A taxonomy of biodiversity indicators for evaluating supply chain sustainability Open Access

Informed supply chain (SC) management is essential because SCs play a central role in global distribution and economic vitality, influencing organizational and national competitiveness, efficiency and resilience (Njoku and Kalu, 2015; Szyliowicz and Zamparini, 2022). At the same time, SCs contribute significantly to social and environmental issues (Kalkanci et al., 2016), prompting growing demands from stakeholders for more sustainable practices. Sustainable SCs (SSCs) promote environmentally responsible production and consumption by addressing challenges such as resource depletion, human rights concerns, emissions and climate change throughout different stages of SC activities (Leal Filho et al., 2023; Njoku and Kalu, 2015; Yu et al., 2022).

Incorporating and balancing the multiplicity of SSC issues is a nontrivial task. SSCs often represent theoretical ideals, while their management presents substantial practical difficulties (Gupta et al., 2020; Narimissa et al., 2020; Nath et al., 2024). Many businesses struggle to govern, manage and transition toward SSCs due to a lack of knowledge, useful frameworks, performance measures or resources.

Biodiversity management – referring to the diversity of life on land and in water, represented by the United Nations’ sustainable development goals (SDGs) 14 and 15, and the complex ecosystems supporting it – represents one of the most important environmental and social goals in contemporary times; humans, of course, are a central part of these ecosystems. Nevertheless, compared with other SSC concerns, relatively little is understood about biodiversity within SCs or how this ecological dimension should be managed (Bogenreuther et al., 2024; Salmi et al., 2023). SSC activities resulting in deforestation, pollution and the overexploitation of natural resources have contributed to historically high levels of biodiversity loss (Alexander et al., 2024; Salmi et al., 2023), thereby drastically weakening ecosystems and continuing to threaten resource-dependent industries (Tilman et al., 2012).

From an SC perspective, biodiversity loss translates into concrete operational and financial risks. Degraded ecosystems can reduce yields in agricultural and forestry SCs, destabilize fisheries and increase volatility in prices and sourcing conditions. Firms dependent on biodiversity-sensitive commodities face increased risks of supply disruptions, regulatory intervention or loss of license to operate when ecosystems are pushed beyond certain thresholds. Therefore, maintaining biodiversity is not only an environmental objective (Pizzutto et al., 2021) but also a precondition for long-term SC resilience and continuity (Liu et al., 2025; Mfuni et al., 2025; Oliver et al., 2015).

While integrating biodiversity considerations into SSC frameworks can help mitigate biodiversity loss, this remains relatively immature in practice (Lähtinen et al., 2016; Wang and Pfister, 2024). A significant reason for this is the fact that chief SC officers and sustainability managers lack clear guidance regarding which biodiversity indicators to apply, where in the SC they should be implemented and how these indicators can be incorporated into risk and performance management systems necessary for long-term resilience (Kashmanian, 2019; Liu et al., 2025; Salmi et al., 2023).

Existing frameworks tend to prioritize traditional sustainability indicators like carbon emissions and energy consumption (Tsai, 2010); however, biodiversity should command greater attention because of its critical connection to ecosystem services across multiple sectors and its importance for SC resilience (Agbelusi et al., 2024; Ali et al., 2024; Bogenreuther et al., 2024; Salmi et al., 2023). There is also a moral and ethical responsibility for industries to account for biodiversity (LeBaron and Lister, 2021; Lim et al., 2017), in addition to its economic and operational ramifications. As a result, biodiversity indicators within SCs must be better understood to support competitive performance and long-term resilience (Heink and Kowarik, 2010).

This study addresses this gap by developing a taxonomy of biodiversity indicators relevant to SCs. This taxonomy categorizes indicators to clarify the relationships among them and outlines how different SC actors may select and apply specific subsets of indicators in practice. Taxonomies and conceptual frameworks for biodiversity indicators can support transdisciplinary, multidisciplinary and interdisciplinary research, helping both academics and practitioners to bridge the knowledge-implementation gap between theory and practice (Ferreira et al., 2022). These can also be used to promote SC sustainability across a multitude of industries. The taxonomy presented here is designed to be comprehensive, identifying connections and possible overlaps to make it more broadly applicable, for the purpose of strengthening and enhancing the resilience of SSCs through improved biodiversity protection.

To further structure these relationships, the indicators are integrated into the drivers–pressures–state–impact–response (DPSIR) framework (European Environment Agency), which provides a cause-and-effect analytical lens (Khoso, 2024). By combining a biodiversity indicator taxonomy with the DPSIR framework, the study bridges an important gap between environmental conservation research and operational decision-making. It also establishes a foundation for a more thorough sustainability assessment that encompasses biodiversity.

The following section provides a literature background that establishes the research foundation and reviews recent developments related to biodiversity indicators for SSCs. The subsequent section describes the methodology used to achieve the research objectives. The findings are then presented and discussed, including the implications of applying the biodiversity indicator taxonomy and framework for advancing both scholarship and practice, particularly in strengthening long-term SC resilience and sustainability. The final section summarizes the study’s main findings and contributions and outlines directions for future research.

This section establishes the theoretical and practical foundation for biodiversity-specific indicators and the development of a taxonomy for SSC management. Although the need for biodiversity-related SC management research is increasingly evident, managing and studying biodiversity beyond the boundaries of individual firms and across SCs is a relatively complex task (Salmi et al., 2023). The following discussion thus highlights this complexity and explains the relevance and value of taxonomies. The section begins with an overview of general SC sustainability principles, performance measurement and the overarching need for taxonomic research. It then discusses how biodiversity loss comprises a critical challenge for SSCs and recent efforts related to biodiversity indicators that inform the present study.

SCs have substantial environmental footprints, resource demands and social impacts. These sustainability impacts necessitate a drastic transformation so that SCs can contribute positively, and potentially regeneratively, to sustainable development rather than increasing the global environmental burden (Acquaye et al., 2017). SSC management seeks to balance economic growth with environmental preservation and social equity. This approach is rooted in the triple bottom line sustainability framework, which integrates economic performance, environmental stewardship and social well-being (Elkington and Rowlands, 1999). The scope of this framework has expanded considerably through the UN’s SDGs (Sarkis and Ibrahim, 2022).

