analyses of those policy measures and their evolution in four Chinese cities: Zhengzhou, Hangzhou, Shanghai and Chengdu. The theoretical framework stems from conceptualisations of urban governance and its role in public health emergencies, wherein crisis management and emergency response are highlighted. In all four cities, the trend curves of cumulative diagnosed cases, critical policies launched in key time nodes and local governance approaches in the first wave were identified and compared. The findings suggest that capable local leadership is indispensable for controlling the coronavirus epidemic, yet local governments' approaches are varied, contributing to dissimilar local epidemic control policy pathways and positive outcomes in the fight against COVID-19. The effectiveness of disease control is determined by how local governments' measures have adapted to geospatial and socioeconomic heterogeneity. The coordinated actions from central to local governments also reveal an efficient, top-down command transmission and execution system for coping with the pandemic. This article argues that effective control of pandemics requires both a holistic package of governance strategies and locally adaptive governance measures/processes, and concludes with proposals for both a more effective response at the local level and identification of barriers to achieving these responses within diverse subnational institutional contexts.

The heterogeneous effects of environmental regulation on industrial carbon emission efficiency in China using a panel quantile regression

In order to verify how environmental regulation affects the improvement of urban industrial carbon emission efficiency, this study first measures the balanced panel data of industrial carbon emission efficiency of 282 cities in China from 2003 to 2019, and evaluates the direct and regulating impact of environmental regulation on China's urban industrial carbon emission efficiency. Meanwhile, in order to investigate the potential heterogeneity and asymmetry, the panel quantile regression method is used. The empirical results show that (1) during the period 2003-2016, China's overall industrial carbon emission efficiency showed a upward trend, with a decreasing spatial pattern from the east-central-west-northeast region. At the urban scale in China, environmental regulation has a significant direct effect on industrial carbon emission efficiency, which is lagged and heterogeneous. At the low quantiles, a one-period lag in environmental regulation has a negative effect on the improvement of industrial carbon emission efficiency. At the middle and high quantiles, a one-period lag in environmental regulation has a positive effect on the improvement of industrial carbon emission efficiency. Environmental regulation has a moderating effect on industrial carbon efficiency. With increasing industrial emission efficiency, the positive moderating effect of environmental regulation on the relationship between technological progress and industrial carbon emission efficiency shows a pattern of diminishing marginal benefits. The main contribution of this study is the systematic analysis of the potential heterogeneity and asymmetry of the direct and moderating effects of environmental regulation on the industrial carbon emission efficiency at the city scale in China by using panel quantile regression method.

Estimating the CO2 emissions of Chinese cities from 2011 to 2020 based on SPNN-GNNWR

Global warming is a serious threat to human survival and health. Facing increasing global warming, the issue of CO₂ emissions has attracted more attention. China is a major contributor of anthropogenic CO₂ emissions and so it is essential to accurately estimate China's CO₂ emissions and analyze their changing characteristics. This study recalculates CO₂ emissions from Chinese cities from 2011 to 2020 using the SPNN-GNNWR model and multiple factors to reduce the uncertainty in emission estimates. The SPNN-GNNWR model has excellent predictions (R²: 0.925, 10-fold CV R²: 0.822) when cross-validation is used. The results indicate that the total CO2 emissions in China calculated by the model are close to those accounted for by other authorities in the world, with the total CO₂ emissions increasing from 9.122 billion tonnes in 2011 to 9.912 billion tonnes in 2020. The city with the largest increase in CO₂ emissions is Tianjin, and the city with the largest decrease is Beijing. The study also reveals the regional differences in CO₂ emissions in Chinese mainland, including emissions, emission intensity and per capita emissions. Capturing and understanding the emissions and the related socioeconomic characteristics of different cities can help to develop effective emission mitigation strategies.

The Heterogeneous Effects of Formal and Informal Environmental Regulation on Green Technology Innovation-An Empirical Study of 284 Cities in China

Promoting green technology innovation (GTI) through environmental regulation is a key measure in reducing the severity of environmental problems. However, the effects of formal environmental regulation (FER) and informal environmental regulation (IER) on GTI have not been clarified. Through theoretical analysis, this paper analyzes the effects of FER and IER on GTI based on OLS and GTWR models. The results show the following: (1) In all Chinese cities, both FER and IER have had a positive impact on GTI. The impact of FER has been much stronger than that of IER. They show a linkage effect, and their interaction (TER) has had a positive impact on GTI. (2) In terms of spatial heterogeneity, the impact of FER, IER, and TER on GTI has decreased across the east-west gradient and has been supplemented by a core-periphery structure. (3) In terms of urban heterogeneity, the impact of FER, IER, and TER has decreased with the size of the city. This study has the potential to strengthen the effect of environmental regulation on GTI. It can provide a decision-making reference for cities to coordinate FER and IER strategies, and provides evidence for adopting regionally differentiated environmental regulation strategies.

