Towards AI-driven Integrative Emissions Monitoring & Management for
  Nature-Based Climate Solutions
Olamide Oladeji Seyed Shahabeddin Mousavi
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.