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Existing literature ignores to consider multiple types of pollutants when analyzing energy efficiency and its convergence. Under this background, using SuperSBM-GML model, the improved entropy method, and convergence model, this paper calculates the green total-factor energy efficiency and its evolution trend of 35 China's industrial sub-sectors considering multiple pollutants, and analyzes its convergence. The results indicate that the average score of industrial green total-factor energy efficiency is low, and there are significant differences among sub-sectors: "Utilization of waste resources" is the highest, while "Manufacture of paper and paper products" is the lowest. The green total-factor energy efficiency shows an overall upward trend from 2006 to 2021, the main driving force comes from technological progress, but numerous sub-sectors have not sufficiently caught up with existing cutting-edge technologies. Specifically, the growth rate of green total-factor energy efficiency in high-energy-consumption sub-sectors is higher than low-to-medium-energy-consumption sub-sectors. There are both σ-convergence and β-convergence in low-to-medium-energy-consumption group, indicating that development of sub-sectors is stable and outstanding. Nevertheless, the high-energy-consumption group only exhibits conditional β-convergence, revealing an imbalance in energy efficiency development. Consequently, formulating the benchmark level of energy efficiency and developing energy efficiency "leader" system are suggested for low-to-medium and high-energy-consumption sub-sectors, respectively. [ABSTRACT FROM AUTHOR]

This paper uses the exogenous shock of China's formal implementation of the environmental information disclosure system in 2008 to construct a quasi-natural experiment. Based on the panel data of prefecture-level cities from 2003 to 2019, the propensity score matching and difference in difference (PSM-DID) approach was used to systematically evaluate the impact of environmental information disclosure on energy efficiency. Overcoming the difficulties in measuring environmental information disclosure and the endogenous problem, this paper investigates the energy-saving effect of environmental information disclosure and illustrates its mechanism for the first time. The results show that environmental information disclosure significantly improved energy efficiency, and public participation played an important role in energy conservation, a conclusion that remained true after a series of robustness tests. The test of the impact mechanism shows that environmental information disclosure can achieve the goal of improving energy efficiency by promoting industrial structure upgrading and technological innovation. This paper enriches the discussion on the relationship between environmental information disclosure and energy efficiency, and provides useful policy inspiration for improving the level of energy efficiency and achieving sustainable economic development. [ABSTRACT FROM AUTHOR]

Effective forecasting of energy consumption structure is vital for China to reach its "dual carbon" objective. However, little attention has been paid to existing studies on the holistic nature and internal properties of energy consumption structure. Therefore, this paper incorporates the theory of compositional data into the study of energy consumption structure, which not only takes into account the specificity of the internal features of the structure, but also digs deeper into the relative information. Meanwhile, based on the minimization theory of squares of the Aitchison distance in the compositional data, a combined model based on the three single models, namely the metabolism grey model (MGM), back-propagation neural network (BPNN) model, and autoregressive integrated moving average (ARIMA) model, is structured in this paper. The forecast results of the energy consumption structure in 2023–2040 indicate that the future energy consumption structure of China will evolve towards a more diversified pattern, but the proportion of natural gas and non-fossil energy has yet to meet the policy goals set by the government. This paper not only suggests that compositional data from joint prediction models have a high applicability value in the energy sector, but also has some theoretical significance for adapting and improving the energy consumption structure in China. [ABSTRACT FROM AUTHOR]

Green consumption has become an important part of an environmentally friendly society. Policy guidance can effectively promote green consumption. Green consumption policies are extensive because of the diversity of green consumption objects. However, the various objects are not completely isolated, and policy must have a certain level of consistency. Therefore, from the perspective of green consumption objects, this paper first uses the text mining method to mine 308 green consumption policies in China, analyzing the objects covered in China's green consumption policies. Then, the evaluation system of green consumption policy consistency was built, seven key policies were selected, and the PMC-index method was used to measure policy consistency. The results show that (i) the main objectives of China's green consumption policy are the promotion of green products and the efficient use of energy. Green services and green recycling have been subject to fewer policy efforts. (ii) In terms of the links involved in green consumption, China pays more attention to the supply and the consumption chain and less attention to the recycling chain. (iii) The quantitative evaluation results of the seven typical policies show that the policy consistency is good but not perfect. The policy shortcomings are mainly reflected in the incentive methods. Therefore, it is recommended to further improve the green consumption objectives and enrich the means and methods of green consumption guidance. [ABSTRACT FROM AUTHOR]

Based on the background of carbon neutrality targets, panel data of 30 Chinese provinces during 2000–2019 were selected in this paper to explore the nonlinear correlation mechanism among transportation, tourism development, renewable and non-renewable energy consumption and economic growth. Firstly, by employing a fixed effects model as the base model, we confirmed the existence of correlations among these variables. And the nonlinear moderating effects were also verified by setting interaction terms in the model. Secondly, we further discussed the characteristics of the nonlinear relationship among the variables by employing a threshold effects model. Our results demonstrated that the effect of transportation on economic growth is positive and significant if and only if renewable energy consumption surpasses a certain threshold. Meanwhile, we also found that the effect of tourism development on economic growth is positive when non-renewable energy consumption is under a certain threshold. Finally, our study indicated that transportation maintains a positive and significant effect on economic growth within any threshold of tourism development, and the positive effect gradually increases as the threshold rises. The findings of this paper suggested that for China, the contribution of transportation and tourism to economic growth can be expanded by further expanding the substitution of renewable energy for non-renewable energy. [ABSTRACT FROM AUTHOR]

Whether the technological finance cooperation pilot (TFCP) policy in China can promote energy efficiency remains under investigated. Using the dataset covering 284 cities in China from 2003 to 2019, this paper adopts the Super-SBM model with undesirable outputs to measure energy efficiency. Based on this efficiency, the Difference-in-Differences model (DID) and Spatial Durbin model (SDM) are employed to discuss the impact of TFCP policy on energy efficiency. Results demonstrate that TFCP policy has significantly promoted energy efficiency. And the conclusion is still valid after the robustness checks and endogenous treatment has been carried out. The impact of the TFCP policy on energy efficiency is heterogeneous, depending on the geographical, administrative, and resource characteristics of cities. To be specific, the promotion effect tends to be pronounced in east-central cities, high-ranking cities, and high-tech cities. Moreover, TFCP policy could significantly improve energy efficiency through the effects of technological innovation, industrial upgrading, and financial development. A further policy spillover analysis shows that TFCP policy has exerted a remarkable incentive influence on energy efficiency locally, while the neighboring cities are inhibitive. To sum up, this research is of important theoretical value and policy-making reference on green economy transformation for cities with differential features and energy utilization capacity, by shedding light on the impacts of such a technological finance cooperation system on energy efficiency. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is an important measure of environmental governance. At present, studies on the impact of high-speed rail on energy efficiency need to be further studied. This paper constructs panel data of 285 cities at prefecture-level and above in China from 2003 to 2017, and uses the difference-in-difference (DID) to study the impact of high-speed railway on urban energy efficiency. Based on the theories of "factor flow," "knowledge spillover," and "center-periphery," this paper discusses the influence mechanism and heterogeneity of high-speed railway on energy efficiency. The empirical results show that high-speed railway can significantly improve energy efficiency, and the conclusion is still valid after parallel trend test, propensity score matching and difference-in-difference (PSM-DID) test, and instrumental variable method. The results remained true after a series of robustness tests. Mechanism analysis shows that high-speed railway can improve energy efficiency by promoting industrial structure upgrading, technological innovation, and market integration. Analysis of heterogeneity shows that the promotion effect of high-speed railway on energy efficiency is greater in central and western cities and core cities. This study provides useful enlightenment for optimizing China's high-speed rail construction planning and seizing the opportunity of high-speed rail development to improve energy efficiency. [ABSTRACT FROM AUTHOR]

