In 2016, the first batch of concentrated solar power (CSP) demonstration projects of China was formally approved. Due to the important impact of the cost-benefit on the investment decisions and policy-making, this paper adopted the static payback period (SP), net present value (NPV), net present value rate (NPVR), and internal rate of return (IRR) to analyze and discuss the costbenefit of CSP demonstration plants. The results showed the following.(1) The SP of CSP systems is relatively longer, due to high initial investment; but the cost-benefit of CSP demonstration plants as a whole is better, because of good expected incomes. (2) Vast majority of CSP projects could gain excess returns, on the basis of meeting the profitability required by the benchmark yield of 10%. (3) The cost-benefit of solar tower CSP technology (IRR of 12.33%) is better than that of parabolic trough CSP technology (IRR of 11.72%) and linear Fresnel CSP technology (IRR of 11.43%). (4) The annual electricity production and initial costs have significant impacts on the cost-benefit of CSP systems; the effects of operation and maintenance costs and loan interest rate on the cost-benefit of CSP systems are relatively smaller but cannot be ignored.

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In recent years, China’s terminal clean power replacement construction has experienced rapid development, and China’s installed photovoltaic and wind energy capacity has soared to become the highest in the world. Precise and effective prediction of the scale of terminal clean power replacement can not only help make reasonable adjustments to the proportion of clean power capacity, but also promote the reduction of carbon emissions and enhance environmental benefits. In order to predict the prospects of China’s terminal clean energy consumption, first of all, the main factors affecting the clean power of the terminal are screened by using the grey revelance theory. Then, an evolutionary game theory (EGT) optimized least squares support vector machine (LSSVM) machine intelligence algorithm and an adaptive differential evolution (ADE) algorithm are applied in the example analysis, and empirical analysis shows that this model has a strong generalization ability, and that the prediction result is better than other models. Finally, we use the EGT–ADE–LSSVM combined model to predict China’s terminal clean energy consumption from 2019 to 2030, which showed that the prospect of China’s terminal clean power consumption is close to forty thousand billion KWh.