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Growing scientific evidence for the indispensable role of environmental sustainability in sustainable development calls for appropriate frameworks and indicators for environmental sustainability assessment (ESA). In this paper, we operationalize and update the footprint-boundary ESA framework, with a particular focus on its methodological and application extensions to the national level. By using the latest datasets available, the planetary boundaries for carbon emissions, water use and land use are allocated to 28 selected countries in comparison to the corresponding environmental footprints. The environmental sustainability ratio (ESR)—an internationally comparable indicator representing the sustainability gap between contemporary anthropogenic interference and critical capacity thresholds—allows one to map the reserve or transgression of the nation-specific environmental boundaries. While the geographical distribution of the three ESRs varies across nations, in general, the worldwide unsustainability of carbon emissions is largely driven by economic development, while resource endowments play a more central role in explaining national performance on water and land use. The main value added of this paper is to provide concrete evidence of the usefulness of the proposed framework in allocating overall responsibility for environmental sustainability to sub-global scales and in informing policy makers about the need to prevent the planet’s environment from tipping into an undesirable state.

In the 21st century, global climate change is a key concern for countries all over the world as, in the future, crops will face several extreme events, including an increase of 2–4 °C in the mean temperature with a possible consequent reduction in yield. Wheat (Triticum durum Desf) is one of the most important foods as it provides 20% of the protein for the world population. Since temperature is one of the most limiting factors of crop development, the aim of this trial was to verify the agronomic response of durum wheat to a temperature increase of about 1.5–2.0 °C through the use of short-time adjustment techniques, such as sowing time and variety choice. The experiment foresaw the comparison between two different temperature conditions (ordinary, OT—in the open field, and high, HT—under a polyethylene tunnel), two sowing times (ordinary—OS, and delayed—DS), and three varieties (Ofanto, modern variety; Cappelli, traditional variety; and a mix of the two). HT conditions caused a decline in the wheat yield (−52.5%), but without differences between the two sowing times. The grain quality resulted positively when affected by late sowing times with an increase in 1000 seeds weight and protein percentages and a decrease in shrunken grains. Therefore, it seems that in areas characterized by high temperatures, delayed sowing can improve grain quality without reducing yield quantity compared to ordinary sowing times.

The Pantanal is the world’s largest and most biodiverse continental sheet-flow wetland. Recently, vast tracts of the Pantanal have succumbed to the occurrence of fires, raising serious concerns over the future integrity of the biodiversity and ecosystem services of this biome, including revenues from ecotourism. These wildfires degrade the baseline of natural ecosystems and the ecotourism economy across the region. Local residents (“Pantaneiros”) anecdotally state that extensive cattle herbivory can solve the contemporary flammability problem of the Pantanal by controlling vegetation biomass, thereby preventing or reducing both fuel loads and fires across the region. Here, we examine the covariation between the presence and density of cattle and the incidence of fires across the Brazilian Pantanal. Variables assessed included bovine cattle density, SPI (Standardized Precipitation Index), GPP (Gross Primary Productivity)/biomass estimate, and fire foci along a 19-year time series (2001 to 2019). Our findings show that fire foci across the Pantanal biome are related to climatic variables, such as lower annual precipitation and higher annual drought indices (SPI) rather than to cattle stocking rates. Therefore, the notion of “cattle firefighting”, a popular concept often discussed in some academic circles, cannot be validated because cattle numbers are unrelated to aboveground phytomass. Gross primary productivity further invalidated the “cattle herbivory” hypothesis because GPP was found to be strongly correlated with cattle density but not with the spatial distribution of fires. Fires throughout the Pantanal are currently aggravated by the presence of livestock and result from a combination of extreme weather events and outdated agricultural practices.

