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Review

Evaluating Multi-Criteria Decision-Making Methods for Sustainable Management of Forest Ecosystems: A Systematic Review

by
Cokou Patrice Kpadé
1,*,
Lota D. Tamini
1,
Steeve Pepin
2,
Damase P. Khasa
3,
Younes Abbas
4 and
Mohammed S. Lamhamedi
5
1
Department of Agricultural Economics and Consumer Science, Faculty of Agricultural and Food Sciences, and Center for Research on the Economics of the Environment, Agri-Food, Transports and Energy, Laval University, Québec, QC G1V 0A6, Canada
2
Department of Soil and Agri-Food Engineering, Faculty of Agricultural and Food Sciences, Laval University, Québec, QC G1V 0A6, Canada
3
Centre for Forest Research and Institute for Systems and Integrative Biology, Laval University, 1030 Avenue de la Medecine, Québec, QC G1V 0A6, Canada
4
Polydisciplinary Faculty, Sultan Moulay Slimane University (USMS), Beni Mellal 23000, Morocco
5
Faculty of Forestry, Geography and Geomatics, Abitibi Price Building, Laval University, Quebec, QC G1V 0A6, Canada
*
Author to whom correspondence should be addressed.
Forests 2024, 15(10), 1728; https://doi.org/10.3390/f15101728
Submission received: 30 July 2024 / Revised: 26 September 2024 / Accepted: 26 September 2024 / Published: 29 September 2024
(This article belongs to the Special Issue Production in Forest Nurseries, Field Performance of Seedlings and Natural Regeneration in the Context of Climate Change)
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Review Reports Versions Notes

Abstract

:
Multi-criteria decision-making (MCDM) methods provide a framework for addressing sustainable forest management challenges, especially under climate change. This study offers a systematic review of MCDM applications in forest management from January 2010 to March 2024. Descriptive statistics were employed to analyze trends in MCDM use and geographic distribution. Thematic content analysis investigated the appearance of MCDM indicators supplemented by Natural Language Processing (NLP). Factorial Correspondence Analysis (FCA) explored correlations between models and publication outlets. We systematically searched Web of Science (WoS), Scopus, Google Scholar, Semantic Scholar, CrossRef, and OpenAlex using terms such as ‘MCDM’, ‘forest management’, and ‘decision support’. We found that the Analytical Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were the most commonly used methods, followed by the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), the Analytic Network Process (ANP), GIS, and Goal Programming (GP). Adoption varied across regions, with advanced models such as AHP and GIS less frequently used in developing countries due to technological constraints. These findings highlight emerging trends and gaps in MCDM application, particularly for argan forests, emphasizing the need for context-specific frameworks to support sustainable management in the face of climate change.

1. Introduction

Climate change is putting pressure on agriculture and food systems worldwide [1,2,3]. Many countries are prone to recurrent arid conditions, and drought events are dramatically increasing [4], presenting a real challenge for agricultural and forest production. For example, in Morocco, climate projections forecast average decreases in precipitation of −13% and −19% by 2045 and 2075, respectively [5].
Efforts are needed to address the conservation of the genetic diversity of forests, including planting in different agricultural settings, and to increase awareness of its importance. An example is argan forest in Morocco [6]. The sustainable development program in Morocco is aimed at rescuing the argan forest [7,8], represents progress in Africa [9], and requires technological, organizational, and institutional development. However, achieving this goal in the context of climate change is becoming increasingly challenging owing to numerous social, environmental, and economic considerations. Economic performance analysis of new production technologies often overlooks the external effects generated by their interactions with the environment or their impact on the overall well-being of society. In the case of climate change mitigation and adaptation scenarios, an analysis of short-, medium-, and long-term effects is required. This is particularly crucial for new forest production technologies, as they must address the challenges of economic, environmental, and social dimensions. They translate into multiple criteria, both quantitative and qualitative [10,11,12,13]. There are several significant external effects on the environment and society, regardless of the costs and benefits of new production technologies [14,15].
The management of forests is very complex and requires a multi-dimensional approach [16,17]. As outlined by Belton and Stewart [18], MCDM methods applied in sustainable development strategies offer complex objectives. They provide solutions to real problems involving complex and contradictory objectives [11,12,13,19,20,21] and multiple parameters, opportunities, and constraints [22,23]. Moreover, they allow alternatives to be compared [23,24]. MCDM considers uncertainties and risks in the decision-making process for data collection and analysis [19] and thus offers a holistic comparison of decision alternatives between scenarios and technologies [25,26]. MCDM analysis addresses long-term horizons [13] and has been applied in various fields to select the most sustainable, credible, and flexible strategies or technologies, such as energy [19,22], bridge design and rehabilitation strategies [12], agriculture [25,27,28,29,30,31,32,33,34], waste management [20,35], and forestry [13,24,36,37].
This study examines the use of MCDM methods in forest management and planning. Our primary purpose is to identify the most used MCDM models to address the economic, social, and environmental aspects of forest management. We quantified trends in the use of different single or combined MCDM models in forestry over time, evaluated their complexity based on the number of indicators used, and examined geographical variations in their use across countries.
The contribution of this study is fourfold. First, we add to the existing literature by indicating that the AHP, as proposed by Saaty [38], is the most utilized method, primarily because of its advantage in pairwise recalibration. Other frequently used methods include GP, PROMETHEE, ANP, and TOPSIS, etc. [12,19,22,37]. Second, while single approaches were more common in earlier years, there has been a growing trend towards using multiple approaches in more recent years in forestry studies, such as the AHP and GP, which aligns with the need for integrated multidisciplinary solutions to address environmental problems [39,40,41]. Third, we evaluate the complexity of MCDM methods based on the number of indicators used, establishing a ranking of these methods based on their complexity level to aid in selecting the appropriate one. Additionally, we highlight the trade-offs between complexity and practical applicability, allowing for adaptation based on the available resources and capacities. Following Sari [37] and Ananda and Herath [13], we find that the complexity of decision-making methods varies widely, with more complex methods offering more comprehensive evaluations. Fourth, we identify the preferences and adoption patterns of MCDM methods by country and region; explore contextual factors such as priorities, capacities, and environment influencing the choice of MCDMs; and highlight disparities in their use. We demonstrate that MCDM methods are used more frequently in developed countries than in developing countries [23,42,43,44,45,46,47].
The remainder of the article is organized as follows: Section 2 presents the materials and methods used, including the research question, search terms, study selection criteria, and data analysis techniques. Section 3 presents the results, which include an analysis of MCDM models used in forest management, their distribution across countries, and the trends in model adoption over time. Section 4 discusses the challenges of applying MCDM methods in forest management and the disparities between developed and developing countries. Finally, Section 5 concludes with key findings, policy implications, and recommendations for future research.

