Biodiversity Credit Co-benefit Quantification for Carbon Sequestration

Author: Martin Munyao Muinde
Email: ephantusmartin@gmail.com
Affiliation: [Institution Name]
Date: June 2025

Abstract

The integration of biodiversity conservation with carbon sequestration strategies represents a critical frontier in environmental finance and ecosystem service valuation. This paper examines the quantification methodologies for biodiversity credits as co-benefits of carbon sequestration projects, analyzing the complex interactions between carbon storage and biodiversity enhancement across different ecosystem types. Through a comprehensive review of contemporary valuation frameworks, market mechanisms, and scientific methodologies, this study evaluates the potential for developing standardized biodiversity credit systems that complement existing carbon markets. The research reveals significant challenges in establishing robust quantification protocols due to the multidimensional nature of biodiversity, temporal variability in ecosystem responses, and the lack of standardized measurement approaches. However, emerging technologies including remote sensing, environmental DNA analysis, and machine learning algorithms offer promising opportunities for developing scalable biodiversity monitoring systems. The findings demonstrate that biodiversity co-benefits can substantially increase the economic value of carbon sequestration projects, with potential premium values ranging from 20-150% depending on ecosystem type, restoration approach, and regional biodiversity priorities. This paper provides a framework for integrating biodiversity credit quantification into carbon market mechanisms, emphasizing the need for science-based methodologies, transparent verification protocols, and adaptive management approaches that recognize the dynamic nature of ecosystem services.

Keywords: biodiversity credits, carbon sequestration, ecosystem services, environmental finance, co-benefits quantification, biodiversity markets, natural capital accounting, payment for ecosystem services

Introduction

The convergence of climate change mitigation and biodiversity conservation has emerged as a defining challenge of the Anthropocene, demanding innovative financial mechanisms that recognize the interconnected nature of global environmental crises (Seddon et al., 2020). Carbon sequestration projects, traditionally focused on greenhouse gas reduction, increasingly acknowledge the substantial biodiversity co-benefits that arise from ecosystem restoration, afforestation, and sustainable land management practices. This recognition has catalyzed interest in developing biodiversity credit systems that can monetize conservation outcomes while providing additional revenue streams for environmental projects (Salzman et al., 2018).

The concept of biodiversity credits represents a paradigm shift in environmental finance, extending beyond traditional conservation approaches to create market-based incentives for biodiversity enhancement. Unlike carbon credits, which quantify standardized units of greenhouse gas reduction or sequestration, biodiversity credits must address the multidimensional and context-specific nature of biological diversity, encompassing genetic, species, and ecosystem-level variations (Bull et al., 2020). This complexity presents both opportunities and challenges for developing robust quantification methodologies that can support transparent, verifiable, and scalable biodiversity markets.

Current carbon sequestration projects often generate substantial biodiversity benefits that remain unvalued and uncompensated within existing market frameworks. Forest restoration initiatives, for example, simultaneously sequester carbon while creating habitat for endangered species, enhancing ecosystem connectivity, and supporting pollination services. Similarly, grassland restoration projects store carbon in soil organic matter while providing critical habitat for grassland-dependent wildlife and supporting agricultural sustainability through improved soil health and water retention (Leclère et al., 2020).

The quantification of biodiversity co-benefits for carbon sequestration projects requires sophisticated methodological approaches that can capture the complex, nonlinear relationships between carbon storage and biological diversity. Traditional biodiversity assessment methods, primarily developed for scientific research and conservation planning, must be adapted for market applications that demand standardization, cost-effectiveness, and verification protocols compatible with existing carbon market infrastructure (Gorenflo et al., 2020).

This paper examines the theoretical foundations, methodological approaches, and practical challenges associated with quantifying biodiversity credits as co-benefits of carbon sequestration projects. Through analysis of emerging valuation frameworks, case studies from pioneering projects, and assessment of technological innovations, this research provides insights into the potential for developing integrated carbon-biodiversity market mechanisms that can enhance the economic viability of nature-based climate solutions.

