Identify Priority Areas Research Articles

Inclusive, needs-based research for addressing Health Literacy in Parents with immigrant backgrounds

Abstract Research indicates that parental health literacy is associated with child health outcomes. A 2021 study by the Norwegian Public Health Department found that certain immigrant groups struggle with health literacy due to language barriers, cultural differences in understanding of health and illness, and unfamiliarity with the Norwegian health system and services. To reduce differences in health literacy and better support parents with immigrant backgrounds to make health promoting choices for their children, and navigate the Norwegian health and social systems effectively, we need knowledge about their resources, challenges, and needs. This knowledge makes it possible to design strategies to promote parental health literacy and make health services more responsive to the needs of the population. Optimising health literacy amongst parents with immigrant backgrounds (OPAL) is a research collaboration between Oslo Metropolitan University and the Bjerke City District, one of the most culturally and linguistically diverse districts in Oslo. Applying the Optimising Health Literacy and Access (Ophelia) process, parents health professionals, and researchers work systematically together to develop knowledge of parents’ health literacy, identify priority areas for research, design and implement effective solutions in primary health care. In this presentation, we will describe how OPAL is rooted in the strategic priorities of the city district, how the project applies methods for needs identification and involves users and stakeholders at all stages. We will share experiences from planning and conducting inclusive data collection, preliminary results and describe how results will be used in development of targeted health literacy actions.

GIS and statistics to analyze scale change consequences on medical desert classification in France

Abstract Background Accessibility is a crucial way to overcome geographical restrictions and provides a path for alleviating the medical desert. Less attention has been paid to the choice of spatial unit in the effect of healthcare multi-accessibility. Inside the OASES European program, the France team proposes to create medical desert classification at the municipality level. The contribution of this study is to highlight the consequences of the choice of spatial scale on spatial distribution and the discrepancies between medical desert classifications. Methods Analyses were carried out in metropolitan France and overseas departments, at the commune scale and two other supra-communal scales used by public policies in France. For each spatial unit, a multidimensional and professional classification has been conducted using a three-step method: scores, principal component analysis and clustering. To demonstrate the presence of spatial dependence and to measure homogeneity between classifications joint count autocorrelation indicators and Theil’s indexes were applied. Results Our results present 7 classes of municipalities, and 5 classes of EPCI or living territories according to their level of healthcare accessibility, the dynamic of supply, and the needs of the population. Spatial representation of the classification demonstrates greater variability at the municipality level than other supra scales. Informative contrasted classes at the municipality level disappeared in favor of the middle class in all accessibility at supra scales. The results demonstrate that spatial dependence between areas at the municipality level reduced the scale effect. Conclusions Spatial approaches have become increasingly popular since they enable decision-makers to identify priority areas and allocate resources. Recognizing the instability across various scales is therefore crucial for thinking about the optimal unit of data aggregation when investigating the medical desert. Key messages • Choice of the scale is a mix between public policy decisions and data availability. • Contiguity constraint is a way to reduce the loss of information after aggregation.

INTEGRATING SOCIO-ECOLOGICAL CONNECTIONS INTO URBAN PLANNING

Urban ecology increasingly recognizes urban vacant lots as spaces with untapped aesthetic, social, and environmental potentials. This study focuses on strategies for converting these lots into green areas in Mossoró, a semi-arid city in Brazil, by analyzing urban connectivity patterns. Using a Geographic Information System, the study identifies priority areas for green space conversion based on factors like urban planning, density, land cover, and vacant lots. The analysis revealed 429 hectares of highly suitable land for conversion. A resistance surface was also created, factoring in public amenities and urban infrastructure, which showed varying levels of resistance to human movement. Areas with well-developed infrastructure had lower resistance, encouraging human and ecological movement. Connectivity modeling highlighted a need for more green infrastructure, especially in peripheral regions, to address socio-spatial fragmentation and enhance urban resilience in Mossoró. Keywords: Connectivity, Green Infrastructure, Urban Vacant Lots.

