야간 위성영상 기반 도시단위 탄소배출량 추정과 영향 요인 분석 = Estimating Carbon Emissions at Urban Scales by Nighttime Light Satellite Imagery and Analysis of Influencing Factors

국문 초록 (Abstract)

지역에 따라 도시 형태, 산업구조, 에너지 사용량, 사회경제적활동 특성 등이 달라지는 만큼 탄소배출의 구조와 강도는 공간적으로 달라질 수밖에 없다(Wang et al., 2022). 지역의 탄소배출량 추정을 위해서는 기존 추정법의 한계를 극복한 상세 단위 분석이 필요하고, 소비 기반의 탄소배출량도 고려할 수 있어야 한다.
야간 위성영상을 활용한 탄소배출량 추정법은 픽셀 단위의 상세분석을 가능하게 해주며, 소비 기반의 추정에 한층 가까워질 수있다. 이에 본 연구는 도시 단위의 탄소배출량을 추정하기 위해야간 위성영상을 활용하였고, 데이터의 활용가능성을 검증한 뒤탄소배출량에 영향을 미치는 도시 요인에 대해 논의하고자 한다.

다국어 초록 (Multilingual Abstract)

As recent climate problems continue, global carbon neutrality has been advanced to reduce the greenhouse gas emissions causing abnormal climate. By 2050, all cities must achieve carbon neutrality, but to move toward a carbon neutral city, it is essential to estimate the amount of carbon emissions per city and analyze the influencing factors. However, current methods of estimating carbon emissions encounter difficulties in city-level analyses. This study estimated carbon emissions using nighttime satellite images to address limitations of previous studies and current greenhouse gas emission calculation methods, and analyzed urban factors affecting carbon emissions using a spatial regression model. The results show that the carbon emissions of Korean cities are increasing over time, and their spatial distribution varies greatly by region. A spatial regression model of the influencing factors identified significant impact of energy consumption, industrial structure, land use, and transportation on carbon emissions. As a result of analysis, carbon emissions increase as energy consumption, productivity of the secondary industry, and the number of automobiles increase. In addition, the higher the compression of land use, the stronger the reduction of carbon emissions, and the higher the diversity of land use, the higher the carbon emissions. This study is meaningful in presenting a new method for sub-space unit estimates to remedy limitations of current carbon emission calculation methods. This carbon emission estimation method sing night satellite images is expected to effectively provide basic data for land use planning and policy research to attain carbon-neutral cities.