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

Xiaodong Chen, Anda Guo, Zhuang Miao and Pengyu Zhu

Applied Energy, 2024, vol. 358, issue C, No S0306261923019797

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.

Keywords: Global supply chain; Transport sector; Efficiency and productivity; MRIO; Non-parameter model (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.apenergy.2023.122615

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