A Bibliometric Systematic Literature Review (B-SLR) of sustainability assessment: Global trends and knowledge mapping

Introduction

Sustainability is commonly understood as the ability to satisfy today's needs without limiting future generations' capacity to meet their own needs, a definition first articulated by the Brundtland Commission, which reframed sustainability as an intergenerational responsibility (World Commission on Environment and Development, 1987) [1] . In its earlier interpretations, sustainability was viewed only through an ecological lens, emphasizing conservation of natural resources, biodiversity protection, and ecological balance, explaining the need for environmental conservation while neglecting economic and social dimensions. By the early 1990s, the concept began to expand into a multidimensional framework. The Food and Agriculture Organization (FAO, 1991) [2] was among the first to modify the concept by introducing measurable indicators for agriculture and socio-economic development. Elkington's Triple Bottom Line (1997) [3] explained that sustainability requires a balance of environmental integrity, economic viability, and social equity. These works established sustainability as a holistic construct rather than only an ecological concern. Further, Agenda 21 (United Nations, 1992) [4] emphasized sustainable development as a foundational principle for international policy, while the Millennium and Sustainable Development Goals in the early 2000s and 2015 extended the concept of sustainability into global governance frameworks (United Nations, 2000, 2015) [5,6] . These initiatives shifted sustainability towards systemic, sitespecific, and multidimensional approaches, requiring assessment across levels from individual farms and communities to global systems. In this era, the emerging complexity of the concept has required the computational and decision support frameworks. Multi-Criteria Decision-Making, fuzzy logic, and machine learning have emerged as dominant methodologies for addressing both qualitative and quantitative uncertainties in sustainability assessment, reflecting the growing need for integrated tools capable of handling diverse indicators and perspectives. While earlier studies have reviewed sustainability assessment frameworks qualitatively, a gap remains in quantitatively mapping the methodological transition from earlier metrics to advanced computational models over the decades. The convergence of environmental science with machine learning and Multi-Criteria Decision-Making, has created a fragmented and vague literature base. Despite global importance on sustainability, there is a absence of bibliometric mapping and systematic assessment frameworks in agricultural sustainability research, particularly in India. While sustainability has been widely discussed globally no proper bibliometric review exists that www.extensionjournal.com captures global and Indian contributions to sustainability assessment. This gap is significant because India, and vulnerability to climate change, requires evidenc based frameworks to guide extension strategies and policy. This study adopts a Bibliometric-Systematic Literature Review of 422 research articles published from 2001 to 2025. By integrating qualitative literature with quantitative bibliometric mapping, this study provides a pathway for understanding the evolution of sustainability assessment methodologies.

Methodology

This study employed a Bibliometric-Systematic Literature Review framework, integrating analysis with qualitative review. Data collection and search strategy: A targeted Boolean search string ("Sustainability Assessment," "Sustainable Agriculture," "Sustainable Development," "Sustainability") was applied in the Scopus database. The dataset was restricted to publications from 2001 to 2025, limited to research articles and review papers, and confined to documents published in English. The systematic search and screening process resulted in a final dataset of 422 documents, exported in CSV format for bibliometric analysis. The metadata was processed using R Studio, the Rpackage bibliometrix, and the Biblioshiny interface. While the 'Science Categories' field was unavailable in the data report, the metadata for 'Keywords Plus' showed a high completion rate. To ensure accuracy, an approach using both author keywords and Keywords Plus within the Biblioshiny interface which compensated for the marginal missing data and provided a comprehensive representation of the conceptual structure. Approximately 100 additional research papers, identified mostly via Google Scholar, were stored in Zotero and reviewed qualitatively to establish the theoretical foundation for sustainability assessment. The B-SLR was conducted in three phases:  Data collection: 422 research articles in CSV format from the Scopus database.

