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Energy efficiency is the key to green development, and the government plays a vital role in energy efficiency. This paper clarifies the mechanism by which the Yangtze River Delta Economic Coordination Committee affects the energy efficiency of urban agglomeration by promoting market integration. Based on panel data of China's prefecture-level cities from 2004 to 2017, we take the Yangtze River Delta Economic Coordination Committee as a quasi-natural experiment of government cooperation and use the difference-in-difference method to test whether this organization has enhanced the energy efficiency of urban agglomeration. The results show that the Yangtze River Delta Economic Coordination Committee can significantly improve energy efficiency in urban agglomerations. The mechanism analysis shows that it reduces the energy consumption per unit of gross domestic product by enhancing the marketization level, perfecting the relationship between the government and the market, and improving the factor market development. The heterogeneity analysis shows that cities with lower city size, lower level of innovation, and cleaner industrial structures gain more benefits in energy efficiency from government cooperation in urban agglomeration. This paper provides empirical evidence for cities to realize integrated energy conservation through government cooperation and market integration. [ABSTRACT FROM AUTHOR]

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Forecasting energy demand accurately is the basis for the formulation and implementation of energy planning. In this paper, energy demand influencing factors are mainly decomposed into scale economy effect, population size effect, energy structure effect, and residential consumption effect based on the Logarithmic Mean Divisia Index (LMDI). Then, the Cointegration and Granger Causality tests are used to discover the influencing factors of energy demand in China. On this basis, a hybrid optimization algorithm, the least-squares support-vector regression optimized by particle swarm optimization (PSO-LSSVR), is proposed to forecast the energy demand of China. Then, three scenarios are set up to analyze the further development of drive factors of energy demand. Finally, in accordance with the forecasting results, some suggestions related to China's energy development policy are given. The main results are as follows. First, gross domestic product (GDP), the total population at the end of the year (POP), the coal consumption ratio in energy (CCR), and residential consumption levels (RCLs) are dominant indicators of energy demand in China. Second, the improved PSO-LSSVR model has significant superiority than other models in energy demand forecasting, a complex and nonlinear system with small samples. Third, China's energy demand will peak in 2022, which is 4.9 million tce in all scenarios. [ABSTRACT FROM AUTHOR]

Climate change is one of the most dangerous threats to human beings, and therefore, it is of great importance for the researchers to inform the policy makers of the threats of climate change and global warming. One of the main causes of climate change is the greenhouse gas emissions, particularly CO2 emissions. In this paper, we try to find a nexus between energy consumption, CO2 emissions, and economic growth in the United States, China, and India, known as three most polluting countries in the world. For this purpose, we applied the wavelet correlation and the partial wavelet coherence approaches during the period 1971–2013. The empirical results for the United States show that the GDP is positively correlated with the CO2 emissions and energy consumption in all frequencies. For China, there is a significant positive relationship between the GDP and CO2 emissions/energy consumption for the short-term horizon. However, for India, although there is a significant positive relationship between the GDP and CO2 emissions, the nexus between the GDP and the energy consumption is not clear. Furthermore, the pollution haven hypothesis was confirmed by the obtained empirical results. Based on our study, we suggest the policy makers in these three countries making supportive decisions for the producers to use modern environment-friendly technologies and renewable energies in their products. [ABSTRACT FROM AUTHOR]

India and China are the biggest developing economies in the world apart from being the most populous nations. The population of China is expected to increase over 1.4 billion by 2050, and India may over take China and become the most populous nation at around 1.6 billion populace. These two nations are home to about 40 % of the total population in the world. Apart from this, India and China have attained remarkable success in the economic growth denoted by higher gross domestic product (GDP). The two nations together contribute about one-fifth of the world GDP. This paper discusses about the changes in the energy consumption pattern of developing countries like China and India aiming towards the sustainable development. Furthermore, this paper also analyses the most important drivers adding to the ecological problems, and gives a further glance at the ecological impacts of these in India and China. Over the last 50-60 years we have been emitting carbon dioxide at an accelerated pace. This has had an effect on the carbon dioxide concentration and other GHG gas concentrations, in the atmosphere. The paper also discusses about the effect of pollutants like NOx, SOx, Particulate matter and Mercury on the environment. [ABSTRACT FROM AUTHOR]

With the development of China's economy, more and more energy consumption has led to serious environmental problems. Faced with the enormous pressure of large amounts of carbon dioxide ( C O 2 ) emissions, China is now actively implementing the development strategy of low-carbon and emission reduction. Through the analysis of the influencing factors of C O 2 emissions in China, five key influencing factors are selected: urbanization level, gross domestic product (GDP) of secondary industry, thermal power generation, real GDP per capital and energy consumption per unit of GDP. This paper applies the Elman neural network optimized by the Firefly Algorithm (FA) to forecast the C O 2 emissions in China. And the results show that the performance of the FA–Elman is better than the Elman neural network and Back Propagation Neural Network (BPNN), verifying the effectiveness of the FA–Elman model for the C O 2 emissions prediction. Finally, we make some suggestions for low-carbon and emission reduction in China by analysing key influencing factors and forecasting C O 2 emissions using the FA–Elman model from 2017 to 2020. [ABSTRACT FROM AUTHOR]

This paper develops an environment input-output (EIO) model to calculate embedded carbon footprints (ECF) from a consumption perspective, based on which an analysis is conducted on the trends and spatial distribution of urban households' ECF in China. The results demonstrate that energy efficiency improvements might not definitely result in a reduction of China's ECF, which is consistent with the Jevons paradox. Survival-oriented emissions account for over 35% of China's ECF, but the percentage is declining gradually. Although development-oriented emissions only take up 15% of the total ECF, the recent years has witnessed a dramatic increase of the percentage. These findings indicate the broad scope of China's ECF. Furthermore, a significant discrepancy on the spatial distribution of ECF is observed and is attributed to the diversity of economic development stages, consumptive policies, and consumption patterns among regions. Finally, relevant policy implications are derived and discussed. [ABSTRACT FROM AUTHOR]

