东北三省县区能源消费碳排放时空变化与影响因素研究

张守忠; 吴相利; 张旖琳

PDF(1467 KB)

中国环境科学 ›› 2025, Vol. 45 ›› Issue (10) : 5907-5920.

碳排放控制

东北三省县区能源消费碳排放时空变化与影响因素研究

张守忠^1,2, 吴相利², 张旖琳²

作者信息 +

Spatio-temporal changes and influence factors of energy-related carbon emissions at county-level in Northeast China

ZHANG Shou-zhong^1,2, WU Xiang-li², ZHANG Yi-lin²

Author information +

文章历史 +

摘要

测度了东北三省182个县区能源消费引致的人均碳排放量,采用核密度估计、空间自相关分析、Theil指数、面板数据模型分析了2000年以来人均碳排放量的时空变化并剖析了影响因素的干扰作用,可为确定碳减排目标和细化政策设计提供依据.结果表明:期间县区人均碳排放量均呈持续增长态势,前10a增速快于后10a,农业县的增长快于林业县,城市中心区最慢.县区人均碳排放存在空间正相关关系,总体上呈现为南高北低格局;从发展趋势看县区间的总差异逐渐缩小,城市间差异是引致总差异减小的主要因子,但总差异主要表现为城市内部各县区间的差异;经济增长和人口素质对所有类型县区均有显著影响,其中经济增长是促进碳排放的首要因素,除林业县外提高人口素质均有助于碳减排;居民消费和人口规模对区域总体、城市中心区以及农业县也有重要影响,其中居民消费表现为促进碳排放,区域总体与农业县的人口收缩和城市中心区的人口增长均会抑制碳排放;城镇化促进了区域总体的碳排放,但会抑制林业县的碳排放;技术水平和老龄化的提高均会促进碳排放,但作用较弱;环境规制对所有类型县区均无影响.实现碳减排,需加强低碳产业发展、提升科技人员效能、推进人口收缩与老龄化治理、倡导低碳生活,走因县制宜多策并举的道路.

Abstract

The per capita carbon emissions(PCCE) of energy consumption in 182 county-level districts in Northeast China were measured in this paper. The spatio-temporal change of the PCCE from 2000 to 2020 were analyzed by using kernel density estimation, spatial autocorrelation analysis and Theil index and its influencing factors were discussed by panel data model, which provides a basis for determining carbon emission reduction targets and refining policy design. The results were as follows: The in county-level districts has shown a sustained growth trend since 2000. The growth rate in the first 10 years was faster than that in the second 10 years. Agricultural counties experienced faster growth than forestry counties, whereas urban core districts experienced the slowest growth.A significant spatial positive correlation was observed among county-level districts, exhibiting an overall south-north gradient with higher values in the southern regions and lower values in the northern areas.The relative difference of PCCE among county-level districts gradually decreased, and the differences among prefecture-level districts constituted the main factors behind the reduction in total differences, whereas total differences were mainly manifested as the differences among counties.Economic growth and population quality had significant effects on all types of counties, with economic growth being the primary promoting factor to PCCE. Improving population quality had contributed to PCCE reductions, except in forestry counties. Consumption and population size also had important effects on the regional overall, urban core, and agricultural counties, with consumption contributing to carbon emissions, population shrinkage in the regional overall and in agricultural counties, and population growth in urban core districts suppressing carbon emissions. Urbanization promoted regional carbon emissions overall, but it suppressed emissions in forest counties. Higher technological development and population aging contributed to carbon emissions, albeit to a lesser extent. Environmental regulation had no effect on all county-level districts. To realize carbon emission reduction, it is necessary to strengthen the development of low-carbon industries, enhance the effectiveness of scientific and technological personnel, promote the management of population shrinkage and aging, advocate a low-carbon life and take a county-specific and multi-pronged approach.

关键词

能源消费碳排放 / 人均碳排放 / 时空变化 / 影响因素 / 县区尺度 / 东北三省

Key words

引用本文

EndNote

Ris (Procite)

Bibtex

导出引用

张守忠, 吴相利, 张旖琳. 东北三省县区能源消费碳排放时空变化与影响因素研究[J]. 中国环境科学. 2025, 45(10): 5907-5920

ZHANG Shou-zhong, WU Xiang-li, ZHANG Yi-lin. Spatio-temporal changes and influence factors of energy-related carbon emissions at county-level in Northeast China[J]. China Environmental Science. 2025, 45(10): 5907-5920

