Abstract：The spatial auto-correlation model and the geographically and temporally weighted regression model were applied in this study to explore the spatial-temporal evolution pattern and influencial factors of carbon emissions in 16cities of Chengdu-Chongqing urban agglomeration during the period of 2008~2018. The results showed that the overall carbon emissions were growing, with the total amount increased from 500 million tons to 660 million tons at a growth rate of about 1500 t/a. The land-average carbon emissions and per capita carbon emissions also had a fluctuating upward trend. The hot spots were Chengdu and Chongqing, accounted for about 20% and 25% of the total carbon emissions respectively, while the cold spot was Ya'an. There were significant spatial differences in both total carbon emissions and land-average carbon emissions. The Moran index of per capita carbon emissions was positive, showing an obvious spatial aggregation pattern. On the whole, the per capita carbon emissions demonstrated the spatial structure characteristics of "lower in the northeast-higher in the southwest", with Nanchong, Suining, and Guang'an being the lower-gathering areas. The influencial factors of each city showed spatial and temporal heterogeneity. Energy intensity, economic development level, and population size all had a significant positive effect on urban carbon emissions, and the effect was strong in the central and western cities; while the positive effect of urbanization level was weak, and the impact on the eastern cities was stronger.
Xu B, Xu L, Xu R, et al.Geographical analysis of CO2 emissions in China's manufacturing industry:A geographically weighted regression model[J].Journal of Cleaner Production, 2017,166(10):628-640.
[2]
Liu Y, Feng C.Decouple transport CO2 emissions from China's economic expansion:a temporal-spatial analysis[J].Transportation Research Part D:Transport and Environment, 2020,79:102225.
[3]
王凯,肖燕,李志苗等.中国旅游业CO2排放区域差异的空间分析[J].中国人口·资源与环境, 2016,26(5):83-90.Wang K, Xiao Y, Li Z M, et al.Spatial Analysis for regional difference of tourism carbon emissions in China[J].China Population, Resources and Environment, 2016,26(5):83-90.
[4]
曾晓莹,邱荣祖,林丹婷,等.中国交通碳排放及影响因素时空异质性[J].中国环境科学, 2020,40(10):4304-4313.Zeng X Y, Qiu R Z, Lin D T, et al.Spatio-temporal heterogeneity of transportation carbon emissions and its influencing factors in China[J].Chinese Environmental Science, 2020,40(10):4304-4313.
[5]
章胜勇,尹朝静,贺亚亚,等.中国农业碳排放的空间分异与动态演进——基于空间和非参数估计方法的实证研究[J].中国环境科学, 2020,40(3):1356-1363.Zhang S Y, Yin C J, He Y Y, et al.Spatial differentiation and dynamic evolution of agricultural carbon emission in China——Empirical research based on spatial and non-parametric estimation methods[J].Chinese Environmental Science, 2020,40(3):1356-1363.
[6]
赵若楠,董莉,白璐,等.光伏行业生命周期碳排放清单分析[J].中国环境科学, 2020,40(6):2751-2757.Zhao R N, Dong L, Bai L, et al.Inventory-analysis on carbon emissionof photovoltaicindustry[J].Chinese Environmental Science, 2020,40(6):2751-2757.
[7]
Chaudhry S M, Ahmed R, Shafiullah M, et al.The impact of carbon emissions on country risk:evidence from the G7 economies[J].Journal of Environmental Management, 2020,265:110533.
[8]
Wang S, Fang C, Ma H, et al.Spatial differences and multi-mechanism of carbon footprint based on GWR model in provincial China[J].Journal of Geographical Sciences, 2014,24(4):612-630.
[9]
刘贤赵,郭若鑫,张勇,等.中国省域碳排放空间依赖结构的非参数估计及其实证分析[J].中国人口·资源与环境, 2019,29(5):40-51.Liu X Z, Guo R X, Zhang Y, et al.Nonparametric estimation and empirical analysis of spatial dependence structure of provincial carbon emissions in China[J].China Population, Resources and Environment, 2019,29(5):40-51.
