The goal of this project is to develop a scientifically robust framework for quantifying the greenhouse gas (GHG) emission reductions enabled by Bayes Studio’s AI-driven wildfire detection and monitoring technology. By leveraging satellite data, remote sensing techniques, and wildfire emission models, the project aims to accurately estimate the carbon emissions avoided through early wildfire intervention. This research will support grant compliance, provide data-driven evidence of environmental impact, and contribute to broader climate change mitigation efforts by informing policy decisions, carbon credit opportunities, and sustainable wildfire management strategies.

Tasks:

Literature Review & Methodology Development

• Research existing GHG accounting frameworks for wildfire emissions (e.g., IPCC, GFED, BlueSky, EDGAR).
• Identify relevant carbon accounting methodologies applicable to avoid wildfire emissions.
• Develop a quantification approach tailored to Bayes Studio’s AI-driven wildfire detection technology.

Data Collection & Analysis

• Gather historical wildfire data (burned area, carbon release, and fire behaviour) from NASA, ESA, NOAA, and local fire agencies.
• Acquire and process multi-spectral remote sensing data from satellites and UAVs.
• Identify baseline emissions from wildfires in regions where Bayes Studio operates.
• Apply machine learning models to estimate the impact of early wildfire detection on carbon emissions reduction.

GHG Impact Calculation & Modeling

• Use statistical models and simulation tools to estimate avoided carbon emissions.
• Cross-validate results with existing wildfire carbon inventories and scientific literature.
• Develop an emission savings projection model for different fire-prone regions.

Documentation & Reporting for Grant Compliance

• Compile findings into a GHG Emissions Reduction Report with transparent methodologies.
• Create an Emission Reduction Framework that can be used for future carbon credit opportunities.
• Prepare responses and documentation required for grant submissions and environmental compliance.

Knowledge Sharing & Stakeholder Engagement

• Develop a presentation deck summarizing the impact of Bayes Studio’s technology on emissions reduction.
• Create an internal guide or whitepaper for company use in future carbon measurement projects.
• Engage with climate scientists, wildfire researchers, and policy experts to validate methodologies and findings.

Deliverables:

1. GHG Emissions Reduction Report: A comprehensive document detailing wildfire-related carbon emissions, avoided emissions through early intervention, and the methodology used.
2. Emission Reduction Framework: A structured, repeatable approach for quantifying carbon savings with Bayes Studio’s technology.
3. Data Analysis Summary: Processed datasets and key insights from remote sensing, satellite imagery, and wildfire behavior models.
4. Modeling & Projections Report: A set of predictive analytics showcasing future emission reductions based on different wildfire scenarios.
5. Grant Compliance Documentation: Technical responses and supporting data aligned with grant application requirements.
6. Presentation Deck: A clear and visually engaging summary of the research findings for internal and external stakeholders.
7. Stakeholder Engagement Report: Summary of feedback from experts, policymakers, and environmental scientists consulted during the project.