中图分类号： X196

参考文献

[1] 苏泳娴,陈修治,叶玉瑶,等.基于夜间灯光数据的中国能源消费碳排放特征及机理 [J]. 地理学报, 2013,68(11):1513-1526. Su Y X, Chen X Z, Ye Y Y, et al. The characteristics and mechanisms of carbon emissions from energy consumption in China using DMSP/ OLS night light imageries [J]. Acta Geographica Sinica, 2013,68(11): 1513-1526.
[2] Kikstra J S, Nicholls Z R J, Smith C J, et al. The IPCC sixth assessment report WGIII climate assessment of mitigation pathways: From emissions to global temperatures [J]. Geoscientific Model Development, 2022,15(24):9075-9109.
[3] Wu L, Sun L, Qi P, et al. Energy endowment, industrial structure upgrading, and CO₂ emissions in China; revisiting resource curse in the context of carbon emissions [J]. Resources Policy, 2021,74, 102329.
[4] 刘华军,邵明吉,吉元梦.中国碳排放的空间格局及分布动态演进——基于县域碳排放数据的实证研究 [J]. 地理科学, 2021,41(11): 1917-1924. Liu H J, Shao M J, Ji Y M. The spatial pattern and distribution dynamic evolution of carbon emissions in China: Empirical study based on county carbon emission data [J]. Scientia Geographica Sinica, 2021,41(11):1917-1924.
[5] Bruckner B, Hubacek K, Shan Y L, et al. Impacts of poverty alleviation on national and global carbon emissions [J]. Nature Sustainability, 2022,5(4):311-320.
[6] Corinne Le Quéré, Korsbakken J I, Wilson C, et al. Drivers of declining CO₂ emissions in 18 developed economies [J]. Nature Climate Change, 2019,9(3):213-219.
[7] Wu Q L, Gu S T. Discerning drivers and future reduction paths of energy-related CO₂ emissions in China: Combining EKC with three- layer LMDI [J]. Environmental Science and Pollution Research, 2021, 28(27):36611-36625.
[8] Zhang Y, Yu Z, Zhang J. Analysis of carbon emission performance and regional differences in China's eight economic regions: Based on the super-efficiency SBM model and the Theil index [J]. PLoS One, 2021, 16(5):e0250994.
[9] 程叶青,王哲野,张守志,等.中国能源消费碳排放强度及其影响因素的空间计量 [J]. 地理学报, 2013,68(10):1418-1431. Cheng Y Q, Wang Z Y, Zhang S Z, et al. Spatial econometric analysis of carbon emission intensity and its driving factors from energy consumption in China [J]. Acta Geographica Sinica, 2013,68(10): 1418-1431.
[10] 高长春,刘贤赵,李朝奎,等.近20年来中国能源消费碳排放时空格局动态 [J]. 地理科学进展, 2016,35(6):747-757. Gao C C, Liu X Z, Li C K, et al. Spatiotemporal dynamics of carbon emissions by energy consumptionin China from 1995 to 2014 [J]. Progressin Geography, 2016,35(6):747-757.
[11] 王少剑,苏泳娴,赵亚博.中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素 [J]. 地理学报, 2018,73(3):414-428. Wang S J, Su Y X, Zhao Y B. Regional inequality, spatial spillover effects and influencing factors of China's city-level energy-related carbon emissions [J]. Acta Geographica Sinica, 2018,73(3):414-428.
[12] 杜海波,魏伟,张学渊,等.黄河流域能源消费碳排放时空格局演变及影响因素——基于DMSP/OLS与NPP/VIIRS夜间灯光数据 [J]. 地理研究, 2021,40(7):2051-2065. Du H B, Wei W, Zhang X Y, et al. Spatio-temporal evolution and influencing factors of energy-related carbon emissions in the Yellow River Basin: Based on the DMSP/OLS and NPP/VIIRS nighttime light data [J]. Geographical Research, 2021,40(7):2051-2065.
[13] 张永年,潘竟虎.基于DMSP/OLS数据的中国碳排放时空模拟与分异格局 [J]. 中国环境科学, 2019,39(4):1436-1446. Zhang Y N, Pan J H. Spatio-temporal simulation and differentiation pattern of carbon emissions in China based on DMSP/OLS nighttime lightdata [J] China Environmental Science, 2019,39(4):1436-1446.
[14] Su, Y X, Chen X Z, Li Y, et al. China’s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines [J]. Renewable & Sustainable Energy Reviews, 2014,35:231-243.
[15] 王少剑,谢紫寒,王泽宏.中国县域碳排放的时空演变及影响因素 [J]. 地理学报, 2021,76(12):3103-3118. Wang S J, Xie Z H, Wang Z H. The spatiotemporal pattern evolution and influencing factors of CO₂ emissions at the county level of China [J]. Acta Geographica Sinica, 2021,76(12):3103-3118.
