Green Silence: Double Machine Learning Carbon Emissions Under Sample Selection Bias

Cathy Yi‐Hsuan Chen, Abraham Lioui and O. Scaillet
Additional contact information
Cathy Yi‐Hsuan Chen: University of Glasgow, Adam Smith Business School; Humboldt Universität zu Berlin
Abraham Lioui: EDHEC Business School
O. Scaillet: Swiss Finance Institute - University of Geneva

No 25-66, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: 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 emitters may exploit vendor estimation bias. Mirroring Heckman sample selection bias, this selfcensorship 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 polluters 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 (search for similar items in EconPapers)
JEL-codes: C12 C13 C33 C51 C52 C82 Q52 Q54 Q56 Q58 (search for similar items in EconPapers)
Pages: 60 pages
Date: 2025-07
New Economics Papers: this item is included in nep-big, nep-ene and nep-env
References: Add references at CitEc
Citations:

Downloads: (external link)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5368583 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2566

Access Statistics for this paper

More papers in Swiss Finance Institute Research Paper Series from Swiss Finance Institute Contact information at EDIRC.
Bibliographic data for series maintained by Ridima Mittal ().