To cope with climate change, China commits that it will strive to achieve carbon peaking by 2030. Using the Cointegration model and the Markov Chain model, this paper forecasts China's carbon emissions during 2019–2030 in six scenarios, and assesses the contribution of optimizing the energy mix to China's carbon peaking. The research obtains three main conclusions. Firstly, optimizing the energy mix will contribute to achieving China's carbon peaking. In the economic slow-growth scenario, taking China's planned target of energy mix (PTEM) into account, the carbon peaking year will be brought forward from 2028 to 2023. In the economic medium-speed-growth scenario, optimizing the energy mix will make China achieve carbon peaking in 2028. Without considering the PTEM, however, the carbon emissions will not peak before 2030. In the economic fast-growth scenario, the peaking year will not occur whether considering the PTEM or not, but the growth rate of carbon emissions with the PTEM will be far lower than that without considering the PTEM. Secondly, in all three economic growth scenarios, optimizing the energy mix will largely reduce the growth rate of carbon emissions, and thus significantly reduce the peak value of carbon emissions. Thirdly, optimizing the energy mix has a negative adjusting effect on the impact of economic growth on the growth rate of carbon emissions, and the negative effect rise as the economic growth rate increases. [ABSTRACT FROM AUTHOR]

With the rapid growth rate of China's economy, the extensive pattern of economic growth of "high energy consumption and low output" has magnified the constraints of energy issues on China's economic development and environmental protection, which highlights the importance of improving energy efficiency. As one of the three major economic zones in China, the Pearl River Delta region also faces high energy consumption and pollution emissions while developing at a high speed. Hence, improving the energy efficiency of the Pearl River Delta region is needed, as it not only is conducive to driving its development of the surrounding green economy, but also promotes the subsequent sustainable economic development. However, there are few literatures on the calculation and analysis of energy efficiency in the Pearl River Delta, and lacking a systematic analysis of input–output index system of energy efficiency measurement. Therefore, this paper calculates the energy efficiencies and the Malmquist indexes based on the panel data of the nine cities in the Pearl River Delta from 2005 to 2019 through super-efficiency SBM model and Malmquist index method by using MAXDEA and MATLAB software. The result illustrates that all regions in the Pearl River Delta except Guangzhou and Shenzhen show obvious energy inefficiency, which is mainly caused by the imbalance between technical efficiency and scale efficiency. Based on the calculation results, this paper gives some relevant suggestions for the further approach of energy reform in the Pearl River Delta according to the calculation results. [ABSTRACT FROM AUTHOR]

Improving green energy efficiency is crucial to promoting China's high-quality economic development and reducing the environmental pollution. In this paper, the Malmquist-Luenberger index measures the green energy efficiency of 30 provinces in China from 2004 to 2019. Based on the measurement results, the spatial Durbin model is used to empirically study the impact of environmental regulation on green energy efficiency and its spatial spillover effects. The results show that China's green energy efficiency is low, and its growth mainly comes from technological progress (TECH) rather than technological efficiency (EFFCH). The eastern region has the highest efficiency of green energy, followed by the West and the lowest in the Central region. The estimation results of the spatial Durbin model show that both environmental regulation and green energy efficiency have a significant spatial correlation. Environmental regulation can improve the green energy efficiency in the local province but inhibit green energy efficiency in the adjacent provinces. Finally, this paper puts forward relevant policy suggestions according to the research results. [ABSTRACT FROM AUTHOR]

The current period of rapid industrialization has not only brought about rapid economic growth but also caused serious energy shortages and environmental pollution, which have affected the sustainable development of the economy. Therefore, it is of great significance to analyse the changes in regional economic, energy, and environmental carrying capacity (3ECC); analyse the interaction among them; and propose corresponding optimization strategies for promoting regional sustainable development. In this study, a 3ECC simulation system is constructed from the pressure and support levels of load-bearing capacity. A system dynamics model is used to simulate the evolution path and action relationship of the 3ECC system, and an empirical test is completed in the Yangtze River Economic Belt region in China. A coupling coordination model and Bayesian vector autoregressive model are used to further explore the degree and action mode of the 3ECC system support and pressure indices. The results show that the model can truly reflect the characteristics of regional development and is suitable for prediction in this study. From 2015 to 2035, driven by technological progress, China's economic aggregate will grow by 223%, oil consumption by 33%, coal consumption by 23%, soil pollution by 197%, water pollution by 134%, and air pollution by 80.9%. Moreover, the coordination degree of the 3ECC supporting indicators gradually increases, while that of the pressure indicators gradually decreases, indicating that economic growth has a synergistic effect on environmental protection and energy conservation, thus further expanding the differences between environmental pollution and energy consumption. Finally, the supporting index of the economic carrying capacity and supporting index of the environmental carrying capacity have the widest influence range, and the optimization of economic and environmental development status is beneficial to the overall improvement of the 3ECC system. The influence range of the energy carrying capacity support index is small, indicating that coal and oil are being gradually replaced by other energy sources. Therefore, the research results of this paper can provide a reference for the sustainable development of other regions. [ABSTRACT FROM AUTHOR]

Cold-chain logistics presents significant challenges to sustainable development in terms of economics, society, and the environment. To address this issue, this paper focuses on solving a real-world problem—the multipath location-routing problem-based cold-chain logistics (MPLRPCCL), which considers soft time windows and heterogeneous fleets. The mathematical model aims to optimize the total logistics costs, including fuel consumption, carbon emissions, cargo deterioration, penalties, operating depots, and renting fleets. To effectively solve this problem, the paper proposes three versions of a memetic algorithm (MA) that incorporate control modules for monitoring the MA's performance and improving its global optimization performance. Computational evaluations were conducted, and the results show that the proposed algorithms are effective and outperform the classic MA framework. Furthermore, the proposed model and algorithms were applied to real-world cases in Hangzhou city, China, demonstrating their practical applicability. Overall, this paper offers a promising approach to tackling the complex challenges posed by cold-chain logistics and contributes to the sustainable development of this important field. [ABSTRACT FROM AUTHOR]

