A Multi-Objective Model for the Optimal Allocation of Development Funds among the Counties of Fars Province, with Emphasis on Spatial Justice, Reducing Regional Disparities, and Balanced Development

Abstract

The optimal allocation of development funds at the regional level is one of the most critical decision-making issues in public planning systems, as government financial resources are limited, while regional development needs differ considerably in terms of their magnitude, nature, and urgency. This study aims to develop a model for allocating development funds among 11 counties in Fars Province based on deprivation indicators for the year of 2021 (1400) and approved budget data for 2022 (1401). Data on eight key indicators across economic, infrastructural, educational, health, digital, and social dimensions were collected from the Statistical Center of Iran, the Ministry of Health and Medical Education, the Ministry of Cooperatives, Labour, and Social Welfare, the Ministry of Energy, the Ministry of Petroleum, and the Planning and Budget Organization of Fars Province. The indicators were weighted using a hybrid approach combining the subjective Analytic Hierarchy Process (AHP), based on the judgments of 15 experts with a consistency ratio of 0.072, and the objective Shannon entropy method. The deprivation index was then calculated using the numerical taxonomy method. The allocation model was solved using goal programming in LINGO, and the results were compared with the approved budget. The findings indicate that the proposed model reallocates the total budget of IRR 92,396,600 million in a manner that increases allocations to the most deprived counties. Arsanjan and Khorrambid receive the largest increases, by 60.7% and 39.1%, respectively, whereas Shiraz, as the most developed county, experiences a 17.6% reduction in its budget allocation. Inequality indicators show that the Gini coefficient decreases from 0.418 to 0.312, representing a 25.4% improvement, while the Theil index decreases from 0.342 to 0.244, representing a 28.7% improvement. In addition, the service gap in deprived counties decreases by an average of 36%. Scenario analysis indicates that the deprivation-oriented scenario, with a Gini coefficient of 0.28, performs best in reducing inequality, although the combined scenario provides a more balanced trade-off between equity and efficiency. Overall, the proposed model demonstrates that need- and deprivation-based allocation can significantly improve spatial equity. However, successful implementation requires careful consideration of absorptive capacity and analysis of alternative budget scenarios.                                                                                  

Keywords: Spatial Justice, Optimal Resource Allocation, Goal Programming, Deprivation Index, Numerical Taxonomy, Hybrid Weighting Method, Analytic Hierarchy Process, AHP, Shannon Entropy, Fars Province.