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Energy system modeling can be used to develop internally consistent quantified scenarios. These provide key insights needed to mobilise finance, understand market development, infrastructure deployment and the associated role of institutions, and generally support improved policymaking. However, access to data is often a barrier to starting energy system modeling, especially in developing countries, thereby causing delays to decision making. Therefore, this article provides data that can be used to create a simple zero-order energy system model for a range of developing countries in Africa, East Asia, and South America, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organisations, journal articles, and existing modeling studies. This means that the datasets can be easily updated based on the latest available information or more detailed and accurate local data. As an example, these data were also used to calibrate a simple energy system model for Kenya using the Open Source Energy Modeling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020-2050. The assumptions used and the results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work., QC 20220503

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., QC 20180130

Energy access is typically viewed as a problem for rural areas, but people living in urban settings also face energy challenges that have not received sufficient attention. A revised agenda in research and practice that puts the user and local planning complexities centre stage is needed to change the way we look at energy access in urban areas, to understand the implications of the concentration of vulnerable people in slums and to identify opportunities for planned management and innovation that can deliver urban energy transitions while leaving no one behind. Here, we propose a research agenda focused on three key issues: understanding the needs of urban energy users; enabling the use of context-specific, disaggregated data; and engaging with effective modes of energy and urban governance. This agenda requires interdisciplinary scholarship across the social and physical sciences to support local action and deliver large-scale, inclusive transformations.

Renewable energy can provide sustainable energy services in rural areas. However, ensuring the continued functioning of the technology in remote locations has been a major challenge. The aim of this study is to identify strategies that have been most successful in enabling access to sustainable energy services in rural areas of developing countries. This thesis draws on literature from the fields of capacity development, technology studies, and renewable energy project implementation. The strategies were identified by comparing existing frameworks with empirical data from three case studies of renewable energy projects in the Inner Mongolia Autonomous Region, China. Data for the case studies were collected via literature search, project document collection, site visits and interviews with a wide variety of stakeholders. The key findings of this thesis are that expanding energy access in developing countries will require significant capacity development efforts. Delivering energy services using decentralised renewable energy systems require the involvement of a diverse range of actors, including governments, program planners, equipment manufacturers, system designers, service networks and end-users. The most suitable delivery mechanism for sustainable energy services will vary from one location to another because of differences in the actors involved, their existing capacity and the history of their interaction. Assessing and building upon existing capacity is essential for delivering energy services in rural areas effectively. A major contribution of this thesis is the development of the “renewable energy capacity pyramid”, an analytical framework that explains how rural electrification projects can enhance local capacity in a systemic way. The framework includes tools; skills and knowledge; organisational structures; sectoral networks; and the institutional environment. This study found that the provision of sustainable energy services in rural areas of developing

Anaerobic digestion has been proven to be the most efficient process for primary treatment of POME. However a major problem in the anaerobic wastewater treatment process is to maintain the sufficient quantity of active biomass in the reactor. In this study membrane separation technology has been applied after anaerobic digestion to increase solids retention time and improve treatment efficiency. The objectives of the study are to evaluate the overall membrane anaerobic system (MAS) treatment efficiency and the applicability of three known kinetic models on the system and determination of kinetic coefficients. The MAS consists of a cross-flow ultrafiltration membrane (PCI Micro 240) for solid-liquid separation. Six steady states were ottained over a range of mixed liquor suspended solids of 12,681 - 30,460 mgtl. The study showed a good fitting of the Monod Model (91.1%), Contois Model (98.5%) and Chen and Hashimoto Model (95%) for the MAS treating raw POME at organic loadings between 1.5 kgCOD/m3/d to 6.5 kgCOD/m3/d. The growth yield coefficient Y, was found to be 0.604 kg VSSlkgCOD while the specific microorganism decay rate was 0.099 day-to The k values were in the range of 0.242 to 0.425 mg COD/mg VSS.d and the Pm values were between 0.145 to 0.257dail. The Monod Model and Chen and Hashimoto Model are better than the Contois Model for solids retention time (SRT), effluent substrate concentration (S) and substrate utilisation rate (E) estimation. Both models are able to produce a good predicted S and E if the SRT >= 50 days. Throughout the study, the removal efficiency of COD was 83.2 to 97.97 %. The methane production rate was between 0.262 to 0.473 1/g-COD utilised/d The MAS treatment efficiency was greatly affected by SRT and OLRs. In this study, membrane fouling and polarization at the membrane surface played a significant role in the formation of a strongly attached cake layer limiting membrane permeability.