A reduction in the CO2 emission intensity (ton per gross domestic product) from a base year to a target year with different rates of reduction in each individual year can yield different cumulative CO2 emissions over time, which could have different effects on the climate. In this paper, an indicator is proposed to measure the extent to which the cumulative CO2 emissions are generated by a specific reduction trajectory of CO2 emission intensity, especially for emerging economies with a relatively higher economic growth rate. The proposed indicator is important for less-developed countries and regions in which economic growth is heavily reliant on energy and in which significant potential exists for improvement of energy efficiency. The indicator is applied to evaluate the CO2 emission reduction performance in China's 12th Five-Year Plan period, and the result is not encouraging, especially when the likely gap between the energy conservation goals and temperature goals is further examined. Mitigation goals should be set by considering not only the performance in the final year, but also the reduction in cumulative emissions over the evaluation period. [ABSTRACT FROM AUTHOR]

Abstract This paper explores the effects of enforced Chinese policies regarding environmental matters at the provincial level. The paper proposes several features and explores datasets from the last four Five-Year Plans, which extensively covered the period from 1995 to 2015. As the datasets are multidimensional, dimensional reduction is applied with a control of information loss described by data variance preservation. A data-based clustering algorithm is applied to identify the similarities, and characteristics are explored in relation to the specific features. The use of this method makes it possible to identify the changes in the relevance of the features in the most recent Five-Year Plan, in which decarbonization is relevant instead of productivity. This method also makes it possible to identify specific efforts several provinces have made to introduce cleaner energy production. Following the analysis, it is concluded that the proposed features enable the proposed method to be considered a tool for analyzing the real-world effects of the adopted policies. [ABSTRACT FROM AUTHOR]

With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China′s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China′s carbon emissions from energy consumption will reach 302.82 million tons in 2020. [ABSTRACT FROM AUTHOR]

Environmental degradation has been recognized as a global issue due to high energy consumption and economic growth. This situation needs researchers to focus on, thereby, the current article examined the impact of renewable energy production (REP), energy import, renewable energy consumption (REC), gross domestic product (GDP), inflation, and foreign direct investment (FDI) on the carbon dioxide (CO2) emission in China. The study considered secondary data and extracted it from the World Bank database covering the period 1981 to 2018. The current article has examined the stationarity of the constructs using Augmented Dickey-Fuller tests and investigated the association among constructs using the quantile autoregressive distributed lag (QARDL) model. The data revealed that REP, energy import, and REC, had a significant and negative linkage with CO2 emission in China. In contrast, GDP, inflation, and FDI are linked with CO2 emission in a positive manner. The article also guided the policymakers regarding the policy development related to reducing carbon emissions using renewable energy production and consumption. [ABSTRACT FROM AUTHOR]

This paper presents a model to analyze the mechanism of the global contribution of energy usage by product exports. The theoretical analysis is based on the perspective that contribution estimates should be in relatively smaller sectors in which the production characteristics could be considered, such as the productivity distribution for each sector. Then, we constructed a method to measure the global contribution of energy usage. The simple method to estimate the global contribution is the percentage of goods export volume compared to the GDP as a multiple of total energy consumption, but this method underestimates the global contribution because it ignores the structure of energy consumption and product export in China. According to our measurement method and based on the theoretical analysis, we calculated the global contribution of energy consumption only by industrial manufactured product exports in a smaller sector per industry or manufacturing sector. The results indicated that approximately 42% of the total energy usage in the whole economy for China in 2013 was contributed to foreign regions. Along with the primary products and service export in China, the global contribution of energy consumption for China in 2013 by export was larger than 42% of the total energy usage. [ABSTRACT FROM AUTHOR]

As a kind of capital, intermediate inputs play an important role that cannot be ignored in the process of production. Their coefficient size reflects a country's economic growth mode. Current mainstream economic growth theories only consider the value added production function, rather than gross output production function, thus ignoring the role of intermediate inputs in the production process and the evaluation of economic efficiency. This paper is based on the statistical characteristics, basic model and empirical test of the basic paradigm. From the perspective of statistical characteristics, China's intermediate inputs coefficients are significantly higher than that of U.S. and Japan and other developed countries. The energy consumption per unit GDP is far over the world average level, which shows that the characteristic of China's economic growth is of extensive growth mode. By using theoretical derivation and numerical simulation, this paper has drawn the conclusion that a country's economic growth mode is related to the intermediate inputs elasticity of substitution, value added rate (VAR), technical level, and some other parameters. Firstly, there exists substitution rather than complementary linkage between primary investment and intermediate inputs. Secondly, economic growth rate shows negative correlation with the size of the elasticity of substitution of intermediate inputs, and the greater, the slower, and vice versa. Thirdly, inverted U shape shows that with the increase of intermediate inputs rate, the equilibrium output increases first and then decreased. In other words, there exists an optimal VAR. In the empirical analysis by using data of China, U.S., Japan and Brazil, the paper proves that there are obvious differences in the elasticity of substitution of intermediate inputs among different countries at their different stages during development. On the one hand, there is a different elasticity of substitution at different stage for a country with a certain independence, and the elasticity of substitution of U.S. is much higher than that of China. On the other hand, there is deviation between the actual VAR and the optimal VAR among these countries, and the deviation of China and Brazil is much larger than that of U.S. and Japan. [ABSTRACT FROM AUTHOR]

The energy consumption pattern dominated by traditional fossil energy has led to global energy resource constraints and the deterioration of the ecological environment. These challenges have become a major issue all over the world. At present, the Chinese government aims to significantly reduce the fossil energy consumption contribution in the terminal energy consumption. The development of renewable energy in the terminal energy and energy conversion links has significantly increased the proportion of clean low-carbon energy. In order to accurately get the proportion of renewable energy terminal power consumption, firstly, this paper selects a primary influencing-factors set including the gross GDP, fixed investment in renewable energy industry, total length of cross-provincial and cross-regional high-voltage transmission lines, etc. as influencing factors of China's electricity consumption fraction produced by renewable energy based on a multitude of papers. Secondly, from the perspective of signal decomposition, the data inevitably has a lot of interference and noise. This paper uses the empirical mode decomposition (EMD) algorithm to reduce the degree of signal distortion and decomposes the signal into natural modes including several intrinsic mode functions (IMFs) and a residual term (Res); afterwards, a new extreme learning machine (ELM) forecasting model optimized by an Inverse Square Root Linear Units (ISRLU) activation function is proposed, and the ISRLU function is used to replace the implicit layer activation function in the original ELM algorithm. Then, a new bacterial foraging algorithm (BFOA) is applied to optimize the parameters of the optimized ELM forecasting model. After multiple learning and training operations, the optimal parameters are obtained. Finally, we superimpose the output of each IMF and Res training task to get the amount of China's power consumption produced by renewable energy. Some statistical indicators including root mean squard error (RMSE) are applied to compare the accuracy of several intelligent machine forecasting algorithms. We prove that the proposed forecasting model has higher prediction accuracy and achieves faster training speed by an empirical analysis. Finally, the proposed combined forecasting algorithm is applied to predict China's renewable energy terminal power consumption from 2018 to 2030. According to the forecasting results, it is found that China's renewable energy terminal power consumption shows a gradual growth trend, and will exceeded 3300 billion kWh in 2030, which will represent a renewable energy terminal power ratio of about 38% in 2030. [ABSTRACT FROM AUTHOR]

