Bibliometric Analysis of Multi-Criteria Decision-Making (MCDM) Methods in Environmental and Energy Engineering Using CiteSpace Software: Identification of Key Research Trends and Patterns of International Cooperation

Abstract

In the context of sustainable development and global challenges such as climate change and energy security, this paper conducts a bibliometric analysis of scientific journals on multi-criteria decision-making (MCDM) methods with an emphasis on their applications in environmental and energy engineering. The study used the CiteSpace software program 6.2.R6 Advanced to analyze citation networks and identify key publications, authors, and research topics. The simulations carried out made it possible to identify the main research centers and patterns of international cooperation, pointing to the key countries and institutions involved in MCDM research. The results of the analysis reveal the research areas of greatest interest and the main directions for future research. These results can support scientists, researchers, and policymakers in making more informed and sustainable decisions, contributing to the achievement of the Sustainable Development Goals.

1. Introduction

Environmental and energy engineering play a key role in achieving sustainable development and meeting global challenges [1,2,3,4,5]. Both fields play a central role in shaping the future of our planet, having a direct impact on the health of ecosystems, the economy, and the quality of life of people [6,7,8,9,10,11]. Environmental engineering deals with a wide range of activities related to the protection and management of natural resources, minimization of the negative impact of human activity on ecosystems, and development of technologies and strategies aimed at improving the state of the environment [12,13,14,15,16,17]. Issues such as air and water pollution, climate change, waste management, and biodiversity conservation are central issues in this field [18,19,20,21,22]. In the face of growing threats to the natural environment, multi-criteria methods allow the assessment and selection of the best solutions, taking into account ecological, economic, and social benefits [23,24,25,26,27,28,29]. Energy, on the other hand, focuses on the production, distribution, and use of energy, which has a direct impact on economic stability, social development, and the state of the natural environment [30,31,32,33,34]. In the face of growing energy demand, depletion of non-renewable resources, and the need to reduce greenhouse gas emissions, multi-criteria methods are necessary to evaluate various energy sources and energy strategies [35,36,37,38,39]. They allow for a comprehensive analysis of various aspects, such as costs, availability, efficiency, environmental impact, and energy security, leading to the development of sustainable and responsible energy policies [40,41,42,43,44,45]. The focus on these two areas is justified by their direct impact on achieving the Sustainable Development Goals and the need to face the challenges of the 21st century [46,47,48,49]. Multi-criteria decision-making (MCDM) methods are a group of tools and techniques used for analysis and decision-making in situations where many criteria are taken into account at the same time [50,51,52,53,54]. Unlike traditional decision-making methods, which usually focus on one criterion, MCDM methods allow you to take into account various, often contradictory criteria, which is particularly important in complex decision-making problems [55,56,57,58].

Decisions made using multi-criteria methods are more balanced and comprehensive because they take into account various aspects of the problem, such as costs, efficiency, environmental impact, risks, and preferences of various stakeholders [59,60,61,62]. This makes it possible to make a more informed and objective choice among the available alternatives. Taking into account multiple criteria allows for the analysis and optimization of various aspects of the problem, leading to a more holistic approach to decision-making [63,64,65,66].

Multi-criteria selection methods are particularly useful in situations where decisions must be made based on the analysis of various data and information and where the results must be consistent with specific goals and constraints [67,68,69]. Examples of typical MCDM applications include site selection for new investments, evaluation of infrastructure projects, natural resource management, energy planning, and crisis management [70]. Thanks to their flexibility and wide range of applications, multi-criteria methods have become an indispensable tool for decision-makers in various sectors. The importance of multi-criteria methods is particularly visible in complex decision-making situations where different stakeholders may have different priorities and expectations [71,72,73,74,75,76,77,78]. For example, in the management of natural resources, decision-makers must consider the economic benefits, environmental protection, and the social impact of their decisions. MCDM methods enable these diverse aspects to be taken into account, leading to more acceptable and effective decisions [79,80,81,82].

In the context of multi-criteria methods, there are several commonly used techniques, each of which is characterized by a unique approach to the decision-making problem and is used depending on the specificity of a given problem and the requirements of decision-makers. The analytic hierarchy process (AHP) is one of the most popular methods, developed by Thomas Saaty [83]. It allows you to prioritize and evaluate different options based on their relative importance by breaking down the problem into a hierarchy of goals, criteria, and alternatives and conducting a series of pairwise comparisons. AHP is particularly useful in situations where decisions must be made based on both objective and subjective criteria [84,85,86,87,88,89,90]. Technique for order preference by similarity to ideal solution (TOPSIS) allows you to identify the option that is closest to the ideal solution that maximizes benefits and minimizes costs [91,92,93,94,95]. The decision-making process in TOPSIS involves calculating the distance of each option from the ideal solution and the worst possible solution and then selecting the option with the smallest distance from the ideal. This method is especially useful when accurate quantitative data are available [96,97,98,99,100,101,102]. The elimination and choice translating reality (ELECTRE) method is used to select and prioritize alternatives based on the preference criteria and acceptance thresholds. It is based on comparing a pair of alternatives against the criteria and determining which of them is preferred and which may be rejected based on specific thresholds. This method is particularly useful in situations where data are incomplete or imprecise [103,104,105,106,107]. VIKOR focuses on achieving a compromise solution by minimizing the maximum deviations from the ideal values for each criterion. This method allows the ranking of the alternatives to be determined based on the trade-offs between competing criteria and enables the best solution to be selected [108,109,110,111,112,113,114,115]. Each of these methods and many others offer unique approaches to solving complex decision-making problems and are used depending on specific needs and contexts [116,117,118].