2. Materials and Methods

2.1. Data Sources and Extraction

We used the systematic review framework proposed by Berrang-Ford et al. [48] which provides a robust methodology for synthesizing and tracking research. The RepOrting standards for Systematic Evidence Syntheses (ROSES) protocol for Systematic Literature Review (SLR) was applied to ensure the replication of research [49]. This framework includes: (a) describing the sources of literature relevant to MCDM methods in forest or argan management and planning; (b) articulating search terms and providing a detailed explanation of the search process used to identify relevant literature; (c) defining criteria for inclusion and exclusion of literature in the review; and (d) documenting the literature included in the review, as well as any literature that was excluded based on the defined criteria.
While the original approach described uses WoS and Scopus [50,51], we expanded our search to include Google Scholar alongside these platforms. In these research engines, a topic search is used to identify publications that refer to MCDM methods in terms of title, abstract, and author keywords. We incorporated Google Scholar because of its comprehensive coverage and accessibility [51,52], which could complement the results obtained from WoS and Scopus. Additionally, Google Scholar’s ability to search across various document types, including journal articles, conference papers, theses, and reports, allows us to capture a broader range of literature relevant to our topic. However, Gusenbauer [53] reported no differences in the disciplinary coverage of Google Scholar, Scopus, and WoS. Instead, we utilized the advanced search features available on Google Scholar to refine our search parameters and ensure relevance. We still emphasized peer-reviewed articles, reviews, book chapters, and books. We also considered the inclusion of gray literature such as institutional reports, particularly if they provided valuable insights into MCDM method application relative to argan forest management and planning within the specified timeframe. We explored other free-access scholarly databases [54] such as Semantic Scholar, CrossRef, and OpenAlex, to broaden our research scope [55]. CrossRef and CrossCite allow researchers to locate and reference high-quality research, thus simplifying the process. Semantic Scholar operates through a literature graph connecting papers, authors, and entities to aid scientists in uncovering and comprehending scientific literature. OpenAlex serves as an openly accessible repository that contains information on academic publications, authors, venues, institutions, and concepts. OpenAlex and CrossRef have larger coverage than WoS and Scopus because they do not limit their coverage to journals or document types [56,57].
Following Rosenstock et al. [58], we combined search terms such as ‘MCDM’ or ‘Multi-criteria decision-making’ with descriptors related to argan forest management and planning. This approach allowed us to capture a comprehensive range of literature pertinent to our research question. We conceptualized argan forest management as encompassing various aspects, including forestry practices, conservation strategies, ecosystem management, and sustainable utilization of forest resources. Argan forest management can integrate adaptive strategies within the framework of long-term ecological restoration. This includes the contribution of advanced geospatial technologies such as geographic information systems (GIS), machine learning, and modeling in the context of a supervised projection of the assisted migration of argan forests under future climatic challenges (adaptative strategy-future plantation areas). We extended this to include planning processes aimed at optimizing resource allocation, land use, and decision support systems within forest management frameworks. Our research equation (‘MCDM’ OR ‘Multi-criteria decision-making’) AND (‘method*’ OR ‘approach*’ OR ‘techniqu*’ OR ‘procedure*’ OR ‘strateg*’ OR ‘practice*’ OR ‘methodolog*’ OR ‘model*’) AND (‘forest* management*’ OR ‘forestry* management*’ OR ‘conservation strategy*’ OR ‘ecosystem management’ OR ‘sustainable utilization of forest* resource*’ OR ‘argan*’) AND (‘planning’ OR ‘decision support’ OR ‘resource allocation’ OR ‘land use’) reflects this conceptualization by integrating key terms relevant to MCDM methods, forest management, and planning (Table 1).
Our research question was the following: how do Multi-Criterion Decision-Making (MCDM) methods intersect with the diverse facets of forest management, including forestry practices, conservation strategies, ecosystem management, sustainable utilization of forest resources, and planning processes aimed at optimizing resource allocation, land use, and decision support systems within forest management frameworks?

