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This study evaluated the environmental conditions of two watersheds selected on the basis of similarities in land use. The environmental conditions of the watersheds were analyzed using two geoprocessing methods and in situ evaluations by applying the rapid assessment protocol (RAP). Geospatial image processing was used to analyze land use, slope, soil classification, and rainfall, while RAP was used to evaluate the connectivity and size of riparian corridors. The results revealed varied uses of land with the landscape matrix in both watersheds, composed of agriculture, pasture, and urban centers. Urban centers were defined as spots and riparian zones as corridors. The analysis of environmental fragility considering all the geospatial variables, classified both watersheds as having medium fragility. The most fragile sites were the urban centers, which had a high slope and degraded riparian zone. Onsite assessments have shown that corridors do not have the size required by legislation; they are fragmented and unstructured, and they contain exotic species. We also propose that a multi-factorial approach be used to evaluate watersheds, associating geospatial assessments and onsite analyses that consider the limitations pointed out in each protocol. This reduces sampling errors and shows the actual state of conservation in riparian zones.

East Lisbon is being exposed to large-scale urban regeneration processes, where luxury residential projects and mixed-use spatial developments are already underway. Thus, it is a living laboratory for “smart”, “creative” and “green” projects, as well as related urban public space interventions. Braço de Prata is an urban space overlooked by developers, being surrounded by obsolete industrial buildings. Concerning the recent interest in international investments in brownfield regeneration and greenfield developments, it represents an attractive urban terrain as a post-industrial working-class neighbourhood, where “smart” and “green” suggest transforming space so that both new and old residents can live and work together and share public space regardless of analysis on their environmental recognitions. The aim of this paper is to present an empirical evaluation model that examines the possible impacts of environmental negligence through the reorganisation of the physical and social fabric. The analyses focus on dwellers’ moral understanding of their changing environment as site-specific domains to address the unique conditions that affect transiently defined presumptions about the collective needs. Taking an evaluative approach in the Braço de Prata case, this paper demonstrates the specific socio-ecological implications of urban inequality in post-industrial neighbourhoods that could be threatened by new decisions, both through urban planning approaches and instruments.

Sustainable nitrogen (N) management in agriculture is one of the most important issues affecting the environmental performance of modern agriculture. It is actually well perceived that coordinated efforts and holistic approaches are required to regulate N use by farmers. The purpose of this study was to provide an initial examination of stakeholders’ views in Japan regarding N use in agriculture and challenges to increase its sustainability. The analysis was based on a questionnaire study of five types of stakeholders (farmers, advisors, researchers, suppliers, policy makers). By means of multivariate analysis techniques it was revealed that consensus was lacking either in the acknowledgment of the causes and effects of unsustainable N management or in the challenges that need to be addressed. N losses from farms and the effects of N use were perceived but not conceived equally by all stakeholders. Organic farming and mandatory measures were the most controversial challenges, while those involving awareness, training and advisory were the most popular. This study cannot provide safe conclusions that can be generalized in the Japanese context, but it indicates domains where further research is required and orientations for future policy design towards more sustainable N use.

The main purpose of this article is to present an overview of the applications of sustainable practices in logistic operations performed by Brazilian companies. To reach this objective, the following steps were carried out: (1) a review of the literature on logistics systems and sustainability in logistics activities; (2) the collection of sustainability reports published by companies that perform logistics operations, which are recognized in Brazil; (3) a content analysis of the reports collected and (4) a discussion of the results, cross-checked with the literature and the extrapolation of conclusions. It was possible to identify 22 sustainable practices, and these practices were grouped into five macro areas. The authors of this paper believe that the findings presented here can be useful for professionals and researchers in the implementation of sustainability practices in logistics systems.

Water scarcity is a driver for society to rethink water management and change the paradigm of use to a fit-for-purpose approach—i.e., separating potable water for human consumption (drinking, cooking or personal hygiene) from all non-potable uses that do not require the same quality level. In this context, urban water reuse is a relevant tool for municipalities and metropolitan areas when dealing with pressure on water resources, among several alternative water sources that can be considered in a site-specific and integrated manner. Through the available literature and specific case studies in the Lisbon Metropolitan Area, this paper explores the benefits and barriers of water reuse and intends to support local authorities in including water reuse in their water management strategies. To the best of our knowledge, this is the first paper focusing on Portugal and the Lisbon Metropolitan Area that globally examines governance, economic, legislative and social aspects regarding water reuse and presents specific implementation examples covering potable and non-potable as well as direct and indirect reuse.

