Analytical Assessment of Ecological Security and Environmental Vulnerability Using the LOPCOW–MABAC Method

Volume : 6 Issue : 2 Year : 2025 Pages : 77-93 e-ISSN : 2717-9230 77 ANALYTICAL ASSESSMENT OF ECOLOGICAL SECURITY AND ENVIRONMENTAL VULNERABILITY USING THE LOPCOW–MABAC METHOD Damla YALÇINER ÇAL a, Orhan Orçun BITRAK b a PhD, Independent Researcher, damlayal[email protected], h9ps://orcid.org/0000-0002-9232-3063. b PhD, Independent Researcher, [email protected], h9ps://orcid.org/0000-0001-5648-4161. ABSTRACT: This study analy-cally examines the ecological threat levels of 207 countries using data presented in the Ecological Threat Report 2024. Four key indicators—demographic pressure, food insecurity, impact of searelated events, and water risk—were u-lized, and all criteria were integrated into a decision matrix to enable a comparable assessment of countries’ environmental vulnerability. The rela-ve importance of the criteria was determined using the LOPCOW method, which is based on data varia-on and eliminates human subjec-vity. The resul-ng weights were calculated as follows: demographic pressure (25.83%), food insecurity (25.59%), impact of sea-related events (26.78%), and water risk (21.79%). These results indicate that the criteria have a nearly equal level of influence on the forma-on of ecological threats. Following the weigh-ng process, countries were evaluated using the MABAC method and ranked according to their overall ecological threat scores. The findings show that Greenland, Bermuda, Malta, Germany, Slovakia, and Estonia are among the countries with the lowest threat levels, whereas Niger, Burkina Faso, Madagascar, Somalia, Afghanistan, and Benin exhibit the highest levels of vulnerability. The results further reveal that ecological threats are predominantly concentrated in low-income regions characterized by arid clima-c condi-ons, limited natural resource management capacity, and heightened sensi-vity to climate shocks. This confirms the strong rela-onship between environmental vulnerability and socio-economic development levels. Overall, the study provides a data-driven analy-cal framework that can support the formula-on of sustainable development policies and contributes to a systema-c understanding of cross-country ecological risk dispari-es while highligh-ng priority regions for environmental interven-on. Keywords: Ecological Threats, Sustainable Development, Environmental Vulnerability, Ecological Security, MulD-Criteria Decision-Making RECEIVED: 16 September 2025 ACCEPTED: 23 October 2025 DOI: hIps://doi.org/10.5281/zenodo.18057043 CITE Yalçıner Çal, D., Bıtrak, O. O., (2025). AnalyDcal Assessment of Ecological Security and Environmental Vulnerability using the LOPCOW–MABAC Method. European Journal of Digital Economy Research, 6(2), 77-93. hIps://doi.org/10.5281/zenodo.18057043 Research Paper Yalçıner Çal, Bıtrak 78 1. INTRODUCTION In an era where global environmental pressures are rapidly intensifying, the sustainable management of natural resources and the measurement of ecological vulnerability have become central concerns for both researchers and policymakers. Challenges such as climate change, rapid populaDon growth, food insecurity, and water scarcity are disrupDng ecological balances and reshaping countries’ environmental resilience (Rockström et al., 2021). Against this backdrop, the development of objecDve, comparable, and datadriven approaches for analyzing ecological threats has gained criDcal importance. The Ecological Threat Report 2024 (ETR-2024), published by the InsDtute for Economics and Peace (IEP), provides an extensive dataset covering 207 countries and offering an up-to-date overview of global ecological pressures. The report evaluates countries’ environmental vulnerabiliDes using four key indicators: demographic pressure, food insecurity, the impact of sea-related events, and water risk (IEP, 2024). These indicators capture the core parameters of ecological security and serve as essenDal reference points for guiding sustainable development policies. Assessing environmental risk oeen requires the simultaneous examinaDon of mulDple interdependent criteria. For this reason, mulD-criteria decision-making (MCDM) methods have become increasingly prominent in environmental studies (Zavadskas et al., 2014). MCDM techniques provide a structured framework that integrates indicators with different scales and orientaDons, enabling