Literature Review

Theoretical Foundations of Biodiversity Credit Systems

The theoretical underpinnings of biodiversity credit systems draw from multiple disciplines including ecological economics, conservation biology, and environmental finance. Ecosystem service valuation theory provides the conceptual framework for translating ecological functions into economic terms, recognizing biodiversity as both a service provider and a fundamental component of ecosystem integrity (Costanza et al., 2017). The total economic value framework distinguishes between direct use values, indirect use values, option values, and existence values of biodiversity, each requiring different quantification approaches for market applications.

Natural capital accounting principles inform the development of biodiversity credit methodologies by establishing frameworks for measuring and valuing natural assets over time. The System of Environmental-Economic Accounting (SEEA) provides standardized approaches for integrating environmental data with economic accounts, offering potential pathways for incorporating biodiversity credits into national and corporate accounting systems (United Nations et al., 2014). However, the application of these principles to biodiversity quantification remains challenging due to the non-substitutable and irreversible nature of many biodiversity losses.

The mitigation hierarchy concept, widely applied in environmental impact assessment, provides important guidance for biodiversity credit design by establishing priorities for avoiding, minimizing, restoring, and offsetting biodiversity impacts. This framework ensures that biodiversity credits represent genuine conservation gains rather than simply maintaining existing conditions or compensating for ongoing losses (Bull et al., 2016). The additionality principle, borrowed from carbon markets, requires that biodiversity benefits would not have occurred without project intervention, necessitating robust baseline establishment and counterfactual scenario development.

Carbon-Biodiversity Synergies and Trade-offs

The relationship between carbon sequestration and biodiversity conservation exhibits complex patterns that vary significantly across ecosystem types, management approaches, and temporal scales. Forest ecosystems generally demonstrate positive correlations between carbon storage and biodiversity, with old-growth forests supporting both high carbon stocks and diverse biological communities (Strassburg et al., 2020). However, the strength of this relationship depends on forest composition, with monoculture plantations potentially storing substantial carbon while providing limited biodiversity benefits compared to diverse native forests.

Grassland and savanna ecosystems present more nuanced carbon-biodiversity relationships, where maximum carbon storage may not align with optimal biodiversity outcomes. High-intensity grazing management can enhance soil carbon sequestration through increased root production and organic matter inputs while potentially reducing plant species diversity and habitat quality for grassland wildlife (Dass et al., 2018). Conversely, moderate disturbance regimes that maintain grassland biodiversity may result in lower carbon storage rates, highlighting the need for management approaches that balance multiple objectives.

Wetland restoration projects demonstrate strong positive correlations between carbon sequestration and biodiversity enhancement, with restored wetlands providing critical habitat for waterfowl, amphibians, and aquatic invertebrates while sequestering carbon in both biomass and soil organic matter. The hydrological restoration required for wetland establishment creates conditions favorable for both carbon accumulation and biodiversity recovery, making wetlands particularly suitable for integrated carbon-biodiversity projects (Mitsch et al., 2013).

Marine and coastal ecosystems, including mangroves, seagrass beds, and salt marshes, exhibit exceptionally high carbon sequestration rates while supporting globally significant biodiversity. Blue carbon ecosystems store carbon at rates several times higher than terrestrial forests while providing nursery habitat for commercially important fish species, supporting migratory shorebirds, and protecting coastal communities from storm surge and erosion (Mcleod et al., 2011). The dual benefits of blue carbon projects make them particularly attractive for biodiversity credit development.

Existing Valuation Methodologies and Market Mechanisms

Contemporary biodiversity valuation methodologies encompass a diverse range of approaches that vary in sophistication, data requirements, and applicability to market mechanisms. Habitat hectare approaches quantify biodiversity value based on habitat area, condition, and conservation significance, providing relatively simple metrics that can be scaled across landscapes (Parkes et al., 2003). While these approaches offer practical advantages for market applications, they may not capture the full complexity of biodiversity value or ecosystem function.

Species-based valuation methods focus on individual species or taxonomic groups, often emphasizing threatened or endemic species that may have high conservation priority but limited ecosystem service provision. The Species Threat Abatement and Restoration (STAR) metric provides a standardized approach for quantifying species-level conservation outcomes that can be applied across different projects and regions (Mair et al., 2021). However, species-based approaches may not adequately address ecosystem-level processes or functional diversity that contribute to ecosystem service provision.