Several SDGs are directly relevant to SC operations, including responsible consumption and production (SDG 12), climate action (SDG 13), life below water (SDG 14) and life on land (SDG 15) (Sorooshian, 2024). These goals underline the need for SCs to embed sustainability into their core strategies to address climate change, conserve biodiversity and promote social equity (Delabre et al., 2020). Among these priorities, biodiversity plays a vital role because ecosystems fundamentally support all life on Earth, including humans (Duffy, 2009). Without the diversity of animals, plants and microorganisms, ecosystems that provide the necessary elements to sustain life – clean air, food and water – could falter.

Several interconnected factors further corroborate the importance of SSCs. A growing number of stakeholders, including governments, investors, consumers and nongovernmental organizations (NGOs), require businesses to accept responsibility for the social and environmental impacts of their SCs (Gurzawska, 2020). As awareness of environmental challenges such as climate change, deforestation, resource depletion and human rights has grown, businesses have begun to recognize the broader impacts of their operations and SCs (Nepstad et al., 2013). Regulatory initiatives, including the European Union’s Green Deal and carbon neutrality targets, also require businesses to develop more innovative and sustainable solutions through environmentally friendly organizational and SC practices (Eckert and Kovalevska, 2021; Filipović et al., 2022; Mentes, 2023). At the same time, unsustainable practices increasingly pose risks to business competitiveness and operational continuity (Schaltegger et al., 2006). While businesses that invest in SSC practices may achieve greater resilience and competitive advantage in markets where sustainability has become a more pertinent public issue, the transition toward SSCs remains difficult due to the complexity of supply networks, economic constraints and the absence of universal sustainability assessment frameworks (Calzolari et al., 2022).

Environmental sustainability is heavily reliant on biodiversity, which encompasses the diversity of life forms and ecosystems on Earth. Ecosystems provide vital services that support industrial operations, such as regulating water purification, pollination and climate (Khanam et al., 2023; Layke, 2009). However, many SC activities, ranging from agricultural deforestation to manufacturing pollution, cause significant biodiversity loss (Cabernard et al., 2024; Crenna et al., 2020; Lenzen et al., 2012). The integration of biodiversity elements, especially metrics and measures, into SSC management systems is thus critical for maintaining organizational performance, SC resilience and ecosystem health.

Efforts to support biodiversity management include incentive-based programs and award systems that encourage conservation and responsible environmental practices (Roberts et al., 2022). Certification standards also provide valuable mechanisms for effective biodiversity management (Boiral et al., 2018). However, effectively integrating biodiversity considerations into SSC requires rethinking how measures and metrics can be used to proactively support efforts. Businesses must move beyond rule compliance and actively work to reduce their biodiversity footprint, for instance, by conducting biodiversity impact assessments and setting measurable goals for conservation and ecosystem restoration within their corporate and SC operations. Rather than focusing solely on damage reduction, reduced environmental impact should entail restorative and regenerative activities that actively improve the environment (Gualandris et al., 2024; Sorooshian, 2025). Such developments have been described as a potential pathway toward a future industrial revolution, sometimes referred to as the industrial revolution 6.0 (Sorooshian, 2025).

Many natural scientists identify biodiversity loss as one of the most critical sustainability issues because several planetary boundaries are already being exceeded. Despite this urgency, studies indicate that industries often devote far less attention to biodiversity compared with other sustainability priorities (Schaltegger et al., 2023). This disconnect can be attributed partly to limited awareness but also to a lack of managerial tools capable of effectively addressing these issues. These concerns lay the foundation for the subsequent exploration of biodiversity indicators and their role in advancing SSC management.

Biodiversity is experiencing a significant global decline, largely driven by increasing consumption of materials and commodities (White et al., 2023; Wilting et al., 2017), making the paucity of research in this area, especially within operations and SCs, disconcerting (Salmi et al., 2023). The problem is further compounded by the tendency of industrial actors to undervalue natural ecosystems in business decision-making processes (Shi et al., 2021).

The relationships between SCs, business activities and biodiversity are indeed complex; impacts can arise through various actions, such as excessive land use, mineral extraction, wildlife exploitation, urban development, pollution and the spread of invasive species. These pressures occur at multiple stages of SCs, including direct operations, upstream suppliers, downstream distribution networks and investment activities. The effects can vary widely depending on species, habitats, geographic location and the pathways through which environmental pressures occur (Crenna et al., 2020).

Growing recognition of the biodiversity crisis has prompted businesses to reassess their business models (Bishop et al., 2009; Van Oorschot et al., 2020; White et al., 2023), while governments are increasingly requiring businesses to disclose and address the ecological impacts of their activities (White et al., 2023). In response to these pressures, businesses have begun setting targets to address biodiversity loss (Zu Ermgassen et al., 2022). However, significant challenges remain in defining appropriate targets and strategies, including metrics and indicators for assessing how their activities affect biodiversity (Addison et al., 2020; Kennedy et al., 2023).

While there is evidence that some forward-thinking companies have begun to take biodiversity issues into consideration, much biodiversity reporting has been criticized as “greenwashing” (Kopnina et al., 2024). One contributing factor is that many existing guidelines do not adequately integrate comprehensive biodiversity factors. Additional barriers prevent many businesses from fully engaging with biodiversity management, including insufficient awareness, capacity or willingness to use the requisite tools that could help them measure and address their ecological impact (White et al., 2023). The diversity and complexity of biodiversity measurement further complicate efforts to identify appropriate indicators for managing activities in a more sustainable manner (Schaltegger et al., 2023).

As a result, understanding biodiversity within both business practice and the related academic literature remains limited (Kopnina et al., 2024). Achieving ambitious biodiversity goals and avoiding accusations of greenwashingali will require businesses to significantly improve their biodiversity management within their direct operations and SCs (White et al., 2023). The development of taxonomies and typologies of biodiversity indicators offers one potential pathway for addressing these challenges.

In business research, taxonomies focus on the classification of data and information to reduce conceptual fragmentation (Nickerson et al., 2013). A taxonomy that encompasses biodiversity indicators can thus help the field identify appropriate measures to support sustainability efforts in SC management.

One of the primary purposes of this study, beyond developing the taxonomy itself, is to conceptualize a framework that can be used to analyze the relationship between SC management and biodiversity. To ensure comprehensiveness and accuracy, a literature review was conducted on biodiversity assessment frameworks using a variety of academic and institutional sources. Searches were performed in the Scopus and Web of Science databases using keywords such as biodiversity indicator framework, biodiversity metrics and SC sustainability. Relevant institutional sources were also examined, including the convention on biological diversity (CBD), the International Union for Conservation of Nature (IUCN) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES).

