Abstract:

It is of great significance to scientifically identify the factors influencing the growth of carbon emissions and accurately predict the peak of carbon emissions and the time to reach the peak of carbon emissions, so that China can realize the goal of “double carbon” as scheduled. This paper selects Guangdong, Hong Kong and Macao Greater Bay Area as the research object and analyzes it based on the NPP-VIIRS nighttime lighting data from 2010 to 2022. Firstly, it estimates the carbon emissions of the 11 cities in Guangdong, Hong Kong and Macao Greater Bay Area, and adopts the Moran index to characterize the spatial and temporal heterogeneity of the carbon emissions among the cities; secondly, it constructs the extended STIRPAT model based on the regional characteristics and thoroughly analyzes the carbon emissions and their influencing factors; finally, it constructs the extended STIRPAT model based on the regional characteristics. In-depth analysis of carbon emissions and its influencing factors; finally, and set three scenarios to predict the changes of carbon emissions from 2022 to 2050. The results show that: (1) the overall carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area show a fluctuating upward trend; (2) carbon emissions show significant spatial clustering characteristics; (3) population size, urbanization and R&D investment are positive influences on carbon emissions in the region; GDP per capita, industrial structure and the level of openness to the outside world negatively influence carbon emissions; (4) in the baseline scenario and the fast-growth scenario, carbon emissions cannot be effectively controlled, while in the carbon peak scenario, carbon emissions are not effectively controlled, while in the carbon peak scenario, carbon emissions are not effectively controlled. (4) Carbon emissions cannot be effectively controlled under the baseline and rapid growth scenarios, while carbon emissions can be effectively controlled under the peak carbon scenario. This paper is of great significance in promoting the low-carbon transition of the Guangdong-Hong Kong-Macao Greater Bay Area and realizing the goals of carbon peaking and carbon neutrality.

Key words: Guangdong, Hong Kong and Macao Greater Bay Area, carbon emission, Moran Index, STIRPAT, scenario prediction

摘要： 科学识别碳排放增长的影响因素，准确预测碳排放峰值及碳达峰时间，对中国如期实现“双碳”目标具有重要意义。本文选取粤港澳大湾区作为研究对象，并基于2010-2022年NPP-VIIRS夜间灯光数据进行分析，首先，估算得出粤港澳大湾区11个城市的碳排放量，并采用莫兰指数测算各城市之间的碳排放量的时空异质性特征；其次，基于区域特色构建了扩展的STIRPAT模型，深入分析碳排放及其影响因素；最后，设定三种情景，预测2022-2050年的碳排放变化情况。结果显示：（1）粤港澳大湾区整体碳排放呈现波动上升趋势；（2）碳排放呈现显著的空间集聚特征；（3）人口规模、城市化和研发投入为为该区域碳排放正向影响因素；人均GDP、产业结构以及对外开放水平负向影响因素；（4）在基准情景和快速增长情景下，碳排放并不能得到有效控制，而在碳达峰情景，碳排放才能得到有效控制。本文对于促进粤港澳大湾区低碳转型，实现碳达峰和碳中和目标具有重要意义。

关键词: 粤港澳大湾区、碳排放、莫兰指数、STIRPAT、情景预测