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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]

In order to explore the decoupling relationship and its influence factors between economic growth and carbon emissions in China, the decoupling elasticity decomposition quantitative model of carbon emissions based on extended Log-Mean Divisia Index and Tapio decoupling models is established in this paper. The carbon emissions induced by household (HOU) sector ranked fifth among the nine sectors; hence, we analyzed the HOU sector with the production (PRO) sector together. The results show that, first, the carbon emissions increased from 3.95 billion tons in 1996 to 10.49 billion tons in 2012, and the contributions of manufacturing sector and electric power, gas and water production and supply sector to carbon emissions account for approximately 81 %. Second, the economic output effect is the main contributor, and the energy intensity effect is the major inhibitor factor to the carbon emissions in both the PRO sector and HOU sector, respectively. Third, the decoupling state of the PRO sector mainly stayed at weak decoupling, while the decoupling state of HOU sector mainly stayed at strong decoupling. Fourth, the impact factors of the carbon emissions and the decoupling elasticity values are in complete agreement. At the end of this paper, we present some policy recommendations for China's government to realize the decoupling between CO emissions and economic growth in the near future. [ABSTRACT FROM AUTHOR]

Abstract Economic development has promoted urbanization in the Beijing-Tianjin-Hebei (BTH) region. With the improvement of living standards, residential consumption have become an important contributor to regional carbon emissions. Using an input-output model, we calculated direct and indirect carbon emissions from both urban and rural residential consumption in the BTH region from 2002 to 2012, and then explored the main factors contributing to indirect carbon emissions from residential consumption using a structural decomposition analysis. Results showed that direct carbon emissions from residential consumption increased from 47.13 million tons (MtC) in 2002 to 123.34 MtC in 2012 and indirect carbon emissions from residential consumption increased from 210.36 MtC in 2002 to 550.00 MtC in 2012. Carbon emission intensity and residential consumption level were the main factors affecting indirect carbon emissions during 2002–2012, with varying effects in different sub-regions. Carbon emission intensity played negative effects in Beijing (−37.19 MtC) and Tianjin (−55.39 MtC), positive effects in Hebei (92.89 MtC). Residential consumption level played positive effects in the three sub-regions (79.94 MtC in Beijing, 31.91 MtC in Tianjin and 155.52 MtC in Hebei). Increases in intermediate demand, residential consumption level and population size enhanced indirect carbon emissions. Based on the analysis, this paper contributed to carbon emission reduction strategies in the BTH region. Highlights • Direct carbon emissions from residential consumption increased to 123.34 MtC in 2012. • Indirect carbon emissions from residential consumption increased to 550.00 MtC in 2012. • Carbon emission intensity and residential consumption level were the main impact factors. • Intermediate demand, residential consumption level and population size played positive effects. [ABSTRACT FROM AUTHOR]

With rapid economic development, energy consumption and environmental pollution have become increasingly prominent, making clear emission reduction targets necessary. Therefore, this paper forecasts input and output variables for China's six industries during the 13th Five-Year Plan by using an EBMDEA model to analyze the distribution efficiency of the industries. Moreover, the ZSG-DEA model is used to optimize carbon emission quotas and establish a reasonable distribution plan. The results show that among the six major sectors in China, the initial allocation of carbon emission rights has relatively low DEA efficiency, with significant efficiency differences among sectors. Only agriculture, forestry, animal husbandry, and fisheries achieved an efficient initial allocation of carbon emission rights, whereas industry and transportation, warehousing, and the postal service exhibited low allocative efficiency. The industrial sector has the greatest room for adjustment and its reduction can be compensated for through an increase in emissions from the other sectors so as to keep total carbon emissions constant. Based on the conclusions of the study, we make suggestions for various stakeholders. [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]

Reducing carbon emissions and managing the aging crisis represent two major challenges in China that involve various requirements for continued economic growth. This paper investigated the relationships between population factors and carbon emissions and further explored the impact of population aging on carbon emissions at the national and regional levels based on the STIRPAT model and provincial panel data from China. Our results show that at the national level, population aging and population quality are positively correlated with China's carbon emissions. The impact of the population living standard on carbon emissions exhibits an urban-rural difference. At the regional level, the impact of population aging on carbon emissions exhibits regional differences. [ABSTRACT FROM AUTHOR]

The paper examines the long-run relationship among per capita income, energy consumption, and carbon emissions in China during 1953–2008. We employ the Toda—Yamamoto procedure to test the Granger causality in a vector autoregressive (VAR) model with levels. We find strong evidence of bilateral Granger causality running between income and energy consumption and unilateral Granger causality running from carbon emissions to income. We further provide policy suggestions to address the energy and sustainable development dilemma in China. These include diversifying the energy mix and exploiting renewable energy, enhancing energy efficiency, transforming the economic growth pattern, and reducing the reliance on resource- and energy-intensive industries. [ABSTRACT FROM PUBLISHER]