Research on Carbon Peak Prediction of Various Prefecture-Level Cities in Jiangsu Province Based on Factors Influencing Carbon Emissions

Yu Wang and Ling Dong ()
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Yu Wang: School of Architectural Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Ling Dong: School of Architecture, Nanjing Tech University, Nanjing 211816, China

Sustainability, 2024, vol. 16, issue 16, 1-24

Abstract: Jiangsu Province is a region with a high concentration of economy and population in China, as well as a spatial unit with relatively concentrated carbon emissions. It is also the pioneer in achieving carbon peak. Analyzing the factors influencing carbon emissions and predicting the peak year of carbon emissions will help Jiangsu Province clarify the direction of carbon reduction and take the lead in achieving carbon peak. This article selects relevant data from Jiangsu Province from 2005 to 2020, uses the STIRPAT model to analyze the influencing factors of carbon emissions in Jiangsu Province, predicts the carbon emissions and peak times of 13 prefecture-level cities in four different scenarios, and constructs a carbon peak prediction model to calculate the carbon peak pressure, carbon emission reduction potential, and carbon peak driving force of each prefecture-level city. Research has found that the population size, wealth level, technological level, urbanization level, and industrial structure have significant impacts on carbon emissions in Jiangsu Province. The prediction results for carbon peak in 13 prefecture-level cities indicate that Nantong, Huai’an, Yancheng, Suzhou, Nanjing, and Wuxi can achieve carbon peak before 2030 in all four scenarios. Changzhou, Xuzhou, Yangzhou, Taizhou, Suqian, Lianyungang, and Zhenjiang are all able to achieve carbon peak between 2025 and 2029 under the low-growth, slow-consumption scenario (P2G2E1) and low-growth, fast-consumption scenario (P2G2E2), but they cannot achieve carbon peak before 2030 under the high-growth, slow-consumption scenario (P1G1E1) and high-growth, fast-consumption scenario (P1G1E2). Finally, based on the carbon peak prediction model, the prefecture-level cities are classified, and differentiated carbon peak implementation paths for different types of prefecture-level cities are proposed.

Keywords: STIRPAT model; influencing factors; carbon peak; scenario analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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