Digital technologies are profoundly influencing all economic sectors and have potential to contribute towards a sustainable society. At the same time, the production, use and waste management of these technologies, which lie at the core of the economic sector of information and communication technology (ICT), are causing environmental impacts. Previous studies have applied life cycle assessment (LCA) methodology and life cycle thinking to assess current and future direct energy use and climate impact of the global ICT sector. These studies frequently arrive at contradictory results regarding future impacts. Calculation approaches applied differ significantly, the consideration of key aspects varies, fast-growing digital technologies are seldom included in future scenarios and uncertainty analyses are typically limited. The aim of this study is to develop guidelines for assessments of the current and future direct energy use and climate impact of the global ICT sector based on LCA methodology and life cycle thinking. The guidelines have been developed based on literature reviews, the authors’ aggregated and broad expertise in this topic and in workshops. Key aspects in influencing the current and future direct energy use and climate impact of the global ICT sector, covering its three subdomains of end-user devices, networks and data centres as well as all life cycle stages, are identified. These include, for example, the number of end-user devices, number of subscriptions and the annual electricity use of networks and data centres. The guidelines address challenges for practitioners and can contribute towards more transparent and coherent future studies.

To meet the 2030 Agenda for Sustainable Development, all Sustainable Development Goals (SDGs) must receive adequate and balanced funding. This study applies artificial intelligence to analyze research proposals accepted between 2015 and 2023 in the European Union and the United States, focusing on datasets from the European Research Council and the National Science Foundation, respectively. Despite the growing application of Artificial Intelligence (AI) in various domains, there remains a lack of comprehensive analysis that applies AI to examine funding allocation across SDGs and gender disparities in scientific research. This study addresses this unmet need by using AI to uncover imbalances in funding distribution, offering insights into current funding instruments. We reveal critical coverage disparities across SDGs, with both funding instruments prioritizing SDG 9 (Industry, Innovation, and Infrastructure), highlighting a potential overemphasis on this goal. Additionally, we document pronounced gender imbalances among principal investigators across nearly all SDGs, except for SDG 5 (Gender Equality), in which female researchers are comparatively better represented. Our results indicate an urgent need for more inclusive and balanced approaches to achieve sustainable development, starting with allocation of research funding. By providing a nuanced understanding of funding dynamics and advocating strategic reallocations, this study offers actionable policy design and planning insights to foster a more equitable and comprehensive support system for sustainability-focused research endeavours.

A key challenge for implementation of a circular economy model in manufacturing systems is the functional dependence of downstream processes on upstream byproducts. Design principles provide a framework for mapping goals to solutions by decomposing complex engineering problems into structured sets of requirements to be satisfied and embodied by design parameters and process variables. Large Language Models can computationally represent such textually-described design elements to quantify interconnections between problems, solutions, and processes. We present a Functional Digital Twin concept, powered by AI language modeling and guided by principles of manufacturing systems design, to identify functionally coupled process variables in an industrial symbiosis and automatically push alerts to stakeholders in a circular manufacturing system. Changes in byproduct composition are pushed downstream, and upstream decision-makers are guided to balance satisfying their design requirements with maintaining circularity of the system. The presented method is demonstrated in a case study of bio-based absorbent materials for intended use in disposable sanitary articles developed from byproducts of the agro-food industry.

