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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]

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]

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]

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]

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]

This paper investigates the impact of urbanization on provincial energy efficiency in China. Using panel data of 30 Chinese provinces between 2003 and 2014, we adopt a stochastic frontier model to estimate energy efficiency in China, and apply a series of spatial panel data models to tackle spatial dependence of energy efficiency between provinces. Our results show that the overall impact of urbanization on energy efficiency is negative in China. These results are established after controlling for spatial correlation and other confounding factors, and are also robust to various model specifications. We also document that the impact of urbanization on provincial energy efficiency manifests through both a direct negative impact induced by the focal province's urbanization process, and an indirect positive spillover effect caused by urbanization in adjacent areas. [ABSTRACT FROM AUTHOR]

The energy consumption of the service sector in China is larger than the total energy consumption of Japan, great importance should be attached to the energy efficiency of Chinese service sector. Considering undesirable output and regional heterogeneity, a meta-frontier slack-based efficiency measure (MSBM) approach is adopted in this paper to measure the energy efficiency of Chinese service sector using provincial panel data of China during 1995–2013. The empirical results show that, the energy efficiency of the service sector in China is 0.62 and 0.85 under meta-frontier and group-frontier technologies. The eastern regions show the best performance in energy efficiency, the central region follows and the western region shows the poorest performance. Only the eastern region shows an increasing trend in the energy efficiency of Chinese service sector. The potential of energy efficiency improvement for Hainan, Guizhou, Qinghai, Ningxia, Gansu and Shaanxi are relatively high. Some policy recommendations are proposed to improve the energy efficiency of Chinese service sector. [ABSTRACT FROM AUTHOR]

For economic activities, energy is a necessary component of production. As the world's largest energy consumer and carbon emitter, China faces greater resource constraints and environmental pressure in the background of global climate change, improving energy efficiency is the inevitable path for long-term development. However, the energy efficiency in China at this stage is relatively low, thus improving energy efficiency is urgent. Based on the panel data from the database of Chinese industrial enterprises and the database of Chinese industrial enterprises' pollutant emissions between 2003 and 2012, this paper empirically conducts a thorough investigation on the nonlinear impact of spatial agglomeration and enterprises' energy efficiency, and further examine its heterogeneity and mechanisms. The results show that: (1) There is a significant U-shaped link between spatial agglomeration and enterprises' energy efficiency. (2) Based on agglomeration externalities, scale effect, learning effect, competition effect, and policy effect are the internal mechanisms through which spatial agglomeration impacts on enterprises' energy efficiency. (3) The effects of spatial agglomeration on enterprises' energy efficiency are influenced by technology level, energy consumption level, agglomeration patterns, and enterprise characteristics. Based on the empirical findings, we suggest that enterprises take full advantage of the benefits brought by spatial agglomeration, and that the government encourage the growth period of spatial agglomeration to become more profound, more synergetic, and more diverse. [ABSTRACT FROM AUTHOR]

For economic activities, energy is a necessary component of production. As the world's largest energy consumer and carbon emitter, China faces greater resource constraints and environmental pressure in the background of global climate change, improving energy efficiency is the inevitable path for long-term development. However, the energy efficiency in China at this stage is relatively low, thus improving energy efficiency is urgent. Based on the panel data from the database of Chinese industrial enterprises and the database of Chinese industrial enterprises' pollutant emissions between 2003 and 2012, this paper empirically conducts a thorough investigation on the nonlinear impact of spatial agglomeration and enterprises' energy efficiency, and further examine its heterogeneity and mechanisms. The results show that: (1) There is a significant U-shaped link between spatial agglomeration and enterprises' energy efficiency. (2) Based on agglomeration externalities, scale effect, learning effect, competition effect, and policy effect are the internal mechanisms through which spatial agglomeration impacts on enterprises' energy efficiency. (3) The effects of spatial agglomeration on enterprises' energy efficiency are influenced by technology level, energy consumption level, agglomeration patterns, and enterprise characteristics. Based on the empirical findings, we suggest that enterprises take full advantage of the benefits brought by spatial agglomeration, and that the government encourage the growth period of spatial agglomeration to become more profound, more synergetic, and more diverse. [ABSTRACT FROM AUTHOR]

This paper investigates whether the China ETS policy has achieved carbon emission reduction at the expense of economic development. Moreover, we are interested in unmasking the role of the institutional factors adopted by the ETS pilots in their ETS effects. Using the province-level panel data during the 2008–2016 period. we employ the DID model to compare carbon emissions and economic development between the ETS and non-ETS regions and between the pre- and post-ETS periods. Some novel empirical findings emerge. First, compared with the non-ETS areas, the ETS policy has significantly reduced carbon emission in the ETS areas. This emission reduction has not come at the cost of economic development. Second, the ETS policy leads to a decline in carbon intensity and fossil fuel energy consumption relative to all energy types. Lastly, some heterogeneity across markets arise. The Beijing carbon market performs the best among all pilots in terms of achieving targets of carbon reductions, followed by the Hubei carbon market. [ABSTRACT FROM AUTHOR]

As an important part of urban carbon emissions, the carbon emissions from household energy consumption are increasing with the continuous improvement of Chinese urbanization. This study divides family population into 4 groups: adolescents (aged 0–17), young people (aged 18–44), middle-aged people (aged 45–59), and elderly people (aged 60 and older) and develops a calculation model for carbon emissions of household energy consumption in accordance with the latest standards of the World Health Organization based on Chinese Family Panel Studies in 2016. Results showed that the marginal and intergenerational effects coexist in various types of household energy consumption: Families with 3–5 people have the most economical consumption scale and the largest marginal sharing effect; multigenerational families or families with a large proportion of middle-aged and older people are beneficial in reducing household energy consumption. This paper suggests it is important to fully consider the impact of the intergenerational effect, marginal effect, and cultural shock caused by the electronic information age on household energy consumption when formulating standards of household energy consumption. [ABSTRACT FROM AUTHOR]