Mapping wild nature areas to identify priority areas for urban rewilding in cities: A process-oriented approach

Urban rewilding initiatives are gaining traction in cities, yet little remains known about the spatial opportunities for transitioning greenspaces into rewilding sites. This paper advances a process-oriented framing of urban wild nature areas (WNAs) and proposes a novel methodological approach for mapping existing WNAs at the city-scale, thereby shedding light on the potentials for urban rewilding across greenspace types and urban environments. Using Vienna as a case study, we identify WNAs as greenspaces predominantly shaped by natural succession processes and devoid of vegetation management. NDVI timeseries derived from Sentinel-2-satellite data for the period 2017–2022 are processed using a Random Forest algorithm to distinguish between unmanaged and managed vegetation annually and integrated into a multi-year composite map of ‘urban vegetation management intensity’. Based on this map and a set of objective cut-off values, 1298 WNAs are identified, representing 29.5 % of the city’s total urban greenspace and 14.9 % of the city’s total area. WNAs are distributed across diverse urban settings and greenspace types, with the majority being found in formal greenspaces such as forests, meadows and parks, and low-density built-up areas. The key strength of the process-oriented approach lies in its ability to detect WNAs dominated by natural succession, regardless of historic origin, greenspace type, and governance structure, thereby providing a baseline against which the full potential for urban rewilding can be evaluated. We demonstrate the methods’ utility in identifying potentials for urban rewilding, emphasising the importance of directing rewilding efforts towards residential greenspaces, urban parks, and street greenery, especially in densely built inner-city districts.

Spatial Anaysis and Modelling to Mappping Priorities Areas for Restauration: Study Case: Natural Protected Area Montes Azules, Chiapas, Mexico

Natural Protected Areas play a fundamental role in the conservation of biodiversity and ecosystems throughout the world and are designated and managed with the purpose of preserving biological and cultural capital, ensuring the sustainable use of natural resources, and maintaining ecosystem services. The Montes Azules Biosphere Reserve, like other Natural Protected in Mexico, faces significant challenges such as deforestation, illegal logging, and loss of vegetation cover, as well as socio-environmental conflicts, factors that hinder the biological and cultural conservation of the area. Likewise, despite their importance, many Natural Protected face significant challenges in terms of effective monitoring and planning. The lack of adequate monitoring and planning strategies, as well as staff and budget, makes it difficult to assess the conservation status of these areas and to implement appropriate management measures. Therefore, creating specific monitoring tools is essential to periodically evaluate their conservation status and identify threats and pressures on ecosystems in order to design adaptive and effective management strategies. The objective of this study is to offer a first methodological approach to identify priority areas for restoration within the Montes Azules Biosphere Reserve, which will allow decision-makers to design and apply conservation strategies in the area. The determination of the priority areas for restoration was based on an environmental management process, where the Land suitability is the base for the territorial arrangement, which was modeled together with the current land use and land cover through rules of decision and having the natural protected area as a frame. For land suitability, the land classification system was used according to its use capacity or American system of the 8 classes developed by the Soil Conservation Service of the United States; for mapping land use land cover, an object base classification method was used. The total transformed area in 2023 occupies an extension of 39,475.38 ha, approximately 50% of this lands corresponds to sloping and very steep land and in some cases with shallow to very shallow soils, not suitable for agriculture. The area identified as priority zones for restoration corresponds to an extension of 19,514.5 ha, which represents nearly 50% of the transformed area, and 6% of the total of study area, from which 6,860.5 ha are top priority for restoration. As main conclusion can be said that the methodological proposal described here serves as a first approximation to identify priority zones for restoration in the Montes Azules Biosphere Reserve, allowing for a more detailed identification of these areas at the local level, which will allow decision-makers to design and apply conservation strategies in the area.

Optimizing soil conservation through comprehensive benefit assessment in river basins

Land degradation from water erosion poses a significant threat to water security and ecosystem stability, driving global efforts in soil conservation. Quantitative assessment of soil conservation benefits—both on-site and off-site—is crucial for guiding effective conservation strategies. However, existing methodologies often fall short in quantifying the value of these combined benefits. Here, we present a comprehensive framework for quantifying soil conservation service flows in monetary terms, evaluating the effectiveness of both on-site and off-site measures. Applying this framework to the Yellow River Basin (YRB), we employ cost-avoidance algorithms related to soil fertility maintenance, dredging cost reduction, and mitigation of nonpoint source pollution. Our results reveal that while many areas contribute to both on-site and off-site benefits, over half of the YRB relies predominantly on off-site services. By strategically enhancing key regions—which constitute 30% of the basin—we demonstrate that the overall soil conservation service supply can increase by 64.2% over the multi-year average from 2001 to 2020 compared to a consideration of on-site only. These findings underscore the essential role of off-site services in fully understanding soil conservation needs, particularly in large river basins, and the identified priority areas can offer valuable insights for optimizing soil conservation efforts.