[16] 莫惠斌,王少剑.黄河流域县域碳排放的时空格局演变及空间效应机制 [J]. 地理科学, 2021,41(8):1324-1335. Mo H B, Wang S J. Spatio-temporal evolution and spatial effect mechanism of carbon emission at county level in the Yellow River Basin [J]. Scientia Geographica Sinica, 2021,41(8):1324-1335.
[17] 陈亮,张楠,王一帆,等.京津冀地区碳排放强度变化驱动因素及归因分析 [J]. 中国环境科学, 2023,43(8):4382-4394. Chen L, Zhang N, Wang Y F, et al. Driving factors and attribution analysis of carbon emission intensity change in Beijing-Tianjin-Hebei region [J]. China Environmental Science, 2023,43(8):4382-4394.
[18] 王正,周侃,樊杰.西部地区县域碳排放核算及主体功能区解析——以四川省为例 [J]. 生态学报, 2022,42(21):8664-8674. Wang Z, Zhou K, Fan J. County-level carbon emission accounting and Major Function Oriented Zones in western regions:Taking Sichuan Province as anexample [J]. Acta Ecologica Sinica, 2022,42(21):8664- 8674.
[19] Apergis N. Environmental Kuznets curves: New evidence on both panel and country-level CO₂ emissions [J]. Energy Economics, 2016, 54:263-271.
[20] Liu J H, Li M X, Ding Y T. Econometric analysis of the impact of the urban population size on carbon dioxide(CO₂) emissions in China [J]. Environment, Development and Sustainability, 2021,23(12):18186- 18203.
[21] 刘辉煌,李子豪.中国人口老龄化与碳排放的关系——基于因素分解和动态面板的实证分析 [J]. 山西财经大学学报, 2012,34(1):1-8. Liu H H, Li Z H. The relationship between Chinese population aging and CO₂ emission: Empirical analysis from decomposition and dynamic panel estimation [J]. Joumal of Shanxi Finance and Economics University, 2012,34(1):1-8.
[22] Dalton M, O'Neill B, Prskawetz A, et al. Population aging and future carbon emissions in the United States [J]. Energy Economics, 2008,30 (2):642-675.
[23] Wang Q, Li L J. The effects of population aging, life expectancy, unemployment rate, population density, per capita GDP, urbanization on per capita carbon emissions [J]. Sustainable Production and Consumption, 2021,28:760-774.
[24] 冯冬,李健.我国三大城市群城镇化水平对碳排放的影响 [J]. 长江流域资源与环境, 2018,27(10):2194-2200. Feng D, Li J. Impacts of urbanization on carbon dioxide emissions in the three Urban agglomerations of China [J]. Resources and environment in the Yangtze Basin, 2018,27(10):2194-2200.
[25] Golley J, Meng X. Income inequality and carbon dioxide emissions: The case of Chinese urban households [J]. Energy Economics, 2012,34(6):1864-1872.
[26] 刘金培,宋晓霞,陈华友,等.中国人均碳排放影响因素的长期均衡与因果动态关系研究——基于结构突变ARDL-VECM模型的实证分析 [J]. 运筹与管理, 2019,28(9):57-65. Liu J P, Song X X, Chen H Y. Study on the long-term equilibrium and causality of the influencing factors of China’s per capita carbon-based on structural break ARDL model and VECM model [J]. Operations Researchand Managment Science, 2019,28(9):57-65.
[27] Wang C J, Wang F, Zhang X L. Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang [J]. Renewable and Sustainable Energy Reviews, 2016,67:51-61.
[28] 张旭亮,周思敏.中国县域人口收缩空间格局与影响因素 [J]. 经济地理, 2023,43(7):42-51. Zhang X L, Zhou S M. Evolution of regional population decline and its driving factors at the county level in China [J]. Economic Geography, 2023,43(7):42-51.
[29] 辛奕霖,刘艳军,柳力玮.中国老工业城市人口增长与收缩对碳排放强度的影响效应 [J]. 地理研究, 2024,43(3):558-576. Xin Y L, Liu Y J, Liu L W. The influence of population growth and shrinkage on carbon emission intensity in old industrial cities of China [J]. Geographical Research, 2024,43(3):558-576.
[30] Yang X, Jin K, Duan Z, et al. Spatial-temporal differentiation and influencing factors of carbon emission trajectory in Chinese cities- A case study of 247 prefecture-level cities [J]. Science of The Total Environment, 2024,928,172325.
[31] 关伟,李书妹,许淑婷.东北三省碳排放时空演变多尺度分析——基于DMSP/OLS夜间灯光数据 [J]. 生态经济, 2022,38(11):19-26. Guan W, Li S M, Xu S T. Multiscale spatio-temporal characteristics of carbon emissions in Northeast Chinabased on DMSP/OLS nighttime light data [J]. Ecological Economy, 2022,38(11):19-26.
