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Towards a common measure of greenhouse gas related logistics activity using data envelopment analysis

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  • Holden, R.
  • Xu, B.
  • Greening, P.
  • Piecyk, M.
  • Dadhich, P.

Abstract

Monitoring company emissions from freight transport is essential if future greenhouse gas (GHG) reductions are to be realised. Modern economies are characterised increasingly by lower density freight movements. However, weight-based measures of freight transport activity (tonne-kilometre, tonnes lifted) are not good at describing volume-limited freight. After introducing the need for performance measurement, the problem of benchmarking is outlined in more detail. A context-dependent undesirable output data envelopment analysis (DEA) model, designed to be sensitive to business context, is then tested on a simulated set of fleet profiles. DEA can produce more consistent measures of good-practice, compared to ratio-based key performance indicators (KPI), providing emission reduction targets for companies and an aggregate reporting tool.

Suggested Citation

  • Holden, R. & Xu, B. & Greening, P. & Piecyk, M. & Dadhich, P., 2016. "Towards a common measure of greenhouse gas related logistics activity using data envelopment analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 105-119.
    • Handle: RePEc:eee:transa:v:91:y:2016:i:c:p:105-119
    DOI: 10.1016/j.tra.2016.06.001
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    Cited by:

    1. Hao Zhang & Jianxin You & Xuekelaiti Haiyirete & Tianyu Zhang, 2020. "Measuring Logistics Efficiency in China Considering Technology Heterogeneity and Carbon Emission through a Meta-Frontier Model," Sustainability, MDPI, vol. 12(19), pages 1-18, October.
    2. Jinying Li & Binghua Zhang & Jianfeng Shi, 2017. "Combining a Genetic Algorithm and Support Vector Machine to Study the Factors Influencing CO 2 Emissions in Beijing with Scenario Analysis," Energies, MDPI, vol. 10(10), pages 1-17, October.

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