 Bibliometric analysis: performance analysis, including citations, annual production, sources, and authors, using the R-package bibliometrix (Biblioshiny interface).  Bibliometric synthesis: a conceptual structure diagram, thematic map, and factorial analysis were produced, and research topics were classified into motor, niche, emerging, and basic themes, enabling identification of dominant and underexplored areas; the social structure analysis included a co-citation network and a countries' collaboration world map.

Theoretical evolution and frameworks

Harrington (1992) [7] argued that sustainable agriculture can be understood through three complementary lenses: agroecology, equity, and sustainable growth. The agroecological perspective emphasizes resilience, efficient input use, and nutrient recycling; the equity perspective highlights intergenerational fairness and recognition of the rights of non-human species; while the sustainable growth perspective focuses on maintaining agricultural productivity alongside preservation of resource quality. Jones (1993) [8] found that any concept of agricultural sustainability must include the dynamic, and sometimes conflicting, interaction between agricultural activities, environment, and society. Roling and Wagemakers (2000) [9] described sustainable agriculture as a regenerative approach that prioritizes efficient use of locally available resources and natural ecological processes, such as nutrient cycling and biodiversity enhancement, minimizing reliance on external inputs like agrochemicals, mined minerals, and nonrenewable energy sources. Velten et al. (2015) [10] described sustainable agriculture as a vague and ambiguous concept but highlighted its value as a framework for integrating ecological, economic, and social dimensions. El Bilali et al.

(2021) [11] argued that while environmental aspects are well studied, social, economic, and governance dimensions remain underrepresented, and called for a holistic approach encompassing planet, people, profit, and policy. The bibliometric analysis [18] identifies an evolution in sustainability assessment, moving from static, qualitative, indicator based frameworks towards a complex integration of Multi-Criteria Decision-Making and artificial intelligence. While earlier indicator sets such as PSR (1994) or RISE (2003) remain effective for site-specific inspection at the field level, they become complex when scaled to regional or global systems. This has required a shift towards mathematical tools such as the Analytic Hierarchy Process and fuzzy logic, which provide the hierarchical weighting and uncertainty modelling needed to manage multidimensional complexity. Thus fuzzy networks and machine learning are used to capture the dynamic nature of sustainability. The bibliometric dataset was evaluated for metadata completeness to ensure the reliability of the analysis. Major bibliometric attributes such as abstracts, authors, DOIs, document types, journals, language, publication year, titles, and citation counts were fully available, with no missing entries, and were therefore rated as excellent. Affiliation data, cited references, author keywords, and corresponding author information were largely complete, with very less missing values, and were rated as good. Keywords Plus were available for the majority of records but showed a missing rate of 15.4 per cent, considered acceptable. A significant gap was observed in 'Science Categories,' which were absent in 422 records. This absence is a known limitation of Scopus exports and was adjusted by relying on co-word analysis and thematic clustering. The tree map visualization highlights the prominent concepts in sustainability assessment research. The largest block is 'sustainability' (260 occurrences, 16 per cent), confirming it as the main anchor of the field. Strong methodological terms such as 'fuzzy mathematics' (171, 10 per cent), 'fuzzy logic' (124, 7 per cent), and 'decision making' (120, 7 per cent) show the central role of computational and multi-criteria approaches. Policy-related concepts such as 'sustainable development' (148, 8 per cent) connect the literature to global governance agendas. Smaller but significant blocks represent specialized applications such as Analytic Hierarchy Process, fuzzy AHP, and multicriteria analysis, while applied domains such as river basin, risk assessment, concrete mix, climate change, and circular design reflect sector specific implementations. Geographic and contextual terms such as India and China indicate regional relevance, while technical extensions such as fuzzy TOPSIS, GIS, and algorithm show methodological diversification. frameworks such as the Sustainable Development Goals. 'Fuzzy mathematics' and 'sustainable development' also show strong growth. 'Fuzzy logic' and 'decision making' increased steadily, highlighting the continuity of computational and multi-criteria approaches. More specialized terms such as the Analytic Hierarchy Process, fuzzy AHP, and multi-criteria analysis grow at a slower pace but remain consistent, showing methodological stability. Applied contexts such as China and supply chain management emerge later, reflecting geographic diversification. The trend topics visualization shows how sustainability assessment research evolved between 2010 and 2023, highlighting both continuity and innovation. Tools such as fuzzy logic, the Analytic Hierarchy Process, multi-criteria analysis, and hierarchical systems appear consistently across the timeline, confirming their role in decision support. From 2015, computational innovations including fuzzy neural networks, simulation, optimization, artificial intelligence, and uncertainty analysis gained prominence, showing the integration of advanced artificial intelligence approaches into sustainability assessments. Applied domains such as supply chain management, risk assessment, environmental protection, and sustainability indices also emerged, showing diversification into sectoral and policy applications. Geographic markers such as China and the United States highlight regional contributions, while specialized techniques such as Delphi, fuzzy sets, and planning methods demonstrate methodological refinement. The collaboration world map highlights a dominant East-West research corridor. China and India lead in total production, both countries facing high population density and climate vulnerability. The density of linkage lines indicates a high degree of international collaboration and academic exchange between Asian, European, and North American institutions. The conceptual structure map, generated in Biblioshiny using Multiple Correspondence Analysis, visualizes how concepts in the dataset are related and grouped into thematic clusters. Dividing lines split the map into four quadrants, with a distinct conceptual orientation; each point represents a keyword or concept extracted from the dataset, and proximity indicates co-occurrence and thematic similarity. The cluster within the highlighted region combines fuzzy logic, fuzzy inference, fuzzy neural networks, machine learning, optimization, sustainability assessment, climate change, energy efficiency, and water quality, showing the integration of artificial intelligence methods with sustainability assessment and emphasizing computational approaches to environmental challenges. Other clusters include terms such as the Analytic Hierarchy Process, multicriteria analysis, and sensitivity analysis, representing Multi-Criteria Decision-Making approaches; concepts such as supply chain management, stakeholder, environmental protection, and economics highlight practical applications of sustainability frameworks; and terms such as innovation, integrated approach, and performance assessment reflect management and policy dimensions. Together, these clusters confirm that sustainability literature is structured around both methodological innovation and practical implementation. The dendrogram visualizes how sustainability assessment publications group into distinct thematic clusters based on similarity. At the lower branches, closely related works cluster together, including specific methodologies such as Multi-Criteria Decision-Making and fuzzy logic applications. At the higher branches, broader clusters emerge, linking these methodologies with applied sustainability frameworks.