The bibliometric analysis is driven by the need to gain a thorough understanding of the development of MCDA methods in key fields such as environmental and energy engineering [119,120,121]. In the face of growing challenges related to environmental protection, natural resource management, and energy transformation, it is crucial to employ tools that enable comprehensive and well-founded decision-making [122]. By mapping research developments, identifying relevant publications, and analyzing international collaboration patterns, a comprehensive overview can be attained that highlights areas of interest. Such insights can be helpful in guiding future research to align with the Sustainable Development Goals and effectively addressing the pressing environmental and energy challenges of our time [123,124,125,126]. Furthermore, an analysis of the literature trends allows for the identification of key researchers and institutions whose work has contributed to the development of these fields, thus supporting better cooperation and knowledge exchange in the scientific community [127]. Understanding these trends can be helpful not only for scientists but also for policymakers and practitioners, who can use this information to develop more effective and sustainable natural resource management and energy planning strategies [128]. The bibliometric approach allows for a deeper understanding of how MCDA methods are used to solve complex problems, such as optimizing energy investment locations, managing environmental risks, and planning sustainable development, thus supporting the development of more robust and influential decision-making frameworks that can have a real impact on improving the quality of life and protecting the environment.

The aim of this study was to conduct a detailed bibliometric analysis of scientific journals regarding multi-criteria methods, with particular emphasis on their applications in the field of environmental and energy engineering. Despite numerous studies focusing on the application of individual MCDA methods in various fields, there is a lack of a comprehensive analysis examining the evolution of these methods in the field of environmental and energy engineering. This study aims to fill this gap by conducting a systematic literature review that identifies key research trends, key publications, and patterns of international cooperation. Previous studies have often focused on specific applications of MCDA, such as water resources management or energy technology assessment, limiting themselves to analyses in narrowly defined contexts. This study integrates these approaches, presenting a wide spectrum of MCDA applications in environmental and energy engineering. The analysis covers the entire period of MCDA research development, not just the recent years, which allows for the identification of trends and key stages in the evolution of these methods from a long-term perspective. Such a wide time frame allows for a more comprehensive understanding of the dynamics of MCDA development and includes both historical and contemporary research aspects. This analysis aims to identify key research trends, assess the evolution of these methods, and indicate the main directions for future research in this area. The analysis carried out allows us to understand the aspects of multi-criteria methods that are of greatest interest to researchers and the dominant research directions. This allows us to determine which methods are most often used and in what contexts, which in turn may indicate their effectiveness and usefulness in solving specific problems. Bibliometric analysis also allows for the identification of the most important publications, authors, and journals in a given research area [129,130,131]. Understanding which research papers have had the greatest impact on the development of multi-criteria methods can provide valuable information regarding future research directions and identify research gaps and needs in this field [132,133,134,135,136,137,138]. Focusing on the application of multi-criteria methods in environmental and energy engineering also allows for the assessment of how these methods contribute to solving specific problems related to the management of natural resources, energy production, and distribution, thus minimizing the negative impact of human activity on the environment. The results of such analysis can be useful not only for scientists and researchers but also for practitioners and policymakers, who can use this information to develop more effective and sustainable management strategies [139,140,141]. To sum up, the aim of this article is to provide a comprehensive literature review and indicate the main directions for future research in the area of multi-criteria methods. The results of this analysis can serve as a basis for further research and support scientists, researchers, and decision-makers in making informed decisions in the area of environmental and energy engineering. The article continues with a detailed discussion of the research methodology, including the tools and techniques of bibliometric analysis. Then, the results of the analysis are presented, taking into account the main research trends and patterns of international cooperation. Finally, the implications of these results for future research and practice are discussed, and conclusions and suggestions for further research in this field are presented.

2. Methodology

To conduct a reliable bibliometric analysis, it is necessary to collect an appropriate database of publications [142,143,144,145,146,147,148,149]. This process involves selecting appropriate keywords, defining selection criteria, and filtering the results according to established standards. This allows a representative sample of publications to be obtained that faithfully reflects the current state of knowledge and the main directions of research in a given field [150]. The criteria for selecting publications included the following:

• Database: All publications were downloaded from the Web of Science database, which guarantees their high quality and recognition in the scientific community.
• Keywords: The study used the keywords “multi-criteria decision-making” and “multi-criteria decision analysis” due to their common use in the scientific literature [151,152,153,154,155].
• Time scope: Publications published without time limits were included to provide a complete overview of the evolution of research in this field.
• Language of publication: Only publications written in English were selected to allow for a broad understanding of the results by the international scientific community.
• Types of documents: Scientific articles, reviews, book chapters, and conference proceedings were analyzed, excluding other types of publications such as abstracts or technical notes;
• Scientific discipline: The focus was on publications related to environmental and energy engineering, which was achieved by filtering publications based on the thematic categories assigned to them in the Web of Science database.

Bibliometric analysis was performed using the CiteSpace software version 6.3.R3 Advanced. CiteSpace is an advanced tool for the analysis and visualization of bibliometric data that enables researchers to track the development of knowledge in various fields of science [156,157,158,159,160,161]. This program was developed by Professor Chaomei Chen and is widely used for citation, co-authorship, and keyword network analysis. CiteSpace allows you to identify key publications, authors, institutions, and research trends, which makes it an extremely useful tool in the analysis of scientific literature [162,163,164,165]. Thanks to advanced visualization functions, CiteSpace enables the presentation of the evolution of knowledge in a transparent and intuitive way [49,166,167,168,169,170]. The study performed the following analyses using CiteSpace:

• Cooperation between countries: This analysis was conducted to identify the countries that most frequently collaborate in multi-criteria decision-making research. The country network visualization feature was used to see the connections between different countries and identify the main centers of international cooperation.
• Inter-institute collaboration: This identified the most important research institutes and their interconnections, allowing the identification of key research centers.
• Cited journals: Analysis of journal citations made it possible to determine which journals are the most influential and play a key role in the development of the field.
• Identification of key research topics through citation analysis: Co-citation analysis was used to isolate key research topics and determine their importance in the context of the entire field. This feature allowed the identification of key publications that had the greatest impact on the development of individual topics.
• Citation burst analysis: Detection of citation bursts allowed us to identify publications that attracted a lot of attention from the scientific community in a short period of time. This analysis was crucial to understand which research topics and publications gained importance in a short period of time.

