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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]

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 examines the relationships between the energy consumption, GDP growth and CO2 emission, using Panel Smooth Transition Regression (PSTR) model for BRIC countries over the period 1960-2006. Our results reveal that environmental quality in these countries has increasingly suffered from high energy consumption. Moreover, rapid economic growth and international trade in energy intensive goods have progressively increased energy consumption. This suggests that excessively high economic growth is a curse for environmental quality and energy conservation policies to reduce unnecessary wastage of energy should be kicked off for energy-dependent BRIC countries. [ABSTRACT FROM AUTHOR]

As the energy supply shortage and environmental pollution have increasingly become the major obstacles to China's economic development, the Chinese government has proposed various policies to reduce energy consumption, one of which is to implement a differentiated electric power price scheme (DEPP) on the energy-intensive industries (EIs). Although it is only imposed on EIs, its indirect impact on other sectors or national economy could be profound. This paper applies an input-output (IO) model, which composes 42 sectors calibrated with data in 2002 to examine the potential impact of DEPP. The results show that DEPP would result in significant energy savings at the expense of declines in gross domestic product (GDP) and increases in consumer price index (CPI). The ancillary benefits include reduction of emissions from the energy-intensive sector. [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]

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]

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]

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]

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]

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]

Abstract: Energy, economy and environment form a mutually-influenced circulatory system. This paper uses econometric theory, according to time series data of energy consumption, GDP and industrial sewage emission from 1983 to 2009 about China, to empirically analyze the dynamic relationship among them. The result shows that: there exist the longterm stable equilibrium relationships among energy consumption, economic growth and environment. The improving action of Energy consumption on economic growth is far less than on environmental pollution. When the system deviated from the equilibrium, the adjusting speed about industry waste gas emission and energy consumption is far lower than that of economy. This paper further reveals the interaction among energy, environment and economy. On this basis, in order to achieve coordinated growth of energy, economy and environment, the paper puts forward several policy suggestions. [Copyright &y& Elsevier]

Abstract: This paper, by adopting the carbon emission calculating method for all kinds of energy proposed by IPCC in 2006, calculates the amount of carbon emissions of Shandong province. It turns out that carbon emissions in Shandong increased 2.63 times, carbon emission intensity increased 2.45 times and the carbon emission intensity and the energy intensity by GDP is obvious higher than the average level of China from 1997 to 2008.Taking into account the characteristics of carbon emissions in Shandong Province, the paper tries to propose the corresponding strategies to develop low-carbon economy. [Copyright &y& Elsevier]

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]

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]

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]

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]

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]

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]

Abstract: Accounting for 7% of energy consumption from all industrial sectors and 15% of total greenhouse gas (GHG) emissions in China in 2009, the cement industry should be one of the main sectors to implement low carbon development. However, a detailed study on presenting its GHG emission trajectory and identifying the key driving factors has yet to be done. Under such a circumstance, this paper first presents a GHG emission inventory of the cement industry in China and then identifies the main driving factors that influence changes of GHG emissions in the cement industry by adopting LMDI method. Our results show that the total GHG emission in 2009 was 1095.1 Mt carbon dioxide equivalent (CO2e), of which 592.8 Mt CO2e was process-related and 502.3 Mt CO2e was energy-related. The major factors responsible for the increase of GHG emissions during 2005–2009 include cement production activity effect and clinker production activity effect, while energy intensity effect played a positive role to offset total GHG emissions. Finally, we present our policy implications and provide possible technical solutions for reducing GHG emissions of the cement industry in China. [Copyright &y& Elsevier]

Abstract: To find the key impact factors of CO2 emissions to realize the carbon intensity target, this paper examined the impact factors of population, economic level, technology level, urbanization level, industrialization level, service level, energy consumption structure and foreign trade degree on the energy-related CO2 emissions in Guangdong Province, China from 1980 to 2010 using an extended STIRPAT model. We employed ridge regression to fit the extended STIRPAT model. Empirical results indicate that factors such as population, urbanization level, GDP per capita, industrialization level and service level, can cause an increase in CO2 emissions. However, technology level, energy consumption structure and foreign trade degree can lead to a decrease in CO2 emissions. The estimated elastic coefficients suggest that population is the most important impact factor of CO2 emissions. Industrialization level, urbanization level, energy consumption structure, service level and GDP per capita are also significant impact factors, but the other factors such as technology level and foreign trade degree are less important impact factors. Some policy recommendations are also given on how to mitigate the growth of CO2 emissions. [Copyright &y& Elsevier]

Abstract: In order to measure the relationship between economic activities and energy consumption for low-carbon economy strategy in ethnic minority autonomous regions, this paper used the Carbon Footprint Model and based the VAR model, used the Impulse Response Function method to describe the dynamic relations between economic growth and the energy Carbon Footprint in these regions. The result indicated that there is a strong interactive response between economic growth and Energy Carbon Footprint. The increases of Carbon Footprint can also cause the increase of GDP, this means the economic growth will be growth more rapidly depends on the massive energy consumption in the short-term, but when the economic growth to the certain extent, energy''s excessive consumption can’t bring higher growth of economic, even give the negative influence to the economic growth, therefore, transforming economic growth pattern, modifying industrial structure and improving energy efficiency are useful ways for low-carbon economy. [Copyright &y& Elsevier]

