Economies committed to environmental goals, such as mitigating global warming, face the challenge that pollution and emissions largely originate from activities in foreign countries. While governments may attempt to tax domestic firms’ sourcing of inputs from brown international firms, such approaches often have to rely on disclosures from these foreign companies. Addressing this issue, we examine international supply chain partners’ disclosure strategies and their interplay with a domestic government’s optimal regulatory policies. Such disclosures are key to SCOPE-3 emissions calculations and meaningful ESG ratings. Optimal policies in our environment account for endogenous information asymmetries and the exercise of market power by domestic firms, leading to predictions that contrast with the notion of a common price for CO2 emissions, as is implied by carbon credit markets. Our analysis characterizes how domestic households’ exposures to global externalities shape both a government’s optimal regulations and the precision of foreign firms’ voluntary disclosures.

We model investors who take into account the amount of public goods firms produce (e.g., carbon emission reductions). In an asset pricing model with production and public goods, we find that more environmentally conscious investors invest more overall, invest more in clean firms, and may invest more in dirty firms. The magnitude and sign of CAPM alphas depends on a comparison between a firm's systematic risk and its relative public good contribution. There is underprovision of the public good. Government provision crowds out private provision, and may reduce overall public good provision. Government provision may be dominated by green subsidies.

A wide range of empirical techniques cannot accurately estimate causal effects of policy events due to anticipation bias---agents making decisions based on beliefs about future policy outcomes. We show that researchers can refine estimates to account for these beliefs, by integrating reduced-form and structural estimation around observed outcomes of a single policy change. We illustrate the importance and implementation of the approach by applying it to the Paris Agreement, which is frequently used to understand how agents respond to a policy event that increased climate regulatory risk. We document that anticipation can bias both the magnitude and sign of the Paris Agreement's average treatment effect on firm risk (estimated from a standard model such as a difference-in-differences regression). We offer concrete guidance on how to account for the divergence between causal and estimated effects.