Citation: | YANG Q,PENG R H,LIU X X,et al.Study on influencing factors of provincial carbon emission based on geographically weighted regression[J].Journal of Environmental Engineering Technology,2023,13(1):54-62 doi: 10.12153/j.issn.1674-991X.20210523 |
Carbon emission reduction has become an urgent problem to be solved in the construction of ecological civilization in the new era. Carbon emission is closely related to regional spatial location. In order to better promote carbon peak and carbon emission reduction, regional differences and trend analysis of carbon emission influencing factors have become the focus of carbon emission reduction analysis. Through the geographically weighted regression method, the impact of population factors, energy consumption and urbanization construction and development on the carbon emission in 30 provinces of China from 2007 to 2017 were studied, and then the correlation between carbon emission and regional socioeconomic development was revealed. The results showed that the spatial aggregation of carbon emissions was strong, and the spatial distribution patterns of various influencing factors were quite different. Among them, the increase of total power consumption and total fossil energy consumption had the greatest positive impact on carbon emissions, and population size also had a certain positive role in promoting carbon emissions. The total consumption of urban public vehicles and main building materials had no significant impact on carbon emissions, showing an unstable positive and negative correlation. Some suggestions were provided for China's carbon emission reduction, including adjusting the energy consumption structure, further improving clean energy technology innovation, integrating urbanization and carbon emission reduction in stages, and increasing the support for green consumption, green building, and green travel.
[1] |
余碧莹, 赵光普, 安润颖, 等.碳中和目标下中国碳排放路径研究[J]. 北京理工大学学报(社会科学版),2021,23(2):17-24.
YU B Y, ZHAO G P, AN R Y, et al. Research on China's CO2 emission pathway under carbon neutral target[J]. Journal of Beijing Institute of Technology (Social Sciences Edition),2021,23(2):17-24.
|
[2] |
WANG C J, WANG F. China can lead on climate change[J]. Science,2017,357:764.
|
[3] |
YANG Y, QU S, CAI B, et al. Mapping global carbon footprint in China[J]. Nature Communications,2020,11(1):2237. doi: 10.1038/s41467-020-15883-9
|
[4] |
张宁, 贺姝峒, 王军锋, 等.碳交易背景下天津市电力行业碳排放强度与基准线[J]. 环境科学研究,2018,31(1):187-193.
ZHANG N, HE S T, WANG J F, et al. Carbon intensity and benchmarking analysis of power industry in Tianjin under the context of cap-and-trade[J]. Research of Environmental Sciences,2018,31(1):187-193.
|
[5] |
张晓梅, 庄贵阳.中国省际区域碳减排差异问题的研究进展[J]. 中国人口·资源与环境,2015,25(2):135-143.
ZHANG X M, ZHUANG G Y. China provincial carbon emissions differences research progress and prospect[J]. China Population, Resources and Environment,2015,25(2):135-143.
|
[6] |
WANG Y N, ZHAO T. Impacts of energy-related CO2 emissions: evidence from under developed, developing and highly developed regions in China[J]. Ecological Indicators,2015,50:186-195. doi: 10.1016/j.ecolind.2014.11.010
|
[7] |
XU X L, XU X F, CHEN Q, et al. The research on generalized regional "resource curse" in China's new normal stage[J]. Resources Policy,2016,49:12-19. doi: 10.1016/j.resourpol.2016.04.002
|
[8] |
马忠, 耿文婷.基于假设抽取法的中国区域间碳排放关联分析[J]. 环境科学研究,2020,33(2):312-323.
MA Z, GENG W T. Correlation analysis of regional carbon emission in China based on the hypothetical extraction method[J]. Research of Environmental Sciences,2020,33(2):312-323.
|
[9] |
邬娜, 傅泽强, 王艳华, 等.“一带一路”沿线国家碳排放EKC检验及脱钩关系分析[J]. 环境工程技术学报,2018,8(6):671-678. doi: 10.3969/j.issn.1674-991X.2018.06.089
WU N, FU Z Q, WANG Y H, et al. EKC test and decoupling analysis of carbon emissions in countries along the “One Belt and One Road”[J]. Journal of Environmental Engineering Technology,2018,8(6):671-678. doi: 10.3969/j.issn.1674-991X.2018.06.089
|
[10] |
李艳红.山东省碳减排系统仿真及政策优化研究[J]. 环境工程技术学报,2020,10(1):150-159. doi: 10.12153/j.issn.1674-991X.20190063
LI Y H. System simulation and policy optimization of carbon emission reduction in Shandong Province[J]. Journal of Environmental Engineering Technology,2020,10(1):150-159. doi: 10.12153/j.issn.1674-991X.20190063
|
[11] |
崔盼盼, 赵媛, 郝丽莎, 等.中国能源行业碳排放强度下降过程中的省际减排成效评价[J]. 地理研究,2020,39(8):1864-1878. doi: 10.11821/dlyj020190688
CUI P P, ZHAO Y, HAO L S, et al. Evaluation on the effectiveness of provincial emission reduction in the process of carbon emission intensity decline in China's energy industry[J]. Geographical Research,2020,39(8):1864-1878. doi: 10.11821/dlyj020190688
|
[12] |
刘彤, 潘晓梦, 宁欣.城镇化对建筑业碳排放的影响效应分析[J]. 工程管理学报,2020,34(6):43-48.