meaningful interpretaDon of both qualitaDve and quanDtaDve informaDon. In this study, ecological threat levels were evaluated using ETR-2024 data through the LOPCOW (Logarithmic Percentage Change-driven ObjecDve WeighDng) and MABAC (MulDAIribuDve Border ApproximaDon Area Comparison) methods. The LOPCOW method objecDvely determines criterion weights by incorporaDng logarithmic percentage variaDon, thereby eliminaDng subjecDve influence (Ecer & Pamučar, 2022). Following this, the MABAC approach ranks countries based on their distance to the border approximaDon area between ideal and anD-ideal soluDons (Pamučar & Cirović, 2015). The combined use of these two methods enables both objecDve weighDng and a mulDdimensional evaluaDon of alternaDves. Importantly, this study does not simply reproduce ETR-2024 classificaDons; rather, it re-analyzes these data through a mathemaDcally transparent, reproducible, and fully objecDve decision-analyDc model. While the ETR provides categorical threat levels, it does not disclose the mathemaDcal weighDng process underlying its composite structure. Thus, the LOPCOW–MABAC framework introduced here offers an added analyDcal layer by quanDfying cross-country differences with higher precision, greater measurability, and enhanced interpretability. The study also clearly disDnguishes among the concepts of “ecological threat,” “environmental vulnerability,” and “ecological security,” which are oeen used interchangeably in the literature. Ecological threat refers to the biophysical pressures a country currently faces, whereas environmental vulnerability reflects its capacity to withstand these pressures. The composite score produced through MABAC directly measures the “ecological threat level,” providing a quanDtaDve indicator that can be readily interpreted by policymakers. Furthermore, the decision to use only four ETR indicators is a deliberate methodological choice. Although the ETR includes numerous dimensions, this study focuses solely on direct biophysical threats. Indicators related to governance, conflict, or socio-economic condiDons were intenDonally excluded to maintain conceptual clarity and isolate the environmental components of ecological risk. This approach allows differences across countries to be observed more clearly and consistently within a strictly ecological context. Overall, the primary aim of the study is to provide a quanDtaDve analysis of ecological threats at the naDonal level, idenDfy high-risk regions, and support sustainable policy development through a data-driven framework. 2. LITERATURE REVIEW This study establishes the theoreDcal foundaDon for a comprehensive mulD-criteria decisionmaking (MCDM) analysis conducted using data from the 2024 Ecological Threat Report. The LOPCOW and MABAC methods, which form the methodological basis of this research, have been widely applied across various fields, including sustainability assessment, risk management, energy planning, supply chain resilience, financial Analytical Assessment of Ecological Security and Environmental Vulnerability Using the LOPCOW–MABAC Method 79 performance evaluaDon, and environmental efficiency analysis. The LOPCOW method stands out due to its ability to generate objecDve criterion weights and its robustness against negaDve performance values. In contrast, the MABAC method offers a mulDdimensional evaluaDon framework by assessing the performance of alternaDves based on their relaDve posiDons within the border approximaDon area—defined between ideal and anD-ideal soluDons. These strengths make both methods parDcularly useful for analyzing large datasets and complex decision environments such as ecological threat assessments. The tables presented in this secDon summarize selected naDonal and internaDonal studies published between 2014 and 2025 in which LOPCOW and MABAC methods have been applied. For each study, the authors, research purpose, methodological approach, and key findings are concisely outlined. This comprehensive literature review aims to strengthen the methodological grounding of the present study and to elucidate the scienDfic foundaDons of decision-making processes based on ecological threat indicators. The reviewed studies are presented in Table 1. Table 1. Literature Review LOPCOW No Author(s) Purpose Method Findings / Results 1 Aydın (2025) To analyze corporate financial performance in the insurance sector. LOPCOW–RANCOM– RAWEC model. The model demonstrated strong discriminatory power in the Sompo Insurance case. 2 Doğan (2025) To evaluate the financial performance of Borsa Istanbul banks. LOPCOW–RAM method. The model reliably revealed financial efficiency differences among banks. 