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Energy consumption has risen much more while the economy has expanded so quickly. As a result, significant environmental contamination is created, which causes public worry. Under this background, enhancing energy efficiency and lowering pollution is particularly crucial for the fulfillment of sustainable economic development. Efficiency evaluation for energy and environment is an effective measure to achieve both energy saving and emission reduction as well as sustainable social development. In this paper, energy and environmental efficiency consists of energy utilization efficiency and pollution treatment efficiency. An improved two‐stage DEA model is proposed, which takes into account how stochastic and environmental factors affect decision‐making units (DMUs). Finally, the author analyses the panel data of 15 cities in Southwest China from 2010 to 2018, which proves the feasibility and effectiveness of this study. The results show Southwest China's poor energy and environmental efficiency as well as the stark differences among its cities. The scientific value revealed by this paper is as follows: (1) for the study of energy and environmental efficiency, this paper is more targeted at the differences between cities and (2) using an improved two‐stage DEA model to consider the effects of stochastic factors on DMUs. [ABSTRACT FROM AUTHOR]

Cities are the core carriers and key positions to achieve the dual carbon goals. It is of great significance to explore whether promoting urbanization can improve carbon emission performance, which is of great significance to comprehensively promote the goal of carbon neutrality. Based on the panel data from 2006 to 2021, this paper analyzes the spatial autocorrelation of carbon emission performance per unit space and the impact mechanism of urbanization process on it. The fixed-effect model was further used to identify the influencing factors of spatial carbon emission performance. The results show that: 1) China's carbon emission performance per unit space is declining year by year. 2) There is a strong positive spatial correlation and stable path dependence on the performance of carbon emissions per unit space in each region. 3) To a certain extent, increasing the level of urbanization will reduce the carbon emission performance per unit space. 4) The urbanization process has a spatial spillover effect on the carbon emission performance per unit space of surrounding provinces, and the spatial spillover effect of industrial structure and energy consumption structure is more obvious than that of economic level, population density and urbanization rate. Based on the conclusions, this paper puts forward specific policy suggestions to reduce the carbon emission performance per unit space to help the low-carbon development of cities. [ABSTRACT FROM AUTHOR]

Based on panel data collected from 2003 to 2020 across 30 provinces in China, the paper employs the spatial vector angle method and spatial Durbin model to investigate industrial agglomeration's nonlinear and spatial spillover effects on the energy consumption structure's low-carbon transition process (Lct). The results indicate the following: First, the influence of industrial agglomeration on Lct exhibits an inverted U-shaped pattern. As the degree of industrial agglomeration expands, its effect on Lct shifts from positive to negative. Second, industrial agglomeration demonstrates spatial spillover effects. It promotes the improvement of Lct in neighboring provinces through agglomeration effects. However, the continuous expansion of industrial agglomeration inhibits the improvement of Lct in neighboring provinces through congestion effects. Third, the heterogeneity test finds that industrial agglomeration has a significant role in promoting Lct in the samples of eastern region, but this effect is not significant in the samples of western and middle regions. [ABSTRACT FROM AUTHOR]

This paper employs the instrumental variable method and the intermediary model to explore the influence of monocentric and polycentric spatial structures of innovation agglomeration on energy efficiency. This paper uses data from 2010 to 2021 in China, and also panel data regression models were used. The findings indicate that the monocentric spatial structure of provincial system innovation agglomeration is primarily concentrated in northeastern and western regions of China, whereas the polycentric spatial structure is mainly distributed in eastern coastal areas. A monocentric spatial structure inhibits the enhancement of energy efficiency, but there is an environmental paradox of excessive resource agglomeration. In contrast, a polycentric spatial structure and its strengthening tendency facilitate the improvement of energy efficiency. A monocentric spatial structure has an inhibitory impact on the improvement of energy efficiency in the three sectors of agriculture, industry, and construction, while a polycentric spatial structure aids it. A monocentric spatial structure further hinders the improvement of energy efficiency by impeding the upgrading of the industrial structure, while the upgrading of the industrial structure driven by the polycentric spatial structure plays a role in enhancing energy efficiency. The policy recommendations of this paper are intended to help coordinate the pace of innovation development between cities, adjust the spatial structure of innovation agglomeration between regions, and promote the upgrading of the comprehensive industrial structure, thereby improving overall energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, 22 indexes are selected at three levels, including the informatization development level, the Internet development level, and the digital transaction development level, based on China's provincial panel data from 2011 to 2020, so as to build a digital economy development index system. Moreover, 28 basic indexes are selected from three aspects, including energy construction, energy production and energy consumption, so as to develop an energy economy development evaluation index system. The development index of China's digital economy and energy economy are measured by using the entropy weight method. The effect of the digital economy on the energy economy and its mechanism are tested by the static panel, the dynamic panel, and the mediating effect and regulating effect models. The results indicate that the digital economy has pronouncedly promoted the development of China's energy economy, and the development of the digital economy can have an effect on the rationalization of the industrial structure and then affect the development of the energy economy, and there is an intermediary effect. Moreover, the upgrading of the industrial structure is conducive to regulating the digital economy and facilitates the development of the energy economy. The development of the energy economy can be better promoted by focusing on the coordinated regional layout of the digital economy development, building a reliable energy commodity trading platform, and expediting the optimization and upgrading of the industrial structure. [ABSTRACT FROM AUTHOR]

This paper examines the nature of the economic activity–energy use nexus for 82 countries and the period from 1971 to 2014. Compared to all other papers, the current study is based on a significantly larger number of observations and was carried out by using new and advanced non-stationary panel data econometric techniques that overcame most of the existing methodical failures. The findings of the Granger non-causality test quite robustly lead to the conclusion that there is bi-directional causality both in the short and the long run. Finally, the obtained estimation results suggest that the increase of energy consumption by 1% results in an increase of GDP per capita between 0.54 and 0.56%. Also, the increase of GDP by 1% results in an increase in energy consumption between 0.47 and 0.48%. Governments cannot simply apply energy conservation policy by reducing total consumption of fossil fuels without negatively affecting economic activity and generating of spiral of mutual effects with positive ecological and very negative economic consequences. It is therefore necessary to make significant efforts to modernize the energy sector and to stimulate companies to invest in new energy-efficient production technologies relying on alternative environmentally friendly energy sources. [ABSTRACT FROM AUTHOR]

The paper analyzes the relationship between energy consumption and green GDP as increased energy consumption could cause an increase in GDP. In order to evaluate how and to what extent the increase in energy consumption affects the size and movement of the green GDP, the aim of this paper is to confirm the existence of a strong link between energy consumption and green GDP. Having a large number of papers linking energy consumption and GDP, through this paper, we want to emphasize an even greater role of green GDP in energy consumption by linking the impact of the consumption of different energy sources with the movements in green GDP. Namely, how much different energy sources affect the gap between green GDP and GDP. Within the empirical analysis, we use a panel cointegration technique to examine long-term relationships among integrated variables. The data analyzed in this model cover 36 countries for the period from 2008 to 2016. These 36 countries include the EU28 countries and potential candidates for accession in the European Union. The results of our analysis follow the theory as we found that an increase in energy consumption causes an increase in GDP, hence the green GDP. However, the second part of the analysis suggests that an increase in consumption of energy in sectors that are environmentally more damaging emphasize the gap between the GDP and green GDP, but that an increase in more environmentally cleaner energy consumption curtails that gap. [ABSTRACT FROM AUTHOR]