China is the largest CO2 emitter in the world, and it shared 28% of the global CO2 emissions in 2017. According to the Paris Agreement, it is estimated that China's CO2 emissions will reach its peak by 2030. However, whether or not the CO2 emissions in China will rise again from its peak is still unknown. If the emission level continues to increase, the Chinese policymakers might have to introduce a severe CO2 reduction policy. The aim of this paper is to conduct an empirical analysis on the long-standing relationship between CO2 emissions and income while controlling energy consumption, trade openness, and urbanization. The autoregressive distributed lag (ARDL) model and the bounds test were adopted in evaluating the validity of the Environmental Kuznets Curve (EKC) hypothesis. The quantile regression was also used as an inference approach. The study reveals two major findings: first, instead of the conventional U-shaped EKC hypothesis, there is the N-shaped relationship between CO2 emissions and real gross domestic product (GDP) per capita in the long run. Second, a positive effect of energy consumption and a negative effect of urbanization on CO2 emissions, in the long run, are also estimated. Quantitatively, if energy consumption rises by 1%, then CO2 emissions will increase by 0.9% in the long run. Therefore, the findings suggest that a breakthrough, in terms of policymaking and energy innovation under China's specific socioeconomic and political circumstances, are required for future decades. [ABSTRACT FROM AUTHOR]

China has a coal addiction that has worsened over time. Its share of the world׳s total final coal consumption more than doubled over the past decade. This fast growth of coal consumption presents serious challenges to the country׳s carbon emission. In 2011, burning coal contributed 72% of China׳s CO 2 emission and 19% of global CO 2 . A better understanding of the trajectory and drivers of Chinese coal usage will help catalyse Chinese efforts to kick its heavy addiction to coal. We analyze China׳s coal consumption during 1955–2011 using time-series data energy consumption, population, gross domestic product, and industrial production. Based on the change of its total coal consumption and coal consumption intensity, China׳s coal use for 1955–2011 is divided into six phases. Next, this paper quantifys the driving forces in each phase and drivers of coal consumption intensity, using the Logarithmic Mean Divisia Index (LMDI) technique. The result shows there is a strong positive correlation between economic growth and coal consumption, the coal intensity and economic output of second industry is the leading contributor to change of coal consumption intensity. Three policy recommendations are offered for China kicking its coal habit. This paper argues that improving coal consumption efficiency is the more optimum policy. [ABSTRACT FROM AUTHOR]

This paper uses the super-efficiency DEA model to study the super-efficiency energy efficiency values of 30 provinces in China from 2006 to 2018, compares the differences between different regions, and verifies the impact of different environmental regulatory tools on energy efficiency. The results show that the regions with the highest energy efficiency in China's provinces are, in order, the eastern region, the central region, and the western region. At the same time, the impact of different environmental regulatory tools on energy efficiency is quite different. The number of environmental administrative penalties has a negative impact on China's energy efficiency, that is, increased number of environmental administrative penalties will reduce China's energy efficiency; market-encouraging environmental regulations tools impact on China's energy efficiency show a positive correlation, which means that market-encouraging environmental regulation tools are conducive to achieving a win-win goal of environmental regulation and energy efficiency in China. The voluntary environmental regulation tool has a negative correlation with energy efficiency, but it is not statistically significant, indicating that it is not the main factor affecting energy efficiency. In terms of other influencing variables, the level of economic development and technological progress are positively related to energy efficiency. The proportion of the secondary industry in regional GDP is positively related to energy efficiency, the relationship between energy consumption structure and energy efficiency is negative correlation, the degree of opening to the outside world has a negative correlation to China's energy efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a model for the projection of Chinese vehicle stocks and road vehicle energy demand through 2050 based on low-, medium-, and high-growth scenarios. To derive a gross-domestic product (GDP)-dependent Gompertz function, Chinese GDP is estimated using a recursive dynamic Computable General Equilibrium (CGE) model. The Gompertz function is estimated using historical data on vehicle development trends in North America, Pacific Rim and Europe to overcome the problem of insufficient long-running data on Chinese vehicle ownership. Results indicate that the number of projected vehicle stocks for 2050 is 300, 455 and 463 million for low-, medium-, and high-growth scenarios respectively. Furthermore, the growth in China's vehicle stock will increase beyond the inflection point of Gompertz curve by 2020, but will not reach saturation point during the period 2014–2050. Of major road vehicle categories, cars are the largest energy consumers, followed by trucks and buses. Growth in Chinese vehicle demand is primarily determined by per capita GDP. Vehicle saturation levels solely influence the shape of the Gompertz curve and population growth weakly affects vehicle demand. Projected total energy consumption of road vehicles in 2050 is 380, 575 and 586 million tonnes of oil equivalent for each scenario. [ABSTRACT FROM AUTHOR]

Nearly thirty years after the inception of reform in early 1979, China replaced Japan as the world's second largest economy. Its population grew from 962.3 million in 1978 to 2011's 1.34 billion. For the period 1979 through 2011, at constant prices, China's real GDP rose from 1978's 3.55 trillion Yuan to 2010's 31.46 trillion Yuan. However, paralleling China's rapid economic growth has been a phenomenal surge in energy consumption and needs. Disconcerting for developed economies of the West has been how China's growing needs for energy imports, especially for petroleum, is to impact the future of the world's markets for conventional sources of fossil fuels. A dozen years after China's economy began to thrive, China imported 0.776 million tons. By 2010, however, imports of the same accelerated to 294.4 billion tons. It was a 39-fold increase in petroleum imports during a brief two-decade period. The objective of this paper is to advance a proposition that, despite China's rapid increases in energy demands in recent decades, the evolving scenario in China is not to markedly affect world energy prices in the future due to the government's energy policy. [ABSTRACT FROM AUTHOR]