Artificial intelligence methods used in various aquaculture applications: A systematic literature review

AbstractThis research article presents a systematic literature review on the current state‐of‐the‐art artificial intelligence (AI) methodologies used in aquaculture applications. As the demand for seafood continues to grow, the aquaculture industry faces numerous challenges, including disease management, feeding optimization, water quality monitoring, and extraction of aquaculture area. To address these challenges effectively and sustainably, AI techniques have been increasingly applied in aquaculture systems over recent years. This review aims to analyze various AI methodologies utilized within different aspects of aquacultural practices. By examining existing studies and identifying trends and gaps in research areas related to AI integration into aquaculture practices, this paper provides valuable insights for further advancements. The purpose was to synthesize current knowledge on application and its challenges in implementing AI technologies within the commercial aquaculture industry. Specifically, the review is to identify and analyze peer‐reviewed studies reporting on applications of AI technologies in aquaculture industry, to classify and summarize the key findings from the selected studies in aquaculture operations through AI, and to evaluate and discuss any challenges reported regarding the implementation and adoption of AI solutions in commercial aquaculture. The overall goal was to comprehensively assess these via a systematic literature review process. Challenges of AI technologies and methods were identified in the research literature for applying AI to optimize commercial aquaculture practices and production. An exhaustive search of a scholarly database from Scopus, was performed and papers published in English between 2020 and 2024 were considered for inclusion. After a rigorous screening process involving over 116 studies, 57 highly relevant works were identified and analyzed according to key themes involving demonstrated AI applications, employed methodologies and challenges that are expected when applying such methods. The findings revealed that AI‐driven tools such as computer vision, machine learning, and predictive modeling hold much potential for enhancing sustainability, efficiency, and productivity within aquaculture operations through applications like disease monitoring, environmental management, and production optimization. However, the review also uncovered substantial challenges that will continue limiting widespread adoption, including restricted access to representative data, prohibitive expenses, technical complexities, lack of social acceptance, and data privacy and security concerns. This comprehensive synthesis of the current evidence available provides an empirical foundation upon which further progress can be built by identifying priority areas requiring additional research efforts to fully address challenges on the responsible integration of suitable solutions for the commercial aquaculture industry globally.

Plastic ingestion in aquatic insects: Implications of waterbirds and landfills and association with stable isotopes

Wetlands provide numerous ecosystem services including freshwater purification. Nonetheless, their functionality is continuously impacted by many pollutants. Plastics are considered as an emerging threat for these ecosystems, but only recently have studies began to focus on plastic and microplastic (MP) contamination in wetlands, especially in biota. This study aims to investigate the abundance of MPs in two ubiquitous aquatic insect taxa (i.e. Corixidae (Hemiptera) and Chironomidae (Diptera)) collected in twelve zones within Mediterranean wetlands belonging to three basins located in Andalusia (south-west Spain). We compared MP contamination across basins and tested the proximity to landfills and presence of colonial waterbirds [i.e. white storks (Ciconia ciconia) and gulls (Larus michahellis and L. fuscus)] on MP abundance in aquatic insects. We also performed stable isotope analyses of nitrogen and carbon (δ15N and δ13C) to evaluate the potential association between MP abundance and isotopic values. We detected 571 suspected MPs (mostly blue fibers) in insects of different developmental stages (i.e., larvae, pupae, nymphs and adults). Polyesters and polypropylene were the most frequent polymers detected. The generalized linear mixed models indicated that MP abundance decreased with increasing distance from landfills; but it also increased in sites with birds that fed on landfills and roost in wetlands. When controlling for landfill effects, sites in the smallest basin (Guadalete) had lower MP contamination than those in Odiel-Tinto and the much larger (>15×) Guadalquivir. Moreover, we found a negative association between MPs items/g (or mean MPs) and 15N isotopes in adult corixids. Our findings showed that MP pollution is present in all the study areas, including strictly protected wetlands. The use of aquatic insects for biomonitoring of MP pollution can help identify priority areas for management actions to mitigate plastic pollution.