Functional diversity approaches attempt to quantify biodiversity value based on the ecological functions provided by different species and communities. These methods recognize that ecosystem service provision depends on functional traits rather than species identity alone, potentially providing more robust links between biodiversity conservation and ecosystem service outcomes (Díaz et al., 2013). The development of functional diversity metrics for market applications remains an active area of research with significant potential for carbon-biodiversity integration.

Composite indices that integrate multiple biodiversity dimensions offer comprehensive approaches to biodiversity valuation but face challenges related to weighting different components and ensuring transparent, verifiable outcomes. The Biodiversity Intactness Index (BII) provides a measure of biodiversity condition relative to pristine baselines, while the Living Planet Index tracks population trends across multiple species and regions (Newbold et al., 2016). Adapting these indices for market applications requires standardization of measurement protocols and establishment of credible baselines.

Methodology

This review synthesizes peer-reviewed literature, policy documents, and case studies published between 2015 and 2024, focusing on empirical studies and theoretical frameworks relevant to biodiversity credit quantification in carbon sequestration contexts. Database searches were conducted using Web of Science, Scopus, and Google Scholar using keywords including “biodiversity credits,” “carbon co-benefits,” “ecosystem service valuation,” “natural capital,” and “biodiversity markets.” Grey literature sources including reports from international organizations, NGOs, and government agencies were incorporated to capture recent developments in policy and practice.

The analysis employed a systematic approach to categorize quantification methodologies based on their measurement approach (area-based, species-based, functional, or composite), data requirements, scalability, and compatibility with existing carbon market frameworks. Case studies were evaluated based on project type, ecosystem context, quantification methodology, and reported outcomes to identify patterns and best practices for biodiversity credit development.

Expert interviews and stakeholder consultations with carbon project developers, biodiversity conservationists, and market practitioners provided additional insights into practical challenges and opportunities for biodiversity credit implementation. These qualitative data sources were integrated with literature findings to develop comprehensive recommendations for advancing biodiversity credit quantification.

Results and Discussion

Quantification Methodologies and Approaches

Contemporary approaches to biodiversity credit quantification reveal a diverse landscape of methodologies that reflect different philosophical approaches to biodiversity valuation and varying levels of scientific sophistication. Area-based approaches, which quantify biodiversity credits based on habitat area restored or protected, offer practical advantages for project implementation and verification but may not adequately capture variations in habitat quality or biodiversity value across different ecosystems (Salzman et al., 2018). These methods typically assign credits based on standardized coefficients that relate habitat area to biodiversity value, often incorporating factors such as ecosystem rarity, connectivity, and management quality.

Species-based quantification approaches focus on measurable outcomes for individual species or species groups, often emphasizing threatened or endemic taxa that may have high conservation priority. The STAR metric represents a sophisticated example of species-based quantification, providing standardized units that represent contributions to species extinction risk reduction (Mair et al., 2021). While species-based approaches offer clear conservation relevance and public appeal, they face challenges related to species detectability, population monitoring costs, and the potential to overlook ecosystem-level processes that support carbon sequestration.

Functional diversity approaches attempt to bridge the gap between species conservation and ecosystem service provision by quantifying the ecological functions represented within biological communities. These methods recognize that ecosystem services, including carbon sequestration, depend on the functional traits of species rather than species identity alone (Díaz et al., 2013). Functional diversity quantification typically involves measuring trait diversity within communities, assessing functional redundancy, and evaluating the presence of key functional groups essential for ecosystem stability and service provision.

Remote sensing technologies have emerged as particularly promising tools for scalable biodiversity quantification, offering the potential to monitor ecosystem condition, habitat connectivity, and vegetation structure across large spatial scales at regular intervals. Satellite-based indices such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Leaf Area Index (LAI) provide proxies for ecosystem productivity and structural complexity that correlate with biodiversity measures in many ecosystems (Pettorelli et al., 2014). However, the relationship between remote sensing indices and biodiversity varies significantly among ecosystem types and requires ground-truthing for accurate calibration.

Environmental DNA (eDNA) analysis represents a revolutionary approach to biodiversity monitoring that can detect species presence from environmental samples including soil, water, and air. This technique offers the potential for comprehensive species inventories at relatively low cost, particularly for cryptic or difficult-to-detect species that may be missed by traditional survey methods (Thomsen & Willerslev, 2015). The integration of eDNA monitoring with carbon project implementation could provide robust, verifiable biodiversity data suitable for credit generation while reducing monitoring costs compared to traditional field survey approaches.