Arguments over equity during abatement goal setting is the principal obstacle to climate mitigation cooperation, while allocating global emissions to each country as deduced from the climate objective according to certain equitable principles offers an effective alternative to ending this dispute. Such an alternative also endows each country with the freedom to determine and develop its own pathway under its emissions budget while ensuring that global climate targets are met. Within this context, this paper integrated economic growth theory with the optimal control model and simulated the optimal abatement pathway as well as the economic growth trajectory for China within the allocated emissions budget. The study found that research and development (R&D) investment is an effective way of improving energy efficiency. Our simulation showed that the R&D intensity of the gross domestic product (GDP) would see a slight decline for the inceptive period, followed by an aggressive rise to a relatively high level before decreasing again. Under the 450 ppm carbon concentration target, the R&D investment intensity would have to increase significantly beginning from 2014 because of the more stringent demands as compared to other less rigorous targets such as 500 ppm. The economy would continue to grow, although growth would occur less rapidly under rigorous targets: relative to 2007 levels, the GDP would grow by 11-fold and 15-fold under the 450 and 500 ppm scenarios, respectively. Before enforcement of an effective R&D investment, the carbon emissions would increase rapidly, while after enforcement, the speed of carbon emissions would slow down. [ABSTRACT FROM AUTHOR]

The article focuses on a research paper which conducts a regression analysis of copper resource consumption trends in China on the basis of per capita gross domestic product, unit energy consumption and import-export volume between 1990-2013. Topics discussed include impact of declining copper stock on copper supply, advantages of secondary copper resources over primary resources in terms of energy consumption and need to develop secondary copper industry to satisfy future copper demands.

Because of rapid economic expansion, China, the USA, and India have become the largest energy producers and sources of CO2 emissions in the world. They burned over 45% of global fuels in 2016. Meanwhile, the developing strategies of 24 polluted states to decrease fossil energy consumption without additional economic output. This paper explores the effect of world top polluted countries' CO2 emission, their GDP and production of electricity by potential indicators and identifies the basic factors that contribute to changes in an environment where petroleum, natural gas, coal, nuclear, biomass, and other renewable energy and hydroelectric sources are examined with GDP per capita. We estimate our data for the period from 1968 to 2017 and use the GLM model. The results show that more production of electricity is causing abnormal CO2 emissions. The Granger causality test shows that there is a unidirectional relationship between energy consumption and economic advancement. Also, there is a short-run bidirectional causality that exists among the energy indicators. We find a unilateral causality between energy consumption and economic growth. Therefore, the consumption of energy might be conductive of 24 (polluted) countries and better economic development; the consumption of energy may be failsafe and guaranteed, while we should limit the resources of countries. [ABSTRACT FROM AUTHOR]

Highlights • Parameters of 60 electricity-saving measures and 16 abatement measures are compiled. • Synergies of electricity use in China's chemical sector at grid level are assessed. • Efficient use of power can cut 154 Mt CO 2eq , 295 kt SO 2 , 322 kt NO x and 64 kt PM 2.5. • North and Northwest grids have the largest economic benefits on emissions reduction. • Grid with high electricity price is more sensitive to price change than other grids. Abstract Industry is a large electricity user. China's chemical industry (globally the largest based on sales) contributes 7% to China's GDP, while it consumes 11% of the total electricity consumption in industry and is responsible for 40% of total CO 2eq , 40% of SO 2 , 59% of NO x and 18% of PM-emissions of the chemical industry emissions. The heterogeneity of GHG and air pollutant emissions across electricity grids (within a country) is rarely included in analyses. In this paper, electricity conservation supply curves are developed (distinguishing the grids) to estimate the cost-effective and technical potentials of electricity conservation in China's chemical industry. The emission factors per grid for GHG (i.e. CO 2 , CH 4 and N 2 O) and air pollutants (i.e. SO 2 , NO x and PM 2.5) are calculated and used to quantify the emissions mitigation achieved by electricity saving technologies in the chemical industry for the period 2012–2035. Results show that significant multiple benefits can be obtained by implementing electricity efficiency measures. There are large differences among the six grids in terms electricity savings and emissions abatement of GHG and air pollutants. 83% of the total electricity saving potential is contributed by the North, Northwest and Central grids, equal to 32% of baseline electricity consumption in 2035. In 2035, 129 Mt of CO 2 , 33 kt CO 2eq of CH 4 , 571 kt CO 2eq of N 2 O, 235 kt of SO 2 , 275 kt of NO x and 52 kt of PM 2.5 in these three grids can be avoided as a result of electricity savings (a reduction of 31–33% compared to baseline emissions). When decision-makers set targets for energy saving and emission reduction, the multiple benefits and grid heterogeneity should not be ignored. [ABSTRACT FROM AUTHOR]

High-Carbon Consumption Behavior (HCCB) of urban residents is one of the main obstacles to the goals of low carbon development in China with the rapid development of the national economy. Among all the uptrend influences, the one caused by conspicuous consumption is worth more attention. In this paper, a structural equation model was applied to analyze the impact path of conspicuous consumption motivation on HCCB. Multiple group analysis was then used to test the moderating effect of individual face conscious and demographics on the impact. Our data originated from a social study of Xuzhou, Jiangsu province, China (n = 351). The results indicated four leading aspects. First, the raising habitual HCCB is mainly affected by three points, materialistic hedonism, interpersonal mediation and social status demonstration, and the influence is reduced in successively. While communication of belonging to/dissociation from a group and ostentation have little effect on habitual HCCB. The second, purchased HCCB of residents is mainly impacted positively by materialistic hedonism, and other factors have weak influence on it. The third, face consciousness has a significant moderating effect on the relationship between residents’ conspicuous consumption motivation and HCCB. People with strong face consciousness are more affected by the social status demonstration and interpersonal mediation; those with weak face consciousness are only driven by material hedonism. The last, gender, marital status, age, education level, and household monthly disposable income have significant moderating effects on the relationship. Among them, the HCCB from males, young people, married persons, highly educated people, and higher income groups is more affected by materialistic hedonism, interpersonal mediation, and social status demonstration, while from females with lower education, and lower income groups is more likely to be driven by social status demonstration. [ABSTRACT FROM AUTHOR]

