Estimation of renewable power generation and greenhouse gas reduction potential in the building sector

The Republic of Korea submitted its updated Nationally Determined Contribution (NDC) to the United Nations Framework Convention on Climate Change (UNFCCC) Secretariat in December 2021. The updated NDC target is to reduce total national greenhouse gas (GHG) emissions by 40% from the 2018 level, which is 727.6 Mt CO₂eq, by 2030. According to the updated NDC, local governments are also required to revise their GHG reduction plans. In addition, local governments should self-inspect the progress and major achievements of the GHG reduction plan every year in accordance with the evaluation guideline of the Ministry of Environment. Of 6 metropolitan cities, Gyeonggi Province shows the highest GHG emissions in the country, which accounts for about 17% of the total national GHG emissions in 2021. Ironically, Goyang City, a basic local government of Gyeonggi Province, was selected as one of the seven best local governments for carbon neutrality in 2021. The City has set a reduction target of 32.8% below BAU by 2030 and prepared a plan to implement reduction targets by sector. Over the last decade, building and transportation sectors have been the major sources of GHG emissions in Goyang City, accounting for approx. 70% of the city’s total GHG emissions. The city promotes zero-energy building (ZEB) for newly constructed buildings and encourages green remodeling for existing buildings in order to reduce GHG emissions in the building sector. It is essential to introduce renewable energy such as solar, geothermal, hydrothermal, etc. for ZEB and green remodeling. In this study, therefore, the potential for solar power generation, which is most easily applicable to the building sector, and GHG reduction were calculated for residential buildings in Goyang City. To calculate the available area for solar power on the roof of residential buildings, spatial data was constructed using high-resolution aerial photographs and the outline of the building roof was extracted through AI training data.

 

Acknowledgements

This research was carried out as a part of KICT Research Program (Data-Centric Checkup Technique of Building Energy Performance) funded by the Ministry of Science and ICT.