This study explores the impacts of environmental pollution and the digital economy on the new energy industry with panel data on 30 Chinese provinces from 2005 to 2020. Mean group regression was performed, and fully modified OLS and dynamic OLS were conducted to check the robustness of the results. The authors reached two conclusions: (1) environmental pollution exerts significant negative impacts on the new energy industry. For every 1% increase in environmental pollution, the level of development of the new energy industry drops by 0.1658%. In other words, environmental pollution levels down the new energy industry. (2) The digital economy produces significant positive impacts on the new energy industry. For every 1% increase in the digital economy, the level of development of the new energy industry rises by 0.4262%. That is, the digital economy levels up the new energy industry. Our policy recommendations place equal stress on both the prevention and the control of environmental pollution, strengthening digital infrastructure, enhancing the government’s digital governance and service capabilities, protecting consumer rights, and replacing conventional energy with new energy.

Using remote sensing combined with machine learning (ML) techniques is a promising approach to classify soybean cultivars. Therefore, the objectives of this study are (i) to verify which input dataset configuration (using only spectral bands, only vegetation indices, or both) is more accurate in the identification of soybean cultivars, and (ii) to verify which ML technique is more accurate in the identification of soybean cultivars. Information was extracted from five central irrigation pivots in the same region and with the same sowing date in the 2015/2016 crop year, in which each pivot was cultivated with a different cultivar, in which the cultivars used were: CV1—P98y12 RR, CV2—Desafio RR, CV3—M6410 IPRO, CV4—M7110 IPRO, and CV5—NA5909 RR. A cloud-free orbital image of the site was acquired from the Google Earth Engine platform. In addition to the spectral bands alone, a total of 13 vegetation indices were calculated. The models tested were: artificial neural networks (ANN), radial basis function network (RBF), decision tree algorithms J48 (DT) and reduced error pruning tree (REP), random forest (RF), and support vector machine (SVM). The five soybean cultivars were classified by the six-machine learning (ML) models in stratified randomized cross-validation with k-fold = 10 and 10 repetitions (100 runs for each model). After obtaining the r and MAE statistics, analysis of variance was performed considering a 6 × 3 factorial scheme (models versus inputs) with 10 repetitions (folds). The means were grouped by the Scott–Knott test at 5% probability. The spectral bands were the most accurate among the tested inputs in the identification of soybean cultivars. ANN was the most accurate model in identifying soybean cultivars.

This work intended to explore some motivations that influence people’s eating habits towards sustainability. This was an observational, cross-sectional study, carried out by questionnaire survey on a non-probabilistic sample of 10,067 participants from 13 countries (Argentina, Brazil, Croatia, Greece, Hungary, Latvia, Lithuania, Poland, Portugal, Serbia, Slovenia, Romania and United States). Results indicated that people prefer fresh local foods from the season, being important because it allows transportation and storage to be reduced, which in many cases implies refrigeration systems and consequent energy expenditure. Although people avoid food waste at home, the awareness for the waste at restaurants still needs to be improved. Consumers seem to prefer foods that have been produced and packed in sustainable ways but still give importance to the package—understandable for food products. The results also indicated significant differences in the food choice motivations between groups for all sociodemographic variables tested (age, sex, marital status, education, professional area, living environment and country), but the association was high only for variable country. Additionally, a tree classification analysis allowed to identify the relative importance of the influential variables on the sustainable food choices, with country being the most important, followed by age and sex. Additionally, discriminant function analysis allowed establishing a model for the relation between country and six variables accounting for preservation of biodiversity, respect for life, save natural resources, save energy, reduce industrial pollution and minimal packaging. Although with some limitations, this study brings valuable insight into some aspects linked with sustainable food choices on a number of countries and how people shape their food choices according to some sustainability issues.