3 Durak (2025) To analyze the corporate sustainability performance of Istanbul Airport (IGA). LOPCOW–MAUT integraXon. IGA was found to exhibit high environmental sustainability performance. 4 Ecer et al. (2025) To assess sustainable aviaXon fuel suppliers. IVF-neutrosophic LOPCOW + MARCOS model. The most energy-efficient and sustainable supplier was idenXfied. 5 Karahaliloğlu (2025) To analyze logisXcs center locaXon selecXon based on sustainability. LOPCOW–Grey RelaXonal Analysis (GRA). The model ensured opXmal site selecXon across environmental and economic criteria. 6 Sharma et al. (2025) To strengthen resilience in the food supply chain through a two-stage decision model. LOPCOW–DOBI and probabilisXc programming. The model enhanced food supply chain resilience. 7 Chaberjee et al. (2024) To select collaboraXve robots (cobots) for producXon lines. LOPCOW–OPTBIAS integrated model. The opXmal cobot improving producXon efficiency was idenXfied. 8 Işık et al. (2024) To analyze the compeXXveness of European ciXes. LOPCOW + CRADIS model. Significant differences were observed in innovaXon and sustainability indicators. 9 Liu et al. (2024) To opXmize resource allocaXon in energy planning. LOPCOW + WASPAS + game theory. The model ensured opXmal resource uXlizaXon. Yalçıner Çal, Bıtrak 80 10 Riaz et al. (2024) To evaluate AI-driven performance in the healthcare supply chain. AI-driven LOPCOW– AROMAN model. Decision efficiency increased under uncertain data condiXons. 11 Rong et al. (2024) To assess risks in industrial robot sogware projects. IVFF-LOPCOW–ARAS model. Accurate weighXng of risk factors contributed to improved project success. 12 Alhntaş (2023) To evaluate the welfare performance of G7 countries. LOPCOW-based CRADIS method. Germany and Canada showed the highest welfare performance. 13 Ecer et al. (2023) To evaluate the role of UAV technologies in agricultural producXon. q-rung fuzzy LOPCOW–VIKOR model. The model improved decision effecXveness under uncertainty. 14 Keleş (2023) To evaluate livable ciXes in G7 countries and Turkey. LOPCOW–CRADIS method. Paris, London, and Istanbul exhibited the highest livability performance. 15 Nila & Roy (2023) To select third-party logisXcs providers in sustainable supply chains. TFN-LOPCOW + FUCOM + DOBI model. Sustainability criteria were objecXvely weighted. 16 Simić et al. (2023) To prioriXze Industry 4.0based material-handling technologies in smart warehouses. Neutrosophic LOPCOW–ARAS model. The most suitable technologies for sustainable warehouse management were determined. 17 Ulutaş et al. (2023) To analyze the effecXveness of natural fibers in insulaXon materials. PSI + MEREC + LOPCOW + MCRAT model. The most suitable material was idenXfied based on environmental and mechanical performance. 18 Yaşar & Ünlü (2023) To examine environmental sustainability levels in universiXes. LOPCOW and MEREC-based CoCoSo method. Green campus pracXces demonstrated the highest sustainability performance. 19 Biswas et al. (2022b) To compare dividendpaying capacity in India’s FMCG and consumer durables sectors. MCDM-based LOPCOW framework. FMCG companies showed a more sustainable financial structure. 20 Biswas et al. (2022c) To manage uncertainty in sales personnel selecXon. Spherical fuzzy LOPCOW model. The model measured sales personnel performance more reliably. 21 Ecer & Pamučar (2022) To evaluate the sustainability performance of banks. LOPCOW–DOBI model. Balanced sustainability measurement across financial and environmental indicators was achieved. 22 Niu et al. (2022) To conduct group decisionmaking in a Fermatean fuzzy environment. Fermatean cubic fuzzy LOPCOW method. Divergence in decision-makers’ views was minimized. Analytical Assessment of Ecological Security and Environmental Vulnerability Using the LOPCOW–MABAC Method 81 MABAC No Author(s) Purpose Method Findings / Results 1 Abdullayev & Çokmutlu (2025) To analyze the impact of the EU Carbon Border Adjustment Mechanism on the Borsa Istanbul cement sector. MEREC-weighted MABAC method. A strong relaXonship was found between financial performance indicators and stock returns. 