Under the dual pressures of energy crisis and environmental pollution, China’s new energy power industry has become a focal point for environmental management and requires greater investment. In this context, as a significant input of investment projects, discount rate requires a well-calibrated evaluation because new energy power investment projects are highly capital intensive. The main objective of this paper is to evaluate the discount rate of China’s new energy power industry. First, we use Moving Average to correct the parameters of capital asset pricing model (CAPM) and weighted average cost of capital, which extends the literature on the avoidance of CAPM noise information problem. Second, we study the industry-level annual discount rates of mainly China’s new energy power industries, including hydropower, nuclear power, wind power, and photovoltaic power industries for the period of 2014–2019. The results show that discount rates in China’s new energy power industries evolved differently between the years of 2014–2019 with average annual discount rates being 7.56%, 5.83%, 5.60%, and 8.64%, for the hydropower, nuclear power, wind power, and photovoltaic power industries, respectively. In 2019, the four annual discount rates were highest for the photovoltaic power industry (8.66%), followed by hydropower (7.17%), wind power (5.72%), and nuclear power industry (5.26%). Forecasting to 2020 from the 2019 evaluation base period, the discount rates are 6.37%, 5.00%, 6.57%, and 9.05% for the photovoltaic power, hydropower, wind power, and nuclear power industries, respectively. Under the different capital structures, their forecasts for the photovoltaic power, hydropower, wind power, and nuclear power industries in 2020 are, respectively, within [4.35%, 9.24%], [3.92%, 7.10%], [4.58%, 10.40%], [5.46%, 14.81%]. We also discussed more details on capital structure and forecast period of discount rates for China’s new energy power industries. Our analysis shows that it is necessary to establish a new energy power industry database and steadily promote the implementation of policies.

The industrialization of using crop straw as energy is currently hindered by systemic bottlenecks, including high collection and storage costs, a poorly coordinated industrial chain, and underdeveloped market mechanism. This study takes Guangdong province as a case study to construct a tripartite evolutionary game model on the transition of straw to energy among the government, enterprises, and farmers. Different from previous studies that focused on the strategy of penalizing the open burning of straw by farmers, this work investigated the cooperation of farmers for straw removal from field, the operational strategies of enterprises for straw utilization as energy, and the selection of government-guided incentive policies. It analyzes the behavioral evolution of these stakeholders under various incentive policies and cooperative scenarios. Numerical simulations were performed to identify the system’s evolutionary stable strategies and assess the potential of expanding straw for energy utilization. It indicated that mild government intervention could lead to a stable equilibrium through facilitating the removal of straw from fields and the utilization of straw as energy by enterprises. Farmers were sensitive to the fluctuation of acquisition price, and their willingness to cooperate would be negatively impacted by a large-scale price reduction. Enterprise expansion was exposed to significant risk under intensive policy intervention. The feasible pathway to increase the proportion of straw utilization as energy in Guangdong began at a small scale. Under mild incentive policies, a scenario targeting a 20% increase was more likely to achieve a market equilibrium for large-scale production than that targeting a 55% increase. The government should draw up positive incentive policies to promote the utilization of straw as energy. By guiding farmers in straw removal from the field and improving the energy enterprises’ competitiveness, the government should curb irrational industry expansion and corporate speculation, and shift from investment support to incentive policies. Meanwhile, the ecological construction of industry and supply chains should be enhanced, and the scale should be used to reduce the high supply-side costs of the straw. It would overcome the central barrier to the commercialization of straw utilization as energy. This work sets an example for conducting dynamic analysis of multi-stakeholder interactions for straw utilization.

In the context of global climate change and the transition to a low-carbon economy, utilizing crop straw as a resource is a key strategy for green transformation. Taking Guangdong province as a case, this study investigates the carbon reduction effects of integrated straw utilization and their spatiotemporal evolution, based on crop yield data from 2019 to 2023 across various municipalities. Different from one-way straw utilization for carbon reduction, this work analyzes the carbon reduction effects of five co-existing pathways to utilize straw as fertilizer, feed, energy, substrate, and raw material. The Theil index, slope value, and exploratory spatial data analysis (ESDA) method are employed to form an analytical framework for the spatiotemporal evolution of carbon reductions by straw utilization. Over this five-year period, the overall and off-field straw utilization steadily increased, and a 6.2% increase in straw utilization was achieved to realize a 19.8% rise in carbon reduction. In 2023, the carbon reduction from straw utilization was chiefly contributed by fertilization, subsequently followed by feed, energy, substrate, and raw material. Over 90% of the carbon reduction contributions came from four major crops, namely rice, peanuts, sugarcane, and potatoes. Carbon reduction across different areas in Guangdong showed positive spatial correlation, with high–high (HH) and low–low (LL) clusters being the primary local autocorrelation patterns. Model applications confirm that incentive policies and industrial development largely facilitate the integrated straw utilization in Guangdong. However, further increases in straw utilization will not necessarily ensure proportional carbon reduction. The regional heterogeneity and coordinated clustering development should be considered to strengthen carbon-reduction intensity. In particular, policies should be tailored to crop straw recovery and utilization, inter-regional straw allocation, and preferentially support straw utilization for energy and as a substrate.

Solid Oxide Fuel Cells (SOFCs) offer high-efficiency electrochemical conversion of fuels like natural gas, yet detailed modeling is crucial for optimization. This paper presents a simulation study of a natural gas-fueled SOFC system, developed using Aspen Plus with Fortran integration. Distinct from prevalent paradigms assuming rated power output, this work adopts rated current density as the primary input, enabling a more direct investigation of the cell’s electrochemical behavior. We conducted a comprehensive sensitivity analysis of key parameters, including fuel utilization, water-carbon ratio, and current density, and further investigated the impact of different interconnection configurations on overall module performance. Results demonstrate that a single unit operating at a current density of 180 mA/cm², a fuel utilization of 0.75, and a water-carbon ratio of 1.5 can achieve a maximum net stack-level electrical efficiency of 54%. Furthermore, optimizing the interconnection of a 400 kW module by combining series and parallel units boosts the overall net system-level electrical efficiency to 59%, a 5-percentage-point increase over traditional parallel setups. This is achieved by utilizing a bottoming cycle for exhaust heat recovery. This research validates the rated current density approach for SOFC modeling, offering novel insights into performance optimization and modular design for integrated energy systems.