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This paper uses dynamic spatial panel models to analyze the effects of industrial structure and technical progress on carbon intensity in order to explore those factors that may lead to a reduction in carbon intensity in China. The results show that there is both significant positive spatial autocorrelation and spatial heterogeneity between provinces in carbon intensity. Although upgrading and optimization of industrial structure are conducive to reducing carbon intensity, in China it is technical progress that plays the most important role. Efficiency change is a leading factor in the reduction of carbon intensity by technical progress, but the effect of technical change is not significant because of the carbon emissions rebound effect. Although technical change itself cannot directly reduce carbon intensity, it can indirectly do so by promoting the upgrading and optimization of industrial structure. Both structure of urbanization and coal-dominated energy consumption increase carbon intensity but the relationship between foreign direct investment and carbon intensity is not significant. Based on these empirical findings, we put forward specific suggestions for the Chinese government in their quest to reduce carbon intensity. [ABSTRACT FROM AUTHOR]

Climate change and global warming have become severe environmental issues of global concern, and many countries have promulgated renewable energy development planning to achieve the targets of carbon emission reduction and sustainable development. Accelerating the development of renewable energy technology innovation is one of the major measures to stimulate the utilization of renewable resources. This paper analyzes the impact of renewable energy technology innovation on carbon intensity in 30 Chinese provinces from 2000 to 2015. Using the recent panel estimation methods that take cross-section dependence and slope heterogeneity into account, the results show that for every 1% increase in the innovation level of renewable energy technology, carbon intensity was significantly reduced by 0.051%. Specifically, renewable energy technology innovation does not affect carbon intensity in the short term. But in the long term, the influences are negative and significant. Moreover, the environmental benefits are most evident in eastern China, while there is no significant influence on the western regions. Finally, this paper provides some policy implications to further discuss the impetus of renewable energy technology innovation. • Renewable energy technology innovation can significantly reduce carbon intensity. • The influences of renewable energy technology innovation will change over time. • There exist differences in the innovation effects in different Chinese regions. [ABSTRACT FROM AUTHOR]

The iron and steel industry is one of major sectors in term of energy consumption and CO 2 emission in China. In this paper, we evaluate the reduction potential of CO 2 emissions in China׳s iron and steel sector, based on the co-integration method and scenario design. We find that there is a long-term relationship between carbon intensity and its affecting factors, such as energy substitution, labor productivity, technology, and energy price. Monte Carlo simulation is further used for risk analysis. The results show that under BAU (business as usual) situation, the carbon intensity of China׳s iron and steel sector will be 0.3693 t CO 2 /100 CNY by 2020. However, the reduction potential of CO 2 emissions in 2020 will be 541.75 million tons and 856.68 million tons under the moderate carbon-reducing scenario and the advanced carbon-reducing scenario, respectively. Based on the results of the elasticities obtained in the long-term equilibrium equation, some policy recommendations for CO 2 emissions reduction in China׳s iron and steel industry are suggested. [ABSTRACT FROM AUTHOR]

Electric power industry is a key to achieve the low-carbon social and economic development. Distributed generation (DG) has great potential in the development of low-carbon power grid because of its non-polluting property and large reserves. Therefore, it is of great importance to have a comprehensive investigation on low-carbon impact of DG on power grids. In this paper, regional power grids are focused as they are crucial supporters of regional economic and social activities and the primary undertakers of low-carbon development. Specifically, first, the carbon dioxide emission coefficient and energy consumption of DG are evaluated from the aspect of its life cycle. Then, the wind farm and photovoltaic power station are compared in terms of carbon dioxide emissions and energy consumption, indicated that wind power is superior in reducing the carbon dioxide emissions. Finally, the carbon intensity of provincial power grid is evaluated by grey prediction model with historical data such as electricity consumption, gross domestic product (GDP), carbon dioxide emission coefficient. Taking Jiangxi provincial power grid as the example, the results demonstrate that the proposed prediction method is of high accuracy and the integration of DGs can effectively reduce carbon dioxide emissions and carbon intensity, thus, DG should be widely integrated into provincial power grids. [ABSTRACT FROM AUTHOR]