The impact of medical infrastructure on regional innovation: An empirical analysis of China's prefecture-level cities

Because of public health emergencies, such as the COVID-19 pandemic, having an optimal medical infrastructure is an important way to maintain the normal operation of society and stimulate vitality in regional innovation. Based on the data on 260 cities at the prefecture level and above in China from 2001 to 2018, this paper investigates the characteristics and mechanisms of medical infrastructure on regional innovation. After a series of regressions, we robustly find that medical infrastructure has a significantly positive impact on regional innovation. In addition, based on the mediating effect model, the mechanism test shows that medical infrastructure can promote regional innovation through the channels of the natural population growth rate, educational level, and the environmental greening level. Finally, considering the urban heterogeneity, we find that the positive impact of medical infrastructure on regional innovation is reflected mainly in eastern and central cities, non-sub-provincial cities, and non-resource-based cities. These conclusions not only enrich the theoretical research on regional innovation from the perspective of medical infrastructure but also shed light on how to better promote regional innovation for China or even other countries.

Convergence analysis of Chinese urban green land-use efficiency

Convergence analysis of China's urban green land-use efficiency (GLUE) will help to explore whether regional differences in GLUE determined by regional economic development and imbalance of resource endowments gradually decrease over time. We construct a DEA model considering variable returns to scale to measure Chinese urban GLUE, and discuss the convergence of Chinese urban GLUE. We find that Chinese urban GLUE is generally low. GLUE of eastern cities is the best. GLUE of non-resource-based (NRB) cities is higher than that of resource-based (RB) cities. GLUE of Pearl River Delta Economic Zone is the highest, while that of Comprehensive Economic Zone of Central 6 Provinces is the lowest. Chinese urban GLUE is mainly affected by technological change. Chinese urban GLUE shows absolute and conditional β convergence, and the gap of GLUE between cities is constantly narrowing. Cities in different locations, resource dependence degrees, and economic zones have different convergence rates. The conclusions help the Chinese government to formulate policies for intensive land use, improve national GLUE, and promote urban intensive and high-quality development.

Digital finance and regional green innovation: evidence from Chinese cities

Digital finance realizes the combination of finance and technology, makes up for many deficiencies of traditional finance, and brings opportunities for green and innovative development. However, systematic research on regional digital finance and green innovation is still lacking. Based on this, this study aims to analyze the impact of digital finance on the regional level of green innovation. For the analysis, the fixed-effect model, the mediating effect model, and the moderating effect model are used to perform regression on the panel data of Chinese cities. The results show that digital finance can significantly improve the level of regional green innovation. Improving the level of regional green financial services is its main mechanism, but the intermediary role of industrial structure optimization and upgrading fails to pass the test. In addition, the results of heterogeneity analysis show that digital finance plays a greater role in promoting green innovation in areas with high levels of traditional financial supply and Internet infrastructure construction. It is worth noting that digital finance does not really play the role of universal benefit, widening the regional green innovation gap. The main contributions of this study are to prove the positive effect of digital finance on green innovation at the regional level, clarify its transmission mechanism and urban heterogeneity analysis, and find that the current digital finance cannot bridge the gap of regional green innovation.

Characteristics and Spatial-Temporal Differences of Urban "Production, Living and Ecological" Environmental Quality in China

The article analyses the spatial and temporal differences in the environmental quality of production, living and ecology of 285 cities in China from 2010 to 2020 by using the entropy method, the Theil index and correlation analysis. The study concludes the following: (1) in terms of overall differences, the overall differences in the "production, living and ecological" environmental quality indices of 285 cities during the study period undergo a process of "narrowing-widening-narrowing". The differences within the four major zones of the country are higher than those between the four major zones, and the differences within the zones show an increasing trend year by year. (2) In terms of temporal differences, the combined scores of "production, living and ecological" environmental quality of the 285 cities in the study period show a decreasing trend, and the contribution of the PLE subsystem scores are, in descending order, production environmental quality > living environmental quality > ecological environmental quality. (3) In terms of overall ranking, the head effect of the combined production, living and ecological environmental quality (PLE) scores of cities in the study period is significant, and the top 10 cities in terms of combined scores are all small and medium-sized cities with significant regionalization characteristics. (4) In terms of spatial pattern, there is a significant spatial gradient in the east, central and western regions, with the overall PLE scores of the four major regions in descending order: eastern region > central region > western region > northeastern region. The regions with high scores in the "production, living and ecological" environmental quality of cities can be divided into three types: multi-core, dual-core and single-core. (5) In terms of influencing factors, there is a logarithmic curve relationship between the combined production, living and ecological environmental quality (PLE) score and the built-up area (BUA) of cities. The study proposes to optimize the layout of urban production, strengthen the industrial links of urban clusters, improve the level of public services, ensure the equalization of urban public services, strengthen the management of ecological environment and improve the quality of ecological environment in order to optimize the quality of urban "production, living and ecological" environment.