Despite the crucial role of investment in energy under public–private partnership (I.E.P.P.P.) in abating environmental pollution and reducing energy poverty (E.P.), the existing literature offers less information about the nexus between I.E.P.P.P. and E.P. In order to identify the E.P. gap based on accessibility, affordability, and availability dimensions, this study investigates the factors influencing E.P., and examines the impact of I.E.P.P.P., globalisation (G.L.O.), output (G.D.P.), risk, technological innovation (T.I.) and renewable energy consumption (R.E.C.) on E.P. in China during the period of 1990 to 2019. The causal relationship between E.P. with its determinants is also examined. Utilising fully modified ordinary least squares (F.M.O.L.S.) econometric approach, we find that investment in energy with a public–private partnership, T.I., and gross domestic product (G.D.P.) bridge the gap for E.P., whereas R.E.C., composite risk index (C.R.I.), and G.L.O. increase the E.P. gap in China. In addition, frequency Domain Causality test reveals that unidirectional causation from I.E.P.P.P., G.D.P., T.I., G.L.O., risk, and R.E.C. to E.P. in the short run to long run. [ABSTRACT FROM AUTHOR]

With the continuous improvement of the economy, more and more attention has been paid to environmental problems. Beijing is China's economic, political, and cultural center, and its low-carbon development by external concerns. In this paper, the relationship between economic development and environmental pollution is analyzed by using the symbolic regression method, which is based on the data of per capita CO2 emissions, total energy consumption, energy intensity, and per capita GDP in Beijing city during 1980-2015. The study found that the presence of the M-curve model between per capita CO2 emissions and per capita GDP, total energy consumption, and per capita GDP are in line with the traditional model of the EKC curve, and that the L-curve model exists between the energy intensity and per capita GDP, respectively, with promising performance. Based on our analysis, we present policy suggestions for reducing carbon emissions and developing a low-carbon economy in Beijing. [ABSTRACT FROM AUTHOR]

Recently the expansion of urban-rural income inequality and the rapid growth of fossil-energy consumption have become two significant challenges China should address to achieve its ambitious goals. For the first time, this study uses the Chinese provincial panel data during the period of 1996–2013 to perform a quantitative analysis on the relationship between the urban-rural income gap and the per capita electricity consumption. Fully considering the potential endogeneity problem, this paper uses the orthogonal-difference Generalized Method of Moments (GMM) as the benchmark estimation method. To ensure the robustness of the estimations, the Theil index and per capita urban-rural income are utilized to measure the urban-rural income gap. The estimation results indicate that the influences of urban-rural income inequality on electricity depend on the income level. At the current stage of economic development, the income disparity has significantly negative impacts on provincial per capita electricity consumption. Moreover, there is also strong evidence for the existence of the inverted U-shaped relationship between per capita electricity consumption and GDP per capita. Furthermore, other economic and social factors, including the enhancement of urbanization and industrialization levels, the adjustment of the population structure, and the development of import-export trade may also promote electricity consumption. [ABSTRACT FROM AUTHOR]

The aim of this research is to examine the link between CO 2 emissions, hydroelectricity consumption, urbanisation and real GDP in China and India during the period of 1965–2013. The long-run cointegration is investigated by the autoregressive distributed lag (ARDL) bounds testing approach, which is augmented with structural breaks. We employ the ARDL cointegration test to establish long run relationship in the variables. Furthermore, we use the ARDL to show that real GDP and urbanisation have long-run positive impact on emission, while hydroelectricity consumption exerts long-run negative impact on emission in both countries. The results support the existence of environmental Kuznets curve (EKC) hypothesis in China and India. Besides, the paper assesses the causal link between the variables by using Granger causality procedures and the results show that there is long-run bidirectional relationship between the variables in both countries. [ABSTRACT FROM AUTHOR]

China intends to rely on urbanization policies to increase its economic growth even against the backdrop of new constraints on energy consumption. This paper investigates the impact of urbanization on economic growth and energy consumption using data from China’s 266 prefecture-level cities over the 2000–2010 period and examines the heterogeneity of this impact across income and urbanization groups. The main results of our empirical estimation indicate that: (1) the effect of urbanization on economic growth is positive and significant for the entire sample, although it is positive and significant only for high- and middle-income groups (and not the low-income group); (2) the impact of urbanization on energy consumption is positive and significant for all groups when the sample is divided into three urbanization groups based on the Northam curve, indicating that urbanization will lead to large increases in energy consumption. In summary, our empirical results show that urbanization targets will be difficult to achieve in terms of increasing GDP per capita under energy consumption constraints. [ABSTRACT FROM AUTHOR]

This paper makes an attempt to introduce energy consumption variable into economic growth model as an input factor in energy economic analysis by taking into consideration China's marine economic zones as the research object. In order to avoid the model setting errors, a semi-parametric linear model comprising of the parametric part to elucidate the impact of capital stock and human capital on the economic growth, and the non-parametric part to explain the impact of energy consumption on the economic growth, is constructed. This paper makes an empirical study on the northern, eastern and southern marine economic zones, and describes in detail the fitting results, monotonicity, convexity of the curve and characteristics of the dynamic changes. By analyzing the concavity and convexity of the fitting curve, the basic form of the energy consumption factors in the production function can be defined fuzzily, and the second derivative shows positive and negative changes under the dynamic time point. Finally, the paper discusses energy economy from production factor input and production factor efficiency. • Designs a semi-parametric partial linear model. • Model indicators include energy, Labor, Capital stock and Human Capital. • Uses first-order and second derivatives to analyze monotonicity and convexity. [ABSTRACT FROM AUTHOR]

Researches revealed that there has been existed a relatively stable and positive relationship between economic growth and electricity consumption. However, the inconsistency between them appeared in 2008. Therefore, it is urgent to analyze whether the GDP in China is cheated and to seek the underlying reasons contributed to the deviation. Anhui province is selected to act our study area. In order to explore the underlying reasons of the deviation between economic growth and electricity consumption, a multivariable regression models is established in this paper. Empirical results are as follows: (1) real GDP, industrial structure (IS), heating degrees days (HDD), cooling degree days (CDD), investment in fixed assets (IF) are all positive factors for the consumption of electricity; (2) IS is the major contributor to raising the consumption of electricity, followed by IF, CDD, HDD, and GDP; (3) energy intensity (EI) and financial development (FD) are negative factors, when the value of EI increase 1.0%, the consumption of electricity will decrease 7.4%; (4) if the electricity consumption caused by other factors except GDP is eliminated, the deviation would almost shrink; (5) the real growth rate of GDP of Anhui province is authentic. [ABSTRACT FROM AUTHOR]