The Clean Heating Policy aims to solve the problems of excessive energy consumption and severe air pollution caused by central heating in northern China. Whether this policy can effectively reduce carbon emissions remains unexplored. Using panel data representing 65 cities in northern China from 2010 to 2019, this paper constructs a dynamic spatial DID model to empirically study the carbon reduction effect of the Clean Heating Policy and its influence channels. The results are summarized as follows. First, the Clean Heating Policy can significantly reduce carbon emissions, and this conclusion holds after multiple robustness tests. The policy has a lag effect, but its spatial spillover effect and long-term effect are not significant. Second, the carbon reduction effect of the Clean Heating Policy is mainly achieved by optimizing the energy structure and improving the thermal efficiency of heat consumer terminals. Third, the carbon reduction effect varies by city and emission field. It is significant only in low-subsidy cities, high-carbon cities, and household fields. Fourth, there is a synergistic reduction relationship between the Clean Heating Policy and the low-carbon city policy. Based on the results of this paper, we propose policy implications, such as promoting policies in multiple ways and improving subsidy efficiency, and provide a reference for other countries. [ABSTRACT FROM AUTHOR]

Under the policy background of "joint prevention and control" of global greenhouse gas emission reduction and regional air pollutants, the power industry, as an important target industry of energy conservation and emission reduction policies, has become an effective choice to release dual pressures. In this paper, the "bottom-up" emission factor method was used to measure the emission of CO2 and NOX from 2011 to 2019. Then, the contributions of six factors to NOX emission reduction in China's power industry were identified using the Kaya identity and logarithmic mean divisia index (LMDI) decomposition methods. The research results show that (1) there is a significant synergistic emission reduction effect between CO2 emission reduction and NOX emission reduction; (2) the factor that inhibits the growth of NOX emissions reduction in the power industry is economic development factor; and (3) the main factors that promote the reduction of NOX emission from the power industry are synergy effect, energy intensity, power generation intensity, and power production structure factors. Several suggestions are proposed, which are the power industry should adjust its structure, improve energy intensity, focus on applying low-nitrogen combustion technology, and improve the air pollutant emission information disclosure system to reduce NOX emissions. [ABSTRACT FROM AUTHOR]

Our paper investigates the association of renewable energy, financial development, urbanization, and growth in China along with technology. The newly developed econometric techniques (asymmetries) have been applied to explore short and long-run nonlinear relation among variables by utilizing annual data for the period of 1965–2021. In nonlinear cointegration, findings highlight the importance of renewable energy consumption for growth and development. Urbanization also confirms a positive association with growth and development where positive shocks were more dominant to support the growth process. The stock of technology brings a positive impact on growth and development whereas financial development results reveal to consider policy carefully. Asymmetric causality supports a uni-directional causal association between renewable energy, financial development, and growth. Policy recommendations are also highlighted according to the results. [ABSTRACT FROM AUTHOR]

Stimulating the shift to low-carbon energy and decreasing energy intensity are crucial strategies for green growth. Reducing energy intensity is an important measure to achieve the goal of "double carbon" and building a beautiful new China. Based on the provincial panel data of China from 2011 to 2020, this paper empirically tests the relationship among digital financial inclusion, industrial structure distortion, and energy intensity by using the spatial Durbin model and the intermediary effect method. The results show that the development of digital financial inclusion can promote the decline of energy intensity, and industrial structure distortion has a partial intermediary effect in the relationship between digital financial inclusion and energy intensity. Because of this, it is important to speed up the development of digital financial inclusion, increase the innovation of digital financial inclusion products and services, strengthen the supervision of digital financial inclusion, and reduce the distortion of the industrial structure so that digital finance can play its full role in reducing energy intensity. [ABSTRACT FROM AUTHOR]

The massive use of energy has caused a rapid increase in global carbon dioxide emissions, resulting in a series of environmental problems such as climate warming. Investment in the energy industry can guide funds into green and clean production, reduce carbon emissions in the energy industry, and promote the green development of the energy industry. This paper considers the energy, the environment, the economy, and other factors and focuses on energy consumption and investment structure. Taking 30 provinces in China as research samples, a dynamic spatial Durbin model is established. The results show that the first-order term of carbon emissions has a driving force of 0.5068% for current carbon emissions at a significance level of 1% and that the increase in current carbon emissions will lead to a continued increase in carbon emissions in the next period. The increase in the carbon emissions of neighbouring provinces will increase their carbon emissions through the spatial spillover effect. Whether in the short term or long term, the increase in energy investment and the optimization of the energy investment structure can reduce carbon emissions. The above conclusions can provide a reference for the formulation of government environmental policies. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study the influencing factors of the ecological pressure of the energy carbon footprint (EPECF) of China's whole industry from 2000 to 2018. First, the EPECF of 48 sub industries is calculated, then divides 48 sub-industries into high-, medium-, and low-pressure industries, and uses the logarithmic mean Divisia index (LMDI) method to analyze and summarize the main driving forces of China's industrial EPECF changes. Finally, policy suggestions for the future industrial decompression are put forward. The main results are as follows: (1) Economic development is the most important factor to promote the growth of EPECF of the three major industries. (2) At present, the population pressure factors of forest and grassland have little effect, and the effect of returning farmland to forest and grassland has not been truly played. (3) The adjustment of industrial structure has gradually become a key factor in reducing EPECF of the three industries. (4) The gradual stability of energy intensity has a certain inhibitory effect on the increase of EPECF in high-pressure industry. (5) The adjustment of energy structure in low-pressure industry has gradually worked. Therefore, the government should establish an economic sustainable development system, vigorously develop clean energy, and realize the green transformation of various industries. This provides an empirical example for other countries in the world to reduce the EPECF. [ABSTRACT FROM AUTHOR]

As one of the industries with high energy-extensive consumption in China, the carbon emissions (CO2-E) generated by heavy industry cannot be ignored. However, the related literatures that take heavy industry energy-related CO2-E as the research object in the past mainly focus on the index decomposition model (IDM), econometric analysis, and other aspects. At present, few literatures use the structural decomposition model (SDM) to analyze the CO2-E and influencing factors of the industry from the perspective of input–output (I-O) analysis. This perspective and model can unify the supply side and demand side energy-related CO2-E of the heavy industry, which is conducive to a more comprehensive analysis of the heavy industry energy-related CO2-E and further various direct and indirect influencing factors. Based on this, this paper employs the energy consumption method (ECM), the I-O analysis method, and SDM to examine the energy-related CO2-E and influencing factors of China's heavy industry. The findings show that while the growth tendency of China's heavy industry energy-related CO2-E has been effectively controlled within the sample interval. Simultaneously, the supply side's optimization of the energy consumption structure (ECS), the upgrading of energy utilization technologies, and the generalized technological progress rate (GTPR) reflecting the input structure effect (ISE) have an inhibitory effect on the heavy industry's energy-related CO2-E, while the final demand effect (FDE) has an increasing effect on the energy-related CO2-E in the heavy industry, indicating that the products produced by the heavy industry are still characterized by high carbonation. This paper not only further enriches the existing research literature on energy-related CO2-E from the heavy industry in theory, but also provides guidance for efficient control of excessive growth of energy-related CO2-E from the perspective of input and output for the heavy industry in practice. [ABSTRACT FROM AUTHOR]

