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Steel production is one of the most carbon-intensive industrial sectors, responsible for 8% of European CO2 emissions. While traditional furnaces strongly rely on the consumption of coal or natural gas, potential opportunities for decarbonization stand in the adoption of alternative technologies such as hydrogen-based Direct Reduction Iron (DRI) coupled with Electric Arc Furnaces (EAF). This work focuses on the Italian steel sector and aims at assessing the potential economic and environmental impact of a switch towards such hydrogen-based technology. Three scenarios have been analyzed, all of which are grounded on the common assumption that hydrogen is produced by employing electrolyzers purchasing electricity from the grid. In the first scenario, the share of electricity production from renewable sources (RES) in the national electricity mix coincides with the one in the current Italian situation. The second scenario reflects the national target of 55% of electricity generated by low-carbon technologies. In the last scenario, the RES share in the electricity mix is 100%, meaning steel production plants are fully supplied with green hydrogen. The analysis is carried out by adopting a multi-regional input-output model for sectorial LCA, which allows to highlight the interlinkages of the steel industry with other sectors in different regional areas. The results show that a switch to DRI with EAF technology, coupled with the increase of RES penetration, allows to reduce the CO2 emissions of the Italian steel sector up to 14%, leading to an increase in employment of about 12 thousand units. It is also worth noting that a larger penetration of electricity produced from RES, which are mostly local, would be a significant improvement in terms of energy security of the steel sector, lowering its dependence on foreign fossil resources.

In the context of last-mile electrification of rural communities, stand-alone solutions like Solar Home Systems are often considered an equivalent option for access to electricity as other technologies, such as minigrids and connection to the main grid. In this work, the authors question this paradigm and propose an alternative approach that takes into account the limited service, and in turn, limited development possibilities, that Solar Home Systems grant to their users. A monetary penalty is applied to this technology based on the missed services. The conceived approach is applied to a case study in the district of Cochabamba, Bolivia, thanks to the cooperation with a local NGO. Results show how the inclusion of shadow costs influences the optimal electrification strategy in terms of users connected to the main grid by up to 33%, highlighting how standard approaches tend to overlook key aspects of access to electricity linked to the possibility of development.

Global challenges, such as chronic hunger in developing and developed regions, loss of wildlife habitat, and the continuing rise of greenhouse gas emissions from human activities, can be addressed only through an integrated approach. The telecoupling concept is one such approach: it explores socioeconomic and environmental interactions among coupled human-natural systems over distances. The telecoupling framework is therefore well-positioned to provide new insights to persistent global sustainability challenges. To operationalize the framework, we have developed the Telecoupling GeoApp, a new web-based component of the Telecoupling Toolbox that provides researchers and practitioners with a useful platform to address globally important issues such as international trade, species invasion, biodiversity conservation, and land-use change. The GeoApp features mapping and geospatial analysis tools to visualize and quantify the five major interrelated components of the telecoupling framework (systems, flows, agents, causes, and effects). In this paper, we demonstrate the GeoApp's functionality by applying it to a case study in which distant systems interact across space and time: the Brazil-China soybean telecoupling. We conclude by highlighting the advantages of the Telecoupling GeoApp in addressing global sustainability challenges. It is our hope that this web application will be valuable to a range of users exploring telecouplings and outcomes across distant coupled human-natural systems for achieving sustainable development goals.

Forest landscape simulation models (FLSMs) – often used to understand and project forest dynamics over space and time in response to environmental disturbance – have rarely included realistic epidemiological processes of plant disease transmission and impacts. Landscape epidemiological models, by contrast, frequently treat forest ecosystems as static or make simple assumptions regarding ecosystem change following disease. Here we present the Base Epidemiological Disturbance Agent (EDA) extension that allows users of the LANDIS-II FLSM to simulate forest pathogen spread and host mortality within a spatially explicit forest simulation. EDA enables users to investigate forest pathogen spread and impacts over large landscapes (> 105 ha) and long time periods. We evaluate the model extension using Phytophthora ramorum as a case study of an invasive plant pathogen causing emerging infectious disease and considerable tree mortality in California. EDA will advance the utility of LANDIS-II and forest disease modeling in general.

