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The use of clean energy can promote the coordinated development of the economy and the ecological environment. However, few studies have paid attention to the changes in the health status of residents in the process of economic development and energy use. To fill this gap, this paper uses health energy intensity, which refers to the amount of energy consumed per unit of health status (composed of population mortality, maternal mortality, and perinatal mortality), to explore the impact of clean energy (expressed by the share of clean energy consumption in total energy consumption) on economic development and healthy energy intensity. By using the panel data of 30 provinces and cities in China from 2005 to 2019, this paper constructs a simultaneous equation model for empirical analysis from the perspective of the whole country and areas with different income levels. The results show that from the national perspective, in the early stage of clean energy development, the level of economic development and healthy energy intensity increased; however, with the further development of clean energy, the sample period shows that the level of economic development and the healthy energy intensity decreased. Heterogeneity analysis shows that in both high-income and moderate-income areas, clean energy has a U-shaped effect on economic development; but in low-income areas, clean energy has an inverted U-shaped effect on economic development. In high-income and low-income areas, clean energy has an inverted U-shaped effect on healthy energy intensity; but in moderate-income areas, clean energy has a U-shaped effect on healthy energy intensity. China's clean energy market is still in its early stage, and the research conclusions of this paper provide a theoretical basis for the realization of China's clean energy development.

PM2.5 has a significant negative impact on human health and atmospheric quality, and accurate prediction of its concentration is necessary. When using common point prediction models for PM2.5 concentration prediction, the influence of various uncertainties on PM2.5 concentrations makes the prediction results suffer from poor accuracy. To address this issue, this paper proposes the quantile regression neural network (QRNN) model based on the least absolute shrinkage and selection operator (LASSO), combined with kernel density estimation (KDE) for probabilistic density prediction of PM2.5 concentrations. The model uses LASSO regression to select the influential factors, and then the quartiles of daily PM2.5 concentrations calculated by the QRNN model are imported into the KDE model to obtain the probability density predictions of PM2.5 concentrations. In the paper, empirical analyses are carried out with the cities of Beijing and Jinan in China as well as six other datasets, and the prediction performance of the model is assessed by using evaluation criteria in both point prediction and interval prediction. The simulation reveals that the predictive performance of the LASSO-QRNN-KDE model is well, and the model is not only effective in filtering high-dimensional data, but also has a higher accuracy compared to common research models. In addition, the model is able to describe the uncertainty of PM2.5 concentration fluctuations and carry more information on the variation of PM2.5 concentrations, which can provide a novel and excellent PM2.5 concentration prediction tool for relevant policy makers.

Under the background of "the Belt and Road" and "the economic corridor of China, Mongolia and Russia" initiatives, it is of great significance to study the temporal and spatial evolution characteristics of urbanization in Russia. This paper studied the population urbanization level, economic urbanization level, social urbanization level, eco-environment urbanization level, and their coupling coordination development degree during 2005-2020 in Russia. First, combining with the Population-Economic-Sociology-Eco-environment model, the paper constructed the index systems to evaluate the urbanization development levels in Russia. Second, based on the comprehensive weighting method of entropy weight and variation coefficient, this paper calculated the population urbanization level, economic urbanization level, social urbanization level, and eco-environment urbanization level in Russia. Third, this paper used the coupling coordination model to measure the coupling coordination degree of the urbanization development levels in Russia. Finally, the spatial differentiation of the population urbanization level, economic urbanization level, social urbanization level, eco-environment urbanization level, and their coupling coupling-coordination degree was performed, respectively, by using ArcGIS. The results are as the following. First, from 2005 to 2020, the economic urbanization level and eco-urbanization level have shown the increasing trend in Russia. The population urbanization level and social urbanization level have shown the stable changing trend in Russia. The eco-environment urbanization and economic urbanization contribute larger share to the urbanization system compared with the population urbanization and social urbanization. The coupling coordination development degree of population urbanization level, economic urbanization level, social urbanization level, and eco-environment urbanization level has showed a slight increasing trend. However, the overall situation of the urbanization in Russia is still in the moderate uncoordinated recession stage. Second, the federal subjects with high urbanization development levels are mainly distributed in Moscow city, Moscow Region, Sverdlovsk Region, Tumen Region, Saint-Petersburg city, Republic of Tatarstan, Krasnoyarsk Territory, Republic of Bashkortostan, Chelyabinsk Region, Nizhny Novgorod Region, Krasnodar Territory, Rostov Region, and Khanty-Mansiysky Autonomous Area. The federal subjects with low ones are mainly located in Republic of Khakasia, Republic of Marii El, Republic of Kabardino-Balkaria, Republic of Tyva, Republic of Karachaevo-Cherkessia, Republic of Kalmykia, Republic of Altay, Jewish Autonomous Area, and Republic of Ingushetia. Third, spatially, from 2005 to 2020, the urbanization pattern of population, economy, society, eco-environment, and their coupling coordination degree in Russia all show unbalanced development characteristics. The population urbanization pattern and the social urbanization pattern have not changed significantly, showing the spatial characteristics of "high west, low east," and "high middle, low north, low south." The economic urbanization pattern has been increasing significantly, showing the spatial characteristics of "high core, low edge." The eco-environment urbanization pattern has not changed significantly, showing the spatial characteristics of "high north, low south." The coupling coordinated development degree of urbanization pattern has showed a slight increasing trend, showing the spatial characteristics of "high middle, low north, low south," "high west, low east". Finally, we suggest policies and strategies that can boost the growth and development of the urbanization in Russia.

Rapid urban expansion and energy transformation are two major challenges facing China. Many researchers have explored urban compactness and energy efficiency, but there is little literature on the coordinated development of the two. Therefore, this paper takes 35 cities in China as research object: The urban compactness evaluation index system is established, and the score is calculated using the entropy weight method; considering the bad output, SBM is used to calculate the green total factor energy efficiency (GTFEE); and the coupling coordination degree model is used to calculate their coordinated degree. The following conclusions can be inferred: (1) Although China's urban compactness has increased, it has not maintained a steady upward trend. Some more developed cities have the phenomenon of compactness rising first and then falling; (2) The overall GTFEE shows a continuous upward trend, but there is still much room for improvement, and regional disparities are significant; (3) The degree of coordination between the two maintains a steady upward trend, and the city's compactness lags behind GTFEE. Based on the findings of the study, this paper proposes policy recommendations that can serve as a reference for key decision-makers and are critical in promoting China's urbanization and energy transformation.

In the current Anthropocene epoch, globalization and urbanization have adversely affected our environment causing global warming. To counter the adverse effects of global warming, research is being conducted into many innovative technologies to identify viable solutions. This paper will focus on one such solution, Living walls and how the built form is enriched by the environmental and psychological benefits provided by Living walls. Buildings with Living walls have lively surroundings which enhance the urban fabric. This review paper shall elaborate on the effects of Living walls on the built environment in the urban realm and analyze how Living walls improve the urban fabric in terms of activity and behavior pattern, streetscape and building frontage.

