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Dynamic changes in the energy–carbon performance of Chinese transportation sector: a meta-frontier non-radial directional distance function approach

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Abstract

The transportation sector is the main energy consumer and carbon emitter in China. To accurately evaluate the dynamic changes in the energy–carbon performance of the sector and to propose alternatives for sustainable development, this paper proposes an approach incorporating the meta-frontier method, global benchmark technology, and non-radial directional distance function. Using this approach, the paper proposes a new definition, named the global meta-frontier non-radial Malmquist energy–carbon performance index (GMNMECPI). GMNMECPI can be decomposed into technical efficiency change (EC), best-practice gap change (BPC), and technology gap change (TGC). This new method was then used to estimate the dynamic changes of energy–carbon performance in China’s transportation sector from 2006 to 2015. The paper also identifies the effect of current policies. The empirical results show that the energy–carbon performance of China’s transportation sector decreased annually by 1.636% during the study period. This reduction was mainly caused by a significant technology lag in the central area while primarily influenced by deterioration in efficiency in both the east and west. There is a distinct heterogeneity in technology across China’s three areas. Based on the findings, the paper closes with policy implications.

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Notes

  1. When measure energy–carbon performance, one way is to consider the scaling factors of the energy input and the undesirable outputs, which indicates that an energy–carbon performance assessment only requires achieving the reduction of energy use and CO2 emission. In contrast, the model in this paper takes into account the scaling factors of the energy input, the gross product, and the CO2 emission. Obviously, our assessment requires higher standards and is a better fit with China's reality. As a developing country, China needs to consider energy conservation, environmental protection, and economic development in its energy and environmental policies. Zhou et al. (2012) also adopted the same idea when assessing energy–CO2 emission performance in electricity generation.

  2. The eastern region includes Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. The central region includes Shanxi, Jilin, Heilongjiang. Anhui, Jiangxi, Henan, Hubei, and Hunan. The western region includes Inner Mongolia, Sichuan, Guizhou, Yunnan, Guangxi, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang. Tibet and Chongqing are excluded due to the absence of relevant data from those provinces.

References

  • Arocena P, Price CW (1999) Generating efficiency: economic and environmental regulation of public and private electricity generators in Spain. Int J Ind Organ 20(1):41–69

    Article  Google Scholar 

  • Barros CP, Managi S, Matousek R (2012) The technical efficiency of the Japanese banks: non-radial directional performance measurement with undesirable output. Omega 40(1):1–8

    Article  Google Scholar 

  • Bi G, Wang P, Yang F, Liang L (2014) Energy and environmental efficiency of China’s transportation sector: a multidirectional analysis approach. Math Probl Eng 2014:1–12

    Google Scholar 

  • Blesl M, Das A, Fahl U, Remme U (2007) Role of energy efficiency standards in reducing CO2 emissions in Germany: an assessment with TIMES. Energy Policy 35(2):772–785

    Article  Google Scholar 

  • BP, Statistical Review of World Energy (2011). http://www.bp.com/statistical review

  • Chambers RG, Chung Y, Färe R (1996) Benefit and distance functions. J Econ Theory 70(2):407–419

    Article  Google Scholar 

  • Chang YT, Zhang N, Danao D, Zhang N (2013) Environmental efficiency analysis of transportation system in China: a non-radial DEA approach. Energy Policy 58:277–283

    Article  Google Scholar 

  • Chen SY (2010) Green industrial revolution in China: a perspective from the change of environmental total factor productivity. Econ Res J 11:21–34

    Google Scholar 

  • Chung YH, Färe R, Grosskopf S (1997) Productivity and undesirable outputs: a directional distance function approach. J Environ Manag 51(3):229–240

    Article  Google Scholar 

  • Cook WD, Seiford LM (2009) Data envelopment analysis (DEA)—thirty years on. Eur J Oper Res 192(1):1–17

    Article  Google Scholar 

  • Cooper WW, Park KS, Pastor JT (1999) RAM: a range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA. J Prod Anal 11(1):5–42

    Article  Google Scholar 

  • Cui Q, Li Y (2015) An empirical study on the influencing factors of transportation carbon efficiency: evidences from fifteen countries. Appl Energy 141:209–217

