Multi-scale Analysis and Synergistic Scenario Simulation of Pollution and Carbon Reduction Efficiency in Guangdong-Hong Kong-Macao Greater Bay Area

Zhengyong Chen, Zhanjie Wen
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Abstract

In the context of China’s “double carbon” goal, pollution and carbon reduction is a consensus. As a demonstration area and model area for China’s development, how to take the lead in realizing the synergistic improvement of pollution and carbon reduction and embark on a green and low-carbon development path with Chinese characteristics is a common concern of the scientific community and the public. However, each city in Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is different regarding resource endowment, energy structure, development conditions, and technical level. The efficiency, ability, and potential of pollution and carbon reduction must differ. The mission objectives, methods, and methods of promoting the “double carbon” work are also different. Only by considering it from the perspective of system collaboration can the “double carbon” work be safe and sustainable. The study proposes that we can, from the dynamic perspective of the production network and industrial transfer, integrate multi-source and multi-mode data and use a multi-scale evaluation method to analyze the multi-dimensional features and driving factors of the interaction effect of pollution and carbon reduction in GBA. The research results can help cities in GBA to understand their weak links in pollution and carbon reduction in a timely, comprehensive, and accurate manner. In addition, this study is conducive to providing decision-making reference for China to formulate regional synergistic effects.
粤港澳大湾区污染减排效率多尺度分析及协同情景模拟
在中国“双碳”目标背景下,污染和减碳是共识。作为中国发展的示范区和示范区,如何率先实现污染治理和碳减排协同改善,走出一条中国特色的绿色低碳发展道路,是科学界和公众共同关心的问题。然而,粤港澳大湾区各城市在资源禀赋、能源结构、发展条件、技术水平等方面存在差异。污染和碳减排的效率、能力和潜力必须有所不同。推进“双碳”工作的任务目标、方法和方法也有所不同。只有从系统协同的角度来考虑,“双碳”工作才能安全、可持续。研究提出,可以从生产网络和产业转移的动态视角,整合多源、多模式数据,采用多尺度评价方法,分析大湾区污染与碳减排交互效应的多维特征及驱动因素。研究结果可以帮助大湾区城市及时、全面、准确地了解其污染和碳减排的薄弱环节。此外,本研究有助于为中国制定区域协同效应提供决策参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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