Several frameworks for biodiversity assessment currently exist. For instance, one common method for determining the environmental impact of a product or service over the course of its useful life is “life cycle assessment” (LCA) (Souza et al., 2015; Teillard et al., 2016). However, existing LCA tools often overlook crucial ecological factors that extend beyond conventional measures like land use, climate change and soil quality, and thus do not fully account for impacts on biodiversity (Teillard et al., 2016). It is also unclear how scientifically comprehensive many LCA biodiversity classifications are. Although an “impact” pathway is typically incorporated with LCA frameworks, improvements and control mechanisms for biodiversity indicators are limited (Winter et al., 2017).

Several studies suggest that LCA methodologies can optimize the incorporation of biodiversity indicators to produce more effective evaluations (Souza et al., 2015; Teillard et al., 2016). To fully grasp biodiversity in SCs, additional indicators – such as ecosystem services, functional diversity and conservation value – should be included (Addison et al., 2020; Crenna et al., 2020; Smith et al., 2024; Teillard et al., 2016). Once again, though, indicators related to governance and control (including indicators for improvement) are missing.

Existing biodiversity assessment methods include both SC and product-level analyses, with various indices and metrics available (Katic et al., 2023). Although these metrics have their advantages and disadvantages, SC managers must select biodiversity indicators based on specific assessment objectives. Additional research in this area remains necessary, as effective biodiversity management within SCs depends on well-established indicators and measurement protocols.

The literature also notes that corporate investments in ecosystem restoration through current initiatives are insufficient to halt biodiversity loss (Kopnina et al., 2024). Corporate narratives often rely on a few activity-based indicators, such as those in the global reporting initiative (GRI) (Halkos and Nomikos, 2021). As a result, businesses frequently struggle with making internal decisions regarding their biodiversity performance across various operational activities based on current indicators (De Silva et al., 2019).

Various initiatives have attempted to develop biodiversity indicators for businesses across multiple sectors, such as finance, extractive industries and agriculture. However, these indicators are typically context-specific and limited to particular applications or segments in the business SC (Addison et al., 2020). Many initiatives have focused primarily on indicators alone without addressing how indicator development fits within broader business sustainability and management processes (Addison et al., 2020). These limitations contribute to the scarcity of biodiversity indicators in SCs.

Many industries continue to neglect biodiversity considerations. In numerous cases, sustainability initiatives either overlook biodiversity entirely or achieve negligible results in this area (White et al., 2023). Both motivational and implementation challenges contribute to this situation.

First, biodiversity represents an inherently complex topic that extends beyond the diversity of species to include genetic diversity, ecosystem conditions and the overall health of the environment (Kosnik et al., 2024). At present, no overarching process exists to guide the development and application of biodiversity indicators across multiple levels of business activity. Such a process would ideally align with business management systems and support informed decision-making related to biodiversity performance (Addison et al., 2020).

Second, many businesses simply lack sufficient knowledge of biodiversity conservation (Salmi et al., 2023; Smith et al., 2019), making it difficult for them to identify and apply appropriate biodiversity indicators. Biodiversity accounting could benefit from greater engagement with approaches developed in the natural sciences (Cuckston, 2018; Feger et al., 2018).

Finally, a substantial hurdle persists in reaching a consensus regarding the use of biodiversity indicators and their connection to specific SC activities (Duelli and Obrist, 2003). Furthermore, standards for measuring biodiversity impacts across SCs have yet to be established (Salmi et al., 2023).

In summary, there is a clear need to identify, classify and standardize critical biodiversity indicators that can be used across SCs to improve sustainability practices – a need that provides further justification for the taxonomy and framework developed in this study.

Traditional taxonomy development methods tend to be resource-intensive and time-consuming; they also often struggle to integrate knowledge across different fields and languages for effective information retrieval and data integration (Cui et al., 2024). To address these issues, this study, following recommendations in the literature (Cui et al., 2024; Yoo and Cheng, 2025; Zhang et al., 2025), employed a multi-stage methodology integrating generative artificial intelligence – specifically, large language models (referred to hereafter as AI) – with literature validation to develop and refine a taxonomy of biodiversity indicators for SSC management.

Previous research (e.g. Fang et al., 2024; Huang et al., 2024) has also relied on AI, particularly large language models, for knowledge-guided taxonomy development and reported promising results. To ensure methodological rigor, the approach used in this study adhered to the AI think tank (AITT) framework (Sorooshian, 2025) for AI-assisted investigations, emphasizing clear documentation, multi-step human verification and triangulation with authoritative sources to address the reliability limitations of AI outputs. Figure 1 illustrates how the four stages of the AITT guidelines align with the biodiversity indicator development process used in this study.

The process began by defining precise, context-specific queries reflecting the overarching research objectives. Each query was carefully structured and divided into smaller subquestions, ensuring clarity in the AI prompts. Three distinct AI models – ChatGPT, AnswerThis and Google Gemini – were used to expand the range of potential indicators while mitigating potential biases that might arise from relying on a single AI source.

To maximize the utility of the AI tools, the prompts were formulated to include the following elements:

• Objective definition: Each query was aligned with clearly defined research objectives, ensuring relevance and specificity.

• Contextualization: Background information, including constraints and goals, was provided to guide AI outputs.

• Query structuring: Complex problems were separated into smaller, more focused questions to enhance the clarity and quality of responses.

The initial prompt was tested using ChatGPT. After evaluating the responses, feedback was provided and the prompt was refined accordingly. The resulting prompt was as follows:

We are conducting research to develop a taxonomy of biodiversity indicators specifically tailored to sustainable SC management. Given the complexities of biodiversity loss – such as deforestation, habitat degradation, pollution and resource overexploitation – please provide a comprehensive list of indicators that could be tracked at various SC stages (from raw material sourcing through end-of-life). For each indicator, explain its relevance to biodiversity in SCs. Draw upon your knowledge of both environmental science and industry best practices to ensure the indicators address key concerns (e.g. ecosystem services, species diversity and habitat connectivity) and are feasible for real-world SC applications.