2.2. Description of Criteria for Inclusion and Exclusion

After conducting a preliminary search, we exported the identified documents into the reference management software EndNote 20 for the initial screening. Following the removal of duplicates and irrelevant publication types, we screened titles, keywords, and abstracts based on predefined inclusion and exclusion criteria. Subsequently, relevant publications underwent a full text review to confirm their eligibility for inclusion. Any inaccessible or excluded materials were noted, and the final curated list consisted of publications that met the relevance and accessibility criteria (Table 2).
We focused on peer-reviewed scientific literature published from January 2010 to March 2024, restricted only to English and French language publications, as our review encompassed a wider range of sources (Table 2). The history of MCDM can be traced back to approximately 40 years [59]. Over time, researchers and practitioners have developed various MCDM models to address specific challenges in forest management [60]. The emphasis within the time framework is justified because it allows for the inclusion of the most recent developments and advancements in the field of MCDM. Additionally, it ensures the relevance of the study by addressing the current challenges and contexts of MCDM.
All the MCDM models used in forest or argan management and planning publications identified during the preliminary search were compiled into a comprehensive database. This database contains essential information like authors’ details, publication year, title, country, source, MCDM methods, indicators, and alternative scenarios. Furthermore, we established an additional database dedicated to relevant publications enriched with detailed information regarding the MCDM methods used, thematic focus, and indicators. This systematic organization of data facilitates a thorough understanding of the literature regarding MCDM models used in forest management and planning, enabling us to explore specific aspects of methods and their indicators. Additionally, the inclusion and exclusion criteria guiding the selection process are outlined in Table 2, providing transparency and clarity in our methodology.
In the next step, we collected in detail the relevant publications to identify (a) authors (year), (b) country, (c) models/methods, (d) number of indicators, (e) indicators, and (f) alternatives studied. The full-text review targeted publications’ thematic focus on forestry rather than geographical scope.

2.3. Data Analyses

We employed descriptive statistics to present quantitative trends, such as the frequency of different MCDM models over time and their geographical distribution. Thematic content analysis is utilized to investigate how MCDM indicators are reflected in relevant publications. These included examining and quantifying the appearance of MCDM indicators in titles, abstracts, keywords, and main texts to assess their importance. The word cloud, known as “Cirrus” in Voyant Tools, generated using Java, displays the most common terms related to MCDM models, with the size of each word indicating its frequency, a process that involves Natural Language Processing (NLP) analysis, including the identification and exclusion of stop words with the statistical analysis of word frequency, which are filtered out in text analysis [61,62]. Also, to address trends over years, we considered a “segment” as a specific period within the range of 2010 to 2024. Because the software (https://voyant-tools.org/, accessed on 27 June 2024.) divides this range into 10 segments, each segment represents approximately 1.4 years. The relative frequency of a specific MCDM model used in each segment ( f ) can be expressed as:
f = n / N
where:
n represents the number of times a specific MCDM model appears within a segment.
N represents the sum of the number of models mentioned in all the papers published within the same time segment.
Factorial Correspondence Analysis (FCA) was used to map the associations between different MCDM models and the journals, revealing how certain models were more prevalent in specific publication outlets, using a specific interpretation of the chi-square test (X2) according to Pearson [63]. This method provides a comprehensive view of set I of elements described by set J of properties through numerical data from the primary basis of this analysis in a rectangular I-J cross-table [64]. The Lambda value ( λ ) , a statistical measure used in FCA, indicates the strength of the association between the variables. A higher λ value suggests a stronger correlation, helping researchers understand the degree to which certain models are linked to journals and aids in interpreting the significance and relevance of the observed patterns in the data. Statistical analyses are conducted using Stata (18.0; StataCorp LLC, College Station, TX, USA), XLSTAT version 2021.1, Voyant Tools version 2016, and Microsoft Excel version 2021.

3. Results

3.1. Overview of Selected Studies

3.1.1. Systematic Mapping and Selection of MCDM Model Applications in Forestry

An initial database search yields 2768 articles that satisfied the predefined inclusion criteria and underwent full-text screening (Figure 1). After applying additional exclusion criteria, 46 articles that investigated MCDM methods and their implications for forestry were retained for systematic mapping. Where a single article reported results from multiple MCDM models, each model was recorded as a separate entry. Consequently, the last sample comprised 49 unique MCDM model applications extracted from 46 articles, including instances of individual MCDM methods and their combinations (Table S1 in Supplementary Materials).

3.1.2. Content Overview and Statistical Distribution of Selected Forest Management Studies

A total of 17 countries are represented, with Turkey, China, and Iran being the most frequently studied. In terms of time distribution, 15% of the studies were published between 2011 and 2015, 45% between 2016 and 2020, and 40% between 2021 and 2023, reflecting an increasing interest in sustainable forest management and multi-criteria decision-making techniques in recent years. The geographic distribution shows that China accounts for 20% of the studies, Turkey for 15%, and Iran for 12%, with the remaining 53% distributed among countries like Sweden, Japan, and Spain. The selected studies employ various empirical methods, such as the AHP, SMART, and BN models, to assess forest management practices (Table S1 in Supplementary Materials).