[32] 杨盛东,杨旭,吴相利,等.环境规制对区域碳排放时空差异的影响——基于东北三省32个地级市的实证分析 [J]. 环境科学学报, 2021,41(5):2029-2038. Yang S D, Yang X, Wu X L, et al. Impact of environmental regulation on spatial-temporal differences of regional carbon emisions: Empiricalanalysis based on 32 prefecture level cities in Northeast China [J]. Acta Scientiae Circumstantiae, 2021,41(5):2029-2038.
[33] 杨迪,杨旭,吴相利,等.东北地区能源消费碳排放时空演变特征及其驱动机制 [J]. 环境科学学报, 2018,38(11):4554-4564. Yang D, Yang X, Wu X L, et al. Spatio-temporal evolution characteristics of carbon emissions from energy consumption and its driving mechanism in Northeast China [J]. Acta Scientiae Circumstantiae, 2018,38(11):4554-4564.
[34] 王勤升,王宁宁,韩欢欢.东北三省碳排放脱钩系数测定与影响因素研究 [J]. 北京信息科技大学学报(自然科学版), 2024,39(1):76-82. Wang Q S, Wang N N, Han H H. Decoupling index measurement and influencing factors of carbon emission in the three northeastern provinces of China [J]. Journal of Beijing Information Science & Technology University(Science and Technology Edition), 2024,39(1):76-82.
[35] 马晓君,董碧滢,于渊博,等.东北三省能源消费碳排放测度及影响因素 [J]. 中国环境科学, 2018,38(8):3170-3179. Ma X J, Dong B Y, Yu Y B, et al. Measurement of carbon emissions from energy consumption in three northeastern provinces and its driving factors [J]. China Environmental Science, 2018,38(8):3170-3179
[36] 王康,李志学,周嘉.环境规制对碳排放时空格局演变的作用路径研究——基于东北三省地级市实证分析 [J]. 自然资源学报, 2020,35(2):343-357. Wang K, Li Z X, Zhou J. The effects of environmental regulation on spatio-temporal carbon emissions patterns:Empirical analysis of prefecture-level cities in Northeast China [J]. Journal of Natural Resources, 2020,35(2):343-357.
[37] 王正,周侃,樊杰.市—县尺度能源消费排放核算方法研究进展 [J]. 地理科学进展, 2023,42(7):1406-1419. Wang Z, Zhou K, Fan J. Progress of research on energy-related carbon emission accounting methods at the prefectural and county levels [J]. Progress in Geography, 2023,42(7):1406-1419.
[38] 施开放.多尺度视角下的中国碳排放时空格局动态及影响因素研究——基于DMSP-OLS夜间灯光遥感数据的分析 [D]. 上海:华东师范大学, 2017. Shi K F. A multiscale analysis on spatiotemporal pattern of carbon emissions and its impact factors in China using DMSP-OLS data [D]. Shanghai: East China Normal University, 2017.
[39] Wu Y Z, Shi K F, Chen Z Q, et al. An improved timeseries DMSP- OLS-like data (1992-2022) in China by integrating DMSP-OLS and SNPP-VIIRS [DB/OL]. 2021.https://doi.org/10.7910/DVN/GIYGJU, Harvard Dataverse, V4.
[40] 田泽源,董治,董治宇,等.基于面板数据模型关中平原城市群交通碳排放峰值预测与脱钩分析 [J]. 中国环境科学, 2024,44(10): 5901-5911. Tian Z Y, Dong Z, Dong Z Y, et al. Predicting and decoupling analysis of transportation peak carbon emissions in Guanzhong Plain urban agglomeration based on panel data modeling [J]. China Environmental Science, 2024,44(10):5901-5911.
[41] Xu B, Lin B. Assessing CO₂ emissions in China’s iron and steel industry: A dynamic vector autoregression model [J]. Applied Energy, 2016,161:375-386.
[42] 范建双,周琳.城镇化及房地产投资对中国碳排放的影响机制及效应研究 [J]. 地理科学, 2019,39(4):644-653. Fan J S, Zhou L. The mechanism and effect of urbanization and real estate investment on carbon emissions in China [J]. Scientia Geographica Sinica, 2019,39(4):644-653.
[43] 丁斐,庄贵阳,刘东.环境规制、工业集聚与城市碳排放强度——基于全国282个地级市面板数据的实证分析 [J]. 中国地质大学学报(社会科学版), 2020,20(3):90-104. Ding F, Zhuang G Y, Liu D. Environmental regulation, industrial agglomeration and urban carbon emission intensity: Empirical analysis based on panel data of 282 prefecture-level cities in China [J]. Journal of China University of Geosciences (Social Sciences Edition), 2020, 20(3):90-104.
[44] Liu W, Luo Z, Xiao D. Age structure and carbon emission with climate-extended STIRPAT model: A cross-country analysis [J]. Frontiers in Environmental Science, 2022(9):719168.