Abstract：To explore the spatial distribution pattern of the county-level carbon emission intensity and the dynamic change in influencing factors, in this paper, ArcGIS spatial statistical model was adopted to measure and analyze the spatial distribution pattern of the carbon emission intensity from 2009 to 2017. Then key influencing factors and their effects change were identified by the random forest model. As results indicated, the average county-level carbon emission intensity fluctuated and decreased from 2009 to 2017. The county-level average carbon emission intensity was 2.02t/10000 yuan in 2017, indicating a significant potential in carbon emission reduction at county-level. Besides, there was significant and rising spatial autocorrelation in carbon emission intensity, but the spatial correlation varied between north-south and east-west regions. The hot-spot area of carbon emission intensity expanded westward, while the cold-spot area expended both southward and northward. Among the key influencing factors, distance to provincial capital, industrial structure, road network density and population played more important role compared with economic development, fiscal revenue and expenditure, number of green patents and high-speed railway. Green patent, total population, and economic development risen in the importance rankings over time, while industrial structure and population density fallen back in the rankings. In addition, most of factors were non-linear correlated with the county-level carbon emission intensity.
张华明,元鹏飞,朱治双.中国城市人口规模、产业集聚与碳排放[J].中国环境科学, 2021,41(5):2459-2470.Zhang H M, Yuan P F, Zhu Z S. City population size, industrial agglomeration and CO2 emission in Chinese prefectures[J]. China Environmental Science, 2021,41(5):2459-2470.
[3]
Wang Zhenhua, Sun Xuetao, Zhang Guangsheng. Structural divi-dends and economic growth in China[J]. Rijeka Faculty of Eco-nomics:Journal of Economics and Business, 2018,36(1):355-372.
[4]
吴敬琏.以深化改革确立中国经济新常态[J].探索与争鸣, 2015,(1):4-7,2.Wu J L. Establishing a new normal for China's economy by deepening reform[J]. Exploration and Free Views, 2015,(1):4-7,2.
[5]
王凯,唐小惠,甘畅,等.中国服务业碳排放强度时空格局及影响因素[J].中国人口·资源与环境, 2021,31(8):23-31.Wang K, Tang X H, Gan C, et al. Temporal-spatial evolution and influencing factors of carbon emission intensity of China's service industry[J]. China population, Resources and Environment, 2021,31(8):23-31.
[6]
Liu X Z, Gao C C, Zhang Y, et al. Spatial dependence pattern of carbon emission intensity in China's provinces and spatial heterogeneity of its influencing factors[J]. Scientia Geographica Sinica, 2018,38(5):681-690.
[7]
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]. China Environmental Science, 2020,40(3):1356-1363.
[8]
王少剑,黄永源.中国城市碳排放强度的空间溢出效应及驱动因素[J].地理学报, 2019,74(6):1131-1148.Wang S J, Huang Y Y. Spatial spillover effect and driving forces of carbon emissionintensity at city level in China[J]. Acta Geographica Sinica, 2019,74(6):1131-1148.
[9]
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. Scientia Geographica Sinica, 2021,41(8):1324-1335.
[10]
马晓君,陈瑞敏,董碧滢,等.中国工业碳排放的因素分解与脱钩效应[J].中国环境科学, 2019,39(8):3549-3557.Ma X J, Chen R M, Dong B Y, et al. Factor decomposition and decoupling effect of China's industrial carbon emissions[J]. China Environmental Science, 2019,39(8):3549-3557.
[11]
J, Wesley, Burnett, et al. Spatial analysis of China province-level CO2 emission intensity[J]. Renewable& Sustainable Energy Reviews, 2014,33(5):1-10.
[12]
石蕾,李洋.中国区域经济增长的碳排放强度差异及其敛散性[J].北京理工大学学报(社会科学版), 2013,15(2):34-38.Shi L, Li Y. Research on the difference of carbon emission intensityand the convergence in the regional economic growth in China[J]. Journal of Beijing Institute of Technology (Social Sciences Edition), 2013,15(2):34-38.
[13]
朱聆,张真.上海市碳排放强度的影响因素解析[J].环境科学研究, 2011,24(1):20-26.Zhu L, Zhang Z. Decomposition analysis of carbon emission intensity in Shanghai City[J]. Research of Environmental Sciences, 2011,24(1):20-26.
[14]
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 andinfluencing factors of CO2 emissions at the county level of China[J]. Acta Geographica Sinica, 2021,76(12):3103-3118.
[15]
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. Scientia Geographica Sinica, 2021,41(11):1917-1924.
[16]
韩会庆,王世尧,马庚,等.我国农业碳排放强度与农业经济空间相关性研究[J].水土保持研究, 2018,25(5):269-274,281.Han H Q, Wang S Y, Ma G, et al. Analysis on spatial correlation between agricultural carbon emission densities and rural economy in China[J]. Research of Soil and Water Conservation, 2018,25(5):269-274,281.
[17]
赵涛,田莉,许宪硕.天津市工业部门碳排放强度研究:基于LMDI-Attribution分析方法[J].中国人口·资源与环境, 2015,25(7):40-47.Zhao T, Tian L, Xu X S. Carbon emission intensity of tianjin industry based on LMDI-Attribution method[J]. ChinaPopulation, Resources and Environment, 2015,25(7):40-47.
[18]
Rw A, Jxh A, Cw A, et al. Embodied CO2 emissions and efficiency of the service sector:Evidence from China[J]. Journal of Cleaner Production, 2020,274(20):116-129.
[19]
蔡博峰,王金南,杨姝影,等.中国城市CO2排放数据集研究——基于中国高空间分辨率网格数据[J].中国人口·资源与环境, 2017,27(2):1-4.Cai B F, Wang J N, Yang S Y, et al. China City CO2 emission dataset:based on the China high resolution emission gridded data[J]. China Population, Resources and Environment, 2017,27(2):1-4.