Once an initial set of biodiversity indicators was generated by each AI tool, the outputs were reviewed, compared and then consolidated into a single data set. Each AI model was then re-queried iteratively, refining and expanding the indicators when partial or incomplete responses appeared. This process continued until no additional logically coherent were produced. This iterative querying ultimately reached a point of saturation, at which the pool of indicators was deemed suitably comprehensive for subsequent analysis. The following prompt was used at this stage:

We are conducting research to develop a taxonomy of biodiversity indicators specifically tailored to sustainable SC management. Given the complexities of biodiversity loss – such as deforestation, habitat degradation, pollution and resource overexploitation – please provide a comprehensive list of indicators that could be tracked at various SC stages (from raw material sourcing through end-of-life). We have compiled the following indicators: [list from previous round]. Draw upon your knowledge of both environmental science and industry best practices to ensure the indicators address key concerns (e.g. ecosystem services, species diversity and habitat connectivity) and are feasible for real-world SC applications; then suggest any additional indicators you believe are missing in this list. For each additional indicator, explain its relevance to biodiversity in SCs.

Although AI tools generated the initial sets of indicators, the resultant taxonomy reflects an iterative, human-guided process. The authors manually reviewed all AI outputs for relevance, redundancy and logical consistency. Each indicator was then verified based on existing theoretical evidence through a targeted literature review. If no peer-reviewed or authoritative institutional source could be identified for an AI-suggested indicator, that indicator was removed from the final taxonomy. All finalized indicators are detailed in Supplementary File S.xls.

Following the AI-based indicator generation, ChatGPT was consulted to identify any overlapping or redundant indicators and then categorize them for improved clarity and applicability in global SC operations. The intent was to correlate the taxonomy with established frameworks; however, at this stage, no specific framework was included in the prompt provided to ChatGPT to avoid influencing the classification process.

The AI mapping procedure involved analyzing biodiversity indicators according to their primary, thematic and relevant classification categories. This process began with a thorough examination of the indicator descriptions to determine their core objectives. Each indicator was then assigned to the category that best suited its purpose, ensuring that the chosen category was logically consistent with the category definitions. The central or most dominant category was chosen to avoid multiple indicator associations. The ChatGPT mapping procedure thus produced a systematic and structured grouping of indicators based on their thematic relevance to biodiversity assessment and analysis. The results were subsequently reviewed and confirmed through manual evaluation.

The supplemental database S.xls (Sheet 2) provides a detailed chain of evidence documenting each methodological step, from the initial AI queries to human review and literature validation. By combining the breadth of AI-assisted indicator generation with literature-based validation and expert review of the classification process, this methodology not only advances scholarship on integrating biodiversity considerations into SSC management but also delivers a replicable, adaptive model for future studies addressing related sustainability challenges.

This section briefly describes the main features of the taxonomy and its mapping to the DPSIR framework; the implications and applications for SC resilience are then discussed in Section 5.

The AI models generated a total of 249 biodiversity indicators, with ChatGPT producing the most items. The outputs generated by the other AI models were largely similar to those produced by ChatGPT. Their unique contributions, consisting of indicators not identified by ChatGPT, represented less than 30% of the total set.

The proposed taxonomy classifies the indicators into ten categories, each consisting of three subcategories. Table 1 provides a summary of the categories and subcategories, along with exemplary measures that further define the categories. The AI-assisted classification offers a coherent mapping of all 249 indicators, organized into ten higher-level categories based on their primary thematic focus and intended application.

For more than two decades, the DPSIR framework has contributed to clarifying causal relationships in environmental sustainability research (Mann, 2025). A substantial body of literature demonstrates that DPSIR is a powerful tool for illustrating environmental causal chains and interactions (Mann, 2025).

Given the ecological nature of the indicators identified in this study, a biodiversity indicator relationship framework was developed using the DPSIR model, which provides a cause-and-effect (or process-oriented) perspective (Khoso, 2024). Table 2 details the integration of biodiversity taxonomy indicators with the DPSIR framework, while Figure 2 illustrates the DPSIR-driven relationships among the indicators. DPSIR helps clarify how different categories of biodiversity indicators relate to each other within broader SC dynamics.

For example, governance and socioeconomic drivers (e.g. resource demand, land-use policies and emissions from industrial activities) manifest as three pressure categories in our proposed taxonomy, which include land use that causes habitat alteration, pollution emissions and climate change. Governance structures also involve institutional and organizational policies related to sustainability in general and biodiversity in particular. Because SCs operate globally, the importance and application of governance-related indicators may vary across different contexts. Similarly, pressure-based indicators may differ depending on industry characteristics, product types or operational processes within a specific SC.

These environmental pressures alter ecosystem functioning, habitat conditions and ecosystem resilience. Such changes, in turn, affect species composition, genetic diversity and population dynamics, including the potential expansion of invasive species. Changes in species and populations (i.e. direct biodiversity metrics) also influence ecosystem services that support human societies. Outcomes related to ecosystem services arise from biodiversity loss and stress on habitats and ecosystem functioning and therefore represent some of the most direct measures of biodiversity change.

SC managers and organizations can use these indicators to assess whether specific facility locations or supplier regions are experiencing ecological stress. Response indicators include methodological, informational and capacity-building mechanisms such as monitoring systems and communication activities. These responses can feed back into the underlying drivers by informing adjustments in governance mechanisms (e.g. regulations) or operational practices (e.g. investments in projects). Monitoring indicators can also help determine whether organizations and SCs are effectively tracking and communicating their biodiversity efforts. Strong monitoring systems implemented by SC actors may improve organizational credibility and address legitimacy concerns, such as accusations of greenwashing, particularly when supported by reliable data collection.

Integrating DPSIR logic into the taxonomy makes explicit the feedback loops between environmental pressures and societal responses, which are vital for designing evidence-based interventions in SCs. This structure demonstrates how biodiversity indicators can support different forms of evaluation across SC activities.

The taxonomy also incorporates factors such as endangered species indicators, uniqueness scores and the presence of culturally significant species, all of which align with the IUCN Red List (IUCN, 2012) for categorizing species according to their risk of extinction. Categories such as “critically endangered,” “endangered” and “vulnerable” provide widely recognized benchmarks that SC managers can use to prioritize monitoring and management activities. Such classifications can help guide sourcing decisions in biodiversity-sensitive regions, such as tropical rainforests or coral reef zones.

The socioeconomic and governance dimensions of the classification system are informed by research demonstrating that effective biodiversity conservation depends on involving stakeholders and establishing corporate governance frameworks. These principles are emphasized by the Taskforce on Nature-related Financial Disclosures (TNFD), which aims to improve transparency and the quantification of nature-related risks and opportunities for businesses and financial institutions (Popovski, 2023), as well as governance and policy subdivisions related to biodiversity offsets. Along with recommended approaches for incorporating stakeholders, promoting active engagement and balancing biodiversity concerns, the taxonomy can support disclosures aligned with TNFD recommendations.

