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Assessing the performance of the transport sector within the global supply chain context: Decomposition of energy and environmental productivity

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  • Chen, Xiaodong
  • Guo, Anda
  • Miao, Zhuang
  • Zhu, Pengyu

Abstract

With globalization and industrialization, the supply chain of the transport sector has become increasingly complex and environmentally polluting. Consequently, the multidimensional and transnational nature of this sector presents numerous challenges in performance estimation, hindering previous studies. To address these issues, we propose an input-output modeling approach based on the Multi-region input-output model (MRIO) and Bounded-adjusted Measure (BAM). This approach allows for a comprehensive assessment of the sector's performance across various dimensions within a global context. Building upon this approach, we construct two indicators: the static transportation sustainability inefficiency (STSI) and the transportation sustainability productivity indicator (TSPI). Moreover, we introduce a systematic decomposition framework, encompassing both horizontal and vertical aspects, to analyze and break down the STSI and TSPI. Empirically, we apply this framework to study the sustainability performance of the global transport sector, encompassing 43 economies, over the period of 2005–2014. The results indicate that within transportation-related Global Supply Chains (GSCs), there is a potential for approximately 5% reduction in energy use, 7% reduction in CO2 emissions, 8% reduction in SO2 emissions, and 8% reduction in NOx emissions over the course of the decade. On average, a 1.0% increase in TSPI was observed, primarily driven by technological progress. From a global perspective, the sustainable development of the transport sector relies more on less developed economies. These findings point to the necessity of subsidizing these economies, which provide spillover effects through global supply chain.

