Recent advances in carbon footprint studies of urban ecosystems: overview, application, and future challenges

Urban ecosystems are complex systems with anthropogenic features that generate considerable CO2 emissions, which contributes to global climate change. Quantitative estimates of the carbon footprint of urban ecosystems are crucial for developing low-carbon development policies to mitigate climate change. Herein, we reviewed more than 195 urban carbon footprint and carbon footprint related studies, collated the recent progress in carbon footprint calculation methods and research applications of the urban ecosystem carbon footprint, analyzed the research applications of the carbon footprint of different cities, and focused on the need to study the urban ecosystem carbon footprint from a holistic perspective. Specifically, we aimed to: (i) compare the strengths and weaknesses of five existing carbon footprint calculation methods [life cycle assessment, input???output analysis, hybrid life cycle assessment, carbon footprint calculator, and Intergovernmental Panel on Climate Change (IPCC)]; (ii) analyze the status of current research on the carbon footprint of different urban subregions based on different features; and (iii) highlight new methods and areas of research on the carbon footprint of future urban ecosystems. Not all carbon footprint accounting methods are applicable to the carbon footprint determination of urban ecosystems; although the IPCC method is more widely used than the others, the hybrid life cycle assessment method is more accurate. With the emergence of new science and technology, quantitative methods to calculate the carbon footprint of urban ecosystems have evolved, becoming more accurate. Further development of new technologies, such as big data and artificial intelligence, to assess the carbon footprint of urban ecosystems is anticipated to help address the emerging challenges in urban ecosystem research effectively to achieve carbon neutrality and urban sustainability under global change.