Similarly, GRI standards and Sustainability Accounting Standards Board (SASB) guidelines emphasize the need for credible, quantitative and practical sustainability indicators in corporate communications (Goswami et al., 2023; Pizzi et al., 2023). Though many existing standards focus primarily on carbon emissions and water use, they increasingly encourage firms to take biodiversity initiatives more seriously (Goswami et al., 2023). The taxonomy presented here thus includes indicators related to genetic diversity, species populations, habitats and ecosystem services, while also building on GRI 304, which specifically addresses biodiversity (Sra and Deo, 2024). These connections corroborate the relevance of the taxonomy framework, particularly because SCs frequently span multiple ecological regions and ecosystems, requiring a consistent yet adaptive way for reporting and ensuring accountability.

Table 3 provides an initial conceptual demonstration of how selected subsets of indicators from the taxonomy could support TNFD processes and GRI 304 disclosures, although comprehensive empirical applications require further development. Section 5.1 extends this conceptual demonstration through an illustrative example involving a biodiversity-sensitive commodity SC.

The taxonomy of biodiversity indicators can serve multiple purposes within a broad range of sustainability and organizational SC frameworks, not just the DPSIR framework introduced earlier. For example, these indicators can be linked to triple-bottom-line categories (Elkington, 1997; Elkington and Rowlands, 1999), which focus on the three primary sustainability dimensions. Socioeconomic indicators correspond to the social and economic dimensions, while indicators related to species, populations and ecosystems, although sometimes connected to economic dimensions, primarily reflect environmental characteristics. Such integration can then support corporate sustainability reporting (Milne and Gray, 2013).

Another potential integration pathway involves the Natural Capital Accounting (NCA) framework and protocol (Whitaker, 2018). SC and sustainability managers may use NCA methods to systematically quantify the value of ecosystem services and natural resources (Ruijs et al., 2019). This model, although not examined in detail here, can incorporate biodiversity indicators into strategic planning. For instance, NCA enables the integration of natural resource considerations into corporate performance measurements, allowing the proposed taxonomy to support improved sustainability practices throughout SCs (Ingram et al., 2024). The protocol evaluates sustainability and financial elements through several stages – “Frame,” “Scope,” “Measure,” “Value” and “Apply;” these stages can then guide the selection and application of indicators from the taxonomy. For example, organizations applying the NCA methodology could use the taxonomy’s subcategories (such as genes, species and populations; habitats and ecosystems; and pressures such as pollution, waste and chemical impacts) in the “Measure” and “Value” stages to collect detailed biodiversity information.

The IPBES conceptual framework emphasizes the dynamic relationships between nature, nature’s contributions to people and quality of life (Borie and Hulme, 2015; Watson et al., 2019). The proposed taxonomy’s categories mirror several IPBES principles by recognizing how environmental changes driven by SC activities can ultimately affect human well-being. The IPBES framework’s focus on direct and indirect drivers of biodiversity loss aligns with the inclusion of climate change, pollution and invasive species as distinct “pressures” within the classification system. Having IPBES as a reference benchmark underlines the necessity of bridging ecological, social and economic perspectives to capture a broader scope of biodiversity impacts and dependencies in global SCs. In this context, the proposed taxonomy can complement and strengthen IPBES-inspired analytical approaches.

The CBD underpins many of the well-established goals for ecosystem and species conservation (Chandra and Idrisova, 2011; Diversity, 2001; Fajardo del Castillo, 2021). Its post-2020 global biodiversity framework emphasizes integrated targets for reducing habitat loss, controlling invasive species and ensuring equitable benefit-sharing – principles that inform the cross-border collaborative aspect of the proposed taxonomy. For instance, indicators related to habitat restoration (ecological restoration progress index), invasive species management and stakeholder engagement demonstrate practical pathways for meeting CBD objectives at the industry level. Aligning the taxonomy with CBD targets acknowledges both the global policy landscape and the on-the-ground realities faced by corporate entities that operate across multiple ecological regions.

The planetary boundaries hypothesis also provides a conceptual reference point by stressing critical environmental thresholds (Rockström et al., 2009), such as preserving biosphere health, controlling land use changes and monitoring biochemical processes. Exceeding such limitations may cause irreparable harm to ecosystems. Managers and organizations that incorporate planetary boundaries into their corporate sustainability strategies (Whiteman et al., 2013) can evaluate whether SC activities adhere to the Earth’s “safe operational range” by associating indicators (e.g. habitat division, ecosystem strength indices and species control techniques) with relevant planetary boundaries. The taxonomy categorizes aspects such as environmental conditions, genetic diversity, trophic relationships ^[1] and governance systems in ways that help identify ecological changes that may be irreversible.

Strategic sustainability and organizational theories can use biodiversity indicators as measurement scales. Several promising linkages can be identified with major theoretical perspectives commonly applied in corporate sustainability research.

The first theoretical linkage involves the natural resource-based view (NRBV) (Hart, 1995). The proposed taxonomy implies that long-term competitive advantage and SC resilience may be gained by effectively managing ecological constraints and commitments. When examined through the NRBVs framework, biodiversity is considered an asset that may help with product creation and innovation, as well as risk management for procuring raw materials and properly disposing of waste.

The taxonomy’s categorization of ecosystem benefits and services (such as pollination and carbon storage) as ecosystem functional diversity highlights how SCs may profit from or harm critical natural processes. According to the NRBV strategy, firms that routinely monitor and respond to biodiversity indicators may create SCs in industries like agriculture and forestry that are affected by climate change or have substantial environmental implications.

Another theoretical perspective relevant to SC management, drawing on resource dependence theory (Hillman et al., 2009), is natural resource dependence theory (NRDT) (Alkhuzaim et al., 2022). NRDT acknowledges that organizations are dependent on natural resources and services: if a critical natural resource is required for SC actors, dependence on the ecosystem increases accordingly. Conversely, SC activities can directly influence ecosystems themselves. The range of indicators included in the proposed taxonomy provides potential measurement tools for assessing these dependencies, as well as measures for how well this theoretical lens holds for a given SC.

Stakeholder theory and legitimacy theory further highlight the need for transparency and successful stakeholder engagement (Mahmud, 2020). Research suggests that collaboration with communities, NGOs and governmental institutions improves the success of biodiversity programs (Boiral and Heras-Saizarbitoria, 2017; Rogalla von Bieberstein et al., 2019). Measuring the incorporation of indigenous knowledge and encouraging stewardship through fair resource utilization are critical aspects of governance models that meet the needs of stakeholders while also ensuring that SC activities are perceived as legitimate and socially acceptable across various regions.