The analysis process in CiteSpace consists of several key steps:

• Data import: Publications are imported into CiteSpace in text format. The data includes bibliographic information, such as title, authors, journal, year of publication, and cited references.
• Creating networks: After importing data, CiteSpace creates a network based on co-citations. Each node in the network represents a publication, and the edges between nodes represent the citation relationships between those publications. These networks can include different types of analysis, such as collaborations between authors, institutes, or countries.
• Clustering: CiteSpace uses clustering algorithms, such as pathfinder network scaling, to identify and group related publications. These clusters represent the main research topics in a given area.
• Visualization: The tool generates interactive visualizations that allow you to explore the citation network. Nodes and edges are color-coded, allowing you to easily distinguish between different citation periods and intensities. Additionally, CiteSpace allows you to add cluster labels, making it easier to interpret the results.
• Citation burst detection: CiteSpace has a burst detection feature that identifies publications with sudden increases in citations over a short period of time. This is carried out by analyzing dynamic changes in the number of citations, allowing you to identify publications that have suddenly become very popular.
• Report generation: At the end of the analysis, CiteSpace generates reports containing detailed information on identified clusters, key publications, and research trends. These reports can be exported in various formats, such as PNG, SVG, or GraphML, for further analysis and presentation.

Thanks to these advanced features, CiteSpace enables researchers to precisely analyze and visualize the dynamics and structure of the study area, which significantly facilitates the interpretation of results and the identification of key research trends.

3. Simulations

The first stage of the bibliometric analysis regarding the use of MCDM in energy and environmental engineering was to examine the connections between countries that actively participate in research on these methods. Analysis of connections between countries allows you to identify scientific leaders and main research centers and assess the intensity of international cooperation. Thanks to this analysis, it is possible to understand how knowledge and innovation spread internationally and which countries play key roles in this process. Figure 1 presents an analysis of connections between countries.

Figure 1 shows a visualization of the global research collaboration network in the field of environmental and energy engineering. The size of the nodes (representing countries) reflects their centrality index, where larger and darker nodes indicate higher centrality. Links between countries indicate mutual collaboration. Based on Figure 1, the top 10 countries with the highest centrality index were identified (

Table 1. Number of publications and centrality by country.

Country	Number of Publications	Centrality
England	249	0.22
USA	494	0.18
China	1825	0.11
India	389	0.09
Germany	145	0.08
Spain	159	0.07
Japan	94	0.07
Iran	467	0.06
Turkey	252	0.06
Italy	195	0.06

). The results of this analysis provide valuable information on global research trends, patterns of collaboration, and potential areas for the development of science policy. This makes it possible to better understand the structure and dynamics of international research in the field of environmental and energy engineering as well as identify leaders and key partners in global research endeavors. The centrality index, used in CiteSpace, is a measure used to assess the importance of individual nodes (e.g., countries, institutions, and publications) in a scientific cooperation network. The centrality indicator used by CiteSpace is primarily an indicator of betweenness centrality, which reflects how often a given node appears on the shortest path connecting other nodes in the network. Betweenness centrality is particularly important because it indicates nodes that play a key role in the flow of information in the network. In the context of scientific research, nodes with high brokerage centrality are often the main channels for knowledge transfer and collaboration, making them strategic points in the research network. A high centrality score means that a country plays a central role in global scientific cooperation, connecting different research groups and facilitating the exchange of knowledge between them.

The results of the analysis presented in the table regarding the number of publications and the betweenness centrality index for various countries in the context of environmental and energy engineering indicate several key trends. China clearly dominates in terms of the number of publications (1825), which proves its intense involvement in research in these fields. The USA (494) and India (389) also contribute significantly, indicating strong research activity. England, despite having fewer publications (249), has the highest centrality index (0.22), which means that it plays a key role in the international research cooperation network. The USA has a lower centrality index (0.18), which may suggest more diversified cooperation. China, despite having the highest number of publications, has a relatively low centrality index (0.11), suggesting that its research is more internally focused or less integrated into the global network. Countries such as Germany, Spain, Japan, Italy, and Iran have a moderate role in cooperation networks, as reflected in their centrality indices (0.06–0.08). This analysis highlights the importance of England as a central node in international research and shows China’s dominance in terms of the number of publications, although its influence on the global network of cooperation is smaller. The USA remains a key player in both publication volume and collaboration, although its centrality is not as high as England. Overall, the analysis helps identify countries playing key roles in research and understand the dynamics of international cooperation in these fields.

The next stage of the bibliometric analysis was the analysis of cooperation and connections between research institutions. Collaboration between institutions is a fundamental element of scientific development because it enables the exchange of knowledge, resources, and technologies, leading to more innovative and impactful research. Collaboration network analysis allows you to identify key institutions in a given field and understand how these institutions cooperate with each other. The map of institutional connections enables identification of the main research centers and cooperation networks that have the greatest impact on the development of research in a given field. This analysis also allows us to understand how cooperation patterns change over time and what factors may influence their evolution. For example, it may be related to the availability of funds, scientific policy, or strategic decisions of individual institutions. In summary, examining collaborations and connections between research institutions provides valuable information about the structure and dynamics of scientific collaborations, which can contribute to a better understanding of the processes leading to breakthrough research results. The results of the analysis are presented in Figure 2.

In the figure, different clusters that are thematically related are marked with different colors, which facilitates the identification and analysis of individual research areas. The size of the nodes, which represent individual research institutions, is proportional to the number of publications coming from these institutions. Gray lines connecting the nodes indicate cross-citations between researchers from different countries, which reflects the degree of their cooperation and influence on the global research network. Based on Figure 2,

Table 2. Top 10 institutions with the highest centrality index.

Country	Number of Publications	Centrality
Azad University	107	0.10
Chinese Academy of Sciences	92	0.09
Indian Institute of Technology	104	0.08
Centre National de la Recherche Scientifique	48	0.08
University of Teheran	101	0.07
Hong Kong Polytechnic University	69	0.07
United States Department of Energy	41	0.07
Aalborg University	40	0.07
North China Electric Power University	219	0.05
Chongqing University	57	0.05

was created, which presents the top 10 institutions with the highest centrality index.