Accurate, reliable and updated information support effective decision-making by reducing uncertainty and enabling informed choices. Multiple crises threaten the sustainability of our societies and pose at risk the planetary boundaries, hence requiring usable and operational knowledge. Natural-language processing tools facilitate data collection, extraction and analysis processes. They expand knowledge utilization capabilities by improving access to reliable sources in shorter time. They also identify patterns of similarities and contrasts across diverse contexts. We apply general and domain-specific large language models (LLMs) to two case studies and we document appropriate uses and shortcomings of these tools for two tasks: classification and sentiment analysis of climate and sustainability documents. We study both statistical and prompt-based methods. In the first case study, we use LLMs to assess whether climate pledges trigger cascade effects in other sustainability dimensions. In the second use case, we use LLMs to identify interactions between the sustainable development goals and detects the direction of their links to frame meaningful policy implications. We find that LLMs are successful at processing, classifying and summarizing heterogeneous text-based data helping practitioners and researchers accessing. LLMs detect strong concerns from emerging economies in addressing food security, water security and urban challenges as primary issues. Developed economies, instead, focus their pledges on the energy transition and climate finance. We also detect and document four main limits along the knowledge production chain: interpretability, external validity, replicability and usability. These risks threaten the usability of findings and can lead to failures in the decision-making process. We recommend risk mitigation strategies to improve transparency and literacy on artificial intelligence (AI) methods applied to complex policy problems. Our work presents a critical but empirically grounded application of LLMs to climate and sustainability questions and suggests avenues to further expand controlled and risk-aware AI-powered computational social sciences.

Approximately 418 million children are beneficiaries of school meal programmes globally. In general, supportive infrastructure is necessary for the successful delivery of school meals, but in many low-income and lower-middle-income countries (LLMICs), schools have poor access to essential facilities such as kitchens, electricity, and clean water. Moreover, schools in LLMICs often rely on charcoal or firewood for cooking with consequent negative health, social, economic, and environmental impacts that disproportionally affect women and children. The increasing availability of electricity and large energy efficient cooking appliances in LLMICs suggests that electric cooking could offer a potential solution. However, although the impacts of providing electricity to schools on educational outcomes have been explored, and the scope for electric cooking transitions at household level is increasingly studied, evidence on the role of electricity in providing sustainable school meals remains scarce, particularly in LLMICs. Most existing studies on school meals focus on the health and nutritional values of school meals and do not consider the energy used in their preparation or associated impacts. To address this gap, this Personal View explores the contribution of electric cooking to providing sustainable school meals. Recent case studies from Kenya, Lesotho, Nepal, and Guinea that introduced electric cooking as an alternative to traditional cooking fuels have shown how electric cooking can contribute to providing sustainable schools meals in LLMICs. This Personal View highlights multiple sustainable benefits from shifting to electric cooking, which include environmental, economic, and health benefits, and time saving, with potential gender benefits intersecting these domains. Sharing lessons learned from each study could improve the delivery and effectiveness of these interventions for other schools, and understanding the range of contexts and challenges could help towards programme design for wider scaling of sustainable school meal provision.

Background: Cooking with traditional fuels can lead to severe health issues caused by household air pollution, and can also affect gender equality and drive environmental degradation. In Nepal, despite government efforts to promote electric cooking, more than half of the population still uses traditional fuels, with electric cooking adoption remaining below 1%. Several of the barriers to and enablers of clean cooking vary geographically; however, few studies have considered spatial explicit information in planning national-scale transitions to clean cooking. In this study we provide a spatially explicit roadmap to estimate the required investments and benefits gained from the transition across Nepal. Methods: This study uses geospatial modelling methods to evaluate strategies to achieve the Government of Nepal's vision for a national-scale transition to clean cooking. We integrate the open-source clean cooking geospatial assessment tool OnStove and a spatial multicriteria analysis model. With OnStove, we evaluate which cooking technologies and fuels maximise the net benefits of a clean-cooking transition across each km2 of the region. With the multicriteria analysis, we weigh stakeholder preferences and prioritise areas of action where policy should be implemented. We used the most up-to-date geospatial data to the year 2023, such as the High Resolution Settlement Layer, Open Street Maps’ road networks, the Global Human Settlement Layer, NASA/USGS forest cover maps, and Facebook's Relative Wealth Index, among others. We also relied on data from the Nepal Oil Corporation, the Nepal Electricity Agency, the Central Bureau of Statistic's 2021 national census, and the Alternative Energy Promotion Center. We evaluate four scenarios capturing advances on clean cooking policy up to the year 2022, current market inefficiencies, and the potential effects of new policies for clean-cooking transition in Nepal. Findings: Our results show that transitional and clean cooking technologies provide higher net benefits than traditional options everywhere across Nepal in all scenarios. Our net-benefit analysis shows that around 9563 deaths could be averted yearly if benefits and externalities were perceived and valued correctly. Furthermore, substantial benefits could be achieved in regard to greenhouse gas emissions avoidance, time saved, and health-cost reductions. Our results also show that the current subsidy strategy from the Government of Nepal is well aligned with the benefits achieved under a cost–benefit analysis. In this context, electric cooking can bring the highest benefits to the largest part of the population. The analysis showed how high subsidies for liquefied petroleum gas in Nepal can present trade-offs with energy security and independence, and how this could be avoided by transferring part of the subsidy to cover differentiated electric cooking tariffs. Accounting for stakeholder preferences and sociodemographic and geographical differences to prioritise areas of focus can balance affordability constraints and target the most vulnerable people first, thus achieving integrated and inclusive planning. Interpretation: Using spatially explicit modelling approaches to evaluate strategies for a clean cooking transition can provide more nuanced results that have not been possible before. This approach can enable data-driven and integrated planning to help to understand which locations of a study area should be prioritised for policy application. Integrated planning can help to reduce affordability constraints on the population and design strategies for a sustainable and inclusive transition. These strategies allow financial institutions, donors, impact investors, development organisations, and government agencies to use their resources, funds, and assistance to create a large impact. Funding: Clean Cooking Alliance.

