Financing Mode Selection and Key Success Factors in Area Comprehensive Development Projects Based on IF-MULTIMOORA

Area comprehensive development projects represent a critical model for accelerating urbanization and fostering industry–city integration. However, such projects face challenges, including extended development cycles, substantial capital investments, incomplete legal frameworks, and financing difficulties, compounded by diverse applicability of available investment and financing modes that heighten the risk of project failure. Previous studies predominantly focused on standalone projects; this study proposes an intuitionistic fuzzy multiobjective optimization by ratio analysis plus the full multiplicative form (IF-MULTIMOORA) method for group decision-making on financing model selection and key success factor (KSF) prioritization. To address this, this study first identifies 20 KSFs through a literature review and the Delphi method, categorizing them into four dimensions: government policies, external environmental factors, project intrinsic factors, and social investor factors. Subsequently, the IF-MULTIMOORA method is applied to evaluate the applicability of three common financing modes, namely, public–private partnership (PPP), social investor + engineering procurement construction (EPC), and the market-oriented operation model, and assess KSF importance in the LinYi (LY) Area Comprehensive Development Project. The results indicated that the social investor + EPC mode demonstrates the highest applicability, and the five most critical KSFs are social investor’s participation in preliminary planning, project investment recovery status, project revenue distribution, land supply plan compatibility, and social investor’s urban comprehensive operation experience. This study constructs a decision-making model integrating the ranking of KSFs with financing mode selection and validates its feasibility through the LY Area Comprehensive Development Project, aiming to anticipate and mitigate potential risks leading to project failure and to improve resource utilization at the decision-making stage.