Exploring surface urban heat island (SUHI) intensity and its implications based on urban 3D neighborhood metrics: An investigation of 57 Chinese cities

Excessive urban temperature exerts a substantially negative impact on urban sustainability. Three-dimensional (3D) landscapes have a great impact on urban thermal environments, while their heat conditions and driving factors still remain unclear. This study mapped urban 3D neighborhoods and their associated SUHI (surface urban heat island) intensities in summer daytime across 57 Chinese cities, and then explored their relationships, driving factors as well as implications. Nine categories of urban 3D neighborhoods existed in Chinese cities and the 3D neighborhood of High Density & Medium Rise (HDMR) contributed the largest share of urban areas. The distribution of 3D neighborhoods varied among cities due to their distinct natural and economic traits. The average SUHI intensity can amount to 4.27 °C across all Chinese 3D neighborhoods. High Density & Low Rise (HDLR) and HDMR presented higher SUHI intensities than other 3D neighborhoods in China. Urban green space (UGI) and building height (BH) had great influences on SUHI intensities. The relative contribution of UGI decreased with the increase of building density and building height, but BH presented the opposite trend. The interaction of urban 3D landscapes and function zones led to highly complicated urban thermal environments, with higher SUHI intensities in industrial zones. Besides, the SUHI intensities of 3D neighborhoods presented great diurnal and seasonal variations, with higher SUHI intensities in HDHR and HDMR at nighttime in winter and summer. What's more, urban residents may suffer unequal heat risk inside cities due to the deviations of SUHI intensities among different 3D neighborhoods. It could be a highly effective way to mitigate SUHI effects in cities by increasing urban greening and improving urban ventilation.

Haze pollution reduction in Chinese cities: Has digital financial development played a role?

Based on the exogenous shock of digital financial development in China in 2013, a difference-in-differences (DID) model is set up in this paper to investigate the causal relationship between digital financial development and haze pollution reduction. The finding of the paper is that a one standard deviation increase in digital finance after 2013 decreases the PM2.5 concentrations by 0.2708 standard deviations. After a number of robustness checks, like placebo tests, instrumental variable (IV) estimations, eliminating disruptive policies, and using alternative specifications, this causal effect is not challenged. In addition, this paper explores three potential mechanisms of digital finance to reduce haze pollution: technological innovation, industrial upgrading, and green development. Moreover, the heterogeneous effects signify that the usage depth of digital finance works best in haze pollution reduction. Digital finance has more positive effects in cities in the north and those with superior Internet infrastructure and higher levels of traditional financial development. However, the quantile regression estimates suggest that for cities with light or very serious haze pollution, the positive impact of digital finance is limited. These findings supplement the research field on the environmental benefits of digital finance, which provides insights for better public policies about digital financial development to achieve haze pollution reduction.

Can Digital Finance Promote Comprehensive Carbon Emission Performance? Evidence from Chinese Cities

Improving urban comprehensive carbon emission performance (CCEP) is the inevitable choice for China's low-carbon development. With the continuous integration of digital technology and financial elements, the development of urban digital finance has also been significantly improved. To further explore the impact of urban digital finance on urban low-carbon development, using the data of 281 cities in China from 2011 to 2019, this paper firstly evaluates the urban CCEP, and further empirically investigates how digital finance influences CCEP. The empirical results show that: (1) Digital finance significantly improves the urban CCEP, and after conducting robustness tests and addressing the endogeneity issue, the above conclusion is robust. (2) For the sub-indicators, there is a U-shaped relationship between the coverage breadth of digital finance and CCEP. Moreover, the improvement of usage depth and digital support services could promote CCEP. (3) The channel tests indicate that digital finance improves the CCEP mainly by promoting green technology innovation and the development of urban tertiary industry. Meantime, digital finance has a stronger impact on improving CCEP in cities with more developed traditional finance, and the positive effect is significant in non-old industrial base cities and a two-control zone. Finally, this paper puts forward relevant policy suggestions.

Interaction and mediation effects of economic growth and innovation performance on carbon emissions: Insights from 282 Chinese cities

China is under rapid urbanization and consequently facing increasing carbon emissions (CE). Economic growth (EG) and innovation performance (IP), as two critical indicators of urbanization, are considered the driving forces of CE. Although economy and innovation are entangled and can jointly affect CE in reality, the measured effects of economy and innovation on CE are often treated separately in traditional studies. We adopted a three-part research framework including the total, interaction and mediation effect tests to elucidate how EG and IP affected CE in China from 2005 to 2015 based on insights from 282 Chinese cities. The empirical results showed that both economy and innovation contributed to CE, although the contribution has reduced over the 11 years. In particular, the interaction effect between economy and innovation for North China, Northeast China, and Southwest China was -4.201, -8.442, and - 3.897, respectively, in 2015, meaning that these regions adversely affect CE. In addition, we found that the economy helps reduce CE via innovation. When considering the changes of economy and innovation, their mediation effect on CE changes varied in different regions, attributable to the level of economy and innovation as well as the stocks of energy resources. Therefore, future planning for low-carbon transition should regard the economy and innovation together. Based on this principle, we propose five detailed policies. Overall, this study is valuable not only for further understanding the triangle relationship among economy, innovation, and CE, but also for reaching low-carbon goals.