[20]
万文玉,赵雪雁,王伟军.中国城市居民生活能源碳排放的时空格局及影响因素分析[J].环境科学学报, 2016,36(9):3445-3455.Wan W Y, Zhao X Y, Wang W J. Spatial-temporal patterns and impact factors analysis on carbon emissions from energy consumption of urbanresidents in China[J]. Acta Scientiae Circumstantiae, 36(9):3445-3455.
[21]
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]. China Environmental Science, 2020,40(5):2312-2320.
[22]
周圣强,朱卫平.产业集聚一定能带来经济效率吗:规模效应与拥挤效应[J].产业经济研究, 2013,(3):12-22.Zhou S Q, Zhu W P. Must industrial agglomeration be able to bring about economic efficiency:Economies of scale and crowding effect[J]. Industrial Economics Research, 2013,(3):12-22.
[23]
陈阳,唐晓华.制造业集聚和城市规模对城市绿色全要素生产率的协同效应研究[J].南方经济, 2019,(3):71-89.Chen Y, Tang X H. Study on the synergistic effect of manufacturing agglomeration and urban size on urban green total factor productivity[J]. South China Journal of Economics, 2019,(3):71-89.
[24]
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]. China Environmental Science, 2020,40(10):4304-4313.
[25]
程开明.城市紧凑度影响能源消耗的理论机制及实证分析[J].经济地理, 2011,31(7):1107-1112.Cheng K M. Theoretical mechanism and empirical study about the influence of city compactness on energy consumption[J]. Economic Geography, 2011,31(7):1107-1112.
[26]
王桂新,武俊奎.产业集聚、城市规模与碳排放[J].工业技术经济, 2012,31(6):68-80.Wang G X, Wu J K. Study on industry agglomeration, urban scale and carbon emission[J]. Journal of Industrial Technological Economics, 2012,31(6):68-80.
[27]
Cheng S, Chen Y, Meng F, et al. Impacts of local public expenditure on CO2 emissions in Chinese cities:A spatial cluster decomposition analysis[J]. Resources Conservation and Recycling, 2021,164(1):105217.
[28]
汪浩,陈操操,潘涛,等.县域尺度的京津冀都市圈CO2排放时空演变特征[J].环境科学, 2014,35(1):385-393.Wang H, Chen C C, Pan T, et al. County scale characteristics of CO2 emission's spatial-temporal evolution inthe Beijing-Tianjin-Hebei metropolitan region[J]. Environmental Science, 2014,35(1):385-393.
[29]
胡孟然.中国县域碳排放空间格局及影响因素研究[D].兰州大学, 2021.Hu M R. Spatial pattern and influencing factors of carbon emissions at county level in China[D]. Lanzhou University, 2021.
[30]
王金南,蔡博峰,曹东,等.中国10km二氧化碳排放网格及空间特征分析[J].中国环境科学, 2014,(1):1-6.Wang J N, Cai B F, Cao D, et al. China 10km carbon dioxide emissions grid dataset and spatial characteristic analysis[J]. China Environmental Science, 2014,(1):1-6.
[31]
Chen J, Gao M, Cheng S, et al. County-level CO2 emissions and sequestration in China during 1997~2017[J]. Scientific Data, 2020, 7:391.
[32]
余文梦,孙小霞,苏时鹏.省域农用地猪粪负荷强度时空演变分析[J].中国生态农业学报(中英文), 2019,27(3):494-506.Yu W M, Sun X X, Su S P. Temporal and spatial changes in pig manure load in agricultural lands in China[J]. Chinese Journal of Eco-Agriculture, 2019,27(3):494-506.
[33]
Breiman L. Random forests[J]. Machine Learning, 2001,45(1):5-32.
[34]
Fang Kuangnan, Wu Jianbin, Zhu Jianping, et al. A review of technologies on random forests. Statistics and Information Forum, 2011,26(3):32-38.
[35]
王超,阚瑷珂,曾业隆,等.基于随机森林模型的西藏人口分布格局及影响因素[J].地理学报, 2019,74(4):664-680.Wang C, Kan A K, Zeng Y L, et al. Population distribution pattern and influencing factors in Tibetbased on random forest model[J]. Acta Geographica Sinica, 2019,74(4):664-680.
[36]
刘卫东,唐志鹏,夏炎,等.中国碳强度关键影响因子的机器学习识别及其演进[J].地理学报, 2019,74(12):2592-2603.Liu W D, Tang Z P, Xia Y, et al. Identifying the key factors influencing Chinese carbon intensity using machine learning, the random forest algorithm, and evolutionary analysis[J]. Acta Geographica Sinica, 2019,74(12):2592-2603.
[37]
范剑勇.产业集聚与地区间劳动生产率差异[J].经济研究, 2006,(11):72-81.Fan J Y. Industrial agglomeration and difference of regional labor productivity:Chinese evidence with international comparison[J]. Economic Research Journal, 2006,(11):72-81.
[38]
Ciccone A. Input chains and industrialization[J]. Ciccone Antonio, 2002,69(3):565-587.
[39]
谭建立,赵哲.财政支出结构、新型城镇化与碳减排效应[J].当代财经, 2021,(8):28-40.Tan J J, Zhao Z. Fiscal expenditure structure, new urbanization and carbon emission reduction effect[J]. Contemporary Finance& Economics, 2021,(8):28-40.
[40]
刘志杰.公路网络交通碳排放影响因素研究[J].交通与运输(学术版), 2012,(1):150-153.Liu Z J. Analysis on influencing factors for carbon emissions of highway network[J]. Traffic& Transportation, 2012,(1):150-153.
