From Carbon Price Shocks to Capital Shortfalls: A Network-Augmented Credit-Risk Framework for Climate Transition Cascades, with an Illustrative Application to Peru

Technical Summary: From Carbon Price Shocks to Capital Shortfalls

Problem Statement
Climate transition risk—the potential for carbon-intensive assets to lose value prior to the end of their economic life due to decarbonization policies—is increasingly recognized as a source of systemic financial risk. However, existing quantitative frameworks linking carbon-price shocks to credit portfolios and interbank contagion are heavily concentrated on advanced economies (e.g., the Euro Area, the United States). There is a significant gap in literature regarding emerging markets, where banking architectures and sectoral concentrations differ materially. Furthermore, while direct exposure to stranded assets has been studied, the second-round amplification of these risks through interbank networks and fire-sale dynamics remains underdeveloped, particularly in the context of country-specific decarbonization policy scenarios.

Methodology
The study proposes a conceptual and methodological framework rather than an empirical assessment. It does not utilize primary supervisory micro-data or perform statistical inference on real portfolios. Instead, it outlines a reproducible, three-module architecture designed to be calibrated with future empirical data:

1. Module 1: Carbon-Sensitive Asset Valuation: This module models the present value of credit exposure using a discounted cash-flow (DCF) expression. It incorporates a carbon-tax rate and sectoral emissions intensity to calculate "stranded value" (the difference between initial and post-shock asset values), drawing on logic from Mercure et al. (2018) and Dietz et al. (2016).
2. Module 2: Structural Credit-Risk Model: Utilizing a Merton-type structural approach, this module estimates the probability of default (PD) as a function of the asset-value-to-debt ratio. It posits that carbon-price shocks reduce asset values and increase effective volatility, thereby raising the modeled PD. This operationalizes price-transmission evidence reviewed by Campiglio et al. (2023).
3. Module 3: Interbank Network Contagion: This module models second-round losses using a network-valuation approach. It calculates capital losses for a specific bank based on its bilateral exposures to other banks, weighted by those banks' probabilities of default and loss-given-default. This logic follows Roncoroni et al. (2021), who applied similar methods to Mexican supervisory data.

The framework is illustrated through stylized, non-empirical scenarios (Orderly, Disorderly, and Accelerated transitions) to demonstrate how the modules interact. These scenarios use illustrative parameter choices bounded by orders of magnitude found in verified literature, explicitly avoiding claims of actual loss estimates for any specific portfolio.

Key Contributions
The paper makes three specific contributions:

1. Methodological Architecture: It provides a reproducible, three-module framework combining asset valuation, structural credit risk, and network contagion, specifically designed for adaptation by emerging-market financial authorities.
2. Verified Literature Matrix: It consolidates quantitative findings from existing, DOI-indexed peer-reviewed sources (e.g., Battiston et al., 2017; Roncoroni et al., 2021) into a comparison matrix. This documents established quantitative evidence without inventing new empirical results.
3. Contextual Application to Peru: It discusses the operationalization of this framework for the Peruvian financial system, characterized by a concentrated banking sector and high exposure to mining, hydrocarbons, and energy. It explicitly identifies the supervisory data (sectoral credit exposure, bilateral interbank exposures) required for a full empirical implementation.

Results and Illustrative Findings
The study does not present empirical results. Instead, it presents:

• A Comparative Literature Matrix (Table 1) documenting that climate transition risk is comparable in magnitude to bank capital in some contexts and that systemic losses can rise sharply when asset-price volatility interacts with deteriorating recovery rates.
• A Stylized Scenario Exercise (Table 2) demonstrating the framework's mechanics. For example, under a "Disorderly transition" scenario (carbon price of US$150–250/tCO₂), the framework illustrates a potential 30–45% devaluation of carbon-intensive cash flows and a "material increase" in probability of default. These figures are explicitly labeled as hypothetical parameter choices for demonstration, not empirical estimates.

Significance and Claims
The paper modestly claims to provide a validated conceptual and methodological foundation for future empirical work, rather than a finished assessment of the Peruvian banking system.

• Limitations: The authors explicitly state that no primary data was used, no statistical significance is claimed, and physical climate risks or behavioral bank responses are outside the scope.
• Policy Relevance: The significance lies in offering a tool for supervisors (such as Peru's Superintendencia de Banca, Seguros y AFP - SBS) to assess climate transition risk. The authors argue that an early, gradual domestic carbon-pricing trajectory, combined with climate-sensitive stress testing, could theoretically reduce systemic amplification.
• Future Work: The study concludes that a definitive quantification of transition risk for Peru remains a task for future research, ideally conducted in partnership with national financial authorities (SBS or BCRP) to access necessary micro-data.