Spatio-Temporal Heterogeneity of the Relationships Between PM2.5 and Its Determinants: A Case Study of Chinese Cities in Winter of 2020

Fine particulate matter (PM2.5) poses threat to human health in China, particularly in winter. The pandemic of coronavirus disease 2019 (COVID-19) led to a series of strict control measures in Chinese cities, resulting in a short-term significant improvement in air quality. This is a perfect case to explore driving factors affecting the PM2.5 distributions in Chinese cities, thus helping form better policies for future PM2.5 mitigation. Based on panel data of 332 cities, we analyzed the function of natural and anthropogenic factors to PM2.5 pollution by applying the geographically and temporally weighted regression (GTWR) model. We found that the PM2.5 concentration of 84.3% of cities decreased after lockdown. Spatially, in the winter of 2020, cities with high PM2.5 concentrations were mainly distributed in Northeast China, the North China Plain and the Tarim Basin. Higher temperature, wind speed and relative humidity were easier to promote haze pollution in northwest of the country, where enhanced surface pressure decreased PM2.5 concentrations. Furthermore, the intensity of trip activities (ITAs) had a significant positive effect on PM2.5 pollution in Northwest and Central China. The number of daily pollutant operating vents of key polluting enterprises in the industrial sector (VOI) in northern cities was positively correlated with the PM2.5 concentration; inversely, the number of daily pollutant operating vents of key polluting enterprises in the power sector (VOP) imposed a negative effect on the PM2.5 concentration in these regions. This work provides some implications for regional air quality improvement policies of Chinese cities in wintertime.

The effect of fiscal decentralization, environmental regulation, and economic development on haze pollution: empirical evidence for 270 Chinese cities during 2007-2016

The current discourse addresses fiscal issues, financial decentralization, and environmental quality and significantly affects economic development and sustainability. This investigation aims to address the research gap in fiscal decentralization and haze pollution for innovation and sustainable growth. This study uses the annual data of 270 Chinese cities from 2007 to 2016 for comprehensive analysis and employs spatial regression methods. The key findings imply that haze pollution in neighbouring cities causes further ecological issues. While the environmental regulations of China tend to have negative impacts on pollution, fiscal decentralization was found to be a key contributor to environmental pollution in Chinese cities. Overall, the study supports the validity of the pollution refuge hypothesis in China. Lastly, the conclusions allow us to conclude that China might need micro-level reforms regarding fiscal decentralization, environmental tax laws, and encouragement of cleaner production technologies.

Exploring the convergence patterns of PM2.5 in Chinese cities

Economic development and ongoing urbanization are usually accompanied by severe haze pollution. Revealing the spatial and temporal evolution of haze pollution can provide a powerful tool for formulating sustainable development policies. Previous studies mostly discuss the differences in the level of PM2.5 among regions, but have paid little attention to the change rules of such differences and their clustering patterns over long periods. Therefore, from the perspective of club convergence, this study employs the log t regression test and club clustering algorithm proposed by Phillips and Sul (Econometrica 75(6):1771-1855, 2007. 10.1111/j.1468-0262.2007.00811.x) to empirically examine the convergence characteristics of PM2.5 concentrations in Chinese cities from 1998 to 2016. This study found that there was no evidence of full panel convergence, but supported one divergent group and eleven convergence clubs with large differences in mean PM2.5 concentrations and growth rates. The geographical distribution of these clubs showed significant spatial dependence. In addition, certain meteorological and socio-economic factors predominantly determined the convergence club for each city.

The dilemma of paraxylene plants in China: Real trouble for the environment?

China's demand for paraxylene, an essential material for the chemical industry, is growing rapidly. However, the public is worried about the influence of paraxylene plants on the environment. The contradiction between supporting paraxylene and opposing it poses a considerable challenge for the Chinese government. Therefore, objective evaluation of paraxylene plants' impact on the environment is of great significance to industrial development. Using the urban panel data of 102 cities in China from 2003 to 2017, this paper applies the PSM-DID method to study the relationship between paraxylene plants and air quality. This paper shows that paraxylene plants play a significantly positive role in aggravating urban air pollution. Specifically, paraxylene plants increase not only industrial dust pollution but also increase SO₂ emissions. This influence is still valid after multiple robustness tests. Moreover, the mechanism analysis reveals that paraxylene plants' construction promotes income growth but hinders foreign direct investment. Finally, this paper provides policy suggestions on promoting paraxylene industrial development in terms of governments, enterprises, and the public.

The dynamics and drivers of environmental performance in Chinese cities: a decomposition analysis

This paper examines urban environmental performance using a unique database of 286 Chinese cities for the period 2002-2014. Both the dynamics of urban total factor environmental efficiency and total factor environmental productivity are explored employing slack-based measures. The findings reveal a U-shaped trend of urban environmental performance over time during the sample period. Energy conservation and pollutant reduction were the key drivers of this transformation, while the deterioration in capital efficiency slowed down this transformation. In terms of regional and city-size heterogeneities, the eastern and large cities showed better environmental performance. In addition, environmental Kuznets relationship existed for waste water and SO2 emissions, but not for CO2 emissions.

