Under the carbon trading mechanism, renewable energy projects can gain additional benefits through Chinese Certified Emission Reduction transactions. Due to the uncertainty of carbon trading system, carbon prices will fluctuate randomly, which will affect the investment timing of renewable energy projects. Thus, the value of the option will be generated. Therefore, renewable energy power generation project investment has the right of option. However, the traditional investment decision-making method can no longer meet the requirements of renewable energy investment in the current stage. In this paper, a real option model considering carbon price fluctuation is proposed as a tool for renewable energy investment. Considering optimal investment timing and carbon price, the model introduces a carbon price fluctuation as part of the optimization, studies the flexibility of enterprises’ delayed investment under the fluctuation of carbon price. A case study is carried out to verify the effectiveness of the proposed real option model by selecting a wind farm in North China. The model is expected to help investors to assess the volatility and risk of renewable energy projects more accurately, and help investors to make a complete plan for the project investment, thus promoting the efficient allocation of resources in the energy industry.

To achieve the commitment of carbon emission reduction in 2030 at the climate conference in Paris, it is an important task for China to decompose the carbon emission target among regions. In this paper, entropy maximization is brought to inter-provincial carbon emissions allocation via the Boltzmann distribution method, which provides guidelines for allocating carbon emissions permits among provinces. The research is mainly divided into three parts: (1) We develop the CO₂ influence factor, including per capita GDP, per capita carbon emissions, carbon emission intensity and carbon emissions of per unit industrial added value; the proportion of the second industry; and the urbanization rate, to optimize the Boltzmann distribution model. (2) The probability of carbon emission reduction allocation in each province was calculated by the Boltzmann distribution model, and then the absolute emission reduction target was allocated among different provinces. (3) Comparing the distribution results with the actual carbon emission data in 2015, we then put forward the targeted development strategies for different provinces. Finally, suggestions were provided for CO₂ emission permits allocation to optimize the national carbon emissions trading market in China.