Voluntary carbon disclosure collapses into a paradox of green silence: firms choose to disclose emissions based on strategic incentives (e.g., correcting vendor overestimates), while high emit- ters may exploit vendor estimation bias. Mirroring Heckman sample selection bias, this self- censorship skews disclosed emissions into non-random samples, distorting climate risk pricing and policy. We bridge economic problem and machine learning, proposing a Heckman-inspired three-step framework in high-dimensional settings to correct for strategic non-disclosure and ensure variable selection consistency in the presence of sample selection bias. By integrating kernel group lasso (KG-lasso) and double machine learning (DML) from neighbouring firms, i.e., using information from carbon next door, we unveil systematic underestimation: empirical analysis of 3444 unique US firms (2010-2023) rejects the null of no selection bias. Our findings indicate that voluntary disclosure induces adverse selection, where green silence rewards pol- luters and undermines decarbonization. Underestimation translates to a $2.6 billion shortfall in tax revenues and up to $525 billion hidden social cost of carbon.

Keywords: carbon emissions, machine learning, sample selection.

JEL Classification: C12, C13, C33, C51, C52, C82, Q52, Q54, Q56, Q58.