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Review

Mapping Agricultural Sustainability Through Life Cycle Assessment: A Narrative Review

by
Konstantinos Spanos
,
Nikolaos Kladovasilakis
*,
Charisios Achillas
and
Dimitrios Aidonis
*
Department of Supply Chain Management, International Hellenic University, Kanellopoulou 2, 601 32 Katerini, Greece
*
Authors to whom correspondence should be addressed.
Environments 2025, 12(11), 436; https://doi.org/10.3390/environments12110436
Submission received: 13 October 2025 / Revised: 6 November 2025 / Accepted: 9 November 2025 / Published: 12 November 2025
(This article belongs to the Special Issue Circular Economy in Waste Management: Challenges and Opportunities)
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Abstract

Over the past few decades, the concept of sustainable agriculture has gained popularity. However, the notion of sustainable agriculture is highly imprecise and unclear, making its application and execution exceedingly challenging. Moreover, disagreements about what sustainability means can lead to a deeper understanding of the intricate empirical procedures and possibly debatable principles involved in any effort to achieve sustainability in agriculture. Practices to increase crop resilience, lower chemical inputs, and boost efficiency are examples of future developments. This review identifies how agricultural life cycle assessment (LCA) studies engage with climate-related metrics such as GHG emissions and land use changes, offering insights for adaptation and mitigation strategies. This review also addresses the need to synthesize existing research on how agriculture and food systems can become more environmentally friendly through LCA. LCA enables the identification of environmental hotspots within agricultural systems, therefore, guiding efforts to limit resource consumption and emissions. For this purpose, a search of a bibliographic database was carried out and the results obtained were analyzed with the open-source tool bibliometrix. There were 2328 results in total with publication years from 1993 to 2025, the latter of which refers to a pre-publication. Then, a post-processing analysis of 1411 articles was conducted and a narrative review of around 100 publications was carried out, where agricultural practices with LCA, current trends, and research gaps were explored. Finally, this paper contributes by identifying three major research gaps derived from the literature synthesis: firstly, the underrepresentation of dynamic LCA models in agriculture; secondly, the lack of geographical balance in case studies; and thirdly, the insufficient integration of socio-economic dimensions in environmental assessments.

1. Introduction

Agricultural systems contribute 10–12% of global greenhouse gas emissions, while facing unprecedented pressure to increase food production by 70% by 2050 to feed 9.1 billion people. Life cycle assessment (LCA) has emerged as the critical methodology for quantifying these environmental impacts across agricultural value chains [1,2,3]. It includes both the natural elements of the Earth, such as trees, air, soil, oceans, lakes, and rivers, and the human-made elements, such as buildings, infrastructure, cities, and communities created by humans. On the one hand, the natural environment refers to all-natural elements that exist independently of human intervention. This includes vegetation, wildlife, ecosystems, and the natural processes that occur on the planet. It is the fundamental basis for the existence of life and plays a critical role in maintaining the balance of the ecosystem [4].
In contrast, the anthropogenic environment refers to what has been created by human activity and intervention. This consists of the buildings, infrastructure, cities, and communities we have developed, as well as the impacts of human activities on the natural environment such as pollution, the overexploitation of natural resources, and climate change [5]. Rapid population growth combined with the adverse effects of climate change and intensifying pressures on both agricultural land and natural resources undermines the planet’s ability to provide safe, nutritious, and adequate food for all its inhabitants without limitations to future generations [6,7].
In recent years, the agricultural sector has adopted intensive farming practices to meet the rising demand, leading to widespread soil degradation, freshwater overuse, and increased greenhouse gas emissions [8]. This has resulted in both the depletion of natural resources and climate change, among other consequences. Natural resource scarcity is a growing concern in many parts of the world. Rapid population growth and increasing industrialization are putting considerable pressure on the world’s finite resources, leading to shortages in many sectors. This is particularly true for key resources such as water, soil, and energy [9]. The pressures on agricultural land, according to the Population Media Center, Education and Social Impact Education for International Development [10], include industrialization, which contributes to agricultural land scarcity through direct land conversion for industrial facilities; urban sprawl that preferentially consumes fertile land near cities; and soil quality degradation from industrial pollution, salinization, and contamination. Without access to productive agricultural land, water resources, and energy, food security and economic development are at risk. Still evident are the impacts of increasing greenhouse gas emissions from agricultural activities and land use changes [11]. Agriculture accounts for 10–12% of global GHG emissions, primarily via enteric fermentation and fertilizer application [12].
According to the European Environmental Agency [13], “Sustainability is about meeting the world’s needs of today and tomorrow by creating systems that allow us to live well and within the limits of our planet”. Food systems contribute one-third of global anthropogenic greenhouse gas emissions (16.5 GtCO2e annually), with synthetic fertilizer production alone accounting for 2.1% of global emissions. This quantifiable environmental burden necessitates systematic assessment through an LCA frameworks to identify mitigation opportunities across agricultural production systems [14]. Therefore, it is necessary to implement policies and practices that promote sustainability and reduce the impacts of climate change to ensure the long-term health of global agricultural systems [15].
In this context, the aim of this paper is to investigate modern techniques and tools for process optimization, LCA methodologies, and their potential integration into the production process so that decisions can be made [16,17].
The evaluation method and framework applied were based on an exhaustive, systematic search of the Scopus scientific database, using specific keywords such as environmental impact, climate change, LCA, and agriculture [18,19,20]. It includes both the natural elements of the Earth, such as trees, air, soil, oceans, lakes, and rivers, and the human-made elements, such as buildings, infrastructure, cities, and communities created by humans [21]. On the one hand, the natural environment refers to all-natural elements that exist independently of human intervention, acting as the fundamental basis for the existence of life and playing a critical role in maintaining the balance of the ecosystem [22,23,24]. In contrast, the anthropogenic environment refers to what has been created by human activity and intervention, consisting of the buildings, infrastructure, cities, and communities we have developed, as well as the impacts of human activities on the natural environment such as pollution, the overexploitation of natural resources, and climate change [25,26]. Additionally, an investigation of ways to improve agricultural practices to minimize negative environmental impacts is critical. LCA is closely connected to sustainability as it delivers quantitative data regarding the environmental performance of agricultural systems [27].
By pinpointing the production stages that exert the greatest environmental impact (such as the use of fertilizers, irrigation, or energy consumption), it assists farmers, policymakers, and agri-businesses in making knowledgeable decisions to mitigate effects [28]. For instance, LCA reveals trade-offs, like decreasing pesticide usage while increasing the need for irrigation; identifies critical issues, such as nitrogen fertilizer use in cereal farming or methane emissions in dairy production; and can establish a framework for comparing different farming approaches (for example, organic versus conventional, or precision agriculture compared with traditional methods).
Lastly, identifying an alignment with policies and regulations is also very important. Integrating LCA results into these established policy and regulatory frameworks ensures that sustainability strategies are not only scientifically robust but also directly support the achievement of environmental objectives and legal requirements such as the European Green Deal (2019–2050) [29], the EU Biodiversity Strategy for 2030 [30], the Farm to Fork Strategy for 2030 [31], the 8th Environment Action Programme (2022–2030) [32], the Common Agricultural Policy (CAP) 2023–2027 [33], and the EU Sustainable Development Goals Implementation 2030 [34].
This literature review aims to investigate how agriculture and food production can become more environmentally friendly, reducing their impact on climate change through LCA [35], of which the specific value added to policymaking lies not in replacing existing economic and agronomic models, but in providing the standardized quantitative environmental data that these models currently lack. While economic models optimize costs and agronomic models optimize yields, they typically rely on incomplete environmental data or single-parameter proxies.
LCA fills this gap by providing (1) a quantified environmental trade-off analysis showing when policies optimizing one environmental goal (e.g., climate) may worsen others (e.g., biodiversity); (2) supply chain environmental transparency revealing that 30–70% of agricultural environmental impacts occur upstream/downstream from farms; (3) standardized environmental metrics enabling consistent policy comparison across regions and production systems. This bibliometric analysis focuses on the development and application of this LCA methodology within agricultural settings, pinpointing methodological shortcomings that hinder the integration of LCA with current policy frameworks. The scientific contribution consists of charting research paths that could enhance LCA’s distinctive systematic approach to environmental assessment.

