电力行业作为最大碳排放行业，其低碳转型是中国实现长期低碳目标的重要环节。因此，中国电力行业的低碳转型路径研究，对于中国低碳发展预测、低碳政策评估有着重要科学与政策意义。结合电力行业现状，该路径研究需要考虑两方面影响因素：一是随着电力市场化改革不断深入，电力行业的长期转型越来越依赖于企业的分散式投资行为；二是电力行业在转型过程中面临着资源与经济两类不确定性因素的影响。以往研究中，上述两方面影响因素仍缺乏全面考虑。本论文基于企业行为模拟来探讨中国电力行业的长期低碳转型路径及其不确定性。为此，本论文结合主体行为模型、效用理论与蒙特卡洛模拟，为中国电力行业构建了不确定性下多主体行为模型，一方面结合不同企业行为偏好情景探讨电力行业低碳转型中企业行为的影响机制，另一方面基于企业行为与不确定性模拟，探讨低碳政策下中国电力行业长期低碳转型的技术与成本路径及其不确定性。研究发现，企业行为偏好与低碳政策设计对中国电力行业低碳转型及其不确定性存在重要影响：（1）电力企业的风险厌恶与自适应技术偏好等行为偏好能促进电力行业长期低碳转型，且存在潜在协同效应，但也会带来经济损失与补贴负担，应在电力行业研究中加以充分考虑。（2）从低碳转型路径来看，低碳政策下电力行业2030年前后将出现化石能源达峰与非化石能源大规模增长，但2050年前化石能源并不会被完全替代。碳排放交易的有偿分配机制较无偿分配机制能促进更早、更大规模的低碳转型，使电力行业碳排放达峰时间提前7-8年，峰值降低17.4%；碳市场的初始碳价与波动率分别会促进与抑制低碳转型，对太阳能与风能的影响尤为显著。（3）从低碳转型路径的不确定性来看，碳排放交易机制对电力行业低碳转型不确定性有着政策效应与价格效应，总体上能减弱转型的不确定性。碳排放交易的有偿分配机制较无偿分配机制，能带来更强的政策效应与更稳定的低碳转型，使电力行业2030年前碳排放达峰的概率达到71-97%，且峰值不确定性缩小60-76%。现有低碳政策下，电力行业难以达到NDC目标的要求，但有潜力达到2℃温升控制目标的要求。碳价波动率能加剧转型不确定性，尤其是有偿分配机制下。（4）从低碳转型的碳减排成本来看，电力行业在转型前期需承担碳减排成本，但长期来看将收获碳减排收益，且消费者将是最大收益者。碳排放交易的有偿分配机制能将实现更早、更高、更稳定的长期碳减排收益，2050年将达GDP的0.5%；而碳价波动率会产生相反的效果。

As the largest carbon emitter, power sector’s low-carbon transition is an important part of China’s low-carbon development. Thus the study on the low-carbon transition path of China's power sector under low-carbon policies has important scientific and policy implications for China's low-carbon development forecast and low-carbon policy assessment. Based on current status of China’s power sector, study on its low-carbon transition should consider two important factors: on one hand, with the deepening of power market reform, power sector’s long-term transition relies increasingly on the decentralized investment behavior of power enterprises; on the other hand, the power industry faces many uncertainties from nature resource and economy development during its transition process. Comprehensive considerations of these two factors haven’t been found in previous studies. The purpose of this study is to explore the long-term low-carbon transition path and its uncertainty in China's power sector, based on the simulation of power enterprises’ behaviors. To this end, this study combines a multi-agent-based model, utility theories and Monte Carlo simulation, to construct a MABUM model (Multi-agent-based Model incorporated with uncertain modelling) for China’s power sector. Based on this MABUM model, this study first analyzes the impacts of enterprise behaviors on power sector’s low-carbon transition by comparing different enterprise preference scenarios; then analyzes the long-term low-carbon transition paths and their uncertainties of China’s power sector under different policy scenarios by considering both enterprise behaviors and many uncertainties. It is found in this study that both enterprises’ preferences and low-carbon policy design have important influence on the low-carbon transition and its uncertainty of China’s power sector. (1) Power enterprises’ risk aversion and adaptive technical preference help promote low-carbon development in power sector with potential synergetic effects, but they also bring economic losses and higher subsidy burden. So power enterprises’ preferences should be considered in study of power sector’s low-carbon transition. (2) As for its low-carbon transition path, it is found that power sector will witness peaking of fossil energy and an outbreak of non-fossil energy development around 2030, but fossil energy won’t be completely replaced before 2050. The paid allocation mechanism of China’s carbon emission trading scheme (ETS) can promote an earlier and larger-scale low-carbon transition than grandfathering mechanism: peaking time of power sector’s carbon emission come 7-8 years earlier with a peak reduction of 17.4%. The initial carbon price and its volatility will promote and curb low-carbon transition in power sector respectively, with significant effects on solar and wind energy. (3) As for uncertainty of its low-carbon transition path, China’s carbon ETS has both policy effect and price effect on the uncertainty of power sector’s low-carbon transition, and overall it can weaken the uncertainty of this transition. Considering different allocation mechanisms of carbon ETS, the paid allocation mechanism can bring stronger policy effect and thus a more stable low-carbon transition. Under paid allocation mechanism, the probability that the power industry will reach its peak carbon emissions by 2030 reaches 71-97%, and the peak uncertainty will shrink by 60-76% compared with unpaid allocation mechanism. Under paid allocation mechanism, although China’s power sector still cannot meet the requirements of the NDC target, it has the probability to achieve the 2°C control target. Also, carbon prices’ volatility can intensify uncertainty of power sector’s low-carbon transition, especially under paid allocation mechanism. (4) As for carbon reduction costs of its low-carbon transition, power sector will bear carbon reduction costs in early stage of transition, but harvest large carbon reduction benefits in the long term, and consumers will benefit the most. The paid allocation mechanism of carbon ETS can achieve earlier, higher, and more stable long-term carbon reduction benefits, reaching 0.5% of GDP in 2050, while carbon prices’ volatility has the opposite impacts.