To clarify whether there is a spatial correlation problem among China's provincial energy consumption, economic growth, and ecological environment, this paper adopts the panel data of 31 provinces in China from 2008 to 2019 and uses a spatial data analysis method. This work studies economic growth and the spatial distribution pattern of the ecological environment, as well as the spatial effects of its energy consumption and economic growth on the ecological environment. Moreover, the spatial autocorrelation model, spatial-temporal transition method, and generalized moment estimation method are adopted to conduct empirical analysis on the research object. The results show that (1) there are spatial correlations among energy consumption, economic growth, and the ecological environment in China's provinces, and there are obviously different agglomeration areas in spatial distribution; (2) energy consumption, economic growth, and the ecological environment level show different growth trends, and the differences between different provinces in provincial units are shrinking; and (3) regarding energy consumption, the spatial impact of economic growth on the ecological environment is obvious, and there are obvious "path dependence" characteristics in the spatial distribution and positive correlations. Finally, this paper puts forward research conclusions and relevant suggestions on the basis of empirical analysis. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to reveal the mechanism and process of the dynamic evolution of the economy-energy-environment (EEE) system and study the variables that determine the speed of system evolution and development. The innovation is to overcome the defect of single index by constructing a system evaluation index and reveal the regional EEE system's evolution mechanism based on Haken's synergetic theory to supplement a systematic perspective's empirical evidence. It selects 30 provinces, municipalities, and autonomous regions in China to empirically analyse the evolution mechanism of the EEE system from 2005 to 2019. The empirical results show that economic development is the order parameter of the evolution and development of the EEE system. In the process of system evolution, the economic development subsystem is the decisive factor in EEE system evolution. In terms of the positive feedback mechanism of order parameter combination, the EEE system forms a positive feedback mechanism for increasing economic development in the stage of evolution and development. The negative feedback mechanism of decreasing environmental pollution emissions is formed in the EEE system. The increase in pollutant emissions has a certain inhibitory effect on economic growth. The improvement in the economic development level has no promoting effect on pollutants, and economic development, in turn, reduces environmental pollution. [ABSTRACT FROM AUTHOR]

The rapid development of digital finance has delivered significant benefits, such as sustainable development and economic growth. We explore the relationship between digital finance and green total factor energy efficiency (GTFEE) for the first time, filling a gap in the existing literature. This paper uses dynamic panel models to explore digital finance's impact on GTFEE at the Chinese city-level panel data from 2011 to 2018. The results show that digital finance can significantly improve urban GTFEE, and the findings remain robust with various tests. Second, the mechanism analysis indicates that digital finance can improve GTFEE by promoting urban green technology innovation and industrial structure upgrading. Further study shows that digital finance has a better effect on the improvement of GTFEE in central and western cities, small cities and non-resource-based cities, but has no significant or small impact on GTFEE in eastern cities, large cities and resource-based cities, reflecting the inclusiveness of digital finance. [ABSTRACT FROM AUTHOR]

This paper investigates the convergence behaviour regarding the share of global energy mix, as measured by primary energy consumption. Recent studies that employ stationary tests of panel data suggest that such data support the convergence hypothesis; however, some drawbacks exist, as these studies rely on methods that do not necessarily imply a sufficient condition for convergence. This paper adopts the concept of relative convergence as proposed by Phillips and Sul (2007), which employs a time‐varying, idiosyncratic component. We choose to focus on various sources' global primary energy consumption and investigate the long‐ run dynamic behaviour by source. The key finding of this paper is that two distinct clubs of convergence of energy can be determined: renewable and non‐renewable energy clubs of convergence. [ABSTRACT FROM AUTHOR]

Green innovation possesses dual externalities of "innovation" and "environmental protection", and enhancing energy efficiency serves as a crucial means to promote high-quality economic development. Building upon the energy rebound effect, we use the balanced panel data of cities at prefecture level and above in China from 2008 to 2018 to explore the impact of urban green innovation on total factor energy efficiency (TFEE). The findings of this study indicate that, firstly, the impact of green innovation on TFEE exhibits a positive U-shaped pattern, characterized by initial suppression followed by subsequent promotion. This conclusion remains robust after undergoing a series of rigorous robustness tests. Second, high-quality green innovation is found to reach the turning point more quickly, implying that substantial green innovation can cross the turning point in smaller quantities. Thirdly, on the whole, in comparison to non-resource-based cities, resource-based cities are capable of reaching the turning point at an earlier stage. Finally, the new energy demonstration cities have not yet reached the turning point, while the non-new energy demonstration cities have crossed the turning point. This study contributes novel insights into the relationship between green innovation and TFEE, which holds significant implications for the formulation and implementation of sustainable development policies. [ABSTRACT FROM AUTHOR]

The planet on which we are living is currently under an imminent threat. Population growth, massive digitalization, environmental pollution, increased scarcity of resources, and the greenhouse effect are putting our future and the future of generations to come at risk. As more and more governmental reforms are aiming to diversify the sources of energy production and many international players are working on market coupling to enhance cross-border cooperation, the question that arises is what developing countries can do in light of an energy crisis. Such so-called transition economies are working hard to promote growth, sustainable development and increase competitiveness in the European arena. Said that, it becomes important to know how market dynamics shape the energy demand, and thus the energy consumption in these countries. To answer this question, this paper employs secondary data from 2007-2021. The empirical analysis builds on unbalanced panel data and studies the main statistically significant determinants steering energy consumption across Balkan region, by examining the case of Albania, Serbia, Bulgaria, Republic of North Macedonia, and Bosnia & Herzegovina through a Fixed Effects model. A wide pool of variables related to innovation, political climate, economy, and finance were chosen to explain the energy consumption in the region. Using a general-to-specific approach, we were able to obtain a model with three regressors, all of them critical in capturing the variability in energy consumption. The results showed that in developing countries the main drivers of energy consumption are HDI, FDI and Innovation Index. This research provides an up-to-date picture on the dynamics of the energy markets in the region by providing a comprehensive analysis, which accounts for a variety of factors and adequate methodology. It is especially useful for policy makers to have a forward-looking approach and embed the results of such a study in their projections, analysis, and future decisions. [ABSTRACT FROM AUTHOR]

