Version 1
: Received: 12 September 2024 / Approved: 12 September 2024 / Online: 14 September 2024 (05:00:25 CEST)
How to cite:
Amiri, N.; Limaei, S. M. Optimal Forest Management Using Multi-Objective and Game Theory Techniques: Considering Environmental and Economic Factors. Preprints2024, 2024091017. https://doi.org/10.20944/preprints202409.1017.v1
Amiri, N.; Limaei, S. M. Optimal Forest Management Using Multi-Objective and Game Theory Techniques: Considering Environmental and Economic Factors. Preprints 2024, 2024091017. https://doi.org/10.20944/preprints202409.1017.v1
Amiri, N.; Limaei, S. M. Optimal Forest Management Using Multi-Objective and Game Theory Techniques: Considering Environmental and Economic Factors. Preprints2024, 2024091017. https://doi.org/10.20944/preprints202409.1017.v1
APA Style
Amiri, N., & Limaei, S. M. (2024). Optimal Forest Management Using Multi-Objective and Game Theory Techniques: Considering Environmental and Economic Factors. Preprints. https://doi.org/10.20944/preprints202409.1017.v1
Chicago/Turabian Style
Amiri, N. and Soleiman Mohammadi Limaei. 2024 "Optimal Forest Management Using Multi-Objective and Game Theory Techniques: Considering Environmental and Economic Factors" Preprints. https://doi.org/10.20944/preprints202409.1017.v1
Abstract
The forest is a complex and dynamic ecosystem that requires integrated planning and management, considering economic, social, environmental, and ecological dimensions. Different stakeholders often have conflicting goals, making optimal management decisions challenging. This study aims to balance increasing economic benefits for forest users with reducing negative environmental impacts, aligning the interests of managers, government institutions, and policymakers. To achieve this, multi-objective optimization methods and game theory were used. The objectives were to maximize the net present value of wood harvesting and the amount of carbon sequestration. The study was conducted in the Shafarood forest in northern Iran. Data collected included stumpage prices, variable harvesting costs, tree density per ha, volume per ha, growth rates, interest rates, carbon sequestration, and labor costs. Using this data, models were developed for the expected mean price of various species, stored carbon, logistics growth, and labor. After adjusting the objective functions and model constraints, the range of Pareto optimal solutions for the multi-objective model and the Nash equilibrium for the game theory model were determined using the epsilon-constrain method. The results indicate that both modeling approaches can identify optimal points for balancing economic and environmental goals. The Pareto optimal range and Nash equilibrium allow for informed decision-making in managing the Hyrcanian forests, accommodating different user goals. Therefore, both models are effective tools for forest management planning and provide a framework for achieving sustainable management practices.
Keywords
decision making; multi-objective; Pareto optimum; game theory; Nash equilibrium; epsilon-constrain; net present value; carbon sequestration
Subject
Biology and Life Sciences, Forestry
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