Social-ecological systems (SES) theory (Ostrom, 2009) provides another relevant perspective by viewing natural systems as interconnected networks that evolve together over time. Following this line of argumentation from SES, several categories within the taxonomy and framework proposed here emphasize the interplay of feedback loops involving actions in a given SC (involvement) and their impacts on ecological conditions (such as shifts in species makeup or genetic diversity), recommending ways for governing bodies to help regulate these interactions. Viewing biodiversity through a structured taxonomy rather than as an isolated environmental concern helps policymakers and practitioners identify areas where actions such as habitat rehabilitation and invasive species management may produce the greatest ecological benefits while balancing economic and social priorities.

Finally, there are nature-based solutions (NbS) perspectives that emphasize the connection between regeneration and sustainable practices (O’Hogain et al., 2018; Sowińska-Świerkosz and García, 2022). As regenerative approaches gain importance within SSCs (Gualandris et al., 2024), indicators that track rewilding progress, ecosystem service restoration and changes in productivity – core NbS principles – can support the integration of biodiversity rejuvenation and risk management within SC contexts. This regenerative interconnectivity also creates potential competitive and financial opportunities for organizations.

Three main insights emerge from the analysis. First, biodiversity indicators relevant to SCs span species and genetic diversity, habitats, ecosystem functions and services, pressures and governance, demonstrating that biodiversity is a cross-cutting dimension of SC performance. Second, mapping these indicators within the DPSIR framework clarifies the relationships linking business drivers and pressures to ecological states, impacts and management responses. Such clarity is immensely valuable for designing resilience-oriented strategies for both businesses and ecosystems. Third, rather than a one-size-fits-all approach, different SC actors are responsible for various parts of the DPSIR causal chain, implying that selection and use of indicators must be specific to the actor and stage of a given SC.

Stakeholders across academic, governmental, industrial and civil society domains have increasingly recognized the implications of biodiversity loss on global sustainability (Katic et al., 2023). Many industries depend on biodiversity both directly and indirectly (Katic et al., 2023). Direct dependence arises when biological resources are used as raw materials (e.g. natural rubber or timber) (Schubert et al., 2022; Warren‐Thomas et al., 2015). The sustainable management of these biologically derived resources is essential for business continuity and SC resilience. An extreme case is when COVID shut down global SCs, local biodiversity can help alleviate these disruptions (e.g. Eggert and Hartmann, 2023). Indirect dependence, on the other hand, arises through the provision of ecosystem services that support a wide range of industries (e.g. water purification, pollination and climate regulation). Disruption of these services through unsustainable practices similarly poses significant risks to business operations, SC resilience and long-term financial performance (Katic et al., 2023).

With these considerations in mind, the findings presented here offer a systematic resource that serves the interests of both academic and practical domains. From a practical perspective, the taxonomy can help organizations incorporate biodiversity considerations into their SC management activities. Firms can use the taxonomy and its associated indicators for procurement decisions, supplier evaluation, risk assessment and sustainability reporting. In doing so, they can identify and monitor biodiversity-related risks and opportunities within their SC operations.

Conceptually, the taxonomy achieves validation through its theoretical grounding and ability to integrate insights from multiple fields while aligning with established international standards and empirical research. The taxonomy (as with other sustainable SC performance taxonomies (e.g. Miemczyk et al., 2012) also provides scholars with a structured approach for organizing measurement scales, conducting comparative studies, developing standardized indicators and constructing sustainability metrics for specific sectors. The primary value of the taxonomy lies in translating a broad environmental concept into a structured set of operationally relevant indicators that can inform both future research and managerial practices. Specifically, the study contributes to the biodiversity management and accounting literature by integrating biodiversity-related reporting, valuation and assessment logics into a supply-chain-oriented taxonomy. It also contributes to the sustainable SC management literature by positioning biodiversity not as a residual environmental issue, but as an important domain of SC performance, governance and resilience.

Nickerson et al. (2013) critique the arbitrary methodologies often used in the development of existing taxonomies, noting that many classifications lack logical coherence and systematic procedures. Schaltegger et al. (2023) similarly emphasize the importance of having tools that can quickly and easily connect business practices to broader societal changes. These critiques reflect broader concerns that many traditional taxonomic approaches remain static, reductionist and confined within narrow disciplinary boundaries. The taxonomy developed in this study addresses these concerns by being inherently transdisciplinary and methodologically rigorous. Its design takes into consideration the complexity of SCs and their impacts on biodiversity and emphasizes the need for both measurability and continuity of practical progress in SC contexts. A hybrid approach that combines standardized elements with flexible customization provides managers with a scalable framework for making decisions and implementing policies in a variety of industrial and ecological settings.

As organizations increasingly seek to address sustainability challenges through their SCs, the proposed taxonomy offers a critical tool that facilitates the translation of theoretical insights into practical interventions. As key figures in the global economy, SC managers play a vital role in mitigating the damage that industrial activities cause to biodiversity (Teixeira et al., 2016). The biodiversity indicator taxonomy can therefore function as a multipurpose guide and decision-making tool for managers and policymakers working within the field of SSC management (Katic et al., 2023).

For practitioners, the taxonomy should be interpreted as a structured menu rather than a rigid checklist. A five-step roadmap is suggested, as shown in Figure 3. To make reporting easier, it is recommended to begin with a small, balanced set of indicators covering the DPSIR framework, with a focus on materiality, controllability and measurability. As data availability improves, the indicator set can be gradually expanded.

An illustrative application can further clarify how the taxonomy could be used in practice. Consistent with prospective theorizing, which is inherently future-oriented and concerned with imagining desirable futures (Gümüsay and Reinecke, 2024), the example is intended to show a plausible and desirable future application rather than report a completed empirical pilot.

Consider a focal firm sourcing palm oil, a commodity associated with deforestation, habitat loss and biodiversity risk in global SCs. The firm first identifies biodiversity-sensitive sourcing locations and traces suppliers to the plantation, farm or cooperative level using traceability systems, certification records, satellite-based monitoring and other chain-of-custody tools. A small, balanced set of indicators can then be selected across the DPSIR chain. Pressure indicators might include deforestation rate, land-use change intensity, agrochemical use, water pollution risk and climate-related hazard exposure. These indicators help identify supplier regions where production places greater pressure on surrounding ecosystems and where additional due diligence, mitigation or exclusion may be necessary. The firm may then evaluate ecosystem conditions and resilience within the sourcing landscape. Relevant indicators may include habitat integrity, soil health, watershed condition, ecological corridor continuity and restoration progress. Data may be drawn from remote sensing, certification audits, community-based observation, regional biodiversity data sets and supplier-provided records.

