Evidence from data analytics and machine learning on Ethiopia’s energy security in the Industry 4.0 era

Evidence from data analytics and machine learning on Ethiopia’s energy security in the Industry 4.0 era | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Evidence from data analytics and machine learning on Ethiopia’s energy security in the Industry 4.0 era Ephrem Tadesse Yohannes, Eshetie Berhan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8566045/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Energy security comprising Availability, Affordability, Accessibility, and Acceptability is critical for Ethiopia’s industrial growth and sustainable development. However, leveraging this potential to achieve security across the 4A dimensions remains a critical challenge, particularly in the context of Industry 4.0. This study analyzes Ethiopia’s energy security from 2011 to 2022 using secondary data from the International Energy Agency, World Bank, U.S. and Energy Information Administration. Descriptive analytics revealed moderate overall energy security (ESI = 52.65), with stable Availability (mean = 59.52), variable Accessibility (mean = 52.11), fluctuating Affordability (mean = 58.82), and moderate Acceptability (mean = 64.18). The analysis indicates a heavy dependence on hydropower, while sectoral energy consumption is rising in industry and transport, with per-capita use remaining low. Machine learning models Ridge (R² = 0.556), Lasso (R² = 0.555), Decision Tree (R² = 0.4484), and Random Forest (R² = 0.659), Support Vector Regression (R² = 0.782) revealed that Accessibility (0.472) and Affordability (0.434) are dominant drivers, while Acceptability (0.594 in linear models, 0.417 in Random Forest) also significantly influences energy security. Availability contributes least across models but can be strengthened through renewable integration. Industry 4.0 technologies emerged as key enablers to directly enhance each dimension of the 4A framework: smart grids and predictive analytics bolster supply Availability and reliability; IoT-enabled monitoring and decentralized generation improve physical and equitable Accessibility; data-driven optimization and automation reduce costs, strengthening Affordability; and blockchain platforms ensure transparency for environmental and social Acceptability. The findings highlight the need for targeted policy interventions, renewable energy investment, digitalized energy management, and stakeholder engagement to enhance Ethiopia’s energy resilience, equity, affordability, and sustainability. Physical sciences/Energy science and technology Physical sciences/Engineering Earth and environmental sciences/Environmental social sciences Energy Security Data Analytics Industry 4.0 Machine Learning Energy Security Dimension Energy Security Index Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction Energy security is a common concern for all societies; however, there is no single, universally accepted definition or standard dimension for quantifying it across nations. According to [ 1 ], notions of energy security frequently differ depending on national contexts, institutional perspectives and time. More importantly [ 2 ] describes energy security as a multidimensional concept that is crucial for both national security and economic stability. International Energy Agency (IEA) defines energy security as the uninterrupted availability of energy sources at an affordable price, highlighting both short-term risks of supply disruptions and long-term challenges of investment and sustainability. As explained by European Commission Energy security is the uninterrupted physical availability of energy products on the market, at a price which is affordable for all consumers (private and industrial), while respecting environmental concerns and promoting sustainable development. From an academic standpoint [ 3 ] conceptualize energy security as the “low vulnerability of vital energy systems,” focusing on system resilience to risks. whereas [ 4 ] advance a broader framework incorporating availability, affordability, efficiency, sustainability, and governance. The fundamental dimensions of energy security include availability, affordability, accessibility, and acceptability [ 5 ][ 6 ]. At its core, energy availability refers to the adequacy of domestic resources and the diversification of imports to meet demand [ 3 ]. Closely linked is affordability, which emphasizes equitable access to energy at stable and reasonable prices that do not hinder economic competitiveness or overburden households [ 7 ]; [ 8 ]. Environmental sustainability and acceptability, now central to the discourse, address the environmental costs of energy systems, particularly climate change driven by greenhouse gas emissions. Modern energy security strategies must therefore align with low-carbon pathways, creating the dual challenge of ensuring reliable supply while transitioning to renewable energy sources[ 9 ][ 10 ]. Industrial revolutions have fundamentally shaped national and global energy security through technological transformations and changing energy demands. Historical analysis reveals that energy factors played crucial roles in the first three industrial revolutions, with each transition bringing new energy challenges and opportunities[ 11 ][ 12 ]. The First Industrial Revolution (late 18th to early 19th century) marked a shift from agrarian economies to industrialized societies, leading to increased demand for coal. This transition improved energy availability and affordability but raised concerns about environmental acceptability due to pollution[ 13 ]The Second Industrial Revolution (late 19th to early 20th century) introduced electricity and oil, diversifying energy sources and enhancing availability and affordability. However, the environmental acceptability of these energy sources became a growing concern as industrial activities expanded.The Third Industrial Revolution (mid-20th century) saw the rise of nuclear power and natural gas, offering cleaner alternatives to coal and oil. These developments improved energy availability and acceptability but introduced new challenges related to safety and waste disposal, affecting public perception and acceptability[ 14 ]. The Fourth Industrial Revolution (Industry 4.0), a concept emerging around 2011, is defined by the integration of cyber-physical systems, the Internet of Things (IoT), big data, and smart factories, enabling real-time data exchange and decision-making[ 15 ][ 16 ] According to [ 17 ] [ 18 ], Industry 4.0 enhances energy availability through real-time monitoring, and integration of renewable energy sources, thereby improving supply reliability and system resilience. Digital platforms also enable decentralized energy production, increasing accessibility for a diverse range of users.[ 19 ][ 20 ] Affordability is supported through process optimization, automation, and data-driven efficiency, which reduce operational costs and minimize energy waste, making energy more cost-effective.[ 21 ][ 22 ][ 23 ].Furthermore, Industry 4.0 supports acceptability by facilitating decarburization, transparent carbon tracking, and ESG compliance, aligning energy use with environmental and social objectives.[ 24 ][ 25 ][ 26 ][ 27 ]. The rise of Industry 4.0, powered by digitalization and advanced data analytics, is reshaping each dimension of energy security. For instance, big data and machine learning platforms facilitate descriptive, predictive, diagnostic, and prescriptive analytics, enabling improved demand forecasting, process optimization, and comprehensive assessment of energy security issues. These technological advancements directly support and enhance all four dimensions of the 4A framework.[ 17 ][ 28 ][ 29 ]. Previous studies on energy security have primarily focused on evaluating dimensions and developing composite indices, with limited consideration of advanced data-analytical methods. In particular, the potential of Industry 4.0 technologies such as data analytics and machine learning to enhance the measurement, forecasting, and management of energy security remains underexplored. This study addresses this gap by developing a data-driven framework that integrates the 4A energy security model (availability, accessibility, affordability, and acceptability) with Industry 4.0 enabled methods. Specifically, machine learning algorithms and descriptive data analytics were employed to analyze longitudinal energy data, energy security dimensions and energy security index. Using this approach, Ethiopia’s energy security was assessed for the period 2011–2022. The analysis reveals a mixed performance across Ethiopia's energy security dimensions, characterized by stable domestic supply yet significant challenges in equitable access and price stability. A heavy reliance on hydropower underscores a climate vulnerability, offset only marginally by nascent wind and solar capacity. Applying a novel machine learning approach, the study identifies Accessibility and Affordability as the dominant drivers of overall energy security, with predictive modeling further quantifying their influence. These findings underscore the critical need for policy to not only diversify the energy supply but also to address the infrastructural and economic barriers that currently constrain Ethiopia's energy resilience. The integration of Industry 4.0 technologies is presented as a strategic pathway to achieve these goals. These strategies would enhance performance across all four dimensions of the 4A framework and support the transition toward a resilient and sustainable energy system. Existing studies on Ethiopia’s energy security have largely employed conventional methods, paying minimal attention to advanced data analytical techniques such as machine learning modeling. This limits their ability to capture complex, multidimensional dynamics critical for informed policy and strategic planning 2. Materials and Methods 2.1. Data and Materials Data were sourced from internationally recognized databases to ensure longitudinal coverage for the selected time frames, facilitating the analysis of national energy security during the Industry 4.0 era. Instead of using random selection, purposive sampling was employed to include datasets that provide comprehensive and consistent national energy information. The selection of internationally recognized databases was used to ensure consistency and reliability, specifically the International Energy Agency (IEA), BP Statistical Review of World Energy, World Bank’s World Development Indicators and Energy Information Administration. These sources were chosen because of their global coverage and annual updates that allow longitudinal analysis. The study used dimensions to collect time-series data from diverse, high-quality sources such as the IEA, World Bank, and the U.S. Energy Information Administration, which are widely recognized and supported by many scholars[ 29 ][ 30 ]. 2.2. Analytical Framework Energy security is a multidimensional concept, and most frameworks for analyzing a nation's energy security include several core dimensions. The most widely used and recognized dimensions are availability, affordability, accessibility, and acceptability (the "4A's")[ 31 ][ 32 ][ 33 ][ 34 ] In Ethiopia, the 4A energy security framework Availability, Accessibility, Affordability, and Acceptability is highly relevant due to the country's socio-economic and energy context. The heavy reliance on hydropower makes energy availability vulnerable to climate variability, while accessibility remains uneven, especially in rural areas where infrastructure is limited. Affordability is critical, as energy costs can burden low-income households and small businesses, and acceptability ensures energy development aligns with environmental sustainability and social priorities, such as reducing deforestation and improving public health. Applying the 4A framework provides a clear, comprehensive approach to assess and guide policies that enhance energy security while supporting Ethiopia’s economic development and social equity[ 35 ](Ethiopian Energy Outlook 2025 )[ 37 ]. Table 1 present core energy security dimension used for the study. Table 1 re Energy Security Dimensions, Metrics, and References Dimension Description Key Metrics / Indicators Used References Availability Reliable and adequate supply of energy to meet demand 1. Energy self-sufficiency (Production/Consumption) 2. Electricity adequacy (Generation/Consumption) [ 30 ][ 10 ] [ 4 ] Accessibility Physical and equitable access to energy infrastructure and services 1. Population access to electricity (%) 2. Energy use per capita (TJ per person) [ 31 ] [ 30 ] Affordability Reasonable and stable energy prices for consumers and industry 1. Energy intensity (Energy Consumption/GDP) 2. GDP per unit of energy use (GDP/Energy Consumption) [ 32 ][ 33 ] Acceptability Environmental sustainability and social compatibility of energy systems 1. CO₂ intensity (CO₂ Emissions/Energy Consumption) 2. Renewables share in electricity generation [ 10 ][ 31 ] Each indicator was standardized to a common scale, and dimension indices were aggregated into a composite Energy Security Index (ESI) [ 8 ]. This framework enabled both longitudinal analysis and predictive modeling. 