文献类型：期刊文献

英文题名：Assessing uncertain technological progress in the decarbonization pathway of China's hydrogen energy system

作者：Zhou, Wenji[1];Ren, Hongtao[2,3];Zhang, Xiao-Bing[1,4];Shao, Shuai[2]

机构：[1]Renmin Univ China, Sch Appl Econ, Beijing 100872, Peoples R China;[2]East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China;[3]Int Inst Appl Syst Anal, Schlosspl 1, A-2361 Laxenberg, Austria;[4]Tech Univ Denmark, DK-2800 Copenhagen, Denmark

年份：2025

卷号：141

外文期刊名：ENERGY ECONOMICS

收录：;EI(收录号：20245117557175);WOS:【SSCI(收录号:WOS:001391375800001)】；

基金：We acknowledge the financial support from the Natural Science Foundation of China (Nos. 72274204, 72243004, and 72371102) and the Major Project of the National Social Science Foundation of China (No. 18ZDA051) .

语种：英文

外文关键词：Hydrogen energy; Energy system model; Stochastic programming; Technological progress; Carbon neutrality

摘要：Hydrogen energy is regarded as a promising solution for decarbonizing hard-to-abate sectors, while its role in the energy transition remains debatable. One of the key reasons is that uncertainty in technological progress has significant impacts on investment decision-making. To assess these effects, this study employs the MESSAGEix framework to develop a hydrogen energy system optimization model in China's context and integrates it with a stochastic scenario-tree generation method to assess the effects of uncertain technological progress on decarbonizing China's hydrogen energy system. The modeled system covers a full range of hydrogen production and consumption associated with different technical options for decarbonization, i.e., renewable energy-based water electrolysis (green hydrogen) and fossil-derived hydrogen coupled with carbon capture and storage. The model simulates a wide range of stochastic crucial cost metrics under the carbon-neutral constraint and compares it to a baseline without an emission constraint. Results show that disruptive technological breakthroughs in renewable electricity generation are essential to decarbonizing the hydrogen production system. The proposed hybrid modeling approach proves that computing is effective and could be applied to many other stochastic programming problems in long-term energy system planning.