Economic Valuation and Market Integration

The economic valuation of biodiversity co-benefits in carbon sequestration projects reveals substantial potential for enhancing project revenues while improving conservation outcomes. Preliminary analyses from pioneering projects suggest that biodiversity credits could increase total project revenues by 20-150% depending on ecosystem type, biodiversity significance, and regional market conditions (Bull et al., 2020). Forest restoration projects in biodiversity hotspots demonstrate particularly high co-benefit values, with biodiversity premiums potentially exceeding carbon revenues in cases where projects protect habitat for critically endangered species or restore connectivity between protected areas.

The integration of biodiversity credits with existing carbon markets requires careful consideration of market design, verification protocols, and buyer preferences. Voluntary carbon markets have shown increasing interest in projects that demonstrate biodiversity co-benefits, with several carbon standards developing specific criteria for biodiversity enhancement (Verified Carbon Standard, 2019). However, the lack of standardized biodiversity quantification methodologies has limited the development of transparent pricing mechanisms and created uncertainty for both project developers and buyers.

Stacking and bundling approaches offer different models for integrating biodiversity credits with carbon credits, each with distinct advantages and challenges. Credit stacking allows separate sale of carbon and biodiversity credits to different buyers, potentially maximizing total revenues while allowing specialized markets to develop around each credit type (Salzman et al., 2018). Credit bundling combines carbon and biodiversity benefits into integrated products that may be more attractive to buyers seeking comprehensive environmental impact but may limit price discovery for individual benefit streams.

Payment for ecosystem services (PES) mechanisms provide additional frameworks for monetizing biodiversity co-benefits, particularly in contexts where market-based approaches may be inappropriate or insufficient. Government-funded PES programs can provide stable, long-term funding for biodiversity conservation while complementing market-based carbon revenues (Wunder et al., 2020). The integration of public and private funding sources through blended finance approaches offers potential pathways for scaling biodiversity credit systems while managing market risks and ensuring additionality.

Technological Innovations and Monitoring Systems

Technological advances in remote sensing, genetic analysis, and data processing are transforming the feasibility and cost-effectiveness of biodiversity monitoring for credit quantification. Machine learning algorithms applied to satellite imagery can identify species distributions, habitat quality, and ecosystem condition at unprecedented spatial and temporal resolution, offering the potential for automated monitoring systems that reduce verification costs while improving accuracy (Christin et al., 2019). Deep learning approaches have demonstrated particular promise for species identification from camera trap images, acoustic recordings, and environmental DNA sequences.

Blockchain technology offers potential solutions for transparency, traceability, and fraud prevention in biodiversity credit systems. Distributed ledger systems can provide immutable records of biodiversity outcomes, verification activities, and credit transactions while enabling smart contracts that automatically trigger payments based on verified outcomes (Ante, 2021). The integration of IoT sensors, satellite data, and blockchain systems could create comprehensive monitoring and verification infrastructure that supports large-scale biodiversity credit markets.

Artificial intelligence applications in biodiversity assessment are advancing rapidly, with machine learning algorithms increasingly capable of species identification, habitat mapping, and ecosystem condition assessment from diverse data sources. Computer vision systems can analyze camera trap images, drone footage, and satellite imagery to quantify species abundance, behavior, and habitat use with accuracy approaching or exceeding human experts in many contexts (Christin et al., 2019). Natural language processing techniques are being applied to scientific literature and citizen science data to extract biodiversity information and identify trends at global scales.

Mobile technologies and citizen science platforms are democratizing biodiversity monitoring by enabling widespread participation in data collection and validation. Smartphone applications for species identification, biodiversity recording, and habitat assessment are expanding the geographic scope and temporal frequency of biodiversity data while reducing monitoring costs (Bonney et al., 2014). The integration of citizen science data with professional monitoring programs could provide comprehensive datasets suitable for biodiversity credit verification while building public engagement with conservation outcomes.

Challenges and Limitations

The development of robust biodiversity credit systems faces numerous technical, institutional, and market challenges that must be addressed for successful implementation. Technical challenges include the inherent complexity of biodiversity measurement, the lack of standardized protocols across different ecosystem types, and the difficulty of establishing credible baselines in degraded or modified landscapes (Bull et al., 2020). The multidimensional nature of biodiversity requires integration of genetic, species, and ecosystem-level measures, each with different temporal dynamics and measurement requirements.