2.3. Methods of Analysis Energy Security Indices (ESIs) are tools used to measure and integrate multiple aspects of energy security [ 34 ] [ 35 ]. Common normalization methods include min–max scaling and conversion to a 0–100 scale, which ensure comparability across diverse metrics. Equal weighting is simple to apply and widely used approah that gives equal importance to all dimensions of energy secuirity[ 36 ][ 10 ] Aggregation typically occurs through additive methods and geometric to combine individual indicators into a single index [ 37 ][ 38 ]. In this study 4A dimensions are also wighted equaly. Availability was assessed using three indicators: self-sufficiency, electricity adequacy, and renewables share. Self-sufficiency was calculated as the ratio of total energy production to total consumption, electricity adequacy as the ratio of total electricity production to total consumption, and renewables share as the proportion of electricity generated from solar, wind, geothermal, and hydro sources. Each indicator was normalized using min–max scaling, and a weighted Availability Index was computed by assigning equal weights to all three indicators, providing a composite measure of energy availability[ 39 ][ 40 ]. Accessibility was evaluated using two indicators: the population’s access to electricity and per capita energy use. Both indicators were first normalized using min–max scaling to ensure comparability. A composite Accessibility Index was then constructed by aggregating the normalized indicators, assigning equal weights to both electricity access and per capita energy consumption.[ 41 ][ 42 ][ 39 ]. Affordability was assessed using two indicators: energy intensity and GDP per unit of energy consumption. Energy intensity, defined as the ratio of total energy consumption to GDP, reflects the economic efficiency of energy use, with lower values indicating better affordability. GDP per energy consumption, calculated as the ratio of GDP to total energy consumption, captures the economic output generated per unit of energy, where higher values indicate better affordability[ 40 ][ 39 ]. Acceptability was evaluated using two indicators: CO₂ intensity and the share of renewables in electricity production. CO₂ intensity, calculated as the ratio of total CO₂ emissions to total energy consumption, reflects the environmental impact of energy use, where lower values indicate better environmental performance. The share of renewables captures the proportion of electricity generated from renewable sources such as solar, wind, hydro, and geothermal, with higher values indicating more sustainable energy production. Both indicators were normalized using min–max scaling, with CO₂ intensity normalized inversely (lower values better) and renewables share normalized directly. A composite Acceptability Index was then constructed by assigning equal weights to both normalized indicators, providing a standardized measure of environmental acceptability in energy consumption and production[ 43 ][ 44 ]. Energy Security Index (ESI) was constructed as the geometric mean of the four normalized sub-indices: Availability, Accessibility, Affordability, and Acceptability. Each sub-index was first divided by 100 to convert it to a proportion, then raised to the power of 0.25, reflecting equal weighting across all dimensions. The resulting product was multiplied by 100 to rescale the final ESI to a 0–100 range[ 45 ][ 46 ][ 47 ]. Data analytics approaches are increasingly vital for analyzing national energy security, offering comprehensive frameworks for assessment and strategic planning [ 42 ]. Combining diverse data sources (energy, economic, environmental) is essential [ 43 ] A robust data analytics approach to national energy security integrates multi-dimensional indicators and data analytics [ 44 ] A comprehensive data analytics approach to assessing national energy security begins with defining its multidimensional nature, which typically encompasses availability domestic production and supply sufficiency; accessibility infrastructure and diversity of supply chains; affordability ensuring stable and reasonable prices; acceptability or sustainability considering environmental impacts and renewable energy integration; and resilience the system’s flexibility and resistance to disruptions[ 45 ][ 46 ]. For the analysis, both descriptive and predictive data analytics methods were employed to examine national energy security during the Industry 4.0 period. Descriptive analytics was first applied to summarize and visualize historical energy trends, including production, consumption, imports, exports and fuel mix diversification. Through measures of central tendency, variability, and trend analysis, descriptive statistics provided insights into the structural characteristics of the energy system and highlighted vulnerabilities related to the 4A energy security dimensions. Descriptive analytics Used to summarize historical incidents, system performance, and identify vulnerabilities in energy systems [ 47 ] [ 48 ][ 43 ]. Given the small dataset, Leave-One-Out Cross-Validation (LOOCV) was applied to robustly evaluate model performance. LOOCV is particularly suitable for small datasets as it maximizes data usage, providing a reliable estimate of model performance by using each data point as a test set exactly once[ 48 ]. For The dataset was used for training and testing and to evaluate the impact of these core energy security variables [ 53 ][ 54 ][ 55 ][56] and to forecast the Energy Security Index (ESI). Predictive analytics were used to anticipate the level of influence of energy security dimensions and overall energy security [ 49 ][ 50 ][ 51 ] [ 52 ]. Table 2 describe reason why the model is considered for prediction. Table 2 Parameters Setup and Justification for Model Consideration Model Key Parameters Used Reason for Application Ridge Regression alpha = 1.0 To establish a regularized linear baseline, controlling for multicollinearity and preventing overfitting via L2 penalty. Lasso Regression alpha = 0.1 To provide a linear baseline capable of feature selection (via L1 penalty) and to assess if the relationship is inherently sparse. Decision Tree max_depth = 3 To capture fundamental non-linear relationships and interactions in an interpretable manner while preventing overfitting through a shallow tree depth. Random Forest n_estimators = 50, max_depth = 3 To improve upon the Decision Tree by leveraging an ensemble approach for greater stability and accuracy, while maintaining a constraint on complexity. Support Vector Regression (SVR) kernel='rbf', C = 1, epsilon = 0.1 To model complex, non-linear relationships between the dimensions and the ESI using the kernel trick, which is effective for smaller datasets. 2.4. Analysis Tool In this study, Python was used as the primary tool to analyze Ethiopia’s energy security through a data-driven approach within the context of Industry 4.0. To achieve the study’s objectives, the researcher collected and processed energy-related data from international sources, including the IEA, World Bank, and BP databases. Pandas and NumPy were employed for data cleaning, normalization, and preparation. Subsequently, Matplotlib and Seaborn were used to visualize the dynamics of energy production, consumption, imports, and exports, highlighting trends and patterns over time. The study also computed and analyzed key dimensions and metrics of energy security using Python. This analysis helped identify the indicators most relevant to Ethiopia’s socio-economic context. Additionally, predictive analytics were performed using the Scikit-learn library and machine learning models, including Linear Regression, Random Forest, Decision Tree, and Support Vector Regression (SVR), to evaluate the impact of these core energy security dimensions and to forecast the Energy Security Index (ESI). 3. Result and Discussion Descriptive analytics focuses on summarizing past and present energy security conditions using historical data, explaining what has happened and providing insight into the current status of the four key dimensions: Availability, Accessibility, Affordability, and Acceptability. In contrast, predictive analytics employs machine learning and statistical models to forecast future energy security trends based on historical data. 3.1. Descriptive Analytics Result Descriptive analytics provides a summary of historical data to identify trends, distributions, and patterns. The descriptive statistics for Ethiopia's energy security dimensions are presented in Table 3 . Table 3 Energy security Dimensions statistical values Index Mean Std Min 25% Median 75% Max Availability Index 59.52 8.25 47.20 56.11 58.71 60.70 78.88 Accessibility Index 52.11 29.25 0.00 30.38 66.43 76.49 86.30 Affordability Index 58.82 29.51 0.00 39.24 65.23 79.98 100.00 Acceptability Index 64.18 21.01 40.00 49.37 58.80 75.13 100.00 Energy Security Index (ESI) 52.65 19.43 0.00 47.00 59.09 63.82 72.61 The analysis of Ethiopia’s energy security from 2011 to 2022, based on the 4A framework, reveals mixed performance across its four dimensions, as illustrated in Fig. 1 . The Availability Index shows a mean value of 59.52 with a relatively low standard deviation of 8.25, indicating a moderately stable energy supply capable of meeting national demand. The relatively narrow range between the minimum (47.20) and maximum (78.88) suggests that fluctuations in energy production or imports have been limited, reflecting consistent investments in generation capacity and diversification of energy sources, including renewables. In contrast, the Accessibility Index exhibits substantial variability, with a mean of 52.11 and a standard deviation of 29.25. The minimum value of 0 and a maximum of 86.30 highlight significant disparities in energy access across the country, with rural and remote areas experiencing limited access while urban centers enjoy reliable supply. This emphasizes the need for enhanced energy distribution infrastructure to ensure equitable access nationwide. The Affordability Index, with a mean of 58.82 and a high standard deviation of 29.51, points to wide fluctuations in the economic accessibility of energy. Periods of high affordability likely reflect government subsidies or favorable pricing, whereas lower values indicate challenges in energy affordability for households and businesses due to market dynamics. This underscores the importance of targeted economic interventions and policy measures to stabilize energy prices and improve affordability for vulnerable populations. The Acceptability Index has a mean value of 64.18 and a standard deviation of 21.01, suggesting moderate progress in social, environmental, and regulatory acceptance of energy use. While Ethiopia shows strong performance in adopting renewable energy and complying with environmental standards, variability across years indicates that sustainability and regulatory measures are not yet uniformly implemented. The composite Energy Security Index (ESI), averaging 52.65 with a standard deviation of 19.43, reflects moderate overall energy security. While energy supply and regulatory acceptance are relatively strong, constraints in accessibility and affordability limit higher energy security outcomes. These findings highlight the need for integrated policy and investment efforts focused on expanding energy infrastructure, enhancing rural access, promoting affordability, and supporting sustainable energy practices to strengthen Ethiopia’s overall energy security. From an Industry 4.0 perspective, Ethiopia’s moderate Availability can be strengthened through real-time monitoring, and smart grids, optimizing supply reliability and integrating renewables efficiently. The wide disparities in Accessibility can be addressed with decentralized energy systems, IoT-enabled microgrids, and predictive analytics, enabling equitable distribution and targeted infrastructure investment. Overall, digitalization and smart energy management act as strategic enablers to enhance Availability and Accessibility, supporting a more resilient, efficient, and socially inclusive energy system. 