There are differences in economic level, population size, and technical level in different stages. At the same time, as a coal-dependent country, other regions have foreign dependence on coal. Then the primary factors and influence degrees of CO2 emissions in different stages and areas with different coal dependents will be various. This paper adds urbanization rate, industrial structure, and energy consumption structure to the traditional STIRPAT model. Based on the expanded STIRPAT model, the panel data of 30 regions in China from 2005 to 2019 are used to analyze the different stages and areas with foreign coal dependence. The main influencing factors and degree of influence of CO2 emissions. Regression finds that population size, energy consumption, and industrial structure will increase regional carbon dioxide emissions. There is an "inverted U-shaped" curve relationship between economic development, technological level, and carbon dioxide emissions. The improvement of the urbanization rate can reduce carbon dioxide emissions. Carbon dioxide emissions. An "inverted U-shaped" curve relationship between per capita GDP and CO2 emissions in three research periods from 2005 to 2010, 2011 to 2015, and 2016 to 2019. At the same time, the inhibition effect of the urbanization rate is constantly strengthened, and the promotion effect of the coal consumption ratio is decreasing year by year. The influence of population size, technical level, and industrial structure on carbon dioxide emissions in the whole country and areas with different coal dependents varies with the study period. To provide a theoretical basis for local conditions and regional management in the future direction of energy-saving and carbon reduction. [ABSTRACT FROM AUTHOR]

Based on the panel data of 227 prefecture-level and above cities in China from 2002 to 2018, a panel linkage equation model is constructed to explore the bidirectional influence relationship between haze pollution and urban sprawl, and the results of the study find that, firstly, there is a bidirectional promotion of causality between haze pollution and urban sprawl. That is, PM2.5 not only has a significant positive effect on urban sprawl, but also urban sprawl has a significant positive correlation with haze pollution, which is further strengthened by adding the air flow coefficient instrumental variable. Second, the heterogeneity analysis yields that haze pollution has different effects on urban sprawl in different regions. Under the sub-regional samples, haze pollution and urban sprawl have a bi-directional significant negative impact relationship in the eastern region, none of the haze pollution and urban sprawl have a bi-directional significant impact relationship in the western region, but both the central region and the northeastern region have a significant positive impact relationship. Under different city sizes, haze pollution and urban sprawl in large, medium and small cities have a bi-directional significant positive impact relationship, and from the numerical size, the degree of influence of haze pollution on urban sprawl in large cities is greater than that in small and medium cities; while the degree of influence of urban sprawl on haze pollution in medium cities is greater than that in large and small cities. Accordingly, it is proposed that urban governance should be adapted to local conditions, focus on innovative technologies to reduce energy consumption, and utilize big data to manage cities. [ABSTRACT FROM AUTHOR]

With the continuous uptick in world energy consumption, green energy plays an increasingly significant role in alleviating energy depletion and promoting economic development. Due to regional differences, transportation restrictions, national policies and other reasons, there is a mismatch between green energy needs and resources across regions in China, which leads to misallocation of capital and labor between heavily polluting and nonpolluting enterprises. In this context, this paper first proposes a new measurement method: namely, the capital and labor mismatch index. Panel data of 30 Chinese provinces from 2008 to 2018 are utilized to calculate the index. The results show significant resource mismatch across provinces in China. In addition, we extend the green Solow model by combining capital mismatch, technological progress and environmental regulation into a dynamic analysis framework of economic growth. The empirical results show that all three factors have significant spatial spillover effects. Specifically, capital misallocation exerts a strong negative impact on economic growth, while the other factors increase it. [ABSTRACT FROM AUTHOR]

This paper, based on the panel data, measures the comprehensive efficiency of green innovation, the economic output efficiency of green innovation, the social output efficiency of green innovation, and the environmental output efficiency of green innovation of listed energy companies in China by means of the window DEA model from the perspective of the triple bottom line. Moreover, the impacts of environmental factors and random factors on the green innovation efficiency are analyzed. It is found that firstly, green innovation efficiency of listed Chinese energy companies shows a V-shaped change trend without excluding environmental factors and random factors. Secondly, environmental factors and random factors are the primary factors to promote the green innovation efficiency of energy listed companies in China. Finally, after removing the interference of environmental factors and random factors, comprehensive efficiency of green innovation of the listed energy companies in China is relatively high and the distribution is relatively concentrated, but there is a downward trend during the research period. [ABSTRACT FROM AUTHOR]

China has launched energy quota right trading system, as one of the sustainable operations, since 2016 to cap the total energy consumption, however the economic and energy saving effect of this new policy is unclear. Based on the input and output panel data of China's 29 manufacturing sub-industries, this paper constructs the non-parametric optimization model under the command-and-control and the energy quota right trading scenario, to compare their potential economic gains and energy savings of these two polices for Chinese manufacturing industries. Results show that, whether at overall level or at the sub-industries level, the economic potential and energy-saving potential from energy quota right trading are higher than those from command-and-control. However, a part of energy-saving potential will be squeezed out due to the negative externality of the energy market trading. We suggest the well integration of both command-and-control and the market mechanism to achieve a Win–Win development. [ABSTRACT FROM AUTHOR]

Introduction: The creation of infrastructure in rural areas is an essential guarantee for the economic development and in improving farmers' living standards. As a critical link between urban and rural production and consumption, rural logistics is essential to maximize the efficiency of rural infrastructure investment. Methods: Based on the panel data of 31 provinces in China from 2011-2020, the study analyzes the impact of high-quality development of rural logistics on the enhancement of residents' consumption in terms of scale and quality, using a fixed-effects model. Results: It suggest that this development promotes the growth of rural residents' consumption and quality, with a noticeable regional heterogeneity. The most significant effect is observed in the eastern region. Further analysis shows that inland and less developed provinces of rural e-commerce stimulate residents' consumption upgrade with the help of high-quality development of rural logistics at a level weaker than coastal provinces and developed provinces of rural e-commerce. Discussion: Consequently, it is recommended to boost the building of logistics infrastructure in rural areas and actively cultivate diversified rural logistics operators in the western and northeastern regions. The contribution of this study is to examine the impact of rural logistics on rural residents' consumption from the perspective of infrastructure, further validating the role of infrastructure development in promoting economic growth. [ABSTRACT FROM AUTHOR]

The energy rebound effect is a potential threat to energy-saving targets based on energy efficiency improvements. This paper employs a stochastic energy demand frontier model to analyze the energy demand and rebound effect in China's urban residential sector. Using a panel data set of 30 Chinese provincial-level regions over the period 2001-2014, for the first time, we investigate the degrees and determinants of China's urban residential energy demand and energy rebound effect. The results show that residents' income level, energy price, temperature deviation, population scale, household size, and district heating system are significant influencing factors of residential energy consumption. Regarding the energy rebound effect, we find that energy price is negatively correlated with the rebound effect, and an inverted U-shaped relationship between residents' income level and rebound-effect size exists. The magnitude of the rebound effect varies across regions, with an average of 65.4%. The main policy implication generated by this study is that it should be in urgent need of energy pricing reform to mitigate the rebound effect in China. [ABSTRACT FROM AUTHOR]

