Carbon Emissions Drivers and Reduction Strategies in Jiangsu Province

Abstract

China embarked on the implementation of a comprehensive national strategy aimed at reducing greenhouse gas (GHG) emissions in 2020, with ambitious targets to achieve peak emissions by 2030 and attain carbon neutrality by 2060. Given the challenges, thoroughly investigating China’s carbon emissions status and outlining reduction pathways for each province is crucial. Based on calculating carbon emissions in Jiangsu Province, this article uses the Logarithmic Mean Divisia Index (LMDI) model to decompose and analyze the factors that affect carbon emissions. This article starts with provincial carbon emissions to find the core factors and then narrows the research scope to the city level to make carbon reduction policies more targeted. When decomposing carbon emissions, this article not only selects energy structure, energy efficiency, economic development, population size, and industrial structure factors commonly used in the LMDI model but also adds the factor of external electricity to research indirect carbon emissions. The final conclusions mainly include the following: firstly, the economic development and energy efficiency factors in Jiangsu Province are the core influencing factors for carbon emissions. The former promotes carbon emissions, while the latter reduces it, and the impact gradually weakens. The energy structure and industrial structure have reduced carbon emissions, while population size and electricity transfer have increased carbon emissions. Furthermore, notable disparities in carbon emissions exist among cities within Jiangsu Province, with varying impacts stemming from diverse driving factors. Upon comprehensive evaluation of the collective carbon reduction impact, Nanjing and Suzhou emerge as cities with a low contribution rate attributable to their industrial structure. Wuxi, Zhenjiang, and Xuzhou, on the other hand, exhibit a low contribution rate associated with their energy structure. Taizhou and Nantong demonstrate a low contribution rate in energy efficiency, while Changzhou, Huai’an, and Yangzhou display a low contribution rate in both industry and energy structure. Lianyungang, Suqian, and Yancheng present low contribution rates across all three factors. Recognizing the distinctive energy and industrial profiles of each city, governmental policies should be formulated with uniformity, fairness, and flexibility, effectively realizing the dual carbon objectives.