能源-经济-环境 (3E) 系统集成模型是进行碳减排政策研究的主流工具，然而多重能源技术刻画能力和内生技术进步处理能力不强一直是此类模型的薄弱点。基于此，本项目从3E系统的技术机制入手，提出政策驱动的多重技术刻画机制，并将其耦合到3E综合模型框架中以描述多重能源技术的替代演变；将基于研发的技术激励效应融入到基于“干中学”的单因素学习曲线中，发展双因素学习曲线方法，用以内生3E系统集成模型的能源技术进步。本研究的主要贡献在于：理论层面，创新多重内生技术机制，构建技术导向型3E系统集成模型，进行大规模复杂系统算法研究，给出跨期最优的求解方案，为进行碳减排政策研究提供核心工具支撑；应用层面，将该模型作为政策分析的工具，考察碳减排背景下的多重技术发展路径选择问题，研究实现既定减排目标的最成本有效的政策组合策略，为国家实现2030年的碳排放达峰目标和20%的非化石能源发展目标提供政策参考和定量依据。

Energy-Economy-Environmental (3E) system integrated model is widely considered to be one of mainstream tools in response to carbon abatement policy research. However, the weak capability of describing multiple energy technologies and endogenizing technological change has long been the bottleneck of this type of model. Based on the great importance of technology in carbon reduction, this project starts from the technical mechanism of conventional 3E integrated models. We propose a new multi-technological model with policy incentives, and bridge the policy-driving multi-technological model and the 3E integrated framework in a highly compatible way; then we develop a two-factor learning curve method by combining the learning-by-doing effect with learning-by-searching effect, this allows us to study endogenous technological change in 3E-integrated model more completely. The main contribution of this project includes two aspects: in theory, we develop a technique-oriented 3E integrated model of China by innovating the multiple endogenous technological mechanisms, and investigate the inter-temporally optimal algorithm of the complex large-scale 3E system, which provides a core tool for carbon reduction policy research; in application, we consider the evolutionary paths of multiple energy technologies in the context of carbon constrains, and study the optimal dynamic policy strategies under the given carbon reduction target, so as to provide constructive policy insights and quantitative evidences for the government to meet the 2030 carbon-peaking goal and 20% non-fossil energy development target.