Carbon emissions performance trend across Chinese cities: evidence from efficiency and convergence evaluation

Improving carbon emissions performance in Chinese cities is a crucial way to promote China's sustainable development. Employing the super-efficiency SBM model, we first estimate the carbon emissions efficiency (CEE) of 262 Chinese cities from 2003 to 2016. Then we study and explain the club convergence of CEE combining Markov and spatial Markov models and Moran's I test method. The results show that CEE has improved, especially for the western and northeastern cities. The efficiency of the northwest cities is low, while those of the central and coastal cities are relatively high. Club convergence exists in China's urban CEE. Cities with high- and low-level efficiency have much higher convergence levels. There are significant spatial agglomeration and spillover effects in China's urban CEE, contributing to the club convergence. Our analysis suggests that "cross-border" cooperation and communication between cities in different clubs should be highly promoted. Cities in high-level efficiency clubs are encouraged to play its role in radiating the lower-level cities. And the Chinese government is encouraged to strengthen carbon emissions mitigation in low-level areas through combining the green "Belt and Road" construction with the establishment of a national carbon market.

Spatial distribution and determinants of PM2.5 in China's cities: fresh evidence from IDW and GWR

While numerous studies have explored the spatial patterns and underlying causes of PM_2.5 at the urban scale, little attention has been paid to the spatial heterogeneity affecting PM_2.5 factors. In order to enrich this research field, we collected PM_2.5 monitoring data from 367 cities across China in 2016 and combined inverse distance weighted interpolation (IDW) and geographically weighted regression (GWR) model. As a result, we could dynamically describe the spatial distribution pattern of urban PM_2.5 at monthly, seasonal, and annual scales and investigate the spatial heterogeneity of the influential factors on urban PM_2.5. Furthermore, in order to make the result more scientific and reasonable, the paper used selection.gwr function and bw.gwr function, respectively, to optimize model, thereby avoiding local collinearity caused by independent variables. The main results are as follows: (1) PM_2.5 in Chinese cities is characterized as time-space non-equilibrium pattern. The Beijing-Tianjin-Hebei region, the Yangtze River corner region, the Pearl River Delta region, and the northeast region have formed a pollution-concentrating core area with Beijing-Tianjin-Hebei region as the axis, which brings greater difficulties and challenges to PM_2.5 governance. (2) The effects of various factors of socio-economic activities on the concentration of PM_2.5 have significant spatial heterogeneity among Chinese cities. (3) There is an inverted "U" curve between economic growth and PM_2.5. When the per capita income reaches 47,000 yuan, the PM_2.5 emission reaches the peak, which proves the existence of environmental Kuznets curve (EKC). These findings could provide a significant reference for policy makers in China to facilitate targeted and differentiated regional PM_2.5 governance measures.

Short-term effects of air pollution on cause-specific mental disorders in three subtropical Chinese cities

BACKGROUND

The effects of ambient air pollution on specific mental disorders are rarely studied, and the reported results are inconsistent.

OBJECTIVE

To assess the short-term effect of ambient air pollution on the morbidity of mental disorders in three subtropical Chinese cities.

METHODS

Daily concentrations of air pollution were averaged from 19 fixed monitoring stations across each city, and data on patients were collected from three psychiatric specialty hospitals. A time-series study combined with a generalized additive Poisson model was conducted to investigate the association between air pollution and mental disorders. The exposure-response relationships were explored and stratified analyses by age and sex were conducted.

RESULTS

A total of 1,133,220 outpatient visits were recorded in three subtropical cities (Huizhou, Shenzhen, and Zhaoqing). The number of daily outpatient visits for mental disorders increased with higher air pollutant (PM_2.5, PM₁₀, SO₂ and NO₂) concentrations, and the effect of NO₂ appeared to be consistently significant across the three cities, with excess risk (ER) of 4.45% (95% CI: 2.90%, 6.04%) in Huizhou, 7.94% (95% CI: 6.28%, 9.62%) in Shenzhen, and 2.19% (95% CI: 0.51%, 3.89%) in Zhaoqing, respectively, at lag03. We also observed significant effect of PM_2.5 at lag0 (ER = 1.20%, 95% CI: 0.28%, 2.13%), PM₁₀ at lag0 (ER = 0.99%, 95% CI: 0.36%, 1.62%), and SO₂ at lag0 (ER = 10.74%, 95% CI: 3.20%, 18.84%) in Shenzhen. For specific mental disorders, significant associations were found in all the air pollutants except between SO₂ and affective disorder and between PM_2.5 and schizophrenia. In addition, we found that air pollution exhibited stronger effects for males and adults (≥18 years).

CONCLUSION

Acute exposure to air pollution, especially NO₂, might be an important trigger of mental disorders.

Platform urbanism and the Chinese smart city: the co-production and territorialisation of Hangzhou City Brain

We analyse an urban platform (Alibaba's City Brain) to show how smart city development is evolving in urban China. In order to do so, we base our analysis on two strands of literature: that on platform urbanism, and on the experimental city. The paper identifies two processes that are shared across both bodies of work on platform urbanism and experimental cities: relational co-production and territorialisation. These processes can also be applied to the case of City Brain as both a platform and an urban experiment. We conclude by reflecting on the significance of urban platforms on the co-production of data-enabled urban governance; local urban context; and citizenship.

