Towards AI-driven Integrative Emissions Monitoring & Management for Nature-Based Climate Solutions
This work addresses the need for integrated decision support in climate policy for policymakers and stakeholders, though it appears incremental as it builds on existing AI methods without introducing new paradigms.
The paper tackles the problem of siloed AI applications in nature-based climate solutions by proposing an integrative framework that combines AI-aided wildfire detection, carbon stock assessment, and other decision support models, aiming to enhance real-world climate policy-making through data exchange and uncertainty mitigation.
AI has been proposed as an important tool to support several efforts related to nature-based climate solutions such as the detection of wildfires that affect forests and vegetation-based offsets. While this and other use-cases provide important demonstrative value of the power of AI in climate change mitigation, such efforts have typically been undertaken in silos, without awareness of the integrative nature of real-world climate policy-making. In this paper, we propose a novel overarching framework for AI-aided integrated and comprehensive decision support for various aspects of nature-based climate decision-making. Focusing on vegetation-based solutions such as forests, we demonstrate how different AI-aided decision support models such as AI-aided wildfire detection, AI-aided vegetation carbon stock assessment, reversal risk mitigation, and disaster response planning can be integrated into a comprehensive framework. Rather than being disparate elements, we posit that the exchange of data and analytical results across elements of the framework, and careful mitigation of uncertainty propagation will provide tremendous value relative to the status-quo for real-world climate policy-making.