Energy is one of the critical factors for economic growth, and carbon emissions are by-products of energy use that degrade environmental quality. This research thus establishes an index of eco-output elasticity of energy consumption to find an eco-friendly production stage in which not only output increases, but also energy consumption degrades. Our observation subject is China, which employs its own energy intensity index to monitor the nexus of economy, energy, and environment. While its energy intensity index is unable to point out the eco-friendly production stage, the index of eco-output elasticity of energy consumption herein does. This paper offers three main empirical findings on China: (i) it presents an eco-friendly production stage from 2012 to 2013; (ii) it has better improvement on energy savings than CO2 reduction; and (iii) in general, its status quo is still high energy consumption, but this phenomenon is improving. [ABSTRACT FROM AUTHOR]

As a financial innovation model to promote sustainable economic development, carbon finance promotes green and inclusive economic development by reshaping the financial system. As a big energy consumer, China has been committed to the green transformation of energy structure, and carbon finance has been regarded as an important tool to support carbon emission reduction. This paper divides carbon finance from three levels, financial macro, environmental governance, and technological development, and selects 17 indexes to measure the development level of carbon finance in China. Time series regression model and the entropy weight method (EWM) are used to analyze the impact of carbon finance on China's energy consumption structure. The empirical results show that carbon finance plays a positive role in promoting China's energy consumption structure, and developing carbon finance is conducive to the use of clean energy and the optimization of energy consumption structure in China; carbon finance can further optimize China's energy consumption structure by investing capital, resources, and technology in industries related to promoting clean energy consumption. Therefore, China should continue to develop carbon finance and promote the establishment of a unified national carbon trading market; at the same time, China should use the CCER trading mechanism to promote the development of renewable energy and enhance the endogenous driving force of energy transformation by promoting technological progress. [ABSTRACT FROM AUTHOR]

High temperature impacts the performance of marathon athletes, and hypothermia harms athletes. Twenty-one runners died, and eight were injured in the China Baiyin marathon on May 22, 2021. It's a typical human life test. The energy equations are combined with the maximum energy supply of Chinese male athletes to study this accident. We analyze the human body's route slope, travel speed, and heat dissipation under low temperatures in this marathon. The study shows that the large slope and long-distance of CP2 to CP3 section and the low temperature during the competition are the main reasons for the accident. The method of quantifying the slope and temperature and calculating the percentage of athletes' physical consumption proposed in this paper can evaluate the route design of field marathons. We suggest that the physical energy consumption ratio of 90%, i.e. 315 cal/min/kg, should be taken as the maximum energy supply for Chinese male marathon runners. Dangerous risk zones for wind speed and temperature on dangerous path sections are also formulated for athletes to make their assessments. This paper's theories and methods can effectively help design the marathon route and determine the race time. [ABSTRACT FROM AUTHOR]

With China's economy entering the stage of high-quality development, manufacturing energy carbon emission efficiency has become the focus of academic attention. It is of great significance to study the convergence of manufacturing energy carbon emission efficiency for realizing high-quality development of manufacturing in China. Based on the panel data of China's manufacturing sub-sectors, this paper measures and analyzes the evolution trend of manufacturing energy carbon emission and its efficiency. On this basis, this paper uses the coefficient of variation and convergence model to test the convergence of manufacturing energy carbon emission efficiency. The results show that China's manufacturing energy carbon emissions and its efficiency demonstrate an increasing trend. Coal was the main source of manufacturing energy carbon emissions. The manufacturing energy carbon emission efficiency does not have σ convergence, but has β convergence, and its convergence has industry heterogeneity. The manufacturing energy carbon emission efficiency exits scale effect and technology effect, but not the effect of opening to the outside world and institutional effect, and its effect exists industry heterogeneity. By reducing carbon emissions, adopting differentiated policies, adjusting the industry scale, and enhancing the industry technology intensity, China's manufacturing can improve the energy carbon emission efficiency and promote high-quality economic development. [ABSTRACT FROM AUTHOR]

To assess the characteristics of household carbon emissions per capita (HCPC), this paper divided China's provinces into 4 groups based on the decoupling relationship between household consumption and related emissions. This classification helped to analyze the correlation and reflected the decoupling status between carbon emissions and household consumption and explored the effect of consumption growth on carbon emissions. Then, according to logarithmic mean divisia index (LMDI) model, HCPC in China's provinces was decomposed into four drivers including carbon coefficient, energy structure, energy consumption, and population structure effect. Through multi-regional (M-R) analysis, temporal evolution and spatial differences of these four drivers in both national and provincial level were studied. This comparison method introduced temporal and spatial decomposition results into the same framework, which may provide a new perspective for analyzing carbon emission trends. The results showed that (a) the HCPC in all 30 provinces increased significantly especially in Inner Mongolia, Tianjin, Xinjiang, Heilongjiang, and Beijing. Energy consumption effect was the leading factor promoting HCPC growth. Energy structure and population structure also promoted HCPC growth slightly, and carbon coefficient was the effect which had inhibitory effect on HCPC growth at regional level. (b) Spatial differences of HCPC between regions narrowed during this period. This is mainly due to the rapid growth of HCPC in region IV. Energy consumption effect was the dominant factor for the spatial differences. Based on the results, this paper proposed to adopt more effective measures to improve energy efficiency, develop clean energy, and optimize energy structure, especially in the provinces with faster growth in carbon emissions. [ABSTRACT FROM AUTHOR]