3.1.1. Total Energy Consumption and Production during Industry 4.0 Era in Tera Joule (TJ) The analysis of Ethiopia’s energy sector during the Industry 4.0 era (2011–2022) shows a consistent increase in Total Energy Consumption (TEC) and Total Energy Production (TEP), as detailed in Table 4 . Table 4 Energy Consumption and Production Variable Mean (TJ) Std (TJ) Min (TJ) 25% (TJ) Median (TJ) 75% (TJ) Max (TJ) Total Final Energy Consumption 1,424,795 128,517 1,194,519 1,326,974 1,430,798 1,512,596 1,617,877 Total Energy Production 1,673,491 162,563 1,445,358 1,593,979 1,707,699 1,804,848 1,973,423 The analysis of Ethiopia’s energy sector during industry 4.0 era from 2011 to 2022 shows a consistent increase in Total Energy Consumption (TEC) and Total Energy Production (TEP) ( Fig. 2 ). In 2011, TEC was approximately 1,194,519 TJ, while TEP was 1,445,358 TJ, indicating a surplus in domestic production. By 2022, TEC increased significantly, reflecting the growing demands from industrial, transport, residential, and commercial sectors. This trend demonstrates the expanding energy needs associated with population growth and economic development.The year to year growth rates show that energy consumption often increased at a faster pace than production, highlighting potential pressures on energy availability. For instance, during 2012–2013, consumption growth was approximately 4–5%, while production grew by about 3%, emphasizing the need for continued investments in generation capacity. The gap between consumption and production underscores the importance of diversifying energy supply sources to enhance energy self-sufficiency and resilience, especially in a country with rapid socio-economic growth (World Energy Council, 2021; IEA, 2022).[ 49 ][ 50 ] 3.1.2. Growth Rates of Sectoral Energy Consumption Figure 3 . depict the analysis of growth rates in sectoral energy consumption provides critical insights into the dynamics of Ethiopia’s energy demand across different economic sectors over the study period. The industrial sector experienced considerable variability in energy consumption growth, ranging from a peak of 28.34% in 2014 to a decline of -7.84% in 2015. These fluctuations may reflect periods of accelerated industrial activity followed by slowdowns, possibly influenced by shifts in production, market demand, or energy availability constraints [ 51 ].Overall, the sector demonstrates a moderate upward trend, highlighting its role as a primary driver of total energy demand in the country. Energy consumption in the transport sector generally exhibited robust growth, with notable increases of 13.44% in 2012 and 21.53% in 2016, though occasional declines, such as -7.78% in 2019, were observed. These variations may be attributed to fluctuations in transport demand, fuel pricing, and economic or regulatory factors[ 52 ]. The long-term pattern indicates rising mobility needs and a growing dependency on energy in the transport sector. Residential energy consumption remained relatively stable, with growth mostly in the range of 1.96–2.76%, except for a slight dip to 0.35% in 2020. This steadiness likely reflects gradual increases in household energy access, driven by electrification programs and urbanization trends( IEA, 2023) The commercial and public services sector showed moderate variability, with peak growth at 15.81% in 2016 and declines such as -5.48% in 2019. These patterns may reflect variations in economic activities, infrastructure development, and expansion of public services( MoWIE, 2021).The agriculture and forestry sector exhibited substantial volatility, with strong growth such as 23.21% in 2016, contrasted by sharp declines of -15.43% in 2019. This reflects seasonal variations, crop cycles, and dependence on traditional energy sources in rural [ 51 ]. These sectors also displayed notable variability, with smaller magnitudes compared to industrial and transport sectors. The non-energy use category, in particular, remained relatively stable, indicating minor fluctuations in energy not directly associated with production or consumption 3.1.3. Energy Generation by Source and Electricity Mix by Source As shown in Fig. 4 , Ethiopia’s electricity generation mix from 2011 to 2022 was heavily dominated by hydropower. Generation from this source increased steadily from 6,264 TJ to 16,770 TJ over this period, underscoring the country’s strategic investment in hydropower projects. Year-on-year growth rates for hydropower fluctuated between 3.7% and 21.5%, with major surges observed in 2016 and 2022, corresponding to new dam commissioning and expansions. This underscores Ethiopia’s policy of leveraging its abundant water resources for renewable energy ( IEA, 2023). In contrast, oil-based electricity generation showed a declining trend, falling from 37 TJ in 2011 to only 2 TJ in 2022. Several years recorded either zero generation (2020) or near-zero levels, with extreme growth rate fluctuations including − 100% in 2019–2020. This decline reflects Ethiopia’s policy shift away from imported fossil fuels due to cost, energy security concerns, and a focus on renewable expansion. Wind energy generation displayed an erratic pattern. Initial growth was rapid, with output rising from 29 TJ in 2011 to 759 TJ in 2015, supported by projects like the Ashegoda Wind Farm. Growth rates were highly volatile, reaching 562% In 2012 and dropping by − 31.8% in 2017 and − 28.2% in 2019. This instability indicates challenges in integrating wind energy into the national grid, possibly due to seasonal variability, maintenance issues, and limited grid flexibility. Solar PV generation, though still small in scale, showed consistent increases, rising from 1 TJ in 2011 to 35 TJ in 2022. Year-on-year growth was initially very high (200% in 2013), reflecting the addition of small-scale PV systems and pilot projects, but stabilized after 2017, with growth rates mostly below 10%. This trend aligns with Ethiopia’s rural electrification strategy, where off-grid solar is being promoted for communities not connected to the central grid [ 55 ]. Overall, the results suggest that Ethiopia’s electricity mix is highly hydro-dependent, with emerging but unstable contributions from wind and solar. The stagnation in geothermal development and the decline of oil-fired generation highlight both opportunities and challenges in diversifying Ethiopia’s energy portfolio. While renewable energy dominance enhances sustainability, over-reliance on hydropower exposes the country to climate risks such as droughts, underscoring the importance of accelerating investment in solar and geothermal as complementary sources. Total energy production and total electricity production in Ethiopia reveal significant patterns that align with the country’s broader energy development trajectory. As shown in the results, total energy production experienced relatively modest but steady growth across the period, with annual rates ranging between approximately 2.7% and 3.9%, except for a minor contraction in 2020 (− 0.33%) (Fig. 5 ). This consistency highlights Ethiopia’s reliance on a predominantly renewable energy mix, particularly hydropower, which has served as the backbone of the national energy system. In contrast, electricity production showed greater variability, with more pronounced peaks such as 19.8% in 2012, 20.1% in 2016, and 12% in 2022, alongside declines in 2021 (− 1.99%). These fluctuations are strongly tied to Ethiopia’s investment in new large-scale hydropower projects and other renewable expansion programs. Comparatively, while total energy production growth remains stable, electricity generation growth appears more sensitive to infrastructural developments and seasonal variations. This difference underscores Ethiopia’s strategic focus on electricity as the key vector for industrialization and economic growth, even as broader energy production (including bioenergy use) evolves more gradually. The upward surge in 2022 electricity growth (+ 12.0%) suggests the positive effect of incremental GERD power generation and grid expansion. 3.1.4. Energy Use Per Capita and Access to Electricity As shown in Fig. 6 , Ethiopia’s energy use per capita remained nearly stagnant from 2011 to 2022, fluctuating between 0.0155 and 0.0162 TJ per person. Growth rates were marginal, characterized by modest increases in some years (e.g., + 1.13% in 2013) that were offset by declines in others (e.g., − 1.77% in 2021). This stagnation suggests that energy demand per individual did not expand significantly despite overall economic growth. A key explanation is the dominance of traditional biomass in Ethiopia’s energy mix, which continues to limit modern energy consumption per. Furthermore, low industrial energy intensity and limited household appliance penetration constrain increases in per-capita usage. In contrast, electricity access increased markedly from 23% of the population in 2011 to 55.4% in 2022. Growth was particularly high in 2016 (+ 47.9%) following grid expansion projects and rural electrification programs under Ethiopia’s National Electrification Program (NEP). However, access growth has slowed since 2019, averaging between 2–7% annually, reflecting challenges in extending infrastructure to remote rural areas. This improvement aligns with Ethiopia’s development goals to achieve universal electricity access by 2030, combining grid expansion with decentralized solar solutions. Nevertheless, the data highlight persistent urban–rural disparities and reliability issues, which are not captured in access percentages but remain critical for energy security. 3.1.5. CO₂ Emission Trend by Energy Consuming Sector The analysis of sectoral CO₂ emissions for 2022, presented in Fig. 7 , reveals that transport and oil-related activities are the dominant contributors. The transport sector emitted 7.275 Mt of CO₂, a slight decrease of 2.96% from the previous year, potentially indicating improvements in efficiency or fuel switching. The oil sector was the largest single contributor at 11.984 Mt. Industrial emissions, at 5.947 Mt, increased by 1.29%, underscoring its role as a persistent driver of emissions. Meanwhile, coal-related emissions saw a substantial rise of 6.36% to 2.660 Mt, pointing to increased consumption that raises environmental concerns. Conversely, electricity and heat production contributed negligibly to overall emissions (0.002 Mt) and remained stable, indicating effective use of renewable energy sources such as hydro, solar, or wind. Smaller sectors, including residential (0.111 Mt, + 2.78% growth) and commercial/public services (0.227 Mt, -5.42% growth), demonstrated minor contributions with varying trends. The residential sector’s growth may reflect increased household energy consumption, whereas the decline in commercial emissions suggests improved energy management practices. Agriculture and forestry emissions (0.540 Mt, -4.93% growth) along with the non-specified “other” sector (0.540 Mt, -4.93% growth) showed moderate reductions, potentially due to sustainable land-use practices and better energy management in miscellaneous activities. Data gaps in the fishing sector, other energy industries, and natural gas limit a full assessment but highlight areas for improved monitoring in the future. Overall, most sectors demonstrated stable or declining year-on-year growth rates, which may indicate the effectiveness of energy efficiency measures and policy interventions. These findings suggest that targeted mitigation strategies should focus on transport, industry, and coal-based activities, while continued promotion of renewable energy, energy-efficient practices, and comprehensive data collection will be crucial for reducing national CO₂ emissions and supporting sustainable energy transition efforts. 3.1.6. Energy Import and Export Trends The analysis of fuel imports from 2011 to 2022 reveals significant and distinct trends for coal and oil products (Fig. 8 ). Coal imports increased from 3,947 TJ to 15,364 TJ, though with substantial year-on-year volatility. This included periods of strong growth (e.g., increases of 58.85% and 56.55%) alongside minor declines, suggesting sensitivity to domestic demand fluctuations, policy shifts, or supply constraints. In contrast, oil product imports were consistently higher, rising from 99,743 TJ to 205,051 TJ. Their generally positive growth rates with peaks exceeding 14% annually reflect steady consumption growth driven by industrial expansion and transport fuel demand, while occasional minor declines indicate periods of moderated use or inventory adjustments. Energy export patterns present a contrasting picture, with negative absolute values throughout the period, reflecting net energy outflows or accounting conventions. The year-on-year growth rates of energy export were highly volatile, with sharp increases, such as 86.11% and 69.54%, and substantial declines, including a -30.18% decrease. This volatility suggests irregularities in energy production, export capacity, or international market demand, highlighting the sensitivity of energy export dynamics to external factors. Overall, the combined analysis of fuel imports and energy exports illustrates that the country relies heavily on imported energy, particularly oil products, while energy exports are unstable. Strategic measures to stabilize imports, improve domestic production, and enhance export efficiency could support energy security and reduce vulnerability to international market fluctuations. 