Thornthwaite's formula is globally an optimum candidate for large-scale applications of potential evapotranspiration and aridity assessment at different climates and landscapes since it has lower data requirements compared to other methods and especially from the ASCE-standardized reference evapotranspiration (formerly FAO-56), which is the most data-demanding method and is commonly used as the benchmark method. The aim of the study is to develop a global database of local coefficients for correcting the formula of monthly Thornthwaite potential evapotranspiration (E_p) using as benchmark the ASCE-standardized reference evapotranspiration method (E_r). The validity of the database will be verified by testing the hypothesis that a local correction coefficient, which integrates the local mean effect of wind speed, humidity, and solar radiation, can improve the performance of the original Thornthwaite formula. The database of local correction coefficients was developed using global gridded temperature, rainfall, and E_r data of the period 1950–2000 at 30 arcsec resolution (∼ 1 km at Equator) from freely available climate geodatabases. The correction coefficients were produced as partial weighted averages of monthly $E_{\text{r}}/E_{\text{p}}$ ratios by setting the ratios' weight according to the monthly E_r magnitude and by excluding colder months with monthly values of E_r or E_p < 45 mm per month because their ratio becomes highly unstable for low temperatures. The validation of the correction coefficients was made using raw data from 525 stations of Europe; California, USA; and Australia including data up to 2020. The validation procedure showed that the corrected Thornthwaite formula E_ps using local coefficients led to a reduction of RMSE from 37.2 to 30.0 mm m⁻¹ for monthly step estimations and from 388.8 to 174.8 mm yr⁻¹ for annual step estimations compared to E_p using as a benchmark the values of the E_r method. The corrected E_ps and the original E_p Thornthwaite formulas were also evaluated by their use in Thornthwaite and UNEP (United Nations Environment Program) aridity indices using as a benchmark the respective indices estimated by E_r. The analysis was made using the validation data of the stations, and the results showed that the correction of the Thornthwaite formula using local coefficients increased the accuracy of detecting identical aridity classes with E_r from 63 % to 76 % for the case of Thornthwaite classification and from 76 % to 93 % for the case of UNEP classification. The performance of both aridity indices using the corrected formula was extremely improved in the case of non-humid classes. The global database of local correction factors can support applications of reference evapotranspiration and aridity index assessment with the minimum data requirements (i.e., temperature) for locations where climatic data are limited. The global grids of local correction coefficients for the Thornthwaite formula produced in this study are archived in the PANGAEA database and can be assessed using the following link: https://doi.org/10.1594/PANGAEA.932638 (Aschonitis et al., 2021).

Background: The increasing frequency and severity of extreme weather events (EWEs) as a consequence of climate change pose critical challenges on the transport and logistics sector, hence requiring systematic evaluation and strategic adaptation. Methods: This study conducts a comprehensive systematic literature review (SLR) of 147 peer-reviewed articles and reports through a PRISMA framework to comprehensively identify key weather-induced challenges, quantify their operational, infrastructural and economic impacts, and explore alternative mitigation strategies. Results: With a greater focus on rainfall, flooding and snowfall, this study highlights their notable impacts causing reductions in transport efficiency, increased maintenance costs and substantial financial losses. Also, it emphasizes the role of advanced technologies, resilient infrastructure, and adaptive policy frameworks as critical enablers for enhancing sector resilience while simultaneously formulating a robust roadmap for cities and companies with actions ranging from direct operational adjustments to long-term transformational changes in policy and infrastructure. Conclusions: This work underscores the importance of using a data-driven approach to safeguard transport and logistics systems against evolving climate risks contributing to the broader goal of sustainable urban resilience and operational continuity.