The analysis of connections between research institutions shows that in the field of environmental and energy engineering, institutions from Asia have the highest centrality index, which indicates their strong involvement in research on sustainable development using multi-criteria analysis methods. Moreover, institutions from Europe and North America also play an important role in global cooperation networks, which emphasizes the importance of international scientific cooperation.

Table 3. Cluster analysis.

Cluster ID	Silhouette Value	Cluster Label	Most Cited Members
#0	0.829	Economic analysis	Islamic Azad University, University of Tehran, Delft University of Technology
#1	0.759	Hydrogen energy technologies	State Grid Corporation of China, Tianjin University, China University of Petroleum
#2	0.860	Sustainable power heat	National Institute of Technology (NIT System), Egyptian Knowledge Bank (EKB), Aalborg University
#3	0.844	Life cycle perspective	Chinese Academy of Sciences, Hong Kong Polytechnic University, Chongqing University
#4	0.958	Pyrolytic decomposition	Centre National de la Recherche Scientifique (CNRS) Shanghai Jiao Tong University, Shandong University
#5	0.909	Hydrocarbon fuel releases	Indian Institute of Technology System (IIT System), China University of Mining & Technology, University of British Columbia
#6	0.928	Three-way decision approach	North China Electric Power University, Huazhong University of Science & Technology, Beijing Institute of Technology
#7	0.985	Polycyclic aromatic hydrocarbon formation	United States Department of Energy (DOE), University of California System, State University System of Florida
#8	0.959	Sustainable energy optimization	Universidade de Lisboa, Polytechnic University of Milan, Helmholtz Association
#9	0.996	Electric car	University of Szczecin, West Pomeranian University of Technology, National Institute of Telecommunications–Poland
#11	0.983	Intuitionistic fuzzy MCDM-based CODAS approach	Istanbul Technical University, Yildiz Technical University, Galatasaray University
#13	0.938	Support tool	Universitat Politecnica de Valencia, Consejo Superior de Investigaciones Cientificas (CSIC), Cranfield University

presents the analysis of the clusters from Figure 2. These clusters are grouped based on the “silhouette value” coefficient, which measures the homogeneity of the cluster and its distinctiveness from other clusters. This value ranges from −1 to 1, where a value close to 1 indicates a well-defined, homogeneous cluster and a value close to 0 or negative indicates cluster overlap. Thanks to this analysis, it is possible to define the research areas in which scientists from given institutions specialize and the most cited members in a given cluster.

The results in Table 3 illustrate the research topics in which multi-criteria analysis methods are used in the field of environmental and energy engineering. Silhouette values for individual clusters indicate high internal consistency of these thematic groups, which indicates well-defined and uniform research within each category. The highest values, such as 0.996 for the electric car cluster and 0.985 for the polycyclic aromatic hydrocarbon formation cluster, suggest particularly strong links between publications in these areas. Overall, these values confirm that the identified clusters are very consistent, which increases the reliability and precision of the bibliometric analysis results.

The next stage of bibliometric analysis was the analysis of cited journals. The analysis of cited journals is an important element of examining the dynamics and structure of knowledge in a given field of science. Selecting the “cited journal” option in CiteSpace allows you to examine which journals are most frequently cited in a selected dataset, which can provide valuable information about the influence and prestige of individual journals and reveal citation patterns in the context of research development. The purpose of citing journal analysis is to identify the most important sources of knowledge in a given field and to understand which journals are central to research development. This analysis can also reveal how citation trends have changed over time and which journals dominate during specific periods. Using various metrics, such as betweenness centrality or burst detection, it is possible to determine which journals play a key role in a given field and which of them experience periods of intense citation growth. An important aspect of the analysis is also the identification of the so-called “burst periods”, i.e., periods in which specific journals were cited much more often than in other periods. This type of information may indicate breakthrough moments in scientific research, new research trends, or paradigm shifts in a given field. The analysis of cited journals also provides information on the interdisciplinarity of research. Based on citations, it is possible to determine which other fields of science influence research in environmental and energy engineering and how these fields interpenetrate. For example, frequent citations to materials science journals may indicate a significant impact of this research on the development of new energy technologies. To sum up, the analysis of cited journals using CiteSpace allows you to understand the structure of citations, identify key journals, and assess the dynamics of research development on multi-criteria methods in environmental and energy engineering. This allows scientists to better understand the sources of knowledge that are most important to their field and the current and future research directions. Figure 1 shows the analysis of the journal citation network divided into clusters.

Based on the analysis of Figure 3,

Table 4. Top 10 institutions with the highest centrality index.

Journal	Journal Impact Factor	Centrality
Fuel	7.4	0.12
European Journal of Operational Research	6.4	0.07
Energy	9.0	0.06
Energy & Fuels	5.3	0.05
Renewable and Sustainable Energy Reviews	15.9	0.04
Journal of Cleaner Production	11.1	0.04
Energy Conversion and Management	10.4	0.04
Energy Policy	9.0	0.04
Progress in Energy and Combustion Science	29.5	0.04
Applied Energy	11.2	0.03

was created, which presents the top 10 journals with the highest centrality index, which allows identification of the key sources of knowledge in the field of environmental and energy engineering. These journals play an important role in the citation network, indicating their importance in connecting different research areas.

The analysis of Table 4, presenting the top 10 journals with the highest centrality index, shows the key sources of knowledge in the field of multi-criteria analyses in environmental and energy engineering. These journals, despite varying impact factor values, play a central role in the citation network, which emphasizes their importance in connecting various research areas. The results of the analysis indicate that not only journals with the highest impact factor but also those with a lower impact factor may be key to the development of research. Such journals influence research directions, shaping new trends and supporting interdisciplinarity in environmental and energy engineering.