2. Methodology and Framework

This study employs a narrative review approach, which differs from systematic reviews by providing a broader, more interpretative synthesis of the literature rather than adhering to strict quantitative protocols. While systematic reviews aim to comprehensively retrieve and meta-analyze all relevant studies, narrative reviews focus on the qualitative synthesis of key concepts, trends, and research gaps within a field.
In order to conduct a thorough bibliometric analysis, it is important to clearly define the research objectives to address specific issues, thus maintaining a focused and relevant analysis [36,37,38]. Bibliometric analysis has gained immense popularity in business research in recent years [39,40,41] and can handle large volumes of scientific data as well as generate a high research impact [42]. This review performed a bibliometric analysis of the specific features of published papers from the last 32 years that have investigated the viability of conditions related to environmental impact assessment and climate change through LCA in agriculture. The strategy of this study was based on a systematic and multi-level approach aiming to thoroughly investigate the environmental and climate-related impacts of agriculture through the LCA methodology [43].
The LCA method was originally developed for the analysis of industrial systems [44,45]; in recent years it has been adapted for use in agriculture [46,47,48,49]. Since the beginning of the 21st century, LCA has gained increased scientific and practical interest, transforming into an interdisciplinary tool applied to a variety of fields. Its standardization, coupled with the global awareness of environmental impacts, has significantly expanded the range of applications and objects of study [50]. The standardization of LCA coupled with a global awareness of environmental issues has broadened the scope of its objects of study as well as its related applications. These applications include impact assessment systems such as eco-indicator 99 [51], IMPACT 2002+ [52], and CML 2002 [53], and other widely used methods including ReCiPe, the Reduction and Assessment of Chemical and other Environmental Impacts (TRACI), the Environmental Design of Industrial Products (EDIP), International Life Cycle Data (ILCD), and the Environmental Product System (EPS) as well as system boundaries and allocation methods [54], spatial differentiation in LCA [55], risk-based LCA [56], dynamic LCA [57], and economic input–output models for environmental life cycle assessment [58].
The search was conducted in the Scopus database with the total findings amounting to 2328, covering the publication years from 1993 to 2025, the latter of which pertains to a pre-publication. Subsequently, a post-processing analysis of 1411 articles was performed, followed by a narrative review of approximately 100 publications, during which agricultural practices involving LCA, current trends, and research gaps were examined.
The first criterion was the publication title, which serves as an initial indicator of the study’s relevance to the field of dynamic LCA in agriculture. The country or region was also considered, referring to the geographical location where the study was conducted or focused, as this provides valuable insight into regional variations in environmental impacts and methodological practices. The subject of the research, such as specific products like kiwi or olive oil, or a more general agricultural focus, was included to further contextualize the study within the broader research landscape. The authors were recorded to help identify the researchers involved and understand their expertise and collaborative networks within the field. The number of citations a publication received was considered as a potential indicator of its impact and influence in the scientific community.
Environmental dimensions were also categorized to identify the types of environmental impacts analyzed, such as CO2 emissions, water use, biodiversity impacts, or the inclusion of more than three specific indicators. The types of environmental impacts analyzed—such as CO2 emissions, water use, impacts on biodiversity, or the inclusion of more than three specific indicators—were key considerations. The year of publication was noted to observe the trends and developments in research over time. Additionally, the source of the publication, referring to the journal or database in which it appeared, was documented.
A crucial aspect of the classification involved identifying whether the study employed a dynamic or static LCA approach. Where a dynamic LCA was used, further specification was made regarding the methodological approach adopted. Tools like SimaPro, Gabi, OpenLCA, and others perform impact calculations using inventory data; they are not models but computational platforms aligned with LCA standards such as ISO 14040 [16]. Other methodologies or tools applied in the study were included as well, such as geographic information systems (GIS), machine learning (ML), or econometric analyses. This study also examined whether energy resource use was considered, specifically documenting fossil fuel consumption and the reliance on renewable energy sources. Furthermore, the inclusion or examination of sustainability policies and regulations was assessed, particularly where the research engaged with agricultural policy or regulatory frameworks relevant to LCA.
