Skip to main content
Intended for healthcare professionals

Special Issue: Measuring Biodiversity Risk in Financial Markets: Methodological Advances and Empirical Applications

The Journal of Economic and Social Measurement (JESM) seeks submissions for a special issue on Measuring Biodiversity Risk in Financial Markets: Methodological Advances and Empirical Applications. The deadline for submission is 30 June 2026.

The accelerating loss of global biodiversity poses fundamental challenges to economic systems and financial markets worldwide. The Kunming-Montreal Global Biodiversity Framework underscores the critical dependence of economic activities on ecosystem services, calling for the systematic integration of biodiversity-related risks into financial decision-making frameworks. Unlike climate change, which has garnered substantial attention in financial economics, biodiversity risk remains significantly understudied despite its profound implications for economic stability and sustainable development (Garel et al., 2024; Giglio et al., 2025).

Recent pioneering studies have begun to illuminate the financial materiality of biodiversity risk. Garel et al. (2024) provide evidence that investors care about biodiversity, with biodiversity-related information affecting stock market valuations. Giglio et al. (2025) establish a comprehensive framework for understanding biodiversity risk in financial contexts, demonstrating its distinct characteristics from climate risk. At the firm level, Li et al. (2025) document significant impacts of biodiversity risk on corporate efficiency, while Duong et al. (2025) reveal its influence on debt maturity structures. Coqueret et al. (2025) identify a biodiversity premium in asset pricing, suggesting that markets are beginning to price biodiversity-related exposures. Adamolekun (2024) links firm-level biodiversity risk to bankruptcy probability, highlighting the systemic implications of ecological degradation. Particularly noteworthy, Zeng et al. (2025) employ advanced quantile-on-quantile methods to uncover substantial cross-national heterogeneity in how biodiversity risk affects stock markets globally, demonstrating the power of sophisticated econometric techniques in revealing complex biodiversity-finance relationships.

Despite these important advances, fundamental measurement challenges persist in biodiversity finance. How should biodiversity risk be quantified across different spatial scales and temporal horizons? What data sources and computational methods are most appropriate for constructing biodiversity risk indicators? How can we integrate traditional ecological data with emerging alternative data sources such as satellite imagery, corporate disclosures, and social media sentiment? What statistical and econometric techniques, including machine learning approaches and advanced quantile-based methods, best capture the nonlinear, threshold-based nature of biodiversity-financial market relationships? These questions align precisely with the Journal of Economic and Social Measurement's mission to advance quantitative methodologies for measuring economic and social phenomena.

A substantial research void exists regarding the employment of sophisticated approaches including artificial intelligence, machine learning, deep learning techniques, and advanced econometric frameworks in quantifying and controlling biodiversity-related risks within financial market contexts. Scholars specialising in academic modelling have comprehensively utilised machine learning algorithms for economic and financial examination across diverse markets, encompassing price forecasting, categorisation, prediction methodologies and risk control procedures. Nevertheless, deploying these technological innovations for biodiversity risk quantification represents an underexplored territory in contemporary research literature. The distinctive characteristics of ML methodologies compared with traditional econometric frameworks reveal certain motivations behind the increasing adoption of computational approaches within sustainability finance disciplines. Furthermore, challenges concerning uncertainty quantification procedures, model interpretability requirements, data integration complexities, and reproducibility standards in biodiversity risk quantification demand immediate consideration from academic researchers. Considering the critical importance of biodiversity preservation for sustainable finance objectives, the quantification difficulties confronting researchers, and the possibilities presented by sophisticated computational and statistical methodologies, this invitation for manuscript submissions encourages enhanced research concentration towards exploring how quantitative approaches can improve our comprehension of biodiversity-financial market interconnections. The themed issue entitled "Measuring Biodiversity Risk in Financial Markets" provides scholars with a forum to examine recent innovations in biodiversity risk quantification, data science implementations, and empirical finance applications. This themed issue addresses the subsequent topics as an illustrative rather than exhaustive catalogue, which authors may contemplate through experimental and theoretical contributions.