Energy and environmental policies are important methods for the government to restrain carbon emissions growth. Identifying the potential dynamic trends of China's carbon emissions under different scenarios has important reference significance for the government's policy implementation. This paper firstly predicted China's carbon emissions from 2017 to 2040 based on three energy transition scenarios at the industrial level. Then, Logarithmic Mean Divisia Index decomposition model was applied to evaluate the driving forces of emissions changes during 1997–2040. Finally, the Spatial–Temporal Logarithmic Mean Divisia Index model was used to explore the emissions reduction potential and the potential reduction path at provincial level. The results showed that (1) as the reduction in energy intensity cannot offset the growth of industrial scale, the carbon emissions of all industries have shown an increasing trend from 1997 to 2017; (2) In the current policies scenario, China's carbon emissions cannot reach the peak before 2040. And only in the sustainable development scenario, the carbon emissions of the three industries will all reach the peaks before 2030. And the development of non-fossil energy will reduce carbon emissions by more than 30%; (3) Hebei, Shanxi, Inner Mongolia, Ningxia, and Heilongjiang are key provinces and improving energy efficiency of the secondary industry is a potential way to promote carbon emissions reduction. The framework and main content of this paper. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to determine the spatial spillover effects of renewable energy on carbon emissions in China's less-developed areas. However, few studies have considered this issue from the perspective of less-developed areas. Based on panel data of 21 provinces in China from 2000 to 2017, this paper analyzes the spatial spillover effects of renewable energy on carbon emissions using Moran's I and Spatial Durbin Model (SDM). The results suggest that, first, Moran's I ranges from 0.378 to 0.519, Moran scatter plot presents that provinces are located in the high–high (HH) and low-low (LL) quadrants, indicating provincial carbon emissions in the study area have a significant spatial correlation and agglomeration. Second, under the three matrices, the direct effect coefficients of renewable energy are -0.2522, -0.2639 and -0.2601, this shows that renewable energy is beneficial to local carbon emissions reduction. In contrast, the indirect effect coefficients of renewable energy are 0.0605, 0.1012 and 0.1125, which means higher renewable energy consumption in a single area is conducive to the improvement of carbon emissions to neighbouring areas. Third, urbanization, industrialization, physical capital and other variables have different impacts on local and nearby carbon emissions. This study provides empirical evidence to achieve carbon emission reduction targets by government policymakers. [ABSTRACT FROM AUTHOR]

The sustainable development of China's economy is bottlenecked by resource shortage and environmental pollution. As the leading resource consumer and pollutant source, the industrial sector needs to improve its energy efficiency. This paper establishes a super epsilon-based measure (Super-EBM) model with bad outputs like environmental cost and evaluates the industrial green total-factor energy efficiencies (IGTFEEs) of 30 provinces in China during 2000–2017. Unlike previous research, the main contribution of this paper is to choose four environmental pollutants as bad outputs (industrial carbon dioxide, industrial sulfur dioxide, industrial chemical oxygen demand, industrial solid waste). By contrast, the previous studies mostly only take one environmental pollutant as bad output, i.e., the bad outputs are not fully measured. Then, the spatiotemporal dynamics and spatial correlations of the IGTFEEs were analyzed, and the influencing factors of IGTFEE were examined empirically with a spatial econometric model. Finally, this paper adopts generalized method of moments (GMM) to solve the endogenous problem, trying to assure the robustness of estimation results. The results show significant provincial differences in IGTFEE. Most eastern coastal provinces achieved satisfactory IGTFEEs, while most inland provinces had undesirable IGTFEEs. Eastern region achieved the highest IGTFEE, followed by central region; western region had the lowest IGTFEE. The IGTFEE improved over time in some provinces while worsened greatly in some provinces. The IGTFEE in most provinces need to be further improved. Global Moran's I values indicate that the provincial IGTFEEs were clustered in space, rather than randomly distributed. Local indication of spatial association (LISA) map reflects significant local spatial clustering of provincial IGTFEEs. In addition, IGTFEE is significantly promoted by industrial structure, technological innovation, human capital, opening-up, and energy structure yet significantly suppressed by ownership structure and environmental regulation. Considering the endogeneity, GMM results show that the estimation results of the model were robust. Specific policy recommendations include vigorously developing high-tech industries, deepening state-owned enterprises reform, diverting more funds to research and development, cultivating versatile talents, introducing environmentally-friendly foreign capital, accelerating the implementation of clean energy development strategy, and widening the fund channels of pollution control investment. [ABSTRACT FROM AUTHOR]

Low-carbon transition has gradually become the focus of research on environmental issues. This paper takes China's eight major economic regions as the entry point. First, carbon emissions are measured according to United Nations' baseline methodologies. Second, the stochastic nonparametric data envelope analysis (StoNED) model is used to measure energy efficiency to improve the accuracy of the measurement. Finally, considering the temporal and spatial nonstationarity of carbon emission data, this paper constructs geographically and temporally weighted regression-stochastic impacts by regression on population, affluence, and technology (GTWR-STIRPAT) model, which can accurately analyze the impact of each driving factor of carbon emissions. This paper also explores efficient emission reduction paths in conjunction with the forcing mechanism. According to the study, China's carbon emissions show a decreasing trend from coastal areas to inland areas. In addition, there are significant problems with carbon emissions in China: some regions focus on improving energy efficiency but neglect increasing energy consumption; some regions focus on industrial development but neglect long-term emission reductions. Among the driving factors, energy efficiency, foreign trade, environmental regulations, and industrial structure have the effects of spatiotemporal heterogeneity, spatial heterogeneity, and time lag on carbon emissions, respectively. [ABSTRACT FROM AUTHOR]

Based on the perspective of market segmentation, this paper empirically tests the impact mechanism of market segmentation on energy efficiency by using China's provincial panel data from 2005 to 2016. The results show that market segmentation has a significant inhibitory effect on energy efficiency by distorting technological progress, scale efficiency, and allocation efficiency. The conclusion of this paper not only provides an important theoretical basis for promoting the reform of China's economic system, but also brings some enlightenment for other developing countries to choose ways to improve energy efficiency. [ABSTRACT FROM AUTHOR]

With the acceleration of China's energy transformation process and the rapid increase of renewable energy market demand, the photovoltaic (PV) industry has created more jobs and effectively alleviated the employment pressure of the labor market under the normalization of the epidemic situation. First, to accurately predict China's solar PV installed capacity, this paper proposes a multi-factor installed capacity prediction model based on bidirectional long short-term memory-grey relation analysis. The results show that, the MAPE value of the GRA-LSTM combined model established in this paper is 5.995, compared with the prediction results of other models, the prediction accuracy of the GRA-BiLSTM model is higher. Second, the BiLSTM model is used to forecast China's installed solar PV capacity from 2020 to 2035. The forecast results show that China's newly installed solar PV capacity will continue to grow and reach 2833GW in 2035. Third, the employment number in China's solar PV industry during 2020–2035 is predicted by the employment factors (EF) method. The results show that the energy transition in China during 2020–2035 will have a positive impact on the future stability and growth of the labor market in the solar PV industry. Overall, an accurate forecast of solar PV installed capacity can provide effective decision support for planning electric power development strategy and formulating employment policy of solar PV industry. [ABSTRACT FROM AUTHOR]

Climate change has a profound impact on human survival and development. Climate change is an energy and economic issue, which should be driven by technology. Total factor energy efficiency (TFEE) improvement is undoubtedly a breakthrough in solving energy problems. In this paper, the fixed effect model, impulse response function, and threshold regression model are used to test the complex relationship between export sophistication and TFEE. The results reveal that export sophistication improvement leads to higher TFEE. The impulse response results verify the existence of a phased and positive correlation between export sophistication and TFEE. Finally, the relationship between export sophistication and TFEE significantly varied across different threshold levels of regional corruption score, economic development, and openness. This article led the foundation for supporting China's export sophistication promotion strategy and also provides a reference for energy efficiency improvement and energy crisis response in the post-pandemic era. [ABSTRACT FROM AUTHOR]