Free-trade zones are highlands of regional development that have a radiating effect on surrounding cities. This paper uses Yangtze River Delta Urban Agglomeration (YRDUA) data from 2003 to 2018, to examine the impact of the China (Shanghai) pilot free-trade zone (SPFTZ) on green total factor productivity (GTFP) in the YRDUA. It uses the regression discontinuity (RD) method and analyzes the influence mechanism with the help of the mediating effect model. The paper presents the following major findings. The establishment of the SPFTZ significantly promoted GTFP in the YRDUA, with technological progress being the main driving force. An analysis using the mediating effect model found that financial efficiency and industrial structure upgrading play a complete mediating role in GTFP growth in the YRDUA. Therefore, the SPFTZ should make use of its policy advantages, take full advantage of the effect of the green growth pole, and promote green mutual development with the YRDUA.

Labor agglomeration with heterogeneous skills has different effects on urban air pollution. Based on the panel data of 263 prefecture level cities in China from 2006 to 2019, this paper constructs a Spatial Durbin Model to explore the impact of skill heterogeneous labor agglomeration and the interaction between skill heterogeneous labor agglomeration on urban air pollution. The results show that there is a positive U-shaped relationship between high-skilled labor agglomeration, low-skilled labor agglomeration, and urban air pollution. From the perspective of restraining urban air pollution, high-skilled labor agglomeration is stronger than low-skilled labor agglomeration. Under the influence of high skilled labor agglomeration, the inhibitory effect of low-skilled labor agglomeration on urban air pollution is enhanced. High-skilled labor agglomeration and low-skilled labor agglomeration reduce the degree of urban air pollution by promoting the improvement of urban innovation level. Based on this, this paper puts forward some policy suggestions, such as further promoting urban labor agglomeration, formulating reasonable urban population management policies, strengthening labor exchange and learning, and carrying out labor knowledge and skills training.

In 2020, China promised to achieve carbon peaking by 2030 and carbon neutrality by 2060, and these targets are famous as “Goal 3060” in China. Chinese resource-based cities are concerned about the realization of Goal 3060 to practice national action against environmental change. In this paper, this study evaluates the impact of population, economic growth, energy intensity, industrial structure, fixed asset investment, and urbanization level on carbon emissions in Chinese cities. To do so, the paper divides 36 Chinese cities into four types (growing city, mature city, recessionary city, and regenerative city) from 2003 to 2017 by factor investigation according to the diverse development stages. The extended STIRPAT model is used to assess the impact of various factors on CO2 emissions in the Yellow River basin and diverse city levels. The panel regression analysis was conducted for the basin as a whole and cities at different development stages through a fixed-effects model and a linear regression model with Driscoll-Kraay standard errors. The results show that (1) the total carbon emissions in the Yellow River basin continued to climb during the study period. However, the growth rate slowed down significantly after 2012. In addition, there are differences in the total carbon emissions and growth rate of different cities. (2) Population, real GDP, energy intensity, industrial structure, and fixed asset investment all have a significant positive impact on carbon emissions in the overall basin except the urbanization level which has a significant negative influence on carbon emissions. (3) There is heterogeneity in the influencing factors of carbon emissions in resource-based cities at various development stages. Based on these results, corresponding policies are proposed for different types of cities to help resource-based cities achieve the 3060 dual carbon goal.

China, as the world's largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities, must enhance efforts in carbon emission reduction to jointly realize China's low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that contribute to carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits carbon emissions, followed by energy structure effect. This paper considers the driving factors of both the production side and the residential consumption side from the city level. The research reveals the main driving factors that effect the carbon emissions of megacities in developing countries, and highlights the leading role of megacities in terms of carbon emission reduction in China and even the world. The paper thereby puts forward policy implications for China's economic policies.

Management of economic growth targets is a universal measure employed by worldwide governments for macroeconomic regulation, as well as an important mechanism for central governments to motivate local governments and officials. This paper extracted panel data on annual economic growth targets and environmental regulation indicators from government work reports (2009–2016) of 284 China’s prefecture-level cities to empirically investigate the impact of economic growth targets set by governments of prefecture-level cities on the environmental regulation intensity. The study concludes that an increase in economic growth targets significantly weakens the intensity of environmental regulation. The conclusion still holds true after robustness tests by changing measurement variables, regression samples and conducting endogenous tests. This also corroborates that empirical results in this study are robust. The underlying reason for the inhibitory effect may be that local governments to achieve economic growth targets prefer less stringent environmental regulations so as to expand outputs in the short term, mainly by increasing the proportion of secondary industry in GDP, land transfer area and fixed asset investment. Further research in this paper also finds that the negative effect of a local government’s annual economic growth target on environmental regulation intensity only exists in cities with low economic development levels and low openness to the outside world.

China's Loess Plateau Region (LPR) plays a significant role in national ecological security and development. Due to the advantage that relates environment with economy, eco-efficiency has become an important indicator of sustainable analysis. Using cross-level panel data for the period 2006-2017, this paper studied LPR's static eco-efficiency and dynamic performance through a combined application of DEA super-efficient slack-based measure and Malmquist Productivity Index at multi-scales. LPR's eco-efficiency was found to experience a slight increase during the study period. The value decreased roughly from east to west, with high eco-efficiency mainly distributed in provincial cities and resource-based cities. The decomposition of the Malmquist Index indicated that technological change contributed most to the improvement of eco-efficiency in the LPR. Besides, this paper explained the variations of eco-efficiency based on the integrated input-output indicators and TOBIT regression model. Economic scale, population density, government regulation, technical innovation, and openness degree were identified as positive influencing factors, while the structure of the industry and land-use intensity were found to have negative impacts on eco-efficiency. Resource-based cities were found to have stronger potentials for eco-efficiency improvement than non-resource-based cities. This paper revealed the characteristics of LPR's eco-efficiency from three perspectives: a spatiotemporal perspective, a macro-meso-micro perspective, and a static-dynamic perspective. The findings of this study hold important implications for policy makers.

The rapid development of the urban economy in China and the accompanying income growth experienced by urban residents have increased demand for tourism and leisure, which has brought pressure on the urban tourism environment system (UTES), making the contradiction between tourism economic development and the ecological environment increasingly acute. While seeking to rationalize the economic, social, and ecological benefits of tourism, reducing the fragility of the UTES and improving its anti-interference and recovery capabilities have become attracted significant attention from scholars in China and elsewhere. This paper establishes a definition of resilience for an UTES and constructs an evaluation index system for it in terms of the social, economic, and ecological environments. It also establishes an entropy weight-TOPSIS resilience evaluation model to measure resilience in regional systems, using ArcGIS to analyze the standard deviation ellipse and center of the gravity track of the resilience. System dynamics was used to construct diagrams of causal relationships and stock flow for the constituent elements of UTES to show the mechanisms that promote its resilience. This paper investigates 14 cities of Gansu Province in particular to simulate the resilience model of a regional system.

