The estimation of building carbon emission using nighttime light images: A comparative study at various spatial scales

Abstract

As one of the fundamental sectors to measure the carbon emission levels in a certain region, building carbon emission plays an important role in determining low-carbon development plans. Most of the carbon emission estimation research mainly focuses on the establishment of bottom-up GHG inventory and the implication of policy-driven approaches, there are still many theoretical gaps in the usage of remote sensing data to predict building carbon emission. This paper presents a comprehensive study to discuss the performance of different regression models using various open nighttime light (NTL) data sources. The Noord Brabant province was employed as a case study to verify the feasibility of using different estimation models at various spatial scales (city-level, district-level, and neighborhood-level). Among all regression models, the geographically weighted regression (GWR) has been proven to better reflect the relationship between building carbon emissions and the NTL index. For practical applications, the carbon intensity (CI) and annual nighttime light index (ANLI) are a pair of optimal sets to establish a reliable estimation model. It exhibits higher utility value at the city-level due to the fewer interferences caused by non-building lighting sources. The results of this comparative study provide a new reference to support the establishment of carbon inventory. By illustrating the differences among various estimation models, the applicable scope of using open remote sensing data to estimate building carbon emissions can be further defined. The conclusion may provide more detailed instructions during the process of developing low-carbon cities.