An approach for quantifying the Social Impact of Carbon Credit Projects and developing a GenAI Tool for UN SDG Claims
- Publication Type:
- Thesis
- Issue Date:
- 2024
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Climate change has become one of the most serious problems of our time, affecting not only the environment but also economies, societies, and human well-being. Governments and industries across the world are under pressure to reduce greenhouse gas emissions, and one widely used approach has been the adoption of carbon credit mechanisms. These systems allow organisations to earn carbon credits by reducing or capturing emissions, which can then be traded with others seeking to offset their carbon footprint. In theory, this creates a market-based incentive for emission reduction without directly harming economic growth.
However, carbon credit projects are usually assessed almost entirely on the basis of how much carbon they reduce or store. This narrow focus ignores the fact that such projects operate within real communities and real social systems. In many cases, carbon credit initiatives influence employment, income stability, access to resources, health outcomes, and long-term development opportunities for local populations. When these effects are not properly considered, projects may meet technical emission targets while offering limited benefits to host communities or, in some cases, contributing to social exclusion and inequality.
This research argues that carbon credit evaluation needs to move beyond carbon accounting alone. To address this gap, the study integrates the United Nations Sustainable Development Goals (UN SDGs) into the assessment of carbon credit projects. The SDGs provide a widely accepted framework that captures social, economic, and environmental dimensions of development, including poverty reduction, education, gender equality, decent work, and climate action. Incorporating these goals into project evaluation allows for a more realistic and fair assessment of overall impact.
The first objective of this research is to develop an integrated assessment framework that evaluates both environmental and social outcomes of carbon credit projects using SDG-based indicators. Rather than treating social impacts as secondary, this framework places them alongside emission reduction outcomes.
The second objective focuses on building and validating statistical and machine learning models that can quantify and predict the social impacts of carbon credit initiatives. These models will use socio-economic and environmental data from diverse projects and regions to support more informed planning and decision-making.
The final objective is the development of a GenAI Auditor, an AI-based system designed to review project documentation and verify reported SDG contributions. This tool aims to improve transparency, accountability, and trust within carbon markets by reducing unsupported or exaggerated claims.
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