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.

城市群内部空间网络的固有属性决定了单个城市的碳达峰路径不可避免地会受到周边城市的影响，但当前针对考虑空间相关性的碳达峰预测研究仍未得到充分开展。为弥补这一研究缺口，我们构建了空间嵌入型深度学习预测模型，并基于中国长江三角洲城市群2000-2019年的历史数据，对2020-2035年的碳排放情况进行了预测。本次预测设置了基准情景与绿色发展情景两种模式。本数据集涵盖了41个城市在上述两种情景下2020-2035年的预测碳排放量。该数据集可用于城市群碳达峰全球战略的制定与相关研究。