The integrated character of the sustainable development goals in Agenda 2030, as well as research in environmental security, flag that sustainable peace requires sustainable and conflict-sensitive natural resource use. The precise relationship between the risk for violent conflict and natural resources remains contested because of the interplay with socio-economic variables. This paper aims to improve the understanding of natural resources’ role in the risk of violent conflicts by accounting for complex interactions with socio-economic conditions. Conflict data was analysed with machine learning techniques, which can account for complex patterns, such as variable interactions. More commonly used logistic regression models are compared with neural network models and random forest models. The results indicate that a country’s natural resource features are important predictors of its risk for violent conflict and that they interact with socio-economic conditions. Based on these empirical results and the existing literature, we interpret that natural resources can be root causes of violent intrastate conflict, and that signals from natural resources leading to conflict risk are reflected in and influenced by interacting socio-economic conditions. More specifically, the results show that variables such as access to water and food security are important predictors of conflict, while resource rents and oil and ore exports are relatively less important than other natural resource variables, contrasting what prior research has suggested. Given the potential of natural resource features to act as an early warning for violent conflict, we argue that natural resources should be included in conflict risk models for conflict prevention.

This research implements a GIS framework model aimed at evaluating the ecological efficiency of urban green areas. The model classifies urban green areas by identifying those that can provide ecosystem services to sustain green infrastructure at an urban district level. This model can also guide decision makers in the proper placement of the green infrastructure. The model works on the interrelation of four indicators of size, shape, vegetation structure and vegetation diversity, and it was tested in the case study of the Western Urban Districts of Naples (Italy). The selection of this study area is because it presents four urban districts that are different for physiography, urban patterns, land use, land cover and for the existing building stock. The proposed GIS-based framework can be a useful tool for planning actions and measures to protect, implement and restore existing green areas through integration into urban green infrastructure.

In Kenya, pastoralists have utilized natural grasslands using practices that often result in overgrazing, low productivity and low income. Such practices have caused environmental problems, which could be exacerbated by climate change. Although knowledge on practices that increase pastoralists’ capacity to adapt to climate and environmental challenges is currently available, the adoption rate remains poor. Hence, there is growing interest in understanding how cross-scale inter-organizational collaboration process either facilitates or hinders climate knowledge communications to and uptake by pastoralists. This study used network analysis to identify how inter-organizational collaborations in knowledge production and dissemination shape knowledge flow and communication to pastoralists in Kenya. A knowledge mapping workshop, key informant interviews and questionnaire surveys were used to identify the key organizations involved in the generation, brokering, and dissemination of adaptation knowledge to pastoralists. Two networks of configurations were explored: (i) relations of collaboration in knowledge production and (ii) relations of collaboration in knowledge dissemination. Measure of clustering coefficient, density, core-periphery location, and degree centrality were used to analyze the network structure and cohesion, and its influence on knowledge flow and adoption. Findings revealed a strong integration across the network with research institutes, NGOs (Non-governmental organizations), and CBOs (Community based organizations) identified as among the central actors, based on their degree centrality. Further, we observed a higher density of ties among actors in the knowledge production network than the dissemination network. The lower density of the dissemination network indicates there are not that many activities by key organizations aimed at ensuring that knowledge reaches the users, compared to activities related to knowledge generation. This also results in poor feedback processes from local pastoralists to knowledge generators and brokers. Knowledge transfer and uptake could therefore be enhanced by improving dissemination activities and feedback mechanisms in the dissemination network as a means of capturing pastoralist perspectives on the relevance, reliability, and usability of knowledge for action. Reflection and revision can be used to improve knowledge so that it is more in sync with a pastoralist context.

This research aims to test a scalable and transferable Geographic Information System (GIS)-based evaluation methodology for the identification, quantification and assessment of multi-functional landscape features. The evaluation of multi-functional features is one of the key tasks required when it comes to identifying the values that people attribute to landscapes, according to the principles of the European Landscape Convention. Mapping the static distribution of Landscape Services (LS) through data-derived estimates and performing spatial composite indicators are fundamental steps in understanding the current state of the Social–Ecological System (SES) of threatened or resilient landscapes. The methodological process is structured in four phases: intelligence (i), design (ii), choice (iii) and outcome (iv), according to the framework of the Multi-Criteria Spatial Decision Support System (MC-SDSS). This process has been implemented in the case study of the National Park of Cilento, Vallo di Diano and Alburni (Italy). The weighting of the spatial indicators, which simulates the model of LS-functioning for the study area, derives from an entropy-based method. Such a method, by which the weights are estimated without decisional agents, concerns a key-concept of information entropy theory, whereby the amount of information for each criterion determines its relative importance within a defined set of spatial criteria. The output of the model concerns mapping composite indicators of the LS; this involves the macro-categories of Regulating, Provisioning, and Cultural Services.