Development of a georeferenced atlas of energy renovation in residential buildings using GIS tool

Abstract This paper presents a method for modelling the energy needs for heating the residential building stock on a macro scale. The method is based on the use of Geographic Information Systems (GIS) tools to manage various existing geolocalised databases and the collection of field data. It combines the calculation of energy demand with the estimation of solar gains received by the building envelope. In order to estimate the heat energy balance, a description of the building geometry is necessary. The geometrical characteristics of the envelope are extracted from the building entities information available in the spatial database. These parameters are then enriched and cross-referenced with other data gathered from the government’s complex taxation files. The TABULA buildings typology was chosen because it is an exhaustive European classification adapted for use in GIS utilities. The objective of this work is to produce a georeferenced atlas that can identify potential energy savings on large zones, such as urban areas. An understanding of energy consumption at this scale is crucial for the effective management of massively scaled energy-saving solutions. The atlas will promote energy efficiency in the residential sector and guide environmental policy measures by identifying priority areas for renovation.

Identifying Priority Areas for the Indian Leopard (Panthera pardus fusca) Within a Shared Landscape.

Habitat loss is one of the primary drivers of large felid decline. The leopard (Panthera pardus), a generalist large felid species, has the behavioural and dietary flexibility to exploit different habitat types of varying human influence. Understanding habitat selection in a shared landscape is critical for the development of conservation strategies and managing negative human-leopard interactions. The development of conservation policy requires data on large spatial scales, which is mostly lacking, especially within shared landscapes in India. This study aims to determine habitat selection by Indian leopards and the anthropogenic, environmental, and climatic variables contributing to this selection. Leopard occurrence records were obtained from an occupancy survey conducted in the five administrative districts in Karnataka (28,375 km2). 267 randomly selected 30 km2 grids were each walked for 10 km and all leopard signs were recorded. Environment variables were chosen to reflect land use, climatic, topographic, and human disturbances that could affect habitat selection at a resolution of 0.1 km2. The mean ensemble model was projected to the state of Karnataka. Habitat selection predicted by the ensemble model was driven by proximity to forest cover and rocky outcrops, higher precipitation, and negatively by distance to cropland and roads. Protected Areas and Reserved Forests in the study covered 47% of the predicted habitat, while 25% is within human-use areas such as human habitation and croplands. This study predicts that half of the habitat selected by leopards is outside of protected areas and reserved forests. The selection of human-use areas is predicted because of the availability of cover from irrigated croplands and the proximity to natural cover that provides refuge. Livestock density did not drive large-scale habitat selection. The preservation of natural cover and rocky outcrops that provide refuge between protected areas is paramount for leopard conservation.

Optimizing afforestation and reforestation strategies to enhance ecosystem services in critically degraded regions

Human activity has caused massive forest ecosystem damage, threatening the global environmental balance. Afforestation and reforestation are crucial strategies for the restoration of forest ecosystem functions. This study was conducted on Belitung Island, Indonesia, which has experienced forest degradation due to mining activity and is currently undergoing forest restoration efforts. This study aimed to identify priority areas for afforestation and reforestation using an innovative approach that integrates multi-criteria analysis (MCA) and machine-learning techniques based on ecosystem service (ES) indicators, wildfire susceptibility, and environmental pressure. This study is the first to combine long-term remote sensing data with machine learning to develop priority scenarios for afforestation and reforestation. Results show that low-priority afforestation areas cover 24,479.66 ha (20.45 %), medium-priority areas 58,703.30 ha (49.04 %), and high-priority areas 36,521.98 ha (30.51 %). For reforestation, low-priority areas cover 23,123.45 ha (30.45 %), medium-priority areas 38,197.36 ha (50.3 %), and high-priority areas 14,618.27 ha (19.25 %). This study is expected to serve as a reference for sustainable forest ecosystem restoration efforts in various regions by leveraging ES approaches and environmental conditions using remote-sensing technology.

