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Scenario Analysis for GHG Emission Reduction Potential of the Building Sector for New City in South Korea

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  • Seo-Hoon Kim

    (Energy ICT Convergence Research Department, Korea Institute of Energy Research, 152, Gajeong-ro Yuseong-gu, Daejeon 34129, Korea)

  • SungJin Lee

    (Energy ICT Convergence Research Department, Korea Institute of Energy Research, 152, Gajeong-ro Yuseong-gu, Daejeon 34129, Korea)

  • Seol-Yee Han

    (Energy ICT Convergence Research Department, Korea Institute of Energy Research, 152, Gajeong-ro Yuseong-gu, Daejeon 34129, Korea)

  • Jong-Hun Kim

    (Energy ICT Convergence Research Department, Korea Institute of Energy Research, 152, Gajeong-ro Yuseong-gu, Daejeon 34129, Korea)

Abstract

A new government report on climate change shows that global emissions of greenhouse gases have increased to very high levels despite various policies to reduce climate change. Building energy accounts for 40% of the world’s energy consumption and accounts for 33% of the world’s greenhouse gas emissions. This study applied the LEAP (Long-range energy alternatives planning) model and Bass diffusion method for predicting the total energy consumption and GHG (Greenhouse Gas) emissions from the residential and commercial building sector of Sejong City in South Korea. Then, using the Bass diffusion model, three scenarios were analyzed (REST: Renewable energy supply target, BES: Building energy saving, BEP: Building energy policy) for GHG reduction. The GHG emissions for Sejong City for 2015–2030 were analyzed, and the past and future GHG emissions of the city were predicted in a Business-as-Usual (BAU) scenario. In the REST scenario, the GHG emissions would attain a 24.5% reduction and, in the BES scenario, the GHG emissions would attain 12.81% reduction by 2030. Finally, the BEP scenario shows the potential for a 19.81% GHG reduction. These results could be used to guide the planning and development of the new city.

Suggested Citation

  • Seo-Hoon Kim & SungJin Lee & Seol-Yee Han & Jong-Hun Kim, 2020. "Scenario Analysis for GHG Emission Reduction Potential of the Building Sector for New City in South Korea," Energies, MDPI, vol. 13(20), pages 1-19, October.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5514-:d:432288
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    1. Chaturvedi, Vaibhav & Eom, Jiyong & Clarke, Leon E. & Shukla, Priyadarshi R., 2014. "Long term building energy demand for India: Disaggregating end use energy services in an integrated assessment modeling framework," Energy Policy, Elsevier, vol. 64(C), pages 226-242.
    2. Mavromatidis, Georgios & Orehounig, Kristina & Richner, Peter & Carmeliet, Jan, 2016. "A strategy for reducing CO2 emissions from buildings with the Kaya identity – A Swiss energy system analysis and a case study," Energy Policy, Elsevier, vol. 88(C), pages 343-354.
    3. Islam, Towhidul, 2014. "Household level innovation diffusion model of photo-voltaic (PV) solar cells from stated preference data," Energy Policy, Elsevier, vol. 65(C), pages 340-350.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.
    6. Lin, Jianyi & Cao, Bin & Cui, Shenghui & Wang, Wei & Bai, Xuemei, 2010. "Evaluating the effectiveness of urban energy conservation and GHG mitigation measures: The case of Xiamen city, China," Energy Policy, Elsevier, vol. 38(9), pages 5123-5132, September.
    7. Davis, Matthew & Ahiduzzaman, Md. & Kumar, Amit, 2018. "How will Canada’s greenhouse gas emissions change by 2050? A disaggregated analysis of past and future greenhouse gas emissions using bottom-up energy modelling and Sankey diagrams," Applied Energy, Elsevier, vol. 220(C), pages 754-786.
    8. Droutsa, Kalliopi G. & Kontoyiannidis, Simon & Dascalaki, Elena G. & Balaras, Constantinos A., 2016. "Mapping the energy performance of hellenic residential buildings from EPC (energy performance certificate) data," Energy, Elsevier, vol. 98(C), pages 284-295.
    9. Peter J. Lenk & Ambar G. Rao, 1990. "New Models from Old: Forecasting Product Adoption by Hierarchical Bayes Procedures," Marketing Science, INFORMS, vol. 9(1), pages 42-53.
    10. V. Srinivasan & Charlotte H. Mason, 1986. "Technical Note—Nonlinear Least Squares Estimation of New Product Diffusion Models," Marketing Science, INFORMS, vol. 5(2), pages 169-178.
    11. Pukšec, Tomislav & Vad Mathiesen, Brian & Duić, Neven, 2013. "Potentials for energy savings and long term energy demand of Croatian households sector," Applied Energy, Elsevier, vol. 101(C), pages 15-25.
    12. Kumar, Rajesh & Agarwala, Arun, 2016. "Renewable energy technology diffusion model for techno-economics feasibility," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1515-1524.
    13. Dai, Hancheng & Masui, Toshihiko & Matsuoka, Yuzuru & Fujimori, Shinichiro, 2011. "Assessment of China's climate commitment and non-fossil energy plan towards 2020 using hybrid AIM/CGE model," Energy Policy, Elsevier, vol. 39(5), pages 2875-2887, May.
    14. Heinz, B. & Graeber, M. & Praktiknjo, A.J., 2013. "The diffusion process of stationary fuel cells in a two-sided market economy," Energy Policy, Elsevier, vol. 61(C), pages 1556-1567.
    15. Koo, Choongwan & Hong, Taehoon & Kim, Jimin & Kim, Hyunjoong, 2015. "An integrated multi-objective optimization model for establishing the low-carbon scenario 2020 to achieve the national carbon emissions reduction target for residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 410-425.
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