    Article  Google Scholar 

  • Cullinane K, Song DW (2006) Estimating the relative efficiency of European container ports: a stochastic frontier analysis. Res Transp Econ 16:85–115

    Article  Google Scholar 

  • Ding JX (2012) Analysis of carbon emission and emission reduction potential in China’s transportation industry. Compr Transp 12:20–26

    Google Scholar 

  • Dyckhoff H, Allen K (2001) Measuring ecological efficiency with data envelopment analysis (DEA). Eur J Oper Res 132(2):312–325

    Article  Google Scholar 

  • Emrouznejad A, Yang GL (2016) CO2 emissions reduction of Chinese light manufacturing industries: a novel RAM-based global Malmquist–Luenberger productivity index. Energy Policy 96:397–410

    Article  Google Scholar 

  • Färe R, Grosskopf S (2010) Directional distance functions and slacks-based measures of efficiency. Eur J Oper Res 200(1):320–322

    Article  Google Scholar 

  • Färe R, Grosskopf S, Lovell CAK, Pasurka C (1989) Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. Rev Econ Stat 71:90–98

    Article  Google Scholar 

  • Färe R, Grosskopf S, Norris M, Zhang Z (1994) Productivity growth, technical progress, and efficiency change in industrialized countries: reply. Am Econ Rev 84(5):1040–1044

    Google Scholar 

  • Färe R, Grosskopf S, Pasurka CA (2007) Environmental production functions and environmental directional distance functions. Energy 32(7):1055–1066

    Article  Google Scholar 

  • Farrell AE, Sperling D (2007) A low-carbon fuel standard for California, parts 1 & 2. Institute of Transportation Studies, University of California

  • Fukuyama H, Weber WL (2009) A directional slacks-based measure of technical inefficiency. Socio-Econ Plan Sci 43(4):274–287

    Article  Google Scholar 

  • Fukuyama H, Weber WL (2010) A slacks-based inefficiency measure for a two-stage system with bad outputs. Omega 38(5):398–409

    Article  Google Scholar 

  • Fukuyama H, Yoshida Y, Managi S (2011) Modal choice between air and rail: a social efficiency benchmarking analysis that considers CO2 emissions. Environ Econ Policy Stud 13(2):89–102

    Article  Google Scholar 

  • Guo XD, Zhu L, Fan Y, Xie BC (2011) Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA. Energy Policy 39(5):2352–2360

    Article  Google Scholar 

  • Hickman R, Ashiru O, Banister D (2009) Achieving carbon-efficient transportation: backcasting from London. Transp Res Rec J Transp Res Board 2139:172–182

    Article  Google Scholar 

  • Hu JL, Lee YC (2008) Efficient three industrial waste abatement for regions in China. Int J Sustain Dev World Ecol 15(2):132–144

    Article  Google Scholar 

  • International Energy Agency (IEA) (2013) CO2 emissions from fuel combustion highlights. Warsow, Poland

  • Lee M, Jin Y (2012) The substitutability of nuclear capital for thermal capital and the shadow price in the Korean electric power industry. Energy Policy 51:834–841

    Article  Google Scholar 

  • Lin BQ, Fei RL (2015) Regional differences of CO2 emissions performance in China’s agricultural sector: a Malmquist index approach. Eur J Agron 70:33–40

    Article  Google Scholar 

  • Lin W, Yang J, Chen B (2011) Temporal and spatial analysis of integrated energy and environment efficiency in China based on a green GDP index. Energies 4(9):1376–1390

    Article  Google Scholar 

  • Liu W, Tian J, Chen L, Lu W, Gao Y (2015) Environmental performance analysis of eco-industrial parks in China: a data envelopment analysis approach. J Ind Ecol 19(6):1070–1081

    Article  Google Scholar 

  • Lu WZ, Huang SJ, Wang L (2014) Environmental efficiency and regional technology gaps in China: a metafrontier non-radial and non-oriental Malmquist index analysis. Pol J Environ Stud 23(1):119–124

    Google Scholar 

  • Machado-Filho H (2009) Brazilian low-carbon transportation policies: opportunities for international support. Clim Policy 9(5):495–507

    Article  Google Scholar 

  • Meng FY, Fan LW, Zhou P, Zhou DQ (2013) Measuring environmental performance in China’s industrial sectors with non-radial DEA. Math Comput Model 58(5):1047–1056

