This website uses cookies.

By clicking the "Accept" button or continuing to browse our site, you agree to first-party and session-only cookies being stored on your device to enhance site navigation and analyze site performance and traffic. For more information on our use of cookies, please see our Privacy Policy.

  1. Home
  2. ASSA Annual Meeting
  3. 2027
  4. Preliminary Program
  5. The Corporate Finance of Climate Change: Theory and Evidence
« Back to Results

The Corporate Finance of Climate Change: Theory and Evidence

Paper Session

Sunday, Jan. 3, 2027 8:00 AM - 10:00 AM (EST)

Westin DC Downtown
Hosted By: American Finance Association
  • Chair: Mariano Croce, Bocconi University

Who Bears the Cost of Natural Disasters?

Mariassunta Giannetti
,
Stockholm School of Economics
Yingjie Qi
,
Copenhagen Business School
Su Wang
,
ShanghaiTech University

Abstract

Using establishment-level data and scanner retail prices, we show that the number of firms decreases and retail prices increase in counties affected by natural disasters. Price increases are particularly pronounced for essential goods and low-priced products, as well as in counties with fewer bank branches per capita, where fewer firms enter following natural disasters. Our results suggest that the costs of climate-related disasters, which are increasingly frequent negative realizations of physical risk, are ultimately borne by consumers.

Financially Constrained Carbon Management

Maria Cecilia Bustamante
,
University of Maryland
Francesca Zucchi
,
European Central Bank

Abstract

We develop a model studying how financing frictions affect a firm’s carbon footprint as well as its transition to sustainable technologies, while allowing for multiple types of green investment: abatement of carbon emissions, adoption of available technologies, and green innovation. Financing frictions impact each type of green investment differently—with abatement unaffected, a negative effect on adoption, and an ambiguous impact on green innovation. Financing frictions reduce current emissions by contracting production, but have a negative impact on the transition to greener technologies in firms relying mainly on adoption. We further show tilting strategies need not boost green innovation, exclusion strategies mainly curb current emissions, and subsidies to adoption help incentivize green innovation too.

Routine and Material: The Integration of Environmental Factors in Analyst Research

Deqiu Chen
,
University of International Business and Economics
Haoyuan Li
,
University of International Business and Economics
Yujing Ma
,
Hong Kong Polytechnic University
Roni Michaely
,
University of Hong Kong

Abstract

We provide comprehensive evidence on how analysts evaluate corporate environmental practices. Combining a survey of 505 analysts with textual analysis of 273,664 reports, we find that analysts routinely cover environmental issues and price these dynamics. Although analysts recognize physical and regulatory risks, particularly for high-polluting industries, they more frequently emphasize environment-related opportunities like green technology transitions. Analysts embed these views into earnings forecasts and recommendations. Most analysts approach environmental issues exclusively through financial value, while a nontrivial minority also consider broader societal values. Finally, analysts identify regulation and public scrutiny, not investor pressure, as primary catalysts for corporate environmental improvement.

Carbon in the Cloud

Tingyu Yu
,
University of Zurich

Abstract

This paper studies how AI investment affects corporate emissions in a large global sample. Firms with higher AI worker shares subsequently reduce Scope 1 emissions on average, but effects are heterogeneous across sectors. Emissions fall broadly through improved forecasting and operational efficiency, whereas Energy and Utilities see emissions increase as AI is disproportionately used to expand carbon-intensive output rather than accelerate the green transition. Instrumental variable estimates using H-1B visa lotteries support a causal interpretation. In the aggregate, the emissions-weighted effect is close to zero, suggesting that brown AI use in a small set of high-emitting sectors offsets emission reductions elsewhere. Estimated emissions from electricity used for AI computing remain modest relative to these operational responses.

Discussant(s)
Ran Duchin
,
Boston College
Maxime Sauzet
,
Boston University
Stefano Rossi
,
Bocconi University
Marco Grotteria
,
London Business School
JEL Classifications
  • G3 - Corporate Finance and Governance