中国交通运输经济发展与碳排放效率评价
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摘要
为评价交通运输经济与碳排放绩效,探寻经济和碳减排双赢的改进路径,提出交通运输经济效率、碳排放效率及联合效率概念。针对中国30个省市区,以资本存量、煤类消费量、油类消费量、新能源消费量、从业人员作为投入要素,交通运输综合换算周转量和行业增加值作为期望产出,CO_2排放量作为非期望产出,采用RAM模型测度评估中国30个省市区的交通运输行业经济、碳排放及联合效率,探究3种效率的分布特征及变动规律,并利用非效率分解模型研究非效率来源,探索改进方向。研究结果表明:各省市区之间的交通运输经济效率、碳排放效率和联合效率均存在明显差异,且东部、中部、西部和东北地区呈现不同的梯度分布特性; 2006～2016年的经济效率、碳排放效率及联合效率总体上可以分为2个阶段,第1阶段为2006～2011年,经济效率、碳排放效率和联合效率均呈波动上升,第2阶段为2011～2016年,经济效率、碳排放效率和联合效率轻微波动,并逐渐趋于平稳。非效率分解模型表明:能源过度消耗是经济效率、碳排放效率和联合效率的主要非效率来源; CO_2过度排放是碳排放效率和联合效率非效率的主要来源;行业增加值产出不足是经济效率和联合效率非效率的主要来源。这说明推广新能源,提升交通运输运营效率,革新交通减排技术是能够实现双赢的重要路径。
To evaluate the performance of transportation economy and carbon emissions and to explore the win-win combination of economic development and carbon emission reduction, this study proposes the concepts of industry economic efficiency, carbon emission efficiency, and joint efficiency. These three efficiencies were then measured for China's 30 provinces using the range adjusted measure(RAM), which used the capital stock, coal consumption, oil consumption, new-energy consumption, and number of employees as inputs and the comprehensive conversion turnover, industry added value, and CO_2 emissions as outputs. Subsequently, the distribution characteristics and tendency of the efficiency measures were explored, and the model of efficiency decomposition was used to study the source of non-efficiency. The results show that: ① The degrees of transportation operational economy efficiency, carbon emission efficiency, and unified efficiency are different for each province, and the efficiencies of the eastern, central, western, and northeast parts of China have gradient distribution features. ② The industry, carbon emission, and joint efficiencies can each be divided into two phases. The efficiencies are observed to increase in volatility during 2006-2011, whereas they decrease slightly and then vary steadily between 2011-2016. ③ The results of the non-efficiency decomposition model indicates that excessive energy consumption is the main source of non-efficiency for all three efficiency measures. Further, excessive CO_2 emission is the main source of inefficiency for both the carbon emission efficiency and unified efficiency. The insufficiency of the industry's value-add is the main source of inefficiency for the economic and unified efficiencies. This shows that the promotion of new-energy modes, improvement of transportation operation efficiency, and innovation of transportation emission reduction technology is important to develop a win-win combination.
To evaluate the performance of transportation economy and carbon emissions and to explore the win-win combination of economic development and carbon emission reduction, this study proposes the concepts of industry economic efficiency, carbon emission efficiency, and joint efficiency. These three efficiencies were then measured for China's 30 provinces using the range adjusted measure(RAM), which used the capital stock, coal consumption, oil consumption, new-energy consumption, and number of employees as inputs and the comprehensive conversion turnover, industry added value, and CO_2 emissions as outputs. Subsequently, the distribution characteristics and tendency of the efficiency measures were explored, and the model of efficiency decomposition was used to study the source of non-efficiency. The results show that: ① The degrees of transportation operational economy efficiency, carbon emission efficiency, and unified efficiency are different for each province, and the efficiencies of the eastern, central, western, and northeast parts of China have gradient distribution features. ② The industry, carbon emission, and joint efficiencies can each be divided into two phases. The efficiencies are observed to increase in volatility during 2006-2011, whereas they decrease slightly and then vary steadily between 2011-2016. ③ The results of the non-efficiency decomposition model indicates that excessive energy consumption is the main source of non-efficiency for all three efficiency measures. Further, excessive CO_2 emission is the main source of inefficiency for both the carbon emission efficiency and unified efficiency. The insufficiency of the industry's value-add is the main source of inefficiency for the economic and unified efficiencies. This shows that the promotion of new-energy modes, improvement of transportation operation efficiency, and innovation of transportation emission reduction technology is important to develop a win-win combination.
引文
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[6] HUANG Z, CAO F, JIN C, et al. Carbon Emission Flow from Self-driving Tours and Its Spatial Relationship with Scenic Spots—A Traffic-related Big Data Method [J]. Journal of Cleaner Production, 2016 (142): 946-955.