In China, the local government's "land for development" strategy has led to a large number of urban construction land allocated to the industrial field, which has promoted the rapid development of industry and economy in the short term but also brought serious environmental quality losses. This paper systematically sets out how land misallocation works on urban air quality and employs the spatial Durbin model (SDM) to conduct an empirical analysis on the panel data of 283 China cities at or above the prefecture level. The result shows that, stimulated by financial maximization and political promotion, in order to obtain more fiscal revenue and growth performance, local governments prefer to allocate a large number of urban construction land to industry and related fields, which leads to the underestimation of industrial land price and the misallocation of land resources. Land misallocation has exerted significant inhibiting effects on the air quality of local and their surrounding cities through inhibiting the upgrading of industrial structure. Further analysis reveals that the bigger the city, the lesser the inhibition effects of land misallocation on upgrading of industrial structure and urban air quality and vice versa. The conclusions of this paper can provide a useful reference for local governments to optimize land allocation, promote economic restructuring, and environmental quality upgrading.

Spatiotemporal variations of industrial carbon emissions (IE) must be scientifically understood, which will be helpful to formulate reasonable emission reduction strategies. Given that spatial distribution of IE is irrelevant to space agents commonly used (such as population and nighttime light), estimation and spatialization methods for total carbon dioxide (CO2) emissions are not entirely suitable for IE. Therefore, this paper used greenhouse gases observing satellite level 4A product to estimate IE at the city level and used industrial land density to obtain the distribution of IE within the administrative districts. Sectoral emission inventories of 182 cities and a mosaic Asian anthropogenic emission inventory named MIX were used to verify the results. Then, spatiotemporal variation characteristics of China's IE were analyzed from multiple levels. Results showed that (1) the mean relative error of estimation results was 56.11%, among which 62 cities had relative error of less than 30%. Gridded IE in this paper had high consistency with MIX. (2) Cities with high IE experienced rapid growth from 2009 to 2012, followed by slower growth from 2012 to 2017. (3) Centroid of significant cold and hot spots moved to the southeast and northwest, respectively. Most cities with high annual IE growth had relatively low emission efficiency, mainly located in Inner Mongolia and Xinjiang. Aggregation of medium and high IE grids may represent high emission efficiency. Significant differences still exist between cities in IE, and sustainable development strategies should be formulated according to local conditions. Regions with high annual growth or low emission efficiency are the key to achieving IE reduction targets in future.

Persisting in opening up and achieving coordinated development of economy, society, and ecology is China's major strategies for achieving sustainable urbanization. Ecological efficiency is a reasonable indicator to measure the development level of a circular economy. Therefore, using the statistical data of 30 provinces in China from 2004 to 2016, this paper uses the super-efficiency SBM model that considers undesired output to measure the eco-efficiency value, which is used to measure the level of circular economy development in each province. Based on this, a panel model is constructed to test the impact of circular economy and FDI on urbanization. The empirical results show that (1) there is a complex nonlinear relationship between the development of circular economy and the advancement of urbanization, and the shape of the curve varies with areas; (2) FDI under environmental regulation is conducive to promoting China's urbanization to achieve green, effective, and sustainable development; and (3) the development of the tertiary industry, human resources, innovation capabilities, and employment situation is conducive to promoting China's urbanization. Finally, based on the empirical results, this paper puts forward policy recommendations to achieve green, efficient, and intelligent development of Chinese cities by promoting the development of a circular economy and strengthening FDI screening.

Solar photovoltaic panels are increasingly being used throughout the world, particularly in Egypt, where a station has been constructed in the city of Aswan with a capacity of 1480 MW, and is classified as one of the largest photovoltaic plants in the Middle East country, where photovoltaic systems are characterized as environmentally friendly and do not produce any pollutants, and photovoltaic systems have the ability to operate with diffuse radiation. It is therefore very important to understand how photovoltaic panels respond to changing weather conditions and how climate conditions affect the performance of photovoltaic cells, as only 15-20% of solar radiation can be converted to electricity, while the rest is wasted as thermal heat. There are two very important factors influencing the efficiency of the photovoltaic cells: the cell temperature and the solar radiation intensity on the photovoltaic cells. Cooling of the optical surfaces is one of the main factors that must be considered in order to achieve the highest efficiency when operating the solar PV systems. By using the appropriate cooling technology for the photovoltaic cells, the electrical efficiency is improved, and the cell degradation rate is decreased over time, which increases the life span of the PV panels. In some applications, such as industrial and domestic applications, excess energy removed with cooling technology could be used. The cooling mediums used to cool the PV panels are water, air, PCM, and nanofluid. Also, the spectral splitting utilization represents a good solution for hybrid solar photovoltaic-thermal applications to obtain higher performance of the photovoltaic. This paper gives a brief analysis of technologies used to improve PV systems efficiency in terms of the nature of cooling media, spectral splitting, and reflectors. Moreover, the economic study of these techniques is presented to demonstrate their economic feasibility. The aims of the present review paper are to provide good knowledge and understanding of all technologies used to improve the performance of PV systems and demonstrate the economic feasibility of these technologies.

Rainfall runoff pollution is one of the main causes of water quality deterioration in urban water system. Setting up initial rainwater storage tank could be one of the rapid and effective methods to control runoff pollution. In order to speed up the water environment management processes, the Chinese government has adopted the Public-Private-Partnership (PPP) mode in the water environment treatment to deal with the shortage of funds. Ensuring water quality and controlling water environment management cost are key to PPP projects. Therefore, factors such as pollutant accumulation characteristics of the catchment, land space availability, sewage treatment plants capacity, and river water management cost should be considered during the design of the initial rainwater storage tank on the premise of ensuring water quality. The empirical design method can hardly meet these requirements simultaneously. Under the background of PPP water environment treatment project, a constrained economic optimization model of the initial rainwater storage tank was presented in this paper. The relationship between the total cost of the water environment management and the interception rate of the initial rainwater storage tanks was established by means of Artificial Neural Network (ANN), while the penalty function was used to transform the constrained optimization problem into an unconstrained optimization problem. The interception rate of the initial rainwater storage tanks was then optimized by means of Particle Swarm Optimization (PSO), and the designed volume of the storage tanks was calculated according to the relationship between the interception rate of the storage tank and the cumulative runoff of the related catchment. Finally, a case study of a PPP demonstration project in a plain city in China was conducted. The results demonstrated that compared with the specification method, the total volume of the initial rainwater storage tank increased by 38.7%, the interception rate increased by 68.4%, and the total cost of river water treatment decreased by 5.7% under the constraints of land space availability and sewage treatment capacity. In addition, the optimized method proposed in this paper could reflect the pollutant accumulation characteristics of the catchment. It not only reduce the total cost of the water environment management but also effectively reduce the impact of non-point source pollution on urban water system, and could be more widely used in other areas and PPP projects.

A detailed characterization of municipal solid waste (MSW) beyond a standard compositional analysis may offer insights useful for improving waste management systems. The present paper contributes to the scarce literature in the field by presenting new data from a rapidly developing Central Asian city, the capital of Kazakhstan, Nur-Sultan. Three sampling campaigns (each 1 week long) have been conducted at the city landfill over a 1-year period (2018–2019), and a detailed characterization for selected waste components and sub-components has been performed. The major fractions of MSW were organics (46.3%), plastics (15.2%), paper (12.8%), and diapers (5.9%). The detailed composition analysis showed high LDPE (low-density polyethylene) content (5.5%) mostly comprised of plastic bags (4.5%), transparent glass (3.2%), pharmaceuticals (0.4%), and fine (i.e., < 12 mm) organic fraction content (29%). The MSW generation rate of Nur-Sultan was estimated as 1.47 kg capita−1 day−1 based on the field collection as well as literature data. Among sustainable waste management recommendations addressed for Nur-Sultan and applicable to other cities in Central Asia, composting is recommended due to high organics fraction in MSW and its great potential to reduce the landfilled waste volume and to help valorizing the waste.

