Journal of Energy Research and Reviews

Renewable Energy-Institution-Environment Trilogy: Time-Series Insights from Nigeria

Olamide Micheal Adediwura, Ifeoluwa Alao-Owunna, Sharon Oluwaseun Adeyeye — Mon, 06 Jul 2026 00:00:00 +0000

Purpose: This study examines the dynamic relationship among renewable energy consumption, institutional quality, and environmental quality in Nigeria from 1995 to 2024, with emphasis on the moderating role of institutional quality.

Methodology: Annual time-series data were analysed using unit root and cointegration tests, and the autoregressive distributed lag (ARDL) approach. Environmental quality was proxied by CO₂ emissions, and the empirical model included renewable energy consumption, the institutional quality index, their interaction term, and an ICT index.

Findings: The results show evidence of a stable long-run relationship among the variables. In the short run, renewable energy consumption reduces CO₂ emissions, indicating immediate environmental benefits from cleaner energy use. Institutional quality has a positive direct association with emissions, suggesting that governance improvements have not yet translated independently into environmental gains. However, the interaction between renewable energy consumption and institutional quality is negative and statistically significant, indicating that stronger institutions improve the emissions-reducing effectiveness of renewable energy. ICT has no significant short-run effect but contributes to environmental improvement in the long run.

Practical Implications: The findings suggest that renewable energy policy should be implemented alongside institutional reforms that strengthen regulatory enforcement, transparency, accountability, and environmental governance. Originality: The study provides Nigeria-specific time-series evidence on the renewable energy-institution-environment nexus within a unified ARDL framework that explicitly captures moderating dynamics.

Integrating Lean, ISO 50001, and Industry 4.0 Analytics for Industrial Energy Management: A Systematic Review of KPIs, Governance, and Implementation Factors

FRANCIS IKECHUKWU ODINAKA — Tue, 07 Jul 2026 00:00:00 +0000

Aims: Effective industrial energy management (IEM) has become a strategic requirement for industrial companies because of rising and fluctuating energy costs, stricter environmental regulations and increasing competitive pressure. Although interest is growing in the integration of Lean/continuous improvement (CI), ISO 50001 energy management systems (EnMS) and Industry 4.0/AI analytics, the evidence base remains fragmented, with inconsistent key performance indicators (KPIs), limited governance models and weak evidence of sustained improvement.

Study Design: Systematic review.

Methodology: This systematic literature review synthesised academic and professional evidence across these three domains. It applied predefined inclusion and exclusion criteria focused on industrial environments and measurable outcomes, following PRISMA 2020 guidance (Page et al., 2021). Searches were conducted in Web of Science, Scopus, IEEE Xplore and Google Scholar for publications from 2000 to 2025, supplemented by practitioner sources from ISO, IEA, DOE and UNIDO. In total, 23 studies met all inclusion criteria after title/abstract and full-text screening.

Results: A framework-based data extraction approach was used to capture Lean/LSS practices, ISO 50001 PDCA elements, analytics capability types and KPI measurement structures. The synthesis produced three main outputs: (i) an integration taxonomy linking AI/Industry 4.0 analytics capabilities with PDCA governance processes and Lean execution mechanisms; (ii) a KPI framework and dictionary designed to address measurement inconsistency; and (iii) a checklist and operating model outlining minimum organisational requirements and boundary conditions for energy-performance implementation. The review also identified continuing inconsistencies in integration specificity, data interoperability, sustainment evidence and KPI comparability.

Conclusion: Collectively, these outputs provide a unified reference model for designing, assessing and maintaining analytics-supported energy management systems in industrial and manufacturing contexts. They should be interpreted as synthesis-based outputs that require empirical validation across diverse manufacturing sectors.

A Comprehensive Review of Fault Detection, Classification, and Location Techniques in Transmission Networks for Developing Countries

Mon, 13 Jul 2026 00:00:00 +0000

Reliable transmission networks underpin national economic development, yet developing countries continue to experience disproportionately high rates of transmission line faults, prolonged outage durations, and constrained investment in protection infrastructure. This review synthesises the current state of knowledge on fault detection, classification, and location techniques applicable to high-voltage transmission systems, with particular attention to the technical, economic, and institutional constraints that shape technology adoption in low- and middle-income power sectors. Conventional protection philosophies based on impedance relaying and overcurrent schemes are examined alongside signal-processing approaches such as wavelet transforms and travelling-wave methods, and against the growing body of work applying machine learning and deep learning architectures, including convolutional neural networks, long short-term memory networks, and hybrid ensembles, to fault diagnosis tasks. The review finds that although artificial-intelligence-based methods report consistently high accuracy under simulated conditions, their transferability to developing-country networks is constrained by sparse instrumentation, weak communication infrastructure, limited synchrophasor coverage, and a shortage of locally labelled fault data. High-impedance faults, series compensation, renewable-integrated feeders, and ageing conductor assets introduce further complications that are underrepresented in the literature, which remains dominated by simulation studies from well-instrumented grids. The review identifies practical pathways for closing this gap, including low-cost phasor measurement architectures, transfer learning from synthetic to field data, and hybrid schemes that combine physics-based fault location with data-driven classification. The synthesis is intended to orient researchers, utility engineers, and regulators in developing economies towards protection strategies that are both technically sound and economically deployable within prevailing infrastructure constraints.