How do carbon trading price and carbon tax rate affect power project portfolio investment and carbon emission: An analysis based on uncertainty theory.

Responding to the social, economic, and environmental call to resolve current sustainability challenges, the concern about carbon dioxide emission reduction should be incorporated into the power investment decision process. Reflecting the low carbon emission requirement, this paper proposes a new optimization model for power project portfolio selection that simultaneously considers both of carbon emission trading scheme and carbon tax imposition. In this model, the initial investment outlay, the power sale price, the carbon trading price, and carbon tax rate are treated as uncertain variables considering the fast-changing environment and complex market situation. Incorporating the constraint on whether the carbon quota is exceeded into the model, two investment strategies are proposed for investors. Using the proposed model, the impact of the rises in carbon trading price and carbon tax rate on the investor's investment strategy selection and the carbon emission is simulated and analyzed through a case study. When the expected carbon price is 203.50 RMB/tCO2-eq or less, a company should invest based on the strategy that annual emissions exceed the quota to obtain a maximum expected NPV which is larger than 408588 million RMB. When future carbon prices are 352.00, 500.50 and 649.00 RMB/tCO2-eq, the government should impose carbon tax rates of 30, 30, and 40 RMB/tCO2 on a power company, respectively, to obtain carbon emission reduction effect. At last, to see the contrast effect of the results from simultaneous implementation of both carbon trading and carbon tax, the results considering the carbon trading alone or carbon tax alone are discussed, respectively.

A new uncertain enhanced index tracking model with higher-order moment of the downside.

Enhanced index tracking (EIT) problem is concerned with selecting a tracking portfolio to beat the benchmark on return while having the minimum tracking error. This paper addresses the EIT problem based on uncertainty theory where stock returns are treated as uncertain variables instead of random variables. Under the framework of uncertainty theory, the paper proposes a new uncertain EIT model where the higher-order moment of the downside is used as the tracking error measure, as higher-order moment makes the model more widely applicable and the downside risk is in line with investors' perception of risk. Besides, some realistic constraints are considered in the new uncertain EIT model. Then, the properties of the proposed model are discussed. To solve the model, we proposed, which is a nonlinear integer programming problem, a meta-heuristic algorithm presented. The efficiency of the algorithm and the applications of the proposed model are illustrated through numerical experiments.