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The 2030 Agenda for Sustainable Development—including 17 interconnected Sustainable Development Goals (SDGs) and 169 targets—is a global plan of action for people, planet and prosperity. SDG7 calls for action to ensure access to affordable, reliable, sustainable and modern energy for all. Here we characterize synergies and trade-offs between efforts to achieve SDG7 and delivery of the 2030 Agenda as a whole. We identify 113 targets requiring actions to change energy systems, and published evidence of relationships between 143 targets (143 synergies, 65 trade-offs) and efforts to achieve SDG7. Synergies and trade-offs exist in three key domains, where decisions about SDG7 affect humanity’s ability to: realize aspirations of greater welfare and well-being; build physical and social infrastructures for sustainable development; and achieve sustainable management of the natural environment. There is an urgent need to better organize, connect and extend this evidence, to help all actors work together to achieve sustainable development. The UN’s Agenda for Sustainable Development has 17 goals with 169 targets for action across a range of issues, with access to sustainable energy for all being Goal 7. This Perspective analyses interlinkages between energy systems, Goal 7 and the other goals at the target level, identifying synergies and trade-offs between them.

We are approaching a reckoning point in 2020 for global targets that better articulate the interconnections between biodiversity, ecosystem services and sustainable development. The Convention on Biological Diversity’s (CBD’s) post-2020 global biodiversity framework and targets will be developed as we enter the last decade to meet the Sustainable Development Goals (SDGs) and targets. Despite recent findings of unprecedented declines in biodiversity and ecosystem services and their negative impacts on SDGs, these declines remain largely unaccounted for in the SDG’s upcoming ‘decade of action’. We use a social–ecological systems framework to develop four recommendations for targets that capture the interdependencies between biodiversity, ecosystem services and sustainable development. These recommendations, which are primarily aimed at the CBD post-2020 process, include moving from separate social and ecological targets to social–ecological targets that: account for (1) the support system role of biodiversity and (2) ecosystem services in sustainable development. We further propose target advances that (3) capture social–ecological feedbacks reinforcing unsustainable outcomes, and (4) reveal indirect feedbacks hidden by current target systems. By making these social–ecological interdependencies explicit, it is possible to create coherent systems of global targets that account for the complex role of biodiversity and ecosystem services in sustainable development. This Perspective uses a social–ecological systems framework to make recommendations for global targets that capture the interdependencies of biodiversity, ecosystem services and sustainable development to inform the Convention on Biological Diversity post-2020 process and the future of the UN’s Sustainable Development Goals.

Marine fisheries constitute a crucial component of global green development, where artificial intelligence (AI) plays an essential role in enhancing green economic efficiency associated with marine fisheries. This study utilizes panel data from 11 coastal provinces and municipalities in China from 2009 to 2020, employing the entropy method and the super-efficiency EBM model to calculate the AI index and the green economic efficiency of marine fisheries. Based on these calculations, we utilize fixed effects models, moderation effect models, and panel threshold models to examine the impact of AI on the green economic efficiency of marine fisheries. The study reveals that: (i) From 2009 to 2020, AI has significantly improved overall, while the green economic efficiency of marine fisheries has shown a fluctuating trend, with substantial regional disparities. (ii) AI significantly enhances the green economic efficiency of marine fisheries. (iii) Green finance, trade openness, and R&D investment act as crucial moderating variables, accelerating AI development and further improving the green economic efficiency of marine fisheries. (iv) The impact of AI on green economic efficiency varies across different intervals of green finance, trade openness, and R&D investment. These findings are crucial for understanding and advancing the informatization strategy of marine fisheries and hold significant implications for the sustainable development of global marine fisheries.

Since 2013, China has initiated a rapid energy transition that replaces traditional solid fuels with modern clean energy. Despite the tremendous success of the energy transition, its impacts on household energy costs and associated energy inequality remain largely unexplored. Here we use data from a large nationwide household survey to investigate these trends. We find that about two-fifths (43.0%) of surveyed households switched from traditional solid fuels to clean energy during 2013–2017. However, 56.1% to ~61.0% of them were from extremely poor or poor households, causing deep concern for increasing household energy burden. Accordingly, the share of surveyed households in energy poverty increased from 30.1% to 34.2%. Despite the declining inequality in energy cost, a growing inequality in energy burden was revealed during 2013–2017. Our results demonstrate that the energy burden on rural households increased due to the dramatic rise in the cost of clean energy, while urban households tend to spend a lower and decreased proportion of their income on energy. Between 2013 and 2017, China enacted a series of policies to improve air quality by promoting a switch to cleaner fuels for households. This study examines the changes in energy cost and associated energy burden across regions and income groups during this period, finding an increased burden on rural households.

The 2030 Agenda and the Paris Agreement share the purpose of creating a more resilient, productive and healthy environment for present and future generations. Nations must seize the opportunity to raise their ambition, realize synergies and minimize trade-offs.