Along with the arrival of the post-Kyoto Protocol era, the Chinese government faces ever greater pressure to reduce greenhouse gases (GHGs). Hence, this paper aims to discuss the drivers of carbon dioxide (CO 2 ) emissions and their impact on society as a whole. First, we analyzed the background and overall situations of CO 2 emissions in China. Then, we reviewed previous studies to explore the determinants behind China's CO 2 emissions. It is widely acknowledged that energy efficiency, energy mix, and economy structure are three key factors contributing to CO 2 emissions. To explore the impacts of those three factors on the economy and CO 2 emissions, we established a computable general equilibrium (CGE) model. The following results were found: (1) The decline of a secondary industry can cause an emission reduction effect, but this is at the expense of the gross domestic product (GDP), whereas the development of a tertiary industry can boost the economy and help to reduce CO 2 emissions. (2) Cutting coal consumption can contribute significantly to emission reduction, which is accompanied by a great loss in the whole economy. (3) Although the energy efficiency improvement plays a positive role in promoting economic development, a backfire effect can weaken the effects of emission reduction and energy savings. [ABSTRACT FROM AUTHOR]

Energy conservation is a major strategy for China to shift to a low-carbon economy and achieve sustainable development. The heating industry uses coal as its main fuel source in China. Energy consumption of China's heating industry grew at an average annual growth rate of 7.75% over 1980–2011. We use the co-integration method to explore the long-run relationship between energy consumption of the heating industry and the factors including GDP, urban population density, central heating supply areas and fuel price. The results indicate that 1% GDP growth yields 2.24% increase in energy consumption of the heating industry. 1% urban population density growth and 1% central heating areas growth result in 0.56% and 0.36% decline in energy consumption of the heating industry, respectively. Under the BAU scenario, energy consumption of the heating industry will be 157.11 Mtce in 2020. Energy conservation potential is estimated to be 22.16 Mtce under the moderate scenario and 43.6 Mtce under the advanced scenario. Moreover, this paper holds the view that the central heating system can be considered as an effective heating method for cities with dense population, both in the south and north. At last, policy recommendations for energy conservation in the heating industry are provided. [ABSTRACT FROM AUTHOR]

In this paper, we first analyze the historical trends in road transportation energy consumption and GDP in developed economies to find out the development characteristics of road energy consumption. The two indexes present obvious ‘S’ type patterns. Then, in order to explore the current status and future trend of road energy transportation in China, we employ path analysis to analyze the impact mechanism of the factors related to road transportation energy consumption. Next, we adopt the BMA model to select the core factors related to road transportation energy consumption in China, and on the basis of the model selection as well as univariate (ETS & ARIMA models) and multivariate (multiple regression) models, the road transportation energy consumption is analyzed and forecast. The results showed that the road transportation energy consumption rises by 0.33 percent for every percent increase in GDP and by 1.26 percentage points for every percent increase in urbanization. The road transportation energy consumption in China is expected to reach around 226181.1 ktoe by the end of 2015, and about 347,363 ktoe by 2020. [ABSTRACT FROM AUTHOR]

Traditional method of forecasting electricity consumption based only on GDP was sometimes ineffective. In this paper, urbanisation rate (UR) was introduced as an additional predictor to improve the electricity demand forecast in China at provincial scale, which was previously based only on GDP. Historical data of Shaanxi province from 2000 to 2013 was collected and used as case study. Four regression models were proposed and GDP, UR, and electricity consumption (EC) were used to establish the parameters in each model. The model with least average error of hypothetical forecast results in the latest three years was selected as the optimal forecast model. This optimal model divides total EC into four parts, of which forecasts can be made separately. It was found that GDP was only better correlated than UR on household EC, whilst UR was better on the three sectors of industries. It was concluded that UR is a valid predictor to forecast electricity demand at provincial level in China nowadays. Being provided the planned value of GDP and UR from the government, EC in 2015 were forecasted as 131.3 GWh. [ABSTRACT FROM AUTHOR]

Extending existing studies based on constant structure, we adopt a time-varying approach to study energy consumption and GDP causality for China in a context of industrialization and urbanization. We find that in light of structural change, China's energy consumption is trend-stationary and thus forms no cointegration with GDP. Further, the relationship between energy consumption and GDP is two-way causal and has been decreasing in strength over time. Finally, industrialization and urbanization, especially the former, have limited effects on energy consumption, suggesting the decreasing energy intensities in individual sectors, instead of structure shift between sectors, as the main reason for China's decreasing energy intensity over the years. [ABSTRACT FROM AUTHOR]

This paper conducts panel analysis to evaluate the effects of a structural economic shift from the industrial to the tertiary sector, a reduction in industrial overcapacity, and improvements in energy efficiency on energy consumption using data for 30 Chinese provinces from 1995 to 2015. We find that, at the national level, the structural shift to the tertiary sector, the reduction in cement and steel production, and the increase in energy efficiency in the industrial sector all have statistically significantly negative effects. We also divide the sample into three geographic and economic regions to evaluate regional differences. We find that the gross domestic product (GDP) share of the tertiary sector shows its greatest impact on reducing energy consumption in the eastern region, a decline in heavy industry production would reduce energy demand more in the central region, and improvement in industrial electricity efficiency would also help reduce energy consumption the most in eastern China. We also forecast energy consumption in China will reach 4.8–4.9 billion tonnes of coal equivalent (tce) in 2020 and further grow to 5.0–5.4 billion tce in 2030. [ABSTRACT FROM AUTHOR]

The household sector has become the second-largest source of energy consumption and CO2 emissions in China. It is important to understand the trends and changing mechanisms of household energy-related CO2 emissions (HECEs) in different social stages for mitigating the impact of climate change. However, the existing trends in HECEs and whether they are congruent with the expectation that HECEs in later developed areas are lower than those in priority areas under the same economic level are unclear. Here, we compared the per capita HECEs (PHECEs) of urban agglomerations under the same economic standards and analyzed the causes of the comparative results. We find that (1) HECEs increased rapidly from 3.65 × 108 t to 12.42 × 108 t during 1995–2017, with an average annual growth rate of 14.19%; (2) urban agglomerations that developed earlier do not have higher PHECEs. The PHECEs of urban agglomerations with moderate and later development do not decrease under increased social, technological, and cognitive conditions; (3) carbon intensity (CI), energy intensity (EI), and per capita regional gross domestic product (PCG) have a positive impact on PHECEs, and the potential impact of EI on PHECEs reduction is greater; (4) the effects of CI, EI, and PCG on PHECEs in different urban agglomerations are various. However, these effects are similar within urban agglomerations. Our study provides a reference for reducing CO2 emissions in the household sector and for the green development of urban agglomerations and emphasizes that the growth of HECE at a reasonable level is necessary when technology does not meet sufficient constraints. [ABSTRACT FROM AUTHOR]