Suppliers operating in landscapes with stronger habitat integrity, lower fragmentation and more regenerative production practices would receive more favorable evaluations in supplier assessments. These results could then be incorporated into procurement scorecards, supplier development plans and biodiversity-related sustainability disclosures. In this way, the taxonomy allows organizations to align selected indicators with decision needs, data availability and operational priorities.

This illustrative example thus demonstrates how the taxonomy can support forward-looking decision-making rather than solely retrospective reporting. Over time, selecting suppliers with lower pressure scores and stronger habitat and ecosystem resilience is expected to improve biodiversity outcomes while also strengthening supply continuity, reducing regulatory and reputational risk and protecting vital ecosystem services. This example also illustrates how the relationships among pressures, ecosystem conditions, biodiversity outcomes and organizational responses can generate testable propositions for future research. Finally, it shows more concretely how biodiversity-informed sourcing strategies may contribute simultaneously to ecological protection and SC resilience.

Beyond this illustrative case, the example also points to the broader contribution of the taxonomy for SC decision-making and performance assessment. The taxonomy’s ten categories and subcategories offer a conceptual framework through which enterprises can evaluate biodiversity-related effects at the SC level. The classification scheme presents a wide range of potential indicators, ranging from genes, species and populations to governance and socioeconomic dimensions that are omnipresent in SSCs (Sarkis, 2012). This actor-specific logic implies that not all indicators are relevant for every SC participant; instead, each actor should select the subset that aligns with its decision-making authority and data access.

A broad array of SC actors – including manufacturers, upstream producers, logistics providers and policymakers – may therefore select different sets of indicators. Managers, for one, should operationalize only those that are relevant and measurable within their organizational or regional contexts. For example, agricultural organizations may focus on habitat quality, ecosystem services such as pollination and water purification, and the ecological consequences of land-use change or resource extraction, while international logistics organizations may be particularly concerned with invasive species control, SC transparency and cross-border regulatory compliance. Linking digital systems to these performance measures can greatly enhance transparency and learning for organizations and policymakers (Cui et al., 2024).

It is worth acknowledging that certain biodiversity indicators may exhibit latency effects, resulting in a more prolonged reaction to ecological pressures or management interventions. This latency may limit the immediate usefulness of some indicators for decision-makers who rely on short-term data for their choices. However, when these delays are anticipated, the indicators remain valuable for guiding long-term strategic planning, risk reduction and ecological resilience assessments.

As mentioned above, the taxonomy serves as a guide rather than a strict checklist. Each indicator selected must be adequately defined, quantified and backed by reliable literature and empirical data. Managers should conduct a gap analysis and engage with stakeholders to determine which indicators are most relevant to the organization’s operations. Users, similarly, should make sure that the chosen indicators are measurable using available monitoring tools or emerging technologies, such as remote sensing and environmental DNA (eDNA) monitoring. For example, Pringle et al. (2025) suggest that Industry 4.0 technologies could be used for monitoring and sensing. Policymakers may also use the taxonomy to develop industry standards and reporting systems that promote transparency and encourage stronger biodiversity management.

Addressing biodiversity challenges within SCs fundamentally requires overcoming institutional and disciplinary silos. Many biodiversity researchers lack formal training in business or SC management, making it difficult for them to contribute to the establishment or adoption of practical indicators. Conversely, SC professionals may have limited familiarity with ecological metrics and how to monitor them. Bridging this gap requires collaboration between academic institutions, organizations and society in general.

Furthermore, trade and standard-setting bodies, such as the World Trade Organization (WTO), the International Organization for Standardization and regional trade blocs, wield significant influence over what is considered sustainable through ecolabels, trade rules and voluntary standards. These demand-side measures, which are frequently based on consumer and investor preferences, can be extremely beneficial in supporting biodiversity protection by encouraging the adoption of appropriate indicators. Standards by various cross-sector organizations – including the United Nations’ CBD, the WTO, the United Nations Conference on Trade and Development, the Food and Agriculture Organization of the United Nations (FAO) and the IPBES – also play important roles in ensuring that biodiversity management is consistent with international economic practices and policies. Their participation may help promote wider adoption of biodiversity indicator frameworks across industries and regions.

During the evaluation of biodiversity indicators, several specialized ecological terms emerged, including zoonotic, riparian, trophic, bryophyte, boreal arctic, phenological, ephemeral wetland, endemism, phylogeographic and ecomorphological, among others. These concepts are often unfamiliar to business practitioners and policymakers unless they have formal training in biological or ecological sciences. Ensuring that such terminology is clearly defined and communicated is therefore essential. If indicator definitions are opaque to managers, both their practical use and credibility will be hindered. Addressing this challenge represents both a practical concern and a promising direction for future research.

The inclusion of biodiversity indicators and frameworks adds additional layers of dynamism and complexity to SC research. Further investigation is necessary to understand how SC characteristics – such as industry sector, institutional context, geographic location and data availability – influence the selection and usefulness of different indicators. Continued validation of both the indicators and the proposed framework is thus needed.

Many relationships among indicators remain assumed rather than empirically verified. Some indicators may function as leading indicators, while others represent current ecological conditions, outcomes or improvements. Determining how these indicators are related, regardless of the theoretical perspective applied, is an important area for future SC research.

Integrating the taxonomy into corporate sustainability reporting may allow firms to better identify areas for improvement, establish targets and measure progress in biodiversity protection across their SCs. Further research examining which biodiversity indicators, or categories of indicators, are most frequently used in corporate sustainability reporting could provide further validation for the taxonomy. Such studies may also reveal whether social, economic or environmental biodiversity indicators play the most significant role in practice.

Indicator selection must ultimately be justified using both quantitative and qualitative criteria and should vary depending on the environmental context of each sector. Analytical techniques that support decision-making regarding which indicators are most applicable in different contexts are another promising area for future research.

The taxonomy also implies a cyclical approach to reviewing and refining selected indicators: as new scientific knowledge is generated and measurement tools are developed, researchers should consider the appropriateness and quantifiability of the identified indicators. This process helps ensure that sustainability procedures are current and capable of capturing the relationships between SC operations and biodiversity outcomes.

