Carbon emission from the electric power industry in Jiangsu province, China: Historical evolution and future prediction

Abstract

This paper takes Jiangsu as an example to measure the carbon emissions from the electric power industry from 2002 to 2017, builds an extended STIRPAT model to quantify its driving factors, and uses the Monte Carlo method to simulate the evolution of carbon emissions in multiple scenarios from 2018 to 2030. The results show that: (1)Population scale, urbanization level, GDP per capita, industrial added value, and electricity consumption intensity promote the increase of carbon emissions in the electric power industry. (2)Trade openness and the transmission level of other provinces play a role in reducing carbon emissions. (3)Under the baseline scenario and the green development scenario, the carbon emissions of the electric power industry have shown a continuous growth trend, but the growth rate of carbon emissions has slowed down significantly under the green development scenario.

Spatial-Temporal Evolution Characteristics of Industrial Carbon Emissions in China’s Most Developed Provinces from 1998–2013: The Case of Guangdong

“…For example, from 1998 to 2013, the industrial output values and carbon emissions of the manufacture of communication equipment, computers, and other electronic equipment rose rapidly, with the average annual growth rates reaching 18.56% and 15.82% respectively, but the industrial CEI was low, which was continuously decreased from only 0.09 ton/10 thousand yuan in 1998 to only 0.06 ton/10 thousand yuan in 2013, and the comprehensive benefits greatly exceeded those of other industries. Similar industries include technology-intensive industries of manufacture of special-purpose machinery (35), manufacture of automobiles (36), manufacture of electrical machinery and equipment (37), etc. With 6 representative advanced manufacturing industries (manufacture of general-purpose machinery, manufacture of special-purpose machinery, manufacture of automobiles, manufacture of electrical machinery and equipment, manufacture of communication equipment, computers, and other electronic equipment as well as the manufacture of instruments and meters) as examples, there were 7832 enterprises increased in PRD Region by 2013 in comparison with the situation in 1998, with the increased number accounting for 94.03% of that in the whole province.…”

“…At present, the influencing elements of ICE in China and the carbon reduction approaches have become another hot spot for research, which attempts to achieve energy conservation and emission reduction without affecting economic development. For example, Yuan and Zhao investigated the drivers of CO 2 emissions in energy-intensive industries from China during 2005 to 2010, believing that external inputs have mainly contributed to increasing the carbon emitted, while the demand change was the key to reducing carbon [34]; Zhang et al analyzed the drivers contributing to CO 2 emissions from electric power industry in Jiangsu Province during 2002-2017 by building extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, and projected carbon emissions from 2018 to 2030 through the Monte Carlo method [35]; by using the STIRPAT model and building a multi-constraint input/output optimization model, Wang et al explored how to update the industrial structure and achieve the goal of low-carbon transformation for China's industry [36]; Zhang et al investigated the drivers of CO 2 emissions of Liaoning's 41 industrial sub-industries by using the LMDI model and believed that energy efficiency dominates in reducing carbon emissions [37]; Zhang et al analyzed the impact elements of ICE of Nanjing through STIRPAT model and believed that the most influencing factor of Nanjing's ICE was the industrial energy structure, followed by the total population [38]; Kong et al investigated various impact elements of CO 2 emissions in China by LMDI model and provided five ideas for reducing carbon emissions [39].…”

Decomposition of drivers and identification of decoupling states for the evolution of carbon emissions from energy consumption in China

“…Aziz and Chowdhury (2022) similarly combined the STIRPAT model with a ridge regression method to investigate the drivers of GHG emissions in the agricultural sector of Bangladesh. Zhang et al (2022) combined the extended STIRPAT model with a Monte Carlo simulation method to simulate the drivers of carbon emissions in the power sector of Jiangsu province under multiple scenarios for 2018-2030. Besides, some scholars have also used other econometric methods to study the drivers of carbon emission evolution at regional or industry levels.…”