Institutional investors seek to make environmentally sustainable investments using environment, social, governance (ESG) ratings. Current ESG ratings have limited validity because they are based on idiosyncratic scores derived using subjective, discretionary methodologies. We discuss a new direction for developing corporate environmental performance (CEP) ratings and propose a solution to the limited validity problem by anchoring such ratings in environmental controversies. The study uses a novel machine learning approach to make the ratings more comprehensive and transparent, based on a set of algorithmic approaches that handle nonlinearity when aggregating ESG indicators. This approach minimizes the rater subjectivity and preferences inherent in traditional ESG indicators. The findings indicate that controversies as proxies for non-compliance with environmental responsibilities can be predicted well. We conclude that environmental performance ratings developed using our machine learning framework offer predictive validity consistent with institutional investors’ demand for socially responsible investment screening.