To comprehend city-level driving mechanisms of carbon emissions, this paper utilizes spectral cluster and two-layer LMDI (logarithmic mean divisia index) method to systematically assess the contribution values of correlative factors from each cluster to Henan's carbon emissions increments, and accordingly comes up with more strategies about how to reduce carbon emissions for each cluster on the basis of driving forces of carbon emissions. The results of clustering and the decomposition are as follows: (1) the 18 prefecture-level cities in Henan were divided into five categories by spectral clustering, and there are similar development patterns within each category, so they can learn from each other to improve their own defects of development; (2) this paper utilizes the two-layer LMDI method to divide the factors affecting each cluster of carbon emissions into four types, which includes energy structure, energy intensity, per capita GDP, and population, and calculates the contribution value of each factor. It was concluded that the contribution value of per capita GDP dominantly drove up carbon emissions, while energy intensity played a significant role in offsetting them. Therefore, it is important for Henan's low-carbon development to control the expansion of economic activity and improve energy efficiency in the future.

This paper discusses the effect of network infrastructure on environmental pollution reduction and the realization mechanism behind it. Based on the panel data of 285 cities in China from 2005 to 2019, this study regards the "Broadband China" pilot policy as a quasi-natural experiment to clarify the pollution emission reduction effect of network infrastructure construction through differences-in-differences method and other methods. The research results show the following: (1) The Broadband China pilot policy has reduced environmental pollution, that is, the construction of network infrastructure has the effect of environmental pollution reduction. The conclusion is still established after a series of robustness tests such as parallel trend test, placebo test, and instrumental variable method. Through the heterogeneity test, it is found that the pollution reduction effect of network infrastructure construction is more obvious in non-resource-based cities, first and second tier cities, and cities in the eastern region (2). The construction of network infrastructure plays a restraining role on local environmental pollution. Due to the insufficient level of regional linkage and the siphon effect of pilot cities, the spatial spillover characteristics of the pollution reduction effect are not obvious (3). The mechanism of action shows that green innovation is an important mediating effect mechanism for network infrastructure construction to reduce environmental pollution. Cities in regions with high degree of marketization and environmental regulation can strengthen the effect of network infrastructure construction on environmental pollution reduction. The research conclusions are conducive to accelerating the development of the digital economy represented by the construction of network infrastructure and provide a useful reference for promoting the level of environmental pollution reduction and achieving high-quality development.

The rapid growth of population and economy leads to a further increase in demand for water resources. The contradiction between supply and demand of water resources has become the main bottleneck restricting sustainable development of a regional social economy. Accurate measurement of regional water usage and the harmonious balance between water consumption (WC) and economic growth (EG) are the premise of regional high-quality development. Based on this premise, this paper studied the arid oasis region, Hexi Corridor, as the research object; utilized the theory of ecological footprint to calculate the ecological footprint (EFW) and ecological carrying capacity (ECCW) of water resources from 2005 to 2019; and quantitatively analyzed the water utilization situation in Hexi Corridor in the past 15 years. Then, combining with the coordinated development decoupling evaluation model, the connection between WC and EG was evaluated. The main results of this study were as follows: (1) During the study period, EFW has shown a fluctuating downward trend, decreasing from 1.745 in 2015 to 1.588 hm

With the advent of the Industry 4.0 era, the digital economy (DE) has become a new driver of sustainable development. This paper focuses on DE's green and environmental value. Based on the panel data of 108 cities in the Yangtze River Economic Belt from 2011 to 2019, we evaluated the temporal and spatial evolution of DE and green total factor productivity (GTFP). The spatial Durbin model analyzes the direct and spatial spillover effects of DE on GTFP, the green efficiency change (GEC), and the green technical change (GTC). The results show that the DE maintains a stable growth trend. GTFP and GTC show a similar fluctuating upward trend, while GEC shows a fluctuating downward trend. Both DE and GTFP show a clustering trend of "high in the lower reaches and low in the upper and middle reaches" and a typical center-periphery pattern over time. Second, for the direct effect, DE has a significant positive impact on GTFP, mainly on GTC rather than GEC. The promotion effect is stronger at the new normal stage, in the lower reaches, and the three major urban agglomerations. Third, for the spatial spillover effect, DE has a trickle-down effect on GTFP and GTC and a siphon effect on GEC, more potent at the new normal stage and in the lower reaches. Compared with peripheral cities, DE has significant trickle-down effects on GTFP, GTC, and GEC in the three major urban agglomerations.

The sustainable development of human beings is facing severe challenges in the context of a new era. The effective reduction of carbon emission intensity is essential to achieve the goal of sustainable development. Obviously, green innovation is an important factor in mitigating carbon emission intensity. It is important to measure the effect of green innovation on carbon emission intensity for accelerating industrial transformation and building a circular economy system. Therefore, this paper uses the data of 30 provinces in China from 2000 to 2019, obtained by the State Intellectual Property Office, using fixed effects model quantile regression model and Spatial Durbin Model to empirically verify the theoretical hypothesis. The conclusions are as follows: (1) Green innovation inhibits carbon emission intensity. Instrumental variable model and robustness test support this conclusion. (2) For carbon emission intensity under different quantiles, green innovation has a more significant effect on provinces with high carbon emission intensity through "target accountability system" and "reverse coercive system." (3) There is a significant spatial correlation between green innovation in China's provinces. The reduction of carbon emission intensity in the region will benefit from the improvement of green innovation in surrounding cities.

The development of urbanization has changed the original land cover and exacerbated the urban heat island effect, seriously affecting the sustainable development of the ecological environment. Research on urban heat island characteristics and land cover changes in five major urban agglomerations in China to provide a reference for preventing thermal environmental risks and urban agglomeration construction planning. This paper estimates the surface urban heat island intensity (SUHII) of the five major urban agglomerations in China from 2003 to 2019 based on Google Earth Engine (GEE) through the urban-rural dichotomy, analyzes their trends through the Sen + M-K trend analysis method, and combines the detrending rate matrix to analyze the impact of land cover type shift on urban heat island change. Research shows that (1) the land cover types of the five major urban agglomerations in China have changed considerably from 2003 to 2019, and all five major urban agglomerations in China experienced varying degrees of urban expansion. (2) The annual average value of SUHII decreases in Beijing-Tianjin-Hebei, Yangtze River Delta, and middle reaches of the urban agglomerations, while the annual average value of SUHII increases in Chengdu-Chongqing and Pearl River Delta urban agglomerations. (3) The spatial composition of land cover types in the five major urban agglomerations in China is highly spatially correlated with urban heat islands, with urban land and bare land urban heat islands being the most pronounced. (4) The land cover type shift has the most significant heat island impact on Beijing-Tianjin-Hebei, Yangtze River Delta, and Chengdu-Chongqing urban agglomerations. (5) The land cover change (LCC) with an increasing trend in SUHII is mainly bare land converted to arable land, and water bodies, grassland, forest land, and arable land converted to urban land.

