中国交通碳排放及影响因素时空异质性

曾晓莹; 邱荣祖; 林丹婷; 侯秀英; 张兰怡; 胡喜生

PDF(1214 KB)

中国环境科学 ›› 2020, Vol. 40 ›› Issue (10) : 4304-4313.

大气污染与控制

中国交通碳排放及影响因素时空异质性

曾晓莹, 邱荣祖, 林丹婷, 侯秀英, 张兰怡, 胡喜生

作者信息 +

Spatio-temporal heterogeneity of transportation carbon emissions and its influencing factors in China

ZENG Xiao-ying, QIU Rong-zu, LIN Dan-ting, HOU Xiu-ying, ZHANG Lan-yi, HU Xi-sheng

Author information +

文章历史 +

摘要

选取30个省级行政单位作为空间单元，采用探索性空间数据分析（ESDA）方法对交通碳排放时空分布格局进行研究，同时考虑空间单元的差异性，构建地理加权回归（GWR）模型分析交通碳排放影响因素的时空异质性.研究发现：2000~2015年交通碳排放量呈现显著的空间聚类特征，聚类趋势逐年加强.双变量空间自相关指数为0.165~0.274，显著性水平介于0.016~0.045，表明交通碳排放同机动车保有量、GDP、货运周转量及客运周转量之间存在显著的空间正相关关系.GWR模型的R²在0.783~0.865之间，而OLS模型的R²在0.675~0.844之间，且GWR模型的AICc值均低于OLS模型的，说明GWR模型的拟合结果明显优于OLS模型，可以更好地解释交通碳排放的影响机制.GWR的回归结果表明碳排放的影响因素存在明显的时空异质性特征，其中GDP是主要的推动因素，部分地区回归系数高达0.91，2000年影响程度由东向西递减，而2005、2010和2015年由北向南递减.客运周转量起到关键的抑制作用，影响程度由东北向西南递减.因此建议应当充分考虑碳排放影响因素的时空异质性特征，制定差异化的碳减排政策.

Abstract

Taking the 30 provinces in Mainland China as spatial analysis unit, the exploratory spatial data analysis (ESDA) method was employed to explore the spatio-temporal pattern of the transportation carbon emissions. Moreover, considering the spatial non-stationary, the geographically weighted regression (GWR) model was applied to analyze the spatio-temporal heterogeneity in the influencing factors of the transportation carbon emissions. The results indicated a significant spatial agglomeration in the transportation carbon emissions, and showing a gradual upward trend across time during the studied period 2000~2015. The Moran's I indices of the Bivariate spatial autocorrelation were 0.165~0.274 and the statistical significance levels were 0.016~0.045, indicating that there was a significant spatial positive correlation between the transportation carbon emissions and the variables, such as motor vehicle population, GDP, freight turnover and passenger turnover. The R² of the GWR models were between 0.783 and 0.865, while the R² of the OLS models were between 0.675 and 0.844; moreover, the AICc values of the GWR model were lower than those of the OLS models', demonstrating the goodness of the GWR models compared to the OLS models. This indicates that we can use the outcomes of the GWR models to better explain the impact mechanism of the transportation carbon emissions. The analysis of the GWR revealed that the influencing factors of the transportation carbon emissions had obvious spatio-temporal heterogeneity. GDP was among the major driving factors, with regression coefficient as high as 0.91 in some areas. The impact of GDP decreased from east to west in 2000, while decreasing from north to south in 2005, 2010, and 2015. The passenger turnover played a key inhibitory role, with its influence decreasing from northeast to southwest in all of the study years. In this context, the spatio-temporal heterogeneity of carbon emission influencing factors should be fully understood to formulate differentiated carbon emission reduction policies.

导出引用

曾晓莹, 邱荣祖, 林丹婷, 侯秀英, 张兰怡, 胡喜生. 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 Xiao-ying, QIU Rong-zu, LIN Dan-ting, HOU Xiu-ying, ZHANG Lan-yi, HU Xi-sheng. Spatio-temporal heterogeneity of transportation carbon emissions and its influencing factors in China[J]. China Environmental Science. 2020, 40(10): 4304-4313

