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Forecasting greenhouse gas emissions from urban regions: microsimulation of land use and transport patterns in Austin, Texas

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  • Tirumalachetty, Sumala
  • Kockelman, Kara M.
  • Nichols, Brice G.

Abstract

Policymakers, planners, engineers, and others seek effective ways to anticipate and manage greenhouse gas (GHG) emissions for a sustainable future. Here, a microsimulation model was developed to demonstrate how one can forecast Austin’s demographic and firmographic attributes over time, using a variety of national and local, aggregate and disaggregate data sets. Year 2030 household energy demands and GHG emissions estimates are compared under five different land use and transport policy scenarios. Application of an urban growth boundary provided the lowest increase in overall vehicle miles traveled (VMT) and GHG emissions, while network additions resulted in the highest rates of increase. Average energy consumption per household are estimated to fall over time (by 11–19% depending on the scenario), but the region’s overall energy consumption is estimated to increase dramatically – by nearly 88% in terms of home energy consumption (in the base scenario) and 108% in the transport sector, relative to the 2005 base-year scenario. Such increases are considerably higher than proposed GHG targets, presenting a serious energy and emissions challenge for Austin as well as other U.S. regions.

Suggested Citation

  • Tirumalachetty, Sumala & Kockelman, Kara M. & Nichols, Brice G., 2013. "Forecasting greenhouse gas emissions from urban regions: microsimulation of land use and transport patterns in Austin, Texas," Journal of Transport Geography, Elsevier, vol. 33(C), pages 220-229.
    • Handle: RePEc:eee:jotrge:v:33:y:2013:i:c:p:220-229
    DOI: 10.1016/j.jtrangeo.2013.08.002
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    Cited by:

    1. Nichols, Brice G. & Kockelman, Kara M., 2014. "Life-cycle energy implications of different residential settings: Recognizing buildings, travel, and public infrastructure," Energy Policy, Elsevier, vol. 68(C), pages 232-242.
    2. Talbi, Besma, 2017. "CO2 emissions reduction in road transport sector in Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 232-238.
    3. Frederik Priem & Philip Stessens & Frank Canters, 2020. "Microsimulation of Residential Activity for Alternative Urban Development Scenarios: A Case Study on Brussels and Flemish Brabant," Sustainability, MDPI, vol. 12(6), pages 1-28, March.
    4. Chow, Alice S.Y., 2016. "Spatial-modal scenarios of greenhouse gas emissions from commuting in Hong Kong," Journal of Transport Geography, Elsevier, vol. 54(C), pages 205-213.
    5. Martos, A. & Pacheco-Torres, R. & Ordóñez, J. & Jadraque-Gago, E., 2016. "Towards successful environmental performance of sustainable cities: Intervening sectors. A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 479-495.
    6. Shoshanna Saxe & Dena Kasraian, 2020. "Rethinking environmental LCA life stages for transport infrastructure to facilitate holistic assessment," Journal of Industrial Ecology, Yale University, vol. 24(5), pages 1031-1046, October.
    7. Saadi, Ismaïl & Mustafa, Ahmed & Teller, Jacques & Farooq, Bilal & Cools, Mario, 2016. "Hidden Markov Model-based population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 1-21.
    8. Yalan Zhao & Yaoqiu Kuang & Ningsheng Huang, 2016. "Decomposition Analysis in Decoupling Transport Output from Carbon Emissions in Guangdong Province, China," Energies, MDPI, vol. 9(4), pages 1-23, April.
    9. Xu, Bin & Lin, Boqiang, 2015. "Carbon dioxide emissions reduction in China's transport sector: A dynamic VAR (vector autoregression) approach," Energy, Elsevier, vol. 83(C), pages 486-495.

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