Journal of the Architectural Institute of Korea (대한건축학회논문집)

Architectural Institute of Korea (대한건축학회)
권호 표지
DOI QR코드

DOI QR Code

Post-2020 Greenhouse Gas Emission Projection in Building Sector

2020년 이후 국가 건물부문 온실가스 배출량 단기 전망 연구

  • Received : 2020.07.22
  • Accepted : 2020.10.05
  • Published : 2020.10.30
⟨ Previous Next ⟩

Abstract

With the signing of the Paris Agreement, an accord concerning the post-2020 climate change regime, in December 2015, all nations around the globe recognized the problem of climate crisis and are proactively reducing the emission of greenhouse gases. The South Korean government has announced plans to reduce the country's greenhouse gas emissions by 37% from the business-as-usual level of 850.6 million tons of carbon dioxide equivalent (Mton CO2e). The plans have set a target of 32.7% minimization for the building sector, which is expected to have a high reduction potential. This study aims to forecast the greenhouse gas emissions in Korea's building sector after 2020 based on its current state of emissions. This study proposes a statistical predictive modeling approach to discover the greenhouse gas emissions projection in building sector by 2030 using regression analysis models, time series models, growth curve model. To this end, the Bass model was applied as the optimal forecasting model as it is assessed to have high predictability. According to the Bass model's predictions of greenhouse gas emissions in the building sector, the level is expected to increase from 156.8 Mton CO2e in 2020 to 173.3 Mton CO2e in 2025, and eventually to 189.0 Mton CO2e in 2030. Compared to the nationwide greenhouse gas emissions forecast, these predictions are higher by approximately 9.8% to 12%. Considering the lack of research on the prospects of domestic greenhouse gas emissions, this study is meaningful as it provides significant results that are necessary for analyzing potential reductions in greenhouse gas emissions and establishing measures for their cutback. Additional research is required on forecasting long-term greenhouse gas emissions through the establishment of optimization models.

Keywords

Acknowledgement

이 연구는 2020년도 국토교통과학기술진흥원 도시건축연구사업의 지원에 의한 결과의 일부임. 과제번호: 20AUDP-C127876-04

References

  1. Geisser S.(1993). Predictive Inference: An Introduction. Chapman and Hall.
  2. GIR (2019). National Greenhouse Gas Inventory Report of Korea, Seoul, Greenhouse Gas Inventory & Research Center of Korea, 37-41, 137-142, 375-376.
  3. EU (2019). The POTEnCIA Central Scenario: An EU energy outlook to 2050, Publication Office of the European Union, http://ec.europa.eu/jrc/en/potencia.
  4. Hwang, I. (2015). Seoul energy demand forecast 2015-2035: An application of the MEAD, Journal of Environmental Policy and Administration, 23(3), 47-76. https://doi.org/10.15301/jepa.2015.23.3.47
  5. IEA (2017a). Energy Technology Perspectives 2017, France, IEA Publications, http://www.iea.org/etp2017.
  6. IEA (2017b). Annal Energy Outlook 2017, IEA Publications.
  7. IPCC (1996). Revised 1996 IPCC guidelines for national greenhouse gas inventories, International Panel on Climate Change, United Nations Environment Programme, International Energy Agency, Japan.
  8. IPCC (2006). 2006 IPCC guidelines for national greenhouse gas inventories, International Panel on Climate Change, Institute for global environmental strategies, Japan
  9. Jeong, Y., & Kim, T. (2019). Estimation and feature of greenhouse gas emission in building sector by national energy statistic, Journal of the Architectural Institute of Korea, 35(7), 187-195
  10. Ji, C., Choi. M., Gwon, O., Jung, H., & Shin, S. (2020). Greenhouse gas emission from building sector based on national building energy database, Journal of the Architectural Institute of Korea, 36(4), 143-152.
  11. Korean Government (2014). The report of the roadmap for Green house gas reduction target from Korea, the related ministries of Korean governments, Republic of Korea
  12. Korean Government (2018). The report of Amendment for the basic roadmap to achieve the national greenhouse gas reduction targets in 2030, the related ministries of Korean governments, Republic of Korea
  13. Kuhn, M., & Johnson, K. (2018). Applied Predictive Modeling, NewYork, Springer, 24-27.
  14. Lewis, C. (1982). Industrial and Buisenss Forecasting Methods, Butterworth, Witt&Witt, 86-87
  15. Lim, S. (2018). A comparative study on the accuracy of tourism forecasting models, Journal of the Korean Data and Information Science Society, 29(6), 1629-1641. https://doi.org/10.7465/jkdi.2018.29.6.1629
  16. Park, N., Yoo, J., Jo, M., Yun, S., & Jeon, E. (2012). Comparative analysis of scenarios for reduction GHG emissions in Korea by 2050 using the low carbon path calculator, Journal of Korean Society for Atmospheric Environment, 28(5), 556-570. https://doi.org/10.5572/KOSAE.2012.28.5.556
  17. Song, K., & Lee, C. (2006). A comparison of accuracy among tourism forecasting models, International Journal of Tourism and Hospitality Research, 20(2), 351-369.
  18. United Nations (2015). United Nations Framework Convention on Climate Change-Adoption of the paris agreement, UNFCCC, FCCC/CP/2015/L.9/Rev.1.
  19. United National Environment Programme (2018). 2018 Global Status Report, UNEP, IEA