China Journal of Econometrics Young Scholar Forum�Beijing, 2025/3

The Carbon Risk Premium Revisited: The Role of Production Networks

Shubo Kou, Kai Li, Minghao Li, Wu Zhu

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Discussant：Zhuo Chen

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Motivation and Overview

• This paper emphasizes that indirect carbon risk exposure to variation in carbon tax transmitted through production networks significantly shapes the cross-sectional variation in firms’ expected returns, beyond firms’ direct carbon emissions.
• Findings:
• Develop a tractable GE model that features both direct and indirect carbon risk exposures to climate regulatory risk, where the latter channel arises from the network effect.
• Empirical measurement of direct and indirect exposures for carbon risk at industry and firm level.
• Firms with higher indirect carbon risk exposure (via supply chain linkages) earn higher expected returns, whereas direct emissions alone show weaker pricing effects​.
• By exploiting the 2016 presidential election, they identify the causal impact of climate regulatory risk on cross-sectional risk premium through the indirect supply chain channel.
• Contribution:
• Theoretical: a GE model incorporating direct and production-network-induced indirect carbon risk exposures that yields clear empirical prediction for carbon risk premium.
• Empirical: propose a input-output-linkage-based indirect carbon risk measure and find the cross-sectional return spread, i.e., greeium, can mostly be explained such indirect measure.
• Policy implication: climate regulation shocks could propagate along supply chains, affecting upstream and downstream firms’ profitability, thus carbon risk is real and systemic

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Comment 1: Measuring indirect carbon exposure – downstream v.s. upstream

• Downstream approach: The paper measures a firm’s indirect carbon risk exposure by looking at its business segments and the industries it sells into.
• They compute a sales-weighted average of each segment’s industry-level indirect exposure.
• If a firm’s customers are highly exposed to carbon costs (due to their own supply chains), the firm inherits that risk indirectly
• This downstream segmentation captures how demand-side linkages transmit carbon risk to the firm
• Potential omission of upstream exposure: supply-side carbon risks.
• A firm that heavily relies on carbon-intensive suppliers could face increased input costs or disruptions when carbon regulations tighten.
• For example, an auto manufacturer sourcing steel from a highly emission-intensive mill would bear carbon cost risk even if its own segment’s average exposure is moderate.
• By emphasizing downstream (customer) industries, the metric might understate risk for firms with carbon-heavy input purchases.
• This downstream-based measure is less relevant for service industries of which customers are households (returns of sorted portfolio are not monotonic across carbon risk groups in Table 6).
• Suggestion: construct an upstream-based indirect carbon risk exposure using firms’ input procurement patterns.

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Comment 2: Passing carbon risk along the supply chain – role of bargaining power

• In supply chain, firms with heterogeneous bargaining power relative to their suppliers/customers may strategically pass through carbon regulatory shocks.
• Does a firm’s position in the network influence its carbon risk?
• Does the industry-specific market structure (fully competitive v.s. oligopolistic) matter?
• The ability to transfer carbon-related costs or risks along the supply chain likely depends on market power and contract structures.
• A dominant supplier facing a new carbon tax can raise its product prices, effectively passing the cost downstream to its customers.
• A supplier in a highly competitive market may be forced to absorb carbon costs to avoid losing business.
• Transmission of carbon risk can be dampened or amplified by bargaining power, pricing power, and competition.
• If some firms can shift carbon costs to their trading partners, the simple network exposure measure might overstate or understate true risk for certain firms, thus the pricing of carbon risk could depend on these industry/market characteristics.
• A comprehensive exploration of this extension could be pursued in future research.
• Some subsample analyses and discussions might help readers better understand the channel proposed by the current paper.

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Comment 3: Additional comments

• Model extensions
• What if, instead of imposing a carbon tax, each firm is allocated a tradable carbon emission quota?
• Can the setup of supply chain network be extended to other type of regulatory risk, e.g., tariffs that are charged to firms that import/export from their upstream/downstream industries? (In a multi-economy setting with country-specific tariffs, you could potentially employ a Bartik instrumental variable identification strategy.)
• The cross-sectional and time series variation of carbon tax for industry j at time t is from heterogeneous but time-invariant industry carbon emission intensity and homogeneous but time-varying government carbon tax rate. What if we relax this functional form, will the model still be tractable?
• Empirical
• When analyzing the impact of direct and indirect carbon exposure on the implied cost of capital, one would expect the effects to manifest at a lower frequency (e.g., annually), reflecting slower yet more persistent influences, whereas the current analysis employs monthly frequency tests using contemporaneous dependent and explanatory variables (Section 4.2).

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