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This paper investigates whether city commercial banks’ expansion can boost energy efficiency to achieve regional carbon-neutral in China spanning the period from 2003 to 2017, including local expansion and cross-regional expansion. We find a significant positive association between the expansion of city commercial banks and energy efficiency. Specifically, the expansion of city commercial banks improved energy efficiency by easing financing constraints. However, the local expansion of city commercial banks worsens credit allocation and the relationship between government and market and decreases energy efficiency. In contrast, cross-regional expansion, which improved credit allocation and the relationship between government and market, increased energy efficiency. This contrasting effect on energy efficiency is pronounced in the eastern region. Our study suggests that the nature of city commercial banks’ expansion crucially affects its potential benefits to energy efficiency improvement.

International audience; This paper investigates whether emerging digital finance can reduce environmental pollution in China based on data from 273 of China's prefecture-level cities spanning the period from 2010 to 2017. The dynamic spatial econometric models (DSDM) find a significant negative association between digital finance and pollutants emissions, and the impacts vary among regions and urban development stages. The impact mechanism test proves that digital finance reduces pollutants emissions through technological innovation, structural adjustment, and capital allocation effects. In addition, we explore the different dimensions of digital finance and find that the depth of use has a more practical effect on reducing emissions. Further analyses based on the threshold model show an inverted N-shaped nexus between digital finance and emissions. The threshold effect also exists in terms of the traditional financial level. Our study proves that emerging digital finance crucially affects its potential benefits to environment and provides an empirical basis for policy-makers to accelerate the digitalization of financial markets, particularly paying attention to its emission-reduction effects.

Sustainable development is one common goal for the whole world, and regional cooperation is imperative to achieve that. Among the 17 sustainable development goals put forward by United Nations, the energy issue is one important part. It is necessary to better understand current energy consumption and trade pattern for energy consumption structure upgrade in the future. Hence, a study on petroleum trade along the “The Belt and Road Initiative” (BRI) can provide practical guidance for regional cooperation and sustainable development. This research uses social network analysis to investigate structural characteristics and evolution pattern with petroleum trade data of countries along BRI. We find that the primary structure of trade network keeps unchanged. Cohesion and core-periphery analysis results indicate that China has constructed a tight relationship with other countries and more trade links are sustained by fewer nations respectively, trade is still correlated to geopolitics. Regional trade network has resilience. Quadratic Assignment Procedure results show that the economic scale has a significant positive impact on trade in this region, neighboring countries tend to establish close trade relations, and the degree of policy communication and facility connectivity between countries needs to be further strengthened. Cultural interoperability also shows significant features, and related follow-up cooperation needs to be further deepened.

This paper presents a new index called sustainable total-factor ecology efficiency (STFEcE), which integrates the ecological footprint and human development index into the framework of total-factor energy efficiency. Compared with the total-factor ecology efficiency index, the new index can better reflect the sustainable development efficiency of a region. This study uses data from 30 provinces of China over the period 2005–2016 to measure the STFEcE. Considering the heterogeneity of regional development in China, the meta-frontier slack-based data envelopment analysis (DEA) model is implemented. The results are as follows. First, the STFEcE for China is low, indicating the great potential for improvement in the sustainable development efficiency of China. Second, the heterogeneity of the STFEcE across the three regions of China (eastern, central, and western) is evident, given the “concave” trend from the eastern region to the western region. Third, decomposing the STFEcE into the technology inefficiency and management inefficiency indexes shows that the management efficiency of the eastern region needs to be improved, whereas the technology efficiency of the central region needs enhancement. For the western region, both the technology efficiency and management efficiency need improvement.

In this study, we created a model based on input-output and carbon dioxide emissions data from WIOD (World Input-Output Database) to investigate the role of transition economies and developed economies in global carbon emissions during 1995–2014. The results show that for transition economies, the carbon emissions calculated based on the production side are higher than those estimated by the consumer side, indicating that the transition economies shoulder more indirect carbon emissions responsibility than developed countries. Subsequent analysis has found that China exports carbon to developed economies, while imports carbon from other transition economies and the rest of world. Finally, the structural decomposition analysis (SDA) shows that the change of export carbon emissions of transition markets during 1995–2014 is mainly affected by the high-carbon sectors and the export scale has the most significant impact on export carbon emissions.

