Search

The gap in demand and supply of energy across Sub-Saharan African (SSA) countries has increased energy insecurity in the region. Therefore, power outages have become pervasive, causing unemployment and a decline in production output. Among the several energy-saving factors identified in the literature across developing economies, technological spillover driven by trade openness appears to be a prominent factor in improving energy efficiency. Thus, this study evaluates the impact of machinery imported from OECD and non-OECD countries and its corresponding research and development (R&D) spillover on energy efficiency performance in 18 SSA countries from 1995 to 2017. Using a stochastic energy distance function, we discover that aggregated data from OECD and non-OECD countries have no significant effect on energy efficiency performance across SSA countries. However, results from disaggregated data for OECD and non-OECD machinery imports show that OECD machinery imports improve energy efficiency contrary to non-OECD imports. Thus, technology spillover from OECD countries is advantageous for SSA countries to reduce long-term energy-based emissions. Furthermore, our results show that human capital has no significant effect on SSA energy efficiency. Consequently, the results possess some policy implications; for instance, policymakers responsible for promoting science and technology could increase investment in human capital development by developing technology and engineering expertise and increasing GDP allocation to R&D activities. For energy efficiency scores, we observe substantial differences in efficiency across SSA countries – implying potential improvements in energy efficiency across SSA countries. [ABSTRACT FROM AUTHOR]

Efficient energy management in WSNs is pivotal for prolonged network lifetime and sustained performance. This research introduces a novel approach to energy optimization through the integration of an ARIMA-driven feature selection process and an Actor-Critic Reinforcement Learning model. The Intel Lab dataset, encompassing data from 54 nodes distributed over a month, serves as the basis for experimentation. In the proposed methodology, the experimental setup ensures the validity of the study, leveraging the realism of the Intel Lab dataset. The data collection process captures the intricacies of WSN dynamics, with temperature records collected from strategically positioned nodes. The ARIMA-driven feature selection method refines the dataset, capturing temporal dependencies critical for energy prediction. The Actor-Critic model, a hybrid of policy and value-based reinforcement learning, dynamically adapts energy allocation strategies based on learned policies and offers a dynamic solution to the challenges posed by WSNs' ever-changing environments. Comparative analysis with methods reveals lower energy consumption of 0.32 mJ, an extended network lifetime of 1501 rounds, and higher prediction accuracy of 98%. The study's implications extend beyond the specific algorithms, suggesting a shift toward adaptive learning models in WSNs. The findings open avenues for future research in the integration of machine learning models for sustainable and efficient WSN deployments, emphasizing the growing importance of dynamic adaptation in sensor networks. Moreover, limitations, such as simulation realism and computational complexity, are acknowledged, prompting avenues for future research. [ABSTRACT FROM AUTHOR]

Power generation comprises high environmental and ecological impacts. The global power industry is under pressure to develop more efficient ways to operate and reduce the impacts of inherent process variability. With the rapid development of technologies within the energy sector, large volumes of data are available due to in-time operational measurements. With increased computer processing capabilities, machine learning is applied to explore these in-time operational measurements for improved process understanding. This research paper investigates machine learning algorithms for energy efficiency improvement at coal-fired thermal power plants by conducting a systematic literature review. This research is essential since it provides guidelines for applying machine learning towards sustainable energy supply and improved decision-making. Subsequently, efficient processes result in the reduction of fuel usage, which results in lower emission levels for equivalent power generation capacity. Furthermore, this study contributes towards future research by providing valuable insights from academic and industry-related studies. [ABSTRACT FROM AUTHOR]

Stakeholder pressure is among the pathways through which firms are being prodded to adopt environmental management practices. Owing to research paucity from the context of developing countries and overall inconclusiveness, this research investigates whether mimetic, normative, and coercive pressures (which encompass stakeholder pressure) sway firms into adopting resource management and energy efficiency. An analysis of data from 852 firms in Kenya using a simple probit model, suggests that all the three types of stakeholder pressure positively influenced corporate resource management and energy efficiency. This augments our postulations on both stakeholder and neo-institutional theories. Based on these findings, it is plausible to view stakeholders as critical 'tools' that can foster corporate sustainability initiatives in developing countries. [ABSTRACT FROM AUTHOR]

This paper investigates the effectiveness of different energy scenarios for achieving early reductions in global energy-related CO2 emissions on trajectories to zero or near-zero emissions by 2050. To keep global heating below 1.5°C without overshoot by 2050, global CO2 emissions must decline by about half by 2030. To achieve rapid, early emission reductions entails substantially changing recent pre-COVID (2000–2019) observed trends, which comprise increasing total primary energy supply (TPES) and approximately constant fraction of TPES derived from fossil fuels (FF fraction). Scenarios are developed to explore the effects of varying future trends in these variables in the absence of substantial CO2 removal, because relying on the latter is speculative and risky. The principal result is that, to reduce energy-related emissions to at least half the 2019 level by 2030 en route to zero or near-zero CO2 emissions by 2050, either TPES must be reduced to at least half its 2019 value by 2050 or impossibly rapid reductions must be made in the FF fraction of supply, given current technological options. Reduction in energy consumption likely entails economic degrowth in high-income countries, driven by policies that are socioeconomic, cultural and political, in addition to technological. This needs serious consideration and international cooperation. Key policy insights If global energy consumption grows at the pre-COVID rate, technological change alone cannot halve global CO2 emissions by 2030 and hence cannot keep global heating below 1.5°C by 2050. In the absence of substantial CO2 removal, policies are needed to reduce global energy consumption and hence foster degrowth in high-income economies. Policies to drive technological and socioeconomic changes could together cut global energy consumption and thus total primary energy supply and associated emissions by at least 75% by 2050. [ABSTRACT FROM AUTHOR]

A wireless sensor network requires judicious utilization of its limited energy resources. As a result, the energy efficient algorithms have become a pre-requisite for healthy and prolonged functioning of these networks. Consequently, the present work minimizes the energy expenditure, maximizes the node utilization, incurs less state switching, and prolongs the network lifetime by proposing a partly centralized and partly distributed sleep wake scheduling for the overlapped dominating sets. The concepts of such a hybrid scheduling and overlapped dominating sets have not been explored to their full potential for continuous sensing. Therefore, the research work presented in this paper shall be a stepping stone to further explore the suggested concept for the purpose of reducing energy consumption in wireless sensor networks. It is envisioned that the proposed approach shall be beneficial in battlefield monitoring, security surveillance, environmental monitoring etc., which necessitate long-term continuous/non-periodic coverage of the target region. The proposed sleep wake scheduling algorithm is hybrid in nature and possesses the advantages of both distributed and centralized approaches. The superiority and significance of the results have been established by a comparative analysis with other existing techniques mentioned in the literature. [ABSTRACT FROM AUTHOR]

The Wireless Sensor Networks (WSNs) are mostly used for applications which hardly have human interaction and WSN is always known for its limited-energy issue. The wireless sensor nodes are equipped with an in-built power supply and have limited energy to complete their assigned tasks in WSNs. Therefore, for WSNs designing energy-efficient protocols is always been a challenging task for researchers. In this paper, we have studied and discussed various WSNs' existing protocols highlighting their merits and demerits. We have also made a comparative study on different parameters and methodologies followed by the respective references while designing different energy-efficient WSN protocols for different scenarios. [ABSTRACT FROM AUTHOR]