LIU T, PAN X M, NING X. Research on the influence of urbanization on carbon emissions in the construction industry[J]. Journal of Engineering Management,2020,34(6):43-48.
|
[13] |
刘丰, 王维国.人口年龄结构变动对碳排放的影响: 基于生育率和预期寿命的跨国面板数据[J]. 资源科学,2021,43(10):2105-2118. doi: 10.18402/resci.2021.10.14
LIU F, WANG W G. The impact of age structure on carbon emissions: based on a cross-country panel data of fertility rate and life expectancy[J]. Resources Science,2021,43(10):2105-2118. doi: 10.18402/resci.2021.10.14
|
[14] |
王雅晴, 谭德明, 张佳田, 等.我国城市发展与能源碳排放关系的面板数据分析[J]. 生态学报,2020,40(21):7897-7907.
WANG Y Q, TAN D M, ZHANG J T, et al. The impact of urbanization on carbon emissions: analysis of panel data from 158 cities in China[J]. Acta Ecologica Sinica,2020,40(21):7897-7907.
|
[15] |
汪明月, 刘宇, 李梦明, 等.区域碳减排能力协同度评价模型构建与应用[J]. 系统工程理论与实践,2020,40(2):470-483. doi: 10.12011/1000-6788-2018-0901-14
WANG M Y, LIU Y, LI M M, et al. Construction and application of evaluation model for coordinated degree of regional carbon emission[J]. Systems Engineering-Theory & Practice,2020,40(2):470-483. doi: 10.12011/1000-6788-2018-0901-14
|
[16] |
洪竞科, 李沅潮, 蔡伟光.多情景视角下的中国碳达峰路径模拟: 基于RICE-LEAP模型[J]. 资源科学,2021,43(4):639-651.
HONG J K, LI Y C, CAI W G. Simulating China's carbon emission peak path under different scenarios based on RICE-LEAP model[J]. Resources Science,2021,43(4):639-651.
|
[17] |
褚力其, 姜志德, 任天驰.中国农业碳排放经验分解与峰值预测: 基于动态政策情景视角[J]. 中国农业大学学报,2020,25(10):187-201.
CHU L Q, JIANG Z D, REN T C. Empirical decomposition and peak prediction of agricultural carbon emissions in China: from the perspective of dynamic policy scenarios[J]. Journal of China Agricultural University,2020,25(10):187-201.
|
[18] |
LI Z W, ZHANG C J, ZHOU Y. Spatio-temporal evolution characteristics and influencing factors of carbon emission reduction potential in China[J]. Environmental Science and Pollution Research International,2021,28(42):59925-59944. doi: 10.1007/s11356-021-14913-3
|
[19] |
王雅楠, 赵涛.基于GWR模型中国碳排放空间差异研究[J]. 中国人口·资源与环境,2016,26(2):27-34. doi: 10.3969/j.issn.1002-2104.2016.02.004
WANG Y N, ZHAO T. Study on spatial difference of carbon emissions in China based on GWR model[J]. China Population, Resources and Environment,2016,26(2):27-34. doi: 10.3969/j.issn.1002-2104.2016.02.004
|
[20] |
苑立波, 葛守中.浙江省“碳排”投入产出表编制方法研究[J]. 统计科学与实践,2013(3):35-37. doi: 10.3969/j.issn.1674-8905.2013.03.008
|
[21] |
彭璐璐, 李楠, 郑智远, 等.中国居民消费碳排放影响因素的时空异质性[J]. 中国环境科学,2021,41(1):463-472. doi: 10.3969/j.issn.1000-6923.2021.01.052
PENG L L, LI N, ZHENG Z Y, et al. Spatial-temporal heterogeneity of carbon emissions and influencing factors on household consumption of China[J]. China Environmental Science,2021,41(1):463-472. doi: 10.3969/j.issn.1000-6923.2021.01.052
|
[22] |
薛瑞晖, 于晓平, 李东群, 等.基于地理加权回归模型探究环境异质性对秦岭大熊猫空间利用的影响[J]. 生态学报,2020,40(8):2647-2654.
XUE R H, YU X P, LI D Q, et al. Using geographically weighted regression to explore the effects of environmental heterogeneity on the space use by giant pandas in Qinling Mountains[J]. Acta Ecologica Sinica,2020,40(8):2647-2654. □
|