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The land transformation process in the last decades produced the urbanization growth in flat and coastal areas all over the world. The combination of natural phenomena and human pressure is likely one of the main factors that enhance coastal dynamics. These factors lead to an increase in coastal risk (considered as the product of hazard, exposure, and vulnerability) also in view of future climate change scenarios. Although each of these factors has been intensively studied separately, a comprehensive analysis of the mutual relationship of these elements is an open task. Therefore, this work aims to assess the possible mutual interaction of land transformation and coastal management zones, studying the possible impact on local coastal communities. The idea is to merge the techniques coming from urban planning with data and methodology coming from the coastal engineering within the frame of a holistic approach. The main idea is to relate urban and land changes to coastal management. Then, the study aims to identify if stakeholders’ pressure motivated the deployment of rigid structures instead of shoreline variations related to energetic and sedimentary balances. The influence of coastal protection (described by an indicator taking into account the linear density of the rigid protection extension alongshore) measures have been considered. Finally, the economical performances in the period just after the land transformation have been studied by means of a dimensionless index based on the concept of the local unit. The method has been applied to the case study of the Abruzzo Region. Results reveal a large urbanization growth and a generalized over-protection of the coast with a resulting spatial alternation of shoreline retreat and advance. On the other hand, the analysis of the local economy reveals that the presence of tourism activities does not necessarily lead to an increase in the local economy. Indeed, the most important improvement in local economies in the region can be associated with coastal stretches characterized by a high environmental value.

Since the 1970s but with greater intensity in the 1980s, strong, social, economic, and cultural transformations have led to the post-Fordist or post-productivist countryside determining what researchers identify as “rural restructuring” [...]

From the comparison of regulations and/or standards for the organic, conventional and/or integrated citrus production method and a voluntary certification, it emerges that farms certified with voluntary non-regulated certification systems, such as the IFA FV GLOBALG.A.P, are obliged to take into account the highest number of aspects, reported in a more complete register, than the organic ones. Moreover, this is also supported by a continuous-time planned process of revision and updating of the applicable versions of the standard. The environmental impact of the food production, the safety aspects of food products, as well as the health, ethics, and safety aspects of workers, are largely considered and inspected in the GLOBALG.A.P., while the organic system, despite the IFOAM suggestions and indications, is only considered partially. This means that, from a practical point of view, the organic product can be considered “clean and safe”, but not more environmentally friendly than the GLOBALG.A.P. products.

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.

Today, the global food security is one of the most pressing issues for humanity, and, according to Food and Agriculture Organisation (FAO), the increasing demand for food is likely to grow by 70% until 2050. In this current condition and future scenario, the agricultural production is a critical factor for global food security and for facing the food security challenge, with specific reference to many African countries, where a large quantities of rice are imported from other continents. According to FAO, to face the Africa’s inability to reach self-sufficiency in rice, it is urgent “to redress to stem the trend of over-reliance on imports and to satisfy the increasing demand for rice in areas where the potential of local production resources is exploited at very low levels” The present study was undertaken to design a new method for land evaluation based on soil quality indicators and remote sensing data, to assess and map soil suitability for rice crop. Results from the investigations, performed in some areas in the northern part of the Nile Delta, were compared with the most common approaches, two parametric (the square root, Storie methods) and two qualitative (ALES and MicrioLEIS) methods. From the qualitative point of view, the results showed that: (i) all the models provided partly similar outputs related to the soil quality assessments, so that the distinction using the crop productivity played an important role, and (ii) outputs from the soil suitability models were consistent with both the satellite Sentinel-2 Normalize Difference Vegetation Indices (NDVI) during the crop growth and the yield production. From the quantitative point of view, the comparison of the results from the diverse approaches well fit each other, and the model, herein proposed, provided the highest performance. As a whole, a significant increasing in R2 values was provided by the model herein proposed, with R2 equal to 0.92, followed by MicroLES, Storie, ALES and Root as R2 with value equal to 0.87, 0.86, 0.84 and 0.84, respectively, with increasing percentage in R2 equal to 5%, 6% and 8%, respectively. Furthermore, the proposed model illustrated that around (i) 44.44% of the total soils of the study area are highly suitable, (ii) 44% are moderately suitable, and (iii) approximately 11.56% are unsuitable for rice due to their adverse physical and chemical soil properties. The approach herein presented can be promptly re-applied in arid region and the quantitative results obtained can be used by decision makers and regional governments.