Anaerobic digestion has been proven to be the most efficient process for primary treatment of POME. However a major problem in the anaerobic wastewater treatment process is to maintain the sufficient quantity of active biomass in the reactor. In this study membrane separation technology has been applied after anaerobic digestion to increase solids retention time and improve treatment efficiency. The objectives of the study are to evaluate the overall membrane anaerobic system (MAS) treatment efficiency and the applicability of three known kinetic models on the system and determination of kinetic coefficients. The MAS consists of a cross-flow ultrafiltration membrane (PCI Micro 240) for solid-liquid separation. Six steady states were ottained over a range of mixed liquor suspended solids of 12,681 - 30,460 mgtl. The study showed a good fitting of the Monod Model (91.1%), Contois Model (98.5%) and Chen and Hashimoto Model (95%) for the MAS treating raw POME at organic loadings between 1.5 kgCOD/m3/d to 6.5 kgCOD/m3/d. The growth yield coefficient Y, was found to be 0.604 kg VSSlkgCOD while the specific microorganism decay rate was 0.099 day-to The k values were in the range of 0.242 to 0.425 mg COD/mg VSS.d and the Pm values were between 0.145 to 0.257dail. The Monod Model and Chen and Hashimoto Model are better than the Contois Model for solids retention time (SRT), effluent substrate concentration (S) and substrate utilisation rate (E) estimation. Both models are able to produce a good predicted S and E if the SRT >= 50 days. Throughout the study, the removal efficiency of COD was 83.2 to 97.97 %. The methane production rate was between 0.262 to 0.473 1/g-COD utilised/d The MAS treatment efficiency was greatly affected by SRT and OLRs. In this study, membrane fouling and polarization at the membrane surface played a significant role in the formation of a strongly attached cake layer limiting membrane permeability.

Energy system modeling can be used to develop internally-consistent quantified scenarios. These provide key insights needed to mobilise finance, understand market development, infrastructure deployment and the associated role of institutions, and generally support improved policymaking. However, access to data is often a barrier to starting energy system modeling, especially in developing countries, thereby causing delays to decision making. Therefore, this article provides data that can be used to create a simple zero-order energy system model for a range of developing countries in Africa, East Asia, and South America, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organisations, journal articles, and existing modeling studies. This means that the datasets can be easily updated based on the latest available information or more detailed and accurate local data. As an example, these data were also used to calibrate a simple energy system model for Kenya using the Open Source Energy Modeling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020-2050. The assumptions used and the results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work. [Abstract copyright: © 2022 The Author(s). Published by Elsevier Inc.]

Energy system modeling can be used to develop internally-consistent quantified scenarios. These provide key insights needed to mobilise finance, understand market development, infrastructure deployment and the associated role of institutions, and generally support improved policymaking. However, access to data is often a barrier to starting energy system modeling, especially in developing countries, thereby causing delays to decision making. Therefore, this article provides data that can be used to create a simple zero-order energy system model for a range of developing countries in Africa, East Asia, and South America, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organisations, journal articles, and existing modeling studies. This means that the datasets can be easily updated based on the latest available information or more detailed and accurate local data. As an example, these data were also used to calibrate a simple energy system model for Kenya using the Open Source Energy Modeling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020-2050. The assumptions used and the results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work. [Abstract copyright: © 2022 The Author(s). Published by Elsevier Inc.]