Targeting climate adaptation to safeguard and advance the Sustainable Development Goals.

The international community has committed to achieve 169 Sustainable Development Goal (SDG) targets by 2030 and to enhance climate adaptation under the Paris Agreement. Despite the potential for synergies, aligning SDG and climate adaptation efforts is inhibited by an inadequate understanding of the complex relationship between SDG targets and adaptation to impacts of climate change. Here we propose a framework to conceptualise how ecosystems and socio-economic sectors mediate this relationship, which provides a more nuanced understanding of the impacts of climate change on all 169 SDG targets. Global application of the framework reveals that adaptation of wetlands, rivers, cropland, construction, water, electricity, and housing in the most vulnerable countries is required to safeguard achievement of 68% of SDG targets from near-term climate risk by 2030. We discuss how our framework can help align National Adaptation Plans with SDG targets, thus ensuring that adaptation advances, rather than detracts from, sustainable development.

Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa.

Human settlements are usually nucleated around manmade central points or distinctive natural features, forming clusters that vary in shape and size. However, population distribution in geo-sciences is often represented in the form of pixelated rasters. Rasters indicate population density at predefined spatial resolutions, but are unable to capture the actual shape or size of settlements. Here we suggest a methodology that translates high-resolution raster population data into vector-based population clusters. We use open-source data and develop an open-access algorithm tailored for low and middle-income countries with data scarcity issues. Each cluster includes unique characteristics indicating population, electrification rate and urban-rural categorization. Results are validated against national electrification rates provided by the World Bank and data from selected Demographic and Health Surveys (DHS). We find that our modeled national electrification rates are consistent with the rates reported by the World Bank, while the modeled urban/rural classification has 88% accuracy. By delineating settlements, this dataset can complement existing raster population data in studies such as energy planning, urban planning and disease response.

The role of artificial intelligence in achieving the Sustainable Development Goals.

The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards.