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Multi-criteria decision making methods to address rural land allocation problems: A systematic review
Ist Teil von
International Soil and Water Conservation Research, 2021-12, Vol.9 (4), p.490-501
Ort / Verlag
Elsevier B.V
Erscheinungsjahr
2021
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
Land allocation has been an important issue in land use planning research studies. Land allocation involves different multifunctional activities of maximizing environmental, economic and social benefits. Multi-criteria decision making (MCDM) method is the most popular tool to optimize land allocation problems by considering decision variables, conflicting objectives, and criteria. Hence, decision-makers face problems on how to optimize the land allocation while minimizing the conflicting trade-offs existing in the decision analysis. With this review study, we aim at identifying and extracting information on MCDM methods to solve land allocation problems from English language articles published between 2000 and 2019 and indexed by four scientific literature databases (Web of Science, Science Direct, Scopus, Google scholar). To this end, we applied a systematic literature review approach, i.e. the Preferred Reporting Items for Systematic Reviews and Meta-Analysis procedure (Moher et al., 2009), with a structured database search expression. 120 articles were selected of which, after careful screening of title, keywords and abstract, 69 were retained for detailed review. This review study report compiles comprehensive information by classifying the papers into application area, optimization objectives, criteria used, decision techniques, publication year and study region. In summary, we found that in the last two decades, the use of the MCDM method has increased, particularly in Europe and China. AHP (analytical hierarchal process) is frequently used for multi-attribute land allocation problems with reference to ecotourism and ecosystem management. LP (linear programming) and SA (simulating annealing) methods are predominantly used to optimize multi-objectives complex agricultural and forest land allocation problems respectively.