Citation analysis is one of the key elements of bibliometric analysis of scientific literature, enabling understanding of how individual publications influence the development of a given research area. Using tools such as CiteSpace allows you to visualize your citation network, making it easier to identify relevant works and their interconnections. Thanks to this method, you can better understand the structure and dynamics of knowledge development as well as detect the main research trends. Citation analysis enables the identification of publications that enjoy high recognition in the scientific community, which is visible by the number of citations. These works often provide theoretical and methodological foundations for other research and may be considered milestones in a given field. Identifying these publications is crucial to understanding which research has had the greatest impact on knowledge development. Additionally, citation analysis allows you to track changes in the importance of individual works over time. It is possible to detect the so-called “citation bursts”, i.e., periods in which a given publication gains popularity and citation in a short time. Such patterns may indicate the emergence of new research trends or breakthrough discoveries that attract the attention of a wide range of researchers. CiteSpace visualizations based on citation analysis can also reveal key citation paths, identifying publications that act as central nodes in the citation network. These central works often connect different research areas, enabling the flow of knowledge between them and supporting the development of interdisciplinary research. The results of the citation analysis are presented in Figure 4.

The analysis of the CiteSpace diagram presented in Figure 4 shows various research areas in which multi-criteria decision-making analysis (MCDA) methods have been used. The largest cluster is the “analytic hierarchy process” (AHP). AHP is a popular MCDA method used to prioritize various decision options based on multiple criteria. It is widely used in environmental planning, resource management, and risk assessment. Examples of AHP applications include the selection of industrial locations taking into account environmental criteria and the planning of sustainable industrial areas.

The next cluster is “battery energy storage system”, focusing on research on battery energy storage systems, which are key to the sustainable development of renewable energy. MCDA is used to evaluate various energy storage technologies for efficiency, cost, environmental impact, and other criteria, allowing the selection of optimal solutions for grid applications.

The third cluster, “wind and solar plant site selection”, concerns the process of selecting the location of wind and solar farms. This is a key aspect of optimizing energy production and minimizing environmental impact. MCDA helps analyze different locations, taking into account factors such as wind speed, land availability, costs, and impact on local communities. Application examples include the selection of locations for wind farms and photovoltaic farms.

The next cluster, “pyrolytic decomposition”, focuses on research on pyrolysis processes that are important for the processing of biomass and the production of renewable fuels. In this context, MCDA can be used to evaluate different pyrolysis technologies and their efficiency.

The “oxy-fuel combustion” cluster includes oxygen combustion technology used to reduce pollutant emissions and improve energy efficiency. Multi-criteria analysis methods can be used for environmental and economic analyses of coal-fired power plants.

The next cluster is “supplier selection”, which concerns the process of selecting suppliers in various industries. MCDA is used here to evaluate suppliers based on multiple criteria such as cost, quality, reliability, and environmental impact.

The “groundwater potential zone” cluster focuses on identifying and assessing potential aquifer zones. MCDA methods are used to evaluate various factors, such as water availability, water quality, and extraction cost. This research is crucial for water resources management, especially in areas with limited resources.

“Air energy storage” is another cluster that includes energy storage in the form of compressed air. MCDA is used to analyze the effectiveness of various air energy storage technologies, which is important, for example, for the stabilization of energy networks.

The “coal pyrolysis” cluster concerns research on coal pyrolysis, a process important for coal processing and fuel production. MCDA can be used to evaluate various coal pyrolysis technologies, allowing processes to be optimized for energy efficiency and minimizing environmental impact.

“Externality assessment” is a cluster focusing on assessing the external effects of economic activity, such as the impact on the environment and public health. MCDA is used to assess and minimize these effects, which is crucial for sustainable development.

The “flood risk management” cluster covers flood risk management. MCDA helps evaluate various risk management strategies, taking into account factors such as cost, effectiveness, and community impact. Application examples include flood risk assessment in urban areas.

The final cluster, “environmental systems planning”, deals with the planning of environmental systems. MCDA is used to evaluate various options for planning and managing environmental systems, allowing more sustainable decisions to be made. This research is important for natural resource management and environmental protection.

The next stage of bibliometric analysis was the use of the “burst detection” function, which allows the identification of sudden increases in the number of citations to publications, which is an indicator of their increased importance and influence in a given period of time. CiteSpace’s “burst detection” feature is an analytical tool used to detect citation bursts, that is, periods in which a given article or set of articles gains a significant number of citations in a relatively short period of time. This type of analysis is particularly useful for identifying new trends and breakthroughs in science that are attracting significant attention from the research community. In practice, “burst detection” allows you to isolate those publications that may be considered pioneering or revolutionary in their field, as well as to track the evolution of research interests over time. The mechanism of “burst detection” is based on modeling the intensity of citations for individual articles. The algorithm identifies moments when the rate of citation of a given article increases significantly, which is interpreted as an increase in interest and a potentially greater impact of this article on the development of a given field. The results of this analysis are visualized in the form of charts, where periods of intense citation are highlighted, enabling researchers to quickly identify key moments in the history of science development. In the context of multi-criteria analysis in environmental and energy engineering, the use of “burst detection” allows the identification of key publications and topics that have attracted a lot of research attention in a short time. This may concern new technologies, research methodologies, or breakthrough discoveries that have the potential to significantly influence the development of these fields. Thanks to this tool, researchers can not only track current trends but also predict future directions of science development, which is invaluable for strategic research planning and innovation development. Use of the “burst detection” function in CiteSpace is an advanced analytical tool that enables the identification of key publications and dynamic changes in research interests. Figure 5 shows the top 10 publications with the highest citation burst. The “year” column indicates the year of publication. The “begin” column indicates the year in which the period of intensive citations for a given publication begins, while the “end” column indicates the year in which this period ends. Interpreting these values allows researchers to understand when a particular publication gained particular attention in the scientific community and how long the increased level of interest in it lasted.

The first publication is Thomas L. Saaty’s book “The Analytic Hierarchy Process” from 1980, presenting a method supporting decision-making in multi-criteria situations. The book is a fundamental work in the field of multi-criteria analysis and decision-making. It introduces the analytic hierarchy process (AHP), a methodology that allows for the structuring and analysis of complex decision-making problems that involve multiple criteria [83]. The period of increased citation in the analyzed field of environmental and energy engineering falls on the years 2004–2016.