Fire-sensitive and threatened plants in the Upper Paraguay River Basin, Brazil: Identifying priority areas for Integrated Fire Management and ecological restoration

Extreme wildfires present an ever-increasing problem in different landscapes around the world. Here, we explore recent wildfires to prioritize recovery efforts and mitigate effects of future wildfires in the Pantanal, the largest continuous wetland in the world. Using ecological niche modeling (ENM), we categorize affected areas based on potential levels of degradation by fire and the predicted suitability for threatened and fire-sensitive plants. Based on this information, we identify key areas for fire prevention. Under land cover change scenarios predicted for 2050, approximately 25 % of the land cover under future threat will have potentially high biomass accumulation. We identified 1272 km2 in priority areas with forest formation, with restoration cost of at least US$ 576,189. This case study provides an analysis workflow that can be used to assess other wildfire-threatened parts of the world. These results can inform public policy to establish new fire brigades, conservation efforts using Integrated Fire Management, and restoration areas to mitigate biodiversity loss.

Priority areas identification for arable soil pollution prevention based on the accumulative risk of heavy metals

Preventing impending heavy metal pollution in arable soil is crucial for ensuring food security and avoiding challenging remediation. The key to effective prevention strategies lies in proactive identifying currently unpolluted regions that are susceptible to future pollution, which current methods, predicated on the assessment of static pollution status, inadequately characterize the potential accumulative changes in soil heavy metals. In this paper, we proposed a framework for identifying priority areas based on the discrepancy between pollution status and accumulative risk, by considering the specific factors that influence heavy metal accumulation in soil. We applied this framework to a region of Xiangtan County to pinpoint priority areas for preventing impending pollution. The result revealed certain areas exhibited a relatively higher accumulative risk of heavy metal pollution, despite not having reached severe pollution levels for the heavy metals Arsenic (As), Cadmium (Cd), Chromium (Cr), Mercury (Hg), and Lead (Pb), among which the area ratio reached nearly 6 %, 36 %, 1 %, 3 %, 4 %, respectively. The priority areas for preventing Cd pollution were primarily concentrated in the mid-southern, mid-western, and eastern regions, while that of the other four heavy metals were predominantly distributed in the mid-northern regions with varying continuous ranges. Moreover, we prioritized the main pollution risks for comprehensive prevention in the following order: Cd, As, Pb, Hg, and Cr, and investigated the key factors contributing to the pollution of these heavy metals. The insights presented in this study have significant implications for soil environmental quality management, offering valuable guidance for implementing precise measures to prevent heavy metal pollution and efficiently control pollution sources.

SYNTHETIC INDICATORS OF QUALITY OF LIFE: EVALUATION OF THE EFFECTIVENESS OF UKRAINE'S ECONOMIC POLICY

This paper examines the effectiveness of Ukraine's economic policies through the lens of quality of life and argues for the adoption of a minimal set of evaluation criteria, ideally a single composite criterion, to optimise policy evaluation. Despite ongoing efforts to identify such criteria, the study introduces a novel approach by using synthetic indicators of quality of life as the basis for evaluation. This approach is particularly relevant in the context of Ukraine's post-war recovery and its aspirations towards European Union integration. These conditions require the adoption of economic policies that meet European standards of quality of life and facilitate the necessary reforms. Such policies are crucial for improving the living conditions of the Ukrainian population and facilitating the return of refugees after Ukraine's victory over Russia, the aggressor country. This study develops a methodology that uses synthetic indicators of quality of life as a system of criteria to assess the effectiveness of Ukraine's economic policy. The research involves a cross-country analysis of data from seven EU Member States and Ukraine, and the construction of regression models that link the determinants of state economic policy to several indicators: population quality, welfare, social quality, and a comprehensive synthetic indicator – quality of life. The obtained results confirm the hypothesis about the key parameters of the state economic policy as determinants of improving the quality of life of Ukrainian citizens. In addition, the analysis of the dynamics of synthetic indicators of the quality of life in Ukraine and their determinants makes it possible to identify priority areas of economic policy aimed at improving these indicators, which will contribute to the overall efficiency of Ukraine's economic policy.

MCF classifier: Estimating, standardizing, and stratifying medicine carbon footprints, at scale.