The objective of the bibliometric analysis was to record and process data related to the scientific publications and to extract relevant bibliometric indicators, such as the number of publications, their citations, their association with specific institutions, scientific fields, etc. [59]. This is an important tool for the quantitative evaluation and analysis of the published scientific literature [60]. The measurements of scientific publications with numerical data are expressed in terms of bibliometric indicators. Of these, the number of publications is the simplest indicator for recording the production of scientific work and hence research work per scientist, organization, discipline, or country [61]. Apart from the number of publications, the most common bibliometric indicators used to assess the impact and originality of scientific work are based on the analysis of the citations of publications from other scientific publications [62]. To ensure as far as possible the accuracy of the results of the literature analysis, three elements must be carefully selected: the literature to be analyzed, the bibliometric techniques to be used, and the software to be used [63].
The search was conducted in the Scopus database, which is widely used for searching the international literature. Its use allows the collection of a complete list of published papers, with high reliability in our results. Given the research topic, the search was performed by entering the following words as criteria in a corresponding combination:
“environmental impact” or “climate change” and “life cycle assessment” or “LCA” or “life cycle” or “life cycle assessment (lca)” or “life cycle analysis” and “agriculture”. These terms were searched in the title, abstract, and keywords of the papers and no other filter was used.
The search results returned 2328 published papers from the Scopus database in the selected years since 1993, with the last paper in January 2025, as a pre-publication. The results of the survey were reviewed in this phase by putting the publications in order, firstly to adjust and delete words that were not considered keywords such as article, controlled study human, humans livestock nonhuman, priority journal, and review, limiting the results to 1457.
Secondly, we excluded papers in languages other than English, because non-English-language articles are often not accessible through international databases (e.g., Scopus, Web of Science) or do not meet the same peer review criteria. A total of 44 papers were excluded, with the maximum number of excluded papers being Chinese, which had 28 results, followed by French with 6 results, and the rest in other languages (4 Spanish, 3 German, 2 Portuguese, 1 Ukrainian, 1 Hungarian, and 1 Finnish). The remaining papers totaled 1411.
The last step, after the statistical analysis of the results using the relevant tool contained in Scopus, was to export the results to a csv file in order to import them into the bibliometrix application and Biblioshiny 5.0.1, a piece of open-source data science software, from Posit PBC (Public Benefit Corporation), which provides a web interface for bibliometrix for the bibliometric analysis of the above features. The programming language R, RStudio 3.6.0+, in combination with the environment in which it operates, namely the “bibliometrix R-Tool”, contributes to the process of analysis using tools for quantitative research [64]. The conclusions of the bibliometric studies aim to capture the collective trends.
In this context, we developed a flowchart that depicts the research framework for the narrative review, as shown in Figure 1. Figure 1 details the process for the 1411 records remaining.
Subsequently, with the visualization program Biblioshiny, the csv file of the results from Scopus was imported for the statistical processing and analysis of several variables. These metrics provide important information about the quality and impact of research by applying standardized metrics [63].
One such metric is the h-index [64] which is used to measure both the productivity and impact of publications and was originally developed for individual scientists or researchers. The h-index correlates with indicators of success such as winning the Nobel Prize, the acceptance of research grants, and holding positions at top universities [65]. The index is based on the total number of the scientist’s most cited papers and the number of citations they have received in other publications. The index was more recently applied to the productivity and impact of a scientific journal [66] as well as a group of scientists, such as a department, university, or country [67]. The index was proposed in 2005 by Jorge E. Hirsch, a physicist at UC San Diego, as a tool to determine the relative quality of theoretical physicists and is sometimes called the Hirsch index or Hirsch number. On the other hand, the g-index is an author-level measure proposed in 2006 by Leo Egghe [68]; the index is calculated based on the distribution of citations received from a particular researcher’s publications, such that given a set of articles ranked in descending order of the number of citations received, the g-index is the single largest number, such that the top g articles together receive at least g2 citations.
In our bibliometric analysis, we employed the h-index and g-index to quantify author-level research influence, and Bradford’s Law to characterize journal dispersion patterns. The motivation for using these metrics is as follows: the h-index balances productivity and citation impact, enabling the identification of consistently influential authors; the g-index gives greater weight to highly cited papers, thus capturing researchers whose work includes a few landmark publications; and Bradford’s Law reveals how articles on a given topic are distributed across journals, guiding the understanding of the “core” versus “periphery” literature outlets.
Last but not least, there are limitations of these metrics that should be acknowledged, such as that the h-index is sensitive to career length and field citation norms, and it does not account for author position or team size; the g-index, while rewarding highly cited work, can overemphasize outlier publications and may disadvantage authors with steady but moderate citation rates; and Bradford’s Law assumes a simple core–zone structure, which may not hold for interdisciplinary or emerging topics; its zone boundaries can also be arbitrary.

