Technical Note on Steps in Baseline Quantification for ARR Carbon Finance Projects using Remote Sensing and GIS
Abstract
This technical note outlines a systematic approach to baseline quantification for ARR (Afforestation, Reforestation, and Revegetation) carbon finance projects using advanced remote sensing (RS) and GIS methodologies. This approach particularly addresses India'sfragmented landscapes, aiming to integrate small and marginal farmers into carbon finance markets, thus enhancing agroforestry potential and providing additional income generation. The challenges in meeting common practice criteria and additionality, as per VERRA/Gold Standard methodologies, are also discussed, offering recommendations to improve inclusivity and applicability.
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Introduction
The increasing emphasis on climate change mitigation has brought afforestation, reforestation, and revegetation (ARR) projects to the forefront as effective tools for sequestering atmospheric carbon dioxide. These projects form a crucial part of global and national climate action strategies. However, ensuring their success requires robust methodologies for baseline quantification and eligibility assessment, particularly in countries like India, where the landscape is highly fragmented, and smallholder participation is key. India'sagricultural landscape is characterized by over 86.1% small and marginal landholdings, making it one of the most fragmented in the world. While this fragmentation poses challenges in scaling carbon finance projects, it also presents an opportunity to integrate millions of small and marginal farmers into these initiatives. Existing methodologies, such as those by VERRA and the Gold Standard, provide a strong framework for ARR projects but often fall short in addressing the complexities of fragmented landscapes and ensuring additionality and inclusivity. This technical note proposes a systematic approach to baseline quantification, leveraging high-resolution RS-GIS tools to overcome these challenges. By tailoring the methodology to India'sunique landscape, this work highlights how agroforestry potential can be utilized not just for environmental benefits but also for generating additional income for smallholders. Furthermore, the methodology addresses critical gaps in existing frameworks, such as the common practice criteria and additionality, ensuring that projects are both credible and scalable. The revised approach aims to bridge the gap between existing standards and the practical realities of fragmented agricultural landscapes. It emphasizes the role of data-driven models and spatial analyses in creating transparent, scalable, and inclusive ARR carbon finance projects that align with national and international goals.
Conclusion
Integrating RS-GIS with linear regression and machine learning enhances baseline quantification for ARR projects in India. This method aligns with eligibility and additionality requirements by providing precise, retrospective carbon stock assessments and enabling continuous monitoring. For India’sagroforestry landscape, where fragmented holdings and smallholder participation are prevalent, this approach offers a practical, transparent, and scalable solution that could significantly improve the confidence of carbon markets. By adopting RS-GIS-based baseline quantification, India can facilitate access to carbon finance for a larger pool of smallholders, transforming agroforestry into a viable tool for sustainable development and climate mitigation. Our enhanced methodology not only addresses the limitations of existing frameworks but also ensures that India’ssmall and marginal farmers are integral beneficiaries of carbon finance projects. By tailoring the approach to fragmented landscapes, this work contributes to both sustainable development and equitable growth.