This paper examines the relationship between climate change and the spatial distribution of population in China. We establish a two-way fixed effects model to investigate the role of temperature difference in the spatial distribution of China's population. We find that the annual variation of temperature has an impact on city size in both large and small cities, and that city size tends to shrink as the temperature difference increases. Meanwhile, we also find that the population in the cities located south of Qinling-Huaihe Line and Aihui-Tengchong Line (Hu's Line) is more sensitive to temperature effects, and that the temperature difference has a significant negative effect on city size. Similarly, the same results are found for prefecture-level cities with low administrative levels. Considering the endogeneity between temperature change and city size, we adopt an instrumental variable using latitude to perform a more robust empirical analysis, the results of a series of robustness tests support these conclusions.

Every year the concern from environmental pollution increases as it affects human health. In the current paper, it is analyzed the evolution of the air quality in different cities on international level related to the reduction in mobility trend due to the emergence of the pandemic. The air pollution is studied in order to achieve healthier cities in the future and create an awareness for the citizens. The COVID-19 pandemic situation was declared during the first months of 2020 and has shown (indirectly) the effects that restrictions on human activity and road traffic have on atmospheric pollutant values. This pandemic condition derived, in most cases, in mobility restrictions in order to decrease the spread of the virus through the density of population in different places and, as a result of these restrictions, pollutant presence decreased equally. This article analyzes some of the largest cities around the world, such as Tsuen Wan district in Hong Kong (China), Los Angeles (USA), London (UK), São Paulo (Brazil), Bangalore (India), Johannesburg (South Africa), and Sydney (Australia) in order to check the different mobility restriction policies established in each location, and how those restrictions led to decrease the levels of some pollutant particles such as NO

Half of the world population live in the cities. Cities energy consumption, environmental impact, and the opportunities they provide for our planet’s sustainability make them attractive for governmental authorities. Any action taken in the cities has immediate repercussions. For this reason, many statistical data are published every year. This paper makes the best use of these data to calculate cities CO2 emissions and their thermal and electric energy consumption. The methodology applied takes into consideration each city size by number of inhabitants and gets results per inhabitant and household. This will make possible to put into practice the right actions to reduce CO2 emissions and to use alternative energy. This paper also defines an index to facilitate and simplify the analysis of results. This study was applied to the case of Spain to show the methodology here proposed. In fact, this type of study has never been carried out in Spain before. With this purpose, the 145 Spanish cities with more than 50,000 people were considered. Results show that cities with larger populations present higher consumptions per inhabitant and household. The smallest the population of a city is, the less energy the city consumes. However, electric energy consumption remains constant regardless of the population size. With regard to the CO2 emissions, results bring to light that the biggest cities produce the highest emissions. Furthermore, comparing emissions produced by electrical sources to the total emissions, it was concluded that the smallest cities produce the highest electrical emissions.

The development of the digital economy radiates to national ecological protection and resource utilization, holding the key to haze governance. However, as the largest developing country, the impact of China's digital economy on haze pollution has received little attention. Based on the panel data of 275 prefecture-level cities in China from 2011 to 2018, this paper studies the impact and mechanism of digital economy development on haze pollution. The results of fixed effect model show that the development of digital economy has a significant negative impact on urban haze pollution in China. The results of heterogeneity analysis show that the development of digital economy has a more significant inhibitory effect on the haze pollution of resource-based cities, non-resource-based cities, eastern cities, and large cities. The quasi-natural experimental results show that the establishment of the "national big data comprehensive experimental area" is conducive to haze pollution control, which proves the conclusion that the digital economy can significantly reduce haze pollution. The development of digital economy can significantly reduce haze pollution by promoting technological innovation and optimizing resource allocation.

Urban regions are the dominant carbon emitter in China. Although the role of urban regions is a crucial concern in peaking China's carbon emission in 2030 or earlier, there were little focus set to the driving factors among urban regions from spatial differences. To excavate more effective abatement strategies to China's cities, this paper improved the environmental Leontief and Ghosh input-output models and structural decomposition analysis to investigate the driving factors of the spatial differences in the CO

In the complex context of urbanization and climate change, how to improve the resilience of cities to deal with various uncertain and unpredictable threats is a new topic with both theoretical and practical challenges. In this paper, the researches on urban resilience are summarized using the bibliometric analysis combined with the visualization analysis. We provide a systematic and objective review of resilience applied to urban development focusing on its conceptual frameworks, research tendencies, and assessment methods. The analysis results demonstrate that an increasing attention has been given to urban resilience, especially in the field of climate change. The degree of research varies significantly in different countries, with the USA dominating in the number of publications, followed by the UK and China. Scholars' attention to urban resilience in different periods is closely related to the development background and disasters experienced by their countries, but there are also some commonalities. Meanwhile, the multi-dimensional research on urban resilience has been recognized by many scholars. Quantitative assessment tools such as simulation model and optimization model have been widely used to assess the level of urban resilience. Based on this, we put forward the future research trends in this field and provide a potential guide for future application of urban resilience.

COVID-19 can cause global pandemics; however, no specific vaccine has been recommended for COVID-19. Nearly 216 countries are trying to stop the spread of the disease, recover from it, and improve its mobility. In a way that people have not experienced in recent years, the COVID-19 pandemic affected humans through the year 2020. To stop the spread of the disease, many governments declared a complete lockdown.The nationwide lockdown had some positive effects on the environment even though it led to a decline in global economic growth. Air pollution levels reduced dramatically as a result of this lockdown on pollution. Most of Europe's populated cities saw a reduction in NO2 concentration of 45-54%. COVID-19 and air, water, and ecology are connected via two pathways, one occurring before the spread of the disease and the other following after. As a result of industrial activity, transportation, and high human density, pollutants were high in many areas before the disease spread. There was a reduction in population movements as well as a decline in human activities which resulted in a reduction in carbon dioxide emissions, an improvement of the ozone layer, as well as improvements in the Earth's weather and environment. As a result of a COVID-19 pandemic, human activities are negatively impacted, and the environment is positively affected. Our objective is to provide an assessment of the impact of human activities on the environment and ecology. During times of lockdown, there is a correlation between atmospheric changes and the behavior of natural creatures. Several significant findings are presented, including air pollution reduction, air quality improvement, ozone healing, and ecological sustainability. COVID-19 is beneficial for aerial health, aquatic health, and ecology in this paper.

Low-carbon city (LCC) pilot is a strategic policy to deal with global climate change and energy poverty. Using the city-level data from 2006 to 2019, this paper applies a multiple difference-in-difference (DID) analysis to explore the impact of LCC policy on urban green total factor energy efficiency(GTFEE) and its potential mechanism. The results show that the LCC pilot policy can significantly improve urban GTFEE, and the finding remains robust with various tests. Secondly, we shed light on the mechanism of the LCC policy and explore the possible channels through green innovation and structural upgrading to improve the urban GTFEE. Third, the policy effect is affected by different levels of urban economic development, urban development scale, and urban development types. In cities with higher levels of economic development, super-large resource-based cities, the pilot policy has a more significant improvement effect on GTFEE. On the other hand, in the less developed regions, pilot policies will hinder the improvement of GTFEE.

