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The metallurgy industry plays a very important role in China's economic growth. Due to its energy concentration, the energy consumption of the industry accounts for a large proportion of China's total energy consumption, and thus contributes to carbon emissions. Whether the metallurgy industry can successfully reduce emissions is related to the future of China's carbon emissions targets. In this paper, the logarithmic mean Divisia index model is used to analyze the change in China's carbon dioxide emissions from 1991 to 2014. The empirical results show that there are three main factors affecting the carbon dioxide emissions of China's metallurgy industry. The three factors are labor productivity, energy intensity and industry size. In addition, we further analyzed the deep motivation of carbon dioxide emissions in the industry, and calculated the future emission reduction potentials. Lastly, according to the empirical results, the paper proposes policy recommendations for the future emission reduction of China's metallurgy industry. [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 As a significant basic industry for the national economic development, coal chemical industry is one of the main contributors to energy consumption and CO 2 emission. In this paper, the CO 2 emission in the time series of 2020 and 2030 was calculated and the Logarithmic Mean Divisia Index (LMDI) method was employed to explore the driving forces of CO 2 emission change. According to the industry development, the CO 2 emission in 2020 and 2030 was tentatively predicted. The results show that China's coal chemical industry has experienced a significant increase in CO 2 emission, from 177.87 million ton CO 2 (MtCO 2) to 427.19 MtCO 2 in 2005–2015, with an average annual growth rate of 9.16%. Currently, the coal-to-ammonia industry is the largest contributor. According to the decomposition analysis, the economic growth and energy intensity are the two positive factors and the industry structure is a negative factor to carbon emission change. In 2020, the CO 2 emission will be 617.34 MtCO 2 under slow development scenario and 759.69 MtCO 2 under rapid development scenario. In 2030, the CO 2 emission will reach 1041.90 MtCO 2 and 1440.13 MtCO 2 under slow and fast development scenarios, respectively. The coal-to-olefins industry will take the place of coal-to-ammonia and turn to be the largest one in 2020 and 2030 if no CO 2 capture, utilization and storage (CCUS) technologies are adopted. It is probable that the contribution from coal-to-methanol will also play important role for CO 2 emission due to its rapid development. In addition, by 2030, if coal-to-natural gas industry develops as planned, its CO 2 emission will significantly increase, which has the potential to be equivalent to that of olefins industry. Highlights • The CO 2 emission evolution trend in China's coal chemical industry is analyzed. • The major driving factors of CO 2 emission change are identified and the effect sizes of various factors are evaluated. • The CO 2 emission in 2020 and 2030 is forecasted. • Policy implications to policymakers are provided. [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]

The surge in energy consumption and CO 2 (carbon dioxide) emissions is exacerbating global warming. The Chinese government has promised to reduce CO 2 emission by 60%–65% per unit of GDP over the 2005 level by around 2030. Analyzing the factors influencing CO 2 emissions is one of the key aspects involved in effectively reducing greenhouse gas emissions. In this paper, we add climatic factors based on socio-economic factors, and adopt the extended STIRPAT model to investigate the mechanism driving emissions by the co-action of natural and socio-economic factors. The results show that the 1995–2014 emissions in China's Zhejiang province had an upward trend with growth rates varying between intermediate periods. The order of the factors' elasticity coefficients is urbanization rate (0.181) > GDP per capita (0.180) > population (0.177) > energy intensity (0.170) > trade openness (0.170) > cooling degree days (0.127) > mean temperature anomaly (0.116). Economic development and urbanization were the main factors promoting CO 2 emissions in 1996–1999, while economic development and foreign trade were the main factors in 2000–2014. Policy makers should pay more attention to keeping a healthy pace of economic development, introducing more low energy consumption and low pollution enterprises and encouraging technological innovation. Improving the quality of urbanization, guiding a sustainable lifestyle, adjusting the structure of foreign trade and curbing energy intensity are also expected to reduce emissions. [ABSTRACT FROM AUTHOR]