3.1.7. Population, GDP and Energy Use per Capital Overview Year-on-year GDP growth rates exhibited strong fluctuations throughout the period (Fig. 9 ). The highest growth reached 35.55% early in the series, with several other years maintaining double digit increases, indicating phases of rapid economic expansion likely driven by industrial growth, investment, or stimulative policies. However, growth slowed markedly in other years, such as to 2.78% in year 10, reflecting periods of economic stabilization or the impact of external constraints on output. Population growth remained relatively stable throughout the period, ranging from approximately 2.66% to 2.84% annually. This consistent growth reflects a steady demographic increase, which could impact demand for energy, infrastructure, and services. Despite the relatively high GDP growth in some years, population growth shows minimal variation, indicating that economic expansion is likely outpacing population increase, contributing to improve per capita metrics. Energy use per capita exhibited low and fluctuating growth, with some years experiencing slight negative growth (e.g., -1.77% and − 1.44%) and others modest positive increases around 1.13%. The relatively small changes in energy use per capita, compared to the stronger GDP growth, suggest improvements in energy efficiency or shifts in economic structure toward less energy-intensive activities. The negative growth in certain years may indicate temporary reductions in energy consumption due to efficiency measures, fuel substitution, or economic adjustments. Overall, the data suggest a decoupling of energy use from economic growth, where GDP continues to grow at a faster rate than per capita energy consumption. This highlights potential progress toward a more energy-efficient economy, but also underscores the need to monitor energy demand to ensure sustainable development. The combination of strong economic growth, steady population increase, and modest per capita energy use growth points toward economic expansion without a proportionate increase in energy consumption, which is a positive indicator for energy policy and sustainability planning. 3.2 Machine Learning Models Result The machine learning analysis of energy security drivers demonstrates the predictive power of statistical methods while quantifying the contributions of different dimensions, as detailed in Table 5 . Table 5 Machine Learning Models, Performance and Feature Model Description R² RMSE Key Feature Importance Ridge Regression 0.5561 14.1762 Availability (0.0708), Accessibility (0.5274), Affordability (0.5434), Acceptability (0.5940), Energy Security Index(57.42) Lasso Regression 0.5552 14.1899 Availability (0.0705), Accessibility (0.5265), Affordability (0.5420), Acceptability (0.5901), Energy Security Index(57.42) Random Forest 0.6590 12.4256 Availability (0.1957), Accessibility (0.2052), Affordability (0.1820), Acceptability (0.4171), Energy Security Index(58.45) Decision Tree 0.4484 15.8031 Availability (0.0170), Accessibility (0.1267), Affordability (0.1538), Acceptability (0.7026), Energy Security Index(57.42) SVR 0.7816 9.9426 Availability (0.1382), Accessibility (0.4716), Affordability (0.4335), Acceptability (0.0776) and Energy Security Index(58.81) The findings of the machine learning analysis are visualized in the accompanying figures. Figure 10 showcases the performance metrics of the developed predictive models, establishing their validity. Subsequently, Fig. 11 compares the actual and predicted values of the Energy Security Index, illustrating the models' accuracy. A detailed breakdown of the predictions generated by each individual model is presented in Fig. 12 . Finally, to interpret the drivers behind these predictions, Fig. 13 summarizes the contribution of key features across the different energy security dimensions, providing critical insight into the factors that most significantly influence the index. The performance metrics of the evaluated models are summarized in Fig. 10 . The comparison between actual and predicted energy security index values. Predicted Energy Security Index by Model Contribution of Features to Each Energy Security Dimension The predicted Energy Security Index (ESI) across the models ranges from 57.42 to 58.81. This reflects a moderate but stable energy security status for Ethiopia. The Ridge and Lasso regression models show an ESI of 57.42 and an R² of about 0.556. These models assign moderate to high importance to Acceptability (0.5940 / 0.5901), Affordability (0.5434 / 0.5420), and Accessibility (0.5274 / 0.5265). In contrast, Availability (0.0708 / 0.0705) is relatively low. This suggests that, within linear modeling frameworks, social and environmental factors, along with cost and infrastructure access, are the main drivers of energy security. Meanwhile, raw energy supply plays a minor role. Industry 4.0 technologies, such as real-time monitoring and optimization platforms, can further improve these areas by allowing better management of energy distribution, cost efficiency, and social compliance. The Random Forest model (ESI = 58.45, R² = 0.659) distributes feature importance more evenly but still emphasizes Acceptability (0.4171), followed by Accessibility (0.2052), Availability (0.1957), and Affordability (0.1820). This suggests that non-linear interactions elevate the role of social and environmental acceptability, reflecting how community trust, regulatory compliance, and environmental considerations shape overall energy security. In Industry 4.0 terms, blockchain-based carbon tracking, IoT-enabled monitoring, and ESG compliance platforms can enhance acceptability by providing transparency and accountability. Meanwhile, the model also captures modest contributions from availability and accessibility, showing the importance of integrated digital platforms for distributed energy management. The Decision Tree model shows that Acceptability has the highest weight at 0.7026, while Affordability accounts for 0.1538, Accessibility for 0.1267, and Availability only for 0.0170. This focus on Acceptability highlights the dangers of oversimplifying issues by relying on a single dimension, even though acceptability plays a key role in social and environmental legitimacy. In this context, tools from Industry 4.0, such as digital ESG dashboards and community engagement platforms, could help balance acceptability with accessibility, affordability, and availability to improve overall energy security. The Support Vector Regression(SVR) model (ESI = 58.81, R² = 0.782) shows Accessibility (0.4716) and Affordability (0.4335) as dominant factors, with smaller contributions from Availability (0.1382) and Acceptability (0.0776). This underscores that physical access to energy infrastructure and cost efficiency are the most critical dimensions in Ethiopia. Industry 4.0 technologies, such as IoT-enabled smart grids, decentralized microgrids, predictive demand analytics, and automated cost optimization, directly support these dimensions. The slightly higher predicted ESI (58.81) compared to other models indicates that applying such technologies could incrementally improve Ethiopia’s overall energy security. 3.3 Limitation of study Overall, the analysis of predicted ESI values alongside the 4A dimension weights indicates that Ethiopia’s energy security is primarily driven by Accessibility and Affordability, while Acceptability gains importance in non-linear modeling approaches, and Availability is consistently lower but can be strengthened via digitalized supply and renewable integration. Leveraging Industry 4.0 innovations smart grids, IoT monitoring, AI-driven cost optimization, decentralized generation, and blockchain transparency can enhance all four dimensions, providing a pathway to elevate Ethiopia’s moderate energy security toward a more resilient, equitable, and sustainable system. Overall, the results suggest that future policy efforts may benefit from placing greater emphasis on improving accessibility and affordability, alongside continued investments in generation capacity. However, this study is subject to limitations, including reliance on available secondary data, the specific energy security index construction and equal weighting of the energy security all dimensions, and the use of associational machine learning models rather than causal inference techniques. Consequently, the findings should be interpreted with appropriate caution. 4. Conclusions This study examined Ethiopia’s energy security from 2011 to 2022 using the 4A framework Availability, Accessibility, Affordability, and Acceptability and explored the potential role of machine learning methods, informed by an Industry 4.0 perspective, in identifying key drivers and possible pathways for improvement. The findings provide a data-driven assessment of the country’s energy security profile over the study period. The results indicate that Ethiopia demonstrates a moderate level of overall energy security (ESI = 52.65), with uneven performance across the four dimensions. Relative strength is observed in Availability (mean = 59.52), reflecting sustained investment in hydropower, and in Acceptability (mean = 64.18), which shows gradual improvement over time. In contrast, Accessibility (mean = 52.11) and Affordability (mean = 58.82) remain key areas of concern, both exhibiting substantial variability. These patterns suggest that energy security challenges in Ethiopia are influenced not only by energy supply but also by infrastructural, economic, and distributional constraints. The application of machine learning models enabled a comparative examination of the relative importance of the four dimensions. Across models, Accessibility and Affordability consistently emerged as influential contributors to variations in the energy security index. Linear models emphasized the role of cost and access-related factors, while non-linear approaches, such as Random Forest, highlighted the increasing relevance of Acceptability. The comparatively lower contribution of Availability across models suggests that, while necessary, supply expansion alone may be insufficient to achieve balanced energy security outcomes. From an Industry 4.0 perspective, the findings suggest that digital technologies may offer supportive mechanisms for addressing several of the identified challenges. For example, smart grids and predictive analytics could improve system reliability and operational efficiency within Ethiopia’s hydropower-dominated energy mix. Similarly, IoT-enabled monitoring and decentralized renewable systems have the potential to support improved access in remote and underserved areas, while data-driven optimization in industrial and commercial energy use may contribute to cost efficiency. Blockchain-based platforms could further enhance transparency related to environmental and social performance, supporting acceptability objectives. These insights should be interpreted as indicative opportunities rather than direct causal effects. Future research could extend this work by incorporating longer time horizons and higher-resolution datasets, exploring alternative weighting schemes for the energy security index, and empirically evaluating the causal impacts of specific digital and Industry 4.0 technology interventions on energy security outcomes. Such efforts would further strengthen the evidence base for data-driven and technology-informed energy policy in Ethiopia. Statements of Declarations Human ethics approval and consent to participate This article does not contain any studies with human participants performed by any of the authors. Availability of data and materials All raw datasets, cleaned datasets, variable definitions, data transformations, and machine learning scripts used for data preprocessing, model development, and analysis have been made available to ensure peer review and reproducibility. The datasets generated and analyzed during this study are available in the Zenodo repository at: https://zenodo.org/records/18464010 raw dataset from World Bank https://zenodo.org/records/18463978 raw datasets from International Energy Agency https://zenodo.org/records/18463683 raw datasets from Energy Information Administration (USA) https://zenodo.org/records/18464081 Processed and analyzed data of Ethiopia energy sector https://zenodo.org/records/18464445 Energy Security Index and Prediction Models code The repository includes all data preprocessing steps, model training scripts, and documentation necessary to reproduce the results reported in this study. Competing interests The authors declare no competing interests. References B. K. Sovacool and M. A. Brown, “Competing dimensions of energy security: an international perspective,” Annu. Rev. Environ. Resour. , vol. 35, no. 1, pp. 77–108, 2010. E. A. Matsui and E. V Kryukova, “Energy security is an important component of national security,” in SHS Web of Conferences , EDP Sciences, 2021, p. 1049. A. Cherp and J. Jewell, “The concept of energy security: Beyond the four As,” Energy Policy , vol. 75, pp. 415–421, 2014. B. K. Sovacool and I. Mukherjee, “Conceptualizing and measuring energy security: A synthesized approach,” Energy , vol. 36, no. 8, pp. 5343–5355, 2011. J. A. Paravantis, “Dimensions, components and metrics of energy security: Review and synthesis,” Spoud. Econ. Bus. , vol. 69, no. 4, pp. 38–52, 2019. B. K. Sovacool and M. A. Brown, Competing dimensions of energy security: An international perspective , vol. 35. 