The main topics of interest are but not limited to such as:

  • Development of biodiversity risk indicators and indices at firm, industry, portfolio, and country levels
  • Measuring corporate dependence on ecosystem services and natural capital
  • Quantifying biodiversity footprints across value chains and supply networks
  • Machine learning and AI methods for biodiversity risk prediction and classification
  • Supervised and unsupervised learning approaches for biodiversity risk pattern recognition
  • Deep learning methods for processing satellite imagery and environmental data
  • Natural language processing for analyzing biodiversity-related disclosures and news
  • Explainable AI and interpretability methods for biodiversity risk models
  • Quantile regression and quantile-on-quantile methods for biodiversity-finance linkages
  • Time-frequency analysis and wavelet methods for biodiversity risk dynamics
  • Panel data methods and spatial econometrics for biodiversity risk analysis
  • Causal inference methods for identifying biodiversity shock impacts
  • Data fusion techniques combining ecological, financial, and alternative data sources
  • Real-time biodiversity risk monitoring and forecasting systems
  • Network analysis of biodiversity risk transmission through financial and supply chain networks
  • Systemic risk perspectives on biodiversity and financial stability
  • Cross-national and regional heterogeneity in biodiversity-financial market relationships
  • Biodiversity risk impacts on stock returns, corporate valuation, and asset pricing
  • Biodiversity risk and corporate financial decisions (debt maturity, capital structure, etc.)
  • Biodiversity-related disclosure quality and market reactions
  • Natural capital accounting and ecosystem service valuation frameworks
  • Uncertainty quantification and robustness analysis in biodiversity risk measurement
  • Stress testing and scenario analysis for biodiversity-related financial risks
  • Policy analysis and evaluation of biodiversity-related financial regulations
  • Biodiversity-aware portfolio construction and optimization
  • Trading strategies incorporating biodiversity risk factors
  • Biodiversity risk in emerging markets and developing economies
  • Others – any contributions related to the theme of the special issue
Instructions for authors can be found at: /author-instructions/JEM
Authors should submit a cover letter and a manuscript by 30 June 2026, via the Journal's online submission site. Please see the Author Instructions on the website if you have not yet submitted a paper through the journal's submission system. When submitting, please indicate that the manuscript is intended for the special issue on "VSI: Biodiversity Risk" to ensure that it will be reviewed for this special issue.

Timeline
Important dates for the publication of this special issue are as follows:
  • Submissions deadline: 30 June 2026
  • Peer review period: January 30 2026 to November 30 2026
  • Expected publication: Q2 2027 Istanbul Review.
Special Issue Guest Editors
Mohammad Abedin, Ph.D. (Managing Guest Editor) School of Management, Swansea University, UK ([email protected])
Hongjun Zeng, Ph.D. (Associate Guest Editor)Department of Finance, College of Finance Nanjing Agricultural University, Nanjing, China ([email protected])

References
  • Adamolekun, G. (2024). Firm biodiversity risk, climate vulnerabilities, and bankruptcy risk. Journal of International Financial Markets, Institutions and Money, 97, 102075.
  • Coqueret, G., Giroux, T., & Zerbib, O. D. (2025). The biodiversity premium. Ecological Economics, 228, 108435.
  • Duong, K. T., Nguyen, T. T., & Tram, H. T. X. (2025). Biodiversity risk and corporate debt maturity.International Review of Financial Analysis, 104556.
  • Garel, A., Romec, A., Sautner, Z., & Wagner, A. F. (2024). Do investors care about biodiversity? Review of Finance, 28(4), 1151-1186.
  • Giglio, S., Kuchler, T., Stroebel, J., & Zeng, X. (2025). Biodiversity risk. Review of Finance, rfaf063.
  • Li, Y., Liu, X., Canil, J., & Cheong, C. S. (2025). Biodiversity risk and firm efficiency. Finance Research Letters, 71, 106414.
  • Zeng, H., Liu, H., Yan, H., & Ma, S. (2025). Biodiversity Risk and Global Stock Markets: A Cross-National Heterogeneity Analysis Based on Quantile-on-Quantile Methods. Borsa Istanbul Review.

Keep up to date

View all options

Also from Sage