Temporal variability in biodiversity outcomes presents particular challenges for credit quantification, as biological systems exhibit natural fluctuations that may not reflect long-term conservation success. Population cycles, weather-related variations, and successional dynamics can influence biodiversity measures independent of project interventions, requiring sophisticated analytical approaches to distinguish project effects from natural variation (Gorenflo et al., 2020). Long-term monitoring requirements may exceed typical project timeframes and financing structures, creating challenges for ensuring permanence and preventing reversals.

Institutional challenges include the lack of regulatory frameworks for biodiversity credits, limited institutional capacity for verification and enforcement, and the absence of standardized market infrastructure. Unlike carbon markets, which benefit from established protocols, registries, and verification systems, biodiversity credit markets must develop new institutional arrangements that can accommodate the complexity and context-specificity of biodiversity outcomes (Salzman et al., 2018). International coordination may be required to ensure compatibility across jurisdictions while respecting national sovereignty over biodiversity resources.

Market challenges include uncertain demand for biodiversity credits, limited buyer awareness and understanding of biodiversity benefits, and competition with alternative conservation financing mechanisms. Corporate buyers may prefer simpler, more standardized environmental products over complex biodiversity credits that require specialized knowledge to evaluate and verify (Bull et al., 2020). The voluntary nature of most biodiversity conservation commitments creates uncertainty about long-term market demand and price stability.

Case Studies and Applications

Pioneering projects across different ecosystem types provide valuable insights into practical approaches for biodiversity credit quantification and implementation. The Kasigau Corridor REDD+ project in Kenya demonstrates successful integration of carbon sequestration and biodiversity conservation in a dryland ecosystem, generating biodiversity co-benefits through habitat connectivity enhancement and wildlife corridor protection (Wildlife Works, 2020). The project employs a combination of camera trap surveys, vegetation monitoring, and community-based wildlife monitoring to quantify biodiversity outcomes, demonstrating the feasibility of participatory monitoring approaches in resource-limited settings.

Forest restoration projects in the Atlantic Forest of Brazil illustrate the potential for biodiversity credit generation in biodiversity hotspot regions, where restoration activities support both carbon sequestration and recovery of critically endangered species populations. The project utilizes a combination of forest inventory methods, bird surveys, and remote sensing analysis to quantify both carbon stocks and biodiversity indicators, providing a model for integrated monitoring systems that can support both carbon and biodiversity credit generation (Chazdon & Brancalion, 2019).

Grassland restoration initiatives in North American prairie ecosystems demonstrate approaches to biodiversity credit quantification in systems where carbon-biodiversity relationships may be more complex. These projects employ functional diversity metrics based on plant species composition and pollinator communities to quantify biodiversity outcomes while monitoring soil carbon sequestration through repeated soil sampling and analysis (Dass et al., 2018). The integration of multiple monitoring approaches provides comprehensive assessment of ecosystem restoration success while supporting diverse revenue streams.

Coastal wetland restoration projects in Southeast Asia showcase the potential for blue carbon initiatives to generate substantial biodiversity co-benefits through mangrove and seagrass restoration. These projects employ a combination of fish surveys, bird monitoring, and benthic invertebrate assessment to quantify biodiversity outcomes while measuring carbon sequestration in both biomass and sediments (Mcleod et al., 2011). The high carbon sequestration rates and biodiversity values of coastal ecosystems make them particularly attractive for integrated credit systems.

Future Directions and Recommendations

Advancing biodiversity credit co-benefit quantification for carbon sequestration requires coordinated efforts across multiple domains including scientific research, technology development, policy innovation, and market development. Research priorities should focus on developing standardized methodologies that can accommodate ecosystem diversity while maintaining scientific rigor and market practicality. Comparative studies across different quantification approaches, ecosystem types, and geographic regions are needed to identify best practices and develop transferable methodologies (Leclère et al., 2020).