The current paper contributes to the literature on the relationship between economic development and energy demand by assembling a wide panel dataset of energy consumption and prices for 37 OECD and 41 non-OECD countries. The unbalanced data spans 1960-2016, with the full 56 years of data for 17 countries and all countries having at least 18 years. In addition, our dynamic panel estimates address nonstationarity, heterogeneity, and cross-sectional dependence. Most results suggest that the GDP elasticity is less than unity (e.g., 0.7)-i.e., energy intensity will fall with economic growth. Most evidence suggests that the GDP elasticity is similar for OECD and non-OECD countries, and for non-OECD countries, similar across income-bands. Also, there is no evidence that individual country elasticity estimates (for GDP or prices) vary systematically according to income. The price elasticity is larger (in absolute terms) for OECD than for non-OECD countries- indeed, it is typically insignificant for non-OECD countries. [ABSTRACT FROM AUTHOR]

Using panel data of 11 provinces in the western region of China, the paper empirically analyzes the influence of public environmental participation on the effectiveness of pollution governance. Based on documents from provincial governments in Western China, the paper uses Nvivo software to extract keywords about local governments' attention to the environment and examines its mediating effect between public environmental participation and pollution governance. Furthermore, a classifiable analysis of government environmental attention is carried out, and the effect of local governments' attention to the environment on pollution governance is studied. The results show that public environmental participation promotes pollution governance by increasing the governments' attention to the environment. Additionally, local governments' environmental attention also has a promotive effect on pollution governance, and the increase in environmental decision-makers' attention to environmental pollution and energy consumption is conducive to the improvement of environmental quality, but the effect of environmental protection and collaborative attention on environmental quality improvement still needs to be explored further. • Local governments' attention to the environment is estimated. • Public environmental participation promotes pollution governance. • Local Governments' Environmental Attention plays mediating role. • Local Governments' Environmental Attention has a promotive effect on environmental quality. • The effect of collaborative attention on environmental quality improvement still needs to be explored further. [ABSTRACT FROM AUTHOR]

This study examines the relationship between renewable and non-renewable energy consumption, economic growth, and ecological footprint for the top 14 Russian-energy-importing countries in 2000–2018. Through PVAR analysis, this study calculates the long-term correlation between variables using a VEC model. The VAR approach addresses the endogeneity issue by including endogenous interactions between the variables in the model. Additionally, the impulse response functions and the influence of various variables on certain lags are evaluated. Cointegration between variables was assessed using DOLS and FMOLS panel analysis to investigate the long-term link further. Our findings demonstrate that there is a cointegration relationship between all series. According to the impulse response analysis results, an energy shock from Russia will negatively affect the countries' economic growth and decrease the ecological footprint. Increasing renewable and non-renewable energy consumption domestically increases ecological footprint, but the effect of renewable energy consumption on ecological footprint seems to be less. According to DOLS findings, a 10 percent increase in Russian fuel exports increases the elasticity of economic growth by 0.61%. In contrast, FMOLS findings indicate that a 10% increase in Russian fuel export increases the elasticity of economic growth by 1%. However, the direction of the relationship is positive when the ecological footprint is considered the dependent variable: While the non-renewable energy consumption elasticity coefficient was 0.030 in the DOLS model, it was found to be 0.023 in the FMOLS model. The elasticity value of the economic growth variable was found to be 0.69 in the FMOLS model. [ABSTRACT FROM AUTHOR]

Based on the panel data of 30 provinces in China from 2006 to 2016, the Gaussian kernel density estimation was initially used to describe the characteristics of the inter-provincial differences in household energy consumption (HEC), a dynamic panel data model was established secondly to study the main factors affecting HEC, and finally the counterfactual decomposition method was used to calculate the contribution of each factor to the inter-provincial differences in HEC. The research results are shown as follows: First, the inter-provincial HEC in China shows a phenomenon of agglomeration and growth, and the HEC level of different regions presents difference and imbalance. Second, the logarithms of consumption habits, total population and residents' income have significant effects on the logarithm of HEC, the influence coefficients are 0.191, 0.073 and 0.745, respectively; HEC not only has significant inertia characteristics, but also has certain population size effect and wealth effect. Third, the logarithms of technological progress, urbanization and household energy price have significant effects on the logarithm of HEC, the influence coefficients are 0.864, 0.195 and −0.346, respectively; the HEC saved by technological transformation is not sufficient to offset the increase in HEC demand, urbanization can improve HEC significantly, but the household energy price significantly inhibits HEC. Finally, from the perspective of contribution, the total population, residents' income and urbanization contribute a lot to the inter-provincial differences, totaling 73.02%. The possible innovations of this paper are as follows: First, the contribution of the influencing factors to the difference of inter-provincial HEC is studied, which is rarely seen in the previous literature. Second, compared with traditional decomposition method, counterfactual decomposition method is more convenient for economic interpretation. Third, the divergent conclusions of scholars are pointed out in this paper, and the actual impact of influencing factors on HEC are clarified. Image 10086681 • The level of inter-provincial HEC shows difference and imbalance. • HEC has significant inertia characteristics, population size effect and wealth effect. • HEC saved by technology is not sufficient to offset the increase in HEC demand. • Total population, residents' income and urbanization contribute 73.02%. • The statistical analysis tool used in this paper is Stata15. [ABSTRACT FROM AUTHOR]

China's transport industry has made rapid progress, which has led to a great amount of carbon emissions. However, it is still unclear how carbon emissions from the transport sector are punctuated by shifts in underlying factors. This paper aims to examine the process of China's carbon emissions from the transport sector as well as its major driving forces at the provincial level during the period of 2000 to 2015. We first estimate the carbon emissions from the transport sector at the provincial level based on the fuel and electricity consumption using a top-down method. We find that the carbon emission per capita is steadily increasing across the country, especially in the provinces of Chongqing and Inner Mongolia. However, the carbon emission intensity is decreasing in most provinces, except in Yunnan, Qinghai, Chongqing, Zhejiang, Heilongjiang, Jilin, Inner Mongolia, Henan and Anhui. We then quantify the effect of socioeconomic factors and their regional variations on carbon emissions using a panel model. The results show that the development of secondary industry is the most significant variable for carbon intensity at both the national and regional levels, while the effects of the other variables vary across regions. Among these factors, population density is the main factor of the increasing carbon emissions per capita from the transport sector for both the whole country and the western region, whereas the consumption level per capita of residents and the development of tertiary industry are the primary drivers of per capita carbon emissions for the eastern and central regions. [ABSTRACT FROM AUTHOR]

