Scenario prediction and critical factors of CO2 emissions in the Pearl River Delta: A regional imbalanced development perspective

Abstract

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. Accordingly, 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 networks 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, specific and targeted countermeasures for local governments and enterprises are ultimately recommended. 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.