2010. doi: 10.1146/annurev-environ-042509-143035. B. W. Ang, W. L. Choong, and T. S. Ng, “Energy security: Definitions, dimensions and indexes,” Renew. Sustain. energy Rev. , vol. 42, pp. 1077–1093, 2015. A. Paul, D. Sahoo, S. Mohapatra, and M. K. Behera, “How does the Energy Transition Shape Inclusive Green Growth in the European Union?,” Jahrb. Natl. Okon. Stat. , no. 0, 2025. P. IEA, “World energy outlook 2022,” Paris, Fr. Int. Energy Agency , 2022. A. Azzuni and C. Breyer, “Global energy security index and its application on national level,” Energies , vol. 13, no. 10, p. 2502, 2020. V. D. Poturukhin, “PROSPECTS FOR THE FOURTH INDUSTRIAL REVOLUTION IN CONTEXT OF GLOBAL ENERGY SECURITY,” Экономика и управление проблемы, решения , vol. 1, no. 9, pp. 17–25, 2020. O. Peter and C. Mbohwa, “Correlation between future energy systems and industrial revolutions,” in Proceedings of the International Conference on Industrial Engineering and Operations Management , 2018, pp. 1953–1961. K. Anderson, “What Was the Industrial Revolution’s Environmental Impact?,” Greenly, https//greenly. earth/en-gb/blog/ecology-news/what-was-the-industrial-revolutions-environmental-impact. Diakses pada , vol. 20, 2023. G. Hu, J. Yang, and J. Li, “The dynamic evolution of global energy security and geopolitical games: 1995~ 2019,” Int. J. Environ. Res. Public Health , vol. 19, no. 21, p. 14584, 2022. C. O. Klingenberg, M. A. V. Borges, and J. A. do Vale Antunes Jr, “Industry 4.0: What makes it a revolution? A historical framework to understand the phenomenon,” Technol. Soc. , vol. 70, p. 102009, 2022. Z. Suleiman, S. Shaikholla, D. Dikhanbayeva, E. Shehab, and A. Turkyilmaz, “Industry 4.0: Clustering of concepts and characteristics,” Cogent Eng. , vol. 9, no. 1, p. 2034264, 2022. V. Pandey, A. Sircar, N. Bist, K. Solanki, and K. Yadav, “Accelerating the renewable energy sector through Industry 4.0: Optimization opportunities in the digital revolution,” Int. J. Innov. Stud. , vol. 7, no. 2, pp. 171–188, 2023. P. F. Borowski, “Digitization, digital twins, blockchain, and industry 4.0 as elements of management process in enterprises in the energy sector,” Energies , vol. 14, no. 7, p. 1885, 2021. M. Li, D. Hu, C. Lal, M. Conti, and Z. Zhang, “Blockchain-enabled secure energy trading with verifiable fairness in industrial Internet of Things,” IEEE Trans. Ind. Informatics , vol. 16, no. 10, pp. 6564–6574, 2020. S. Otoum, I. Al Ridhawi, and H. Mouftah, “A federated learning and blockchain-enabled sustainable energy trade at the edge: A framework for industry 4.0,” IEEE Internet Things J. , vol. 10, no. 4, pp. 3018–3026, 2022. G. Arana-Landín, N. Uriarte-Gallastegi, B. Landeta-Manzano, and I. Laskurain-Iturbe, “The contribution of lean management—Industry 4.0 technologies to improving energy efficiency,” Energies , vol. 16, no. 5, p. 2124, 2023. N. Mohamed, J. Al-Jaroodi, and S. Lazarova-Molnar, “Leveraging the capabilities of industry 4.0 for improving energy efficiency in smart factories,” Ieee Access , vol. 7, pp. 18008–18020, 2019. M. Ghobakhloo and M. Fathi, “Industry 4.0 and opportunities for energy sustainability,” J. Clean. Prod. , vol. 295, p. 126427, 2021. E. Mastrocinque, F. J. Ramírez, A. Honrubia-Escribano, and D. T. Pham, “Industry 4.0 enabling sustainable supply chain development in the renewable energy sector: A multi-criteria intelligent approach,” Technol. Forecast. Soc. Change , vol. 182, p. 121813, 2022. J. Oláh, N. Aburumman, J. Popp, M. A. Khan, H. Haddad, and N. Kitukutha, “Impact of Industry 4.0 on environmental sustainability,” Sustainability , vol. 12, no. 11, p. 4674, 2020. T. Nitlarp and S. Kiattisin, “The impact factors of industry 4.0 on ESG in the energy sector,” Sustainability , vol. 14, no. 15, p. 9198, 2022. S. I. Vallarta-Serrano, E. Santoyo-Castelazo, E. Santoyo, E. O. García-Mandujano, and H. Vázquez-Sánchez, “Integrated sustainability assessment framework of industry 4.0 from an energy systems thinking perspective: bibliometric analysis and systematic literature review,” Energies , vol. 16, no. 14, p. 5440, 2023. J. C. Kabugo, S.-L. Jämsä-Jounela, R. Schiemann, and C. Binder, “Industry 4.0 based process data analytics platform: A waste-to-energy plant case study,” Int. J. Electr. power energy Syst. , vol. 115, p. 105508, 2020. T. Ahmad et al. , “Energetics Systems and artificial intelligence: Applications of industry 4.0,” Energy Reports , vol. 8, pp. 334–361, 2022. L. Gitelman, E. Magaril, and M. Kozhevnikov, “Energy security: new threats and solutions,” Energies , vol. 16, no. 6, p. 2869, 2023. J. Fouladvand, “Behavioural attributes towards collective energy security in thermal energy communities: Environmental-friendly behaviour matters,” Energy , vol. 261, p. 125353, 2022. J. Ren and B. K. Sovacool, “Quantifying, measuring, and strategizing energy security: Determining the most meaningful dimensions and metrics,” Energy , vol. 76, pp. 838–849, 2014. B. Kruyt, D. P. Van Vuuren, H. J. M. de Vries, and H. Groenenberg, “Indicators for energy security,” Energy Policy , vol. 37, no. 6, pp. 2166–2181, 2009. I. T. Jolliffe and J. Cadima, “Principal component analysis: a review and recent developments,” Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. , vol. 374, no. 2065, p. 20150202, 2016. Z. Yu, J. Li, and G. Yang, “A Review of Energy Security Index Dimensions and Organization,” Energy Res. Lett. , vol. 3, no. 1, 2022. P. Gasser, “A review on energy security indices to compare country performances,” Energy Policy , vol. 139, p. 111339, 2020. Z. Tajani, C. Tajani, and M. Sabbane, “Assessment of energy supply security using an analytic hierarchy process based approach,” Indones. J. Electr. Eng. Comput. Sci. , vol. 32, no. 1, pp. 555–562, 2023. J. Brodny and M. Tutak, “Assessing the energy and climate sustainability of European Union member states: an MCDM-based approach,” Smart Cities , vol. 6, no. 1, pp. 339–367, 2023. E. Desogus, E. Bompard, and D. Grosso, “A Composite Index for Tracking the Evolution towards Energy Transition at Urban Scale: The Turin Case Study,” Energies , vol. 17, no. 6, p. 1281, 2024. G. B. Bello, L. B. M. V. Viana, G. M. P. de Moraes, and D. Ferraz, “Renewable Energy Index: The Country-Group Performance Using Data Envelopment Analysis,” Energies , vol. 18, no. 14, p. 3803, 2025. M. L. Martí Selva and R. M. Puertas Medina, “Sustainable energy development analysis: Energy Trilemma,” 2022. W. E. Forum, “Fostering effective energy transition,” Insight Rep. , 2021. F. J. Hasanov, S. Mukhtarov, and E. Suleymanov, “The role of renewable energy and total factor productivity in reducing CO2 emissions in Azerbaijan. Fresh insights from a new theoretical framework coupled with Autometrics,” Energy Strateg. Rev. , vol. 47, p. 101079, 2023. H. V. Singh, R. Bocca, P. Gomez, S. Dahlke, and M. Bazilian, “The energy transitions index: An analytic framework for understanding the evolving global energy system,” Energy Strateg. Rev. , vol. 26, p. 100382, 2019. A. Stavytskyy, G. Kharlamova, O. Komendant, J. Andrzejczak, and J. Nakonieczny, “Methodology for calculating the energy security index of the state: Taking into account modern megatrends,” Energies , vol. 14, no. 12, pp. 1–19, 2021, doi: 10.3390/en14123621. S. Chakrabartty, “Geometric mean approach to measure Composite Index,” [RMd] Rev. , vol. 6, no. 1, pp. 109–131, 2024. M. Mohsin et al. , “Developing low carbon economies: an aggregated composite index based on carbon emissions,” Sustain. Energy Technol. Assessments , vol. 35, pp. 365–374, 2019. R. Sreedharan, J. Prajapati, P. Engineer, and D. Prajapati, “Leave-one-out cross-validation in machine learning,” in Ethical Issues in AI for Bioinformatics and Chemoinformatics , CRC Press, 2023, pp. 56–71. I. Citaristi, “International energy agency—iea,” in The Europa directory of international organizations 2022 , Routledge, 2022, pp. 701–702. E. Asmelash, G. Prakash, R. Gorini, and D. Gielen, “Role of IRENA for global transition to 100% renewable energy,” in Accelerating the transition to a 100% renewable energy era , Springer, 2020, pp. 51–71. M. A. H. Mondal, E. Bryan, C. Ringler, D. Mekonnen, and M. Rosegrant, “Ethiopian energy status and demand scenarios: prospects to improve energy efficiency and mitigate GHG emissions,” Energy , vol. 149, pp. 161–172, 2018. W. Kruger, S. Fezeka, and A. Olakunle, “Ethiopia country report,” Rep. 5 Energy Econ. Growth Res. Program. (W01 W05) PO Number PO00022908 , 2019. P. Dechamps, “The IEA World Energy Outlook 2022–a brief analysis and implications,” Eur. Energy Clim. J. , vol. 11, no. 3, pp. 100–103, 2023. E. Eludoyin et al. , “Energy system development pathways for Ethiopia: Final project report,” 2021. J. Thorne and M. Gerhard, “Off-Grid Funding Strategy,” 2021. Additional Declarations No competing interests reported. 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Trends\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8566045/v1/0c7ef099dac40a8d999c3e04.png"},{"id":106344270,"identity":"7c4d4df6-7cc0-408e-925f-990971e9b98a","added_by":"auto","created_at":"2026-04-07 16:13:06","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":103422,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePopulation, GDP and Energy Use per Capital Trend\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8566045/v1/6b49f1d42fe78eb6e2434759.png"},{"id":106344272,"identity":"68f749cb-3214-4f7e-b088-8e0c1600cf5b","added_by":"auto","created_at":"2026-04-07 16:13:06","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":31584,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModels Performance\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8566045/v1/3c5fda7c3ba7336ce5361c4e.png"},{"id":106344093,"identity":"ea5b9605-cf67-4a4b-b30d-0a6be2c39da6","added_by":"auto","created_at":"2026-04-07 16:12:15","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":81224,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eActual versus Predicted Energy Security Index\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8566045/v1/a3546dd2b86ede409fcf5697.png"},{"id":106344213,"identity":"01414c44-c13a-4557-8b33-44ac51ae419b","added_by":"auto","created_at":"2026-04-07 16:12:50","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":57932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted ESI per Model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-8566045/v1/96608553ecd07df5ec3c0c3f.png"},{"id":106344274,"identity":"24c62e7b-73d1-489c-aed9-ea8237ab466b","added_by":"auto","created_at":"2026-04-07 16:13:07","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":66122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFeature Contributions per Energy Security Dimension\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-8566045/v1/2c185560b97f26bce0afe35a.png"},{"id":109240084,"identity":"9bd338ad-2a4f-4a78-ad0b-f587f520fb56","added_by":"auto","created_at":"2026-05-14 06:25:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1830561,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8566045/v1/3aa3e4ba-2b3d-496a-944c-4bf083ec65b3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evidence from data analytics and machine learning on Ethiopia’s energy security in the Industry 4.0 era","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEnergy security is a common concern for all societies; however, there is no single, universally accepted definition or standard dimension for quantifying it across nations. According to [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], notions of energy security frequently differ depending on national contexts, institutional perspectives and time. More importantly [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] describes energy security as a multidimensional concept that is crucial for both national security and economic stability. International Energy Agency (IEA) defines energy security as the uninterrupted availability of energy sources at an affordable price, highlighting both short-term risks of supply disruptions and long-term challenges of investment and sustainability. As explained by European Commission Energy security is the uninterrupted physical availability of energy products on the market, at a price which is affordable for all consumers (private and industrial), while respecting environmental concerns and promoting sustainable development. From an academic standpoint [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] conceptualize energy security as the \u0026ldquo;low vulnerability of vital energy systems,\u0026rdquo; focusing on system resilience to risks. whereas [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] advance a broader framework incorporating availability, affordability, efficiency, sustainability, and governance.