A dataset of carbon emission projections for Yangtze River Delta Urban Agglomeration, China, 2020-2035.

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.