Healthcare accounts for 5% of global greenhouse gas emissions, with medicines making a sizeable contribution. Product-level medicine emission data is limited, hindering mitigation efforts. To address this, we created Medicine Carbon Footprint (MCF) Classifier, to estimate, standardize, stratify and visualize medicine carbon footprints. We used molecular weight and chemical structure to estimate the process mass intensity and global warming potential of the active pharmaceutical ingredient in small molecule medicines. This allowed us to estimate medicine carbon footprints per dose, which we categorized into MCF Ratings, accessible via a searchable web application, MCF Formulary. We performed comparison and sensitivity analyses to validate the ratings, and stratification analyses by therapeutic indication to identify priority areas for emission reduction interventions. We generated standardized medicine carbon footprints for 2214 products, with 38% rated LOW, 35% MEDIUM, 25% HIGH and 2% VERY HIGH. These products represented 2.2 billion NHS England prescribed doses in January 2023, with a total footprint of 140 000 tonnes CO2e, equivalent to the monthly emissions of 940 000 cars. Notably, three antibiotics-amoxicillin, flucloxacillin and penicillin V-contributed 15% of emissions. We estimate that implementing the recommended 20% antibiotic prescription reduction could save 4200 tonnes CO2e per month, equivalent to removing 29 000 cars. Standardized medicine carbon footprints have utility in assessing and addressing the carbon emissions of medicines, and the potential to inform and catalyse changes needed to align better healthcare and net zero commitments.

Measuring physical disorder of architectural façades for informing better urban renewal using deep learning and space syntax

With the emergence of human-centered urban development goals and the increasing pursuit of a better quality of life, the architectural façades of cities are receiving growing attention. However, during the process of urban development, architectural façades often experience physical disorder. This phenomenon tends to be overlooked in targeted urban management practices or lacks cohesive urban renewal planning at a macro scale. This oversight can negatively impact the livability and attractiveness of a region. This study aims to quantify the architectural façades encountered daily by urban residents by measuring the physical disorder of architectural façades to inform better urban renewal using deep learning and space syntax. First, streetscape images of architectural façades were collected using the Baidu Maps Street View service. Subsequently, an evaluation system for architectural façades was developed, and machine learning was employed to conduct high-resolution measurements and assessments of these façades. Simultaneously, street network data is extracted and analyzed using space syntax to quantify the accessibility of architecture on each street. Finally, by integrating the analysis of architectural façades and accessibility, the study identifies priority areas for building renewal, thus providing a decision-support tool for sustainable urban renewal planning. Overall, the paper presents an innovative method that combines image data, deep learning, and space syntax-derived architectural accessibility for a quadrant analysis. It offers designers and decision makers new perspectives and enhances the livability of residents by focusing on the physical condition of architectural façades, thereby making urban renewal practices more human-centered and better aligned with the actual needs of city dwellers.

Nature-Based Solutions Scenario Planning for Climate Change Adaptation in Arid and Semi-Arid Regions

Extreme climatic conditions cause a decrease in ecosystem services, the disruption of the ecological balance, and damage to human populations, especially in areas with socially vulnerable groups. Nature-based solutions applying blue-green infrastructure (BGI) against these negative impacts of climate change have an important role in planning sustainable cities. This study aims to identify priority areas and develop scenarios and strategies for spatial planning to understand the tradeoffs in approaches and to maximize the benefits of ecosystem services provided by BGI in cities with arid and semi-arid climates, using Phoenix, Arizona, a swiftly urbanizing city in the Sonoran Desert, as the study area. Using GIS-based multi-criteria decision-making techniques and the Green Infrastructure Spatial Planning model integrated with the city’s existing water structures, this study is conducted at the US census scale. The hotspots for BGI are mapped from the combined GIS-based multi-criteria evaluation and expert stakeholder-driven weighting. In the hotspots where priority areas for BGI in Phoenix are identified, the city center area with a high density of impervious surfaces is identified as the highest priority area. It is revealed that social vulnerability and environmental risks (flooding, heat) have a positive correlation in Phoenix, and stormwater management and the urban heat island are the criteria that should be considered first in BGI planning.