Results and discussion

Metadata quality and scientific production

Global collaboration and conceptual structure

Conclusion

This Bibliometric systematic literature review using the R studio and R package shows that sustainability assessment has evolved into a globally collaborative and continuously evolving discipline spanning multiple fields. Multi-Criteria Decision-Making and fuzzy logic remain the dominant assessment methods, while artificial intelligence tools such as fuzzy neural networks and machine learning increasingly serve as complementary approaches. The leadership of China and India in research output and citations highlights the growing geographic diversification of the field, alongside the influence of open access publishing. Looking forward, research should give more attention to the policy dimension, the refinement of decision support tools, and the integration of resilience thinking with sustainability assessment, so that the field remains responsive to emerging socio-ecological challenges at both individual and global levels. India must invest in developing standardized sustainability assessment tools that integrate environmental, economic, and social dimensions, also including governance and policy indicators. Agricultural extension systems should adopt computational approaches such as multi-criteria decision-making, fuzzy logic, and machine learning to handle sustainability as an abstract concept. Strengthening collaboration along with national level institutes, State Agricultural Universities, and extension agencies will be crucial for ensuring farmer level adoption. By bridging methodological innovation with policy relevance, India can improve sustainability assessment practices and provide a strong foundation base for extension strategies, thereby aligning national agricultural development with globally recognized Sustainability Development Goals.

References

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