In this study, we follow the concept of “promotion tournament” and extend the competition model to describe a Chinese government official's behavior, and examine the impact of economic performance and environmental quality on a Chinese mayor's political career. Empirical results show that both indicators affect the promotion of Chinese mayors, while such impacts vary across regions. We also find that there are heterogeneous results across pollution types. The main results hold for pollution that can be detected easily by the public. In contrast, less eye-catching pollution do not block a mayor from promotion significantly. Public opinions do exert pressures toward mayors' promotion. However, when considering the mayors' personal characteristics, the performance assessment mechanism is no longer remarkable, which demonstrates that the promotion of a Chinese administrative official depends more heavily on his political resume.

This paper combines the concept of Ecological Footprint (EF) with the framework of total-factor energy efficiency to develop a new index of total-factor ecology efficiency (TFEcE), which is constructed as the ratio of the target EF input obtained from SBM (slack-based measure) model to the actual ecology input under the consideration of labor and capital inputs. This paper computes the TFEcE of 28 provinces in China for the period 2000–2012. Findings show that China’s TFEcE remains a low level of 0.5, which urgently needs to be improved. China’s regional TFEcE is extremely unbalanced and the eastern area ranks first with the highest score. Compared with total-factor energy efficiency and traditional single-factor ecological efficiency, The TFEcE index evaluates ecology efficiency more comprehensively through taking EF in conjunction with the total-factor framework.

Based on China's regional extended input-output tables from 2002 to 2012, we built non-competitive input-output models in order to measure China's embodied carbon emissions (ECEs) from international trade and inter-provincial trade, while also using a structural decomposition analysis (SDA) approach to examine the driving factors of variation in ECEs from international trade and inter-provincial trade. The findings were as follows. First, the ECEs of China's 30 provincial administrative regions all first increased and then decreased under four modes of trade. Second, there was notable carbon leakage in both international trade and inter-provincial trade, and this was particularly severe in terms of the transfer of ECEs in inter-provincial trade. Third, judging from both a consolidated perspective and using the SDA of driving factors of ECEs under four modes of trade, intermediate production technology, trade structure, and total trade volume all promoted the growth of ECEs. Specifically, total trade volume was the primary driving factor of the growth of ECEs, whereas the direct carbon emission coefficient had a significant inhibiting effect on the growth of ECEs.

The sustainable energy consumption in northeast Asia has a huge impact on regional stability and economic growth, which gives price volatility research in the energy market both theoretical value and practical application. We select China’s fuel oil futures market as a research subject and use recurrence interval analysis to investigate the price volatility pattern in different thresholds. We utilize the stretched exponential function to fit the pattern of the recurrence intervals of price fluctuations and find that the probability density functions of the recurrence intervals in different thresholds do not show the scaling behavior. Then the conditional probability density function and detrended fluctuation analysis prove that there is short-term and long-term correlation. Last, we use a hazard function to introduce the recurrence intervals into the (value at risk) VaR calculation and establish a functional relationship between the mean recurrence interval and the threshold. Following this result, we also shed light on policy discussion for hedgers and government.

The haze pollution caused by fine particulate matter (PM 2.5) emissions has become one of the most crucial topics of sustainable environmental governance in China. Using the average concentration of PM 2.5 in China’s key cities from 2000 to 2012, as measured by aerosol optical depth, this study tested the time-spatial convergence of fine particulate matter pollution in China. The results show that there is a trend of absolute convergence between timespan and China’s PM 2.5 emissions. At the same time, in the geographic areas divided by the east, middle and west zones, there is a significant difference in the convergence rate of PM 2.5. The growth rate of PM 2.5 in the middle and west zones is significantly higher than that of the east zone. The correlation test between regional economic growth and PM 2.5 emissions suggest a significant positive N-type Environmental Kuznets Curve (EKC) after considering spatial lag and spatial error effect.

We have entered the “New Normal” economy, with more emphasis on economic growth driven by innovation than resource. This paper investigates the impacts of firms considering corporate social responsibility and environmentally sound technology by building a three-stage Cournot competition model with asymmetric cost. The sustainable development of economic and endogenous firm growth achieves the win–win result in the theoretical model. Using data from 31 firms in China, this paper empirically researches on the relationships among corporate social responsibility, environmentally sound technology and firm endogenous growth. The results show that: (1) Marginal cost decreased with the increase of innovation, as well as getting government research and development subsidy, which has a positive effect on firm growth. (2) Consumers respond positively to corporate social responsibility initiative, the reputation of the firm can be improved. At the same time, environmentally sound technology objectively reduces the marginal cost of competitors because of the technology spillover. (3) Profit of a firm undertaking corporate social responsibility partly decreases, which has a negative effect on firm growth. The contradiction between corporate social responsibility and profit of firm could be adjusted, such as socially responsible investment fund hosed by institutional investors.