2 Aşan et al. (2025) To measure the project management performance of regional development agencies. LOPCOW and MABAC methods. Significant regional differences were observed in agency project performance. 3 Aydın (2025) To conduct corporate performance analysis in the insurance sector. LOPCOW–RANCOM– RAWEC model. Criteria influencing the financial performance of insurance companies were idenXfied. 4 Doğan (2025) To measure the financial performance of Borsa Istanbul banks. LOPCOW–RAM method. Performance differences among banks were reliably determined. 5 Durak (2025) To examine the corporate sustainability performance of Istanbul Airport (IGA). LOPCOW and MAUT integraXon. IGA demonstrated strong sustainability performance. 6 Jaleel & Mahmood (2025) To develop a decision support system for supply chain management. Bipolar complex fuzzy sog MABAC. The model opXmized supply chain performance. 7 Karahaliloğlu (2025) To analyze logisXcs center locaXon selecXon based on sustainability. LOPCOW–GRA model. The selected locaXon achieved both economic and environmental balance. 8 Sharma et al. (2025) To enhance food supply chain resilience. LOPCOW–DOBI and probabilisXc programming. The model presented strong results in risk reducXon. 9 Fan et al. (2024) To evaluate wearable health technologies. MEREC–MABAC and CPT-based approach. A user-friendly performance evaluaXon of health technologies was performed. 10 Jafari & Naghdi Khanachah (2024) To assess supplier informaXon-sharing and resilience. Pythagorean fuzzy MABAC. Supply chain resilience was effecXvely measured. 11 Sun et al. (2024) To evaluate technology use in post-producXon film and media. MABAC-based analysis. MulXmedia technology performance was assessed. 12 Mandal & Seikh (2023) To opXmize the plasXc waste management process. Interval-valued spherical fuzzy MABAC. The opXmal waste management method was idenXfied. 13 Tan et al. (2023) To evaluate investment risks in the Belt and Road IniXaXve. Prospect theory + Fermatean fuzzy MABAC. Improved accuracy in risk assessment was achieved. 14 Torkayesh et al. (2023) To examine MABAC applicaXons in sustainability. SystemaXc literature review. The growing use of MABAC in sustainability studies was demonstrated. Yalçıner Çal, Bıtrak 82 15 Wang et al. (2023) To develop a MABAC algorithm using picture fuzzy sets. Prospect theorybased MABAC. The method ensured high reliability under uncertainty. 16 Ahmad et al. (2022) To provide decision support for emergency response systems. Non-linear DiophanXne fuzzy MABAC model. The model improved decision effecXveness under uncertainty. 17 Mishra et al. (2022) To select sustainable suppliers in the automoXve sector. HF-DEA-FOCUMMABAC technique. Sustainability criteria were effecXvely weighted. 18 Tešić et al. (2022) To improve decisionmaking processes. Rough-numbersbased modified MABAC. The method enhanced decision stability under uncertainty. 19 Deveci (2021) To opXmize offshore wind farm site selecXon in the U.S. Type-2 neutrosophic MABAC. Environmental and economic factors were balanced. 20 Lukić (2021) To analyze sectoral efficiency in Serbia. Classical MABAC method. The financial services sector showed the highest efficiency. 21 Zhang et al. (2021) To evaluate green supplier selecXon. Spherical fuzzy CPTMABAC. The model effecXvely assessed environmental performance. 22 Zhao et al. (2021) To reduce uncertainty in group decision-making. IntuiXonisXc fuzzy CPT-MABAC. Decision consistency was improved. 23 Irvanizam et al. (2020) To solve mulX-criteria group decision-making problems. Triangular fuzzy neutrosophic MABAC. The model reduced uncertainty in group decisions. 24 Liu & Cheng (2020) To improve decisionmaking in a neutrosophic environment. Regret theory & likelihood-based MABAC. Risk axtudes of decision-makers were incorporated. 25 Mishra et al. (2020) To develop a decision support model for smartphone selecXon. Extended intuiXonisXc fuzzy MABAC. Criterion sensiXvity in the decision process was improved. 26 Wang et al. (2020) To conduct group decisionmaking in q-rung orthopair fuzzy environments. Fuzzy MABAC. Group decision accuracy increased. 27 Mulliner et al. (2016) To evaluate sustainable housing affordability. ComparaXve MABAC analysis. MABAC was found effecXve for sustainable housing assessment. 28 Pamučar et al. (2015) To opXmize logisXcs resource selecXon. MABAC method. Efficient selecXon of transportaXon resources was achieved. 