[10]
刘玉珂,金声甜.中部六省能源消费碳排放时空演变特征及影响因素[J].经济地理, 2019,39(1):182-191.Liu Y K, Jin S T.Temporal and spatial evolution characteristics and influencing factors of energy consumption carbon emissions in six provinces of central China[J].Economic Geography, 2019,39(1):182-191.
[11]
邱立新,徐海涛.中国城市群碳排放时空演变及影响因素分析[J].软科学, 2018,32(1):109-113.Qiu L X, Xu H T.Analysis of spatial-temporal evolution and impact factors of urban agglomerations carbon emissions in China[J].Soft Science, 2018,32(1):109-113.
[12]
Chen L, Xu L, Cai Y, et al.Spatiotemporal patterns of industrial carbon emissions at the city level[J].Resources, Conservation and Recycling, 2021,169:105499.
[13]
苏凯,陈毅辉,范水生,等.市域能源碳排放影响因素分析及减碳机制研究——以福建省为例[J].中国环境科学, 2019,39(2):859-867.Su K, Chen Y H, Fan S S, et al.Influencing factors and reduction mechanism of carbon emissions at the city-range:an empirical study on Fujian province[J].Chinese Environmental Science, 2019,39(2):859-867.
[14]
Ehrlich P R, Holdren J P.Impact of population growth[J].Science, 1971,171(3977):1212-1217.
[15]
王丽,欧阳慧,马永欢.经济社会发展对环境影响的再认识——基于IPAT模型的城市碳排放分析[J].宏观经济研究, 2017,(10):161-168.Wang L, Ou Y H, Ma Y H.Reconceptualization of the environmental impact of economic and social development——analysis of urban carbon emissions based on IPAT model[J].Macroeconomics, 2017(10):161-168.
[16]
Kaya Y.Impact of carbon dioxide emission control on GNP growth:Interpretation of proposed scenarios[R].Paris:Intergovernmental Panel on Climate Change (IPCC), 1989.
[17]
李庚欣,胡纯,梅运军,等.基于Kaya模型的湖北省农业碳排放时空特征及影响因素研究[J].绿色科技, 2020,(4):217-220.Li G X, Hu C, Mei Y J, et al.Study on the spatiotemporal characteristics and rmpact factors of agricultural carbon emissions in Hubei based on Kaya model[J].Journal of Green Science and Technology, 2020,(4):217-220.
[18]
Xiong C, Chen S, Xu L.Driving factors analysis of agricultural carbon emissions based on extended STIRPAT model of Jiangsu Province, China[J].Growth and Change, 2020,51(3):1401-1416.
[19]
Huang J, Li X, Wang Y, et al.The effect of energy patents on China's carbon emissions:evidence from the STIRPAT model[J].Technological Forecasting and Social Change, 2021,173:121110.
[20]
Zhang Y, Zhang Q, Pan B.Impact of affluence and fossil energy on China carbon emissions using STIRPAT model[J].Environmental Science and Pollution Research, 2019,26(18):18814-18824.
[21]
朱冬元,纪磊.基于STIRPAT模型的长江经济带碳排放驱动因素研究[J].湖北农业科学, 2021,60(11):50-54,61.Zhu D Y, Ji L.Study on driving factors of carbon emission in the Yangtze river economic belt based on STIRPAT model[J].Hubei Agricultural Sciences, 2021,60(11):50-54,61.
[22]
黄琳琳,王远,张晨,等.闽三角地区碳排放时空差异及影响因素研究[J].中国环境科学, 2020,40(5):2312-2320.Huang L L, Wang Y, Zhang C, et al.A spatial-temporal decomposition analysis of CO2 emissions in Fujian Southeast Triangle Region[J].Chinese Environmental Science, 2020,40(5):2312-2320.
[23]
Quan C, Cheng X, Yu S, et al.Analysis on the influencing factors of carbon emission in China's logistics industry based on LMDI method[J].Science of The Total Environment, 2020,734:138473.