This paper presents a forecast and analysis of population, economic development, energy consumption and CO2 emissions variation in China in the short- and long-term steps before 2020 with 2007 as the base year. The widely applied IPAT model, which is the basis for calculations, projections, and scenarios of greenhouse gases (GHGs) reformulated as the Kaya equation, is extended to analyze and predict the relations between human activities and the environment. Four scenarios of CO2 emissions are used including business as usual (BAU), energy efficiency improvement scenario (EEI), low carbon scenario (LC) and enhanced low carbon scenario (ELC). The results show that carbon intensity will be reduced by 40–45% as scheduled and economic growth rate will be 6% in China under LC scenario by 2020. The LC scenario, as the most appropriate and the most feasible scheme for China’s low-carbon development in the future, can maximize the harmonious development of economy, society, energy and environmental systems. Assuming China's development follows the LC scenario, the paper further gives four paths of low-carbon transformation in China: technological innovation, industrial structure optimization, energy structure optimization and policy guidance. [ABSTRACT FROM AUTHOR]

As a developing country, extensive carbon and sulfur emissions are associated with China’s rapid social and economic development. Chief among them are the emissions from coal and oil consumption. This paper focuses on the demand side, attempting to regulate the range of relative price of oil to coal at the consumption level. Through the adjustment of the relative price, the goal of reducing the emissions of carbon and sulfur could be achieved in the market of energy consumption. Data regression is applied to investigate the functional relationship between emissions and energy prices. The results indicate that when the coal price is less than 300, the higher relative price leads to less carbon and sulfur emissions; when the coal price is more than 300 and less than 500, there exists an optimal relative price which has the least carbon emissions, and this value is not more than 11.5; when the coal price is more than 500, the smaller relative price is beneficial to decline carbon and sulfur emissions. The changed trend of relative price-sulfur emissions is very similar to relative price-carbon emissions. Compared to the present energy situation in China, the relative price of oil to coal still need to be reduced especially when coal price is more than 500. [ABSTRACT FROM AUTHOR]

The transportation sector is one of the major driving forces of carbon emissions. Identifying the factors that affect CO 2 emissions from the transportation sector is important to build a low-carbon city. Most existing research focuses on the total effect of factors on CO 2 emissions while the indirect influence is also the driving force of CO 2 emissions. Additionally, identifying the causal relationship between variables is helpful to study the mutual acting mechanism. Therefore, this paper uses the path analysis model to estimate the direct, indirect and total influences of driving factors on transportation CO 2 emissions in Beijing and investigate the causality relationships between variables. The results show that reducing energy intensity and transportation intensity are the key factors in controlling the increase of transportation-related CO 2 emissions. Population has the greatest positive impact on CO 2 emissions because an increasing population is leading to growth in energy consumption and the number of motor vehicles. However, population could indirectly affect the energy intensity and transportation intensity to reduce carbon emissions. Moreover, motor vehicles increase CO 2 emissions due to the growth in private car population and its low energy efficiency. And, the change in the economic growth pattern somewhat inhibits the growth rate of CO 2 emissions by reducing the energy intensity and transportation intensity indirectly. To further suppress the growth of transportation carbon emissions, the following steps should be taken: appropriately improve the quality of population, control the scale of motor vehicles, develop and promote clean energy, and reduce traffic energy intensity and transportation intensity. [ABSTRACT FROM AUTHOR]

China has achieved significant progress in terms of economic and social developments since implementation of reform and open policy in 1978. However, the rapid speed of economic growth in China has also resulted in high energy consumption and serious environmental problems, which hindering the sustainability of China's economic growth. This paper provides a framework for measuring eco-efficiency with CO 2 emissions in Chinese manufacturing industries. We introduce a global Malmquist-Luenberger productivity index (GMLPI) that can handle undesirable factors within Data Envelopment Analysis (DEA). This study suggested after regulations imposed by the Chinese government, in the last stage of the analysis, i.e. during 2011–2012, the contemporaneous frontier shifts towards the global technology frontier in the direction of more desirable outputs and less undesirable outputs, i.e. producing less CO 2 emissions, but the GMLPI drops slightly. This is an indication that the Chinese government needs to implement more policy regulations in order to maintain productivity index while reducing CO 2 emissions. [ABSTRACT FROM AUTHOR]