Will polycentric cities cause more CO2 emissions? A case study of 232 Chinese cities

From 2000 to 2010 China experienced rapid economic development and urbanization. Many cities in economically developed areas have developed from a single-center status to polycentricity. In this study, we used exploratory spatial data analysis (ESDA) to identify the population centers, which identified 232 cities in China as having urban centers. COMP was used to represent urban agglomeration, and POLYD (representing how far is the city's sub-centers to the main center), POLYC (representing the number of a city's centers), and POLYP (representing the population distributed between the main center and the sub-centers) were used to indicate urban polycentricity. Night light data were used to determine the CO₂ emissions from various cities in China. A mixed model was used to study the impact of urban aggregation and polycentric data on the CO₂ emission efficiency in 2000 and 2010. The study found that cities with higher compactness were distributed in coastal areas, and the cities with higher multicentricity were distributed in the Yangtze River Delta and Shandong Province. The more compact the city was, the less conducive it was to improving CO₂ emission efficiency. Polycentric development of the city was conducive to improving the CO₂ emission efficiency, but the number of urban centers had no significant relationship with the CO₂ emission efficiency. Our research showed that the compactness and multicentricity of the city had an impact on the CO₂ emission efficiency and provided some planning suggestions for the low carbon development of the city.

Heterogeneity of influential factors across the entire air quality spectrum in Chinese cities: A spatial quantile regression analysis

Most of the previous researches estimate influencing factors impact on air quality average without considering the heterogeneity of influential factors on different levels of air quality. In order to detect the different effects of influencing factors on air quality index (AQI) between lower-AQI and higher-AQI cities, this study applies a spatial quantile regression model (SQRM) to investigate heterogeneity of influential factors on AQI, while accounting for spatial autocorrelation of AQI. The results show that heterogeneity effects of windspeed, terrain slope, urbanization sprawl and spatial autocorrelation on AQI are large across the entire AQI spectrum, while heterogeneity effects of precipitation, temperature, relative humidity, terrain fluctuation and urbanization intensity on AQI are not obvious. The spatial positive autocorrelation of AQI in higher-AQI cities is greater than that in lower-AQI cities. Compared with higher-AQI cities, the negative impact of terrain slope on AQI is lager in lower-AQI cities. One unit increase in wind speed contributes AQI to decrease 9.31 to 5.64 then to 5.39 for lower, medium and higher-AQI cities. One unit increase in urbanization sprawl would lead AQI increase 25.6 to 15.6 then to 10.5 for lower, medium and higher-AQI cities. The heterogeneity analysis of meteorological, topographic and socioeconomic factors effects on air quality are of guiding significance for realizing the differentiation of policy measures for air pollution prevention and control.

Spatiotemporal variations of chlorinated paraffins in PM2.5 from Chinese cities: Implication of the shifting and upgrading of its industries

To highlight the levels and distributions and to assess the risk of human exposure of chlorinated paraffins (CPs) in PM_2.5 in China, the concentrations and homologue patterns of short-chain chlorinated paraffins (SCCPs) and medium-chain chlorinated paraffins (MCCPs) in PM_2.5 from 10 cities in China were studied in 2013 and 2014. The mean concentrations of ΣSCCPs and ΣMCCPs were 19.9 ± 41.1 ng m^-3 and 15.6 ± 18.6 ng m^-3, respectively. Unexpectedly, the highest pollution levels occurred in two central cities (Xinxiang and Taiyuan) rather than in well-known eastern megacities such as Beijing, Nanjing, Shanghai, and Guangzhou. By comparing with earlier research, it has indicated the trend of CPs industry shifting from large eastern cities to small and medium-sized cities in central China to some extent. In addition, the composition pattern of SCCPs demonstrated an obviously differences from previous studies, with C₁₁ and Cl₇ predominating and accounting for 45.1% and 24.9%, respectively. Meanwhile, the ratio of MCCPs/SCCPs in most cities was less than 1.00 except for Guangzhou (1.92), Shanghai (1.29), and Taiyuan (1.11). Combined with the results of correlation analysis and principal component analysis, the observed pollution characteristics of CPs in PM_2.5 had similar sources, which were more influenced by the ratio of MCCPs/SCCPs than by organic carbon, elemental carbon, temperature, population, and gross domestic product. Overall, the composition of CPs reflected the characteristics of local industrial production and consumption, and also implied efforts of Chinese enterprises to reduce the content of short carbon groups of CPs production. The CPs mainly deposited in head airways during the process of entering the human respiratory system. However, at the present levels, there was no significant carcinogenic effect for human health.

Policy effect of the Clean Air Action on green development in Chinese cities

The Clean Air Action is considered an important measure to control air pollution. Despite extensive studies on the benefits or the cost of the Clean Air Action, the overall effect of such an action on green development is largely unknown. This paper tries to fill this gap. Based on panel data of 278 Chinese cities, this paper begins with the construction of a comprehensive indicator, namely green production efficiency, to reflect the green development over the period 2011 to 2016, we then implement the quasi-difference-in-differences framework to identify the policy effect of the Clean Air Action on green development. The following findings are obtained: (1) The Clean Air Action has enhanced the green development of Chinese cities, especially in areas with relatively high reduction target and rich resource endowment; (2) The dynamic analysis reveals that the positive effect of the Clean Air Action on green development presents an intensifying trend with time. This paper provides new insights to understand the Clean Air Action, based on these findings, we propose that future policies should focus on the transformation of overall green development and take full account of regional heterogeneity.