Renewable energy consumption has a strong impetus in promoting energy conservation and emission reduction, which is a new path leading to clean and low-carbon development. Based on that, this paper uses the data of carbon productivity, renewable energy power consumption level, technological progress, national economic development level, population, energy efficiency, industrial structure rationality, and other data in 30 provinces in China from 2011 to 2020, based on the STIRPAT model, conducts an empirical analysis on the impact of renewable energy power consumption on carbon productivity in Chinese provinces while considering both the spatial horizontal dimension and the temporal vertical dimension. The empirical results show that (1) Chinese carbon productivity presents an obvious spatial spillover effect and presents the spatial positive correlation distribution characteristics of "high-high" type agglomeration and "low-low" type agglomeration. (2) The utilization of renewable energy plays a positive role in promoting the development of low-carbon economy. The perspective of the horizontal spatial dimension shows a positive spatial spillover effect. The perspective of the longitudinal time dimension shows a marginal increase in the overall improvement of the environment. (3) Among the seven regions in China, the consumption of renewable energy in North China, East China, and Central China brings a dominant effect on carbon productivity. (4) About 29% of the positive effect of renewable energy consumption on carbon productivity is indirectly realized by technological progress. Finally, the article puts forward targeted policy suggestions. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to strengthen research concerning the impact of climate change on China's water resources, in order to avoid extreme weather events such as regional rainstorms and floods affecting the stable supply of energy and electricity. The goal is to reorganize energy industry allocation to make China's energy the most efficient, which would thus help achieve sustainable development goals. The biggest contribution and of this paper is not only to enlarge the scale and take into account the sustainable aspects, but also to consider climate change factors that have been ignored in the past as exogenous variables. For the first time, we take stock of the optimal allocation of new and tradition energy sources and carbon dioxide. This study uses the Dynamic ZSG-DDF model with exogenous variables and undesirable outputs to find a way to redistribute carbon dioxide, new energy, and tradition energy from different provinces on the premise of maximizing efficiency, thus achieving the goal of reducing carbon and saving the earth. The research results show that the energy efficiency of tradition energy is higher than that of new energy in various provinces. At the same time, the China government must comprehensively review the quota targets of new energy, tradition energy, and carbon dioxide (CO2) in each province as quickly as possible. Currently, the quotas of various provinces are seriously misplaced, and 26 provinces in total have great room for improvement. [ABSTRACT FROM AUTHOR]

The level of energy green consumption is an essential factor influencing China's economy's high-quality development. In this paper, we select relevant panel data from 2010 to 2019 for 28 cities in Beijing-Tianjin-Hebei and surrounding areas, and use the DPSIR model and spatial econometric model to analyze the spatial divergence characteristics and driving factors of the energy green consumption index based on the construction of the energy green consumption level evaluation index system and index measurement. The results of this research are as follows: (1) The energy green consumption index has a distribution pattern of "high in the east and low in the west, high in the north, and low in the south," and the driving force (D) and response force (I) are the key factors influencing the spatial variation of the energy green consumption index. (2) The measurement results show that GDP per capita and built-up green coverage have a considerable positive effect on the energy green consumption index, whereas non-domestic energy structure has a significant inhibitory effect. (3) In terms of spatial spillover effects, greening coverage of built-up areas, industrial structure, and domestic energy structure, all have positive effects on neighboring regions' energy green consumption indexes, with positive spatial spillover effects, whereas GDP per capita, urbanization level, and non-domestic energy structure, all have negative spatial spillover effects. In light of the issues at hand, this paper suggests making good efforts to create and put into practice laws and policy norms, cultivate green energy-saving markets, vigorously promote the use and promotion of energy-saving technologies, and lower energy consumption in secondary industry and domestic consumption. The study's findings can help advance the modernization of the region's industrial structure and the growth of a green economy by providing the local government with a reference point for timely policy adjustments. [ABSTRACT FROM AUTHOR]

In recent years, the rapid rise of carbon dioxide concentration makes global warming increasingly serious, which leads to sea-level rise, coastal lowland inundation, glacier melting, extreme weather, species extinction and other environmental problems. China, as the largest carbon dioxide emitter, is facing climate change. In 2020, the Chinese government promised at the UN General Assembly to achieve the peak of carbon by 2030, and also put forward the goal of achieving carbon neutrality by 2060. To achieve the carbon peak and carbon in target, China needs to reduce the use of fossil energy and replace the use of fossil energy with renewable energy with less carbon dioxide emissions. Tidal energy, as a low-carbon renewable energy, is an important alternative to fossil energy. But can the tide be developed at the expense of the environment? In this paper, the impact of the development of tidal energy on the environment is analyzed and elaborated. According to the environmental impact, the solutions of developing environment-friendly tidal energy, wave energy and temperature difference energy are given. Based on this, policy suggestions are put forward to the Chinese government. The research results of this paper will provide reference for developing countries to develop and utilize tidal energy for power generation. [ABSTRACT FROM AUTHOR]

The dual-credit policy, implemented by the Chinese government on April 1, 2018, aims at boosting the production of new energy vehicles and further reducing fuel consumption of internal combustion engine vehicles. However, whether the policy is effective has caused controversy among many scholars in China and one of the hot spots of debate is about the effectiveness of the pricing method. In this paper, the government pricing model of dual-credit policy and the market pricing model are compared by the optimization theory. The factors include the transaction price of credits, the quantity of vehicles, the monopoly manufacturer's profits, and social welfare. Our results show that the dual-credit policy benefits energy saving and emission reduction in the transport sector. In addition, it is suggested that the government should formulate the transaction price of credits and subsidy the automobile enterprise in the early stage of the policy while adopting free market pricing in the late stage of the policy. [ABSTRACT FROM AUTHOR]

As an essential energy and chemical base in China, carbon reduction in the Energy "Golden Triangle" (EGT) area is significant. This paper used the logarithmic mean Divisia index (LMDI) method to analyze the drivers of carbon emissions from secondary industry energy consumption (CESEC) in EGT from 2005 to 2019 and then used the GM (1,1) method to simulate carbon emissions in 2030. Meanwhile, the decoupling relationship between carbon emissions and economic development was also analyzed using the two-dimensional decoupling model to test the effectiveness of carbon reduction by the region's government. This paper showed the following: (1) CESEC in the EGT area increased from 1.89×108t to 2.617×108 t; (2) the economic output effect is the main factor influencing carbon emissions in the EGT area, followed by population effect and energy structure effect, while energy intensity effect mitigates carbon emissions; and (3) CESEC will peak at 12.362×108t in 2030, leaving an arduous task on carbon reduction. The two-dimensional decoupling condition between carbon emissions and economic growth in the EGT area is low level-weak decoupling (WD-LE) for 2005–2019. The decoupling condition in Yulin and Ningdong is concentrated in low level-expansion connection (EC-LE) and low level-weak decoupling (WD-LE). Furthermore, Erdos reached high level-expansion negative decoupling (END-HE) condition during 2015–2019. Based on the above findings, a low-carbon development strategy for EGT should consider improving emission reduction technologies for high-carbon energy sources like coal, adjusting the energy consumption structure and seeking government policy support for carbon reduction. [ABSTRACT FROM AUTHOR]

