Integrated social-ecological data for regional natural resource management

Adams, Vanessa M.; Allen, Stuart; Steel, Ruth; Stoeckl, Natalie; Larson, Silva

doi:10.1016/j.dib.2023.109806

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ESG is becoming increasingly important in the financial world, as there's a growing recognition that ESG factors can have a significant impact on a company's long-term value. [2] TCFD (Task Force on Climate-related Financial Disclosures) and [3] TNFD (Task Force on Nature-related Financial Disclosures) are both initiatives aimed at providing guidance and standards for companies to report on specific environmental risks (Dye et al, 2021 ; HOEKSTRA, 2022 ; Rudman et al, 2022 ; Chiu et al, 2023 ; Irvine-Broque and Dempsey, 2023 ; Lee et al, 2023 ; Adams et al, 2024 ).…”

Section: Detailed Data Requirements For Green Finance (And How Synthe...mentioning

confidence: 99%

Opportunities for synthetic data in nature and climate finance

Tkachenko

2024

Front. Artif. Intell.

View full text Add to dashboard Cite

This paper delves into the intricacies of synthetic data, emphasizing its growing significance in the realm of finance and more notably, sustainable finance. Synthetic data, artificially generated to simulate real-world data, is being recognized for its potential to address risk management, regulatory compliance, and the innovation of financial products. Especially in sustainable finance, synthetic data offers insights into modeling environmental uncertainties, assessing volatile social and governance scenarios, enhancing data availability, and protecting data confidentiality. This critical review attempts first ever classification of synthetic data production methods, when applied to sustainable finance data gaps, elucidates the methodologies behind its creation, and examines its assurance and controls. Further, it identifies the unique data needs of green finance going forward and breaks down potential risks tied to synthetic data utilization, including challenges from generative AI, input quality, and critical ethical considerations like bias and discrimination.

show abstract