3. Reference Results

3.1. Bibliography Review

Firstly, a statistical analysis was performed using the internal Scopus analysis tool. Figure 2 shows the temporal evolution of the publications for the period from 1993 to the date of data extraction, i.e., 23 September 2024, which includes 1 pre-publication in January 2025 of the 1411 papers in our database. The temporal evolution of the number of published papers and their references during the years 1993–2025 allows the published work to be distinguished into three stages. The first stage (1993–2007) includes a very small number of articles (52 papers, 3.69% of the total). The second stage (2008–2016), during which 374 articles were produced, represents 26.51% of the total, and the number of articles is consistently in the double digits. The percentage of citations is 4.4% with a total of 227 citations. In the third stage (2017–2025), the growth rate of publications accelerates, with a total of 985 articles published, a percentage of 69.80% of the total, and a constant triple-digit number of publications per year; this post-2017 boom indicates a fertile and highly active domain.
Table 1 summarizes each author’s career impact metrics. The h-index values range from 6 to 17, reflecting differences in cumulative citations. The mean h-index of 9.4 highlights the group’s overall research influence. The authors Nemecek T., Bacenetti J., Rieradevall J., Van Der Werf H.M.G., and Wang X. attained the highest h-indices, indicating particularly strong citation records. In contrast, more recent entrants such as Nabavi-Pelesaraei A. (PY start 2017) show promising early-career momentum. The m-index differences (all within one point) suggest similar rates of citation growth across authors.
Regarding the local impact of authors based on the TC (Total Citations) index, i.e., how many times their papers have been cited, the largest circle belongs to Nemecek T, who has the largest number of citations (1613), followed by Van der Werf HMG. with 1203 citations, which means that Nemecek T. has the largest local impact in terms of citations. Also, the other authors, such as Gaillard G. (913) and Rieravevall J. (755), also have a high performance, but with fewer citations.
Bradford’s Law, as shown in Figure 3, explains how research articles on any particular topic are dispersed or disseminated in different journals. It was first reported in 1934 in the journal Mechanics by S.K. Bradford and then in a book documented by the same author in 1948 explaining the verbal formulation and graphical representation of his law [69]. Bradford’s Law is based on the doctrine that a minority of journals will present a majority of articles on a given topic, while a significant number of journals will present a smaller number of articles.
This principle is commonly referred to as the “Core–District” model [70,71]. R-Biblioshiny 5.0.1 and RStudio R 3.6.0+ were used, software tools for the bibliometric analysis of the presented journals located in the “Core Sources” area, which are considered the most important and recognized journals in the research field. An analysis of the distribution of publications based on Bradford’s Law (Figure 3) reveals that most relevant articles are concentrated in a small group of high-impact journals, such as the Journal of Cleaner Production and Sustainability. The most dominant and most central journal for the topic (Table 2) is the Journal of Cleaner Production with 287 publications, second is Sustainability (Switzerland) which is a strong contributor, though with a reduced density with 70 publications, and last in zone 1 is the Sustainable Production and Consumption Jornal with 20 publications.
The network analysis shown in Figure 4a displays the collaboration between researchers. The nodes as shown represent authors, for example, Wang X, Nemecek T, or Mira-de-Val J, involved in the review, while the different colors indicate different research fields or topic areas. Also shown is the link between the groups, with a line indicating collaboration between authors. The denser a network is shown, the closer the collaborations between authors. Finally, there is a large dispersion between groups of authors, suggesting that they handle slightly different subject areas and do not collaborate closely with each other.
From the co-referral network shown in Figure 4b, it can be seen that the central nodes in the network are “Wernet G.” and “Brentrup”, which means that these authors have an influential role in the investigated field. The color units depicted indicate different research interests. Also, some groups of nodes depicted are less connected to the main core of the network. Finally, Wernet G is shown with many links to other studies and authors which demonstrates the importance of his research work.
The vertical axis of Figure 5 shows the impact of publications, while the horizontal axis shows the importance of publications. The cluster with the highest impact is the one that includes agriculture themes, with a percentage of 39.6%. This is followed by life cycle and environmental impact, with 33.8% each. This cluster has the greatest impact on this study, as well as the one shown in the bottom right section with similar characteristics. The caption defines “conf.” as the “confidence”’ within the cluster analysis. The cluster with the least impact and centrality is the one depicted in the bottom left with very low impact percentages. Finally, it is evident that the life cycle and environmental impact themes have a central role.
The topics shown in the upper right part of the figure (e.g., environmental impact, life cycle, agriculture) indicate high centrality and impact, as well as a significant influence in the scientific community involved in environmental and agricultural studies. The clustering that is correlated is centered around the concepts of environmental impact and life cycle analysis. Finally, according to Table 3, the dominance of environmental impact in the clusters that are second (21.9%) and third (56.2%) indicates that the studies focused on these topics not only have a high impact, but are also used as key references in the development of other topics.
