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Dynamic energy and carbon footprints of urban transportation infrastructures: Differentiating between existing and newly-built assets

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  • Wei, Ting
  • Chen, Shaoqing

Abstract

Transportation infrastructures play an important role in building energy-efficient and low-carbon cities. Yet, how existing and newly-built assets contribute differently to the footprints of transportation infrastructures is not clear. In this paper, we develop a life-cycle approach to quantify the energy and carbon footprints of urban transportation infrastructures over time, with a clear differentiation between existing and newly-built assets. Taking Guangzhou, China as a case study, we evaluate the dynamic footprints of various types of urban transportation infrastructure (roads, subway, and railway) across their life-cycle stages until 2017, and perform a scenario analysis to 2035 to explore future pathways of footprint mitigation. We find that the energy and carbon footprints of road assets built prior to 2005 are around 36 times higher than those of the subway and 5 times higher than those of the railway. However, this situation has begun to shift over 2006–2017 as the subway and railway have become more popular means of transportation and bigger contributors to the city’s energy and carbon footprints. The pace of technological advances in building transportation infrastructure needs to exceed that of the average urban economy to steadily reduce the energy and carbon footprints of the new projects planned. These findings offer a dynamic life-cycle approach and tool to evaluate long-term regulatory policies toward a more sustainable transportation infrastructure system in cities.

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  • Wei, Ting & Chen, Shaoqing, 2020. "Dynamic energy and carbon footprints of urban transportation infrastructures: Differentiating between existing and newly-built assets," Applied Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:appene:v:277:y:2020:i:c:s0306261920310667
    DOI: 10.1016/j.apenergy.2020.115554
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    References listed on IDEAS

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    7. Lin, Boqiang & Xu, Bin, 2020. "Effective ways to reduce CO2 emissions from China's heavy industry? Evidence from semiparametric regression models," Energy Economics, Elsevier, vol. 92(C).

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