3.2. Analysis of MCDM Use over Time

A “single approach” study uses only one model, whereas a multiple approach study uses more than one model to conduct the research. Overall, there were slightly more studies (26) that used a single approach than studies (21) that used multiple approaches (Figure 2). However, the trend seems to be shifting towards the use of multiple approaches in recent years. In 2022, seven studies used multiple approaches, the highest number across all years. In contrast, only two studies used a single approach in 2022. Similarly, in 2023, three studies used multiple approaches, and only one study used a single approach. The years with the highest number of single-approach studies were 2017 (four studies) and 2019 (five studies). The number of years with the highest number of multiple-approach studies was 2019 (four studies) and 2022 (seven studies).
The most studied models in the reviewed papers are AHP and TOPSIS, followed by PROMETHEE, ANP, GIS, and GP. The strong dominance of AHP and TOPSIS may indirectly indicate the preference of researchers in the region for these models, possibly because of their perceived effectiveness or familiarity. However, it is worth noting the presence of other models suggests diverse approaches in the field. Thus, some studies utilize multiple methods, such as the Delphi method, AHP, and Spatial Multi-Criteria Evaluation (SMCE), or combined AHP with the Geographic Information System (GIS). Few studies have explored certain models, such as the BN, Decision-Making Trial and Evaluation Laboratory (DEMATEL), and MCDM with the Rapid Appraisal of Mangrove Ecosystem Sustainability (RAPMECS), which could suggest a lower interest in or awareness of these specific methodologies among researchers and policymakers (Figure 3).
Figure 4 shows an overview of the relative frequencies of 35 different models used in forestry management studies across ten-time segments from 2010 to 2023. AHP stands out as the most frequently employed model across most of these segments (Figure 3), serving as a benchmark for comparison with the other models depicted in Figure 4. Figure 4A shows the relative frequencies of six models over the time segments. The AHP method is the most frequently used in most segments, with a relative frequency ranging between 0.01 and 0.045. Although the relative frequency decreased slightly in some segments, it remained predominant throughout the examined period. Other methods such as GIS, VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje method), TOPSIS, and PROMETHEE have also made recurring appearances. However, their relative frequencies are generally lower than those of the AHPs. We observe variability in the relative use of each method over time. Other methods such as SMART (Simple Multi-Attribute Rating Technique), ANP, and ANN also appear in certain segments, albeit with lower frequencies compared to AHP (Figure 4B). However, its relative frequency is lower than that of AHP and other methods. Figure 4C shows that other methods such as SMCE (Spatial Multi-Criteria Evaluation), QMCA (Qualitative Multi-Criteria Analysis), the Delphi method, CP (Compromise Programming), CM (Cognitive Mapping), and BCP (Balanced Compromise Programming) also appear in certain segments, although their relative frequencies are generally lower compared to AHP.
Figure 4D shows the relative frequencies by models over ten-time segments. Compared to the AHP, the Pareto Frontier module, IIPT (Iterative Ideal Point Thresholding), CPA (Compromise Programming Approach), CA (Cluster Analysis), BWM (Best – Worst Method), and BN (Bayesian Network Model) also make appearances in specific segments, albeit with lower relative frequencies (0.011) compared to AHP, indicating their occasional utilization in forestry management. Overall, while AHP emerges as a prevalent method across most segments (Figure 4D), the presence of other methods reflects a diversified approach for tackling forestry management challenges. Additional methods, such as PCMs (Pairwise Comparison Matrices), MCDM_RAPMECS (Criteria Decision-Making Analysis with the Rapid Appraisal of Mangrove Ecosystem Sustainability), FIS (Fuzzy Inference System), EWT (Entropy Weighting Techniques), DEMATEL (Decision-Making Trial and Evaluation Laboratory), Decision Analysis by Ranking Techniques (DART), DEMATEL-based ANP (DANP), and CASA (CASA models), also emerged in specific segments, albeit with lower relative frequencies (0.013) compared to AHP (Figure 4E), indicating their intermittent utilization. Figure 4F presents the relative frequencies of the model over ten-time segments, and the AHP method exhibits a consistent presence across many segments, with relative frequencies ranging from 0.01 to 0.045. This indicates its enduring importance and widespread usage. Methods such as SWARA (Stepwise Weight Assessment Ratio Analysis), SAW (Simple Additive Weighting), PFT (Pareto Front Technique), NDIVT (Normalized Difference Vegetation Index), FDANP (Hybrid Fuzzy-DEMATEL-ANP), EGP (Extended Goal Programming), and BN also emerge in specific segments, albeit with lower relative frequencies (0.0125) compared to AHP, indicating their sporadic utilization. Overall, while AHP maintains dominance across most segments, the presence of alternative methods underscores a diversified approach to addressing forestry management challenges.
The relative frequencies are low because they represent the proportion of studies using a particular model out of the total number of studies in each time segment. Given that there are 35 MCDM models used across the studies, it is likely that the usage of each individual model is spread out, resulting in lower relative frequencies.
Across the segments, a diverse array of model combinations involving AHP is observed (Figure 5). In the early segments, the prevalence of specific combinations is limited, suggesting a period of experimentation or exploration of different methods. However, as time passed, a clear pattern emerged, with AHP paired with TOPSIS consistently dominating the landscape. This dominance is particularly notable in segments 3, 4, 6, 7, 8, 9, and 10, where AHP-TOPSIS exhibits significant prominence, indicating its preference for addressing forestry management challenges. While other combinations such as AHP-VIKOR, AHP-PROMETHEE, and AHP-GIS also appeared, their presences remained comparatively less pronounced.

