| Title |
Post-2020 Greenhouse Gas Emission Projection in Building Sector |
| Authors |
정영선(Jeong, Young-Sun) ; 조수현(Cho, Suhyun) |
| DOI |
https://doi.org/10.5659/JAIK.2020.36.10.117 |
| Keywords |
Projection; Greenhouse Gas; CO2e Emission; Building Sector; Climate Change |
| 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. |