\u003c/p\u003e \u003cp\u003eThe fundamental dimensions of energy security include availability, affordability, accessibility, and acceptability [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e][\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. At its core, energy availability refers to the adequacy of domestic resources and the diversification of imports to meet demand [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Closely linked is affordability, which emphasizes equitable access to energy at stable and reasonable prices that do not hinder economic competitiveness or overburden households [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]; [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Environmental sustainability and acceptability, now central to the discourse, address the environmental costs of energy systems, particularly climate change driven by greenhouse gas emissions. Modern energy security strategies must therefore align with low-carbon pathways, creating the dual challenge of ensuring reliable supply while transitioning to renewable energy sources[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIndustrial revolutions have fundamentally shaped national and global energy security through technological transformations and changing energy demands. Historical analysis reveals that energy factors played crucial roles in the first three industrial revolutions, with each transition bringing new energy challenges and opportunities[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e][\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The First Industrial Revolution (late 18th to early 19th century) marked a shift from agrarian economies to industrialized societies, leading to increased demand for coal. This transition improved energy availability and affordability but raised concerns about environmental acceptability due to pollution[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]The Second Industrial Revolution (late 19th to early 20th century) introduced electricity and oil, diversifying energy sources and enhancing availability and affordability. However, the environmental acceptability of these energy sources became a growing concern as industrial activities expanded.The Third Industrial Revolution (mid-20th century) saw the rise of nuclear power and natural gas, offering cleaner alternatives to coal and oil. These developments improved energy availability and acceptability but introduced new challenges related to safety and waste disposal, affecting public perception and acceptability[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The Fourth Industrial Revolution (Industry 4.0), a concept emerging around 2011, is defined by the integration of cyber-physical systems, the Internet of Things (IoT), big data, and smart factories, enabling real-time data exchange and decision-making[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e][\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAccording to [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], Industry 4.0 enhances energy availability through real-time monitoring, and integration of renewable energy sources, thereby improving supply reliability and system resilience. Digital platforms also enable decentralized energy production, increasing accessibility for a diverse range of users.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e][\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Affordability is supported through process optimization, automation, and data-driven efficiency, which reduce operational costs and minimize energy waste, making energy more cost-effective.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e][\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].Furthermore, Industry 4.0 supports acceptability by facilitating decarburization, transparent carbon tracking, and ESG compliance, aligning energy use with environmental and social objectives.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e][\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e][\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e][\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe rise of Industry 4.0, powered by digitalization and advanced data analytics, is reshaping each dimension of energy security. For instance, big data and machine learning platforms facilitate descriptive, predictive, diagnostic, and prescriptive analytics, enabling improved demand forecasting, process optimization, and comprehensive assessment of energy security issues. These technological advancements directly support and enhance all four dimensions of the 4A framework.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e][\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e][\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies on energy security have primarily focused on evaluating dimensions and developing composite indices, with limited consideration of advanced data-analytical methods. In particular, the potential of Industry 4.0 technologies such as data analytics and machine learning to enhance the measurement, forecasting, and management of energy security remains underexplored. This study addresses this gap by developing a data-driven framework that integrates the 4A energy security model (availability, accessibility, affordability, and acceptability) with Industry 4.0 enabled methods. Specifically, machine learning algorithms and descriptive data analytics were employed to analyze longitudinal energy data, energy security dimensions and energy security index.\u003c/p\u003e \u003cp\u003eUsing this approach, Ethiopia\u0026rsquo;s energy security was assessed for the period 2011\u0026ndash;2022. The analysis reveals a mixed performance across Ethiopia's energy security dimensions, characterized by stable domestic supply yet significant challenges in equitable access and price stability. A heavy reliance on hydropower underscores a climate vulnerability, offset only marginally by nascent wind and solar capacity. Applying a novel machine learning approach, the study identifies Accessibility and Affordability as the dominant drivers of overall energy security, with predictive modeling further quantifying their influence. These findings underscore the critical need for policy to not only diversify the energy supply but also to address the infrastructural and economic barriers that currently constrain Ethiopia's energy resilience. The integration of Industry 4.0 technologies is presented as a strategic pathway to achieve these goals. These strategies would enhance performance across all four dimensions of the 4A framework and support the transition toward a resilient and sustainable energy system. Existing studies on Ethiopia\u0026rsquo;s energy security have largely employed conventional methods, paying minimal attention to advanced data analytical techniques such as machine learning modeling. This limits their ability to capture complex, multidimensional dynamics critical for informed policy and strategic planning\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data and Materials\u003c/h2\u003e \u003cp\u003eData were sourced from internationally recognized databases to ensure longitudinal coverage for the selected time frames, facilitating the analysis of national energy security during the Industry 4.0 era. Instead of using random selection, purposive sampling was employed to include datasets that provide comprehensive and consistent national energy information. The selection of internationally recognized databases was used to ensure consistency and reliability, specifically the International Energy Agency (IEA), BP Statistical Review of World Energy, World Bank\u0026rsquo;s World Development Indicators and Energy Information Administration. These sources were chosen because of their global coverage and annual updates that allow longitudinal analysis. The study used dimensions to collect time-series data from diverse, high-quality sources such as the IEA, World Bank, and the U.S. Energy Information Administration, which are widely recognized and supported by many scholars[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e][\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Analytical Framework\u003c/h2\u003e \u003cp\u003eEnergy security is a multidimensional concept, and most frameworks for analyzing a nation's energy security include several core dimensions. The most widely used and recognized dimensions are availability, affordability, accessibility, and acceptability (the \"4A's\")[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e][\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e][\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e][\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn Ethiopia, the 4A energy security framework Availability, Accessibility, Affordability, and Acceptability is highly relevant due to the country's socio-economic and energy context. The heavy reliance on hydropower makes energy availability vulnerable to climate variability, while accessibility remains uneven, especially in rural areas where infrastructure is limited. Affordability is critical, as energy costs can burden low-income households and small businesses, and acceptability ensures energy development aligns with environmental sustainability and social priorities, such as reducing deforestation and improving public health. Applying the 4A framework provides a clear, comprehensive approach to assess and guide policies that enhance energy security while supporting Ethiopia\u0026rsquo;s economic development and social equity[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e](Ethiopian Energy Outlook 2025 )[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e present core energy security dimension used for the study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ere Energy Security Dimensions, Metrics, and References\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKey Metrics / Indicators Used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReliable and adequate supply of energy to meet demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Energy self-sufficiency (Production/Consumption)\u003c/p\u003e \u003cp\u003e2. Electricity adequacy (Generation/Consumption)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccessibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical and equitable access to energy infrastructure and services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Population access to electricity (%)\u003c/p\u003e \u003cp\u003e2. Energy use per capita (TJ per person)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAffordability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReasonable and stable energy prices for consumers and industry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Energy intensity (Energy Consumption/GDP)\u003c/p\u003e \u003cp\u003e2. GDP per unit of energy use (GDP/Energy Consumption)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e][\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcceptability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental sustainability and social compatibility of energy systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. CO₂ intensity (CO₂ Emissions/Energy Consumption)\u003c/p\u003e \u003cp\u003e2. Renewables share in electricity generation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e][\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEach indicator was standardized to a common scale, and dimension indices were aggregated into a composite Energy Security Index (ESI) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This framework enabled both longitudinal analysis and predictive modeling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Methods of Analysis\u003c/h2\u003e \u003cp\u003eEnergy Security Indices (ESIs) are tools used to measure and integrate multiple aspects of energy security [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Common normalization methods include min\u0026ndash;max scaling and conversion to a 0\u0026ndash;100 scale, which ensure comparability across diverse metrics. Equal weighting is simple to apply and widely used approah that gives equal importance to all dimensions of energy secuirity[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Aggregation typically occurs through additive methods and geometric to combine individual indicators into a single index [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e][\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In this study 4A dimensions are also wighted equaly. Availability was assessed using three indicators: self-sufficiency, electricity adequacy, and renewables share. Self-sufficiency was calculated as the ratio of total energy production to total consumption, electricity adequacy as the ratio of total electricity production to total consumption, and renewables share as the proportion of electricity generated from solar, wind, geothermal, and hydro sources. Each indicator was normalized using min\u0026ndash;max scaling, and a weighted Availability Index was computed by assigning equal weights to all three indicators, providing a composite measure of energy availability[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e][\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Accessibility was evaluated using two indicators: the population\u0026rsquo;s access to electricity and per capita energy use. Both indicators were first normalized using min\u0026ndash;max scaling to ensure comparability. A composite Accessibility Index was then constructed by aggregating the normalized indicators, assigning equal weights to both electricity access and per capita energy consumption.