Policy-making optimization based on generative adversarial networks: A case study of mapping energy transition pathways to China's carbon neutrality

Mapping energy transition pathways is pivotal for achieving carbon neutrality. However, potential pathways delineated by rule-based models differ significantly due to model characteristics, posing a grand challenge in subsequent policy-making. Inspired by the ability of deep convolutional generative adversarial networks (DCGAN) to extract features and generate images, we integrate model outputs concerning 16 energies' transition pathways to carbon neutrality through DCGAN, assimilating the uncertainties among these outputs. Since DCGAN absorbs the patterns of published data, it offers new insights into policy-making of energy transitions. DCGAN indicates that natural gas and its application with carbon capture and storage play more crucial roles than these currently suggested levels. Additionally, hydrogen and nuclear energies require further development over 2020-2060, serving as a cushion during the substantial energy restructuring. Our study not only provides novel insights into methods mapping the trajectories of critical variables to carbon neutrality but extends DCGAN's application into policy-making optimization.