As part of the country's efforts to achieve green development, China implemented a mandatory energy intensity reduction target in its 11th "Five-Year Plan (FYP)" in 2006, and then began to roll out a series of relevant measures. However, existing studies have paid little attention to the actual effects of China's energy intensity constraint policy (EICP). In this paper, using panel data from China's 36 industrial sub-sectors covering the years from 2001 to 2014, we adopt the difference-in-differences (DID) method to investigate for the first time the EICP's (marginal) effect on total factor energy efficiency growth (TFEEG). We also estimate the superposition effect caused by the introduction of a carbon intensity constraint policy (CICP) on TFEEG, through the difference-in-difference-in-differences (DDD) strategy. Finally, using counterfactual, re-grouping and quasi-DID analyses, we conduct a series of robustness tests of the empirical results. The results show that the TFEEG in China's industrial sector experienced an overall declining trend between 2001 and 2014. The implementation of the EICP has had a significantly negative effect on the improvement of the TFEEG of sub-sectors with higher levels of energy intensity. After the implementation of the EICP, the TFEEG rate of these sub-sectors declined by 4.31%, compared to the rate of the other sub-sectors. The results of a series of robustness tests indicate that such a negative effect is credible. The marginal effect in the first two years after the implementation of the EICP was significantly negative, while the superposition effect of the introduction of a CICP on industrial TFEEG remained negative. Thus, the Chinese government should reinforce the implementation of energy-saving policies by introducing additional market-oriented auxiliary policies to propel the green development transformation of China's industrial sector. [ABSTRACT FROM AUTHOR]

This paper has used a stirpat model to investigate the synergistic effect of CO2 emission, energy consumption, energy intensity, economic growth, population, urbanization and trade openness to demonstrate growthenvironment nexus in four selected developing Asian economies. Taking a panel data set from Bangladesh, China, India and Indonesia this study applies Autoregressive Distributed Lag (ardl) model and vec Granger Causality/Block Exogeneity Wald Tests. The empirical results show that energy intensity, urbanization, population, and per capita gdp growth are the raison d'être of CO2 emissions whereas trade openness is found to be negatively related to CO2 emissions. Conversely, energy consumption, urbanization, population and trade openness are positively related to per capita gdp. In addition, it also investigates the Environmental Kuznets Curve (EKC) hypothesis and the findings substantiate an inverted Ushaped relationship. Cross-section short-run coefficients of country-level data are inquired into to check the robustness of the panel outcomes. [ABSTRACT FROM AUTHOR]

This study aims to provide empirical evidence on whether using renewable and non-renewable energy, CO2 emissions, and the resulting changes in temperature and precipitation can alter cereal yields in European Union countries. To this end, a panel data set covering 1992–2021 has been used to analyse the impact of climate change and economic factors on cereal production in European Union countries. The study employed various analytical techniques to better understand the impact of climate change and economic factors on cereal crop yields. These included using annual average temperature and rainfall to measure climate change, energy consumption, and economic growth to analyse the economic aspects. To achieve the objective, feasible generalised least squares (FGLS) models with robust standard errors and bootstrap and a quantile regression (QR) model with marginal analysis were employed. The Westerlund cointegration test confirmed the presence of cointegration between cereal production and climate variables, economic growth, and energy consumption. The results indicate that increases in temperature and precipitation may contribute to increased cereal production in European Union countries. In contrast, energy consumption, including renewable energy and CO2 emissions, may have a nonlinear effect on cereal production. Quantile regression analysis suggests climate change may impact crop yields most in countries with low and moderate cereal production per hectare. Increased CO2 emissions may increase yields in the initial period, but excessive CO2 may negatively impact cereal production in the long term. It would be advisable for countries with low and moderate crop productivity to consider implementing technological advances and combating global warming by modernising cultivation methods and making greater use of renewable energy sources. [ABSTRACT FROM AUTHOR]

To gain a deeper understanding of the carbon emission mechanism from transportation facilities, all system elements affecting carbon emissions from regional transportation facilities are identified and analyzed according to panel data from 30 regions in China. A spatial econometric model for carbon emissions from transportation facilities is constructed using the Spatial Dolbin model from 2004 to 2022 as the research period. From the results, the carbon dioxide emissions from transportation facilities added from 318 million tons in 2004 to 752 million tons in 2022, with an average annual growth rate of 4.9%. The global spatial auto-correlation coefficient was significant at the 5%, with an obvious spatial correlation between carbon dioxide emissions within a geographical range. In addition, through stability testing, the model showed high stability in both spatial lag testing and spatial error testing, demonstrating strong ability to interpret data. The research shows that the carbon emission is affected by independent variables, including population, economy, technology, and transportation, and exhibit significant spatial distribution characteristics in different regions and years, providing a basis for policy formulation and carbon emission management. [ABSTRACT FROM AUTHOR]

Energy intensity convergence can be used to assess the effectiveness of policies in reducing energy intensity. This study analyzes the energy intensity convergence in Indonesia based on panel data of 33 provinces from 2010 to 2018. Spatial econometrics techniques are used in the estimation of beta convergence to measure the spatial dependence of energy intensity. Empirical results show that there is evidence of both absolute and conditional beta convergences with no evidence of sigma convergence. From the results, this study found that other variables, such as provincial income, the role of manufacturing industries, the role of international trade, FDI, and population density, might encourage energy intensity convergence. From the estimation results, several policy recommendations are derived to increase energy efficiency: First, using a more efficient industrial technology. Second, attracting foreign investment to non-industrial sectors. Third, developing exports from sectors that use less energy to increase energy efficiency. [ABSTRACT FROM AUTHOR]

Currently, the manufacturing development in Yangtze River Basin is featured by high energy consumption and high pollution. This consumes lots of energy and creates severe environmental pollution. For the sustainable development of manufacturing, it is necessary to scientifically evaluate the manufacturing energy efficiency of Yangtze River Basin. In this paper, the suitable metrics are firstly selected to evaluate the manufacturing energy efficiency (MEE) under environmental constraints. Then, the panel data 2010–2018 on the manufacturing of the 11 regions of the Yangtze River Basin were taken as the object. On this basis, the MEE in the Belt was analyzed statically and dynamically with the slack-based measure (SBM) model and Malmquist-Luenberger metric, respectively. In addition, the Tobit model was adopted for regression analysis on how various factors affect the Belt MEE. The results show that: the MEEs in the Belt were severe polarized, with significant differences between regions; in most regions, the ML was improved to varied degrees in the sample interval; comparatively, technological progress boasts stronger promoting effect than technical efficiency improvement; the Belt MEE is significantly promoted by technological innovation, foreign trade, and environmental regulation, significantly suppressed by property-right structure and energy price, and not significantly affected by industrial structure or energy structure. [ABSTRACT FROM AUTHOR]

Changjiang Economic Zone (CEZ) faces the urgent task to promote the energy conservation and emission reduction of the transportation industry. This study constructs an evaluation system for transportation industry energy efficiency (TIEE) and evaluates TIEEs of 11 CEZ provinces in 2000–2017, using the super-slack-based measure (Super-SBM) model containing undesired output. On this basis, the panel data model was adopted to explore the impactors of TIEE. The main results are as follows: CEZ provinces varied significantly in TIEE. In the sample period, Jiangsu, Jiangxi, Zhejiang, Sichuan, Shanghai, and Anhui achieved relatively satisfactory TIEEs; Hunan, Hubei, and Guizhou performed generally on TIEE, calling for some improvement; Chongqing and Yunnan did not perform well, leaving a huge room for improvement. Judging by TIEE trends in the lower reaches, middle reaches, and upper reaches, TIEE of the lower reaches exhibited a U-shaped trend (first decrease and then increase) and TIEEs of the middle reaches and upper reaches did not fluctuate significantly, except for a few years. There was a marked difference between the three regions in TIEE: TIEE in the lower reaches was much higher than that in the middle reaches and upper reaches. In addition, the panel data model demonstrates that TIEE is significantly promoted by economic growth and transportation structure, obviously suppressed by industrial structure, opening-up, and transportation infrastructure, and not clearly affected by government influence or environmental regulation. [ABSTRACT FROM AUTHOR]