[24]
陈军华,李乔楚.成渝双城经济圈建设背景下四川省能源消费碳排放影响因素研究——基于LMDI模型视角[J].生态经济, 2021, 37(12):30-36.Chen J H;Li Q C.Research on the influencing factors of energy consumption carbon emission in Sichuan province under the background of the construction of Chengdu-Chongqing double city economic circle:from the perspective of LMDI method[J].Ecological Economy, 2021,37(12):30-36.
[25]
Xu Q, Dong Y, Yang R.Urbanization impact on carbon emissions in the Pearl River Delta region:kuznets curve relationships[J].Journal of Cleaner Production, 2018,180:514-523.
[26]
马景富.辽宁省碳排放的环境库兹涅茨曲线实证研究[J].节能, 2021,40(5):58-62.Ma J F.An empirical study on the environmental Kuznets curve of carbon emission in Liaoning province[J].Energy Conservation, 2021, 40(5):58-62.
[27]
Wang B, Yu M, Zhu Y, et al.Unveiling the driving factors of carbon emissions from industrial resource allocation in China:a spatial econometric perspective[J].Energy Policy, 2021,158:112557.
[28]
Liang S, Zhao J, He S, et al.Spatial econometric analysis of carbon emission intensity in Chinese provinces from the perspective of innovation-driven[J].Environmental Science and Pollution Research, 2019,26(14):13878-13895.
[29]
郭莉,邹梦瑶.空间溢出视角下的碳排放强度影响因素研究[J].生态经济, 2020,36(9):32-37.Guo L, Zou M Y.Research on influencing factors of carbon intensity from the perspective of spatial spillover[J].Ecological Economy, 2020,36(9):32-37.
[30]
胡艳兴,潘竟虎,李真,等.中国省域能源消费碳排放时空异质性的EOF和GWR分析[J].环境科学学报, 2016,36(5):1866-1874.Hu Y X, Pan J H, Li Z, et al.Spatial-temporal analysis of provincial carbon emissions in China from 1997 to 2012 with EOF and GWR methods[J].Chinese Environmental Science, 2016,36(5):1866-1874.
[31]
陈操操,蔡博峰,孙粉,等.京津冀与长三角城市群碳排放的空间聚集效应比较[J].中国环境科学, 2017,37(11):4371-4379.Chen C C, Cai B F, Sun F, et al.Spatial agglomeration effects of carbon dioxide emissions between Beijing-Tianjin-Heibei region and Yangtze River delta region[J].Chinese Environmental Science, 2017, 37(11):4371-4379.
[32]
李建豹,黄贤金,揣小伟,等.长三角地区碳排放效率时空特征及影响因素分析[J].长江流域资源与环境, 2020,29(7):1486-1496.Li J B, Huang X J, Chuai X W, et al.Spatio-temporal characteristics and influencing factors of carbon emissions efficiency in the Yangtze River delta region[J].Resources and Environment in the Yangtze Basin, 2020,29(7):1486-1496.
[33]
莫炜程.FDI对区域二氧化碳排放的影响研究——基于广东省空间面板数据模型的分析[J].产业与科技论坛, 2020,19(15):69-71.Mo W C.Study on the impact of FDI on regional CO2 emissions——eased on spatial panel data model of Guangdong province[J].Industrial & Science Tribune, 2020,19(15):69-71.
HUANG B, WU B, BARRY M.Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices.International Journal of Geographical Information Science, 2010,24(3):383-401.
[36]
彭耕,熊琳,朱直君,等.新时期成都——资阳同城化空间发展规划探索[J].规划师, 2021,37(11):63-68.Peng G, Xiong L, Zhu Z J, et al.Theoretical exploration and planning practice of Chengdu-Ziyang integrate development[J].Planners, 2021, 37(11):63-68.
[37]
王海军,张彬,刘耀林,等.基于重心-GTWR模型的京津冀城市群城镇扩展格局与驱动力多维解析[J].地理学报, 2018,27(6):1076-1092.Wang H J, Zhang B, Liu Y L, et al.Multi-dimensional analysis of urban expansion patterns and their driving forces based on the center of gravity-GTWR model:A case study of the Beijing-Tianjin-Hebei urban agglomeration[J].Acta Geographica Sinica, 2018,27(6):1076-1092.
