Carbon Crediting When Better Measurement Is Not Enough

Abstract

Carbon-crediting methodologies determine how imperfect monitoring, reporting, and verification (MRV) evidence is translated into issued credits. When project developers can influence measured outcomes, greater reliance on project-specific data improves targeting but also strengthens incentives to manipulate the signal. We develop a model in which a crediting authority commits to a crediting rule while anticipating the project developer's response. The framework distinguishes statistical accuracy from gaming robustness. The optimal rule generally attenuates the MRV signal: greater accuracy and robustness justify stronger reliance on project-specific evidence, whereas higher credit prices and greater heterogeneity in gaming ability call for a flatter rule. Even when manipulation becomes prohibitively difficult, measurement noise alone implies attenuation. We also characterize how market and project conditions affect the relative value of improving accuracy versus robustness. An illustration using project-level data on cookstove carbon credits shows how independent reassessments can inform the framework and highlights the data requirements for empirical implementation.