This paper presents the results of multidisciplinary research which addresses the issue of sustainability within the design of urban agricultural landscapes through the joint solution of architecture and hydro-geomorphology for the management, accessibility and use of urban agricultural landscapes. If the traditional separation between urban and productive land has now been overcome by the inclusion of agricultural areas within the cities, what is still under discussion is how to make its planning sustainable, especially in densely urbanized contexts. This research was developed with a methodology able to combine different types of data in order to satisfy needs relating to the availability of water for land supply and to make this landscape—both rural and urban—accessible to the city. Historical traces, interpretative insights, and hydro-geomorphological reading models of the territory were analysed. Their interweaving has made it possible to develop a prototype of a water and landscape infrastructure that combines a system for subsurface water, cycle–pedestrian paths and small-scale architecture features around the water tanks. The research has been tested in the city of Pozzuoli (NA) in Southern Italy, within the Monteruscello neighbourhood. The result led to a design solution of an integrated system which contributes to implementing the green transition within cities.

Current industrial production is driven by increasing globalization, which has led to a steady increase in production volumes and complexity of products aimed at the pursuit of meeting the needs of customers. In this context, one of the main tools in the management of customer value is Lean Manufacturing or Production, though it is considered primarily as a set of tools to reduce the total cost of the resources needed to achieve such needs. This philosophy has recently been enriched in the literature with case studies that link Lean Management (LM) with the improvement of environmental sustainability. The consequence is an expansion of the Computer Integrated Manufacturing (CIM); indeed, CIM, currently, combining and integrating the key business functions (e.g., business, engineering, manufacturing, and information management) with a view of the life cycle, does not highlight the strategic role of the environmental aspects. In order to deal with the increasingly rapid environmental degradation that is reflected in society, in terms of both economy and quality of life, Industrial Ecology (IE) introduced a new paradigm of principles and instruments of analysis and decision support (e.g., Life Cycle Assessment—LCA, Social Life Cycle Assessment -SLCA, Material Flow Account—MFA, etc.) that can be considered as the main basis for integrating the environmental aspects in each strategy, design, production, final product, and end of life management, through the re-engineering of processes and activities towards the development of an eco-industrial system. This paper presents the preliminary observations based on a analysis of both theories (LM-IE) and provides a possible assessment of the key factors relevant to their integration in a “lean environmental management”, highlighting both positives (lights) and possible barriers (shadows).

To contribute to small and medium enterprises (SMEs) sustainable transition into the circular economy, the study proposes the activation of organizational learning (OL) processes – denoted here as multi-level knowledge creation, transfer, and retention processes – as a key phase in introducing circular business models (CBMs) at SME and supply chain (SC) level. The research employs a mixed-method approach, using the focus group methodology to identify contextual elements impacting on CBM-related OL processes, and a survey-based evaluation to single out the most frequently used OL processes inside Italian construction SMEs. As main result, a CBM-oriented OL multi-level model offers a fine-grained understanding of contextual elements acting mutually as barriers and drivers for OL processes, as possible OL dynamics among them. The multi-level culture construct – composed of external stakeholders’, SC stakeholders’, and organizational culture – identify the key element to activate CBM-oriented OL processes. Main implications are related to the identification of cultural, structural, regulatory, and process contextual elements across the external, SC, and organizational levels, and their interrelation with applicable intraorganizational and interorganizational learning processes. The proposed model would contribute to an improved implementation of transitioning into the circular economy utilizing sustainable business models in the construction SMEs.