The global adoption rate of artificial intelligence (AI) is rising, indicating its transformative potential. However, this adoption is far from uniform, with low-income countries (LICs) trailing behind significantly. Despite needing AI for development, LICs face multiple challenges in harnessing its benefits, exacerbating existing global disparities in technology adoption. In spite of the potentially important role that AI can play in the development of LICs, AI literature overlooks these countries, with research predominantly focused on more advanced economies. This lack of inclusivity contradicts the principles of distributive justice and global equity, prompting us to explore the importance of AI for LICs, offer a theoretical grounding for AI catch-up, identify effective AI domains, and propose strategies to bridge the AI gap. Drawing insights from the leapfrogging and absorptive capacities literature, our position paper presents the feasibility of AI catch-up in LICs. One crucial finding is that there is no one-size-fits-all approach to achieving AI catch-up. LICs with strong foundations could favor leapfrogging strategies, while those lacking such foundations might find learning and acquisition prescriptions from absorptive capacity literature more relevant. The article also makes policy recommendations that advocate for the swift integration of AI into critical LIC domains such as health, education, energy, and governance. While LICs must address challenges related to digital infrastructure, human capital, institutional robustness, and effective policymaking, among others, we believe that advanced AI economies and relevant international organizations like UNESCO, OECD, USAID, and the World Bank can support LICs in AI catch-up through tech transfer, grants, and assistance. Overall, our work envisions global AI use that effectively bridges development and innovation disparities.

Cash transfers (CTs) are increasingly recognized as a scalable intervention to alleviate financial hardship. A large body of evidence evaluates the impact of CTs on subjective well-being (SWB) and mental health (MH) in low- and middle-income countries. We undertook a systematic review, quality appraisal and meta-analysis of 45 studies examining the impact of CTs on self-reported SWB and MH outcomes, covering a sample of 116,999 individuals. After an average follow-up time of two years, we find that CTs have a small but statistically significant positive effect on both SWB (Cohen’s d = 0.13, 95% confidence interval (CI) 0.09, 0.18) and MH (d = 0.07, 95% CI 0.05, 0.09) among recipients. CT value, both relative to previous income and in absolute terms, is a strong predictor of the effect size. Based on this review and the large body of existing research demonstrating a positive impact of CTs on other outcomes (for example, health and income), there is evidence to suggest that CTs improve lives. To enable comparisons of the relative efficacy of CTs to improve MH and SWB, future research should meta-analyse the effects of alternative interventions in similar contexts.In a systematic review and meta-analysis of 45 studies, covering a sample of 116,999 individuals across 22 countries, McGuire et al. find that cash transfers improve the subjective well-being and mental health of recipients in low- and middle-income countries.

Research examines the influence of multisource urban data on residents’ perceptions of safety. Utilizing the SHAP machine learning model, the research conducts a comprehensive analysis of the nonlinear relationships and interactive effects between built environment factors and psychological perception factors on urban residents’ safety perceptions. Focusing on Nanchang City as a case study, the research integrates multidimensional data encompassing urban spatial environments, resident perceptions, and socioeconomic indicators. The findings highlight the critical role of perceived urban vitality and perceived wealth in shaping residents’ safety perceptions, addressing the insufficient consideration of individual psychological factors in previous research, this study innovatively incorporates psychological perception data, thereby extending traditional built environment theories. By employing nonlinear models to elucidate the influence mechanisms of different variables across spatial zones, it provides a scientific foundation for urban planning and safety governance. Additionally, selecting Nanchang, a representative medium-sized city characterized by historical and cultural heritage, as the sample addresses the previous research gap in such urban contexts. By establishing an evaluation framework for urban safety perception based on multi-source data, this study offers theoretical support and practical guidance for precision planning in medium-sized cities dominated by historical and cultural heritage. This contributes to advancing sustainable urban development and enhancing residents’ well-being.

This study attempts to measure the caste-based dietary gap and relative contribution of different socioeconomic factors in Odisha, India, through the lens of the Capability Approach. We used National Sample Survey 68th (2011–12) and 79th (2022–23) rounds data that includes 13,113 sample. Dietary diversity was assessed using a 13-food group score, categorized into low, medium, and high based on established FAO/FANTA thresholds. The caste gap in dietary disparities is assessed using Oaxaca-Blinder decomposition for non-linear models. The decomposition analysis reveals the magnitude of caste-based dietary gap and the contribution of observed and unobserved factors to such gap. A gradual improvement in dietary diversity from low to medium category has been observed among SC/ST between 2011 and 12 and 2022-23 in Odisha. The marginal effects show socioeconomic status, household composition, cooking fuel &location are the increases the probability of being in the medium and higher dietary diversity. About 76% and 57% of caste-based inequalities in high and medium dietary diversity (with reference to low dietary diversity) explained by observable differences in endowments. While education and wealth (endowment component) are the key drivers of dietary diversity, the large unexplained component for shift from low to medium dietary status underscores the structural barriers such as social exclusion, cultural norms and differential market access that reinforces caste inequalities. Addressing the caste-based dietary gap therefore requires equity-focused, multidimensional nutrition strategies that combine economic development with targeted interventions.