Network Effects in Corporate Emissions: Evidence from a Data-Dependent Spatial Panel Model

TL;DR

This paper uncovers unobserved influence networks among U.S. industrial facilities affecting emissions, demonstrating the importance of data-driven network estimation for understanding systemic environmental risks and informing targeted regulation.

Contribution

It introduces a novel high-dimensional network econometrics approach to uncover unobserved influence networks from data, improving understanding of spillovers in corporate emissions.

Findings

Estimated networks explain 28% of total impact of firm characteristics.

Distance-based networks show no significant spillovers.

Data-driven networks outperform a priori networks in capturing spillovers.

Abstract

We study spillover effects in corporate toxic emissions using a heterogeneous panel network of U.S. industrial facilities from 2000-2023. Rather than imposing a network structure a priori, we uncover an unobserved web of influence directly from the data using recent advances in high-dimensional network econometrics. Indirect effects transmitted through the estimated network account for about 28% of the total impact of key firm balance-sheet characteristics. By contrast, distance-based networks generate no statistically discernible spillovers, while a priori firm- or industry-based networks substantially overstate within-group spillins relative to the data-driven network. These findings show that who is linked to whom, and with what strength, matters critically for assessing systemic environmental risk and for designing targeted regulation. Methodologically, the paper provides a flexible framework for quantifying facility-level emissions spillovers and their consequences in financial and policy settings.