We combine machine learning for data collection with quasi-experimental econometric methods to measure the social and environmental impact of private investments. Our evaluations help businesses communicate results to stakeholders (such as governments, investors, and the public) with credibility and clarity. We also estimate how these sustainability outcomes affect key business performance metrics.

Building on our evaluation results, we estimate the return on investment (ROI) of social and environmental initiatives, not only in terms of public value (e.g., savings for governments and taxpayers), but also in business gains. This helps position impact as both a value-add and a strategic, cost-saving investment for companies.

Using supervised and unsupervised machine learning, we predict how different investment strategies are likely to affect social and environmental outcomes, and how those improvements, in turn, influence key business performance metrics. These predictive tools help companies and investors anticipate risks, quantify trade-offs, and design smarter responses to complex sustainability challenges.