基于特征优化贝叶斯分类算法的中国城市碳强度分析

宋文明; 邹嘉龄; 唐志鹏

doi:10.7523/j.ucas.2023.090

中国科学院大学学报 >

2025 , Vol. 42 >Issue 6: 747 - 757

DOI: https://doi.org/10.7523/j.ucas.2023.090

环境科学与地理学

基于特征优化贝叶斯分类算法的中国城市碳强度分析

宋文明 ,
邹嘉龄 ,
唐志鹏

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1 中国科学院地理科学与资源研究所区域可持续发展分析与模拟院重点实验室, 北京 100101;
2 中国科学院大学资源与环境学院, 北京 100049;
3 广东外语外贸大学广东国际战略研究院, 广州 510020

收稿日期: 2023-06-15

修回日期: 2023-12-04

网络出版日期: 2023-12-04

基金资助

中国科学院战略性先导科技专项(A类)(XDA28060301)资助

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Carbon intensity analysis of Chinese cities based on feature optimization Bayesian classification algorithm

SONG Wenming ,
ZOU Jialing ,
TANG Zhipeng

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1 CAS Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China;
2 College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
3 Guangdong Institute for International Strategies, Guangdong University of Foreign Studies, Guangzhou 510020, China

Received date: 2023-06-15

Revised date: 2023-12-04

Online published: 2023-12-04

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摘要

基于改进型的贝叶斯分类算法，通过2005—2019年期间省级能源消费数据核算所得碳强度类型，结合社会经济等多重属性指标，将省级碳强度及其对应的优化属性特征作为样本，训练降尺度至所辖城市单元应用识别其碳强度类型，并与CEADs数据以及传统基于夜间灯光数据拟合得到的碳排放数据进行对比分析。结果显示本文提出的基于特征优化的贝叶斯分类方法具有一定优势。进一步采用该方法揭示中国282座主要城市2005—2019年间的碳强度演化特征，发现绝大部分城市的碳强度均呈现由高向低的转变态势，其中南北区域的城市在碳强度类型和改善状况方面长期存在显著差异，未来中西部资源型城市将是碳强度减排的重点关注地区。此外基于特征优化的贝叶斯分类法也具有良好的可扩展性，可望在区县小尺度的碳强度核算中应用。

关键词： 特征优化; 贝叶斯分类算法; 城市碳强度; 演化

本文引用格式

宋文明 , 邹嘉龄 , 唐志鹏 . 基于特征优化贝叶斯分类算法的中国城市碳强度分析[J]. 中国科学院大学学报, 2025 , 42(6) : 747 -757 . DOI: 10.7523/j.ucas.2023.090

Abstract

Cities serve as the primary hubs for human activities, and the successful realization of China’s “Dual Carbon” goals critically hinges on the effective reduction of carbon emissions in urban areas. However, due to the lack of detailed disaggregated data on energy consumption by source, urban carbon emission accounting has emerged as a crucial research area. This study, based on an enhanced Bayesian classification algorithm, leverages provincial-level energy consumption data from 2005 to 2019. It combines this data with various multi-dimensional attributes, including socioeconomic indicators, to determine carbon intensity types. The approach involves training on optimized attributes corresponding to provincial-level carbon intensity and then downscaling them to identify carbon intensity types at the city level. Comparative analysis with data from the carbon emission assessment database system (CEADs) and traditional methods highlights the advantages of the proposed feature-optimized Bayesian classification method. Furthermore, this method unveils the carbon intensity evolution of 282 major Chinese cities from 2005 to 2019, revealing a notable shift from high to low carbon intensity in the majority of cities. Notably, significant disparities persist in carbon intensity types and improvement trends between cities in the northern and southern regions. In the future, special attention should be paid to carbon intensity reduction efforts in resource-rich cities in central and western China. Additionally, the feature-optimized Bayesian classification method proposed in this study exhibits strong scalability, holding promise for applications at smaller scales, including county-level carbon intensity assessments.

Key words： feature optimization; Bayesian classification algorithm; urban carbon intensity; evolution

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