3.3. Analysis of MCDM Use across Countries

Across countries, a diverse array of models is employed, reflecting the multifaceted nature of forestry management and decision-making. The hierarchy diagram depicts the distribution of decision-making models across countries, with each model represented by a spoke extending from the center of the chart (Figure 6). The length of each spoke corresponded to the frequency or prevalence of the respective model’s usage in the surveyed countries. Several models have emerged that are widely used across multiple countries. AHP stands out prominently and is implemented in countries such as Angola, India, Italy, Japan, and Turkey, among others. Similarly, GIS has been extensively employed in countries such as Canada, China, Iran, and Portugal, reflecting its widespread use in mapping and monitoring forest resources. Furthermore, models such as PROMETHEE and Interval-Valued Intuitionistic Fuzzy Sets (IIPT) demonstrate consistent usage across different regions. PROMETHEE’s adaptability to handle MCDM scenarios is evident in its adoption in countries such as Australia, Ecuador, and the USA. Similarly, IIPT, utilized in Austria, Europe, and Japan, provides a robust framework for integrating uncertain and imprecise information into decision-making processes.

3.4. Correlations between Models and Publication Journals

As indicated in Section 2.3, we used Factorial Correspondence Analysis (FCA) to evaluate the correlation between models and journals (Table 3). The Lambda (symmetric) value (λ = 0.444) indicates a moderate yet statistically significant association between the two categorical variables, “Models”, and “Journal” (p < 0.001). However, the directional measures reveal an asymmetric relationship, with the strength of association and predictive ability to vary based on which variable is treated as the dependent variable. When predicting the ‘Models’ variable, λ = 0.276 suggests a 27.6% reduction in error using the independent variable, indicating a moderate association. In contrast, when predicting the ‘Journal’ variable, λ = 0.588 points to a stronger association, with a 58.8% reduction in error. The Goodman and Kruskal’s tau values further corroborate these findings, showing a moderate association (tau = 0.388) for predicting ‘Models’ but a relatively strong association (tau = 0.603) for predicting ‘Journal’. Collectively, these results consistently demonstrate a statistically significant yet directional relationship between the variables, where the strength of the association ranges from moderate to strong, contingent on the specific measure used and the direction of the relationship. The significant values of the directional measures indicate a meaningful association between the two categorical variables being analyzed, implying that they are not independent of each other. This suggests the potential for using one variable’s value to make inferences about the other, and these findings demonstrate an asymmetric directional relationship between the two variables.
Models such as the Normalized Difference Vegetation Index (NDVI) and Qualitative Multi-Criteria Analysis (QMCA) demonstrate clear associations with specific journals, indicating targeted avenues for dissemination in environmental geoinformatics and forest planning research, respectively (Figure 7). Conversely, GP, CM, and ANP exhibit dispersed associations across multiple outlets, suggesting the need for a broad approach to journal selection. In contrast, AHP shows notable associations with journals focusing on forest policy and economics, emphasizing the importance of targeted dissemination in these areas.
Models like PROMETHEE and the Delphi Method exhibit varied associations, indicating publication across diverse outlets within the forestry research domain. Additionally, models such as Spatial Multi-Criteria Evaluation (SMCE), Expert Judgement and Scoring Process, Expert Choice 11 software, Best - Worst Method (BWM), and VIKOR demonstrate dispersed associations, highlighting the need for researchers to explore various publishing avenues.

3.5. Indicators Used in MCDM Models

The radar chart depicts the average number of indicators employed by various models and methods in the decision-making scenarios (Figure 8). High-complexity methods, such as Qualitative Multicriteria Analysis and ANP, utilize an average of 21 and 18 indicators, respectively, suggesting a comprehensive approach to decision-making with a diverse set of criteria. Similarly, the CM employs an average of 18 indicators, indicating extensive data analysis and a broad consideration of criteria. Moderately complex methods, including AHP and GP, employ 15 and 11 indicators on average, respectively, balancing comprehensiveness with practicality. Low-complexity methods, such as PROMETHEE, Delphi method, SMCE, and expert judgement, each utilize an average of eight indicators, suggesting simpler, more focused approaches to decision-making.