    Article  Google Scholar 

  • National Bureau of Statistics of China (NBSC) (2011) China statistical year book 2011. China Statistics Press, Beijing

    Google Scholar 

  • O’Donnell CJ, Rao DP, Battese GE (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empir Econ 34(2):231–255

    Article  Google Scholar 

  • Oh DH, Lee JD (2010) A metafrontier approach for measuring Malmquist productivity index. Empir Econ 38(1):47–64

    Article  Google Scholar 

  • Pastor JT, Lovell CK (2005) A global Malmquist productivity index. Econ Lett 88(2):266–271

    Article  Google Scholar 

  • Picazo-Tadeo AJ, Reig-Martinez E, Hernandez-Sancho F (2005) Directional distance functions and environmental regulation. Resour Energy Econ 27(2):131–142

    Article  Google Scholar 

  • Pittman RW (1983) Multilateral productivity comparisons with undesirable outputs. Econ J 93(372):883–891

    Article  Google Scholar 

  • Seiford LM, Zhu J (2002) Modeling undesirable factors in efficiency evaluation. Eur J Oper Res 142(1):16–20

    Article  Google Scholar 

  • Song M, Song Y, An Q, Yu H (2013) Review of environmental efficiency and its influencing factors in China: 1998–2009. Renew Sustain Energy Rev 20:8–14

    Article  Google Scholar 

  • Song X, Hao Y, Zhu X (2015) Analysis of the environmental efficiency of the Chinese transportation sector using an undesirable output slacks-based measure data envelopment analysis model. Sustainability 7(7):9187–9206

    Article  Google Scholar 

  • Song M, Zhang G, Zeng W, Liu J, Fang K (2016) Railway transportation and environmental efficiency in China. Transp Res Part D Transp Environ 48:488–498

    Article  Google Scholar 

  • Steenhof PA, McInnis BC (2008) A comparison of alternative technologies to de-carbonize Canada’s passenger transportation sector. Technol Forecast Soc Change 75(8):1260–1278

    Article  Google Scholar 

  • Sun Z, Luo R, Zhou D (2015) Optimal path for controlling sectoral CO2 emissions among China’s regions: a centralized DEA approach. Sustainability 8(1):28

    Article  Google Scholar 

  • Tao Y, Zhang S (2013) Environmental efficiency of electric power industry in the Yangtze River Delta. Math Comput Model 58(5):927–935

    Article  Google Scholar 

  • Tian D, Zhao F, Mu W, Kanianska R, Feng J (2016) Environmental efficiency of Chinese open-field grape production: an evaluation using data envelopment analysis and spatial autocorrelation. Sustainability 8(12):1246

    Article  Google Scholar 

  • Tone K (2001) A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 130(3):498–509

    Article  Google Scholar 

  • Trappey AJ, Trappey C, Hsiao C, Ou JJ, Li S, Chen KW (2012) An evaluation model for low carbon island policy: the case of Taiwan’s green transportation policy. Energy Policy 45:510–515

    Article  Google Scholar 

  • Tulkens H, Eeckaut PV (1995) Non-parametric efficiency, progress and regress measures for panel data: methodological aspects. Eur J Oper Res 80(3):474–499

    Article  Google Scholar 

  • Wang Z, Feng C (2015a) A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: an application of global data envelopment analysis. Appl Energy 147:617–626

    Article  Google Scholar 

  • Wang Z, Feng C (2015b) Sources of production inefficiency and productivity growth in China: a global data envelopment analysis. Energy Econ 49:380–389

    Article  Google Scholar 

  • Wang Z, Yin F, Zhang Y, Zhang X (2012a) An empirical research on the influencing factors of regional CO2 emissions: evidence from Beijing city, China. Appl Energy 100:277–284

    Article  Google Scholar 

  • Wang ZH, Zeng HL, Wei YM, Zhang YX (2012b) Regional total factor energy efficiency: an empirical analysis of industrial sector in China. Appl Energy 97:115–123

    Article  Google Scholar 

  • Wang K, Yu S, Zhang W (2013) China’s regional energy and environmental efficiency: a DEA window analysis based dynamic evaluation. Math Comput Model 58(5):1117–1127

    Article  Google Scholar 

  • Wang Z, Feng C, Zhang B (2014) An empirical analysis of China’s energy efficiency from both static and dynamic perspectives. Energy 74:322–330

