We measure our success through real change on the ground. Our approach involves three essential steps: Count It, Change It, and Scale It.
Count It We start with data. We conduct independent research and draw on the latest technology to develop new insights and recommendations. Our rigorous analysis identifies risks, unveils opportunities, and informs smart strategies. We focus our efforts on influential and emerging economies where the future of sustainability will be determined.
Change It We use our research to influence government policies, business strategies, and civil society action. We test projects with communities, companies, and government agencies to build a strong evidence base. Then, we work with partners to deliver change on the ground that alleviates poverty and strengthens society. We hold ourselves accountable to ensure our outcomes will be bold and enduring.
Scale It We don’t think small. Once tested, we work with partners to adopt and expand our efforts regionally and globally. We engage with decision-makers to carry out our ideas and elevate our impact. We measure success through government and business actions that improve people's lives and sustain a healthy environment.
|Project name||Summary||Status||Social impact area|
|Creating a well-being data layer using machine learning, satellite imagery and ground-truth data||Conducting economic surveys requires huge resources; thus, modern means of acquiring this information using publicly available data and open source technologies create the possibilities of replacing current processes. Satellite images can act as a proxy for existing data collection techniques such as surveys and census to predict the economic well-being of a region. The aim of the project is to build on a prototype that was created using Census data and LandSat data for India. In the next iteration, opportunities for Demographic Health Surveys, Open Street Map, Sentinel and nightlight data will be explored. The initial prototype created a model that had an accuracy of almost 70 percent. The aim is to create a model for India that can be adapted and scaled to other countries.||done_all Completed||
|Identifying economic and financial incentives for forest and landscape restoration in Latin America using Natural Language Processing||Forest and landscape restoration is a cross-cutting agenda that traverses sectors such as agriculture, forestry, water and natural resources. While this cross-cutting nature makes restoration an attractive policy measure for carbon sequestration, mitigation, and adaptation, it complicates policy analysis. The sheer volume of text impedes researchers and decision makers from identifying misalignment and monitoring evolving policy and agenda shifts. Analyzing such a large corpus of documents exacerbates policy analysis’ transparency, objectivity, access, and scalability. Our proposal is to standardize and scale policy analysis, alignment, and agenda setting with natural language processing (NLP). A previous proof-of-concept we developed demonstrated the utility of NLP to quickly summarize agenda-specific information from policies. The aim of this project would be to identify financial and economic incentives to support enabling conditions for Nature Based Solutions.||In progress||
Years in operation: 25 or more years
Yearly budget: >$50MM
Geographical scope: Multi-national
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Washington, DC 20002
United States of America
Phone number: 2025093417