4. Discussion

4.1. Challenges or Disparities for MCDM Model Use in Sustainable Forest Management

While single-approach studies were more common in the earlier years, there has been a growing trend towards using multiple approaches in more recent years, possibly due to the desire to combine the strengths of different models. The trend towards using multiple methods in forestry management, such as the AHP and TOPSIS, aligns with the need for integrated and multidisciplinary solutions to address complex environmental problems [39,40,41]. This shift reflects a growing recognition of the limitations of single-method approaches in adequately capturing the intricacies of forestry management issues [65]. The absence of certain models in the reviewed papers, such as Principal Component Analysis (PCA) and hybrid fuzzy DEMATEL-ANP (FDANP), does not necessarily imply a lack of published information regarding them in the region. However, it may indicate a relative lack of interest or focus compared to more commonly used models, such as AHP and TOPSIS, possibly reflecting their perceived effectiveness or applicability in the research context. The prevalence of AHP and TOPSIS in forestry research is consistent with previous studies emphasizing their extensive use in decision-making processes [66]. These methods offer structured frameworks that can effectively address the multifaceted nature of environmental projects, providing a transparent and scientifically sound basis for sustainability assessments. Certain models, such as AHP and GIS, are widely unused in developing countries. This observation is consistent with past research and scientific discourse, suggesting the adoption of advanced decision-making models in forestry may be constrained by many factors in developing countries. Disparities in the adoption of MCDM across countries can be attributed to differences in research priorities, institutional capacities, and environmental contexts [43,44,45,46,47,67]. McHenry et al. [68] emphasized the disparities in the usage of MCDM in forestry, reflecting variations in research priorities, institutional capacities, and environmental contexts. The limited adoption of advanced models in developing countries underscores challenges related to resource constraints and technological infrastructure. Baynes et al. [69] identified key factors that influence the success of community forestry in developing countries and highlighted the importance of understanding the contextual factors that shape decision-making processes in forestry. There is a lack of specific empirical studies on the adoption and effectiveness of advanced decision-making models, such as MCDM, in the context of developing country forestry [42]. So, efforts to develop context-specific MCDM frameworks that account for the unique social, economic, and environmental factors are warranted in developing countries [59]. The association between decision-making models and publishing outlets highlights the strategic implications for researchers in disseminating their findings effectively. Understanding these associations can guide researchers in selecting appropriate journals to maximize the visibility and impact of their studies within the forestry research community [60]. Unique ecosystems, such as argan forests, may require specialized decision-making frameworks tailored to address their specific environmental and socioeconomic dynamics. Furthermore, our results clearly demonstrate that the MCDM method has never been applied to argan tree development strategies, let alone in the current context of severe climate change and the demographic and economic pressures facing argan forests in Morocco [70,71,72].

4.2. Implications of MCDM Application in Forest Management Design under Climate Change

The MCDM methods are indeed valuable for assessing options on climate change and sustainable development [73]. MCDM methods provide a comprehensive framework to address complex environmental challenges by considering multiple criteria and stakeholder perspectives [61]. The integration of MCDM methods of designing sustainable strategies in developing countries in general, has been extensively unexplored. Existing literature emphasizes the importance of reconciling economic, social, and environmental aspects of decision-making processes [62,74]. Castella and Lestrelin [75] further underscored the importance of integrating environmental considerations, particularly in the context of climate change, while Lacaze et al. [67] provided a methodological framework for this integration, using a multidisciplinary approach to assess the natural and human environment of forests.
The research conducted on MCDM models in sustainable forest management [76] holds theoretical significance across multiple crucial domains. It underscores the significance of integrated approaches that consider the intricate and multifaceted nature of sustainability challenges, placing emphasis on the necessity of context-specific frameworks and a comprehensive sustainability approach [76,77]. The research highlights the importance of involving stakeholders in decision-making processes [78], considering a range of perspectives and priorities [76,78]. Moreover, it emphasizes the importance of interdisciplinary research collaborations in effectively addressing emerging sustainability challenges [75,77]. Furthermore, it emphasizes the need to consider the consequences of climate change in forest management practices [75,77]. The interaction between climate factors and pathogen dynamics poses a significant risk to the sustainability of forests [79]. Increased temperatures and thermal stress are projected to further degrade, for example, argan trees, hindering their regeneration capabilities [80]. The research results have substantial policy implications, encouraging the spread of knowledge and the creation of accessible decision support tools for sustainable forest management [76,77]. These implications collectively underscore the necessity for more comprehensive, adaptable, and inclusive approaches to sustainable forest management, particularly in unique ecosystems like argan forests. MCDM methods are showcased to tackle environmental challenges, incorporating stakeholder perspectives and climate change impacts, indicating a change in forestry research and management.

5. Conclusions and Research Needs

This literature review examines the use of MCDM methods in forest management, with a particular focus on sustainable development. It highlights a growing trend towards integrating multiple MCDM approaches, with methods such as the AHP and TOPSIS being the most commonly used. This reflects an increasing recognition of the complexity of forestry management challenges. However, there remains a significant gap in applying MCDM methods to developing countries forest management, despite their proven effectiveness in other contexts. This presents an opportunity to develop decision-making frameworks tailored to the unique ecological, economic, and social dimensions of forests.
Efforts should aim to address barriers to MCDM adoption in developing countries, such as limited resources and technological infrastructure. Interdisciplinary collaboration is key to applying MCDM to complex ecosystems. Future studies should integrate local knowledge, climate science, and economic factors into MCDM frameworks. Additionally, investigating the long-term impacts of MCDM-based decisions on forest sustainability could provide valuable insights for policymakers and forest managers. This review sets the stage for more targeted, context-specific applications of MCDM in sustainable forest management, contributing to ecosystem resilience in the face of climate change.
Forests facing drought and precipitation scarcity require innovative technologies. Assisted migration, including genetic selection of drought-resistant plants and reduced chemical inputs, is one approach. These innovations aim to address current economic, environmental, and social challenges of forest management. Future studies should apply the AHP method to compare and select effective and sustainable technologies. Data on economic (operating costs, total investment costs, energy costs, etc.), environmental (water savings, mineral fertilizer savings, CO2 equivalent sequestration, biodiversity status, etc.), and social impacts (jobs created, social acceptability, impact on human health, impact on economic development) from the adoption of these new production technologies in nurseries and forest plantations will help generate sustainability indicators to guide decision-making. Additionally, adopting a holistic approach could help manage the trade-off between conserving water resources and maximizing yield, as well as reduce nitrous oxide (N2O) emissions resulting from the application of mineral nitrogen fertilizers to mitigate climate change. This ensures that innovations not only enhance forest productivity but also support broader sustainable development objectives.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15101728/s1, Table S1: Literature review of MCDM models used in forest management. References [36,37,47,65,77,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121] are cited in supplementary materials.