[7] MEYER I, LEIMBACH M, JAEGER C C. International Passenger Transport and Climate Change: A Sector Analysis in Car Demand and Associated CO2 Emissions from 2000 to 2050 [J]. Energy Policy, 2007 (35): 6332-6345.
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[9] TAPIO P. Towards a Theory of Decoupling: Degrees of Decoupling in the EU and the Case of Road Traffic in Finland Between 1970 and 2001 [J]. Transport Policy, 2005, 12 (2): 137-151
[10] GRAY D, ANABLE J, ILLINGWORTH L, et al. Decoupling the Link Between Economic Growth, Transport Growth and Carbon Emissions in Scotland[R]. Aberdeen: Robert Gordon University, 2006.
[11] 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 Chang-wei, ZHANG Shuai, JIAO Ping, 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.
[12] FARRELL M J. The Measurement of Productive Efficiency [J]. Journal of the Royal Statistical Society, 1957, 120 (3): 253-290.
[13] SUEYOSHI T, GOTO M. DEA Approach for Unified Efficiency Measurement: Assessment of Japanese Fossil Fuel Power Generation [J]. Energy Economics, 2011 (33): 292-303.
[14] ZHOU P, ANG B W. Linear Programming Models for Measuring Economy-wide Energy Efficiency Performance [J]. Energy Policy, 2008 (36): 2911-2916.
[15] RENSHAW E. Energy Efficiency and the Slump in Labor Productivity in the USA [J]. Energy Econo- mics, 1981 (3): 36-42.
[16] AIDA K, COOPER W W, PASTOR J T, et al. Evaluating Water Supply Services in Japan with RAM: A Rang-adjusted Measure of Inefficiency [J]. Omega, 1998 (26): 207-232.
[17] COOPER W, SEIFORD L M, TONE K. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software [M]. Berlin: Springer-Verlag 2007.
[18] 冯梅,王成静.我国各地区软件与信息技术服务业绩效评价研究[J].经济问题,2015(8):66-70. FENG Mei,WANG Cheng-jing. Evaluation Research on Performance of Software and Information Technology Service Industry in Different Regions in China [J]. On Economic Problems, 2015 (8): 66-70.
[19] 王冬冬,李丽琴,肖亮.基于DEA的陕西省交通运输效率评价与分析[J].数学的实践与认识,2014,44(20):33-38. WANG Dong-dong, LI Li-qin, XIAO Liang. Evaluation and Analysis of Shaanxi Province Transportation Efficiency Based on DEA [J]. Mathematics in Practice and Theory, 2014, 44 (20): 33-38.
[20] 韩晶,陈超凡,施发启.中国制造业环境效率、行业异质性与最优规制强度[J].统计研究,2014, 31(3): 61-67. HAN Jing, CHEN Chao-fan, SHI Fa-qi. Environment Efficiency of Chinese Manufacturing, Industry Heterogeneity and Optimal Regulation Intensity [J]. Statistical Research, 2014, 31 (3): 61-67.
[21] 李杰伟,张国庆.中国交通运输基础设施资本存量及资本回报率估算[J].当代财经,2016(6):3-14. LI Jie-wei, ZHANG Guo-qing. Estimation of Capital Stock and Capital Return Rate of China’s Transportation Infrastructure [J]. Contemporary Finance & Economics, 2016 (6): 3-14.
[22] 赵光辉.交通强国愿景下我国交通行政管理趋势及应对[J].广西社会科学,2018(4):97-100. ZHAO Guang-hui. China's Traffic Administrative Management Trends and Countermeasures Under the Vision of Strong Traffic Country [J]. Guangxi Social Sciences, 2018 (4): 97-100.
[2] 喻洁,达亚彬,欧阳斌.基于LMDI分解方法的中国交通运输行业碳排放变化分析[J].中国公路学报,2015,28(10):112-119. YU Jie, DA Ya-bin, OUYANG Bin. Analysis of Carbon Emission Changes in China's Transportation Industry Based on LMDI Decomposition Method [J]. China Journal of Highway and Transport, 2015, 28 (10): 112-119.
[3] SAIJA S, ROMANO D. A Methodology for the Estimation of Road Transport Air Emissions in Urban Areas of Italy [J]. Atmospheric Environment, 2002, 36 (34): 5377-5383.
[4] SCHOLL L, SCHIPPER L. CO2 Emissions from Passenger Transport: A Comparison of International Trends from 1973 to 1992 [J]. Energy Policy, 1996, 24 (1): 17-30.
