Roughly 60 per cent of Africans lack access to electricity, negatively impacting development opportunities. Non-governmental organisations (NGOs) have started promoting distributed generationsmallscale, localised electricity generationto change this situation. Despite widespread need, however, the dispersion of these distributed generation NGOs (DG-NGOs) is uneven, with high concentrations in a few African countries. Drawing on an original database and field research, we analyse location variation among DG-NGOs across the continent. We find that DG-NGOs are likely to operate in democratic settings with large populations that lack access to electricity. International DG-NGOs are also likely to operate where aid allocation levels are relatively high.

Marine litter is a significant threat to the marine environment, human health, and the economy. In this study, beach litter surveys along Vietnamese coasts were conducted in a local context to quantify and characterize marine litter using the modified GESAMP marine litter monitoring guideline. A total of 21,754 items weighing 136,820.2 g was recorded across 14 surveys from September 2020 to January 2021. Plastic was the most abundant type of litter by both quantity (20,744 items) and weight (100,371.2 g). Fishing gear 1 (fishing plastic rope, net pieces, fishing lures and lines, hard plastic floats) and soft plastic fragments were the most frequently observed items (17.65% and 17.24%, respectively). This study not only demonstrates the abundance and composition of marine litter in Vietnam, it also provides valuable information for the implementation of appropriate preventive measures, such as the redesign of collection, reuse, and recycling programs, and informs policy and priorities, with a focus on action and investment in Vietnam. Moreover, insights from this study indicate that citizen science is a useful approach for collecting data on marine litter in Vietnam.

<p>Plastic waste finds its way to the ocean often through rivers: it is estimated that between 1.15 and 2.41 million tons of plastic waste enters the ocean every year from rivers. Out of the 1500+ rivers that are estimated for being responsible for 80% of the riverine plastic emissions, around 70 of these rivers are located in Vietnam which highlights the importance of further investigating the plastic waste situation in its rivers.</p><p>The aim for this study was to develop a method for machine learning based measuring several types of plastic litter (in particular floating, trapped and submerged) in riverine systems. By considering the combined information of various litter categories this methodology is able to draw a holistic picture of plastic transport in riverine systems.</p><p>Two different methodological components were set up: (i) an AI (artificial intelligence) based litter detection algorithm which analyses imagery gathered by bridge-installed action cameras for floating and trapped plastic waste items in terms of abundances and waste types and (ii) a net-based sampling method which measures floating as well as submerged plastics at the bridge locations. The applied AI-based litter detection algorithm was originally developed for plastics detection in an aquatic environment in Cambodia for drone imagery. Within this framework, this approach was further developed and applied to detect floating and trapped plastic litter in polluted rivers captured with action cameras in Vietnam. The complementing net-based sampling for submerged plastics was applied in parallel to calibrate the continuous camera-based sampling with direct measurements.</p><p>Within this study it was shown that the combination of the two presented approaches provides a suitable methodology for the measurement of plastic transport along a river. Calibration of the continuous camera-based method showed that about 50% of the litter was transported at the surface and was thus directly detectable by AI. The methodology is relevant to the remote sensing community focusing on plastics detection and to researchers addressing plastic waste. The continuous assessment of plastic quantities transported by rivers will be key for policy makers to identify main polluters and to understand the impacts of any taken measures to reduce plastics pollution. Increasing the understanding of plastic types through these measurements is key for policy makers to develop the right measures which can target the items responsible for the majority of plastics pollution in rivers, as typically only few items are responsible for the majority of plastics leakage. The achievements of this study aim to fill these knowledge gaps by enhancing the litter detection method. As a next step, this method could be scaled up to be tested for a longer time period and at additional sites. The results of such longer-term measurements of surface and submerged plastics may allow for extrapolation of floating plastics to total transported plastics.</p>