In the second article, the authors propose a multi-criteria decision-making (MCDM) method called the best–worst method (BWM) [171]. This method involves the decision-maker identifying the best (most desirable and most important) and worst (least desirable and least important) criteria and then conducting pairwise comparisons between these criteria and the remaining ones. The criteria weights are determined based on the solution to the maximin problem. These weights are then used to evaluate alternatives against various criteria. The final alternative ratings are obtained by aggregating weights from different sets of criteria and alternatives, allowing the selection of the best alternative. A concordance coefficient for BWM is also proposed, which checks the reliability of comparisons. The method is illustrated on the example of a real decision-making problem (choosing a mobile phone) and compared with the AHP method. The results show that BWM is more consistent and requires less data for comparison, leading to more reliable results compared to AHP. The period of increased citation began in 2020 and is ongoing.

In the third article, the authors review the applications of multi-criteria decision-making (MCDM) methods in the context of selecting locations for renewable energy sources [33]. MCDM methods are becoming increasingly popular in renewable energy plant siting decisions because they take into account many of the conflicting goals and preferences of decision-makers. The article covers a systematic review of the literature on the applications of MCDM for the selection of renewable power plant locations, covering 85 articles published between 2001 and 2018 in reputable journals. The review focuses on exclusion and assessment criteria for five energy sources and five steps in site selection: criteria selection, data normalization, criteria weighting, evaluation of alternatives, and validation of results. The article shows that different energy sources emphasize different criteria, but there are also some similarities. The most frequently used methods for selecting criteria are literature reviews and expert opinions. Recoding is most often used to normalize data. The analytic hierarchy process (AHP) is popular for weighting criteria. Geographic information system (GIS) and weighted linear combination (WLC) are most commonly used in evaluating alternatives. Variation of criteria weights is most often used to validate results. The authors note that the renewable energy site selection process typically involves six main steps: formulation of alternatives, selection of criteria, normalization of data, weighting of criteria, evaluation of alternatives, and validation of results. The article presents a detailed discussion of each of these stages and the classification of articles according to the publication date, energy source, publication journal, author’s country of affiliation, and research area. The period of increased citation began in 2021 and continues.

The fourth article discusses the problem of selecting locations for photovoltaic (PV) power plants using an approach based on the geographic information system (GIS) and analytical hierarchical process (AHP) with Saudi Arabia as an example [172]. Selecting an appropriate site for utility-scale PV projects is crucial due to the importance of weather factors, proximity to infrastructure, and the presence of protected environmental areas. The model takes into account various aspects, such as economic and technical factors, aiming to maximize energy achievements while minimizing project costs. AHP was used to evaluate the criteria weights and calculate the land suitability index (LSI) to evaluate potential locations. The LSI model groups locations into five categories: “least suitable”, “marginally suitable”, “moderately suitable”, “very suitable”, and “most suitable”. A case study was conducted for Saudi Arabia using real climate and legal data, such as roads, mountains, and protected areas. The period of increased citation began in 2019 and continues.

In the fifth article, the authors present a multi-criteria model for selecting the location of wind farms in Nigeria, based on the geographic information system (GIS) and analytical hierarchical process using type-2 sets (interval type-2 fuzzy AHP) [173]. The appropriate location of a wind farm requires taking into account economic, social, and environmental factors. A GIS model with type-2 fuzzy AHP is proposed to analyze the suitability of wind farm locations, considering uncertainty, ambiguity, and inconsistency in decision-making. The model uses two sets of criteria (weighted and limiting), encompassing economic, social, and environmental factors, to evaluate suitable sites for wind farms. The article provides valuable scientific information for decision-makers and planners, helping them choose optimal locations for the development of wind farms. Although the study concerns Nigeria, the proposed methodology can be used to select wind farm locations in other regions of the world. The period of increased citation began in 2020 and continues.

The sixth paper presents an assessment of the suitability of siting wind and solar farms in Songkhla Province, Thailand, using the geographic information system (GIS) and analytical hierarchical process (AHP) [174]. The aim of the study was to identify ideal locations for utility-scale wind and solar farms. Data obtained mainly from government organizations and maps of solar and wind resources were used. The study presents a scientific approach to site selection for utility-scale wind and solar farms in Songkhla Province. The integration of GIS and AHP has proven effective in assessing site suitability, and the results may increase investor confidence in renewable energy investments in Thailand. The process outlined in the study provides valuable information for policymakers and planners, supporting Thailand’s efforts to reduce its dependence on fossil fuels. The period of increased citation began in 2021 and continues.

The seventh publication in this analysis is the book “Multiple Attribute Decision Making: Methods and Applications” by Ching-Lai Hwang and Kwangsun Yoon, which is a comprehensive review of multiple attribute decision making (MADM) methods [175]. This publication serves as a resource for advanced students and researchers who need an updated overview of this rapidly developing area of operations research. The book discusses the development of the MADM theory, defining basic concepts and introducing standard notation. The review covers the seventeen main MADM methods, presenting their basic concepts, computational procedures, and characteristics. Each method is illustrated with simple numerical examples. The following sections present various MADM models, attribute transformations, fuzzy decision rules, and weight evaluation methods. The book provides a systematic classification of MADM methods that have been proposed by researchers from various fields. The application section discusses practical applications of these methods in commodity selection, facility location, personnel selection, project planning, and public amenities selection. The authors also propose an integrated approach to MADM problems and rules for selecting MADM methods. The period of increased citation began in 2021 and continues.

The eighth article deals with the application of geographic information system (GIS) and analytical hierarchical process (AHP) technologies to select optimal locations for photovoltaic (PV) power plants in Malatya Province, Turkey [176]. The study aimed to identify the best locations for PV power plants, taking into account many factors, such as energy potential, proximity to roads, transmission lines, transformers, terrain slope, environmental aspects, and distance from critical infrastructure. Using the AHP method, the weights of individual criteria were calculated and then integrated in the GIS database. As a result, a map was created showing optimal locations for solar power plants. The period of increased citation began in 2021 and continues.