Author Contributions

Study’s design, C.P.K., L.D.T., S.P., D.P.K., Y.A. and M.S.L.; methodology, C.P.K.; validation, L.D.T., S.P., D.P.K., Y.A. and M.S.L.; formal analysis, C.P.K. and L.D.T.; resources, C.P.K. and L.D.T.; data curation, C.P.K. and L.D.T.; writing—original draft preparation, C.P.K. and L.D.T.; writing—review and editing, S.P., D.P.K., Y.A. and M.S.L.; visualization, L.D.T., S.P., D.P.K., Y.A. and M.S.L.; supervision, L.D.T., S.P., D.P.K., Y.A. and M.S.L.; project administration, S.P. and Y.A.; funding acquisition, L.D.T., S.P., D.P.K., Y.A. and M.S.L. All authors have read and agreed to the published version of the manuscript.

Funding

Fonds de recherche du Québec (FRQ) and the Centre National de Recherche Scientifique et Technique (CNRST) du Maroc, Programme de collaboration de recherche CNRST—FRQ, project N°327244.

Data Availability Statement

The data supporting this study are available from the corresponding author on request.

Acknowledgments

The authors thank the Fonds de Recherche du Québec (FRQ) and the Centre National de Recherche Scientifique et Technique (CNRST) du Maroc for funding this study. The authors are grateful to the Editor and three anonymous reviewers for providing helpful comments which allowed us to improve and enhance the content of our article.

Conflicts of Interest

The authors have no relevant financial or non-financial interests to disclose.