Based on the MCA (Multiple Correspondence Analysis) method, it is possible to interpret the distribution of concepts in space, i.e., how concepts are thematically related to each other and how close they are to each other. Figure 6 illustrates thematic clusters, with the X-axis representing the centrality (importance) of themes within the research network, and the Y-axis showing the impact (influence) of these themes based on citation metrics. In Figure 6, it can be observed that LCA is at the center, which demonstrates the importance of the concept in the analysis of environmental impacts as well as in the different activities. Climate change is placed close to concepts such as carbon footprint, alternative agricultural practices, greenhouse gases, and more generally the actions taken to mitigate the effects of climate change. Finally, sustainability is linked to thematic axes such as environmental considerations and alternative agriculture.
The figure above illustrates the aspects of environmental impacts and agriculture and how important LCA is for quantifying environmental impacts and improving sustainability. A useful visual illustration of what is happening in the environmental field from a research perspective is Figure 7, which presents a strategic map, which breaks down the individual topics into the dimensions of importance (centrality) and the degree of development [72]. The diagram is divided into four areas as follows: Motor Themes (upper right), Niche Themes (upper left), Basic Themes (lower right), and Emerging or Declining Themes (lower left). Basic Themes include foundational topics such as the LCA methodology and environmental impact assessments. A representative study is Nemecek’s [73] on Swiss farming systems’ LCA, which established benchmarks for agricultural sustainability. In the Motor Themes area are the topics that are currently being researched, such as farms, economic and social effects, and economic analysis; characteristic studies are from Bacenetti [74] who investigated the economic and social effects of precision agriculture, while Rieradevall [75] focused on integrating sustainability policies in farm-level decision-making. In the Niche area are the topics that are well developed and less popular, such as waste management and ethanol. In the Basic area are basic topics, such as LCA, environmental impact, and agriculture, which are very important in the sector but not yet fully developed. These topics are essential but need more research to reach maturity.
For a more complete picture of the literature review and the scientific search, the publications were classified according to specific criteria in order to identify the 100 publications that best fit our research field [76]. The following criteria were carried out:
When evaluating the comprehensiveness of sustainability assessments, it was recorded whether the study combined environmental and economic data to provide more integrated sustainability [77]. Similarly, whether a cost–benefit assessment was performed to quantify the economic value of environmental impacts was also determined.
Finally, the classification included the nature of the data used, distinguishing between primary data (newly collected) and secondary data (pre-existing or historical). The source of funding or sponsorship was analyzed for insights into the relationship between funding structures and scientific impact. Moreover, the studies were examined for references to specific Sustainable Development Goals (SDGs), highlighting the alignment of the research with broader global sustainability objectives. In terms of subject/production, according to the figure below, the dominant subject is apples with 7%, followed by lettuce at 5%, while the rest, olive oil, peach, soybean, and wheat, occupy 3%, and the other items appear with 1%.
Most publications, an overwhelming 65%, examine several environmental indicators or general environmental impacts rather than just one specific indicator. The most citations are provided by the publication Life Cycle Assessment of Swiss Farming Systems: I and II, which together have 438 citations, followed by the publication Evaluation of the environmental impacts of apple production using LCA: a case study in New Zealand with 204.
In terms of the year of publication from the year 2003, we have three publications in the year 2024, and up to August when the data was extracted we have thirteen, as Figure 8 presents.
The source that dominated (Table 4) by publications is by far the Journal of Cleaner Production with 31%, followed by Sustainability (Switzerland) with 6%, followed by the other sources.
Regarding the countries in which the studies were conducted, international studies and Italy led the way with 12%, followed by the USA and Iran with 10% and 7%, respectively. Of the articles that meet the criteria, 99% do not use a dynamic LCA while 25% of the LCA research focuses on the cradle-to-farm-gate stage. Most of the papers did not use software for their environmental impact analysis. However, 21% used versions of the SimaPro software (version from 7.1 to 9.1), 14% used OpenLCA, and 3% used ReCiPe and DEA. The use of energy resources was addressed by only 18 publications and sustainability policies/regulations were taken into account by 25 publications, while the number of articles containing a combination of environmental and economic data was 26. Finally, cost–benefit assessment was only addressed 12 publications and the use of primary data for data input and use was only addressed in 2 publications. Most of the publications were funded either by the EU or by public bodies (57%). Finally, of great interest are the objectives of the publications related to the SDGs, as they show a trend in publications. Figure 9 shows that Goal 12 (Responsible Consumption and Production) was addressed in 98% of the publications, followed by Goal 9 (Industry, Innovation and Infrastructure) with 93 references, Goal 13 (Climate Action) with 63 references, and Goal 2 (Zero Hunger) with 60%.

