Purpose: To propose a new investment-income valuation model by real options approach (ROA) for old community renewal (OCR) projects, which could help the government attract private capital's participation.Design/methodology/approach: The new model is proposed by identifying the types of options private capital has in the OCR project, selecting the option model most suitable for private capital investment decisions, improving the valuation model through the triangular fuzzy numbers to take into account the uncertainty and flexibility, and demonstrating the feasibility of the calculation model through an actual OCR project case.Findings: The new model can valuate OCR projects more accurately based on considering uncertainty and flexibility, compared with conventional methods that often underestimate the value of OCR projects.Practical implications: The investment-income of OCR projects shall be re-valuated from the lens of real options, which could help reveal more real benefits beyond the capital growth of OCR projects, enable the government to attract private capital's investment in OCR, and alleviate government fiscal pressure.Originality/value: The proposed OCR-oriented investment-income valuation model systematically analyzes the applicability of real option value (ROV) to OCR projects, innovatively integrates the ROV and the net present value (NPV) as expanded net present value (ENPV), and accurately evaluate real benefits in comparison with existing models. Furthermore, the newly proposed model holds the potential to be transferred to various social welfare projects as a tool to attract private capital's participation.

Forest fires pose significant threats to ecological security, human settlements, and sustainable regional development, particularly in mountainous regions with complex environmental and anthropogenic conditions. Previous studies have yet to construct a forest fire risk assessment framework that integrates multi-source data, which limits the comprehensiveness and accuracy of existing assessments. To address this gap, taking Liangshan Prefecture in China as a case study, this research selected eight risk factors to characterize vegetation conditions, topographic features, climatic conditions, and human activities. A combined weighting approach integrating the mandatory determination method and the coefficient of variation method was employed to determine the weights of different indicators. Forest fire risk probability was calculated using a weighted comprehensive evaluation model, and spatial autocorrelation analysis based on global Moran’s I and local indicators of spatial association (LISA) was further conducted to investigate spatial clustering characteristics. The results indicate that high-risk and very high-risk areas are mainly concentrated in southeastern Liangshan, particularly in Xichang, Jinyang, Ningnan, Huili, and Huidong, where warmer climatic conditions, dense vegetation coverage, mountainous terrain, and intensive human activities jointly contribute to elevated forest fire risk. The global Moran’s I value of 0.219175 indicates significant positive spatial autocorrelation in forest fire risk distribution. Validation using historical fire-scar data from 2010 to 2020 showed that 83.66% of the fire scars were distributed within medium-, high-, and very high-risk areas, suggesting that the proposed assessment framework provides a reasonable representation of forest fire risk patterns in Liangshan. The findings of this study can support regional forest fire prevention planning, targeted resource allocation, and risk management in mountainous areas.