基于BCF模型的城市人口规模对二氧化碳排放强度的影响

doi:10.6046/zrzyyg.2024003

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全球气候变化形势严峻,实现“双碳”目标意义重大。在控制其他驱动变量的条件下,研究某种因子对二氧化碳排放强度影响效应仍然面临一定挑战。该文首先以中国地级市尺度的二氧化碳排放强度为数据源,采用地理探测器模型和空间自相关方法分别分析二氧化碳排放强度空间异质性和空间相关性;其次,构建贝叶斯因果森林(Bayesian causal forest,BCF)模型,在控制混杂因子的基础上,得到了2005—2020年城市人口规模对二氧化碳排放强度的因果效应,结果呈现出“U”型曲线特征,探究了中国城市人口规模对二氧化碳排放强度的影响机制;最后,基于上述分析,针对不同地区提出合理减排政策建议。研究可为增强城市的可持续发展提供参考依据。

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	田丽君
	晁晖
	王春磊
	焦琳琳

关键词 ：二氧化碳排放强度, 空间异质性, 空间相关性, 贝叶斯因果森林模型, 因果效应, 城市人口规模

Abstract：

Under severe global climate change, achieving carbon peak and neutrality goals is of great significance. Exploring the influence of a specific factor on carbon dioxide (CO₂) emission intensity while controlling other driver variables remains a challenge. With CO₂ emission intensity data at the prefecture-level city scale as a data source, this study analyzed the spatial heterogeneity and spatial correlation of CO₂ emission intensity using the geodetector model and the spatial autocorrelation method, respectively. Using the constructed Bayesian causal forest model, and controlling other drivers, this study obtained the causal effects of China’s urban population size on CO₂ emission intensity from 2005 to 2020, presenting a U-shaped curve. Accordingly, this study explored the influence mechanism of China’s urban population size on CO₂ emission intensity. Based on the above analysis, this study proposed reasonable emission reduction policy recommendations for different regions, serving as a reference to enhance urban sustainable development.

Key words： carbon dioxide (CO₂) emission intensity spatial heterogeneity spatial correlation Bayesian causal forest model causal effect urban population size

收稿日期: 2024-01-02 出版日期: 2025-07-01

ZTFLH:	TP79
	P237

基金资助:国家自然科学基金项目“沙尘气溶胶影响下的地表长波辐射遥感估算”(41801264);河北省自然科学基金项目“沙尘气溶胶影响下的地表温度反演研究”(D202009074);河北省中央引导地方科技发展资金项目“基于深度学习和树种类型的塞罕坝区域人工林地上碳储量时空变化及未来趋势研究”(246Z5901G)

通讯作者: 晁晖(1987-),女,硕士,讲师,主要研究方向为遥感与地理信息。Email: hchao@ncst.edu.cn。

作者简介: 田丽君(2001-),女,学士,主要研究方向为测绘与遥感。Email: tlijun0725@163.com。

引用本文:

田丽君, 晁晖, 王春磊, 焦琳琳. 基于BCF模型的城市人口规模对二氧化碳排放强度的影响[J]. 自然资源遥感, 2025, 37(3): 183-191.
TIAN Lijun, CHAO Hui, WANG Chunlei, JIAO Linlin. Exploring the influence of China’s urban population size on carbon dioxide emission intensity based on the Bayesian causal forest model. Remote Sensing for Natural Resources, 2025, 37(3): 183-191.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2024003 或 https://www.gtzyyg.com/CN/Y2025/V37/I3/183

Fig.1 研究区概况(审图号:GS京(2025)0941号)

Tab.1 变量的选取及来源

Fig.2 2000—2020年研究区二氧化碳排放强度箱线图

Fig.3 二氧化碳排放强度的空间分布(审图号:GS京(2025)0941号)

Fig.4 2000—2020年间研究区每5年分区域q统计值

Fig.5 2000—2020年全局莫兰指数

Fig.6 研究区二氧化碳排放强度LISA聚集图(审图号:GS京(2025)0941号)

Fig.7 整体因果效应值

Fig.8 超大人口规模城市因果效应值分布(审图号:GS京(2025)0941号)

Fig.9 特大人口规模城市因果效应值分布(审图号:GS京(2025)0941号)

Fig.10 大人口规模城市因果效应值分布(审图号:GS京(2025)0941号)

Fig.11 中等人口规模城市因果效应分布(审图号:GS京(2025)0941号)

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