With the deterioration caused by environmental pollution, studies on the low-carbon economy have received more attention. As far as we know, electricity production accounts for a large portion of total carbon emissions. Therefore, based on panel data from 2005 to 2014, we used the non-radial directional distance function model based on the data envelope analysis (DEA) to evaluate the static electricity product efficiency of various provinces in China. Further, the global Malmquist index was introduced to analyze the main factors. The relationship between the environmental policy stringency and the regional power production efficiency was also investigated. Static results show that China&rsquo, s regional power production efficiency has a significant difference between coastal developed and inland areas. Dynamic evaluation further proved that the difference is mainly due to technological change. Finally, regression results show that technological change is the only factor directly affecting regional environmental policy stringency.

Urbanization is considered as a main indicator of regional economic development due to its positive effect on promoting industrial development; however, many regions, especially developing countries, are troubled by its negative effect — the aggravating environmental pollution. Many researchers have indicated that rapid urbanization stimulated the expansion of industrial production scale and increased industrial pollutant emissions. However, this judgement contains a grave deficiency in that urbanization not only expands industrial production scales but can also increase industrial labour productivity and change the industrial structure. To modify this deficiency, we first decompose the influence which urbanization impacts on industrial pollutant emissions into the scale effect, the intensive effect and the structure effect by using the Kaya Identity and the LMDI Method; second, we perform an empirical study of the three effects’ impacts by applying the spatial panel model with data from 282 Chinese cities between 2003 and 2013. Our results indicate that (1) there are significant reverse U-shapes between Chinese urbanization rate and its industrial pollutant emissions; (2) the scale effect and the structure effect have aggravated Chinese industrial waste water discharge, sulphur dioxide emissions and soot (dust) emissions, while the intensive effect has generated a decreasing and ameliorative impact on that aggravated trend. The definite relationship between urbanization and industrial pollutant emissions depends on the combined influence of the scale effect, the intensive effect and the structure effect; (3) there are significant spatial autocorrelations of industrial pollutant emissions between Chinese cities, but the spatial spillover effect from other cities does not aggravate local urban industrial pollutant emissions, we offer an explanation to this contradiction that the vast rural areas surrounding Chinese cities have served as sponge belts and have absorbed the spatial spillover of cities’ industrial pollutant emissions. According to the results, we argue that this type of decomposition of the influence into three effects is necessary and meaningful, it establishes a solid foundation for understanding the relationship between urbanization and industrial pollutant emissions, and effectively helps to meet relative policy making.

In recent years, China has faced tremendous pressure to reduce carbon dioxide emissions. At the COP 15 United Nations Climate Change Conference in Copenhagen in 2009, China committed itself to achieve a 40%¨C45% per GDP carbon dioxide emission reduction in the near future. To reach this goal, China is willing to adopt a series of new policies, including attempts to introduce a carbon tax, and to start an energy-resource-tax pilot program in the western provinces. For this research, we constructed a Multi-Regional Computable General Equilibrium model. Then we used six scenarios to evaluate the economic effects and effectiveness of energy-resource tax policy for control of carbon dioxide emissions for different regions of China. The main result of this research is the finding that an ad valorem energy resource tax can reduce carbon dioxide emissions in China. At the same time, fiscal policy might engender different effects in different regions of China. Additionally, this policy is more efficient for controlling petroleum and natural gas resources than it is for coal resources.

China’s water pollution is severe and has a negative impact on its residents. Establishing an emissions trading mechanism will be helpful for reducing the pollution. However, the government in China controls the emission rights market. The “GDP Only” preference blocks equitable rules to address the externalities. To modify this distortion, we develop a multi-objective primary distribution model that optimizes economic efficiency, environmental contribution, and fairness. In addition, the geographical location of a company and the industry differential are two key factors that would affect the local government’s decision. According to the simulation results using data from Tai Lake in China, this model can effectively help to meet the political expectation that large-scale manufacturers with poor technology can take the initiative to reduce emissions through emission-rights distribution.

The significant fluctuation of industrial electricity consumption has a high impact on power load, which makes the research on recurrence intervals between extreme events of theoretical and practical significance. The study uses a high-frequency data of heavy and light industries and employs recurrence interval analysis in different thresholds. We find that the reoccurrence interval of volatility can fit with the stretched exponential function and the probability density functions of recurrence intervals in various thresholds shows a scaling behavior. Then, the conditional probability density function and the multifractal detrended fluctuation analysis demonstrate the existence of short-range correlation, long-range correlation, and multifractal properties, respectively. We further construct a hazard function, introduce recurrence intervals into VaR calculation and establish a functional relationship between average recurrence interval and threshold. Following this result, we also shed light on policy discussion for multi-industrial electricity supply management.