29 Zavadskas et al. (2014) To evaluate MCDM methods comprehensively. Extensive literature analysis. MABAC demonstrated strong performance in mulX-criteria evaluaXons. 3. METHOD 3.1. LOPCOW Method The LOPCOW (Logarithmic Percentage ChangeDriven ObjecDve WeighDng) method, developed by Ecer and Pamučar (2022), is one of the newgeneraDon objecDve weighDng techniques introduced to the mulD-criteria decision-making (MCDM) literature. The method is parDcularly noteworthy for its ability to determine criterion weights independently of decision-makers’ Analytical Assessment of Ecological Security and Environmental Vulnerability Using the LOPCOW–MABAC Method 83 subjecDve judgments, especially when dealing with large-scale datasets or decision matrices containing negaDve values. Unlike tradiDonal objecDve approaches that rely solely on measures such as variance or entropy, LOPCOW is based on the logarithmic percentage changes of the series’ standard deviaDon and mean-square values. This structure minimizes the influence of measurement units or scale differences among criteria and eliminates scale bias arising from the magnitude of the data series (Ecer & Pamučar, 2022). Another disDnguishing feature of LOPCOW is its insensiDvity to negaDve performance values within the decision matrix. This makes the method more stable and reliable in mulDdimensional decision environments where criteria contain negaDve or mixed values (Biswas et al., 2022b). Depending on the influence level of criteria, dataset size, and performance variaDons among alternaDves, the LOPCOW method can be effecDvely applied to decision problems characterized by high variability. It is designed to yield more balanced and realisDc weight differences among criteria, parDcularly in data series with large variances (Biswas et al., 2022c). According to Ecer and Pamučar (2022), the LOPCOW method consists of three main steps: construcDng the normalized decision matrix, calculaDng the logarithmic percentage change coefficient for each criterion, and converDng these values into criterion weights. The weights obtained at the end of this process provide an objecDve, scale-independent, and stable evaluaDon by considering both the degree of criterion variaDon and the discriminatory power among alternaDves (Ecer & Pamučar, 2022; Biswas et al., 2022b). The procedural steps of the LOPCOW method are presented as follows (Keleş, 2023; Yaşar & Ünlü, 2023): 1. Construc,on of the Decision Matrix: In the first step of the LOPCOW method, the decision problem is structured by defining m alternaDves and n criteria. The performance values corresponding to these alternaDves and criteria are compiled to form the decision matrix. As shown in EquaDon (1), this matrix represents the fundamental dataset that will be used throughout the decision-making process. IDM=%x!! x"! x!" x"" ⋯ ⋯x!# x"# ⋮ )))))⋮ ⋮ ⋮ x$! x$" ⋯ x$#* (1) 2. Construc,on of the Normalized Decision Matrix: In this step, the criterion values contained in the decision matrix are standardized using the linear normalizaDon technique. The normalizaDon procedure is applied differently depending on the orientaDon of each criterion. If a criterion has a cost-oriented structure—meaning that lower values are preferred—EquaDon (2) is used. Conversely, if the criterion is benefit-oriented and higher values are preferred, EquaDon (3) is applied. The resulDng normalized decision matrix (IDM) ensures that all criteria are transformed onto a comparable scale, enabling a consistent and meaningful evaluaDon across alternaDves. r!"=#!"#$#$% #!"#$#!$& (2) r!"=#$%$#!$& #!"#$#!$& (3) 3. Construc,on of the Percentage Value Matrix: In this stage of the analysis, the percentage values for each criterion are calculated using the formula presented in EquaDon (4). In this calculaDon, the percentage of the standard deviaDon for each criterion is determined by taking the mean-square values into account. This approach eliminates scale differences arising from the magnitude of the data series, ensuring that criteria measured in different units become comparable. The resulDng percentage value matrix serves as the basis for idenDfying the relaDve importance levels of the criteria, grounded in the distribuDonal characterisDcs of the dataset. PV!"