This paper uses the Global Malmquist-Luenberger Index (GMLI) based on directional distance function (DDF) super efficiency model to measure the urban green land use efficiency (UGLUE) of 108 cities in the Yangtze River Economic Belt (YREB) from 2007 to 2018, and it utilizes the spatial economic model to analyze the impact of land finance on the UGLUE and its mechanism of action. The results show that, firstly, the UGLUE in the YREB shows a steady development trend, the overall efficiency level is high, and there are spatial agglomeration characteristics. Secondly, the impact of land finance on the UGLUE presents "inverted U-shaped." With the continuous expansion of the scale of land finance, the impact of land finance on the UGLUE in the city has changed from positive to negative. Thirdly, land finance has a spatial spillover effect. Land finance will inhibit the improvement of UGLUE in surrounding areas through the "peer effect." With the continuous expansion of land finance scale, land finance will promote the improvement of UGLUE in surrounding cities through the "warning effect."

Green total factor productivity (GTFP) is an essential indicator to measure economic and environmental efficiency. Moreover, formulating a reasonable environmental regulation system and promoting green technological innovation is a systematic way to improve GTFP. However, previous related studies lack to investigate the impact of environmental regulation on GTFP from the perspective of green technological innovation. For this purpose, this paper aims to examine the specific impact of environmental regulation on GTFP based on the perspective of green technology innovation, so as to provide some policy insights for the formulation of more effective implementation of environmental regulation, improve green technology innovation level, and achieve a win-win situation for both economic growth and environmental protection. Furthermore, epsilon-based measure (EBM), which includes both radial and non-radial distance functions, is used to measure the GTFP. The spatial autoregressive method is also employed to quantify the impact of environmental regulation on GTFP from the perspective of green technological innovation using panel data of 269 prefecture-level cities in China from 2004 to 2018. The main findings indicate that there is a significant spatial autocorrelation between environmental regulation and GTFP. Environmental regulation has a significant positive effect on GTFP. Environmental regulation in the local regions also significantly contributes to GTFP in neighboring regions. Besides, environmental regulation indirectly promotes GTFP by enhancing green technological innovation level. Regional heterogeneity results show that environmental regulation can not only directly promote GTFP but also indirectly significantly promote GTFP through green technological innovation in the eastern and central regions, but insignificant in the western region. Based on the above findings, we conclude that policymakers should not only develop differentiated environmental regulation standards and steadily improving the intensity and rationality of environmental regulation but also add green innovation funds supply, enhance green innovation factor allocation efficiency, and strengthen RD talents, funds, and policies to green technology innovation, so as to drive GTFP improvement.

Under the influence of global climate change and rapid urbanization, urban flood disaster becomes more and more serious. Urban flood disaster risk assessment and risk response strategy have become a hot issue in sponge city research. This study introduce the basic concept of sponge urban construction risk and the basic theory, using the grounded theory to determine the sponge risk factors for urban construction, building based on system dynamics dynamic evolution model of the urban construction risk, sponge city construction in Jiangxi province as the research object, the application of Vensim PLE software carries on the simulation results. This paper analyzes the dynamic development situation and key risk factors of each sub-risk system in the whole life cycle, and discusses what countermeasures should be taken to reduce the risk of sponge city construction.

Despite governmental efforts toward the development of policies, funds, and technologies, the inherent characteristics of rural solid waste (RSW) discharge have led to great difficulties in RSW pollution control. However, establishing a realistic management strategy requires greater knowledge of RSW generation. Therefore, the RSW of 72 typical towns and villages from 12 provinces of China was analyzed for physicochemical characteristics, as well as its spatiotemporal distribution. The largest proportion of kitchen waste, coal ash, plastic, and paper of RSW was 33.70% ± 17.87%, 26.50% ± 17.61%, 13.48% ± 5.68%, and 10.75% ± 5.75%, respectively, in 2015. Although RSW had the potential for composting, it was still necessary to pay special attention to heavy metals pollution of RSW. The spatiotemporal distributions of RSW components were extremely non-homogenous, and significant variations existed in the kitchen residue, coal ash, plastic, and paper because of differences in economic growth, climatic changes, dietary habits, energy consumption structure, and consumer preferences. No obvious differences in RSW components were observed between villages and market towns. Overall, RSW treatment and management approaches should be considered based on local conditions of RSW generation.

Soiling has a crucial importance regarding its impact mainly for countries that have high soiling levels, dust storms, water scarcity and a great solar energy potential as the case of Morocco. Soiling mitigation is therefore mostly required during spring, due to higher pollen concentration, and summer, due to the lack of heavy precipitation. In this work, systematic measurements of soiling ratio were made in Rabat, city of north-western Morocco, during almost 1 year. Soiling has been evaluated considering the effect of dual-axis tracking that was compared to photovoltaic (PV) on fixed structure. A soiling rate of about 0.22%/day has been found for static PV while only 0.1% was found for PV on tracker. An additional approach of cleaning has been proposed in this paper which aims to use dew water. Using glass samples that were exposed to real environmental conditions, similar to the exposure conditions of PV panels, the soiling ratio was determined. At sunrise time, the subsequent change of the glass sample, from the horizontal position at night into the position of 30° tilt angle during the day, is performed to promote dew flow by the force of gravity acting on the droplets of dew. It has been found that relying only on the change of tilt angle, the average soiling losses were only 3.8% compared with 11.8% for fixed PV. This approach can reduce soiling similarly to solar trackers.

Coercive and constraints mechanism analysis between rural ecosystem health (REH) and urbanization is required to comprehend spatio-temporal coupling coordination relationship and promote rural ecosystem health development under the pressure of urbanization, especially those with dramatic changes in Chinese countryside. This paper, taking Chongqing municipality as the case study, analyzed the temporal and spatial characteristics of the coupling coordination between urbanization and REH of 38 districts and counties from 2010 to 2018 with support of coordination coupling degree (CCD) model, entropy method, and GIS spatial methods. The main conclusions were as follows: (1) the EUS (34.84%) and RSceS (35.64%) separately made the highest share contribution to urbanization and REH, the proportion of non-agricultural population in total population and consumption of farmland chemical fertilizer intensity were the main two indicators. (2) The study has confirmed the existence of an obvious differentiated U-shaped curves between urbanization and REH, a dynamic coupling coordination-X was found to exist, which conformed to a linear continuous growth curve, and has transitioned to "high quality coupling coordination" stage from "severe disorder" stage from 2000 to 2018; (3) the spatial differentiation characteristics of coupling coordination in 2018 have shown a significant regional differences pattern, and the coupling coordination in more developed areas was generally higher than most developed areas and less developed areas, namely western Chongqing metropolitan areas northeastern Chongqing and southeastern Chongqing. This report could provide policy implications for Chinese decision makers to formulate sustainable measures to balance urbanization development and REH protection.