Whether embedding in global value chain(GVC)can promote China's manufacturing industry green growth is of great importance for China to accelerate the realization of green economy transformation and building of China's high-quality modern ecological economic system. This paper analyzes the impact of embedding in GVC on China's manufacturing industry green growth from perspectives of embedding position and embedding degree, using panel data from 17 manufacturing industries from 2000 to 2014. The study finds that during the inspection period, GVC embedding position and embedding degree of China's manufacturing industry have a fluctuation and there is a large difference between industries, most industries are still in the downstream of GVC, and low-tech industries embedding in GVC to a deeper degree. In the meantime, China's manufacturing industry green growth level has declined, and green growth situation is not optimistic. Dynamic panel results show that raising the GVC embedding position can significantly promote China's manufacturing industry green growth. Further, threshold regression is used to find that when the embedding position crosses a certain threshold, the effect of increasing the GVC embedding degree on green growth changes from inhibition to promotion. And the effect of GVC embedding position and degree on China's manufacturing industry green growth has significant industry heterogeneity. Finally, based on the research conclusions, concrete implications for promoting China's manufacturing industry green growth are put forward: First, China should improve the GVC embedding position of manufacturing industry and enhance the value-added and control capabilities of the value chain. Second, China should increase the GVC high-end links embedding degree of manufacturing industry. While realizing value chain functional upgrading and chain upgrading, increase GVC embedding degree. Third, China should accelerate energy technology innovation, promote the development of new energy, and accelerate the transformation of energy consumption structure. Last of all, China should implement industry differentiation policies, enhance the overall competitiveness of the high-tech industry, and achieve the "essential" improvement of the GVC position of the low-tech industry. At the same time, for all industries, China must improve the level of environmental regulation, increase R&D investment, and reduce the proportion of coal consumption and the proportion of state-owned enterprises. [ABSTRACT FROM AUTHOR]

China's transportation industry, with its typical characteristics of "high energy consumption and high emissions", is a key point for China's CO 2 emission reduction. An investigation of the driving factors of China's transport energy-related CO 2 emissions has great importance for clarifying the "gains" and "losses" as well as the future focuses of CO 2 emissions emission reduction in this sector. Existing studies on the driving factors of transport CO 2 emissions in China fail to incorporate effects of technology and efficiency into decomposition analysis framework. Based on the provincial panel data of China's transportation industry for the period of 2004–2016, this paper uses a comprehensive decomposition framework, which combines the logarithmic mean Divisia index method (LMDI) and production-theoretical decomposition analysis (PDA), to decompose China's transport CO 2 emissions into nine components: emission factor effect, energy structure effect, scale effect, regional structure effect, energy-saving technology effect, production technology effect, energy efficiency effect, production efficiency effect, and potential energy intensity effect. The main results indicate that: (1) during the sample period, China's transport industry witnessed 498.998 million tons (Mt) CO 2 emissions growth. Scale effect was the largest contributor, followed by production technology, energy-saving technology, and energy structure; (2) the improvement of energy efficiency and production efficiency, the decline of electricity's emission factor, and regional structure adjustment played active roles in the CO 2 emission reduction of transportation industry; (3) the performance of various driving factors varies greatly in different provinces. Local governments should establish and implement policies tailored to their characteristics. • A production-theoretical approach was used for China's transport CO 2 emissions. • It can decompose CO 2 emissions from technology and efficiency perspectives. • Results show that during 2004–2016, China's transport CO 2 emissions increased by 498.998 Mt. • Output effect was the largest contributor to this remarkable increase of transport CO 2 emissions. • Technology and efficiency factors cumulatively reduced 242.1 Mt transport CO 2 emissions. [ABSTRACT FROM AUTHOR]

Environmental regulation is of great concern to international academics and policy makers. Previous studies often regarded environmental regulation as an influencing factor when calculating eco-efficiency or energy efficiency. More attention should be given to the efficiency of environmental regulation, especially in industrial field. According to exiting studies, industrial environmental regulation efficiency (IERE) may be influenced by external environmental factors, to explore the influence and obtain an actual efficiency, this paper calculated IERE in China by using the panel data of 30 regions from 2011 to 2015 (the 12th Five-Year Plan period) through the three-stage data envelopment analysis approach. Firstly, the Undesirable-slacks-based model method was used to calculate the original IERE. Secondly, the economic base, industrial structure, urbanization level, and education level were taken as external environmental variables, and the effect of external environmental factors and stochastic disturbance were excluded. Thirdly, the actual IERE can be calculated after adjusting input values. The results showed that China's IERE was underestimated due to the impact from external environmental factors. Moreover, the IERE was more influenced by pure technical efficiency than scale efficiency. The IERE of each region varied, thus, targeted policies should be formulated according to local conditions. This research provided a new analytical method for calculating environmental regulation efficiency. The method can be applied either in other countries or in other sectors. • The environmental regulation efficiency in the industrial field of China was evaluated. • The three-stage DEA method was utilized, the role of environmental variables could be considered. • Industrial environmental regulation efficiency was more influenced by pure technical efficiency. • Three kinds of efficiencies were all underestimated due to the influence of external environmental factors. [ABSTRACT FROM AUTHOR]