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Floods are among the most devastating disasters in terms of socio-economics and casualties. However, these natural disasters can be managed and their effects can be minimized by flood modeling performed before the occurrence of a flood. In this study, flood modeling was developed for the Göksu River Basin, Mersin, Türkiye. Flood hazard and risk maps were prepared by using GIS, HEC-RAS, and HEC-HMS. In hydraulic modeling, Manning’s n values were obtained from 2018 CORINE data, return period flow rates (Q25, Q50, Q100, Q500) were obtained from HEC-HMS, and the application was carried out on a 5 m resolution digital surface model. In the study area, the water depths could reach up to 10 m, and water speeds were approximately 0.7 m/s. Considering these values and the fact that the study area is an urban area, hazard maps were obtained according to the UK Department for Environment, Food and Rural Affairs (DEFRA) method. The results indicated that possible flood flow rates from Q25 to Q500, from 1191.7 m3/s to 1888.3 m3/s, were detected in the study area with HEC-HMS. Flooding also occurred under conditions of the Q25 flow rate (from 4288 km2 to 5767 km2), and the impacted areas were classified as extremely risky by the DEFRA method.

Increasing material use efficiency is important to mitigate future supply risks and minimize environmental impacts associated with the production of the materials. The policy mix presented in this paper aims to contribute to reducing the use of virgin metals in the EU by 80% by 2050 without significant shifting of burdens to other material resources, environmental impacts, or parts of the world. We used a heuristic framework and a systems perspective for designing the policy mix that combines primary instruments designed to increase material efficiency, recycling and substitution of materials (a materials tax, the extended producer responsibility, technical regulations, and environmental taxes) and supportive instruments aimed to reduce barriers to implementing the primary instruments and to contribute towards the policy objectives (e.g., research and development support, and advanced recycling centers). Furthermore, instruments were designed so as to increase political feasibility: e.g., taxes were gradually increased as part of a green fiscal reform, and border-tax adjustments were introduced to reduce impacts on competitiveness. However, even in such a policy mix design ongoing ex-ante assessments indicate that the policy mix will be politically difficult to implement—and also fall short of achieving the 80% reduction target. Nonetheless, we suggest combining primary and supportive instruments into coherent and dynamic policy mixes as a promising step towards system reconfigurations for sustainability.

This study will review the environmental implications of dynamic policy objectives and instruments outlined in the European Union 7th Framework Programme (EU-FP7) Project DYNAmic policy MIXes for absolute decoupling of EU resource use from economic growth (DYNAMIX) to address reductions in food consumption, food waste and a change in waste handling systems. The environmental implications of reductions in protein intake, food waste reductions, food waste management and donations are addressed using a life cycle approach to find the greenhouse gas (GHG) emissions, land use and water consumption. Data are provided from the Statistics Division of the Food and Agriculture Organization (FAOSTAT) food balance sheets for the European Union (EU) with a base year of 2010 and life cycle inventory (LCI) data from a meta-study of available GHG, land use and water consumption data for major food products. The implications are reviewed using a number of scenarios for the years 2030 and 2050 assuming policy instruments are fully effective. Results indicate that reductions in animal-based protein consumption significantly reduce environmental impacts, followed thereafter by reductions in food waste (assuming this also reduces food consumption). Despite the positive implications the policy mixes may have for targets for decoupling, they are not enough to meet GHG emissions targets for the EU outlined in the DYNAMIX project, although land and water use have no significant change compared to 2010 levels.

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

As a consequence of climate change and urbanization, many cities will have to deal with more flooding and extreme heat stress. This paper presents a framework to maximize the effectiveness of Nature-Based Solutions (NBS) for flood risk reduction and thermal comfort enhancement. The framework involves an assessment of hazards with the use of models and field measurements. It also detects suitable implementation sites for NBS and quantifies their effectiveness for thermal comfort enhancement and flood risk reduction. The framework was applied in a densely urbanized study area, for which different small-scale urban NBS and their potential locations for implementation were assessed. The overall results show that the most effective performance in terms of flood mitigation and thermal comfort enhancement is likely achieved by applying a range of different measures at different locations. Therefore, the work presented here shows the potential of the framework to achieve an effective combination of measures and their locations, which was demonstrated on the case of the Sukhumvit area in Bangkok (Thailand). This can be particularly suitable for assessing and planning flood mitigation measures in combination with heat stress reduction.

Cyclists, pedestrians and elderly people’s specific needs in urban road infrastructures are often neglected. They rarely benefit from safety measures or innovations. Inspired by playgrounds and aiming to reduce vulnerable road users (VRUs) injuries, the development of the rubber-based impact-absorbing pavements (IAP) offers a possibility to rethink the design of urban pavements and address safety on roads, which constitutes a major challenge in terms of attaining more sustainable, resilient, and safe cities. Therefore, bituminous mixtures with four different crumb rubber contents, 0%, 14%, 28%, and 33% (in total weight), were produced by partial aggregates substitution using the dry process. After the assessment of the geometrical and volumetric properties, the mechanical performances were evaluated. Finally, the samples were tested to measure the abrasion and impact attenuation with the well-known head injury criterion (HIC), at different temperatures from −10 to 40 °C, to obtain a wide range of values referring to possible weather conditions. A significant effect of the rubber percentage and layer thickness on impact attenuation was observed. All observations and results confirm the feasibility of the IAP concept and its positive effect on future injury-prevention applications.

Regional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution provides typically the best fit. We offer hydrological-meteorological insights into the differences among the six stations. For the period studied, almost all stations exhibit statistically insignificant trends, which renders the conclusions about flood risk as relevant for hydrological sciences and the design of regional flood protection infrastructure.