Urban road traffic network (URTN) plays an important role in city operation, while it is also suffered a lot from the urban flood disasters which caused negative impacts frequently, like traffic congestion, and road collapse. The function loss of URTN not only destroy normal urban life and work order, but also pose a serious threat to people's lives and properties. Therefore, it is urgent to quantitatively explore the flood resilience of URTN. The concept of resilience puts forward new ideas to help solve the problem of urban flooding disasters from a holistic view. Exploring the flood resilience of urban traffic network may help to mitigate urban flooding and improve the urban resilience. This paper developed a flood resilience evaluation model of URTN, which contains 26 indicators based on the 4R theory. A case study was conducted in southern China to validate the model with real data. It evaluated the urban flood resilience of road traffic network with a comparison of before and after reconstruction of the pipeline. The results demonstrated that the flood resilience of URTN is at a relatively low level in the study area, and the limitation of single traditional engineering measure to the flood resilience of URTN. Suggestions such as strengthening the citizen participation and enhancing the complementary capability of multiple engineering measures are proposed to further promote the flood resilience of the URTN.

The ecological transformation of the mineral resource-based cities (MRBCs) is the key to promoting the construction of ecological civilization and realizing the sustainable development of the social economy and society in China. Existing research ignores the influence of public participation on other subjects in the process of transformation, which is essential to solve the dilemma faced by the ecological transformation of MRBCs. In view of this, from the perspective of stakeholders, this study constructs a new evolutionary game model, which is jointly participated in by the government, enterprises, and social factors (SF) (including public, self-media, and non-governmental organizations). This paper discusses the interests of the main parties and their inter-relationships and reveals the causal mechanism and influencing factors of the dilemma of the ecological transformation of the MRBCs. Our results demonstrate that the evolution and convergence of strategies among the tripartite game agents exhibit strong interaction. First, the single industrial structure, low regulatory efficiency, and weak ecological awareness substantially increase the cost of transformation, thus exacerbating the dilemma of MRBC transformation. Second, the transformation intensity and comprehensive income are important factors affecting the transformation, and the effect intensity of influencing factors in different transition stages is different, which implies that the focus of policies in different stages is different. Third, in the process of promoting enterprise ecological transformation, government supervision and SF supervision have significant complementary effects. However, high-intensity supervision has a significant crowding-out effect on the willingness of SF to participate. Therefore, the government needs to balance the intensity of supervision and willingness of SF to participate. Compared with the existing research, this study comprehensively reflects the complex dynamic game process of the ecological transformation of MRBCs in China and can provide a reference for the policy of promoting the ecological transformation of MRBCs in China.

This paper presents the characterization of municipal solid waste (MSW) randomly collected from two material recovery facilities in São Paulo city, before (input — recyclables) and after (output — rejects) the sorting processes. Geo-environmental and geotechnical tests were performed on shredded samples and a digestion method was applied to detect the metals As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn concentrations using an ICP OES. The objective was to assist future activities of integrated solid waste management and soil pollution. Results showed different particle sizes comparing the input (44.6%) and the output MSW (75.1%) passing through the 100-mm sieve. Organic matter and ash contents indicated the influence of inorganic carbon due to the plastics’ presence, with values varying between 6 and 13%. The pH values obtained were neutral and the electrical conductivity of the MSW rejects suggested a higher amount of ions, with values above 1000 µS/cm. Metals analyses show that Cd, Cu, Ni, Pb, and Zn are present in high concentrations, depending on the types of the materials. Standard Proctor compaction curves yielded maximum dry unit weight varying from 6.6 to 10.0 kN/m3 and optimum moisture contents from 20 to 42%. Cohesion ranged from 1.3 to 31.3 kPa and friction angle from 3.2 to 42.9°. The results are comparable with those obtained for other countries using different MSW treatments and contribute to the data basis for MSW from the selective collection, aiming the integrated solid waste management, serving for other countries that adopt MSW sorting and recycling. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-18281-w.

Industrial structure change is an important cause of air pollution in China. Regarding the "Triple Play" reform (TPR) led by the Chinese government as a quasi-natural experiment of industrial convergence, this study constructs an analysis framework for the impact of industrial convergence on air pollution. Based on panel data at the Chinese city level from 2004 to 2016, this paper empirically examines the impact of industrial convergence on air pollution by using a difference-in-difference model, the propensity score matching method, and a mediation model. The results show that (1) the TPR significantly alleviates air pollution of cities; (2) the TPR reduces air pollution by improving green technological innovation and industrial structural upgrading; (3) the TPR significantly reduces air pollutants in the southern, coastal, non-resource-based cities and cities with higher technological absorption capacity, but its impact in the northern, inland, resource-based cities and cities with lower technological absorption capacity is not remarkable. This research provides feasible suggestions for the government to promote industrial convergence through deregulation policies and then reduce air pollution.

The increasing discrepancy between a regional economy and transportation imposes higher requirements for their coordinated development. This paper utilized the coupling coordination degree (CCD) model and entropy method (EM) to quantitatively study the coupling coordination state between regional economy and transportation and its spatial distribution of 30 provinces in China from 2004 to 2017. The results show the following: (1) The comprehensive level of regional economy and transportation development in China has shown a growing trend, and the development of regional economy is faster than that of the transportation. (2) The CCD between regional economy and transportation in China is changing from incoordination to high-level coordination. The imbalance of CCD levels among the regions studied varies significantly. The CCD in the eastern regions is slightly higher than that in the central, western, and northeast regions. (3) In the region with a higher CCD, the discrepancy between the development of regional economy and transportation is higher than that in other regions. Moreover, suggestions are provided to promote the coordinated development from a regional perspective. This study provides references to policymakers to help them properly plan and design transportation systems while considering the regional economy, thus stimulating the coordinated and sustainable development between regional economy and transportation.

To achieve a win-win situation for both urbanization and carbon emissions reduction from a spatiotemporal perspective, we need to identify the salient links between urbanization and carbon emissions in different dimensions. Using 2008-2018 panel data on the Yangtze River Delta urban agglomeration, this paper constructs a Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model based on four dimensions of urbanization: population, economy, land, and ecology. Additionally, it uses a whole group of variables for reference, constructs a Spatial Durbin model (SDM) to estimate the spatial effect, and empirically investigates the spatial dependence of carbon emissions and the influence of various driving factors. The results show that (1) in the temporal dimension, the historical carbon emissions of the study area continue to increase. However, the extent to which they are doing so is slowing, the number of low carbon emissions areas has significantly decreased, the number of medium carbon emissions areas have significantly increased, the number of high and relatively high carbon emissions areas are relatively stable, and energy intensity continues to decline. (2) In the spatial dimension, Shanghai, Suzhou, and their surrounding cities have always been carbon emissions hotspots, high and relatively high carbon emissions areas are mainly concentrated in these cities. Low carbon emissions areas and cold spots are mainly distributed in Anhui Province. Medium carbon emissions areas show a great spatial and temporal evolution and are distributed in all provinces. (3) In the four dimensions of urbanization, per capita GDP will not only affect regional carbon emissions but also have a spatial spillover effect. For every 1% increase in the economic factors, carbon emissions in neighboring regions will increase by 0.38-0.43%. Population, economic, and technological factors have significant positive effects on carbon emissions, and economic factor is the most important factor. (4) In different dimensions of urbanization, there are obvious heterogeneities in the impacts of different factors on carbon emissions. Among them, the elasticity coefficient of per capita GDP and energy intensity is the smallest among the dimension of land urbanization, and the elasticity coefficient of the total population is the smallest among the dimension of population urbanization. Therefore, when formulating carbon emissions reduction policies, it is necessary to fully consider the spatial spillover effects, determine the optimal population size threshold, advocate for a low-carbon lifestyle, promote clean technology, and realize information exchange and policy interaction across regions from the perspective of holistic governance.