Under the new mode of labor division for global production, the method of calculating a country's energy consumption and carbon emissions is based on a "production side" principle that disregards the embodied energy and carbon emissions caused by international trade. This method is unfair to China and other large, exporting countries. From the perspective of value-added trade, the multiregional input–output model based on the world input–output table and environmental account from the World Input–Output Database are used to measure the scale of China's value-added trade; subsequently, the import and export net values of China's foreigntraderelated embodied energy and carbon emissions are calculated. The results show that: (1) China's value-added exports in 2009 amounted to US $1,045.37 billion, which constitutes 21% of China's Gross Domestic Product (GDP) in that year. Nearly half of the value-added exports are to fulfill the final demand from North America and European Union countries; manufacturing and service are the main value-added export industries of China. (2) China has a relatively high unit coefficient for value-added energy consumption and carbon emissions, both representing a net export of embodied energy and embodied carbon emissions in foreign trade. In this regard, energy and mid-level technology manufacturing industries, such as coke, refined oil, and nuclear fuel processing, are the main exporters of embodied energy and embodied carbon. [ABSTRACT FROM AUTHOR]

China launched seven emissions trading scheme (ETS) pilots in 2011, and a national ETS, at the end of 2017 in an attempt to reduce its greenhouse gas emissions (GHGs) and drive its extensive energy transition in a cost-effective way. The Hubei province pilot ETS has operated since 2014, and an analysis of its effectiveness can provide lessons for the nascent national system. This paper disaggregates Hubei's industrial carbon dioxide (CO 2) emissions from 2005 to 2018 into economic scale, economic structure, energy efficiency, and energy structure effects using the logarithmic mean Divisia index (LMDI). Difference-in-difference (DID) models then assess the implementation effectiveness of this ETS, and its impact on the main influencing factors of the LMDI. Ten cities each in the Hubei and Hunan provinces are selected as treatment and control groups, respectively, in the DID models. Results show that the change in industrial CO 2 emissions is mainly due to the economic scale and energy efficiency effects. After the ETS implementation, the energy structure effect has a limited impact on emissions, while the economic structure effect makes a negligible contribution. Moreover, the implementation of the ETS has had little demonstrable impact on industrial CO 2 emissions, gross domestic product (GDP), energy consumption intensity, and CO 2 emission intensity. This lacking of impact is caused by the GHG monitoring capacity deficits of the local Development and Reform Commission, and irrational quota allocations. Effective supervision and quota allocation should be developed to increase the impacts of the ETS. [ABSTRACT FROM AUTHOR]

PM2.5 pollution has emerged as a global human health risk. The best measure of its impact is a population's PM2.5 exposure (PPM2.5E), an index that simultaneously considers PM2.5 concentrations and population spatial density. The spatiotemporal variation of PPM2.5E over the Beijing-Tianjin-Hebei (BTH) region, which is the national capital region of China, was investigated using a Bayesian space-time model, and the influence patterns of the anthropic and geographical factors were identified using the GeoDetector model and Pearson correlation analysis. The spatial pattern of PPM2.5E maintained a stable structure over the BTH region's distinct terrain, which has been described as "high in the northwest, low in the southeast". The spatial difference of PPM2.5E intensified annually. An overall increase of 6.192 (95% CI 6.186, 6.203) ×103 μg/m3 ∙ persons/km2 per year occurred over the BTH region from 1998 to 2017. The evolution of PPM2.5E in the region can be described as "high value, high increase" and "low value, low increase", since human activities related to gross domestic product (GDP) and energy consumption (EC) were the main factors in its occurrence. GDP had the strongest explanatory power of 76% (P < 0.01), followed by EC and elevation (EL), which accounted for 61% (P < 0.01) and 40% (P < 0.01), respectively. There were four factors, proportion of secondary industry (PSI), normalized differential vegetation index (NDVI), relief amplitude (RA), and EL, associated negatively with PPM2.5E and four factors, GDP, EC, annual precipitation (AP), and annual average temperature (AAT), associated positively with PPM2.5E. Remarkably, the interaction of GDP and NDVI, which was 90%, had the greatest explanatory power for PPM2.5E ′ s diffusion and impact on the BTH region. [ABSTRACT FROM AUTHOR]

Considering the enlarging inter-provincial disparities in China as regards carbon emissions and carbon intensity (carbon emissions per unit gross domestic product), this paper is the first study to investigate the inter-provincial carbon imbalance by constructing and employing the Kaya-Zenga index. We use China's panel data of provincial-level carbon emissions over 1995–2016 to quantitatively measure the levels of inter-provincial imbalance and polarization in carbon emissions and carbon intensity. Further, we decompose the Kaya-Zenga index into different contributing factors both regionally and structurally and perform a scenario analysis to identify the corresponding regionally differentiated countermeasures regarding carbon emission reduction. The results show that the imbalance in carbon emissions is mainly caused by imbalances in population scale and income level, while the imbalance in carbon intensity predominantly results from imbalances in energy efficiency and energy mix. In addition, for heavy manufacturing provinces, the respective emission-reduction strategy should aim at lowering energy intensity through local technology improvement and inter-regional technology transfer. For light manufacturing and high technology provinces, carbon emission reduction is harder to be achieved; however, a mix of policies of improving energy efficiency, optimizing energy mix, and industrial upgrading should be implemented. The results of the scenario analysis indicate that reducing imbalance in carbon intensity under different scenarios can lead to a substantial reduction in carbon emissions (up to 10%). • We investigate the inter-provincial carbon imbalance within China. • We construct the Kaya-Zenga index and decompose the index into four contributors. • We use a panel data set of 30 provincial-level regions in China over 1995–2016. • Carbon imbalance is mainly caused by imbalances in population and energy efficiency. • Reducing intra-group imbalance in carbon intensity can abate carbon emissions. [ABSTRACT FROM AUTHOR]