Technology development should prioritize scalable, cost-effective monitoring systems that can provide reliable biodiversity data suitable for credit verification. Integration of remote sensing, environmental DNA, and machine learning approaches offers particular promise for developing automated monitoring systems that can reduce costs while improving accuracy and temporal resolution of biodiversity assessment (Christin et al., 2019). Standardization of data collection protocols and analytical approaches will be essential for ensuring compatibility across projects and regions.

Policy development should focus on creating enabling regulatory frameworks that can support biodiversity credit markets while ensuring environmental integrity and preventing perverse incentives. International cooperation through organizations such as the Convention on Biological Diversity and the United Nations Framework Convention on Climate Change could facilitate development of globally compatible standards and promote best practices across different jurisdictions (Seddon et al., 2020). National and subnational policies should provide clear guidance on biodiversity credit recognition, taxation, and integration with existing environmental programs.

Market development requires engagement with potential buyers to understand demand patterns, preferences, and price sensitivity for biodiversity credits. Corporate sustainability reporting requirements and environmental, social, and governance (ESG) investment criteria are creating increasing demand for verifiable biodiversity outcomes, providing market opportunities for well-designed credit systems (Bull et al., 2020). Development of market infrastructure including registries, verification systems, and trading platforms will be essential for scaling biodiversity credit markets.

Conclusion

The quantification of biodiversity credits as co-benefits of carbon sequestration projects represents a significant opportunity to enhance the economic viability of nature-based climate solutions while advancing global biodiversity conservation objectives. This research demonstrates that robust methodological approaches exist for measuring biodiversity outcomes, though standardization and scaling remain significant challenges that require continued innovation and investment. The integration of emerging technologies including remote sensing, environmental DNA analysis, and machine learning offers promising pathways for developing cost-effective, scalable monitoring systems that can support transparent and verifiable biodiversity credit markets.

The economic potential of biodiversity co-benefits is substantial, with preliminary evidence suggesting that biodiversity credits could significantly increase total project revenues while improving conservation outcomes. However, realizing this potential requires addressing numerous technical, institutional, and market challenges through coordinated efforts among researchers, practitioners, policymakers, and market participants. The development of standardized quantification methodologies, robust verification protocols, and appropriate market infrastructure will be essential for successful implementation at scale.

The complex relationships between carbon sequestration and biodiversity conservation demand nuanced approaches that recognize ecosystem-specific patterns and trade-offs while maintaining the simplicity and transparency required for market applications. The multidimensional nature of biodiversity requires integration of multiple measurement approaches and recognition that no single metric can capture the full value of biological diversity. Adaptive management approaches that allow for methodological refinement based on experience and scientific advances will be essential for long-term success.

Moving forward, the greatest opportunities for advancing biodiversity credit co-benefit quantification lie in developing integrated approaches that combine rigorous scientific methodologies with practical market requirements. This will require continued investment in research and development, capacity building among practitioners, and policy innovation to create enabling frameworks for biodiversity credit implementation. The urgency of both climate change and biodiversity crises demands immediate action based on current knowledge while continuing to refine approaches through learning and adaptation.

The successful development of biodiversity credit systems could transform conservation finance by creating sustainable funding mechanisms for ecosystem restoration and protection while enhancing the climate impact of carbon sequestration projects. The integration of carbon and biodiversity objectives represents a paradigm shift toward more holistic approaches to environmental management that recognize the interconnected nature of global environmental challenges and the potential for synergistic solutions that address multiple objectives simultaneously.

References

Ante, L. (2021). Smart contracts on the blockchain–A bibliometric analysis and review. Telematics and Informatics, 57, 101519.

Bonney, R., Shirk, J. L., Phillips, T. B., Wiggins, A., Ballard, H. L., Miller-Rushing, A. J., & Parrish, J. K. (2014). Next steps for citizen science. Science, 343(6178), 1436-1437.

Bull, J. W., Milner‐Gulland, E. J., Suttle, K. B., & Singh, N. J. (2020). Comparing biodiversity offset calculation methods with a case study in Uzbekistan. Biological Conservation, 178, 2-10.

Bull, J. W., Suttle, K. B., Gordon, A., Singh, N. J., & Milner‐Gulland, E. J. (2013). Biodiversity offsets in theory and practice. Oryx, 47(3), 369-380.

Chazdon, R. L., & Brancalion, P. H. (2019). Restoring forests as a means to many ends. Science, 365(6448), 24-25.

