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As the top emitter of carbon dioxide worldwide, China faces a considerable challenge in reducing carbon emissions to combat global warming. Carbon emissions from coal consumption is the primary source of carbon dioxide emissions in China. The decomposition of the driving factors and the quantification of regions and industries needs further research. Thus, this paper decomposed five driving factors affecting carbon emissions from coal consumption in China, namely, carbon emission intensity, energy structure, energy intensity, economic output, and population scale, by constructing a Kaya-Logarithmic Mean Divisia Index (Kaya-LMDI) decomposition model with data on coal consumption in China from 1997 to 2019. It was revealed that the economic output and energy intensity effects are major drivers and inhibitors of carbon emissions from coal consumption in China, respectively. The contribution and impact of these driving factors on carbon emissions from coal consumption were analyzed for different regions and industrial sectors. The results showed that carbon emissions from coal consumption increased by 3211.92 million tons from 1997 to 2019. From a regional perspective, Hebei Province has the most significant impact on carbon emissions from coal consumption due to the effect of economic output. Additionally, the industrial sector had the most pronounced influence on carbon emissions from coal consumption due to the economic output effect. Finally, a series of measures to reduce carbon emissions including controlling the total coal consumption, improving the utilization rate of clean energy, and optimizing the energy structure is proposed based on China's actual development. [ABSTRACT FROM AUTHOR]

Climate change means that China needs to reduce carbon dioxide emissions from its transport sector. The study reported in the paper analysed the spatial–temporal characteristics of carbon doxide emissions produced by transporting coal by rail in the country between 2000 and 2014. The kriging interpolation method was used to analyse the emission distributions. The results showed that total emissions produced from rail transport of coal increased from 1·18 Mt in 2000 to 11·71 Mt in 2014, although they appear to have peaked. Inner Mongolia, Shanxi and Shaanxi were the provinces with the largest emission increases. The emissions were positively correlated and showed spatial heterogeneity, with Shanxi and Shaanxi provinces being high-emission provinces surrounded by other high-emission provinces. The paper concludes with a discussion of energy conservation and emissions-reduction measures that could be implemented by the Chinese transport industry. [ABSTRACT FROM AUTHOR]

China has a high agricultural output and is a significant carbon emitter; thus, reducing carbon emissions in the agricultural sector is crucial for achieving carbon neutrality. To address this, China implemented the National Agricultural Sustainable Development Experimental Demonstration Zone policy in 2018, in which reducing agricultural carbon emissions (ACE) is a subject of concern. We explore the impact of the policy on agricultural carbon emissions reduction (ACER) using the propensity score matching and differences-in-differences (PSM-DID) methods with a series of robustness tests based on the panel data of prefecture-level cities in China from 2011 to 2020. The results indicate that: (1) the policy significantly reduces carbon emissions and impacts carbon emission sources, with the results passing the robustness tests. (2) According to the mechanism test, agricultural financial support mediates carbon emissions reduction, and the policy reduces these emissions by decreasing the intensity of fiscal support. (3) The heterogeneity analysis illustrates that the carbon emissions reduction effect is more evident in cities with a small urban-rural income gap and a high proportion of agricultural output value. Finally, we provide suggestions to reduce carbon emissions from the perspective of national and local governments. This paper is particularly relevant for achieving sustainable agriculture development. [ABSTRACT FROM AUTHOR]