[5] 徐雅楠,杜平志.我国交通运输业的碳排放测度及因素分解[J].物流技术,2011,30(6):16-18. XU Ya-nan, DU Ping-zhi. Measurement and Factor Analysis of Carbon Emissions of Transportation Industry of China [J]. Logistics Technology, 2011, 30 (6): 16-18.
[6] HUANG Z, CAO F, JIN C, et al. Carbon Emission Flow from Self-driving Tours and Its Spatial Relationship with Scenic Spots—A Traffic-related Big Data Method [J]. Journal of Cleaner Production, 2016 (142): 946-955.
[7] MEYER I, LEIMBACH M, JAEGER C C. International Passenger Transport and Climate Change: A Sector Analysis in Car Demand and Associated CO2 Emissions from 2000 to 2050 [J]. Energy Policy, 2007 (35): 6332-6345.
[8] 谢守红,蔡海亚,夏刚祥.中国交通运输业碳排放的测算及影响因素[J].干旱区资源与环境,2016,30(5):13-18. XIE Shou-hong, CAI Hai-ya, XIA Gang-xiang. Calculation of the Carbon Emissions of Chinese Transportation Industry and the Driving Factors [J]. Journal of Arid Land Resources and Environment, 2016, 30 (5): 13-18.
[9] TAPIO P. Towards a Theory of Decoupling: Degrees of Decoupling in the EU and the Case of Road Traffic in Finland Between 1970 and 2001 [J]. Transport Policy, 2005, 12 (2): 137-151
[10] GRAY D, ANABLE J, ILLINGWORTH L, et al. Decoupling the Link Between Economic Growth, Transport Growth and Carbon Emissions in Scotland[R]. Aberdeen: Robert Gordon University, 2006.
[11] 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 Chang-wei, ZHANG Shuai, JIAO Ping, 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.
[12] FARRELL M J. The Measurement of Productive Efficiency [J]. Journal of the Royal Statistical Society, 1957, 120 (3): 253-290.
[13] SUEYOSHI T, GOTO M. DEA Approach for Unified Efficiency Measurement: Assessment of Japanese Fossil Fuel Power Generation [J]. Energy Economics, 2011 (33): 292-303.
[14] ZHOU P, ANG B W. Linear Programming Models for Measuring Economy-wide Energy Efficiency Performance [J]. Energy Policy, 2008 (36): 2911-2916.
[15] RENSHAW E. Energy Efficiency and the Slump in Labor Productivity in the USA [J]. Energy Econo- mics, 1981 (3): 36-42.
[16] AIDA K, COOPER W W, PASTOR J T, et al. Evaluating Water Supply Services in Japan with RAM: A Rang-adjusted Measure of Inefficiency [J]. Omega, 1998 (26): 207-232.
[17] COOPER W, SEIFORD L M, TONE K. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software [M]. Berlin: Springer-Verlag 2007.
[18] 冯梅,王成静.我国各地区软件与信息技术服务业绩效评价研究[J].经济问题,2015(8):66-70. FENG Mei,WANG Cheng-jing. Evaluation Research on Performance of Software and Information Technology Service Industry in Different Regions in China [J]. On Economic Problems, 2015 (8): 66-70.
[19] 王冬冬,李丽琴,肖亮.基于DEA的陕西省交通运输效率评价与分析[J].数学的实践与认识,2014,44(20):33-38. WANG Dong-dong, LI Li-qin, XIAO Liang. Evaluation and Analysis of Shaanxi Province Transportation Efficiency Based on DEA [J]. Mathematics in Practice and Theory, 2014, 44 (20): 33-38.
[20] 韩晶,陈超凡,施发启.中国制造业环境效率、行业异质性与最优规制强度[J].统计研究,2014, 31(3): 61-67. HAN Jing, CHEN Chao-fan, SHI Fa-qi. Environment Efficiency of Chinese Manufacturing, Industry Heterogeneity and Optimal Regulation Intensity [J]. Statistical Research, 2014, 31 (3): 61-67.
[21] 李杰伟,张国庆.中国交通运输基础设施资本存量及资本回报率估算[J].当代财经,2016(6):3-14. LI Jie-wei, ZHANG Guo-qing. Estimation of Capital Stock and Capital Return Rate of China’s Transportation Infrastructure [J]. Contemporary Finance & Economics, 2016 (6): 3-14.
[22] 赵光辉.交通强国愿景下我国交通行政管理趋势及应对[J].广西社会科学,2018(4):97-100. ZHAO Guang-hui. China's Traffic Administrative Management Trends and Countermeasures Under the Vision of Strong Traffic Country [J]. Guangxi Social Sciences, 2018 (4): 97-100.