Search

In China, manufacturing is the industry that consumes the most energy and emits the most carbon, and the effect of emission reduction on the process of reaching carbon peaking and carbon neutrality is decisive. The existing research on the driving factors of manufacturing carbon emissions has not analyzed the specific structural characteristics of manufacturing carbon emissions from the perspective of industrial relevance, and little attention has been paid to the discussion of carbon emission reduction paths of different manufacturing sectors from the perspective of final demand. This study examines the direct carbon emissions and carbon emissions from final demand in China's manufacturing sector, and decomposes the carbon emissions from final demand into six distinct components using input-output analysis. In addition, this study examines the carbon emission path in manufacturing production activities, as well as the carbon emission reduction potential and scenario prediction of the factors influencing manufacturing carbon emissions. In 2018, the direct carbon emissions and carbon emissions from final demand were approximately 4.61 billion tons and 3.50 billion tons, respectively. Meanwhile, direct and indirect spillovers accounted for 62.1% and 23.1% of carbon emissions from final demand, respectively. Using the carbon emission transfer route map of the manufacturing industry, the direction and amount of carbon emission transfer from various energy sources can be accurately determined. The CR scenario predicts that the manufacturing industry will reach its carbon peak between 2025 and 2030, with a corresponding peak between 4.02 and 4.06 billion tons, and that carbon emissions in 2060 will be 40% lower than in 2018. [ABSTRACT FROM AUTHOR]

To study the key drivers of China's carbon dioxide emissions, this study combines the structural decomposition analysis method and the input-output subsystem analysis method to construct a decomposition model of the factors influencing the amount of change of the carbon dioxide emissions in China. From the perspective of final demand, this decomposition model (own internal component, feed-back component, spillover component, scale component) is used to analyze the amount of change of China's carbon dioxide emissions in the two periods of 2012–2017 and 2017–2018. Four scenarios are set up to predict the carbon dioxide emissions of China before 2035. The results show that in 2012, 2017 and 2018, the carbon dioxide emissions generated by final demand were respectively 9,018,810, 8,715,226, and 8,803,201 kt, of which the spillover component accounted for the largest share, namely 85.5%, 85.6% and 84.7% respectively. This indicates that the increase of carbon dioxide emissions in China is due to intersectoral pulling effects, and the activities of the Construction sector have had pulling effects on the carbon dioxide emissions of other sectors to a greater extent. The energy intensity effect is the significant influencing factor that inhibits carbon dioxide emissions and economic development is the main driver of the increase in carbon dioxide emissions; this implies that changing the current energy structure is necessary to reduce carbon dioxide emissions. The technical progress scenario (QN) and the energy structure optimization scenario (GN) are predicted to achieve the carbon peak in 2025 and 2030, respectively. [Display omitted] • Total carbon emissions generated by final demand are decomposed into four subsystems, with a structural decomposition. • The spillover components account for the largest proportion of carbon emissions. • Construction industry has pulled the carbon emissions of other sectors to a greater extent. • Under GN and QG scenarios, it is expected to achieve carbon peak in 2025 and 2030 respectively. • Technical improvements are effective in most sectors. [ABSTRACT FROM AUTHOR]

Globally, the transportation industry is one of the leading fields that generate the largest share of greenhouse gas emissions. While undergoing rapid development, countries worldwide aim to solve the problems involved in high energy consumption. Taking China as an example, this paper studies the main factors of carbon emissions in the transport sector and analyses the decoupling states between carbon emission and economic growth, making energy efficiency policies accordingly. In order to better demonstrate the dependence of the economy on the carbon emissions in China's transportation industry comprehensively, combined with the C D production function, this paper develops the decomposition and decoupling technology based on the LMDI approach. Additionally, it quantifies seven effects: energy emission intensity effect, energy structure effect, energy intensity effect, transportation intensity effect, technology state effect, labor input effect and capital input effect. The results show three major points: (1) From 2001 to 2018, the cumulative carbon emissions of China's transportation industry increased by 633.46 million tons, in which the capital input effect is the key factor driving carbon emissions, accounting for 157.70% of the total cumulative increased emissions, followed by energy structure effect at 10.39%. The labor input effect accounted for the smallest proportion at 2.26%. In this case, the technology state effect is the primary factor in restraining carbon emissions. During the study period, it reduced carbon emissions by 292.27 million tons, accounting for 46.14%. To a certain extent, energy intensity effect, transportation intensity effect and energy emission intensity inhibited carbon emissions, representing 16.67%, 5.32% and 2.22%, respectively. (2) During the research period, two decoupling states existed between carbon emissions and economic growth in China's transportation industry, specifically weak decoupling and expansive coupling. (3) The analysis of decomposition and decoupling state of influencing factors of carbon emissions shows that, on the one hand, factors promoting carbon emissions (capital input effect, energy structure effect and labor input effect) hinder the decoupling process. On the other hand, factors restraining carbon emissions (technology state effect, transportation intensity effect, energy intensity effect and energy emission intensity effect) accelerate the decoupling process. The research findings provide a new perspective for achieving carbon emission reduction in the transportation industry and curbing energy consumption growth. [Display omitted] • Develop the carbon emissions decomposition framework in transportation industry. • The framework is composed of LMDI decomposition model and C-D production function. • Fixed asset investment is the main factor influencing carbon emissions in China. • Optimizing capital use and labor efficiency are the keys to reduce carbon emissions [ABSTRACT FROM AUTHOR]