Prof. Concetta Carnevale, Prof. Danilo Drago, Dr. Sandro Rondinella (p), Prof. Francesco Trivieri

This paper analyses the relationship between extreme weather events and local credit market outcomes, with a specific focus on territorial heterogeneity across Italian provinces over the period 2002–2022. The study explores whether and how climate-related shocks affect credit risk, lending conditions, and financial dynamics across different segments of the local economy in a bank-based system characterized by strong regional disparities.
The empirical strategy relies on provincial panel data models with fixed effects, exploiting both time and cross-sectional variation to isolate the impact of extreme weather exposure while controlling for local economic conditions and structural characteristics. Credit risk is measured through non-performing loans, disaggregated by micro-firms, non-financial companies, and households. Additional indicators capture lending rates, guarantee intensity on outstanding loans, deposit dynamics, and medium- and long-term loan growth, allowing for a comprehensive assessment of financial adjustments.
The results show that extreme weather events are associated with a deterioration in local credit quality, particularly among micro-firms and non-financial companies. These effects are accompanied by tighter lending conditions, stronger collateral intensity, and a slowdown in investment-related lending. Overall, the evidence suggests that climate shocks reshape local financial conditions through multiple transmission channels and that their impact is spatially differentiated, highlighting the relevance of incorporating climate exposure into regional financial stability assessments.

Dr. David Castells-quintana (p)

Climate change is one of the most pressing challenges of this century. This is more so for developing and least developed countries, which have limited means of adaptation and a high
proportion of vulnerable populations. In this paper, we empirically study the interconnection between climate and multidimensional poverty. To do so, we rely on gridded climatic data matched with multidimensional poverty data, both aggregated at different spatial levels. We first provide evidence at the cross-country level (for over 70 countries worldwide) as well as the subnational level (for 343 subnational regions in Asia). We then focus on India, looking at regions, districts, and households. We implement several econometric techniques (including alternative fixed-effects specifications, as well as long-differences and difference-in-difference specifications, plus a battery of robustness checks). Our findings indicate that rising (and more variable) temperatures, as well as increased incidences of drought, are associated with higher levels of multidimensional poverty across all analyzed levels. Benefiting from rich micro data at the household level in India, we find that climate-effects are mainly driven by deprivations in living conditions and nutrition. Finally, supporting evidence, exploiting detailed remote-sensing data, suggests that these effects may be (partly) driven by decreases in vegetation (proxied by greenness) and lower economic activity (as proxied by nightlight
intensity).

Dr. Marek Walacik (p), Dr. Aneta Chmielewska (p)

Growing environmental risk exposure and increasing spatial heterogeneity across property markets challenge conventional valuation methodologies. While hedonic and mass appraisal models are widely applied in both public and private valuation systems, they often insufficiently account for spatial dependence, localized environmental risk, and high-resolution locational attributes derived from geospatial data.
This paper proposes a GIS-based valuation framework that integrates environmental risk indicators and spatial econometric techniques into mass appraisal modelling. The approach extends traditional hedonic specifications by incorporating:
(1) detailed locational variables extracted from Geographic Information Systems (GIS),
(2) environmental exposure metrics such as flood susceptibility, heat intensity, or land-use transitions, and
(3) formal spatial dependence structures through spatial lag and spatial error models.
By embedding environmental risk layers directly into automated valuation models (AVMs), the framework addresses omitted variable bias and spatial autocorrelation effects that frequently distort price estimation. The study also examines how spatially explicit modelling alters marginal price gradients and improves predictive performance compared to non-spatial benchmarks.
The proposed methodology is particularly relevant in markets characterized by uneven data quality and information asymmetry, where traditional comparability approaches may be limited. Empirical evidence illustrates how the integration of geospatial risk attributes modifies valuation outcomes and enhances model transparency.
The paper contributes to regional science by demonstrating how spatial econometric tools and GIS-driven data integration can refine property valuation systems and strengthen the analytical linkage between environmental exposure and market pricing mechanisms. It highlights the role of advanced spatial modelling as a bridge between real estate economics, environmental risk assessment, and quantitative regional analysis.

Dr. Paolo Bottero (p)

Disasters such as earthquakes are hinge events that can produce lasting effects on local economic structures. They disrupt production and employment but may also transform local comparative advantages that support regional medium and long-term competitiveness. In the disaster literature, attention often focuses on short-term performance indicators. Less consideration is given to whether and how such shocks alter long-term regional specialisation. Within this context, specialisation may strengthen competitiveness and recovery capacity thanks to agglomeration economies and dense production networks, but it may also amplify vulnerability when the shock directly affects dominant sectors and local interdependencies propagate the disturbance.

This paper analyses the long-term structural consequences of the 2012 Emilia-Romagna earthquake, asking whether pre-existing industrial specialisation amplified vulnerability compared to more diversified local economies. Emilia-Romagna is an informative case due to the dense manufacturing fabric of SMEs and industrial districts, characterised by localised value chains and consolidated specialisations. The coexistence within the seismic “crater” of strongly specialised district areas and more diversified non-district areas allows us to examine how different productive configurations condition the long-term effects of the shock.

The empirical design is based on a panel of 29 Labour Market Areas (LMAs) included in the official 2012 seismic crater, observed over 2004–2021. According to the ISTAT, 15 affected LMAs are industrial districts and 14 are non-district areas. Causal effects are identified through a systematic application of microsynth across a broad portfolio of structural indicators, providing a comprehensive view of disaster impacts by capturing both immediate disruptions and medium-term adjustments across interconnected dimensions of the local productive structure. Outcomes include two-digit sectoral Revealed Comparative Advantage and aggregate indicators (Economic Complexity Index, Herfindahl–Hirschman Index, Krugman Specialisation Index, and diversity measures), estimated using both local units and employment.

Results show that in industrial districts comparative advantages in key manufacturing sectors decline and remain lower for several years, alongside a shift towards a production structure closer to the national average. In non-district LMAs, deviations are smaller and less persistent. The evidence suggests that highly specialised production systems may display structural fragility when shocks affect sector-specific assets and dense local production networks, highlighting a trade-off between efficiency in stable periods and robustness under systemic disruption, with implications for post-disaster industrial and regional policy.