[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e][\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e][\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAffordability was assessed using two indicators: energy intensity and GDP per unit of energy consumption. Energy intensity, defined as the ratio of total energy consumption to GDP, reflects the economic efficiency of energy use, with lower values indicating better affordability. GDP per energy consumption, calculated as the ratio of GDP to total energy consumption, captures the economic output generated per unit of energy, where higher values indicate better affordability[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e][\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Acceptability was evaluated using two indicators: CO₂ intensity and the share of renewables in electricity production. CO₂ intensity, calculated as the ratio of total CO₂ emissions to total energy consumption, reflects the environmental impact of energy use, where lower values indicate better environmental performance. The share of renewables captures the proportion of electricity generated from renewable sources such as solar, wind, hydro, and geothermal, with higher values indicating more sustainable energy production. Both indicators were normalized using min\u0026ndash;max scaling, with CO₂ intensity normalized inversely (lower values better) and renewables share normalized directly. A composite Acceptability Index was then constructed by assigning equal weights to both normalized indicators, providing a standardized measure of environmental acceptability in energy consumption and production[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e][\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEnergy Security Index (ESI) was constructed as the geometric mean of the four normalized sub-indices: Availability, Accessibility, Affordability, and Acceptability. Each sub-index was first divided by 100 to convert it to a proportion, then raised to the power of 0.25, reflecting equal weighting across all dimensions. The resulting product was multiplied by 100 to rescale the final ESI to a 0\u0026ndash;100 range[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e][\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e][\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Data analytics approaches are increasingly vital for analyzing national energy security, offering comprehensive frameworks for assessment and strategic planning [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Combining diverse data sources (energy, economic, environmental) is essential [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] A robust data analytics approach to national energy security integrates multi-dimensional indicators and data analytics [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eA comprehensive data analytics approach to assessing national energy security begins with defining its multidimensional nature, which typically encompasses availability domestic production and supply sufficiency; accessibility infrastructure and diversity of supply chains; affordability ensuring stable and reasonable prices; acceptability or sustainability considering environmental impacts and renewable energy integration; and resilience the system\u0026rsquo;s flexibility and resistance to disruptions[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e][\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor the analysis, both descriptive and predictive data analytics methods were employed to examine national energy security during the Industry 4.0 period. Descriptive analytics was first applied to summarize and visualize historical energy trends, including production, consumption, imports, exports and fuel mix diversification. Through measures of central tendency, variability, and trend analysis, descriptive statistics provided insights into the structural characteristics of the energy system and highlighted vulnerabilities related to the 4A energy security dimensions. Descriptive analytics Used to summarize historical incidents, system performance, and identify vulnerabilities in energy systems [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e][\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Given the small dataset, Leave-One-Out Cross-Validation (LOOCV) was applied to robustly evaluate model performance. LOOCV is particularly suitable for small datasets as it maximizes data usage, providing a reliable estimate of model performance by using each data point as a test set exactly once[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. For The dataset was used for training and testing and to evaluate the impact of these core energy security variables [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e][\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e][\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e][56] and to forecast the Energy Security Index (ESI). Predictive analytics were used to anticipate the level of influence of energy security dimensions and overall energy security [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e][\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e][\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e describe reason why the model is considered for prediction.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters Setup and Justification for Model Consideration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKey Parameters Used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReason for Application\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRidge Regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealpha\u0026thinsp;=\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo establish a regularized linear baseline, controlling for multicollinearity and preventing overfitting via L2 penalty.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLasso Regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealpha\u0026thinsp;=\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo provide a linear baseline capable of feature selection (via L1 penalty) and to assess if the relationship is inherently sparse.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecision Tree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emax_depth\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo capture fundamental non-linear relationships and interactions in an interpretable manner while preventing overfitting through a shallow tree depth.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en_estimators\u0026thinsp;=\u0026thinsp;50, max_depth\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo improve upon the Decision Tree by leveraging an ensemble approach for greater stability and accuracy, while maintaining a constraint on complexity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupport Vector Regression (SVR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekernel='rbf', C\u0026thinsp;=\u0026thinsp;1, epsilon\u0026thinsp;=\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo model complex, non-linear relationships between the dimensions and the ESI using the kernel trick, which is effective for smaller datasets.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Analysis Tool\u003c/h2\u003e \u003cp\u003eIn this study, Python was used as the primary tool to analyze Ethiopia\u0026rsquo;s energy security through a data-driven approach within the context of Industry 4.0. To achieve the study\u0026rsquo;s objectives, the researcher collected and processed energy-related data from international sources, including the IEA, World Bank, and BP databases. Pandas and NumPy were employed for data cleaning, normalization, and preparation. Subsequently, Matplotlib and Seaborn were used to visualize the dynamics of energy production, consumption, imports, and exports, highlighting trends and patterns over time.\u003c/p\u003e \u003cp\u003eThe study also computed and analyzed key dimensions and metrics of energy security using Python. This analysis helped identify the indicators most relevant to Ethiopia\u0026rsquo;s socio-economic context. Additionally, predictive analytics were performed using the Scikit-learn library and machine learning models, including Linear Regression, Random Forest, Decision Tree, and Support Vector Regression (SVR), to evaluate the impact of these core energy security dimensions and to forecast the Energy Security Index (ESI).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result and Discussion","content":"\u003cp\u003eDescriptive analytics focuses on summarizing past and present energy security conditions using historical data, explaining what has happened and providing insight into the current status of the four key dimensions: Availability, Accessibility, Affordability, and Acceptability. In contrast, predictive analytics employs machine learning and statistical models to forecast future energy security trends based on historical data.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Descriptive Analytics Result\u003c/h2\u003e \u003cp\u003eDescriptive analytics provides a summary of historical data to identify trends, distributions, and patterns. The descriptive statistics for Ethiopia's energy security dimensions are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnergy security Dimensions statistical values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailability Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccessibility Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e76.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e86.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAffordability Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e79.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcceptability Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy Security Index (ESI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of Ethiopia\u0026rsquo;s energy security from 2011 to 2022, based on the 4A framework, reveals mixed performance across its four dimensions, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The Availability Index shows a mean value of 59.52 with a relatively low standard deviation of 8.25, indicating a moderately stable energy supply capable of meeting national demand. The relatively narrow range between the minimum (47.20) and maximum (78.88) suggests that fluctuations in energy production or imports have been limited, reflecting consistent investments in generation capacity and diversification of energy sources, including renewables. In contrast, the Accessibility Index exhibits substantial variability, with a mean of 52.11 and a standard deviation of 29.25. The minimum value of 0 and a maximum of 86.30 highlight significant disparities in energy access across the country, with rural and remote areas experiencing limited access while urban centers enjoy reliable supply. This emphasizes the need for enhanced energy distribution infrastructure to ensure equitable access nationwide.\u003c/p\u003e \u003cp\u003eThe Affordability Index, with a mean of 58.82 and a high standard deviation of 29.51, points to wide fluctuations in the economic accessibility of energy. Periods of high affordability likely reflect government subsidies or favorable pricing, whereas lower values indicate challenges in energy affordability for households and businesses due to market dynamics. This underscores the importance of targeted economic interventions and policy measures to stabilize energy prices and improve affordability for vulnerable populations. The Acceptability Index has a mean value of 64.18 and a standard deviation of 21.01, suggesting moderate progress in social, environmental, and regulatory acceptance of energy use. While Ethiopia shows strong performance in adopting renewable energy and complying with environmental standards, variability across years indicates that sustainability and regulatory measures are not yet uniformly implemented. The composite Energy Security Index (ESI), averaging 52.65 with a standard deviation of 19.43, reflects moderate overall energy security. While energy supply and regulatory acceptance are relatively strong, constraints in accessibility and affordability limit higher energy security outcomes. These findings highlight the need for integrated policy and investment efforts focused on expanding energy infrastructure, enhancing rural access, promoting affordability, and supporting sustainable energy practices to strengthen Ethiopia\u0026rsquo;s overall energy security.\u003c/p\u003e \u003cp\u003eFrom an Industry 4.0 perspective, Ethiopia\u0026rsquo;s moderate Availability can be strengthened through real-time monitoring, and smart grids, optimizing supply reliability and integrating renewables efficiently. The wide disparities in Accessibility can be addressed with decentralized energy systems, IoT-enabled microgrids, and predictive analytics, enabling equitable distribution and targeted infrastructure investment. Overall, digitalization and smart energy management act as strategic enablers to enhance Availability and Accessibility, supporting a more resilient, efficient, and socially inclusive energy system.