[38]
王少剑,谢紫寒,王泽宏,等.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, et al.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.
[39]
蔺雪芹,边宇,王岱.京津冀地区工业碳排放效率时空演化特征及影响因素[J].经济地理, 2021,41(6):187-195.Lin X Q, Bian Y, Wang D.Spatiotemporal evolution characteristics and influencing factors of industrial carbon emission efficiency in Beijing-Tianjin-Hebei region[J].Economic Geography, 2021,41(6):187-195.
[40]
肖宏伟,易丹辉.基于时空地理加权回归模型的中国碳排放驱动因素实证研究[J].统计与信息论坛, 2014,29(2):83-89.Xiao H W, Yi D H.Empirical study of carbon emissions drivers dased on geographically time weighted regression model[J].Statistics & Information Forum, 2014,29(2):83-89.
[41]
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].Chinese Environmental Science, 2014,29(2):83-89.
[42]
张雪华,董会忠."2+26"城市碳排放时空演变特征及其驱动因素研究[J].资源开发与市场, 2021,37(12):1448-1456.Zhang X H, Dong H Z.Study on the spatial-temporal evolution of carbon emissions in "2+ 26" cities and its driving factors[J].Resource Development & Market, 2021,37(12):1448-1456.
[43]
黄蕊,王铮,丁冠群,等.基于STIRPAT模型的江苏省能源消费碳排放影响因素分析及趋势预测[J].地理研究, 2016,35(4):781-789.Huang R, Wang Z, Ding G Q, et al.Trend prediction and analysis of influencing factors of carbon emissions from energy consumption in Jiangsu province based on STIRPAT model[J].Geographical Research, 2016,35(4):781-789.
[44]
沈杨,汪聪聪,高超,等.基于城市化的浙江省湾区经济带碳排放时空分布特征及影响因素分析[J].自然资源学报, 2020,35(2):329-342.Shen Y, Wang C C, Gao C, et al.Spatio-temporal distribution and its influencing factors of carbon emissions in economic zone of Zhejiang Bay Area based on urbanization[J].Journal of Natural Resources, 2020,35(2):329-342.
[45]
徐婕,潘洪义,曹文亚,等.基于LUCC的眉山市县域碳排放效应[J].四川师范大学学报(自然科学版), 2020,43(5):683-689.Xu J, Pan H Y, Cao W Y, et al.County carbon emission effect of Meishan city based on LUCC[J].Journal of Sichuan Normal University (Natural Science), 2020,43(5):683-689.
[46]
邓晓臻,陈思,汤银英.区域产业结构调整下县域城市物流需求空间关联协同研究[J].物流技术, 2020,39(7):43-50.Deng X Z, Chen S, Tang Y Y.Research on spatial correlation and synergy of county-level urban logistics demand under regional industrial structure adjustment[J].Logistics Technology, 2020,39(7):43-50.
[47]
陈军华,李乔楚,何京.碳中和目标下四川省低碳效率区域差异性[J].天然气工业, 2021,41(6):162-170.Chen J H, Li Q C, He J.Regional diversity of low-carbon efficiency in Sichuan province under the goal of carbon neutrality[J].Natural Gas Industry, 2021,41(6):162-170.
[48]
洪业应,向思洁,陈景信.重庆市人口规模、结构对碳排放影响的实证研究——基于STIRPAT模型的分析[J].西北人口, 2015,36(3):5.Hong Y Y, Xiang S J, Chen J X.Impact of demographic factors on carbon emission in Chongqing city:a study base on Stirpat model[J].Northwest Population Journal, 2015,36(3):13-17.
[49]
孙秀锋,施开放,吴健平.县级尺度的重庆市碳排放时空格局动态[J].环境科学, 2018,39(6):2971-2981.Sun X F, Shi K F, Wu J P.Spatiotemporal dynamics of CO2 emissions in Chongqing:an empirical analysis at the county level[J].Environmental Science, 2018,39(6):2971-2981.
