Analysis and Measurement of Carbon Emission Aggregation and Spillover Effects in China: Based on a Sectoral Perspective

Abstract

Faced with the environmental pressure of global warming, China has achieved certain results in emission reduction, but this needs to be completed more efficiently. Therefore, this article conducts a more comprehensive and in-depth study of China’s carbon emissions from the perspective of the development of national economic sectors and taps the potential for emission reduction in various sectors. Taking into account the adjustment of the national economic sector and the current status of carbon emissions, the study period was from 2003 to 2017. The logarithmic mean Divisia index (LMDI) method was used to measure and analyze the impact of seven factors, including urban construction conditions, on the carbon emissions of various sectors. According to the commonalities and differences of the impacts, 42 sectors were aggregated into four categories. At the same time, the input–output structure decomposition analysis (IO–SDA) model was used to analyze the spillover effects of intersectoral carbon emissions. According to the research results, based on the characteristics of the four types of sectors, and fully considering the spillover effects, the improvement of life cycle management to control energy consumption in the entire supply chain was taken as the leading idea. Moreover, combined with the actual development situation, four types of sectoral carbon emission reduction paths and optimization strategies are proposed to establish a more sustainable demand structure in order to achieve emission reduction.

A Calculation Model of Carbon Emissions Based on Multi-Scenario Simulation Analysis of Electricity Consumption

“…In other words, key indirect variables, such as GDP, population, and other factors, are selected to analyze their quantitative relationship with carbon emissions and predict the peak of CO 2 emissions. The main methods include the Kaya identity [7], the STIRPAT model [8], the environment Kuznets curve (EKC) [9], the LMDI model [10,11], etc. Fan et al (2020) studied influencing factors and countermeasures of industrial carbon emissions in Hebei Province based on the Kaya model [7].…”

Transportation Carbon Emissions from a Perspective of Sustainable Development in Major Cities of Yangtze River Delta, China

“…Generally, carbon emission estimation in the existing literature only considers fossil fuels, and few studies consider electricity as a carbon calculation source [25]. From an ecological perspective, transportation considerations with electricity instead of fossil fuels are becoming increasingly common [26].…”

“…An electric vehicle license plate's application time is much shorter and less complicated than for traditional vehicles in Beijing and Shanghai. It can be expected that by 2030, electricity will account for more than 70% of the energy consumption in the transportation sectors of YRD [25]. However, it should be emphasized that the high consumption rate of electricity can only represent a potential energy greenness (low-carbon degree), because China's current electricity is mostly delivered by thermal power plant but less from other clean energy sources (e.g., solar, wind, nuclear, etc.).…”