=% % ln⎝ ⎜ ⎛ %∑($% )! $*+ ! &⎠ ⎟ ⎞ .100% % (4) 4. Calcula,on of Criterion Weights: In the final step, the objecDve weight values for each criterion are computed using the formula presented in EquaDon (5). These weights are determined based on the degree of variaDon exhibited by each criterion, thereby capturing their relaDve influence within the decision-making process. The resulDng weights consDtute the primary output of the LOPCOW method and can be directly uDlized in subsequent mulD-criteria decision-making analyses. W"='($% ∑'($% & $*+ (5) Yalçıner Çal, Bıtrak 84 3.2. MABAC Method MulD-Criteria Decision-Making (MCDM) approaches provide a systemaDc framework for evaluaDng mulDple alternaDves based on several assessment criteria (Zavadskas et al., 2014). One of these approaches, the MABAC (MulD-AIribuDve Border ApproximaDon Area Comparison) method, was developed by Pamučar and Cirovic (2015) and relies on the concept of the border approximaDon area in assessing decision alternaDves. The primary aim of MABAC is to simultaneously consider each alternaDve’s closeness to the ideal soluDon and its distance from the negaDve soluDon. The MABAC method is characterized by a mathemaDcally robust structure and high interpretability, making it parDcularly suitable for evaluaDng alternaDves under mulDple criteria. In this approach, once the decision matrix is normalized, a weighted decision matrix is created. Subsequently, the border approximaDon area is computed for each criterion, and the distance of each alternaDve from this area is determined. A posiDve distance indicates that the alternaDve performs beIer than the reference boundary, whereas a negaDve distance signals weaker performance (Pamučar et al., 2015). Due to its computaDonal simplicity and strong interpretability, MABAC is considered a powerful alternaDve to more tradiDonal MCDM methods such as TOPSIS and VIKOR (Mulliner et al., 2016). In recent years, the MABAC method has been widely applied in various domains, including sustainable supplier selecDon (Mishra et al., 2022), performance evaluaDon of energy systems (Wang et al., 2020: 208), and sectoral efficiency analysis (Lukić, 2021). Moreover, it can be integrated with weighDng methods such as Entropy, CRITIC, MEREC, and SWARA, allowing for the combined evaluaDon of objecDve and subjecDve criteria (Tan et al., 2023). Through these capabiliDes, MABAC has emerged as an effecDve tool for analyzing sustainability indicators, environmental performance criteria, and regional development indices. The procedural steps of the MABAC method are presented below (Pamučar et al., 2015): 1. Construc,on of the Decision Matrix: The construcDon of the decision matrix is presented in EquaDon (1). 2. Normalized Decision Matrix: To ensure that the criteria can be compared on a common scale, the values are normalized. The normalizaDon formulas for both benefit-based and cost-based criteria are given in EquaDon (6). n!"=2#$%$*+,-(#%) *012#%3$*+,-(#%),𝑓𝑎𝑦𝑑𝑎8𝑘𝑟𝑖𝑡𝑒𝑟𝑖 45#2#%3$#$% *012#%3$*+,-(#%),𝑚𝑎𝑙𝑖𝑦𝑒𝑡8𝑘𝑟𝑖𝑡𝑒𝑟𝑖 (6) This ensures that all criteria are scaled into the [0,1] range. 3. Construc,on of the Weighted Decision Matrix: Aeer determining the weights (wⱼ) that represent the relaDve importance of each criterion, the normalized matrix is mulDplied by these weights. The weighted decision matrix is calculated using EquaDon (7). v!"=𝑛!"∗𝑤" (7) 4. Calcula,on of the Border Approxima,on Area: A disDncDve feature of the MABAC method is the calculaDon of the border approximaDon area (gⱼ) for each criterion. This value serves as a reference point for comparing the performance of the alternaDves, and it is computed using EquaDon (8). g"=∏7$% ! $_+ 4 (8) 5. Calcula,on of the Alterna,ves’ Distance from the Border Area: The deviaDon of each alternaDve from the border value for each criterion is calculated using EquaDon (9). g!"=𝑣!"∗𝑔" (9) Here, if qᵢⱼ > 0, alternaDve i performs above the average for that criterion, whereas qᵢⱼ < 0 indicates that it performs below the average. 6. Calcula,on of MABAC Scores and Ranking: Finally, the overall score of each alternaDve is computed using EquaDon (10), aeer which the alternaDves are ranked accordingly. S"=∑𝑞!" 8 "9: (10) A larger Sᵢ value indicates that the alternaDve exhibits beIer performance. Therefore, the alternaDves are ranked in descending order based on their Sᵢ scores. 