Journal
URBAN CLIMATE
Volume 44, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.uclim.2022.101226
Keywords
CO2 emissions; Pearl River Delta; Regional imbalanced development; Scenario prediction; Back propagation neural network
Funding
- National Natural Science Foundation of China [72002018]
- Ministry of Education Humanities and Social Sciences Fund [17XJC630001]
- Youth Innovation Team of Shaanxi Universities [21JP009]
- Innovation Capacity Support Plan of Shaanxi Province [2020KJXX-054, 2022KRM012]
- Major projects of Shaanxi Social Science Federation [2021HZ0777]
- Fundamental Research Funds for the Central Uni [300102230613, 300102231639]
- Social Science Planning Fund of Shaanxi Province [2020R028]
- Social Science Planning Fund of Xi'an City [22GL92]
- Natural Science Basic Research Plan in Shaanxi Province of China [2019JQ-500]
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This paper studies the carbon emissions in the Pearl River Delta urban agglomeration and explores the impact of development disparities on low-carbon achievement. By using BPNN and scenario analysis, it predicts the CO2 emissions of four different types of cities and identifies critical factors at the urban agglomeration level. The research findings indicate that the urbanization rate is the major contributor to emissions increase in Guangzhou and potential development cities, while the growth of industrial structure is critical for Shenzhen. Thus, it suggests prioritizing these factors when designing reduction policies and recommending specific measures for local governments and enterprises.
The Pearl River Delta urban agglomeration (PRD) is the main body responsible for achieving carbon neutrality in China. However, high carbon dioxide (CO2) emissions are significantly affected by internal development disparities, hindering the realization of low carbon. Accord-ingly, considering the imbalanced development, the PRD is divided into four types: Guangzhou, Shenzhen, active development cities (ADCs), and potential development cities (PDCs). On this basis, this paper employs a back propagation neural network (BPNN) to establish a set of net-works to predict the CO2 emissions of four city types. Then, in combination with scenario analysis, the BPNN is extended to explore critical factors at the urban agglomeration level. The findings show that the urbanization rate is the major contributor to increasing emissions in Guangzhou and the PDCs, whereas the growth of the industrial structure is the critical factor for Shenzhen. These factors should be given priority when designing reduction policies. Thus, spe-cific and targeted countermeasures for local governments and enterprises are ultimately recom-mended. Overall, this paper not only provides a novel perspective of regional imbalances for emission mitigation but also bears significance to policies and actions for urban agglomerations, which are conducive to realizing emission reduction targets and achieving low-carbon development.
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