Ranking the factors influencing the degradation of the Arasbaran forest through a multi-criteria decision-making approach using the Best-Worst Method (BWM)

Document Type : Research Paper

Authors

1 Corresponding Author, Associate Prof., Department of Forestry, Ahar Faculty of Agriculture and Natural Resources, University of Tabriz, Iran. Email: royaabedi@tabrizu.ac.ir

2 MSc student, Department of Forestry, Ahar Faculty of Agriculture and Natural Resources, University of Tabriz, Iran

Abstract

Background and objectives: Degradation factors cause disruptions in the natural course of a forest ecosystem, leading to a decline in its capacity and, ultimately, the destruction of forest habitats. By examining and distinguishing these changes, it is possible to predict the current status and future trends of forests and make informed decisions to improve conditions. This study aimed to identify and prioritize the main degradation factors in the Arasbaran forest using the multi-criteria Best-Worst Method (BWM) to determine the significance of each factor. Additionally, the sub-criteria of each factor were identified and ranked in detail.
Methodology: The study was conducted in the protected forests of the Arasbaran Biosphere Reserve, located in the north of East Azerbaijan Province in northwestern Iran. In this research, degradation factors were categorized into three main criteria: human-caused, managerial-executive, and natural factors. The sub-criteria for each category were identified, selected, and grouped, consisting of nine, nine, and six sub-criteria, respectively. The survey participants included experts and specialists in the field of natural resources. A specialized questionnaire was designed based on the principles of the BWM method. After collecting responses and performing the BWM analysis, the weights of the criteria and sub-criteria, along with the consistency index, were calculated.
Results: The majority of participants (75.43%) had between 10 and 20 years of work experience in natural resources, 38.59% held a master's degree, and 63.40% specialized in forest sciences. The results of prioritizing criteria and sub-criteria indicated that human-caused factors, with a weight of 0.400, played the most significant role in the degradation of the Arasbaran forest. The ranking of sub-criteria revealed that Livestock grazing leads to the destruction of vegetation, hindering the regeneration, and compaction of soil, and animal husbandry (Wagg= 0.119) was the primary contributor to human-caused destruction in the study area. The ranking of the managerial and executive factor sub-criteria indicated that the Lack of collaborative management and protection (Wagg= 0.130) was ranked first. The ranking of natural sub-criteria revealed that pest infestations and plant diseases (Wagg= 0.230) was effective natural factor.
Conclusion: Comparing the findings of this study with the current situation in the study area demonstrated that the BWM analysis method provided reliable and acceptable results. The prioritization of forest degradation factors will assist planners and policymakers in designing and implementing targeted strategies to mitigate or adjust these destructive elements. Additionally, forest managers will be able to identify key challenges and adopt suitable conservation measures to improve or modify forest management plans. It is recommended that further research be conducted to identify and prioritize the most effective solutions for preventing or addressing these degradation factors and their sub-criteria. By obtaining expert recommendations, the findings can be made accessible to decision-makers to facilitate effective actions for protecting this vital ecosystem. Moreover, any solution for preventing degradation should comprehensively address human, managerial-executive, and natural aspects to ensure the ecosystem’s recovery by minimizing these destructive factors. Furthermore, exploring the potential of remote sensing technologies and artificial intelligence-based analyses for developing a deforestation monitoring approach, particularly concerning natural factors, is suggested.

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References

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History

  • Receive Date: 25 November 2024
  • Revise Date: 19 January 2025
  • Accept Date: 09 March 2025

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