    Article  Google Scholar 

  • Wang QW, Su B, Sun J, Zhou P, Zhou D (2015) Measurement and decomposition of energy-saving and emissions reduction performance in Chinese cities. Appl Energy 151:85–92

    Article  Google Scholar 

  • Wang QW, Su B, Zhou P, Chiu CR (2016) Measuring total-factor CO2 emission performance and technology gaps using a non-radial directional distance function: a modified approach. Energy Econ 56:475–482

    Article  Google Scholar 

  • Wu J, Zhu Q, Chu J, Liu H, Liang L (2016) Measuring energy and environmental efficiency of transportation systems in China based on a parallel DEA approach. Transp Res Part D Transp Environ 48:460–472

    Article  Google Scholar 

  • Yu Y, Choi Y, Zhang N (2015) Strategic corporate sustainability performance of Chinese state-owned listed firms: a meta-frontier generalized directional distance function approach. Soc Sci J 52(3):300–310

    Article  Google Scholar 

  • Yu C, Shi L, Wang Y, Chang Y, Cheng B (2016) The eco-efficiency of pulp and paper industry in China: an assessment based on slacks-based measure and Malmquist–Luenberger index. J Clean Prod 127:511–521

    Article  Google Scholar 

  • Zhang N, Choi Y (2013) A comparative study of dynamic changes in CO2 emission performance of fossil fuel power plants in China and Korea. Energy Policy 62:324–332

    Article  Google Scholar 

  • Zhang YJ, Da YB (2013) Decomposing the changes of energy-related carbon emissions in China: evidence from the PDA approach. Nat Hazards 69(1):1109–1122

    Article  Google Scholar 

  • Zhang B, Wang Z (2014) Inter-firm collaborations on carbon emission reduction within industrial chains in China: practices, drivers and effects on firms’ performances. Energy Econ 42:115–131

    Article  Google Scholar 

  • Zhang N, Wei X (2015) Dynamic total factor carbon emissions performance changes in the Chinese transportation industry. Appl Energy 146:409–420

    Article  Google Scholar 

  • Zhang N, Zhou P, Kung CC (2015) Total-factor carbon emission performance of the Chinese transportation industry: a bootstrapped non-radial Malmquist index analysis. Renew Sustain Energy Rev 41:584–593

    Article  Google Scholar 

  • Zhang J, Zeng W, Shi H (2016) Regional environmental efficiency in China: analysis based on a regional slack-based measure with environmental undesirable outputs. Ecol Indic 71:218–228

    Article  Google Scholar 

  • Zhou P, Poh KL, Ang BW (2007) A non-radial DEA approach to measuring environmental performance. Eur J Oper Res 178(1):1–9

    Article  Google Scholar 

  • Zhou P, Ang BW, Wang H (2012) Energy and CO2 emission performance in electricity generation: a non-radial directional distance function approach. Eur J Oper Res 221(3):625–635

    Article  Google Scholar 

  • Zhou Y, Xing X, Fang K, Liang D, Xu C (2013) Environmental efficiency analysis of power industry in China based on an entropy SBM model. Energy Policy 57:68–75

    Article  Google Scholar 

  • Zhou G, Chung W, Zhang Y (2014) Measuring energy efficiency performance of China’s transport sector: a data envelopment analysis approach. Expert Syst Appl 41(2):709–722

    Article  Google Scholar 

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Acknowledgements

This study was funded by National Social Science Foundation of China (No.15BGL200), National Natural Science Foundation of China (Nos. 71573186, 71473107, 71603105, 71673119), Natural Science Foundation of Jiangsu, China (No. SBK2016042936), Science Foundation of Ministry of Education of China (No. 16YJC790067).

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  1. School of Management, Jiangsu University, Zhenjiang, 212013, China

    Gang Tian, Jian Shi, Licheng Sun, Xingle Long & Benhai Guo

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  1. Gang Tian
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Correspondence to Gang Tian.

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Tian, G., Shi, J., Sun, L. et al. Dynamic changes in the energy–carbon performance of Chinese transportation sector: a meta-frontier non-radial directional distance function approach. Nat Hazards 89, 585–607 (2017). https://doi.org/10.1007/s11069-017-2981-5

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