Abstract.
I examine the impact of information intermediaries' emissions estimates on firms' emissions disclosures. In recent years, estimates of corporate emissions provided by intermediaries have proliferated. I develop an analytical framework to theorize how such estimates interact with firms' disclosures. In my model, investors penalize firms' emissions, and higher-emitting firms abstain from disclosure. Thus, intermediaries would underestimate firms' emissions because their statistical models are calibrated using a self-selected sample of lower-emitting firms that choose to disclose. The model predicts that the biased emissions estimates further deter firms' disclosures if at least some investors naively rely on these estimates. I provide empirical support for my model's building blocks: investors' emissions pricing, firms' strategic disclosures, and intermediaries' biased estimates. Next, I exploit the expansion of a major intermediary as shock to the presence of intermediary estimates. I document evidence consistent with the predicted deterrence effect of intermediaries' estimates on firms' emissions disclosures. Informed by my model and corroborating empirical evidence, I structurally estimate the model to explore the potential consequences of refining intermediaries' estimates and the roles played by disclosure mandates.
Presented at CBS Brown Bag (2022), 2022 Federal Reserve Stress Testing Research Conference*, 2022 Accounting for an Ever-Changing World Conference*, 2022 Fixed Income and Financial Institutions Conference, Wharton*, University of Virginia*, Ohio State University*, 2022 HKUST Accounting Research Symposium*, 2023 HARC*, 2023 FARS Midyear Meeting, and 2023 EAA*
Presented at CBS Brown Bag (2023), 2023 MIT Asia Conference in Accounting, 2024 HARC, 2024 FARS Midyear Meeting, University of Oklahoma*, University of Amsterdam*, Erasmus University Rotterdam*, 2024 Cheung Kong Graduate School of Business Accounting Conference*, and 2024 China International Conference in Finance*