The smart cities paradigm has gained significant attention as a tool to address the multifaceted challenges posed by contemporary urban mobility systems. While cities are eager to integrate cutting-edge technologies to evolve into digital and intelligent hubs, they often deal with infrastructure and governance bottlenecks that prevent the rapid adoption of industry-driven innovations. This study introduces a three-step methodological approach to forecast a city’s innovation readiness in urban mobility, thus facilitating city-led innovation and identifying key areas within urban mobility systems that require attention. Initially, a comprehensive literature review was undertaken to ascertain the most impactful innovation indicators influencing a city’s ability to embrace new technologies. Subsequently, Principal Component Analysis (PCA) was applied to identify these indicators, highlighting the primary markers of innovation for each city. The final step involved the application of both random and fixed-effects regression models to quantify the influence of distinct unobserved variables—such as economic, cultural, and political factors—on the innovation readiness of various cities. The methodology’s effectiveness was tested using data from cities across diverse regions. The findings underscore that merely 7 out of 21 innovation indicators are critical for assessing a city’s innovation readiness. Moreover, the random-effects model was identified as the most suitable for capturing the nuances of unobserved variables in the studied cities. The innovation readiness scores at the city level revealed a diverse range, with cities like Madrid, Gothenburg, and Mechelen demonstrating high readiness, while others like Kalisz and Datong showed lower scores. This research contributes to the strategic planning for smart cities, offering a robust framework for policymakers to enhance innovation readiness and foster sustainable urban development, with a newfound emphasis on city-specific analysis.

As ports evolve to meet sustainability targets, seamless coordination between road and rail operations becomes fundamental to success. This study addresses the Train Loading Planning Problem (TLPP) which focuses on assigning outbound containers to train wagons under slot, weight, and pattern constraints aiming to examine its broader systemic implications. A compact mixed-integer programming formulation is developed and enhanced through a column-generation approach that efficiently prices feasible wagon plans. The optimization module is embedded within a discrete-event simulation of terminal processes including yard handling, gate operations, and train timetables. The study tests a TLPP-based rail planning algorithm within a DES of terminal and hinterland operations to quantify the impact under realistic variability. Using operational data from the Port of Valencia, realistic planning scenarios are evaluated across varying demand mixes and train frequencies. Results indicate that integrating rail capacity with optimized wagon loading reduces set-up time by 20%, delivery lead time by 54%, container dwell time by 80%, and greenhouse gas emissions by 54% compared with a trucking forwarding baseline, while maintaining throughput and alleviating congestion at terminal gates and yards. From a computational perspective, the column-generation approach achieves improved runtimes to the compact MIP and scales linearly to the number of variables. The proposed framework delivers ready to use load plans and practical insights for the deployment of additional rail capacity, supporting sustainable logistics in port environments.

Urban pluvial flooding can disrupt road networks, delay emergency medical services and produce spatially uneven losses of access to urgent healthcare. This study evaluates how flood-induced road disruption affects ambulance travel performance and identifies buildings, neighbourhoods and populations that experience reduced emergency healthcare accessibility under rainfall events with return periods of 10, 20 and 100 years in Thessaloniki, Greece. High-resolution flood-depth outputs were integrated with direction-specific road operating speeds, building footprints, population estimates, an ambulance terminal and seven public hospitals. Road-section speeds were adjusted according to local flood depth and impassable sections were removed, while minimum-time routes were then calculated for the terminal-to-patient, patient-to-hospital and hospital-to-terminal legs across 21 two-hospital duty configurations. Flooding produced substantial increases in travel time and distance even though only a small proportion of road links became impassable. Under the 100-year scenario, the median full-service-cycle time increased from 24.8 to 33.0 min, patient-delivery completion declined from 100.0% to 86.1% and the proportion of journeys completed within 30 min fell from 97.3% to 56.1%. The patient-to-hospital leg deteriorated most, while comparison with baseline revealed that the greatest flood-related reductions in patient-delivery accessibility within 30 min occurred in northern and northwestern Thessaloniki under the modelled terminal and hospital-duty configurations. The study provides an integrated framework that combines ambulance travel modelling with building- and population-level accessibility analysis under flood conditions. The findings show that limited but strategically located road disruptions can generate disproportionate operational and spatial impacts, highlighting the importance of protecting critical corridors, supporting flood-aware routing and considering hospital availability in emergency preparedness planning.