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

Stylianos Asimakopoulos, George Kapetanios, Vasilis Sarafidis, Alexia Ventouri

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.

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

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.
Paper Structure (18 sections, 2 theorems, 36 equations, 1 figure, 3 tables)

This paper contains 18 sections, 2 theorems, 36 equations, 1 figure, 3 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Network Effects and Corporate Spillovers
  4. Corporate Emissions and Environmental Performance
  5. Environmental Signals in Financial Markets
  6. Spatial and Network Econometrics
  7. Model Specification and Motivation
  8. Estimation Approach
  9. Empirical Findings
  10. Point Estimates and Standard Errors
  11. Direct and Indirect Effects
  12. Network Estimation
  13. Network Formation and Homophily Sources
  14. Homophily along Firm, Industry, and State Dimensions
  15. Homophily in Financial Characteristics
  16. ...and 3 more sections

Key Result

Proposition 1

Consider the DGP eq:dgp1, where each unit $i$ has $k_{i}$ link units, $k_{i}^{\ast}$ proxy units, and $N - k_{i} - k_{i}^{\ast}$ distant units. Let $c_{p}(N,\delta)$ be defined as in cvf, where $0 < p < 1$ is a significance level and $f(N,\delta) = cN^{\delta}$ for some $c > 0$ and $\delta > 0$. Let

Figures (1)

  • Figure 1: Directed Network Graph with Node Size and Color Scaled by Out-Degree

Theorems & Definitions (4)

  • Proposition 1
  • Remark 1
  • Proposition 2
  • Remark 2