The 2025 Los Angeles (LA) County wildfires destroyed more than 16,000 structures and were followed by more than $20 billion of insurance payments within seven months. We use a difference-in-differences (DiD) design to examine how this acute physical climate shock transmitted through banks’ capital, credit risk, profitability, management efficiency, liquidity, funding, and credit supply. Treated banks are commercial banks headquartered in LA County; control banks are headquartered in counties with no FEMA major disaster declaration over 2022Q1–2025Q4 and have no LA County branch. We estimate entropy-balanced DiD and event-study models with bank and quarter fixed effects on a seven-quarter window (2024Q1–2025Q3) with onset in 2025Q1. To separate the effects of the wildfires from pre-existing trends and from differences between LA County banks and other banks that appear even in a year without a fire, we examine each effect with parallel-trends and placebo-timing diagnostics. The cleanest evidence comes from the single-county sample. Relative to the control banks, single-county treated banks’ non-performing loan ratio rose, and their management efficiency and return on assets deteriorated, while their total and real estate lending grew and the yields on these loans and their cost of deposits fell. We term this coexistence of rising credit risk and expanding intermediation the disaster liquidity paradox. For the full sample, only the increase in non-performing loans and the decline in the cost of deposits pass all diagnostics. The full-sample deposit and lending expansion, however, is of a size that LA County banks also display in a year without a fire, so we present it as a description of the post-wildfire period rather than as a causal effect of the wildfires. Insurance and relief payments sustaining bank funding are a possible explanation for this pattern; because Call Report data cannot trace these flows, the analysis does not directly demonstrate this mechanism. As all treated banks are headquartered in a single county and experienced a single event, the estimates describe this episode and may not generalize to other disasters or banking markets.
The purpose of this paper is to investigate if loan loss provisions (LLPs) incorporate forward-looking information regarding future changes in non-performing loans (NPLs), and whether this relationship changed after 2018. To evaluate the predictive relationship between current LLPs and future credit deterioration, fixed-effects specifications are employed with bank and year effects as well as NPL persistence and bank-specific controls. LLPs are positively and significantly related to the change in future NPLs in all of the specifications. The LLP coefficient is 0.0394 and the interaction between LLPs and a post-2018 indicator is 0.0767 in this fully controlled model, implying a post-2018 marginal effect of 0.1161. The coefficients remain significant after controlling for bank size, capitalisation, and profitability, indicating that the predictive content of provisioning is not confounded by observable bank characteristics. The change in future NPLs is characterised by a high degree of persistence, with the lagged NPL coefficient rising to 0.350 in the full specification. Larger banks are more likely to experience higher subsequent NPLs, while capitalisation and profitability have a negative relationship with future credit deterioration. Overall, these results are consistent with the information-content role of LLPs and show that its relationship to future credit risk became significantly stronger following 2018.
Pseudospectral analysis provides a natural framework for assessing robustness, systemic vulnerability, and transient shock amplification in non-normal dynamical systems, but its direct application becomes computationally demanding in large-scale problems. Recently, a scalable framework for matrices of the form A=UV*, with U,V∈Cd×r and r≪d, was developed, and the computation of the smallest singular value of zI−A, together with several related pseudospectral problems, was reduced to structured eigenvalue problems whose dimensions depend only on the low rank r. Building on this framework, the present paper develops further theoretical and computational results motivated by systemic-risk analysis in large-scale economic and financial interaction models. We extend the analysis to diagonally shifted low-rank systems and derive a characterization of pseudospectral boundary intersections with circular trajectories centered at the diagonal shift. For approximately low-rank matrices, known localization results are used to derive computable lower and upper bounds for the distance to instability. As a new theoretical contribution, we establish an analytical lower bound for the smallest eigenvalue of the reduced matrix MU,V(z), together with computable refinements involving only reduced-order quantities; the bound is shown to be sharp in a representative example. The reduced framework is further employed in the computation of the Kreiss constant and the analysis of transient amplification. Numerical experiments on large-scale economic interaction models illustrate how these results enable computationally tractable assessment of robustness and transient risk through reduced problems whose dimensions are independent of the full system size.
Digital transformation strategies at universities present significant challenges regarding structure and strategy, especially in digital risk governance. A systematic literature review was conducted in accordance with the PRISMA guidelines to examine how these risks are managed and identify the frameworks applied, particularly in contexts where the integration of emerging technologies reshapes organizational processes. Indexed databases (Scopus, Web of Science, and Dimensions) were examined, focusing on publications from 2019 to 2025. Following the removal of duplicates and the application of inclusion and exclusion criteria, 40 pertinent papers were identified through an iterative filtering process of key themes associated with the topic. The results illustrate the governance strategies implemented and the interaction of rules, standards, and management models in digital transformation strategy. The review identifies conceptual fragmentation across digital transformation, digital culture, risk governance, and smart university research, and proposes an integrative synthesis grounded in the reviewed corpus.
Climate transition risks are increasingly relevant to financial development, particularly in resource-dependent economies. However, the existing literature has largely examined how financial development affects environmental outcomes, while the reverse relationship remains comparatively understudied. This study investigates whether carbon emissions and renewable energy consumption influence financial development in Kazakhstan over 1996–2024, controlling for economic growth, inflation, and foreign direct investment. Financial development is measured by domestic credit to the private sector provided by banks (% of GDP). Using the Autoregressive Distributed Lag (ARDL) bounds testing approach and accounting for structural breaks, the study examines short-run and long-run dynamics. The results confirm a long-run equilibrium relationship among the variables, but none of the individual long-run coefficients is statistically significant. This finding does not imply that climate-transition factors are economically irrelevant; rather, it suggests that their effects have not yet translated into persistent, statistically identifiable changes in aggregate bank-based financial development. In the short run, economic growth has a positive effect, while inflation has a negative effect, with renewable energy consumption and foreign direct investment showing lagged effects. The study contributes new evidence on the climate–finance nexus from a resource-dependent transition economy and highlights the evolving, but still limited, transmission of climate-transition dynamics through Kazakhstan’s banking system.
