The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors across the society requires an assessment of its effect on sustainable development. Here we analyze published evidence of positive or negative impacts of AI on the achievement of each of the 17 goals and 169 targets of the 2030 Agenda for Sustainable Development. We find that AI can support the achievement of 128 targets across all SDGs, but it may also inhibit 58 targets. Notably, AI enables new technologies that improve efficiency and productivity, but it may also lead to increased inequalities among and within countries, thus hindering the achievement of the 2030 Agenda. The fast development of AI needs to be supported by appropriate policy and regulation. Otherwise, it would lead to gaps in transparency, accountability, safety and ethical standards of AI-based technology, which could be detrimental towards the development and sustainable use of AI. Finally, there is a lack of research assessing the medium-and long-term impacts of AI. It is therefore essential to reinforce the global debate regarding the use of AI and to develop the necessary regulatory insight and oversight for AI-based technologies.

Perennial rivers and streams make a disproportionate contribution to global carbon (C)cycling. However, the contribution of intermittent rivers and ephemeral streams, which

Target 19, set by the Convention on Biological Diversity, seeks to improve the knowledge, science base, and technologies relating to biodiversity. We will fail to achieve this target unless prolific biases in the field of conservation science are addressed. We reveal that comparatively less research is undertaken in the world’s most biodiverse countries, the science conducted in these countries is often not led by researchers based in-country, and these scientists are also underrepresented in important international fora. Mitigating these biases requires wide-ranging solutions: reforming open access publishing policies, enhancing science communication strategies, changing author attribution practices, improving representation in international processes, and strengthening infrastructure and human capacity for research in countries where it is most needed.

Despite growing recognition of the importance of a natural flow regime in river-floodplain systems, researchers struggle to quantify ecosystem responses to altered hydrological regimes. How do frequency, timing, and duration of inundation affect fundamental ecosystem processes such as leaf litter decomposition? Along the semi-natural Tagliamento River corridor, located in northeastern Italy, we employed in situ experiments to separate effects of different inundation components on breakdown rates of black poplar (Populus nigra). We used a litter-bag method with two different mesh sizes to investigate how fungi and macroinvertebrates influence leaf breakdown rates. Ten treatments, each representing a specific combination of duration and frequency of inundation, were deployed in two seasons (summer, winter) to mimic complex inundation patterns. After 30 days of exposure, mean percentage of remaining leaf litter (ash free dry mass) ranged between 51% (permanent wet) and 88% (permanent dry). Leaf breakdown was significantly faster in winter than in summer. Duration of inundation was the main inundation component that controlled leaf breakdown rates. Leaf-shredding macroinvertebrates played only a role in the permanent wet treatment. Fungal parameters explained the faster leaf breakdown in winter. Our study suggests that modifications of the inundation regime will directly modify established decomposition processes. Factors reducing duration of inundation will decelerate leaf breakdown rates, whereas a decrease in flow variation will reduce leaf breakdown heterogeneity.