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Figure 1. Systematic literature review process based on ROSES protocol; adapted from Ishtiaque [49].
Figure 1. Systematic literature review process based on ROSES protocol; adapted from Ishtiaque [49].
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Figure 2. Single (one MCDM model only) versus multiple (>1 MCDM model) approaches adopted in the reviewed studies (n = 46).
Figure 2. Single (one MCDM model only) versus multiple (>1 MCDM model) approaches adopted in the reviewed studies (n = 46).
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Figure 3. Radar showing the frequency of MCDM models.
Figure 3. Radar showing the frequency of MCDM models.
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Figure 4. Relative frequency comparison of MCDM models in forestry over time (A–F).
Figure 4. Relative frequency comparison of MCDM models in forestry over time (A–F).
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Figure 5. Relative frequency comparison of most model combinations of reviews over time.
Figure 5. Relative frequency comparison of most model combinations of reviews over time.
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Figure 6. Hierarchy chart showing the number of countries and models used in forestry.
Figure 6. Hierarchy chart showing the number of countries and models used in forestry.
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Figure 7. Factorial correspondence analysis between models used in forestry and publishing journals. Legend: Ejud: Expert_judgment; Spr: Scoring_process; PFr: Pareto_Frontier; PROM: PROMETHEE; GMCDM: GIS-MCDM; Dme: Delphi_method; DEM: DEMATEL; Flogn: Fuzzy_logic_norm; GISM: GIS_SMCD; CJFR: Canadian Journal of Forest Research; ECOL: Ecological Indicators; JFOR: Journal of Forest Planning; ISAH: ISAHP Proceedings; FORp: Forest Policy and Economics; ENVI: Environmental Monitoring and Assessment; LIFE: Life Science Journal; FORt: Forests; JENV: Journal of Environmental Planning and Management; ANFR: Annals of Forest Research; COMP: Computers and Electronics in Agriculture; IOPC: IOP Conference Series: Materials Science and Engineering; SUST: Sustainability; SUMA: Sumarski List; IJEG: International Journal of Environment and Geoinformatics; ICIE: International Conference on Industrial Engineering and Management; IJSA: International Journal of Sustainable Agricultural Management and Informatics; JINN: Journal of Innovation & Knowledge; FIRE: Fire; JCLE: Journal of Cleaner Production; JPLA: Jurnal Ilmiah PLATAX; APPL: Applied Geography; FORm: Forest Ecology and Management; E3SW: E3S Web of Conferences; SCIR: Scientific Reports; SSRN: SSRN Electronic Journal; RESQ: Research Square; CEFJ: Central European Forestry Journal; ECOI: Ecology of Iranian Forests; GEOS: Geo-spatial Information Science; F1: First dimension of FCA, and F2: Second dimension of FCA.
Figure 7. Factorial correspondence analysis between models used in forestry and publishing journals. Legend: Ejud: Expert_judgment; Spr: Scoring_process; PFr: Pareto_Frontier; PROM: PROMETHEE; GMCDM: GIS-MCDM; Dme: Delphi_method; DEM: DEMATEL; Flogn: Fuzzy_logic_norm; GISM: GIS_SMCD; CJFR: Canadian Journal of Forest Research; ECOL: Ecological Indicators; JFOR: Journal of Forest Planning; ISAH: ISAHP Proceedings; FORp: Forest Policy and Economics; ENVI: Environmental Monitoring and Assessment; LIFE: Life Science Journal; FORt: Forests; JENV: Journal of Environmental Planning and Management; ANFR: Annals of Forest Research; COMP: Computers and Electronics in Agriculture; IOPC: IOP Conference Series: Materials Science and Engineering; SUST: Sustainability; SUMA: Sumarski List; IJEG: International Journal of Environment and Geoinformatics; ICIE: International Conference on Industrial Engineering and Management; IJSA: International Journal of Sustainable Agricultural Management and Informatics; JINN: Journal of Innovation & Knowledge; FIRE: Fire; JCLE: Journal of Cleaner Production; JPLA: Jurnal Ilmiah PLATAX; APPL: Applied Geography; FORm: Forest Ecology and Management; E3SW: E3S Web of Conferences; SCIR: Scientific Reports; SSRN: SSRN Electronic Journal; RESQ: Research Square; CEFJ: Central European Forestry Journal; ECOI: Ecology of Iranian Forests; GEOS: Geo-spatial Information Science; F1: First dimension of FCA, and F2: Second dimension of FCA.
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Figure 8. Radar chart depicting the average number of indicators used in empirical studies by models.
Figure 8. Radar chart depicting the average number of indicators used in empirical studies by models.
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Table 1. Key words and search string.
Table 1. Key words and search string.
Key WordsSearch String
# 1 Multicriteria decision-making‘MCDM’ OR ‘Multi-criteria decision-making’
# 2 Methods‘method*’ OR ‘approach*’ OR ‘techniqu*’ OR ‘procedure*’ OR ‘strateg*’ OR ‘practice*’ OR ‘methodolog*’ OR ‘model*’
# 3 Forest management‘forest* management*’ OR ‘forestr* management*’ OR ‘conservation strateg*’ OR ‘ecosystem management’ OR ‘sustainable utilisation of forest* resource*’ OR ‘argan*’
# 4 Planning‘planning’ OR ‘decision support’ OR ‘resource allocation’ OR ‘land use’
# 5 # 1 AND # 2 AND # 3 AND # 4
Table 2. Inclusion and exclusion criteria for literature selection.
Table 2. Inclusion and exclusion criteria for literature selection.
NºInclusion CriteriaExclusion Criteria
1Text in English and FrenchText in languages other than English and French
2Article journal (empirical data)Article review, book, chapters in book, book series, conference
Proceeding
3Focuses on the forestry sectorFocuses on sectors other than forestry (e.g., agriculture, energy, and transport sectors)
3Addresses methods and indicators of MCDM models used in forest management and planningDo not address methods and indicators of MCDM models
4The text includes sufficient detail to carry out data analysisThe text does not provide sufficient detail to carry out data analysis
5Empirical or Review of empiricalNon-empirical studies/theoretical and conceptual framework
62010–2024<2010
Table 3. Directional measures of models by journal of cross-tabulations.
Table 3. Directional measures of models by journal of cross-tabulations.
Directional Measures
ValueAsymptotic Standardised Error aApproximate T bApproximate Significance
Nominal by nominalLambda (λ)Symmetric0.4440.04480570.000
Models dependent0.2760.06241650.000
Journal dependent0.5880.06185780.000
Goodman and Kruskal’s tauModels dependent0.3880.013 0.930 c
Journal dependent0.6030.003 0.275 c
a. Not assuming the null hypothesis. b. Using the asymptotic standard error, assuming the null hypothesis. c. Based on chi-square approximation.
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Kpadé, C.P.; Tamini, L.D.; Pepin, S.; Khasa, D.P.; Abbas, Y.; Lamhamedi, M.S. Evaluating Multi-Criteria Decision-Making Methods for Sustainable Management of Forest Ecosystems: A Systematic Review. Forests 2024, 15, 1728. https://doi.org/10.3390/f15101728

AMA Style

Kpadé CP, Tamini LD, Pepin S, Khasa DP, Abbas Y, Lamhamedi MS. Evaluating Multi-Criteria Decision-Making Methods for Sustainable Management of Forest Ecosystems: A Systematic Review. Forests. 2024; 15(10):1728. https://doi.org/10.3390/f15101728

Chicago/Turabian Style

Kpadé, Cokou Patrice, Lota D. Tamini, Steeve Pepin, Damase P. Khasa, Younes Abbas, and Mohammed S. Lamhamedi. 2024. "Evaluating Multi-Criteria Decision-Making Methods for Sustainable Management of Forest Ecosystems: A Systematic Review" Forests 15, no. 10: 1728. https://doi.org/10.3390/f15101728

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