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Socioeconomic driving forces and scenario simulation of CO2 emissions for a fast-developing region in China

Author:
Wang, Shaojian, Wang, Jieyu, Li, Shijie, Fang, Chuanglin, Feng, Kuishuang
Source:
Journal of cleaner production 2019 v.216 pp. 217-229
ISSN:
0959-6526
Subject:
assets, carbon dioxide, data collection, energy, greenhouse gas emissions, industrialization, issues and policy, models, population growth, urbanization, China
Abstract:
Guangdong is one of China's fastest developing provinces, and thus faces the challenge of reducing CO2 emissions whilst fostering economic growth. In order to advance the goal of developing a low-carbon economy in China, this study explored influencing factors, change trends, and reduction potential in relation to CO2 emissions in Guangdong Province using a provincial dataset covering the period 1995 to 2014. We used extended STIRPAT (stochastic impacts by regression on population, affluence, and technology) model and the technique of ridge regression in order to identify key influencing factors behind Guangdong's CO2 emissions. We also forecasted emission trends and estimated reduction potential in the period 2015–2030. Our empirical results indicate that economic development, population growth, urbanization, fixed asset investment, and industrialization level all positively affected CO2 emissions during the period studied, while the impacts of energy consumption structure and technology progress were found to be negative. Scenario simulations reveal that the aggregate CO2 emissions of Guangdong Province will increase continuously up to 2030 under all of the twenty scenarios developed and tested in this study; despite this, we argue that the potential for emission reduction remains large. An optimal scenario was identified after cross-sectional comparison. Our analysis casts new light on the importance of exploring socioeconomic determinants and reduction potential in relation to emissions, in order to plan for rapidly developing regions like Guangdong. The empirical findings have significant implications for the Chinese government in implementing policy measures in order to promote low-carbon development.
Agid:
6286316