3.2. Narrative Review

This bibliometric analysis shows that the leading and central topics in the research area are LCA, environmental impact, and agriculture as they are at the center of scientific research, with an increasing trend in recent years [78].
LCA is a well-established method that contributes significantly to the study of the environmental impacts of agricultural practices and economic challenges [79]. In the context of the environmental challenges facing the planet, LCA is emerging as a critical tool that allows the estimation of energy consumption, greenhouse gas emissions, water use, and pollution from agricultural activities [80,81]. This means that these issues are considered fundamental for the continuation of scientific understanding and development in the field of agriculture and environmental science. Agriculture is one of the most intense agents of environmental impact due to its intensive consumption of natural resources and production of pollutants [82]; at the same time, the focus on key issues such as sustainability and greenhouse gases is directing increasing attention to environmental concerns that have a direct impact on agricultural practices [83]. These issues are in the Basic Themes zone, which suggests that they are very important but are still in a maturing phase where research continues to grow and evolve. From an economic and social perspective, the strong presence of economic and social effects (economic analysis) in the driving themes shows that the critical role of economic and social factors in agriculture is recognized. Research has shown that an LCA helps to assess the environmental impacts of not only physical but also socio-economic factors. Incorporating socio-economic elements into an LCA offers a more holistic approach to the analysis of agricultural systems and highlights the impacts of agricultural activities at local and global levels [84]. This approach is central to the development of policies that promote sustainable agriculture. This finding highlights the debate around the socio-economic consequences of agricultural practice and how these consequences interact with environmental sustainability [85]. The integration of these parameters and their importance makes it clear that any sustainable strategy must be both environmentally and economically sustainable [86]. Themes such as waste management and uncertainty analysis are in the Niche Themes which suggests that, although they are not the most widely researched topics, they have matured in specific research communities and can offer deep knowledge in niche contexts [87].
At the same time, the fact that topics such as animals and life cycle stages are in the Emerging or Declining Themes area indicates that they are either being downgraded or are in the early stages of emerging research [88]. The link between greenhouse gases and sustainability and alternative agriculture shows that environmental management is inextricably linked to agricultural practices. This finding brings to the fore the need to reduce environmental impacts through innovative agricultural practices and LCA [89], such as precision agriculture, agroforestry, integrated pest management, and smart irrigation systems. In addition, the high centrality of issues such as sustainability and alternative agricultural practices suggests the research should focus on finding innovative solutions that can reduce environmental impacts while improving the performance of agricultural systems [90]. The agricultural sector’s significant contribution to greenhouse gas (GHG) emissions and water consumption has led to increased attention to the study of the life cycle of products and processes, with a focus on reducing the environmental impacts [91]. The agricultural sector contributes significantly to global greenhouse gas (GHG) emissions mainly because of the methane emitted from enteric fermentation and manure management, the nitrous oxide released from fertilizer application and soil processes, the carbon dioxide emissions linked to land use change and energy use, as well as the methane from flooded rice paddies [92].
Additionally, recent studies have demonstrated the effectiveness of Environmental Water Footprint (EWF) assessment combined with life cycle assessment frameworks for addressing food security and sustainability challenges. For instance, research on bridging food demand and agricultural self-sufficiency through integrated water–energy–food nexus approaches illustrates the practical applications of EWF methodologies [93,94].
The data suggest that LCA and environmental impact research are at a critical point where more researchers are beginning to focus on these issues. At present, the need for further the development and systematic analysis of these factors will be central to the future of agricultural systems and environmental management. Reducing environmental impacts and integrating their socio-economic factors with others will also be central [95]. Dynamic LCA models incorporating temporal and spatial variations show a 15–30% improved accuracy over static approaches. Recent advances include spatiotemporal DLCA frameworks that integrate agent-based modeling and real-time sensor data for field-specific emission monitoring [96,97,98]. Based on research and innovative practices, policies will be developed that are expected to play a key role in the future of environmental management and sustainable agriculture, especially when combined with data on energy use, emissions, and socio-economic factors [99].