Emission drivers of cities at different industrialization phases in China

As cities are the center of human activity and the basic unit of policy design, they have become the focus of carbon dioxide reduction, especially metropolitan areas that are high energy consumers and carbon dioxide emitters in countries such as China. The fact cities differ in their levels of development and stages of industrialization points to the need for tailor-made low-carbon policies. This study is the first to consider cities' different phases of industrialization when analyzing city-level emission patterns and drivers, as well as the decoupling statuses between economic growth and their emission levels in China. The results of 15 representative cities at different phases of industrialization show that various decoupling statuses, driving factors and decoupling efforts exist among cities, and that heterogeneity among these factors also exists among cities at the same industrialization phase. For further decomposition, energy intensity contributed the most to emissions reduction during the period 2005 to 2010, especially for cities with more heavy manufacturing industries, whereas industrial structure was a stronger negative emission driver during the period 2010 to 2015. Based on those findings, we suggest putting into practice a diversified carbon-mitigation policy portfolio according to each city's industrialization phase rather than a single policy that focuses on one specific driving factor. This paper sets an example on emissions-reduction experience for other cities undergoing different industrialization phases in China; it also sheds light on policy initiatives that could be applied to other cities around the world.

Air Pollution as a Cause of Obesity: Micro-Level Evidence from Chinese Cities

Chinese air pollution is obviously increasing, and the government makes efforts to strengthen air pollution treatment. Although adverse health effects gradually emerge, research determining individual vulnerability is limited. This study estimated the relationship between air pollution and obesity. Individual information of 13,414 respondents from 125 cities is used in the analysis. This study employs ordinary least squares (OLS) and multinomial logit model (m-logit) to estimate the impact of air pollution on obesity. We choose different air pollution and Body Mass Index (BMI) indicators for estimation. Empirical results show Air Quality Index (AQI) is significantly positively associated with the BMI score. As AQI adds one unit, the BMI score increases 0.031 (SE = 0.002; p < 0.001). The influence coefficients of particle size smaller than 2.5 μm (PM_2.5), particle size smaller than 10 μm (PM₁₀), carbon monoxide (CO), nitrogen dioxide (NO₂), ozone (O₃), and sulfur dioxide (SO₂) to the BMI score are 0.034 (SE = 0.002; p < 0.001), 0.023 (SE = 0.001; p < 0.001), 0.52 (SE = 0.095; p < 0.001), 0.045 (SE = 0.004; p < 0.001), 0.021 (SE = 0.002; p < 0.001), 0.008 (SE = 0.003; p = 0.015), respectively. Generally, air pollution has an adverse effect on body weight. CO is the most influential pollutant, and female, middle-aged, and low-education populations are more severely affected. The results confirm that the adverse health effects of air pollution should be considered when making the air pollution policies. Findings also provide justification for health interventions, especially for people with obesity.

Characterization of polycyclic aromatic hydrocarbon (PAHs) source profiles in urban PM2.5 fugitive dust: A large-scale study for 20 Chinese cites

Polycyclic aromatic hydrocarbons (PAHs) in road dust (RD) and construction dust (CD) in PM_2.5 were quantified in the samples collected in 20 Chinese cities. The PAHs profiles in urban PM_2.5 fugitive dusts were determined and their potential health risks were evaluated. Seven geographical regions in China were identified as northwest China (NWC), the North China Plain (NCP), northeast China (NEC), central China (CC), south China (SC), southwest China (SWC), and east China (EC). The overall average concentrations of total quantified PAHs (ΣPAHs) were 23.2 ± 18.9 and 22.8 ± 29.6 μg·g^-1 in RD and CD of PM_2.5, indicating that severe PAHs pollution to urban fugitive dusts in China. The differences of ΣPAHs between RD and CD were minor in northern and central regions of China but much larger in southern and east regions. The ƩPAHs for RD displayed a pattern of "high in northern and low in southern", and characterized by large abundance of high molecular weights (HMWs) PAHs, indicating that vehicle emission was the predominant pollution origin. Additionally, higher diagnostic ratios of fluoranthene/(fluoranthene + pyrene) in NCP, CC, and SWC suggest critical contributions of biomass burning and coal combustion for RD in these areas. In comparison, gasoline combustion was the major pollution source for CD PAHs in NWC, NCP, NEC, and CC, whereas industrial emissions such as cement production and iron smelting had strong impacts in the heavy industrial regions. The total benzo[a]pyrene (BaP) carcinogenic potency concentrations (BaP_TEQ) for RD and CD both showed the lowest in SC (0.05 and 0.07, respectively) and the highest in NCP (10.99 and 7.67, respectively). The highest and lowest incremental life cancer risks (ILCR) were found in NCP and SC, coinciding with the spatial distributions of ambient PAHs levels. The total CD-related cancer risks for adults and children (~10^-4) suggest high potential health risks in NCP, SWC, and NWC, whereas the evaluated values in EC and SC indicate virtual safety (≤10^-6).

