Abstract
China is facing huge pressure on CO2 emissions reduction. The heavy industry accounts for over 60% of China’s total energy consumption, and thus leads to a large number of energy-related carbon emissions. This paper adopts the Log Mean Divisia Index (LMDI) method based on the extended Kaya identity to explore the influencing factors of CO2 emissions from China’s heavy industry; we calculate the trend of decoupling by presenting a theoretical framework for decoupling. The results show that labor productivity, energy intensity, and industry scale are the main factors affecting CO2 emissions in the heavy industry. The improvement of labor productivity is the main cause of the increase in CO2 emissions, while the decline in energy intensity leads to CO2 emissions reduction, and the industry scale has different effects in different periods. Results from the decoupling analysis show that efforts made on carbon emission reduction, to a certain extent, achieved the desired outcome but still need to be strengthened.
Highlights
The heavy industry mainly produces production and other materials, and serves as the technical basis for the economy
The proportion of heavy industry to total industry sector increased from 48.6% in 1981 to 75.5% in 2001, and to 79.9% in 2016
index decomposition analysis (IDA) has different forms, among which Laspeyres decomposition and Divisia decomposition are the commonly used ones. [22] proposed the Log-Mean Divisia Index Decomposition Method (LMDI), which is one of the most commonly used methods in IDA. [23] reviewed IDA. [24] analyzed the driving forces of energy consumption and carbon emission in China’s cement industry, and the results show that output growth is the most important factor driving energy consumption up, while structural shifts mainly drives energy consumption down
Summary
The heavy industry mainly produces production and other materials, and serves as the technical basis for the economy. Compared with SDA and PDA, the method of IDA has relatively lower requirements for data, especially the results of PDA on the structural effect of output and energy may be inconsistent with reality [21]. In this case, IDA is originally used in the study of industrial energy consumption, and gradually used in energy-environmental analysis. [24] analyzed the driving forces of energy consumption and carbon emission in China’s cement industry, and the results show that output growth is the most important factor driving energy consumption up, while structural shifts mainly drives energy consumption down.
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