Dataset for multidimensional assessment to incentivise decentralised energy investments in Sub-Saharan Africa

Abstract

In this data article, we present datasets from the construction of a composite indicator, the Photovoltaic Decentralised Energy Investment (PV-DEI) index, presented in detail in [1] . This article consists of a comprehensive energy-related data collected in practice from several sources, and from the outputs of the methodology described in [1] . The PV-DEI was designed and developed to measure the multidimensional factors that currently direct decentralised renewable energy investments. The PV-DEI index includes 52 indicators and was constructed because factors stimulating investment cannot be captured by a single indicator, e.g. competitiveness, affordability, or governance [1] . The PV-DEI index was built in alignment with a theoretical framework guided by an extensive review of the literature surrounding investment in decentralised Photovoltaic (PV), which led to the selection of its indicators. The structure of the PV-DEI was evaluated for its soundness using correlational assessments and principal component analyses (PCA). The raw data provided in this article can enable stakeholders to focus on specific country indicators, and how scores on these indicators contributed to a countries overall rank within the PV-DEI index. The data can be used to weight indicators depending on the specifications of several different stakeholders (such as NGOs, private sector or international institutions).

Performance Evaluation of Regression Analysis Algorithms in Predicting the Enrollment of Basic Education

“…The creation of model and testing the performance in the dataset will be the last stage. As a result, the dataset will be evaluated with multiple algorithms to see whether the model is the best match for the dataset [19]. The comparison will also display each process's distinct topologies, relative error, standard deviation, training time, and testing time [20].…”

Review and Assessment of Energy Policy in the Economic Community of West African States region

“…It is known that the manufacturing companies that service the oil and gas industry account for a major share of the energy consumed in the manufacturing sector. Also, the application of solar energy in the oil and gas sector (especially upstream) is a major way to drive solar energy integration and environmental sustainability (Bender et al, 2021). Nigeria, Ghana, and Côte d'Ivoire are the obvious leaders in terms of actual power generation.…”

“…Due to the high dependency of the main livelihoods in Senegal River Basin (SRB) on water (agriculture, livestock, fisheries), around 85% of its population lives close to the river (Bender et al, 2021). The SRB is highly vulnerable to climate variability and changes, due to the great interdependence between climate and socioeconomic activities, and it could be further challenged by the increasing pressures posed by its population dynamics on natural resources, the subsequent changes in land use and the competition among sectors and users.…”

“…The SRB is highly vulnerable to climate variability and changes, due to the great interdependence between climate and socioeconomic activities, and it could be further challenged by the increasing pressures posed by its population dynamics on natural resources, the subsequent changes in land use and the competition among sectors and users. There is a high hydropower potential in the basin and even if currently only two plants are being exploited (one under development), the four riparian countries have planned to increase the number of reservoirs, in order to meet the expected growing demands as well as to regulate the high inter-and intra-annual water availability of the basin (Bender et al, 2021). In the middle valley and delta, agriculture, pastoralism, and fishing are the main activities.…”

Predictive Analysis of the Enrolment of Elementary Schools Using Regression Algorithms

“…One of the leading predictive algorithms is Regression [7] ; this algorithm has been used in medical, statistical, environmental predictions, and even enrollment analysis [8][9]. It has also been proven that the regression algorithm fits multidimensional datasets [10]. In this case, using this method would allow a broader scope with higher accuracy [11].…”