Identifying temperature refuges in Utah using temperature, biota, and habitat data

AbstractUnderstanding where on landscapes to make investments, such as designating protected areas, is a critical component of biodiversity management. Locations for management actions should achieve current management objectives while also having the best chance of continued success in the future. Climate change has the potential to undermine biodiversity management, as it may lead to substantial changes in environmental conditions that are outside local managers' control. Following changes in environmental conditions, areas on the landscape may become unsuitable for the species or habitats that the initial actions were intended to benefit. The potential for local actions to be undermined by global‐scale threats makes it essential to account for and minimize exposure to temperature change. We present a series of analyses identifying priority areas for wildlife and habitat management. We conducted our analyses using a systematic landscape planning approach that identifies areas within species' ranges or current distributions of key habitats that are predicted to be less affected by future temperature change. We used the ranges of 142 animal and 149 plant species identified as species of greatest conservation need (SGCN) together with the distributions of 14 terrestrial and 19 aquatic key habitats in Utah, USA. We measured temperature change in 2 ways: as changes in mean annual temperature between 2020 and the year 2100 (temperature difference) and by quantifying how far a species range or habitat would have to shift to maintain its current temperature envelope (climate velocity). We identified the sub‐watersheds with hydrologic unit code 12 (HUC 12) that collectively encompassed the ranges of our SGCNs and key habitats while minimizing overall exposure to temperature change. These high priority HUC 12s represented areas that were not only hotspots for SGCNs and key habitats but also acted as temperature refugia, where management actions are likely to be robust to temperature change. We hope that our identification of high‐priority HUC 12s will help inform and guide future management actions to improve their long‐term outcomes.

Assessment of green hydrogen production by volatile renewable energy under different SSPs scenarios in China

In the context of increasing global concerns about climate change and sustainable energy, the production of green hydrogen (GH) from volatile renewable energy (VRE) sources, such as solar and wind, is a natural choice for China to meet its climate change mitigation goals. However, there are a number of challenges in current research on the assessment of green hydrogen potential. This study evaluates the potential of GH production through VRE at the regional level in China, under the various scenarios of Shared Socioeconomic Pathways (SSPs). For this purpose, a power generation prediction model suitable for VRE is proposed to predict the energy supply capacity for GH production in six regions of China, which combined Multilevel Discrete Wavelet Decomposition and Bi-directional Long Short-Term Memory with attention mechanism (MDWD - AMBiLSTM). The total amount of GH could potentially reach up to 428.01 Mt under the SSP1, with the northern region leading at the regional level at 104.21 Mt. Furthermore, the study evaluated the potential for GH promotion in six regions of China between 2023 and 2035 b y combining the three dimensions of GH production potential, economic evaluation, and environmental contribution. This study tentative identify priority areas and development scenarios for promoting GH production in China, and made policy recommendations for the promotion of green hydrogen in disparate areas.

Decision support for United States—Canada energy integration is impaired by fragmentary environmental and electricity system modeling capacity

Abstract The renewable energy transition is leading to increased electricity trade between the United States and Canada, with Canadian hydropower providing firm lower-carbon power and buffering variability of wind and solar generation in the U.S. However, long-term power purchase agreements and transborder transmission projects are controversial, with two of four proposed transmission lines between Quebec, Canada and the northeast U.S. cancelled since 2018. Here, we argue that controversies are exacerbated by a lack of open-source data and tools to understand tradeoffs of new hydropower generation and transmission infrastructure in comparison to alternatives. This gap includes impacts that incremental transmission and generation projects have on the economics of the entire system, for example, how new transmission projects affect exports to existing markets or incentivize new generation. We identify priority areas for data synthesis and model development, such as integrating linked hydropower and hydrologic interactions in energy system models and openly releasing (by utilities) or back-calculating (by researchers) hydropower generation and operational parameters. Publicly available environmental (e.g. streamflow, precipitation) and techno-economic (e.g. costs, reservoir size,) data can be used to parameterize freely usable and extensible models. Existing models have been calibrated with operational data from Canadian utilities that are not publicly available, limiting the range of scientific and commercial questions these tools have been used to answer and the range of parties that have been involved. Studies conducted using highly resolved, national-scale public data exist in other countries, notably, the United States, and demonstrate how greater transparency and extensibility can drive industry action. Improved data availability in Canada could facilitate approaches that (1) increase participation in decarbonization planning by a broader range of actors; (2) allow independent characterizations of environmental, health, and economic outcomes of interest to the public; and (3) identify decarbonization pathways consistent with community values.