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Assessing the employment agglomeration and social accessibility impacts of high speed rail in Eastern Australia

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  • David Hensher
  • Richard Ellison
  • Corinne Mulley

Abstract

There is growing interest in establishing additional evidence, under the umbrella of the wider economy impacts of transport infrastructure projects, to support transport projects in general and public transport projects in particular that struggle to obtain benefit–cost ratios sufficient to gain the support of financial agencies. This paper focuses on one element of wider economy impacts, often referred to as effective economic (employment) density or employment agglomeration impacts, and another, less usually identified, social accessibility impact (SAI) which we refer to as effective social density, which in broad terms provide, correspondingly, evidence of the potential gains in work-related output (often referred to as productivity gains) and potential gains in non-work-related outputs. Both are associated with gains in individual and household benefit attributable to improved accessibility to services linked with populations and particular locations. The SAIs may capture some of the induced benefits in those jurisdictions where these are included routinely in benefit–cost analysis, and the methodology here is most appropriate to those settings where an existing calibrated demand curve may not be available. Using the proposed high speed rail (HSR) project between Sydney and Melbourne as the empirical setting, we identify economic agglomeration and social accessibility benefits for work and non-work related activity respectively. We find the former to be relatively small compared to the significant gains associated with non-work related travel activity, suggesting the greatest benefits associated with HSR, especially for those residents outside of the major metropolitan areas, will be non-work related travel activity. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • David Hensher & Richard Ellison & Corinne Mulley, 2014. "Assessing the employment agglomeration and social accessibility impacts of high speed rail in Eastern Australia," Transportation, Springer, vol. 41(3), pages 463-493, May.
  • Handle: RePEc:kap:transp:v:41:y:2014:i:3:p:463-493
    DOI: 10.1007/s11116-013-9480-7
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    References listed on IDEAS

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    Cited by:

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    2. Yutong Xue & Pengcheng Xiang, 2020. "The Social Risk of High-Speed Rail Projects in China: A Bayesian Network Analysis," Sustainability, MDPI, vol. 12(5), pages 1-23, March.
    3. Shi, Wenming & Lin, Kun-Chin & McLaughlin, Heather & Qi, Guanqiu & Jin, Mengjie, 2020. "Spatial distribution of job opportunities in China: Evidence from the opening of the high-speed rail," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 138-147.
    4. Merlin, Louis A. & Levine, Jonathan & Grengs, Joe, 2018. "Accessibility analysis for transportation projects and plans," Transport Policy, Elsevier, vol. 69(C), pages 35-48.
    5. Stewart, Anson F. & Zegras, P. Christopher, 2016. "CoAXs: A Collaborative Accessibility-based Stakeholder Engagement System for communicating transport impacts," Research in Transportation Economics, Elsevier, vol. 59(C), pages 423-433.
    6. Liqian Deng & Yaodong Zhou & Zhipeng Li & Zujie Zhang & Jiaoli Cai, 2024. "Do High-Speed Rail Networks Promote Coupling Coordination between Employment and Industry Output? A Study Based on Evidence from China," Sustainability, MDPI, vol. 16(3), pages 1-16, January.
    7. Repolho, Hugo M. & Church, Richard L. & Antunes, António P., 2016. "Optimizing station location and fleet composition for a high-speed rail line," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 437-452.
    8. Li, He & Lu, Juan & Guo, Feiyu, 2022. "High speed rail and corporate social responsibility performance: Analysis of intra-regional location and inter-regional spillover," Transport Policy, Elsevier, vol. 128(C), pages 65-75.
    9. Zhao Yun & Bi Chongren, 2019. "An Agent-Based Simulation Model of Knowledge Spillover Under the Influence of High-Speed Railway," Journal of Systems Science and Information, De Gruyter, vol. 7(2), pages 99-114, April.

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