Machine learning climate finance framework for environmental pollution credits among smallholder farmers in the Western Cape, South Africa

Abstract

ACKGROUND: The growing global population, expected to reach 9.7 billion by 2050, is increasing the demand for sustainable food system practices and resilient food systems. The food system contributes to nearly one-third of global emissions, while smallholder farmers, who survive on farming, face challenges related to climate change and inefficient resource use. Existing research suggests a lack of innovative approaches to reduce food system emissions and waste while improving sustainability in the face of climate change. OBJECTIVES: The study's primary objective is to propose a conceptual climate finance framework to enable small-scale farmers to reduce pollution and generate verifiable environmental pollution credits. The study addresses a significant gap in the literature by proposing a machine learning-based conceptual climate finance framework for an environmental pollution credit system, aimed at small-scale farmers in the Western Cape, South Africa. METHOD: The study adopted an organisational cybernetics systems approach to propose a conceptual climate finance framework. This framework will use machine learning (ML) techniques, such as supervised learning, that can accurately predict and classify new and previously unseen data, learning from labelled datasets collected from various datasets in food systems. FINDINGS: The study's findings suggest that the proposed climate finance framework will not only help optimise farm practices but also allow farmers to earn pollution credits, offering new revenue streams. The study supports the COP29 agenda and drives advancement towards the Sustainable Development Goals (SDGs). The proposed framework contributes to advancing the SDGs and driving meaningful environmental change in the region.