Estimating carbon emissions in urban functional zones using multi-source data: A case study in Beijing. (15th March 2022) - Zheng, Yunqiang; Du, Shihong; Zhang, Xiuyuan; Bai, Lubin; Wang, Haoyu
Abstract: Carbon emissions have been increasing for decades, resulting in serious environmental problems. To achieve sustainable development goals, it is necessary to estimate carbon emissions related to urban energy consumption. As previous studies have addressed carbon emissions from the building-, community-, and district-levels, urban functional zones (a basic unit serving for urban planning) remain unexplored. In this study, carbon emissions derived from energy consumption in Beijing were explored using multi-source data, and a novel framework was developed to calculate the residential and urban facility carbon emissions. The methods were based on the mapped relationship between geographic entity locations and multi-source data, adopting the coefficient method of the Intergovernmental Panel on Climate Change (IPCC) to calculate emissions. The results showed large spatial auto-correlation, where peak emissions were represented by aggregation patterns, and outliers mostly occurred in the forested and farmland zones. Carbon emissions distributions per unit area also showed a mode of high-value clusters in centre of Beijing while using Moran's I test (p < 0.0001). Suburban carbon emissions are also of interest owing to their similar aggregation centres surrounding urban areas. This study revealed that the institutional, residential, and industrial zones held the highest levels of carbon emissions. Accordingly, this work will aid low-carbon city management efforts and energy allocation optimization. Highlights: Multisource data estimated urban functional zone (UFZ) carbon emissions in Beijing The IPCC carbon inventory method was used to calculate emissions Distributions per unit area showed a mode of high-value clusters in central Beijing Suburban emissions were also of interest for their similar aggregation centres Results can inform low-carbon city management and energy allocation optimization