Environmental changes and shifting resource demands may also alter the relevance of particular indicators across industrial sectors. A comparable situation arises for other environmental SC issues, such as net-zero measurement, where adjustments in stakeholder and industry requirements are needed (Singh, 2025). To avoid misinterpreting biodiversity impacts, research examining indicators within specific geographic or sectoral contexts is needed. Given the complexity of biodiversity management, applying critical success factor theory to identify and prioritize the most relevant indicators across SCs may provide valuable insights (Bai and Sarkis, 2014; Kouhizadeh et al., 2022).

SC location and context are also important factors to take into consideration (Boscari et al., 2024). For example, extraction stages often involve direct ecological impacts and may require more direct measures. Later stages of SCs may rely more heavily on less direct indicators like socioeconomic factors or pollution emissions. Determining where in the SC particular indicators are most useful remains an important research question. The successful implementation of the taxonomy is dependent on the collaboration of cross-disciplinary teams of sustainability professionals, data scientists, ecologists and industry experts. Such collaboration can support the interpretation of indicators, the identification of practical implications and the development of actionable recommendations that strengthen both ecological integrity and SC resilience.

This combined theoretical and practical perspective ensures that the taxonomy remains flexible and open to improvements as new environmental risks emerge and societal awareness for sustainable business practices grows. Further research on how the indicators can be operationalized as measurement scales within established frameworks, such as Natural Capital Accounting or NRDT, would also strengthen the analytical foundations of the taxonomy. Although the taxonomy provides a structured framework for selecting biodiversity indicators, its real-world effectiveness ultimately depends on selecting measurable indicators that are relevant to each organization’s operational context. When applied appropriately, the taxonomy can support both managerial decision-making and policy development aimed at promoting sustainable and resilient SCs.

Biodiversity is immensely vital to ecosystem health and the long-term viability of the global economy. For many sectors, it is also a key determinant of long-term SC resilience, as ecosystem degradation can destabilize production yields, increase sourcing risks and trigger regulatory or reputational shocks. SC managers play an important role in reducing the negative effects of industrial activities on biodiversity; however, the lack of a standardized biodiversity taxonomy has made it difficult to compare indicators and develop effective management strategies. This article has highlighted the importance of incorporating biodiversity considerations into SC management practices and proposed a comprehensive taxonomy of potential biodiversity indicators. Nevertheless, advances in SSC practices can only come about through active participation from industry actors and clear policy support. Implementing biodiversity indicators remains challenging due to data scarcity and the complexity of balancing economic, social and environmental objectives.

The taxonomy of biodiversity indicators introduced in this study addresses a blind spot in existing SSC performance indicator frameworks by explicitly incorporating biodiversity into sustainability performance assessments in SCs. By integrating biodiversity measurements with established standards, theoretical frameworks and practical management principles, the taxonomy provides a structured yet flexible foundation applicable across diverse industries and geographic contexts. The study differs from previous frameworks by applying an AI-assisted approach to identify 249 biodiversity indicators, including measures related to genetic diversity and ecosystem services, supported by literature from conservation science, economics and ecology. Additionally, the study proposes relationships among indicator categories through a DSPIR-informed framework.

Existing frameworks and tools, such as GRI 304, often provide only limited indicators, sometimes restricted to binary reporting of whether biodiversity is referenced at all (Chuzairi and Sutopo, 2024). Similarly, frameworks such as the TNFD tend to focus solely on broad nature-related risks at the organizational level (Popovski, 2023). In contrast, the taxonomy developed in this study organizes indicators into 10 categories and 30 subcategories, with the goal of connecting theoretical models with real-world SC management practices to provide precise, adaptive recommendations for picking context-specific indicators. This approach advances current sustainability indicator sets and supports improved operational decision-making and environmental accountability on a global scale. In this sense, the paper extends the biodiversity management and accounting literature by integrating otherwise fragmented disclosure-, valuation- and assessment-oriented approaches into a supply-chain-oriented taxonomy. It also extends the sustainable SC management literature by showing how biodiversity indicators can inform supplier evaluation, reporting, governance and resilience-oriented decision-making.

The study builds on the extensive body of research regarding sustainability indicators, which has emphasized the importance of context-specific, measurable and adaptable metrics. The taxonomy provides a structured tool that industry practitioners and future empirical research can use to select appropriate biodiversity indicators through a systematic process tailored to specific SC needs. The taxonomy’s hierarchical organization, ranging from genetic diversity to ecosystem conditions and socioeconomic factors, mirrors the layered complexity of biodiversity systems. This structure highlights that a single set of indicators is rarely sufficient to capture the full spectrum of biodiversity dynamics in business operations; instead, a flexible, multi-level system is necessary. The taxonomy provides professionals with a structured list of biodiversity indicators that can be selected based on data availability, allowing them to choose the ones best suited to their situation. The proposed five-step roadmap further illustrates how indicator selection can be integrated into existing SC processes and sustainability reporting systems.

This study represents an initial step toward integrating biodiversity indicators across SCs. Although Section 5.1 presents an illustrative application, the framework has not yet been empirically tested within a specific company or SC, which remains an important limitation and a priority for future research. Continued scientific validation and empirical refinement of the taxonomy will be necessary as new ecological knowledge and technological tools become available.

One of the most significant challenges in biodiversity measurement is the lack of high-quality data across different ecosystems and geographic regions (Freitas et al., 2020). Expanding data collection efforts, potentially through citizen science initiatives, can improve data coverage and foster local support, provided that appropriate quality control protocols are implemented (De Sherbinin et al., 2021; König et al., 2021). Future research should concentrate on improving data collection and analysis, refining methodological approaches and improving communication and engagement. Furthermore, it is critical to carefully weigh the trade-offs between economic development and environmental protection to strike a balance between biodiversity conservation and economic and social objectives (Feiock and Stream, 2001; Klein et al., 2008). Accordingly, contextual factors such as ecosystem type, industry and objectives should be considered when selecting suitable indicators (Teixeira et al., 2016). Greater industry-level standardization will also be necessary to improve the comparability and reliability of the proposed taxonomy. By addressing these issues, SC managers and policymakers can strengthen biodiversity conservation efforts for the purpose of creating a more sustainable future.

The authors gratefully acknowledge the assistance of technology in refining the manuscript’s language, shortening longer text and improving the clarity and flow of the presentation; it was also helpful in terms of brainstorming to address the reviewers’ comments. All AI-suggested text was carefully reviewed, verified and, where necessary, rewritten by the authors, who remain fully responsible for the accuracy and integrity of the final paper.

The supplementary material for this article can be found online.