7. Ranking of Alterna,ves and Decision Making: PosiDve scores indicate that the decision alternaDve outperforms the border approximaDon area, whereas negaDve scores reflect weaker performance. Through this approach, decisionmakers obtain both numerical comparisons and a clear assessment of relaDve performance among the alternaDves. Analytical Assessment of Ecological Security and Environmental Vulnerability Using the LOPCOW–MABAC Method 85 4. METHODOLOGY 4.1. Research Aim and Scope This study aims to analyDcally assess the ecological threat levels of countries using data from the Ecological Threat Report 2024 (ETR-2024). The core objecDve is to evaluate the key indicators that shape countries’ environmental vulnerability through objecDve weighDng and mulD-criteria ranking techniques, thereby providing a holisDc analyDcal framework. In this context, the LOPCOW method is employed to objecDvely determine the relaDve importance of the criteria, aeer which the MABAC method is applied to rank countries according to their overall ecological threat levels. Using these two methods in combinaDon preserves the staDsDcal structure of the dataset while eliminaDng human subjecDvity in the evaluaDon process, allowing differences in countries’ environmental performance to be idenDfied more transparently. The study ulDmately aims to offer policymakers, internaDonal organizaDons, and researchers an analyDcal framework applicable to sustainable development, ecological security, and environmental risk management. An important contribuDon of this study lies in the addiDonal analyDcal value it provides compared with ETR2024. While the ETR presents summary threat classificaDons for countries, it does not disclose how indicator weights are mathemaDcally constructed. The LOPCOW–MABAC framework, by contrast, analyzes the same dataset through enDrely transparent, traceable, and reproducible staDsDcal procedures. As a result, the study produces an independent and comparable ecological threat index that can be evaluated alongside the ETR’s own classificaDons, enabling a more precise differenDaDon among countries. The selecDon of indicators used in this study is also a deliberate methodological choice. Although ETR2024 includes a wide range of variables, only four core biophysical threat indicators—demographic pressure, food insecurity, impact of sea-related events, and water risk—are included in the analysis. These four indicators are used because they are available for all countries, are directly comparable, and represent the fundamental biophysical drivers of ecological threats. Variables related to governance capacity, conflict intensity, economic stability, or social vulnerability are intenDonally excluded, as they reflect societal resilience rather than ecological threat itself. Similarly, indicators such as climate anomalies, temperature increases, or carbon emissions are not included because they are not methodologically consistent or uniformly reported across all countries. Therefore, these four indicators represent the most staDsDcally coherent and conceptually appropriate variables for conducDng a mulD-criteria ecological threat assessment. Through this structure, the LOPCOW– MABAC approach not only reinterprets ETR data but also enables ecological threats to be evaluated within a mathemaDcally transparent, comparable, and objecDve decision-making framework. Consequently, the study evolves into an analyDcal assessment tool that reconstructs ecological threat levels independently of the ETR’s internal classificaDons, offering methodological added value and a strengthened theoreDcal foundaDon. This methodological contribuDon enhances the model’s pracDcal applicability and facilitates the interpretaDon of results by decision-makers. In sum, the study seeks to provide policymakers, internaDonal insDtuDons, and researchers with a comprehensive and data-driven evaluaDon framework for sustainable development, ecological security, and environmental risk management. By doing so, it clarifies the spaDal distribuDon of global ecological risks and supports strategic decision-making aimed at prioriDzing regions facing the highest levels of threat. 4.2. Dataset and Variables The dataset used in this study was obtained from the “Ecological Threat Report 2024” published by the InsDtute for Economics and Peace (IEP) (Vision of Humanity, 2024). The dataset provides quanDtaDve ecological threat indicators for 207 countries. Four main criteria were used in the analysis: (1) Demographic Pressure: Represents pressures arising from populaDon growth, urban density, natural resource demand, and the environmental carrying capacity. 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