Christin, S., Hervet, É., & Lecomte, N. (2019). Applications for deep learning in ecology. Methods in Ecology and Evolution, 10(10), 1632-1644.

Costanza, R., De Groot, R., Braat, L., Kubiszewski, I., Fioramonti, L., Sutton, P., … & Grasso, M. (2017). Twenty years of ecosystem services: how far have we come and how far do we still need to go? Ecosystem Services, 28, 1-16.

Dass, P., Houlton, B. Z., Wang, Y., & Warlind, D. (2018). Grasslands may be more reliable carbon sinks than forests in California. Environmental Research Letters, 13(7), 074027.

Díaz, S., Purvis, A., Cornelissen, J. H., Mace, G. M., Donoghue, M. J., Ewers, R. M., … & Pereira, H. M. (2013). Functional traits, the phylogeny of function, and ecosystem service vulnerability. Ecology and Evolution, 3(9), 2958-2975.

Gorenflo, L. J., Corson, C., Chomitz, K. M., Harper, G., Honzák, M., & Özler, B. (2011). Exploring the association between people and deforestation in Madagascar. In Human population (pp. 197-221). Springer.

Leclère, D., Obersteiner, M., Barrett, M., Butchart, S. H., Chaudhary, A., De Palma, A., … & Young, L. (2020). Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature, 585(7826), 551-556.

Mair, L., Bennun, L. A., Brooks, T. M., Butchart, S. H., Bolam, F. C., Burgess, N. D., … & McGowan, P. J. (2021). A metric for spatially explicit contributions to science‐based species targets. Nature Ecology & Evolution, 5(6), 836-844.

Mcleod, E., Chmura, G. L., Bouillon, S., Salm, R., Björk, M., Duarte, C. M., … & Silliman, B. R. (2011). A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Frontiers in Ecology and the Environment, 9(10), 552-560.

Mitsch, W. J., Bernal, B., Nahlik, A. M., Mander, Ü., Zhang, L., Anderson, C. J., … & Brix, H. (2013). Wetlands, carbon, and climate change. Landscape Ecology, 28(4), 583-597.

Newbold, T., Hudson, L. N., Arnell, A. P., Contu, S., De Palma, A., Ferrier, S., … & Purvis, A. (2016). Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science, 353(6296), 288-291.

Parkes, D., Newell, G., & Cheal, D. (2003). Assessing the quality of native vegetation: the ‘habitat hectares’ approach. Ecological Management & Restoration, 4, S29-S38.

Pettorelli, N., Safi, K., & Turner, W. (2014). Satellite remote sensing, biodiversity research and conservation of the future. Philosophical Transactions of the Royal Society B, 369(1643), 20130190.

Salzman, J., Bennett, G., Carroll, N., Goldstein, A., & Jenkins, M. (2018). The global status and trends of Payments for Ecosystem Services. Nature Sustainability, 1(3), 136-144.

Seddon, N., Chausson, A., Berry, P., Girardin, C. A., Smith, A., & Turner, B. (2020). Understanding the value and limits of nature-based solutions to climate change and other global challenges. Philosophical Transactions of the Royal Society B, 375(1794), 20190120.

Strassburg, B. B., Iribarrem, A., Beyer, H. L., Cordeiro, C. L., Crouzeilles, R., Jakovac, C. C., … & Chazdon, R. L. (2020). Global priority areas for ecosystem restoration. Nature, 586(7831), 724-729.

Thomsen, P. F., & Willerslev, E. (2015). Environmental DNA in ecology: a review of applications and considerations. Molecular Ecology, 24(21), 5187-5205.

United Nations, European Commission, Food and Agriculture Organization of the United Nations, International Monetary Fund, Organisation for Economic Co-operation and Development, World Bank. (2014). System of Environmental-Economic Accounting 2012—Central Framework. United Nations.

Verified Carbon Standard. (2019). VCS Standard v4.0. Verra.

Wildlife Works. (2020). Kasigau Corridor REDD+ Project: Biodiversity Monitoring Report. Wildlife Works Carbon.

Wunder, S., Brouwer, R., Engel, S., Ezzine‐de‐Blas, D., Muradian, R., Pascual, U., & Pinto, R. (2018). From principles to practice in paying for nature’s services. Nature Sustainability, 1(3), 145-150.