2026-10-13T11:55:39Z https://www.scidb.cn/oai

10.57760/sciencedb.17027 2024-11-29T08:54:27Z

2024-11-29 A dataset of carbon emission projections for Yangtze River Delta Urban Agglomeration, China, 2020-2035. doi:10.57760/sciencedb.17027 en The characteristics of spatial networks within urban agglomerations determine that the peak carbon path of a single city is inevitably affected by neighboring cities, but the prediction of peak carbon considering the spatial correlation is still not well researched. To address this situation, we developed a spatially embedded deep learning forecasting model and conducted carbon emission forecasts for 2020-2035 based on the 2000-2019 data of China's Yangtze River Delta city cluster. The prediction scenarios include a baseline scenario and a green scenario. The provided dataset includes the projected carbon emissions of 41 cities under these two scenarios for the period 2020-2035. This dataset can be used for the study of the development of a global strategy for carbon peaking in urban agglomerations. carbon emission; urban agglomeration; temporal-spatial characteristics shi changfeng yu yue yao xiao pang qinghua PUBLIC https://creativecommons.org/publicdomain/zero/1.0/ dataset Science Data Bank