This paper mainly investigates the relationship among energy consumption, carbon emissions and economic growth in China from 1952 to 2012. First, we implement unit root and cointegration analysis to test the stationary. Furthermore, we analyze the mutual influence among energy consumption, carbon emissions and economic growth through Granger causality analysis. Next, we also conduct static and dynamic regression analysis on the determinants of carbon emissions and economic growth. Last, we predict the future influence of different energy consumption on carbon emissions and economic growth. We find that coal has dominant impact on economic growth and carbon emissions. GDP (Gross Domestic Product) has bi-directional relationship with CO 2 (carbon dioxide) emission, coal, gas, and electricity consumption. It is imperative to change energy consumption structure in China. We had better decrease coal consumption rate. It is significant to develop hydro and nuclear power in China. [ABSTRACT FROM AUTHOR]

China's emissions continue to rise rapidly in line with its mounting energy consumption, which puts considerable pressure on China to meet its emission reduction commitments. This paper assesses the impacts of CO2 mitigation measures in China during the period from 2010 to 2050 by using a computable general equilibrium method, called AIM/CGE. Results show that renewable energy makes a critical difference in abating emissions during the period from 2010 to 2020. The scenarios with emission trading would drive more emission reductions, whereby the emission-cutting commitment for 2020 would be achieved and emission reductions in 2050 would be more than 57.90%. Meanwhile, the share of non-fossil energy increases significantly and would be more than doubled in 2050 compared with the BAU scenario. A carbon tax would result in a significant decline in emissions in the short term, but would have an adverse effect on economic growth and energy structure improvements. It is also observed that the integrated measures would not only substantially decrease the total emissions, but also improve the energy structure. [ABSTRACT FROM AUTHOR]

China has been the world's largest producer and consumer of air conditioners, and more and more RACs 1 1 The RACs is short for room air conditioners. would be owned by China's households along with the rapid economic development. Air Conditioner is also considered as one of the largest potential contributors to energy reduction among home appliances because of the huge energy consumption. Therefore, the national energy efficiency standards were issued to promote the use and production of high-efficient RACs. According to China's energy efficiency standards, this paper investigated the electricity savings and CO 2 emission reductions from RACs over the period of 2005–2025. The results indicate that the rural RAC market which develops more slowly than the urban one still has great potential, and government has to revise subsidy policies to make the standards more effective, especially for rural areas. In 2025, the total electricity consumption of RACs is projected to be 598–674 TW h, while the amount without energy efficiency standards is 753 TW h. From 2005 to 2025, the energy efficiency standards for RACs can save 1430–2540 TW h electricity and reduce 908.3–1610. 1 Mt CO 2 emissions in different scenarios. Finally, we suggest that the standards should be revised every 4 or 5 years with higher revision pace of 8% to 10% [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the relationship between renewable energy consumption and economic growth in China for the period 1977–2011. Autoregressive Distributed Lag approach (ARDL) to cointegration and Johansen cointegration techniques are employed by including intermittent variables namely carbon dioxide emissions and labor. We also employed Granger causality test in order to determine the direction of the causality among the variables. The results show that there is a bi-directional long term causality between renewable energy consumption and economic growth. This finding implies that growing economy in China is propitious for the development of renewable energy sector which in turn helps to boost economic growth. We also find that labor influences renewable energy consumption in the short term. However, there is no evidence of long or short run causality between carbon emissions and renewable energy consumption. This implies that actual level of renewable energy in China is still insignificant and not considerably exploited in order to contribute to the mitigation of carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

With the rapid economic development, the transportation sector becomes one of the high-energy-consumption and high-CO2- emissions sectors in China. In order to ensure efficient use of energy and to reduce CO2 pollution, it is important to gain the best performance standards in China's transportation sector. Data envelopment analysis (DEA) has been accepted as a popular tool of efficiency measurement. However, previous studies based on DEA are mainly restricted to the radial expansions of outputs or radial contractions of inputs. In this paper, we present a nonradial DEA model with multidirectional efficiency analysis (MEA) involving undesirable outputs for the measurement of regional energy and environmental efficiency of China's transportation sector during the period 2006-2010. We not only evaluate the energy and environmental efficiency level and trend of China's transportation sector but also investigate the efficiency patterns of 30 regions and three major areas of China. Additionally, we identify the energy saving potential and CO2 emissions reduction potential for each province and area in China in this study. [ABSTRACT FROM AUTHOR]