Rural electrification plays a key role in terms of achieving the target of universal access to electricity by 2030. In order to deploy reliable and sustainable solutions, the sizing phase of decentralized power systems requires specific inputs such as long-term forecasts of the electricity load. In rural contexts, access to electricity is extremely interlinked to social-economic complex dimensions that in turn, influence the electricity demand. In this paper, we rely on System Dynamics for modelling such complexities, and we calibrate the results with data from a hydroelectric-based electrification programme implemented in 2005 in the rural community of Ikondo, Tanzania. We coupled the model with a newly extended version of a load stochastic tool, namely Lifetime – LoadProGen, in order to introduce a more reliable and calibrated approach to long-term electricity load forecasts.

The Asian subterranean termite, Coptotermes gestroi (Wasmann) (Isoptera: Rhinotermitidae), was discovered on Grand Cayman Island in 2000 and, by 2014, had been recorded from 102 land-based localities. These data were used in a hierarchical cluster analysis to identify homogeneous clusters of sites to estimate separate introduction points on the island. Results suggest 4 separate introductions of C. gestroi to Grand Cayman by boat and 1 by land transport from other previously infested parts of the island. The infestations by boat could be either primary introductions (originating from another island) or secondary introductions (originating from other previously infested parts of Grand Cayman). An individual-based model was used to simulate non-anthropogenic spread of C. gestroi over Grand Cayman from 2014 to 2050. The model predicts that by 2050, most of the western part of Grand Cayman will likely be heavily infested by C. gestroi, whereas patches of unsuitable habitat restrict the expansion of the species over the central and eastern parts of the island. In the absence of further human introductions, it will likely take a century for C. gestroi to saturate the island by natural dispersal only. Based on detailed termite diversity surveys, we provide updated records for 14 termite species, collectively, on Grand Cayman, Little Cayman, and Cayman Brac.

Scientists and land managers are increasingly monitoring forest microclimate environments to better understand ecosystem processes, such as carbon sequestration and the population dynamics of species. Obtaining reliable time-series measurements of microclimate conditions is often hindered by missing and erroneous values. In this study, we compare spatio-temporal techniques, space–time kriging (probabilistic) and empirical orthogonal functions (deterministic), for reconstructing hourly time series of near-surface air temperature recorded by a dense network of 200 forest understory sensors across a heterogeneous 349 km2 region in northern California. The reconstructed data were also aggregated to daily mean, minimum, and maximum in order to understand the sensitivity of model predictions to temporal scale of measurement. Empirical orthogonal functions performed best at both the hourly and daily time scale. We analyzed several scenarios to understand the effects that spatial coverage and patterns of missing data may have on model accuracy: (a) random reduction of the sample size/density by 25%, 50%, and 75% (spatial coverage); and (b) random removal of either 50% of the data, or three consecutive months of observations at randomly chosen stations (random and seasonal temporal missingness, respectively). Here, space–time kriging was less sensitive to scenarios of spatial coverage, but more sensitive to temporal missingness, with less marked differences between the two approaches when data were aggregated on a daily time scale. This research contextualizes trade-offs between techniques and provides practical guidelines, with free source code, for filling data gaps depending on the spatial density and coverage of measurements.

The Formosan subterranean termite, Coptotermes formosanus Shiraki, is a pest of major economic concern. This termite is particularly known for its tendency to establish populations in nonendemic areas via maritime vessels as well as human-aided transport of infested materials. The natural spread of this species after new introductions occurs in part by dispersal flights originating from mature colonies. Dispersal flight activity is also the primary variable for the evaluation of area-wide management programs. Few studies exist describing the dynamics and distribution of a typical dispersal flight for this species. The present study used data collected by mark-recapture of C. formosanus alates over 12 individual evenings of dispersal flights in the New Orleans French Quarter. In this study, we found that for one selected flight dispersal location, which was not affected by a high density of trap locations nearby, alates flew on average 621 m from their parent colony. A new record of a 1,300-m dispersal flight was recorded. Spatial analysis showed that neither wind nor light affected the direction of flight, which may, however, be attributed to scarce light and wind measurements in the study region.