The ninth article analyzes the selection of the best renewable energy alternative using fuzzy axiomatic design (AD) and fuzzy analytic hierarchy process (FAHP) [177]. AD and AHP methodologies are often used in the literature for decision-making, and fuzzy set theory helps deal with uncertainty in the presence of incomplete or unclear information. The article proposes two fuzzy methods for multi-criteria decision-making. The first method, based on AHP, allows the evaluation of experts’ results in the form of linguistic expressions or exact or fuzzy numbers. The second method, based on AD, evaluates alternatives according to functional requirements from experts. This paper uses fuzzy AD to select the best renewable energy alternative and compares it with fuzzy AHP. The study covers four main and seventeen sub-criteria and five different renewable energy alternatives for Turkey. The results show that the use of AD and AHP methods is effective in accounting for uncertainty and ambiguity in expert judgments, supporting investments in sustainable renewable energy. The period of increased citation falls on the years 2011–2018.

The tenth paper conducts a comparative analysis of multi-criteria decision-making (MCDM) methods for assessing and ranking renewable energy sources for electricity generation in Taiwan [178]. The study includes the use of four MCDM methods—WSM (weighted sum method), VIKOR, TOPSIS, and ELECTRE—as well as the Shannon entropy weight method to assess the importance of each criterion. The results of the analysis indicate that efficiency is the most important criterion, followed by job, operational and maintenance costs, and land use. Hydropower was rated as the best energy alternative in Taiwan, followed by solar, wind, biomass, and geothermal. The study also analyzed the sensitivity of the results to changes in criteria weights, which showed that classification results depend on the weights of financial, technical, environmental, and social criteria. For financial and technical aspects, hydropower turned out to be the best source of renewable energy, while the best choice was wind energy for environmental aspects and photovoltaics for social aspects. The period of increased citation began in 2020 and continues.

Based on the analysis of publications with the highest citation burst index, it can be seen that one of the main areas of interest in the field of environmental and energy engineering is the location of renewable energy sources. Research and publications focus on identifying the best places to install energy sources, such as wind and solar farms. This process requires taking into account many criteria that reflect the complexity and multi-aspect nature of decision-making in this area. The analyzed works often emphasize the importance of economic, social, and environmental factors, which proves the need for a holistic approach to the planning and development of energy projects. An important element of this research is the use of advanced analytical tools that enable accurate and comprehensive assessments of potential locations. The growing number of citations in recent years indicates an increasing interest in these topics, which is consistent with the global trend of striving for sustainable development and reducing dependence on fossil fuels. The analysis results provide valuable information that can support decision-makers and investors in making informed decisions about investing in renewable energy sources, which is crucial for the future of energy around the world.

In addition to the above examples resulting from bibliometric analysis, the MCDM method is widely used in various fields of engineering and management, allowing more informed and sustainable decisions to be made in complex multi-criteria situations. In environmental engineering, MCDM is used to select water purification technologies, assess waste management systems, and plan air protection activities [179,180]. For example, the use of the TOPSIS method to assess water purification technologies allows for the selection of the optimal technology based on many criteria, such as pollutant removal efficiency and operating costs [181]. In energy engineering, MCDM is used to assess and select the most efficient and sustainable energy sources, such as wind farms, solar farms, or biomass, as well as to optimize the energy mix in order to minimize costs and environmental impact [182,183,184,185,186,187,188,189,190,191]. For example, the use of the AHP (analytic hierarchy process) to assess the location of wind farms allows many factors to be taken into account, such as the wind speed and land costs. In addition, MCDM is used in urban planning when selecting the location of new infrastructure investments, which allows for the sustainable development of cities and minimizes negative impacts on the local community [192,193]. In water resources management, this method helps in making decisions about water resource allocation in conditions of limited resources and changing climatic conditions, as shown in studies on water resources management in drought-affected regions [194,195,196]. In the industrial context, MCDM is used for project risk assessment, optimization of production processes, and supply chain management, which allows operational efficiency to be improved and costs to be reduced [197,198,199]. An example is the application of the ELECTRE method for risk assessment of construction projects, where various criteria, such as cost, time of completion, and quality of workmanship, are taken into account [200,201].

In the context of environmental and energy engineering, the application of multi-criteria analysis methods can lead to the creation of projects that not only improve efficiency and sustainability but also address the needs of disadvantaged communities. For example, in the energy sector, the use of MADM can help select the most appropriate renewable energy technologies for remote and poor communities that often lack access to reliable energy sources. By analyzing multiple criteria, such as installation costs, availability of local raw materials, and minimization of negative environmental impacts, it is possible to develop solutions that provide access to clean and affordable energy. This in turn supports the UN Sustainable Development Goals, such as “Affordable and Clean Energy” (Goal 7) and “Reduce Inequalities” (Goal 10). In environmental engineering, research results can be used to design waste management systems that are more efficient and accessible to low-income communities. An example would be the implementation of recycling and composting systems that not only reduce the amount of waste going to landfills but also create local jobs and additional sources of income for residents. Integrating communities into the decision-making and operational processes of such systems can increase their effectiveness and social acceptance.

4. Conclusions and Future Research Directions

4.1. Conclusions

The bibliometric analysis that was conducted provides valuable conclusions on key trends and research topics in the field of environmental and energy engineering. These conclusions have important implications for future research and investment strategies in the renewable energy sector.

One of the main conclusions is the growing importance of multi-criteria decision-making (MCDM) methods in the decision-making process regarding the location of renewable energy sources. Methods such as the analytical hierarchical process (AHP), best–worst method (BWM), and geographic information system (GIS) are widely used to evaluate and select locations for wind and photovoltaic farms. These advanced analytical tools enable the consideration of many criteria, including economic, social, and environmental, which is key to maximizing energy efficiency and minimizing negative impacts on the environment.