To tackle climate change, Chinese government has applied an array of mitigating measures to reduce CO2 emissions. During the 11th Five-year Plan, Chinese government set the emission reduction target policy to reduce energy consumption per unit of gross domestic product (GDP) by 20%. This paper attempts to estimate the effect of this emission reduction target policy on industrial performance from complexity perspective. The result shows more complex industrial structure is related to less coal consumption. In general, emission reduction target policy has a negative effect on the probability of branching into new industries and lower the productivity and profitability of a given industry. However, this negative effect is weaker for more complex industries. Only for industries with very high complexity, emission reduction target policy can improve the performance of an industry rather than exerting a negative effect. Our finding not only helps to design a more effective policy to achieve industrial development strategy, but also provides a potential way to achieve economic growth while reduce the emission of greenhouse gases at the same time. • We investigate the impact of Emission reduction target policy(ERT) on industrial performance from complexity perspective. • We apply the method of reflection to calculate the Economic Complexity of provinces and Industry Complexity of industries. • More complex industrial structure contributes to the coal consumption reduction. • ERT has negative effects on the probability of branching into new industries, lowers the productivity and profitability of a given industry. • ERT has weaker negative effects for more complex industries. [ABSTRACT FROM AUTHOR]

• Decoupling status and its determinants of China's TECE and GDP were analyzed. • Factors behind decoupling progress were quantified by Kaya identity and Tapio index. • Results show that during 2005–2016, the decoupling status changed from extensive to weak. • Decline of energy intensity is the dominant factor for the decoupling progress. • Scenario analysis was used for finding out pathways of decoupling China's TECE from GDP. China's transport industry is energy intensive and high-polluting. While with the surging urbanization and the development of service industry, China's economic relies more and more on the transport sector. Therefore, exploring the relationship between transport energy-related carbon emission (TECE) and economic development is crucial to the realization of China's "Post Paris" mitigation target. The paper carries out a decoupling research between TECE and Gross domestic product (GDP) at both national level and province level based on Logarithmic Mean Divisia Index (LMDI) decomposition analysis with the extended Kaya identity and Tapio decoupling model. The model quantifies eight factors' effects on the relationship with focusing on external macro socio-economic related factors (i.e., spatial pattern, urbanization, per capita service industry output value, reciprocal of the service industry's share of GDP, and demographic variable) successfully. The key conclusions are indicated as follows: (1) the national decoupling status was extensive coupling during 2004–2010 and then weak decoupling during 2010–2016. The progress can be attributed to the decline of energy intensity. (2) Per capita service output was always the prominent factor to promote carbon emissions growth in different time periods and provinces with inhibiting the advancement of decoupling process, followed by urbanization. (3) Scenario analysis shows that with the continuous growth of traffic demand and the promotion of urbanization, improving energy efficiency has become the key link to realize the decoupling between China's TECE and its economy. [ABSTRACT FROM AUTHOR]

CO2 emissions are of global concern because of climate change. China has become the largest CO2 emitter in the world and presently accounts for 30% of global emissions. Here, we analyze the major drivers of energy-related CO2 emissions in China from 1978 when the reform and opening-up policy was launched. We find that 1) there has been a 6-fold increase in energy-related CO2 emissions, which was driven primarily (176%) by economic growth followed by population growth (16%), while the effects of energy intensity (-79%) and carbon intensity (-13%) slowed the growth of carbon emissions over most of this period; 2) energy-related CO2 emissions are positively related to per capita gross domestic product (GDP), population growth rate, carbon intensity, and energy intensity; and 3) a portfolio of command-and-control policies affecting the drivers has altered the total emission trend. However, given the major role of China in global climate change mitigation, significant future reductions in China's CO2 emissions will require transformation toward low-carbon energy systems. [ABSTRACT FROM AUTHOR]

Civil buildings are among the structures that consume the most energy in society. Reducing the energy consumption of civil buildings has become an important component of China’s energy policy. Identifying the key influencing factors of the energy consumption of civil buildings and formulating effective energy-saving countermeasures are important to enhance the energy efficiency of civil buildings. This study conducted a systematic analysis of the influencing factors of the energy consumption of civil buildings in Hubei Province, China. First, existing studies on the influencing factors of the energy consumption of buildings in foreign developed countries were reviewed. The status of the population, economic life, and social life that influenced the energy consumption of civil buildings in Hubei Province in the past 10 years was summarized. Relevant influencing factors were estimated via ridge regression. Results demonstrated that developed countries generally investigated the influencing factors of the energy consumption of civil buildings from the energy supply-demand relation, family factors, and out-of-family factors. Gross domestic product (GDP), total population, tertiary industry development, per capita floor space, household consumption level, and urbanization facilitated the energy consumption of civil buildings in Hubei Province during the aforementioned period, and the regression coefficients were significant at the 5% level. Lastly, policy countermeasures were proposed to accelerate the energy-saving development of civil buildings. Research conclusions are important to enrich theories regarding the energy consumption reduction of civil buildings, to help relevant government sectors of Hubei Province recognize action points for the energy-saving development of civil buildings, and to accelerate the benign development of energy-saving buildings. [ABSTRACT FROM AUTHOR]

Mankind is constantly pursuing economic growth and social development, and while these bring convenience to people, they involve large amounts of greenhouse gas emissions under the heavy use of energy, which produces quite serious air pollution, which not only affects people's health but also leads to ecological environmental damage. Nowadays, the impact of the environment and the negative impacts of the world's economies while promoting economic growth are the most important issues for economies to achieve balanced development. This study uses the dynamic slack‐based data envelopment analysis (DEA) model to assess the environmental energy efficiency of high‐income economies (including China) and explore the negative impacts on the environment, to obtain a basis for energy‐saving emission reduction methods or configurations by using 48 high‐income economies (including China) from 2010 to 2014. The interperiod (carryover) variable is gross domestic product (GDP). The empirical results show that economies with high energy efficiency have a large consumption of energy and are unable to reduce carbon dioxide emissions. We also find that there exists a difference in the results depending on whether GDP is considered as the carryover variable. European economies are more efficient in energy consumption than others, and Asian economies are the most inefficient. In order to pursue GDP growth, economies need to consider reducing their energy consumption and CO2 emissions and improve their energy usage efficiency, further implementing sustainability. [ABSTRACT FROM AUTHOR]