Abstract: This paper examines the driving forces for reducing China’s CO2 emission intensity between 1998 and 2008, utilizing the logarithmic mean divisia index (LMDI) technique. By first grouping the CO2 emissions into two categories, those arising from activities related to the electric power industry and those from other sources, emission intensity is further broken down into the effects of the CO2 emission coefficient, energy intensity of power generation, power generation and consumption ratio, electricity intensity of the gross domestic product (GDP), provincial structural change, and the energy intensity of the GDP for other activities. The decomposition results show that improvements in the energy intensity of power generation, electricity intensity of GDP, and energy intensity of GDP for other activities were mainly responsible for the success in reducing China’s CO2 emission intensity and that activities related to the electric power industry played a key role. It is also revealed that performance varied significantly at the individual province level. The provinces with higher emission levels contributed the most to China’s improvements in CO2 emission intensity. [Copyright &y& Elsevier]

Abstract: Energy intensity is an important indicator to evaluate the coordination of energy-economy-environment system, so reducing energy intensity has become one of the greatest challenges of economy development. In order to study the relation between energy intensity and influential factors of energy intensity in China, a regression equation is used in this paper, using the method of stepwise regression analysis. By introducing the data of real GDP, total energy consumption and energy consumption mix, all data over the period 1990-2008, the regression equation can rationally simulate the energy intensity. Empirical results show that energy structure and energy conversion efficiency have a great impact on energy intensity. In the future, we should raise R&D expenditure on energy technology accounted for the proportion of GDP, optimize energy Structure, upgrade the industry structure and develop the new energy. As the regression equation has a very good simulation results, it can be used to forecast energy intensity of China. [Copyright &y& Elsevier]

Abstract: Traditional DEA is an evaluation method based on the self-assessment system; the decision-makers cannot choose a frame of reference freely. In order to solve the DEA method can be based on both self-assessment system and any frame of reference for evaluation, the generalized output-oriented DEA model based on BC2 is given in this paper. Then through this model to evaluate the efficiency of Chinese energy consumption and GDP from 1990 to 2007, and compared the efficiency with several developed countries in 2006. [Copyright &y& Elsevier]

Abstract: The “low-hanging fruits” (LHF) (or cream-skimming) problem receives some attention in recent years. However, few studies provide formal analysis on the LHF problem and other flaws based on economic theory. This paper investigates the LHF problem of Clean Development Mechanism (CDM) in China. The energy consumption and the Green House Gas (GHG) emission in China are outlined. The details of the CDM projects, including their distribution in China, and the types of projects are summarized. In order to test whether there were LHF problems in China, a simple statistical model is developed. The statistical analysis provides an indirect evidence that the LHF problem exists in China. The results indicate that areas with higher emission of GHG and low level of GDP per capita attract more CDM projects with higher amount of Certified Emission Reductions (CERs). The results are robust after eliminating HFC-23 decomposition projects from the sample. [Copyright &y& Elsevier]

Abstract: taking energy consumption per ten thousand Yuan unit GDP as energy consumption quality indicator and using, this paper analyses the relationship between energy consumption quality and the level of education and technology of Jiangsu province based on grey relation theory. Conclusion shows that, according the importance order, the influence factors of Jiangsu energy consumption quality is as following: scientific and technological personnel, number of college students per ten thousand in number, number of R&D funds. Therefore,to improve the energy consumption quality, we should improve technology personnel level and education level.at the same time,we should enlarge fund investment. [Copyright &y& Elsevier]

Abstract: The global energy issues have stimulated increasing research interest in the linkages between the energy consumption and economic growth. As the biggest developing country, China has surpassed United States to be the world''s largest energy consumption country. This paper examines the casuality between GDP and energy consumption by using updated China provincial panel data for the period 1985–2007 within a multivariate framework by adopting Pedroni''s panel methodology. The results show that cointegration is present between real GDP, energy consumption, the labor force, and real gross fixed capital. The results of Granger-causality test indicate the presence of Bi-directional causality between energy consumption and economic growth which supports the feed back hypothesis.Economic interpretations of the empirical results are also presented. [Copyright &y& Elsevier]

Abstract: The relationship between energy consumption and economic growth has long been the focus at home and abroad. The general view is: energy consumption promotes economic growth; economic growth affects energy consumption on the other hand. In sight of the original theory, this paper adopts the statistical data of Shandong Province from 1980 to 2008, which include Gross Domestic Products (GDP), energy consumption, fixed asset investment and employees. Using unit root, co-integration and Granger causality test, we examined the relationship between energy consumption and economic growth in Shandong Province. The results show that energy consumption and economic growth have long-term trend relation, and there is two-way causality between them. The econometric model is estimated using Generalized Least Square (GLS) method. The conclusions are as follows: energy consumption and economic growth is positively correlated in Shandong province, and economic growth has highly depended on energy consumption. [Copyright &y& Elsevier]

