Abstract
In order to achieve China’s target of carbon intensity emissions reduction in 2030, there is a need to identify a scientific pathway and feasible strategies. In this study, we used stochastic frontier analysis method of energy efficiency, incorporating energy structure, economic structure, human capital, capital stock and potential energy efficiency to identify an efficient pathway for achieving emissions reduction target. We set up 96 scenarios including single factor scenarios and multi-factors combination scenarios for the simulation. The effects of each scenario on achieving the carbon intensity reduction target are then evaluated. It is found that: (1) Potential energy efficiency has the greatest contribution to the carbon intensity emissions reduction target; (2) they are unlikely to reach the 2030 carbon intensity reduction target of 60% by only optimizing a single factor; (3) in order to achieve the 2030 target, several aspects have to be adjusted: the fossil fuel ratio must be lower than 80%, and its average growth rate must be decreased by 2.2%; the service sector ratio in GDP must be higher than 58.3%, while the growth rate of non-service sectors must be lowered by 2.4%; and both human capital and capital stock must achieve and maintain a stable growth rate and a 1% increase annually in energy efficiency. Finally, the specific recommendations of this research were discussed, including constantly improved energy efficiency; the upgrading of China’s industrial structure must be accelerated; emissions reduction must be done at the root of energy sources; multi-level input mechanisms in overall levels of education and training to cultivate the human capital stock must be established; investment in emerging equipment and accelerate the closure of backward production capacity to accumulate capital stock.
Highlights
Rapid industrialization has brought increased wealth for society and subsequently improved people’s living standards
Where Ei is the mean energy consumption of the country i; Yi is average Gross Domestic Product (GDP) of the country i; Ki is average capital stock of the country i; Hi is average human capital of the country i; Wi is average winter temperature of the country i; yki is the ratio of agriculture, mining, manufacturing, or service sector in its GDP of the country i; eki is the ratio of coal, oil, natural gas, bioenergy, and primary electricity in its total energy consumption of the country i; vi is the normally distributed random error term; ui is potential un-efficiency term, which follows a truncated distribution with a non-negative expectation
The CPS, NPS, and 450S scenarios are used for energy structure; the low economy scenario (LEC) and high economy scenario (HEC) scenarios for economic structure; the Kruger and HC14 scenarios for human capital; the EIU and HCS scenarios for capital stock; and the β-USA, U4, U5, and U6 scenarios for potential energy efficiency
Summary
Rapid industrialization has brought increased wealth for society and subsequently improved people’s living standards. Yang et al [21] developed transcendental logarithmic functions of production and growth to study China’s energy structure and its evolution, and discovered that human capital investments, capital stock investments, and progressing fossil fuel technologies were helpful towards achieving a win-win scenario of both growth and emissions reduction. Al-Mulali et al, Sheng and Guo [22,23] found that urbanization, to a certain extent, could effectively improve the energy efficiency and thereby reduce carbon emissions Among their studies, researchers postulated many strategies and methods to realize reduction targets, including better energy efficiency or demand structure, more FDI, newer trade models, more environment-related innovations, and higher urbanization rates. Investigators have discussed various pathways for increasing energy efficiency, such as optimizing its energy demand structure, attracting FDI, improving its trade model, encouraging environment-related innovations, and furthering urbanization etc. We describe the parameters for setting up the scenarios in the analysis
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