中国省域物流业碳排放空间差异分析
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摘要
随着中国社会经济的发展,物流行业也进入到飞速发展阶段,而低碳经济是近年来各行业一直关注的焦点。文章采用省域物流碳排放作为环境压力的衡量指标,利用空间面板数据对物流业进行空间计量分析,区别于传统普通最小二乘法(OLS)仅是对变量整体估计,空间计量方法可以得到区域差异化的回归系数,更能进行局域分析。研究结果发现:物流业周转货物量、物流业人均和物流业能源强度这三个变量对物流业碳排放均属于正向影响关系,其中物流能源强度对物流碳排放的影响程度最为显著。空间滞后效应的系数为0.336,且通过了显著性检验,说明相邻省份存在明显的空间扩散(溢出)效应。
With the development of China's social economy, the logistics industry has entered the stage of rapid development. Low carbon economy has been the focus of attention in various industries in recent years. Therefore, the research on the influencing factors of carbon emissions in the logistics industry has become particularly important. Using provincial logistics carbon emissions as an indicator of environmental pressure, using spatial panel data to conduct spatial econometric analysis of logistics industry,which is different from the traditional ordinary least squares(OLS) method, which only estimates variables as a whole. Spatial econometric method can obtain regress coefficients of regional differentiation, and can better carry out local analysis. The results show that the three variables of logistics industry turnover cargo volume, per capita and energy intensity of logistics industry have a positive impact on carbon emissions of logistics industry, and the impact of logistics energy intensity on carbon emissions is the most significant. The coefficient of spatial lag effect is 0.336, and has passed the significance test. It shows that there is obvious spatial diffusion(spillover) effect in adjacent provinces.
With the development of China's social economy, the logistics industry has entered the stage of rapid development. Low carbon economy has been the focus of attention in various industries in recent years. Therefore, the research on the influencing factors of carbon emissions in the logistics industry has become particularly important. Using provincial logistics carbon emissions as an indicator of environmental pressure, using spatial panel data to conduct spatial econometric analysis of logistics industry,which is different from the traditional ordinary least squares(OLS) method, which only estimates variables as a whole. Spatial econometric method can obtain regress coefficients of regional differentiation, and can better carry out local analysis. The results show that the three variables of logistics industry turnover cargo volume, per capita and energy intensity of logistics industry have a positive impact on carbon emissions of logistics industry, and the impact of logistics energy intensity on carbon emissions is the most significant. The coefficient of spatial lag effect is 0.336, and has passed the significance test. It shows that there is obvious spatial diffusion(spillover) effect in adjacent provinces.
引文
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[2]林伯强,蒋竺均.中国二氧化碳的环境库兹涅兹曲线预测及影响因素分析[J].管理世界,2009(4):27-36.
[3]陈诗一.能源消耗、二氧化碳排放与中国工业的可持续发展[J].经济研究,2009(4):41-55.
[4]刘燕华,葛全胜,何凡能,等.应对国际CO2减排压力的途径及我国减排潜力分析[J].地理学报,2008,63(7):675-682.
[5]王铮.气候保护及其对社会经济影响的模拟研究[J].地理研究,2010(11):1921-1931.
[6] ANG B W. Decomposition analysis for policymaking in energy:which is the preferred method?[J]. Energy Policy, 2004,32:1131-1139.
[7] REITLER W, RUDOLPH M, SCHAEFER H. Analysis of the factors influencing energy consumption in industry:a revised method[J]. Energy Economics, 1987,14(1):49-56.
[8] HOWARTH R B, SCHIPPER L, DUERR P A, et al. Manufacturing energy use in eight OECD countries[J]. Energy Economics, 1991,13(2):135-142.
[9] PARK S H. Decomposition of industrial energy consumption:an alternative method[J]. Energy Economics, 1992,14(4):265-270.
[10] KAYA Y. Impact of carbon dioxide emission on GNP growth:interpretation of proposed scenarios[R]. Presentation to the Energy and Industry Subgroup, Response Strategies Working Group, IPCC, 1989.
[11] BIRDSALL N. Another look at population and global warming population, health and nutrition policy research[C]//Working Paper, Washington, DC:World Bank, WPSl020, 1992.
[12] CHANG T C, LIN S J. Grey relation analysis of carbon dioxide emissions from industrial production and energy uses in Taiwan[J]. Journal of Environmental Management, 1999,56:247-257.
[13]谢守红,王利霞,邵珠龙.国内外碳排放研究综述[J].干旱区地理,2014(4):720-730.
[14]韩晶,王赟,陈超凡.中国工业碳排放绩效的区域差异及影响因素研究——基于省域数据的空间计量分析[J].经济社会体质比较,2015(177):113-124.
[15]张淘新,曾熬志.中国交通碳排放空间计量分析[J].低碳城市,2013,10(20):14-20.
[16]高鸣,宋洪远.中国农业碳排放绩效的空间收敛与分异——基于Malmquist-luenberger指数与空间计量的实证分析[J].经济地理,2015,4(35):142-148.
[17] Anselin L R J G. M Florax. Small sample properties of tests for spatial dependence in regression models:Some further results[C]//New Directions in Spatial Econometrics, 1995:21-74.
[18] Anselin L. Spatial Econometrics:Methods and Models[D]. Kluwer Academic, Dordrecht, 1988.
[19]陈建先,郑玉歆.空间计量经济学文献综述[J]. 21世纪数量经济学,2011,9(12):79-93.
[20]魏艳旭,孙根年,李静.基于技术进步的中国能源消耗与经济增长:前后两个30年的比较[J].资源科学,2011,33(7):1338
[21]吴玉鸣,李建霞.省域经济增长与电力消费的局域空间计量经济分析[J].地理科学,2009,29(1):30-34.