Abstract: Assuming that energy consumption is the main source of GHG emissions in China, this paper analyses the effect of population, urbanisation level, GDP per capita, industrialisation level and energy intensity on the country''s environmental impact using the STIRPAT model with data for 1978–2006. The analysis shows that population has the largest potential effect on environmental impact, followed by urbanisation level, industrialisation level, GDP per capita and energy intensity. Hence, China''s One Child Policy, which restrains rapid population growth, has been an effective way of reducing the country''s environmental impact. However, due to the difference in growth rates, GDP per capita had a higher effect on the environmental impact, contributing to 38% of its increase (while population''s contribution was at 32%). The rapid decrease in energy intensity was the main factor restraining the increase in China''s environmental impact but recently it has also been rising. Against this background, the future of the country looks bleak unless a change in human behaviour towards more ecologically sensitive economic choices occurs. [Copyright &y& Elsevier]

Abstract: In this paper, in contrast to the usual rough estimation, we present a model to simulate and evaluate the direct, indirect economic and environmental impacts of the implementation of building energy efficiency standards on Chinese economic system and environment by 12 indicators in two scenarios. Four indicators are used to evaluate the direct economic impact degree, five indicators are used to evaluate the direct environmental impact degree, three indicators are used to evaluate the indirect economic impact degree of 34 sectors and the whole Chinese economic system. This research makes it possible to link developments in the implementation of building energy efficiency standards with environmental and economic structure change. The most important finding is that the implementation of building energy efficiency standards can reduce a large amount of pollutants emissions and increase the GDP at the same time. [Copyright &y& Elsevier]

This paper investigates the existence and direction of Granger causality between economic growth, energy consumption, and carbon emissions in China, applying a multivariate model of economic growth, energy use, carbon emissions, capital and urban population. Empirical results for China over the period 1960–2007 suggest a unidirectional Granger causality running from GDP to energy consumption, and a unidirectional Granger causality running from energy consumption to carbon emissions in the long run. Evidence shows that neither carbon emissions nor energy consumption leads economic growth. Therefore, the government of China can purse conservative energy policy and carbon emissions reduction policy in the long run without impeding economic growth. [Copyright &y& Elsevier]

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]

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]

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]

The forecasting of energy consumption in China is a key requirement for achieving national energy security and energy planning. In this study, multi-variable linear regression (MLR) and support vector regression (SVR) were utilized with a gated recurrent unit (GRU) artificial neural network of Chinese energy to establish a forecasting model. The derived model was validated through four economic variables; the gross domestic product (GDP), population, imports, and exports. The performance of various forecasting models was assessed via MAPE and RMSE, and three scenarios were configured based on different sources of variable data. In predicting Chinese energy consumption from 2015 to 2021, results from the established GRU model of the highest predictive accuracy showed that Chinese energy consumption would be likely to fluctuate from 2954.04 Mtoe to 5618.67 Mtoe in 2021. [ABSTRACT FROM AUTHOR]

Herein we develop a means to differentiate between the energy required to expand and the energy required to maintain the economies of cities. A nonlinear model is tested against historical data for two cities, Hong Kong and Singapore. A robust fit is obtained for Hong Kong, with energy for maintenance close to that for growth, while Singapore, with a weaker fit, is growth dominated. The findings suggest that decreases in either of the per unit maintenance or growth demands can simultaneously cause gross domestic product (GDP) and total energy use to increase. Furthermore, increasing maintenance demands can significantly limit growth in energy demand and GDP. Thus, the low maintenance demands for Hong Kong, and especially Singapore, imply that, all other things being equal, GDP and energy use of these cities will continue to grow, though Singapore's higher energy use for growth means it will require more energy than Hong Kong. [ABSTRACT FROM AUTHOR]

China has achieved notable success in developing its economy with approximate 10 percent average annual GDP growth over the last two decades. At the same time, energy consumption and CO2 emissions almost doubled every five years, which led China to be the world top emitter in 2007. In response, China's government has put forward a carbon mitigation target of 40%-45% reduction of CO2 emission intensity by 2020. To better understand the potential for success or failure of such a policy, it is essential to assess different driving forces such as population, lifestyle and technology and their associated CO2 emissions. This study confirms that increase of affluence has been the main driving force for China's CO2 emissions since the late 1970s, which outweighs reductions achieved through technical progress. Meanwhile, the contribution of population growth to CO2 emissions was relatively small. We also found a huge disparity between urban and rural households in terms of changes of lifestyle and consumption patterns. Lifestyles in urban China are beginning to resemble Western lifestyles, and approaching their level of CO2 emissions. Therefore, in addition to the apparent inefficiencies in terms of production technologies there is also a lot of room for improvement on the consumption side especially in interaction of current infrastructure investments and future consumption. [ABSTRACT FROM AUTHOR]