Ecological deterioration, air pollution, and resource depletion have shrouded the vast regions of China, raising widespread concerns about the sustainable development of the domestic economy. Although financial agglomeration has become a pivotal approach for China to realize green transformation, there is a lack of evidence against the causal correlation between financial agglomeration and sustainable development of the regional economy. To fill this gap, using the data of 29 provincial capital cities in China spanning from 2009 to 2019 and adopting individual time bidirectional fixed effect model, IV-GMM approach, and alternative modeling techniques, this paper investigates the impact of financial agglomeration on sustainable development of the regional economy for the first time. The results indicate that there is a significant positive correlation between financial agglomeration and the sustainable development of the regional economy. Financial agglomeration facilitates the improvement of regional energy efficiency, and the latter further mediates the relationship between financial agglomeration and sustainable development of the regional economy. In addition, the empirical results also demonstrate that the higher the economic policy uncertainty, the weaker the positive relationship between financial agglomeration and energy efficiency. The present study is of great significance for China to implement energy-saving and emission-reduction tasks and achieve sustainable urban construction. [ABSTRACT FROM AUTHOR]

With the advent of the era of big data, large-dimensional spatial panel data is gradually used to do empirical research in the macroeconomic field. This paper adopts a Data Envelopment Analysis(DEA) approach to calculate regional energy efficiency based on the perspective of total-factor energy efficiency using statistical data of 30 administrative regions in China. On basis of spatial effects, the paper assesses the convergence of regional energy efficiency in China using large-dimensional spatial panel data model. Our results indicate that (1) There is significant spatial autocorrelation and clear spatial effects in China's regional energy efficiency. Thus, spatial effects should not be ignored when assessing the convergence of regional energy efficiency in China; (2) During the period from 2000 to 2014 China's regional energy efficiency not only exhibits absolute β -convergence but also exhibits conditional β -convergence, the convergence rate is higher than the rate of absolute convergence after controlling for the initial conditions of the level of economic development, foreign direct investment and government influence; (3) The convergence rate of China's regional energy efficiency from 2004 to 2014 is higher than the convergence rate for the period of 2000–2004, which indicates that industry transfer has contributed to improvement in the convergence of regional energy efficiency in China since 2004. [ABSTRACT FROM AUTHOR]

Based on the panel data of 30 Chinese provinces in 2000–2018, this paper measures the green total factor energy efficiency (GTFEE) of China with the super-slack-based measure (Super-SBM) model. Then, the spatial correlation of provincial GTFEEs was analyzed, a spatial econometric model was established based on the panel data, and was used to empirically verify the effect of financial agglomeration, technological innovation, and financial agglomeration and technological innovation interaction on GTFEE. The results show that: in the sample period, most Chinese provinces had a low GTFEE, and the low-GTFEE provinces mainly belong to inland area; only a few eastern coastal provinces achieved a satisfactory GTFEE. China and its three regions exhibited similar trends of GTFEE. By mean GTFEE, eastern region ranked the highest, followed by central region; the western region came at the bottom. The Global Moran's I values show strong spatial correlations between provincial GTFEEs, that is, adjacent provinces often have similar GTFEEs, and the provinces tend to cluster in terms of GTFEE. According to the spatial econometric model, GTFEE is significantly inhibited by financial agglomeration, but significantly promoted by technological innovation. GTFEE is not clearly affected by financial agglomeration and technological innovation interaction. Opening up has a significant positive impact on GTFEE, while industrial structure, energy structure and environmental regulation have a significant negative impact on GTFEE. [ABSTRACT FROM AUTHOR]

With the panel data of 21 China's industrial industries from 2008 to 2020, the relationship models between intelligent industry, energy regulation and ecological transformation are constructed and tested from two dimensions of resource saving and environmental friendliness, then equity financing is introduced into this model as moderating variable to discuss the moderating effects on the relationships between intelligent industry, energy regulation and ecological transformation. Results show that: ⑴China's industrial industries significantly transformed to the resource-saving type, and the environment-friendly level stayed in a slow progression. ⑵Intelligent industry affected ecological transformation positively and significantly. The impact of energy regulation on ecological transformation was nonlinear. The regulation of energy consumption can significantly stimulate the transformation of resource saving, and restrain the transformation of environmental friendliness; the regulation of energy structure can significantly stimulate the transformation of environmental friendliness. ⑶ Equity financing can positively moderate the relationship between intelligent industry and ecological transformation, and it can also moderate the regulation of energy structure and promote the transformation to environmental friendliness, especially in the low consumption industries. [ABSTRACT FROM AUTHOR]

The focus of this study is to examine the effect of energy consumption, energy intensity, energy prices on the financial development of some selected top oil exporting countries in Africa. The study takes the period from 1976 until 2019 for the analysis. Both the cross-sectional and the homogeneity tests confirmed the presence of cross-sectional dependency and heterogeneity across the panel data set. Likewise, the dynamic and heterogeneous co-integration result reveals the presence of long run relationship among the scrutinized variables. The estimation result further indicates that both the energy intensity and energy use positively relate with the financial development of the sampled economies, while energy price indicates a negative relationship towards financial development. In addition, the causality results by Dumitrescu and Hurlin causality shows a unidirectional causal relationship from energy use to financial development. It also indicates another one-way causal linkage from energy intensity to financial development. Meanwhile, there exists a feedback causal effect between energy price and the financial development for all the sampled economies. In the last section of the paper, the analysis makes available policy recommendations. [ABSTRACT FROM AUTHOR]

This paper aims to explore the relationships between environmental quality, economic growth, and energy use. A panel data set of 25 years (1990–2014) for five OPEC countries (Algeria, Nigeria, Indonesia, Saudi Arabia, and Venezuela) is used, and panel unit root tests, panel cointegration tests, panel Granger causality tests, fully modified ordinary Least square (FMOLS), and dynamic ordinary least square (DOLS) methods are employed as estimation strategies. Our results show that in the long-run, there are two-way causal relationships between GDP and energy consumption for all countries. Bidirectional causal relationships between GDP and CO2 emissions are also observed in all countries except Algeria. The same relationships are also observed between energy consumption and CO2 emissions in all countries with an exception in Venezuela where unidirectional causality running from CO2 to energy consumption is found. The impact of GDP on the CO2 emissions is the highest in Saudi Arabia followed by Venezuela, Nigeria, and Indonesia. The effect of energy use on the CO2 emissions is the greatest in Algeria followed by Indonesia and Nigeria. [ABSTRACT FROM AUTHOR]