Carbon dioxide emission driving factors analysis and policy implications of Chinese cities: Combining geographically weighted regression with two-step cluster

Global warming and climate change have become a serious environmental problem and China's carbon emissions are currently the highest in the world. Cities are the main sources of carbon emissions and the key to solving these problems. Therefore, research on reducing carbon dioxide emissions is a matter of concern. In this study, a spatial autocorrelation analysis was performed to understand the spatial characteristics of carbon dioxide emissions in 171 Chinese cities. Then, stepwise and geographically weighted regressions were used to explore the processes that drive carbon dioxide emissions in Chinese cities. A two-step cluster was used to classify Chinese cities into different categories based on the degree of impact of each driver. The results showed that there is a spatial aggregation relationship between urban carbon dioxide emissions. High-high clusters mainly occur in the Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations, while low-low clusters occur in the central, western, and southwestern cities. Among all variables, freight volume, per capita gross domestic product, population density, and the proportion of secondary industries correlate positively with carbon dioxide emissions, whereas the number of buses per 10,000 people correlates negatively with carbon dioxide emissions. The geographically weighted regression model provided more detailed results and revealed the spatial heterogeneity of the effects of the different drivers. The impact of population, economic factors, and industrial factors in the eastern region is significantly greater than that in the central and western regions. Freight volume and public transport have the most significant impact in the northeast region. The clustering results showed that cities can be divided into four types. These findings provide a reference and policy suggestions for how cities in different regions should reduce carbon dioxide emissions.

Evaluation of China's Environmental Pressures Based on Satellite NO2 Observation and the Extended STIRPAT Model

China’s rapid urbanization and industrialization have affected the spatiotemporal patterns of nitrogen dioxide (NO₂) pollution, which has led to greater environmental pressures. In order to mitigate the environmental pressures caused by NO₂ pollution, it is of vital importance to investigate the influencing factors. We first obtained data for NO₂ pollution at the city level using satellite observation techniques and analyzed its spatial distribution. Next, we introduced a theoretical framework, an extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, to quantify the relationship between NO₂ pollution and its contributing natural and socio-economic factors. The results are as follows. Cities with high NO₂ pollution are mainly concentrated in the North China Plain. On the contrary, southwestern cities are characterized by low NO₂ pollution. In addition, we find that population, per capita gross domestic product, the share of the secondary industry, ambient air pressures, total nighttime light data, and urban road area have a positive impact on NO₂ pollution. In contrast, increases in the normalized difference vegetation index (NDVI), relative humidity, temperature, and wind speed may reduce NO₂ pollution. These empirical results should help the government to effectively and efficiently implement further emission reductions and energy saving policies in Chinese cities in a bid to mitigate the environmental pressures.

Influence of Urban-Growth Pattern on Air Quality in China: A Study of 338 Cities

Air pollution in China is a serious problem and an inevitable threat to human health. This study evaluated the relationship between air quality and urban growth pattern in China by conducting empirical research involving 338 prefecture-level and above cities. Spatial regression techniques considering spatial autocorrelation were applied to correct the calculation bias. To obtain local and accurate results, a conception of eight economic zones was adopted to delineate cities into different groups and to estimate regression separately. An additional six urban form and socioeconomic indicators served as controlling variables. Significant and positive relationships between the aggregated urban growth pattern index and air pollution were observed in Northeast China, northern coastal China, and Northwest China, indicating that a high degree of urban aggregation is associated with poor air quality. However, a negative parameter was obtained in southern coastal China, showing an opposite association on urban aggregation and air quality. Nonsignificant connections among the other four zones were found. The findings also highlighted that land use mix, population density, and city size exerted varied and significant influence on air quality across eight economic zones. Overall, this study indicated that understanding the quantitative relationships between urban forms and air quality can provide policymakers with alternative ways to improve air quality in rapidly developing China.

The weight of unfinished plate: A survey based characterization of restaurant food waste in Chinese cities

Consumer food waste has attracted increasing public, academic, and political attention in recent years, due to its adverse resource, environmental, and socioeconomic impacts. The scales and patterns of consumer food waste, especially in developing countries, however, remain poorly understood, which may hinder the global effort of reducing food waste. In this study, based on a direct weighing method and a survey of 3557 tables in 195 restaurants in four case cities, we investigated the amount and patterns of restaurant food waste in China in 2015. Food waste per capita per meal in the four cities was 93g, consisting mainly of vegetables (29%), rice (14%), aquatic products (11%), wheat (10%), and pork (8%). This equals to approximately 11kg/cap/year and is not far from that of western countries, although per capita GDP of China is still much lower. We found also that food waste per capita per meal varies considerably by cities (Chengdu and Lhasa higher than Shanghai and Beijing), consumer groups (tourists higher than local residents), restaurant categories (more waste in larger restaurants), and purposes of meals (friends gathering and business banquet higher than working meal and private dining). Our pilot study provides a first, to our best knowledge, empirically determined scales and patterns of restaurant food waste in Chinese cities, and could help set targeted interventions and benchmark national food waste reduction targets.

Meeting critical challenges and striving for urban sustainability in China

China has experienced outstanding economic growth during the last three decades through urbanization. But at the same time, many ecological and social issues have been marginalized, leading to problems in public safety, health, and social equity. Such a pattern of development is unlikely to be sustainable. In this article, we examine these issues and the challenges that come with resolving them, and advocate a holistic and pragmatic approach to the research and practice of urban sustainability in China.