The type of data an individual contributor adds to OpenStreetMap (OSM) varies by region. The local knowledge of a data contributor allows for the collection and editing of detailed features such as small trails, park benches or fire hydrants, as well as adding attribute information that can only be accessed locally. As opposed to this, satellite imagery that is provided as background images in OSM data editors, such as ID, Potlatch or JOSM, facilitates the contribution of less detailed data through on-screen digitizing, oftentimes for areas the contributor is less familiar with. Knowing whether an area is part of a contributor’s home region or not can therefore be a useful predictor of OSM data quality for a geographic region. This research explores the editing history of nodes and ways for 13 highly active OSM members within a two-tiered clustering process to delineate an individual mapper’s home region from remotely mapped areas. The findings are evaluated against those found with a previously introduced method which determines a contributor’s home region solely based on spatial clustering of created nodes. The comparison shows that both methods are able to delineate similar home regions for the 13 contributors with some differences.

The ability to manage geospatial data has made Geographic Information Systems (GIS) an important tool for a wide range of applications over the past decades, including manage- ment of natural resources, analysis of wildlife movement, ecological niche modeling, or land records management. This paper illustrates, using invasive termite species as examples, how GIS can assist in identifying their potential sources of infestations and model their spread in urban South Florida. The first case study shows that the Formosan subterranean termite, Coptotermes formosanus Shiraki, and the Asian subterranean termite, Coptotermes gestroi (Wasmann) (Isoptera: Rhinotermitidae), were introduced into and dispersed across South Florida by sailboats and yachts. The second case study shows an agent-based model to simulate the natural spread of Nasutitermes corniger (Motschulsky) (Isoptera: Termitidae) in Dania Beach, Florida. This paper provides an overview of basic functionalities in GIS and demonstrates how they can be customized for advanced modeling and simulation.

Invasive termites are destructive insect pests that cause billions of dollars in property damage every year. Termite species can be transported overseas by maritime vessels. However, only if the climatic conditions are suitable will the introduced species flourish. Models predicting the areas of infestation following initial introduction of an invasive species could help regulatory agencies develop successful early detection, quarantine, or eradication efforts. At present, no model has been developed to estimate the geographic spread of a termite infestation from a set of surveyed locations. In the current study, we used actual field data as a starting point, and relevant information on termite species to develop a spatially-explicit stochastic individual-based simulation to predict areas potentially infested by an invasive termite, Nasutitermes corniger (Motschulsky), in Dania Beach, FL. The Monte Carlo technique is used to assess outcome uncertainty. A set of model realizations describing potential areas of infestation were considered in a sensitivity analysis, which showed that the model results had greatest sensitivity to number of alates released from nest, alate survival, maximum pheromone attraction distance between heterosexual pairs, and mean flight distance. Results showed that the areas predicted as infested in all simulation runs of a baseline model cover the spatial extent of all locations recently discovered. The model presented in this study could be applied to any invasive termite species after proper calibration of parameters. The simulation herein can be used by regulatory authorities to define most probable quarantine and survey zones.

The analysis and forecasting of extreme climatic events has become increasingly relevant to plan effective financial and food-related interventions in third-world countries. Natural disasters and climate change, both large and small scale, have a great impact on non-industrialized populations who rely exclusively on activities such as crop production, fishing, and similar livelihood activities. It is important to identify the extent of the areas prone to severe drought conditions in order to study the possible consequences of the drought on annual crop production. In this paper, we aim to identify such areas within the South Tigray zone, Ethiopia, using a transformation of the Normalized Difference Vegetation Index (NDVI) called Absolute Difference NDVI (ADVI). Negative NDVI shifts from the historical average can generally be linked to a reduction in the vigor of local vegetation. Drought is more likely to increase in areas where negative shifts occur more frequently and with high magnitude, making it possible to spot critical situations. We propose a new methodology for the assessment of drought risk in areas where crop production represents a primary source of livelihood for its inhabitants. We estimate ADVI return levels pixel per pixel by fitting extreme value models to independent monthly minima. The study is conducted using SPOT-Vegetation (VGT) ten-day composite (S10) images from April 1998 to March 2009. In all short-term and long-term predictions, we found that central and southern areas of the South Tigray zone are prone to a higher drought risk compared to other areas.