Quantifying the Impact of Forest Landscape Restoration Through Predictive Analytics

Background

Climate change poses significant global risks, with potential costs of ~$792 trillion over the next 80 years. Forest landscape restoration (FLR) provides a powerful solution, offering co-benefits like mangroves absorbing 70–90% of storm surges and generating $7–$30 in economic benefits per dollar invested. However, these co-benefits remain undervalued by markets, creating a $400 billion annual investment gap. The Trillion Tree Fund aims to scale conservation finance to restore 1.2 trillion trees—offsetting a decade of carbon emissions and recognizing nature as a critical social and economic partner.

Objective

To develop a predictive impact analytics dashboard that quantifies the social, economic, and environmental benefits of forest landscape restoration investments, encouraging institutional investors to close the funding gap.

Approach

The team focused on creating a comprehensive solution:

1. Methods: Developed a predictive impact analytics model capable of calculating diverse co-benefits in USD terms.
2. Data Sources: Leveraged datasets for inputs such as cost of investment, tree species, land area, tree maturity, and ecoregion.
3. Analysis Techniques: Quantified benefits including reduced damages, enhanced economic productivity, improved health outcomes, increased property values, and carbon sequestration.
4. Tools Used: Integrated GIS mapping, scenario modeling, and impact radius calculations into a user-friendly dashboard.

Results and Impact

The project produced a predictive dashboard that provides institutional investors with clear, quantifiable data to support FLR financing decisions. Key outcomes include:

• Investment Insights: Identified datasets for evaluating ROI and co-benefits like reduced health costs, increased property values, and enhanced ecotourism revenues.
• Co-Benefits Quantification: Highlighted specific benefits such as $1,000/ha in ecotourism revenues and $800M in increased property values from urban tree planting.
• GIS Mapping: Added a cost-of-inaction map and visualized co-benefits within defined impact radii (e.g., 5–10 miles).
• Scenario Analysis: Modeled reduced climate risks and co-benefits at full restoration potential, such as floodwater absorption and wildfire spread mitigation.

The solution bridges the gap between FLR benefits and market valuation, encouraging investments that generate long-term, multidimensional returns.

Future Implications

The dashboard sets a new standard for quantifying FLR benefits, influencing policies and investment strategies. Future developments could include integrating real-time data, expanding geographic coverage, and refining ROI models to further boost investor confidence in conservation finance. By addressing undervaluation, this work supports scaling global efforts to restore landscapes and mitigate climate risks.