At this stage, it is an important goal for China to solve environmental problems to limit the carbon emission peak by 2030 and then to lower its quality gradually thereafter. The outbreak of the COVID-19 pandemic in 2020 seriously impacted all aspects of China's social economy and brought many uncertainties to the realization of the carbon emission peak. Based on the fact, it has important theoretical and practical significance to take the problem of China's carbon emissions during the COVID-19 as the research object. Thus, this paper analyzes the current situation of China's CO2 emissions and finds out that in the context of ongoing COVID-19 response, stimulated by China's economic stimulus policies, CO2 emissions decrease firstly and then gradually rebound. On this basis, the paper constructs a dynamic model of China's CO2 emission system to simulate the change in China's CO2 emissions under different economic stimulus policies. The relevant simulation results demonstrate the following: (1) China cannot realize the CO2 emission goal by 2030 only by adopting traditional economic stimulus policies. (2) The green economic stimulus policy oriented to the development of clean energy will rebound China's CO2 emissions in the short term, but it can effectively reduce CO2 emissions in the long run. The most important factors affecting CO2 emission reduction are industrial energy consumption and industrial structure, while the energy power generation structure and the transportation structure have a relatively weak impact on the emission reduction effect. (3) The green economic stimulus policy combined with economic stimulus measures and a variety of low-carbon measures can enable China to peak CO2 emissions before 2030, with a peak value of 11.059 billion tons. In general, green economic stimulus policies can achieve a win–win situation for China's economic recovery and carbon emission peak. [ABSTRACT FROM AUTHOR]

This paper examines whether financial technology (FinTech) development affect household energy consumption. The proposed point that FinTech can reduce household energy consumption is theoretically discussed and empirically tested using data from the 2017 Digital Financial Inclusion Index, the 2018 China Family Panel Studies (CFPS), the 2018 China Environmental Statistical Yearbook and the 2018 China Science and Technology Statistical Yearbook. The results show that FinTech contributes to reducing household energy consumption. Several retests, including the instrumental variable, replacement sample and propensity score matching methods, prove its robustness. Mechanism tests show that investment in environmental governance and technological innovation promotion are the two main transmission channels. We also find that the reducing effect is more significant in the following groups: the low-middle income level classes, the eastern regional residents, those with bachelor's degrees and above, the those aged over 60 and rural residents. The outcomes of this paper call for government departments to positively guide FinTech development to reduce household energy consumption. From another perspective, the conclusions drawn from our analysis make a great reference value for countries and provide new ideas for Chinese carbon peaking and carbon neutralisation goals. [ABSTRACT FROM AUTHOR]

In order to promote the balanced development of regional economy, governments at all levels are constantly introducing regional coordinated development policy (hereinafter referred to as "the Policy"). However, there is an important and interesting issue, namely, with the increasingly severe environmental problems resulted from rapid regional economic growth, what kind of impact will the Policy have on carbon emissions reduction? This is attracting wide attention from relevant stakeholders. Therefore, taking the Beijing–Tianjin–Hebei (BTH) region in China for example and through constructing the difference-in-differences (DID) model, this paper evaluated the effect of the implementation of the Policy on carbon emissions reduction. Results indicated that the Policy significantly reduced the level of regional carbon emissions in the BTH region. After carrying out a series of robustness tests, this paper still found that the above conclusions were reliable. Moreover, the mediation effect test shown that the Policy indirectly lessened carbon emissions by optimizing energy structure and reducing the intensity of carbon emissions, while the expanding of economic scale would lead to an increase in carbon emissions due to the effect from the Policy. Additionally, heterogeneity analysis revealed that the Policy had a more significant effect on carbon emissions reduction in underdeveloped regions with low environmental constraints. Overall, this paper would be beneficial to understanding the environmental effects of the Policy at the urban regional scale, thus providing an important basic theoretical basis for promoting the green and sustainable development of regional economy. [ABSTRACT FROM AUTHOR]

Energy efficiency (EE) plays an important role in achieving the dual-carbon goal, and improving EE is thus indispensable. This paper evaluates the impact of carbon emission trading policy (CETP) on EE based on a difference-in-difference (DID) method, using 16-year data of 30 provinces and cities from 2005 to 2020. Conclusions are as follows: (1) CETP significantly promotes EE, and this conclusion still appears valid after robustness tests. (2) The positive impact of CETP on EE is more effective in regions of high foreign direct investment (FDI) and high government intervention (GOVI). (3) The positive impact of CETP on EE is through impact mechanisms of energy structure adjustment (ESA), green innovation (GI), and industrial structure upgrading (ISU). The findings in this paper may enrich current research in CETP and offer more pragmatic suggestions for policy advancement as well. [ABSTRACT FROM AUTHOR]

Heating and cooling in buildings accounts for over 20% of total energy consumption in China. Therefore, it is essential to understand the thermal requirements of building occupants when establishing building energy codes that would save energy while maintaining occupants' thermal comfort. This paper introduces the Chinese thermal comfort dataset, established by seven participating institutions under the leadership of Xi'an University of Architecture and Technology. The dataset comprises 41,977 sets of data collected from 49 cities across five climate zones in China over the past two decades. The raw data underwent careful quality control procedure, including systematic organization, to ensure its reliability. Each dataset contains environmental parameters, occupants' subjective responses, building information, and personal information. The dataset has been instrumental in the development of indoor thermal environment evaluation standards and energy codes in China. It can also have broader applications, such as contributing to the international thermal comfort dataset, modeling thermal comfort and adaptive behaviors, investigating regional differences in indoor thermal conditions, and examining occupants' thermal comfort responses. [ABSTRACT FROM AUTHOR]

China's economic growth has reached a new plateau. It is no longer appropriate to use the old economic growth model, which relied on labor, land resources, mineral resources, and other economic considerations. Under the background of artificial intelligence, high-quality economic development is an inevitable trend. A new financial paradigm called "digital finance" integrates financial services with information technologies. Digital financial technology is thought to be a crucial foundation for fostering high-quality and sustainable economic and social development since it may offer more economic entities reduced cost of capital and more realistic financial service skills than in traditional financial models. In the era of artificial intelligence, how to reasonably release the momentum of digital finance for China's sustained economic growth has become a hot topic of discussion at this stage. This paper studies the impact of digital finance on the economic efficiency of the energy industry in the context of artificial intelligence. Relevant metrics were also calculated. The findings revealed that: The benchmark regression result of digital finance on the efficiency of the green economy was 0.4685 before adding the main restrictions; the benchmark regression result of digital finance on the efficiency of the green economy was 0.2243 after adding the main constraints. As a result, data finance had a favorable impact on the effectiveness of the green economy. [ABSTRACT FROM AUTHOR]