The empirical conclusions regarding the relationship between energy price and energy efficiency are relatively mixed. This paper systematically examines the influence of energy price on energy efficiency in China based on data from 30 provinces between 2003 and 2017, using linear and nonlinear effect analysis. We found that the impact of energy price on energy efficiency in China was positive in general. However, there existed heterogeneous effects of energy price on energy efficiency in various regions, and the effect differed with differences in energy efficiency levels based on the panel quantile regression analysis. Finally, the nonlinear effect analysis based on the panel threshold model indicated that the effect of energy price on energy efficiency increased with the rise of the environmental regulation level and economic growth rate, while it decreased with the ascent of the degree of energy price distortion and economic development level. In particular, when the value of a region's economic development level and economic growth rate was within a certain range, the impact was not statistically significant. Overall, these findings contribute to a deeper understanding regarding the effect of energy price on energy efficiency in China. [ABSTRACT FROM AUTHOR]

This paper investigates energy use efficiency at the province level in China using the stochastic frontier panel data model approach. The stochastic frontier model is a parametric model which allows for the modeling of the relationship between energy use and its determinants using different control variables. The main control variables in this paper are energy policy and environmental and regulatory variables. This paper uses province level data from all provinces in China for the period 2010–2017. Three different models are estimated accounting for the panel nature of the data; province-specific heterogeneity and province-specific energy inefficiency effects are separated. The models differ because of their underlying assumptions, but they also complement each other. The paper also explains the degree of inefficiency in energy use by its possible determinants, including those related to the public energy policy and environmental regulations. This research supplements existing research from the perspective of energy policy and regional heterogeneity. The paper identifies potential areas for improving energy efficiency in the western and northeastern regions of China. Its findings provide new empirical evidence for estimating and evaluating China's energy efficiency and a transition to cleaner energy sources and production. [ABSTRACT FROM AUTHOR]

CO2 emission is one the major contributor to climate change that the top CO2 emitting countries are always trying to mitigate. In an attempt to fill the gap in energy and environmental literature, this study explores the interaction between economic growth, energy usage, trade and urbanization on CO2 emission for MINT economies using the time coverage from 1980 to 2018, providing new perspectives into the literature by employing panel data analysis. Aiming to create robust outcomes, this paper deployed both conventional and modern econometric techniques. The panel co-integration test revealed evidence of the co-integration between CO2 and its determinants in the MINT economies. In order to explore the linkages between CO2 and its determinants, the ARDL PMG model was utilized in MINT economies. Findings based on the ARDL PMG reveals; (i) positive interconnection between CO2 emissions and energy usage; (ii) no significant link was found between CO2 and economic growth; (iii) urbanization influence CO2 positively while a negative link was found between CO2 and trade. Furthermore, the Dumitrescu-Hurlin Causality test revealed; (i) uni-directional causality from CO2 to urbanization; (ii) GDP growth cause CO2 while CO2 causes energy usage. Based on these findings, recommendations were put forward. [ABSTRACT FROM AUTHOR]

This paper has used a stirpat model to investigate the synergistic effect of CO2 emission, energy consumption, energy intensity, economic growth, population, urbanization and trade openness to demonstrate growthenvironment nexus in four selected developing Asian economies. Taking a panel data set from Bangladesh, China, India and Indonesia this study applies Autoregressive Distributed Lag (ardl) model and vec Granger Causality/Block Exogeneity Wald Tests. The empirical results show that energy intensity, urbanization, population, and per capita gdp growth are the raison d'être of CO2 emissions whereas trade openness is found to be negatively related to CO2 emissions. Conversely, energy consumption, urbanization, population and trade openness are positively related to per capita gdp. In addition, it also investigates the Environmental Kuznets Curve (ekc) hypothesis and the findings substantiate an inverted Ushaped relationship. Cross-section short-run coefficients of country-level data are inquired into to check the robustness of the panel outcomes. [ABSTRACT FROM AUTHOR]

One of the critical challenges facing China is environmental pollution caused by insufficient use of clean energy. This paper empirically investigates the impact of rural labor mobility on household clean energy use using data from the China Family Panel Studies (CFPS). The results indicate that labor mobility significantly promotes the adoption of clean energy by rural households, with income effects and information channel effects serving as the underlying mechanisms. The study also finds that health risks, as an important consideration for household energy use choices, enhance this positive effect. Furthermore, heterogeneity analysis reveals that labor mobility has a greater impact on households with lower levels of education, lack of social security, and those located in central and western regions. Therefore, the government should take measures to increase the income of the rural migrant population and continuously widen the information channels for rural households to promote clean energy use. The findings of this study provide a new perspective and evidence for understanding household energy use behavior in China, offering valuable insights for policymakers aiming to expand the use of clean energy. • Labour mobility significantly contributes to clean energy use in rural households. • Income effects and information channel effects are the main mechanisms of action. • Health risks faced by households play a moderating role. • Heterogeneity in region, educational attainment, social security status will influence the effect of labour mobility. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of the impact of renewable/non-renewable energy consumption on ecological quality and economic growth can serve to the sustainability of energy, ecology, and economy. This paper provides a new perspective of the comprehensive system of the 3E model, and introduces indicator of social factor to examine the non-linear effects of urbanization on energy, economy, and the environment. A threshold panel regression model is developed using the data of 120 countries in the last 20 years. In the proposed model, renewable energy and non-renewable energy are explanatory variables, economic growth and ecological footprint are explained variables, and urbanization are threshold variable. Non-linear relationships are concretized and quantified in the research of global and different income groups. The results show that global renewable energy can promote economic growth while improving the environment. As the urbanization rate increases, the negative effect of renewable energy on the ecological footprint first weakens and then increases, and the positive coefficient on the economy maintains a growth trend. Non-renewable energy has a more obvious positive effect on economic growth, but it increases the ecological footprint. The development of urbanization strengthens its promotion of the economy and reduces its pressure on the environment. From the perspective of different income groups, renewable energy does not always suppress the ecological footprint. The energy transition reduces economic growth in the initial stages of urbanization in certain regions. After crossing the threshold, the coefficient changes of different income groups have similarities and differences. Based on the conclusions, some recommendations are put forward, including promoting the transformation of energy consumption structure, improving energy efficiency and accelerating the process of urbanization. The model in this paper is suitable for investigating the nonlinear impact of urbanization on the 3E system through panel data, and can provide theoretical support for studying the threshold effect of a country or economy. This study also shows the importance of renewable energy and the indirect effects of urbanization, which is helpful for formulating global sustainable and coordinated development policies. [Display omitted] • A new perspective of the energy-economy-environment (3E) model is proposed. • Threshold models enable the non-linear relationships to be concretized and quantified. • Global renewable energy improves the environment while promoting economic growth. • Urbanization reduces the pressure on the environment from energy consumption. • Urbanization strengthens the positive effect of energy on economic growth. [ABSTRACT FROM AUTHOR]