The analysis showed that in recent years, there has been a significant increase in the number of citations to works on the application of MCDM and GIS in environmental and energy engineering. This increase indicates the growing interest of researchers and practitioners in these methods, which is consistent with the global trend towards sustainable development. The use of these methods allows for more accurate and comprehensive assessments, which in turn supports better investment decision-making in the renewable energy sector.

The work discussed in the analysis also highlights the importance of integrating different analytical tools and approaches, which allows for a more comprehensive and effective solution to problems related to the location and assessment of renewable energy technologies. This makes it possible not only to identify the best locations but also to optimize the planning and management processes of energy projects.

The analysis of the literature trends in research on multi-criteria decision analysis (MCDA) methods reveals dynamic changes and evolution of key research areas. The growth of popularity of methods such as analytic hierarchy process (AHP) and other MCDA techniques can be attributed to several reasons. One of them is the growing demand for sustainable development and effective management of natural resources, which makes these tools indispensable in making complex decisions that take into account many criteria and constraints. Also, global challenges such as climate change, urbanization, and the need to switch to renewable energy sources increase the need for precise and comprehensive assessment and planning methods. For example, MCDA technologies are widely used in the optimization of wind and solar farm locations, where technical, economic, social, and environmental aspects must be taken into account. Another aspect is the development of information technologies and analytical tools, such as GIS (geographic information system), which allows more advanced and accurate analyses and increases confidence in the results obtained using MCDA. The increase in the number of citations of key publications in this field indicates their growing importance and acceptance among researchers and practitioners.

The noticeable increase in the popularity of MCDA methods is also due to their versatility and adaptability to various decision-making contexts. These methods are used not only in environmental and energy engineering but also in other sectors, such as project management, urban planning, agriculture, and the health sector. Their ability to integrate various criteria and preferences of decision-makers makes them extremely useful in complex decision-making scenarios.

The conclusions of this analysis are important not only for the scientific community but also for decision-makers and investors who can use this information to make more informed and effective decisions regarding renewable energy investment. Taking into account the results of the bibliometric analysis, important policy recommendations can be formulated to support and promote multi-criteria decision analysis (MCDA) methods in the renewable energy sector. It is important for decision-makers and politicians to recognize the importance of integrating MCDA into the planning and implementation process of energy projects, which will enable more sustainable decision-making. Public policies should encourage the use of advanced analytical tools such as MCDA to consider multiple criteria when selecting locations and optimizing the management of energy projects. In addition, governments should promote the development of technologies supporting MCDA by funding research, supporting innovative projects, and creating educational programs and training for specialists in this field. The research results provide concrete guidelines on how to effectively use advanced analysis methods in practice, which can help accelerate the development of sustainable energy worldwide. International cooperation and exchange of best practices can further strengthen global efforts towards sustainable energy development by contributing to faster adoption and implementation of these advanced analytical methods worldwide. Implementation of these recommendations can accelerate the development of renewable energy sources while minimizing negative impacts on the environment and local communities. However, it should be noted that the bibliometric analysis conducted has some limitations. One of the main limitations is the dependence on the available data in the Web of Science database, which may not include all the relevant publications in the field of MCDA and GIS. Furthermore, the analysis focused mainly on English-language publications, which may lead to missing important scientific works published in other languages. Another limitation is the dynamic nature of research in the field of environmental and energy engineering, which means that the results of the analysis may quickly become outdated as new research and technologies emerge.

4.2. Future Research Directions

In future studies, it is worth extending the analysis to additional databases and including publications in other languages to obtain a more complete picture of the global trends in MCDA research. Further studies may also focus on developing new methods for integrating MCDA with other analytical tools and their application in different decision-making contexts. Finally, it is important to continue research on the effectiveness and practical application of MCDA in real energy projects, which may contribute to the further development of sustainable energy.

Based on the analysis of topics in environmental and energy engineering and multi-criteria decision-making (MCDM) methods, it has been determined that the integration of MCDM with renewable energy sources (RES) is of most significance for future research.

In recent years, the increasing demand for sustainable and efficient energy sources, driven by climate change and the depletion of natural resources, has rendered alternative energy sources a critical area of research. However, the selection of an appropriate energy source is complex, necessitating the consideration of numerous technical, economic, reliability, and environmental factors. The existing literature underscores the necessity of a comprehensive approach to the analysis of these factors. Nevertheless, many studies are constrained to narrowly defined aspects, thereby neglecting the holistic approach essential for making well-informed decisions.

Future research in multi-criteria decision-making (MCDM) in environmental and energy engineering should focus on several key areas to enhance the field’s contribution to sustainable development. Integrating advanced analytical tools like the geographic information system (GIS), artificial intelligence (AI), and machine learning can significantly improve the precision and comprehensiveness of MCDM processes, especially in complex scenarios with multiple criteria. There is a growing need to apply MCDM methods to emerging technologies such as hydrogen energy and advanced battery storage systems in order to evaluate their feasibility, efficiency, and environmental impacts. Cross-disciplinary approaches should be expanded, incorporating insights from environmental engineering, economics, social sciences, and policy studies to develop balanced and sustainable decision-making frameworks.

Aligning future research with the United Nations Sustainable Development Goals (SDGs) is crucial, particularly in areas like affordable and clean energy, climate action, and sustainable urban development. Enhancing stakeholder engagement by developing participatory MCDM approaches will ensure that the views and preferences of diverse groups, including local communities, industry experts, and policymakers, are considered. Adapting MCDM methods to address climate change challenges is essential, focusing on resilience, mitigation measures, and long-term sustainability.

Optimizing the integration of renewable energy sources into existing grids is another critical area, requiring evaluation of different configurations, storage solutions, and grid management strategies. Developing new MCDM methods to handle the increasing complexity and uncertainty in decision-making, including dynamic and multi-dimensional criteria, is vital. Longitudinal studies tracking the evolution of MCDM applications over time will provide valuable insights into the effectiveness of various approaches and the impact of technological advancements. Finally, translating MCDM outcomes into practical policy recommendations and implementation frameworks will support informed decision-making, promoting sustainable and resilient development in environmental and energy sectors.