China is facing the large-scale urbanization and great pressure of energy consumption and environmental protection in the past few decades. In this process, urban green technology management efficiency (GMEC) is of great importance for the future work of greener transformation. Using China's 286 city-level panel data from 2006 to 2018, this paper proposes a global Malmquist index, PVAR, and panel quantile regression model to explore the heterogeneous dynamic effect of financial development (FIN) and environmental regulation (ER) on GMEC. The results indicate that during 2006-2018, (1) the growth rate of GMEC fluctuated violently, with a change of more than 28%. In addition, there is "one tight and one loose" phenomenon of GMEC. This indicates that there is much room for the improvement of GMEC; (2) in northeast cities, FIN is not conductive to GMEC, but in other regional cities, FIN has dynamic beneficial effect on GMEC. In addition, FIN represents a changing trend of dropping at first to rising afterwards in all regional cities; (3) furthermore, from time angle, ER has the heterogenous dynamic effect on GMEC among regional cities. Specifically, in northeast, eastern, central, and western cities, GMEC has an "M," "N," "U," and "U"-shaped relationship with the level of ER, respectively. From space angle, there is distinct heterogeneous effect of ER on GMEC at different quantiles within regional cities. Therefore, the local government are supposed to establish and implement policies based on regional cities' characteristics.

As the digital economy develops rapidly and the network information technology advances, new development models represented by the network economy have emerged, which have a crucial impact on green economic growth. However, the relevant previous studies lacked the role of analyzing the direct and indirect effects of internet development on green economic growth at the prefecture-level city level. For this purpose, this paper aims to examine the intrinsic mechanism of the impact of internet development on green economic growth and provide empirical support for cities and regions in China to increase internet construction. Furthermore, the mixed model (EBM), which includes both radial and non-radial distance functions, is applied to calculate the green economic growth index. Fixed effect model and mediation effect model are also employed to test influence mechanisms of the internet development on green economic growth using panel data of 269 prefecture-level cities in China from 2004 to 2019. The statistical results reveal that internet development has contributed significantly to green economic growth. When the internet development level increases by 1 unit, the green economic growth level increases by an average of 5.0372 units. However, regional heterogeneity is evident between internet development and green economic growth, that is, the promoting effect of internet development on green economic growth is gradually enhanced from the eastern region to the western region. We also find that internet development guides industrial structure upgrading improves environmental quality and accelerates enterprise innovation, which indirectly contributes to green economic growth. And internet development mainly achieves green economic growth through enterprise innovation. Based on the above findings, we concluded that policymakers should not only strengthen the guiding role of social actors to promote the stable development of the internet industry, but also foster the construction of the three models of "internet+industry integration," "internet+environmental governance," and "internet+enterprise innovation" to promote green economic growth.

The relationship between environmental regulation and firms' total factor productivity (TFP) has always been a hot topic in environmental economics, but the conclusions are still mixed. Employing a sample of 14,375 firm-year observations in China from 2010 to 2018, our research explores whether and when environmental regulation could trigger firms, to enhance TFP. The available evidence leads us to cautiously conclude that: (1) Environmental regulation notably improves firms' TFP, the conclusion still holds after alleviating the endogenous problems and a battery of robustness tests. (2) Firms' bargaining power significantly weakens the effect of environmental regulation on firms' TFP. (3) Compared with non-state-owned firms and non-heavy-polluting industries, environmental regulation has a greater impact on state-owned firms and heavy-polluting industries; higher executive compensation does not motivate firms to improve TFP. Compared with firms headquartered in non-provincial capital cities, environmental regulation has a greater impact on firms' TFP in provincial capital cities. Overall, the findings of our research are extremely relevant for the governments, investors, and firms' managers; this paper provides China's micro-firm-level evidence for the Porter hypothesis.

Cemetery waste (CW) constitutes one of the streams of municipal waste (MW). Based on the available data, it can be said that it accounts for about 1% of the mass of MW being generated in Poland. CW management should be carried out selectively. It is estimated that the level of CW selective collection is lower than the average level achieved for MW. This paper presents some solutions concerning the selective collection of CW implemented in selected municipal and denominational cemeteries in Poland. Based on research conducted, the levels of proper separation of glass and biodegradable waste in containers for their selective collection were determined, accordingly as >85% and >80%. Due to the contamination with paraffin residues, the purity of the selectively collected plastic fraction was lower than 20%. The human factor was recognized as the most significant determining efficiency of the selective collection of CW. Therefore, further education of society, as well as promoting good practices in the field of CW management, should be regarded as desirable. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-16351-7.

With the development of China's economy, the problem of environmental pollution has become increasingly more serious, affecting the sustained and healthy development of Chinese cities and the willingness of residents to invest in fixed assets. In this paper, a panel data set of 70 of China's key cities from 2003 to 2014 is used to study the effect of environmental pollution on home prices in China's key cities. In addition to the static panel data regression model, this paper uses the generalized method of moments (GMM) to control for the potential endogeneity and introduce the dynamics. To ensure the robustness of the research results, this paper uses four typical pollutants: per capita volume of SO2 emissions, industrial soot (dust) emissions, industrial wastewater discharge, and industrial chemical oxygen demand discharge. The analysis shows that environmental pollution does have a negative impact on home prices, and the magnitude of this effect is dependent on the level of economic development. When GDP per capita increases, the size of the negative impact on home prices tends to reduce. Industrial soot (dust) has the greatest impact, and the impact of industrial wastewater is relatively small. It is also found that some other social and economic factors, including greening, public transport, citizen income, fiscal situation, loans, FDI, and population density, have positive effects on home prices, but the effect of employment on home prices is relatively weak.

As a new production factor, digitalization plays a vital role in society, economy, and the environment. Based on the expanded STIRPAT model, this paper empirically tests the impact of energy structure and digital economy on carbon emissions by panel data from 2011 to 2017 in 30 provinces of China. The results show that the energy structure mainly based on coal has a significant driving effect on carbon emissions. Compared with non-resource-based provinces, the increase of energy structure dominated by coal has a greater effect on carbon emission in resource-based provinces. It is worth noting that this kind of influence has a greater impact on the central region of China, followed by the western region and the eastern region. Besides, the digital economy has a significant moderating effect. With the development of digital economy, the impact of coal-based energy structure on carbon emissions is gradually decreasing. This effect is more significant in non-resource-based provinces and eastern China, but not significant in resource-based cities and central and western China.