Human activities are closely related to energy consumption and carbon emissions. Nevertheless, in the context of energy transition and carbon emission reduction policies, the impact of population migration on energy consumption and carbon emissions is still unclear. Based on the panel data of 30 provincial regions in China from 2000 to 2019, this paper uses the spatial Durbin model to perform a spatial econometric investigation on this issue. In particular, this study innovatively embeds two spatial weight matrices, an economic gravity weight matrix and a population migration weight matrix, into the spatial Durbin model to capture the spatial spillover effects of population migration. The results show that population migration has obvious adverse impacts on the provinces with net outward population migration. Specifically, on the one hand, population migration increases energy consumption and energy poverty, but energy poverty is more severe in provinces with net outward population migration. On the other hand, population migration increases the carbon emissions and carbon reduction barriers of the provinces with net outward population migration and has no significant impact on the carbon emissions and carbon reduction barriers of the provinces with net inward population migration. Besides, the results of heterogeneity analysis show that population migration causes gaps in the energy structure among regions and even triggers the carbon transfer to provinces with net outward population migration. On the whole, the research results of this paper all prove that population migration increases the carbon reduction barriers in the provinces with net outward population migration. These empirical insights enrich the relevant theoretical achievements of the impact of population migration on energy consumption and carbon emissions and have a high reference value for formulating more reasonable regional energy transition and carbon emission reduction policies. • The spatial spillover effects of population migration on energy consumption and carbon emissions are explored. • The spatial Durbin model is used in this paper. • The spatial Durbin model is embedded with the economic gravity weight matrix and the population migration weight matrix. • Population migration has obvious adverse impacts on the provinces with net outward population migration [ABSTRACT FROM AUTHOR]

• The impact of economic growth pressure on energy efficiency improvement is discussed. • Economic growth pressure has hindered the improvement of energy efficiency. • Heterogeneity effects exist in individual dimension and temporal dimension. Economic growth target management has been a typical feature of Chinese governments at all levels for a long time in the past decades. Based on a panel data of 188 Chinese cities, this paper examines the impact of pressure brought by economic growth target on energy efficiency improvement. The results suggest that economic growth pressure has hindered the improvement of energy efficiency. For every unit of increase in economic growth pressure, the improvement indicator of energy efficiency will decrease by 3.3%. This finding is robust, after replacing explained variables and core explanatory variables, no substantial changes are found. Sub-sample regression confirms the existence of heterogeneity effects in individual dimension and temporal dimension. Finally, by summarizing existing studies, this paper finds that the investment imbalance effect, the hindrance effect on industrial structure upgrading, and the crowding-out effect on innovation are three important ways for economic growth pressure to restrain the improvement of energy efficiency. Our findings are helpful to understand the role of the government's economic growth target management in resource allocation and energy utilization and provide a certain practical reference for China to promote high-quality economic development. [ABSTRACT FROM AUTHOR]

Abstract: Recently, energy production in China fell behind energy consumption. This poses important challenges for the rapidly growing Chinese economy. As a consequence, the causal relationship between energy consumption and GDP is an important empirical issue. This paper examines Granger causality between energy consumption and GDP in China using province-level data. The current paper extends the Granger causality analysis employed in previous studies by taking into account panel heterogeneity. Specifically, four different causal relationships are examined: homogeneous non-causality (HNC), homogeneous causality (HC), heterogeneous non-causality (HENC), and heterogeneous causality (HEC). HC and HNC hypotheses are rejected for causality in either direction, from GDP to energy or from energy to GDP, which implies that the panel made up of Chinese provinces is not homogeneous. Then, heterogeneous causality tests (HEC ad HENC) are conducted for each province. For the causality running from GDP to energy, 19 provinces exhibit HEC and 11 provinces exhibit HENC. For the causality running from energy to GDP, 14 provinces exhibit HEC and 16 provinces exhibit HENC. The results suggest that the Chinese government should incorporate a regional perspective while formulating and implementing energy policies. [Copyright &y& Elsevier]

This paper investigates the economic impact of an energy efficiency improvement policy on electricity-intensive firms in Ghana. The policy imposed a penalty on these electricity-intensive firms, which are referred to as special load tariff (SLT) customers, when their power factor was below 90%. This paper applies the regression discontinuity design (RDD) to the panel data of these SLTs ranging from 1994 to 2012, excluding those years characterized by energy crisis. The results show adverse impacts of the policy on the employment and salary levels of the firms in the long run, in particular, the small- and medium–voltage firms. The results indicate that small- and medium–voltage firms are economically vulnerable to the penalty policy in the long run and recommend two policies to overcome this challenge. Firstly, the penalty for power factor improvement should not be imposed identically across firms with different voltage levels. Secondly, firms that satisfy the power factor standard should receive subsidies to improve their competitiveness in the market. [ABSTRACT FROM AUTHOR]

Abstract: This paper is dedicated to explore the dynamic performance of energy efficiency in Africa, with panel data in country level, taking energy yield, power consumption, electricity transmission and distribution losses into account, the paper employ stochastic frontier mode,highlighting a dummy variable in energy output in terms of net imports of energy and power, which minify the deviation of estimated variables. The results show that returns of scale did not appear in energy and power industry in Africa, electricity transmission and distribution losses contribute most to GDP per unit of energy. In country level, Republic of Congo and Botswana suggest an obvious energy efficiency advantage. Energy efficiency in Mozambique and Democratic Republic of Congo are not very satisfying during the studying year [Copyright &y& Elsevier]

Renewable energy plays an important role in reducing global carbon (CO 2) emission. This paper builds a RER (renewable energy consumption rate) index to represent the energy structure of a country and proposes a U-shaped RKC (renewable energy Kuznets Curve) hypothesis between RER and economic growth. We also examine the dynamic relationship between RER and the Environmental Kuznets Curve (EKC) hypothesis using two panel data sets of 17 major developing and developed countries as well as six geo-economic regions of the world during 1990–2014. Panel co-integration tests indicate that a long-run relationship exists among economic growth, RER and carbon emission. We employ the fully modified ordinary least squares (FMOLS) and the dynamic ordinary least squares (DOLS) techniques to estimate the co-integration coefficients of the panels and individual countries/regions respectively. The results verify both the EKC and RKC hypotheses, indicating that a 10% rise in RER would to a 1.6% carbon emission reduction. It is also found that the RKC turning points of individual countries and the entire samples in general take place before the turning points of the respective EKCs. It suggests that promoting renewable energy consumption to make RKC cross its turning point earlier can accelerate EKC to reach its turning point more quickly. This finding has important policy implications with respect to the development and utilization of renewable energy and environmental protection. The dynamic relationship between EKC and RKC. Image 1 • We examined the relationship between renewable energy and GDP. • A U-shaped curve exists between GDP and renewable energy consumption. • The turning points of RKCs take place earlier than that of the EKCs. • Promoting RKC arrive its turning point in advance can accelerate EKC to reach its turning point more quickly. [ABSTRACT FROM AUTHOR]