Suggested Citation

  • Chen, Xiaodong & Guo, Anda & Miao, Zhuang & Zhu, Pengyu, 2024. "Assessing the performance of the transport sector within the global supply chain context: Decomposition of energy and environmental productivity," Applied Energy, Elsevier, vol. 358(C).
  • Handle: RePEc:eee:appene:v:358:y:2024:i:c:s0306261923019797
    DOI: 10.1016/j.apenergy.2023.122615
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    as
    1. Yan, Jia & Sun, Xinyu & Liu, John J., 2009. "Assessing container operator efficiency with heterogeneous and time-varying production frontiers," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 172-185, January.
    2. H. Wang & B.W. Ang & P. Zhou, 2018. "Decomposing aggregate CO2 emission changes with heterogeneity: An extended production-theoretical approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    3. William Cooper & Kyung Park & Jesus Pastor, 1999. "RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA," Journal of Productivity Analysis, Springer, vol. 11(1), pages 5-42, February.
    4. Binlei Gong, 2020. "New Growth Accounting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 641-661, March.
    5. Acquaye, Adolf & Ibn-Mohammed, Taofeeq & Genovese, Andrea & Afrifa, Godfred A & Yamoah, Fred A & Oppon, Eunice, 2018. "A quantitative model for environmentally sustainable supply chain performance measurement," European Journal of Operational Research, Elsevier, vol. 269(1), pages 188-205.
    6. Chen, Kun & Zhu, Joe, 2020. "Additive slacks-based measure: Computational strategy and extension to network DEA," Omega, Elsevier, vol. 91(C).
    7. Boussemart, Jean-Philippe & Ferrier, Gary D. & Leleu, Hervé & Shen, Zhiyang, 2020. "An expanded decomposition of the Luenberger productivity indicator with an application to the Chinese healthcare sector," Omega, Elsevier, vol. 91(C).
    8. Fare, R. & Grosskopf, S. & Hernandez-Sancho, F., 2004. "Environmental performance: an index number approach," Resource and Energy Economics, Elsevier, vol. 26(4), pages 343-352, December.
    9. Ramli, Noor Asiah & Munisamy, Susila, 2015. "Eco-efficiency in greenhouse emissions among manufacturing industries: A range adjusted measure," Economic Modelling, Elsevier, vol. 47(C), pages 219-227.
    10. Robert M. Solow, 1956. "A Contribution to the Theory of Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 70(1), pages 65-94.
    11. Dervaux, Benoît & Kerstens, Kristiaan & Vanden Eeckaut, Philippe, 1998. "Radial and nonradial static efficiency decompositions: a focus on congestion measurement," Transportation Research Part B: Methodological, Elsevier, vol. 32(5), pages 299-312, June.
    12. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    13. Guo, I-Lung & Lee, Hsuan-Shih & Lee, Dan, 2017. "An integrated model for slack-based measure of super-efficiency in additive DEA," Omega, Elsevier, vol. 67(C), pages 160-167.
    14. Xie, Chunping & Bai, Mengqi & Wang, Xiaolei, 2018. "Accessing provincial energy efficiencies in China’s transport sector," Energy Policy, Elsevier, vol. 123(C), pages 525-532.
    15. Miao, Zhuang & Chen, Xiaodong, 2022. "Combining parametric and non-parametric approach, variable & source -specific productivity changes and rebound effect of energy & environment," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    16. Kim, Bosung & Park, Kun Soo & Jung, Se-Youn & Park, Sang Hun, 2018. "Offshoring and outsourcing in a global supply chain: Impact of the arm’s length regulation on transfer pricing," European Journal of Operational Research, Elsevier, vol. 266(1), pages 88-98.
    17. A. Charnes & W. W. Cooper, 1962. "Programming with linear fractional functionals," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 9(3‐4), pages 181-186, September.
    18. Tokito, Shohei, 2018. "Environmentally-Targeted Sectors and Linkages in the Global Supply-Chain Complexity of Transport Equipment," Ecological Economics, Elsevier, vol. 150(C), pages 177-183.
    19. Morris A. Cohen & Suman Mallik, 1997. "Global Supply Chains: Research And Applications," Production and Operations Management, Production and Operations Management Society, vol. 6(3), pages 193-210, September.
    20. Charnes, A. & Cooper, W. W. & Golany, B. & Seiford, L. & Stutz, J., 1985. "Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 91-107.
    21. Shen, Zhiyang & Baležentis, Tomas & Chen, Xueli & Valdmanis, Vivian, 2018. "Green growth and structural change in Chinese agricultural sector during 1997–2014," China Economic Review, Elsevier, vol. 51(C), pages 83-96.
    22. Zhang, Runsen & Fujimori, Shinichiro & Dai, Hancheng & Hanaoka, Tatsuya, 2018. "Contribution of the transport sector to climate change mitigation: Insights from a global passenger transport model coupled with a computable general equilibrium model," Applied Energy, Elsevier, vol. 211(C), pages 76-88.
    23. Cui, Qiang & Li, Ye, 2018. "Airline dynamic efficiency measures with a Dynamic RAM with unified natural & managerial disposability," Energy Economics, Elsevier, vol. 75(C), pages 534-546.
    24. Miao, Zhuang & Chen, Xiaodong & Baležentis, Tomas & Sun, Chuanwang, 2019. "Atmospheric environmental productivity across the provinces of China: Joint decomposition of range adjusted measure and Luenberger productivity indicator," Energy Policy, Elsevier, vol. 132(C), pages 665-677.
    25. Zhi Wang & Shang-Jin Wei & Xinding Yu & Kunfu Zhu, 2017. "Characterizing Global Value Chains: Production Length and Upstreamness," NBER Working Papers 23261, National Bureau of Economic Research, Inc.
    26. Ang, Frederic & Kerstens, Pieter Jan, 2020. "A superlative indicator for the Luenberger-Hicks-Moorsteen productivity indicator: Theory and application," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1161-1173.
    27. Fare, Rolf, et al, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    28. Heydari, Chiman & Omrani, Hashem & Taghizadeh, Rahim, 2020. "A fully fuzzy network DEA-Range Adjusted Measure model for evaluating airlines efficiency: A case of Iran," Journal of Air Transport Management, Elsevier, vol. 89(C).
    29. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    30. Cui, Qiang & Lin, Jing-ling & Jin, Zi-yin, 2020. "Evaluating airline efficiency under “Carbon Neutral Growth from 2020” strategy through a Network Interval Slack-Based Measure," Energy, Elsevier, vol. 193(C).
    31. Adler, Nicole & Martini, Gianmaria & Volta, Nicola, 2013. "Measuring the environmental efficiency of the global aviation fleet," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 82-100.
    32. Aida, Kazuo & Cooper, William W. & Pastor, Jésus T. & Sueyoshi, Toshiyuki, 1998. "Evaluating Water Supply Services in Japan with RAM: a Range-adjusted Measure of Inefficiency," Omega, Elsevier, vol. 26(2), pages 207-232, April.
    33. Fare, Rolf & Knox Lovell, C. A., 1978. "Measuring the technical efficiency of production," Journal of Economic Theory, Elsevier, vol. 19(1), pages 150-162, October.
    34. Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August.
    35. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    36. William Cooper & Jesús Pastor & Fernando Borras & Juan Aparicio & Diego Pastor, 2011. "BAM: a bounded adjusted measure of efficiency for use with bounded additive models," Journal of Productivity Analysis, Springer, vol. 35(2), pages 85-94, April.
    37. Wang, H. & Pan, Chen & Wang, Qunwei & Zhou, P., 2020. "Assessing sustainability performance of global supply chains: An input-output modeling approach," European Journal of Operational Research, Elsevier, vol. 285(1), pages 393-404.
    38. Chen, Xiaodong & Wu, Ge & Li, Ding, 2019. "Efficiency measure on the truck restriction policy in China: A non-radial data envelopment model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 140-154.
    39. Lucas, Robert Jr., 1988. "On the mechanics of economic development," Journal of Monetary Economics, Elsevier, vol. 22(1), pages 3-42, July.
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