Background: A large proportion of individuals experience functional impairment in everyday life due to climate worry. However, the current understanding of this functional impairment is limited by the use of suboptimal measures. Furthermore, it is not known whether functional impairment due to climate worry affects pro-environmental behaviors (PEBs) or whether individuals who experience such impairment perceive a need for support. The aims of the current study were (1) to extend previous research using an established measure of functional impairment (the Work and Social Adjustment Scale, WSAS), (2) to explore the associations between climate worry, functional impairment, and PEBs, and (3) to describe the characteristics and the perceived need for support of individuals with functional impairment due to climate worry.

Methods: A cross-sectional survey targeting adult individuals who experience climate worry. Participants were recruited nationally in Sweden between September and October 2022. The survey included measures of climate worry severity, climate worry frequency, functional impairment, PEBs, depressive symptoms, sleep problems, and questions related to perceived need for support.

Results: A total of 1221 adults (75% women, mean age 46.3 years) were included in the analyses. Multivariate structural equation modeling revealed that climate worry severity and frequency were significantly associated with PEBs (β = 0.34 and β = 0.45, respectively). Climate worry frequency was associated with functional impairment (β = 0.41). Functional impairment was only marginally associated with PEBs (β = 0.05). Approximately 40% of the sample (n = 484) reported a high frequency and high severity of climate worry. Among these, one-third (n = 153) scored above the cutoff for significant impairment on the WSAS. Individuals in this group (high severity and frequency of climate worry as well as significant functional impairment) were more likely to experience depressed mood and sleep problems and were more interested in receiving support, specifically concerning strategies for worry management and sustainable behavior change.

Conclusions: Using an established measure of functional impairment, we found an association of climate worry with functional impairment and PEBs. Importantly, as there is a perceived need for support in individuals with impairment due to climate worry, interventions targeting this specific subgroup should be developed.

The world should redouble its efforts on the SDGs, not abandon them. Here's how to progress the United Nations' agenda towards 2050.

The energy sector is the largest emitter of greenhouse gas emissions, accounting for 37% of the world's combined emissions, and plays a key role in achieving the Sustainable Development Goals. However, there is no systematic means for planners and practitioners to integrate climate considerations throughout the lifecycle of energy infrastructure projects. Using a thematic document analysis, we provide a comprehensive list of activities related to climate mitigation and adaptation which can be systematically mainstreamed into the energy sector project lifecycle to support a range of sustainable development outcomes. Two renewable energy projects were used to integrate the results into a practical context and demonstrate the range of potential SDG target synergies. The case studies demonstrate the varied means by which climate action can be integrated through mainstreaming in project lifecycle stages, holistically achieving wider SDG impacts. This work provides a practical means to maximise progress within the framework of climate-compatible development.