Renewable energy (RE), the key to realizing the low-carbon transformation of energy, is significant in reducing social carbon emissions (CEs). However, the regional differences in the impact of RE on CEs and the impact mechanism are still unclear. Clarifying the regional differences and impact mechanisms will encourage the government to formulate a development plan for RE in line with the actual situation. To explore the heterogeneous impacts and conduction paths of RE on CEs, this paper uses province-level data from 2000 to 2019 in China. Based on clarifying regional CE efficiency, panel quantile regression models are introduced to assess the differential impacts of RE at different CE efficiencies. And the conduction mechanism of the effect of RE on CE efficiency is explored through the parallel multiple mediation effect model. The results show that RE development, the accumulation of social affluence, and technological innovation can improve carbon efficiency while increasing population size and energy consumption inhibit carbon efficiency. Urbanization rate, industrial structure, and opening to the outside world affect travel alienation in regions with different levels of CE efficiency. RE development improves regional CE efficiency, which is more evident in regions with low and high efficiency. In addition, RE can increase CE efficiency indirectly through effects on population size, urbanization rate, scientific and technological innovation, and intensity of fossil energy consumption. However, it decreases CE efficiency indirectly through social enrichment. These findings will provide broader insights for improving the development of regional RE and formulating differentiated policies. [ABSTRACT FROM AUTHOR]

The construction of green campus is related to the global sustainable development. Under the guidance of campus energy-saving policy, China's green campus mainly focuses on campus' energy saving. In contrast, the green campus in the USA pays more attention to environmental problems. This study focuses on the impact of "energy saving" on the sustainable development of green campus. On the one hand, it puts forward targeted optimization suggestions for the Chinese universities. On the other hand, it provides the experience reference and new development ideas for the global campus sustainability. Based on the characteristics of the development mode of green campus in China and the USA, this paper studies the motivation of "energy saving" to "sustainability" in the construction of green campus in China. In terms of research methods, this paper selects Zhejiang University a typical representative of China's green campus that combined with the comparative group of four American universities and conducts a quantitative analysis of G-value and S-value that represented energy consumption and sustainability, respectively. Zhejiang University's G-value (0.019) in 2016 is one-sixth of that of the comparative group in the same period, indicating that the implementation effect of the energy-saving monitoring platform is remarkable. From 2014 to 2017, the G-values are basically stable at 0.02, indicating that its future development potential is insufficient. Zhejiang University's scores in the four categories of S-value are significantly lower than those in the comparative group, showing that there are great differences in the construction achievements of the five universities. According to the score rate of 17 sub-items of S-value, the five universities have different advantages and disadvantages. Therefore, the green campus construction of China's case university is too one-sided and only stays at the level of energy conservation. The diversification of construction and the balanced development of all aspects should be paid more attention in the future. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between "replacing business tax with value-added tax" (RBTVT) and the total factor energy efficiency (TFEE) of the logistics industry using regression discontinuity (RD) method, and the research shows that the TFEE of the logistics industry in China has presented a benign growth trend. RBTVT has significantly promoted the development of TFEE in the logistics industry, and its influence has mainly stemmed from the improvement of green technology progress. Moreover, the promotion mechanism of policy in the eastern region reacts more sharp than that in the central and western regions does. So this paper claims that the government should built a tax incentive mechanism of green production and energy conservation. It is necessary to use RBTVT to promote the high-quality development of the logistics industry and create an environment which coexist low-carbon environmental protection and economic benefits. [ABSTRACT FROM AUTHOR]

With economic growth, China's energy consumption and industrial agglomeration have increased significantly. This paper uses China's provincial-level energy-intensive industries from 2004 to 2017 as the research object and employs a multi-dimensional panel fixed effect model to investigate the impact of industrial agglomeration on energy efficiency and its mechanism. Results show the following: (1) In general, industrial agglomeration and energy efficiency have a significant "inverted U-shaped" relationship, with an inflection point of 3.309. As the degree of agglomeration increases, energy efficiency first increases and then decreases, but the degree of agglomeration does not cross the inflection point. (2) The impact of industrial agglomeration on energy efficiency has regional heterogeneity. In the eastern and central regions, there is no significant non-linear relationship between industrial agglomeration and energy efficiency but a monotonous positive correlation. In the western region, industrial agglomeration has a significant "inverted U-shaped" impact on energy efficiency, with an inflection point of 3.495, but the agglomeration degree is still on the left of the inflection point, suggesting that a moderate increase in agglomeration is conducive to the improvement of energy efficiency. (3) Industrial agglomeration improves energy efficiency by increasing human capital and promoting fixed asset investment. Therefore, this paper argues that the agglomeration of energy-intensive industries in various regions should be increased to improve energy efficiency. However, it is necessary to raise the industrial threshold in the western region to prevent industrial agglomeration from crossing the inflection point. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As it is known that the linkage between financial performance and environmental performance of enterprises has not obtained conclusive results, and the role of liability and its structure have not been considered yet. In view of these gaps, this paper employed the data of Chinese manufacturing firms from 2008 to 2011, to examine the impact of liability structure on enterprises' carbon emissions abatement. The results manifest that both the relative and absolute proportions of long-term liabilities have a significant negative effect on the level value and growth rate of enterprise carbon emissions in China. Heterogeneity analysis demonstrates that enterprises in high energy consumption group have higher total carbon emissions and growth rate compared with enterprises in low and middle energy consumption groups. For enterprises of different ownership, the level of private-owned enterprises is higher and the growth rate is lower. The findings imply that policy makers should not adopt a one-size-fits-all policies to reduce firm carbon emissions. [ABSTRACT FROM AUTHOR]

Many developing countries are facing the difficulty of choosing between economic growth and energy conservation and emission reduction (ECER). China has strengthened the implementation of ECER by setting environmental accountability as the development goal of local governments, hoping to have better governance effects. To evaluate the actual intervention effect of this approach, this paper constructs panel data covering 46 countries from 1995 to 2014 and uses the difference-in-differences (DID) method and the composite control method to quantitatively analyse the policy effect. The results show that China can effectively curb energy consumption and carbon emission intensity per unit of GDP by adding ECER targets to the government's five-year plan, which has significant effects on ECER. Furthermore, we use an intermediary mechanism to test and identify low-carbon alternatives and an ECER promotion mechanism for technological advancement. The conclusion shows that economic development is compatible with low carbon and energy consumption. Combined with China's long-term goals for ECER, it can be considered that on the road to achieving carbon peaking and carbon neutrality in the future, the economy and tertiary industry should be rationally developed, the degree of urbanization should receive more attention, and the proportion of thermal power generation should be reduced. [ABSTRACT FROM AUTHOR]