中图分类号： X511

参考文献

[1] 张诗青,王建伟,郑文龙.中国交通运输碳排放及影响因素时空差异分析[J]. 环境科学学报, 2017,37(12):4787-4797. Zhang S Q, Wang J W, Zheng W L. Spatio-temporal difference of transportation carbon emission and its influencing factors in China[J]. Acta Scientiae Circumstantiae, 2017,37(12):4787-4797.
[2] Lin D T, Zhang L Y, Chen C, et al. Understanding driving patterns of carbon emissions from the transport sector in China:evidence from an analysis of panel models[J]. Clean Technologies and Environmental Policy, 2019,21(6):1307-1322.
[3] 蒋自然,金环环,王成金,等.长江经济带交通碳排放测度及其效率格局(1985~2016年)[J]. 环境科学, 2020,41(6):2972-2980. Jiang Z R, Jin H H, Wang C J, et al. Measurement of traffic carbon emissions and pattern of efficiency in the Yangtze River Economic Belt (1985~2016)[J]. Environmental Science, 2020,41(6):2972-2980.
[4] 庄颖,夏斌.广东省交通碳排放核算及影响因素分析[J]. 环境科学研究, 2017,30(7):1154-1162. Zhuang Y, Xia B. Estimation of CO₂ emissions from the transport sector in Guangdong Province, China and analysis of factors affecting emissions[J]. Research of Environmental Science, 2017,30(7):1154-1162.
[5] Bai C Q, Zhou L, Xia M L, et al. Analysis of the spatial association network structure of China's transportation carbon emissions and its driving factors[J]. Journal of Environmental Management, 2020, 253:109765-109476.
[6] 袁长伟,张帅,焦萍,等.中国省域交通运输全要素碳排放效率时空变化及影响因素研究[J]. 资源科学, 2017,39(4):687-697. Yuan C W, Zhang S, Jiao P, et al. Temporal and spatial variation and influencing factors research on total factor efficiency for transportation carbon emissions in China[J]. Resources Science, 2017,39(4):687-697.
[7] Li F Y, Cai B F, Ye Z Y, et al. Changing patterns and determinants of transportation carbon emissions in Chinese cities[J]. Energy, 2019, 174:562-575.
[8] 李若影.基于空间计量模型的中国交通运输业碳排放影响因素分析[D]. 西安:长安大学, 2017. Li R Y. Analysis on the factors affecting carbon emissions of Chinese transportation industry based on spatial econometric model[D]. Xi'an:Chang'an University, 2017.
[9] Bai C Q, Chen Y B, Yi X, et al. Decoupling and decomposition analysis of transportation carbon emissions at the provincial level in China:perspective from the 11th and 12th Five-Year Plan periods[J]. Environmental science and pollution research international, 2019, 26(15):15039-15056.
[10] 吕倩.京津冀地区汽车运输碳排放影响因素研究[J]. 中国环境科学, 2018,38(10):3689-3697. Lü Q. Study on the driving factors of vehicle transport carbon emissions in Beijing-Tianjin-Hebei region[J]. China Environmental Science, 2018,38(10):3689-3697.
[11] 李丹丹,刘锐,陈动.中国省域碳排放及其驱动因子的时空异质性研究[J]. 中国人口·资源与环境, 2013,23(7):84-92. Li D D, Liu R, Chen D. Research on space-time heterogeneity of carbon emission and influencing factors in provinces of China[J]. China Population, Resources and Environment, 2013,23(7):84-92.
[12] 刘贤赵,高长春,张勇,等.中国省域碳强度空间依赖格局及其影响因素的空间异质性研究[J]. 地理科学, 2018,38(5):681-690. 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.
[13] 林海燕,李正红,胡喜生,等.基于地理加权回归的福建省公路通车里程数研究[J]. 井冈山大学学报(自然科学版), 2017,38(3):70-75. Lin H Y, Li Z H, Hu X S, et al. Research on highway traffic mileage of Fujian province based on geographical weighted regression[J]. Journal of Jinggangshan University (Natural Science), 2017,38(3):70-75.
[14] Liu Y, Wang Y, Huo H. Temporal and spatial variations in on-road energy use and CO₂ emissions in China, 1978~2008[J]. Energy Policy, 2013,61:544-550.
[15] Cai B, Yang W, Cao D, et al. Estimates of China's national and regional transport sector CO₂ emissions in 2007[J]. Energy Policy, 2012,41:474-483.
[16] 国家发展改革委气候司.省级温室气体清单编制指南(试行)[R]. 北京, 2011. Climate Department of National Development and Reform Commission Guidance for Compiling Provincial Greenhouse Gas Emission Lists (Trial)[R]. Beijing, 2011.
[17] 国家统计局能源司.中国能源统计年鉴[M]. 北京:中国统计出版社, 2001~2016. Energy Department of the National Bureau of Statistics of China. China energy statistical yearbook[M]. Beijing:China Statistics Press, 2001~2016.
[18] Anselin L. Local indicators of spatial association-LISA[J]. Geographical analysis, 1995,27(2):93-115.
[19] 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]. China Environmental Science, 2019,39(2):859-867.
[20] 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]. China Environmental Science, 2017, 37(11):4371-4379.
[21] Wang S J, Fang C L, Ma H T, 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.
[22] 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.
[23] 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.
[24] 国家统计局.中国统计年鉴[M]. 北京:中国统计出版社, 2001~2016. National Bureau Statistics of China. China Statistical Yearbook[M]. Beijing:China Statistics Press, 2001~2016