4. Research Gaps and Proposal for Future Research

It is important to clearly distinguish the final stage of our approach from a rigorous systematic review. While a systematic review follows strict protocols to minimize bias and comprehensively retrieve all the relevant literature for meta-analysis, a narrative review aims for a broader, more contextualized synthesis of key concepts and trends. In our methodology, the quantitative bibliometric analysis (stages 1–3) provided a comprehensive overview, while the subsequent narrative review of the most central and high-impact publications (approx. 100) was conducted to offer a qualitative synthesis and deep interpretation of the emergent research gaps and trajectories.
While the present study offers a comprehensive bibliometric and narrative review of the existing literature, certain limitations must be acknowledged, which in turn suggest promising avenues for future investigation.
  • The predominance of static LCA approaches: A significant portion (99%) of the analyzed studies rely on static LCA models, neglecting the inherent dynamism of agricultural systems and the temporal variability of key parameters, such as climate change impacts and evolving agricultural practices.
  • Geographical bias: The geographical scope of the existing literature exhibits a marked bias towards European and North American contexts (82% of studies), potentially limiting the applicability of the findings to tropical and African agricultural systems.
  • The limited integration of environmental and economic dimensions: Only a minority of studies (26%) integrate both environmental and economic indicators, thereby constraining the ability to formulate holistic policy recommendations.
The underutilization of dynamic LCA and integrated environmental–economic models is primarily due to several methodological and practical barriers. Firstly, dynamic LCA requires intensive primary data collection from agricultural operations, supplemented by remote sensing data, and the integration of spatiotemporal models to account for time-dependent parameters and location-specific variations, which is resource-intensive and complex (only two publications addressed the use of primary data for data input and use). Secondly, the integration of environmental and economic models is challenging because economic models often rely on incomplete environmental data or single-parameter proxies. Overcoming this requires more specialized interdisciplinary research to link LCA’s standardized quantitative environmental data with economic evaluation tools and policy frameworks.
Future research should prioritize the development of time-dependent simulation models for key agricultural sectors (e.g., olive groves, vineyards) [100], incorporating climate change projections (Representative Concentration Pathways—RCPs) and machine learning algorithms [101]. The increasing trend of publications in this area since 2017 (Figure 2 from the bibliometric analysis) suggests a growing recognition of the need for dynamic assessments [102]. It is necessary to expand the geographical scope, and investigations should focus on comparative analyses of modern and traditional agricultural systems in under-represented regions (e.g., South Asia, the Sahel). Additional focus must be given to the utilization of Geographic Information Systems (GIS) to map spatial variations in environmental impacts [103].
Efforts are needed to link LCA methodologies with economic evaluation tools and policy frameworks (e.g., the European Green Deal) and build upon frameworks [104]. Bibliometric analysis underscores the need for interdisciplinary research that combines environmental and economic perspectives [105]. Finally, future research should incorporate primary data collection from agricultural operations, supplemented by remote sensing data, to improve the accuracy and representativeness of LCA models [106].
It is important to acknowledge that environmental assessments used in policymaking contexts typically involve integrated decision support systems that combine multiple analytical tools, stakeholder engagement, and political considerations. Life cycle assessment serves as one analytical component within these broader frameworks, rather than a standalone policy tool. Policymakers often rely on multi-criteria decision analysis, cost–benefit analysis, and participatory assessment processes that incorporate LCA results alongside economic, social, and political factors. Understanding this context is essential for positioning LCA research contributions appropriately within the policy landscape. The adoption of these research directions can strengthen the methodological robustness and data quality of life cycle assessment in agricultural systems [27], thereby improving LCA’s potential contribution to integrated environmental assessment frameworks. While LCA is rarely used as a standalone for policy decisions, enhanced LCA methodologies can provide more reliable inputs for multi-criteria decision analysis and comprehensive sustainability assessment tools that inform agricultural policy development and support evidence-based decision-making processes. The bibliometric analysis provides several key insights:
  • The thematic map reveals LCA as a central concept in the assessment of environmental impacts and the promotion of sustainability within the field.
  • The analysis of the co-citation network identifies key researchers and influential publications that have shaped the field.
  • The network of research collaborations indicates some dispersion between research groups, potentially suggesting specialization within different sub-areas of the field.
The limitations identified in this review underscore the need for methodological advancements and broader geographical representation in future LCA studies [107]. The development of dynamic modeling approaches, the integration of economic considerations, and the use of primary data sources offer promising avenues for enhancing the relevance and applicability of LCA in supporting sustainable agricultural practices and policies [108].

5. Conclusions

These findings confirm the indispensable role of life cycle assessment (LCA) in quantifying agricultural environmental impacts, successfully meeting this study’s objective to map the integration and methodological landscape of LCA in the sector.
The bibliometric analysis of LCA in agriculture underlines its key role in addressing environmental and socio-economic challenges. LCA has become an established and indispensable tool for quantifying the environmental impacts of agricultural practices, encompassing energy consumption, greenhouse gas emissions, water use, and pollution. By integrating socio-economic dimensions, LCA offers a more holistic approach to understanding the complex interaction between agricultural activities and sustainability, thereby supporting the development of policies that promote environmentally sound and economically viable practices.
The findings reveal that key issues such as sustainability, greenhouse gas emissions, and alternative agricultural practices are central to advancing research in this area. These issues highlight the urgent need for innovation in agricultural systems to reduce environmental impacts while enhancing performance. These innovations include technologies like sensor-based irrigation and the GPS-guided application of farm inputs; farming methods such as no-till farming and crop rotation; systems that combine trees with crops or mix crops with livestock; using natural fertilizers and pesticides; and approaches that help restore soil health and boost the amount of carbon stored in the soil. In addition, specialized topics such as waste management and uncertainty analysis provide specialized knowledge that can be extended to wider applications, while emerging topics such as animal life cycle stages present opportunities for future exploration.
This study further underlines the importance of exploiting technological developments to improve LCA methodologies. Innovations such as digital monitoring, renewable energy integration, and carbon footprint assessments are critical to improving agricultural sustainability. These approaches not only address environmental concerns but also align with global sustainability goals, enabling the more efficient use of resources and reducing ecological footprints.
In conclusion, the integration of LCA into agricultural systems is at a transformative juncture. As research continues to evolve, it is imperative to focus on systematic analyses and innovative practices that address both environmental and socio-economic dimensions. This will pave the way for resilient agricultural systems that are capable of meeting the demands of a growing population while safeguarding natural resources for future generations.

Author Contributions

Conceptualization, K.S. and D.A.; methodology, K.S.; formal analysis, K.S.; investigation, K.S. and N.K.; writing—original draft preparation, K.S.; writing—review and editing, K.S., N.K. and C.A.; visualization, K.S. and N.K.; supervision, D.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Research framework and flowchart.
Figure 1. Research framework and flowchart.
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Figure 2. Production of articles per year.
Figure 2. Production of articles per year.
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Figure 3. Key sources from Bradford’s Law.
Figure 3. Key sources from Bradford’s Law.
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Figure 4. (a) Cooperation network displaying author collaboration, showing a high dispersion between groups which suggests specialization or limited cross-group collaboration. (b) Co-referral network identifying Wernet G. and Brentrup as central nodes, demonstrating their influential role as foundational publications in the field.
Figure 4. (a) Cooperation network displaying author collaboration, showing a high dispersion between groups which suggests specialization or limited cross-group collaboration. (b) Co-referral network identifying Wernet G. and Brentrup as central nodes, demonstrating their influential role as foundational publications in the field.
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Figure 5. Coupling clusters of articles by impact (vertical axis) and centrality (horizontal axis). The upper right quadrant confirms that themes like agriculture, life cycle, and environmental impact have the highest centrality and impact within the reviewed literature.
Figure 5. Coupling clusters of articles by impact (vertical axis) and centrality (horizontal axis). The upper right quadrant confirms that themes like agriculture, life cycle, and environmental impact have the highest centrality and impact within the reviewed literature.
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Figure 6. Clusters of coupling sources showing the thematic relationships. LCA is the central concept. Climate change is closely linked to carbon footprint and GHG emissions. Sustainability is associated with environmental considerations and alternative agriculture.
Figure 6. Clusters of coupling sources showing the thematic relationships. LCA is the central concept. Climate change is closely linked to carbon footprint and GHG emissions. Sustainability is associated with environmental considerations and alternative agriculture.
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Figure 7. Thematic map.
Figure 7. Thematic map.
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Figure 8. Number of publications per year, on life cycle assessment.
Figure 8. Number of publications per year, on life cycle assessment.
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Figure 9. Distribution of publications over time categorized by Sustainable Development Goals (SDGs), highlighting trends in agricultural LCA research aligned with global sustainability targets.
Figure 9. Distribution of publications over time categorized by Sustainable Development Goals (SDGs), highlighting trends in agricultural LCA research aligned with global sustainability targets.
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Table 1. Author productivity by year.
Table 1. Author productivity by year.
Authorh-Indexg-Indexm-IndexTCNPPY Start
Nemecek T17220.89473681613222006
Bacenetti JJ10130.9090909513132014
Rieravevall J10110.6666666755112010
Van der Werf HMG10110.51205112005
Wang X10130.7692307386132012
Gaillard G9100.4736842913102006
Nabavi—Pelesaraei A88164682017
Gabarrell X780.466666648482010
Zhang Y790.437524392009
Aubin J660.37535162009
Table 2. Distribution of key sources based on Bradford’s Law.
Table 2. Distribution of key sources based on Bradford’s Law.
SourceRankFreqcumFreq
Journal of Cleaner Production1287287
Sustainability (Switzerland)270357
Agricultural Systems333390
Environmental Science and Pollution Research424414
Renewable and Sustainable Energy Reviews524438
Journal of Industrial Ecology621459
Sustainable Production and Consumption720479
Table 3. Analysis of coupling clusters from sources.
Table 3. Analysis of coupling clusters from sources.
LabelGroupFreqCentralityImpact
Environmental impact—conf 21.9%
Life cycle—conf 14.6%
Agriculture—conf 13.3%		1270.542983174.72050265
Life cycle—conf 29.3%
Agriculture—conf 26.7%
Environmental impact—conf 21.9%		3180.529818571.84077243
Life cycle—conf 56.1%
Agriculture—conf 60%
Environmental impact—conf 56.2%		5481.431214331.66410422
Table 4. Sources with the most publicity.
Table 4. Sources with the most publicity.
SourcePublications
Journal of Cleaner Production31
Sustainability (Switzerland)6
Sustainable Production and Consumption5
Agricultural Systems4
Environmental Impact Assessment Review3
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Spanos, K.; Kladovasilakis, N.; Achillas, C.; Aidonis, D. Mapping Agricultural Sustainability Through Life Cycle Assessment: A Narrative Review. Environments 2025, 12, 436. https://doi.org/10.3390/environments12110436

AMA Style

Spanos K, Kladovasilakis N, Achillas C, Aidonis D. Mapping Agricultural Sustainability Through Life Cycle Assessment: A Narrative Review. Environments. 2025; 12(11):436. https://doi.org/10.3390/environments12110436

Chicago/Turabian Style

Spanos, Konstantinos, Nikolaos Kladovasilakis, Charisios Achillas, and Dimitrios Aidonis. 2025. "Mapping Agricultural Sustainability Through Life Cycle Assessment: A Narrative Review" Environments 12, no. 11: 436. https://doi.org/10.3390/environments12110436

APA Style

Spanos, K., Kladovasilakis, N., Achillas, C., & Aidonis, D. (2025). Mapping Agricultural Sustainability Through Life Cycle Assessment: A Narrative Review. Environments, 12(11), 436. https://doi.org/10.3390/environments12110436

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