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Total Energy Consumption and Production during Industry 4.0 Era in Tera Joule (TJ)\u003c/h2\u003e \u003cp\u003eThe analysis of Ethiopia\u0026rsquo;s energy sector during the Industry 4.0 era (2011\u0026ndash;2022) shows a consistent increase in Total Energy Consumption (TEC) and Total Energy Production (TEP), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnergy Consumption and Production\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (TJ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd (TJ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin (TJ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25% (TJ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian (TJ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75% (TJ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMax (TJ)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Final Energy Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,424,795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128,517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,194,519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,326,974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,430,798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,512,596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,617,877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Energy Production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,673,491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162,563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,445,358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,593,979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,707,699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,804,848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,973,423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of Ethiopia\u0026rsquo;s energy sector during industry 4.0 era from 2011 to 2022 shows a consistent increase in Total Energy Consumption (TEC) and Total Energy Production (TEP) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In 2011, TEC was approximately 1,194,519 TJ, while TEP was 1,445,358 TJ, indicating a surplus in domestic production. By 2022, TEC increased significantly, reflecting the growing demands from industrial, transport, residential, and commercial sectors. This trend demonstrates the expanding energy needs associated with population growth and economic development.The year to year growth rates show that energy consumption often increased at a faster pace than production, highlighting potential pressures on energy availability. For instance, during 2012\u0026ndash;2013, consumption growth was approximately 4\u0026ndash;5%, while production grew by about 3%, emphasizing the need for continued investments in generation capacity. The gap between consumption and production underscores the importance of diversifying energy supply sources to enhance energy self-sufficiency and resilience, especially in a country with rapid socio-economic growth (World Energy Council, 2021; IEA, 2022).[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e][\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Growth Rates of Sectoral Energy Consumption\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. depict the analysis of growth rates in sectoral energy consumption provides critical insights into the dynamics of Ethiopia\u0026rsquo;s energy demand across different economic sectors over the study period. The industrial sector experienced considerable variability in energy consumption growth, ranging from a peak of 28.34% in 2014 to a decline of -7.84% in 2015. These fluctuations may reflect periods of accelerated industrial activity followed by slowdowns, possibly influenced by shifts in production, market demand, or energy availability constraints [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].Overall, the sector demonstrates a moderate upward trend, highlighting its role as a primary driver of total energy demand in the country. Energy consumption in the transport sector generally exhibited robust growth, with notable increases of 13.44% in 2012 and 21.53% in 2016, though occasional declines, such as -7.78% in 2019, were observed. These variations may be attributed to fluctuations in transport demand, fuel pricing, and economic or regulatory factors[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The long-term pattern indicates rising mobility needs and a growing dependency on energy in the transport sector.\u003c/p\u003e \u003cp\u003eResidential energy consumption remained relatively stable, with growth mostly in the range of 1.96\u0026ndash;2.76%, except for a slight dip to 0.35% in 2020. This steadiness likely reflects gradual increases in household energy access, driven by electrification programs and urbanization trends( IEA, 2023) The commercial and public services sector showed moderate variability, with peak growth at 15.81% in 2016 and declines such as -5.48% in 2019. These patterns may reflect variations in economic activities, infrastructure development, and expansion of public services( MoWIE, 2021).The agriculture and forestry sector exhibited substantial volatility, with strong growth such as 23.21% in 2016, contrasted by sharp declines of -15.43% in 2019. This reflects seasonal variations, crop cycles, and dependence on traditional energy sources in rural [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. These sectors also displayed notable variability, with smaller magnitudes compared to industrial and transport sectors. The non-energy use category, in particular, remained relatively stable, indicating minor fluctuations in energy not directly associated with production or consumption\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3. Energy Generation by Source and Electricity Mix by Source\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Ethiopia\u0026rsquo;s electricity generation mix from 2011 to 2022 was heavily dominated by hydropower. Generation from this source increased steadily from 6,264 TJ to 16,770 TJ over this period, underscoring the country\u0026rsquo;s strategic investment in hydropower projects. Year-on-year growth rates for hydropower fluctuated between 3.7% and 21.5%, with major surges observed in 2016 and 2022, corresponding to new dam commissioning and expansions. This underscores Ethiopia\u0026rsquo;s policy of leveraging its abundant water resources for renewable energy ( IEA, 2023). In contrast, oil-based electricity generation showed a declining trend, falling from 37 TJ in 2011 to only 2 TJ in 2022. Several years recorded either zero generation (2020) or near-zero levels, with extreme growth rate fluctuations including \u0026minus;\u0026thinsp;100% in 2019\u0026ndash;2020. This decline reflects Ethiopia\u0026rsquo;s policy shift away from imported fossil fuels due to cost, energy security concerns, and a focus on renewable expansion.\u003c/p\u003e \u003cp\u003eWind energy generation displayed an erratic pattern. Initial growth was rapid, with output rising from 29 TJ in 2011 to 759 TJ in 2015, supported by projects like the Ashegoda Wind Farm. Growth rates were highly volatile, reaching 562% In 2012 and dropping by \u0026minus;\u0026thinsp;31.8% in 2017 and \u0026minus;\u0026thinsp;28.2% in 2019. This instability indicates challenges in integrating wind energy into the national grid, possibly due to seasonal variability, maintenance issues, and limited grid flexibility. Solar PV generation, though still small in scale, showed consistent increases, rising from 1 TJ in 2011 to 35 TJ in 2022. Year-on-year growth was initially very high (200% in 2013), reflecting the addition of small-scale PV systems and pilot projects, but stabilized after 2017, with growth rates mostly below 10%. This trend aligns with Ethiopia\u0026rsquo;s rural electrification strategy, where off-grid solar is being promoted for communities not connected to the central grid [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, the results suggest that Ethiopia\u0026rsquo;s electricity mix is highly hydro-dependent, with emerging but unstable contributions from wind and solar. The stagnation in geothermal development and the decline of oil-fired generation highlight both opportunities and challenges in diversifying Ethiopia\u0026rsquo;s energy portfolio. While renewable energy dominance enhances sustainability, over-reliance on hydropower exposes the country to climate risks such as droughts, underscoring the importance of accelerating investment in solar and geothermal as complementary sources.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTotal energy production and total electricity production in Ethiopia reveal significant patterns that align with the country\u0026rsquo;s broader energy development trajectory. As shown in the results, total energy production experienced relatively modest but steady growth across the period, with annual rates ranging between approximately 2.7% and 3.9%, except for a minor contraction in 2020 (\u0026minus;\u0026thinsp;0.33%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This consistency highlights Ethiopia\u0026rsquo;s reliance on a predominantly renewable energy mix, particularly hydropower, which has served as the backbone of the national energy system. In contrast, electricity production showed greater variability, with more pronounced peaks such as 19.8% in 2012, 20.1% in 2016, and 12% in 2022, alongside declines in 2021 (\u0026minus;\u0026thinsp;1.99%). These fluctuations are strongly tied to Ethiopia\u0026rsquo;s investment in new large-scale hydropower projects and other renewable expansion programs.\u003c/p\u003e \u003cp\u003eComparatively, while total energy production growth remains stable, electricity generation growth appears more sensitive to infrastructural developments and seasonal variations. This difference underscores Ethiopia\u0026rsquo;s strategic focus on electricity as the key vector for industrialization and economic growth, even as broader energy production (including bioenergy use) evolves more gradually. The upward surge in 2022 electricity growth (+\u0026thinsp;12.0%) suggests the positive effect of incremental GERD power generation and grid expansion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4. Energy Use Per Capita and Access to Electricity\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Ethiopia\u0026rsquo;s energy use per capita remained nearly stagnant from 2011 to 2022, fluctuating between 0.0155 and 0.0162 TJ per person. Growth rates were marginal, characterized by modest increases in some years (e.g., +\u0026thinsp;1.13% in 2013) that were offset by declines in others (e.g., \u0026minus;\u0026thinsp;1.77% in 2021).\u003c/p\u003e \u003cp\u003eThis stagnation suggests that energy demand per individual did not expand significantly despite overall economic growth. A key explanation is the dominance of traditional biomass in Ethiopia\u0026rsquo;s energy mix, which continues to limit modern energy consumption per. Furthermore, low industrial energy intensity and limited household appliance penetration constrain increases in per-capita usage. In contrast, electricity access increased markedly from 23% of the population in 2011 to 55.4% in 2022. Growth was particularly high in 2016 (+\u0026thinsp;47.9%) following grid expansion projects and rural electrification programs under Ethiopia\u0026rsquo;s National Electrification Program (NEP). However, access growth has slowed since 2019, averaging between 2\u0026ndash;7% annually, reflecting challenges in extending infrastructure to remote rural areas. This improvement aligns with Ethiopia\u0026rsquo;s development goals to achieve universal electricity access by 2030, combining grid expansion with decentralized solar solutions. Nevertheless, the data highlight persistent urban\u0026ndash;rural disparities and reliability issues, which are not captured in access percentages but remain critical for energy security.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.5. CO₂ Emission Trend by Energy Consuming Sector\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of sectoral CO₂ emissions for 2022, presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, reveals that transport and oil-related activities are the dominant contributors. The transport sector emitted 7.275 Mt of CO₂, a slight decrease of 2.96% from the previous year, potentially indicating improvements in efficiency or fuel switching. The oil sector was the largest single contributor at 11.984 Mt. Industrial emissions, at 5.947 Mt, increased by 1.29%, underscoring its role as a persistent driver of emissions. Meanwhile, coal-related emissions saw a substantial rise of 6.36% to 2.660 Mt, pointing to increased consumption that raises environmental concerns. Conversely, electricity and heat production contributed negligibly to overall emissions (0.002 Mt) and remained stable, indicating effective use of renewable energy sources such as hydro, solar, or wind. Smaller sectors, including residential (0.111 Mt, +\u0026thinsp;2.78% growth) and commercial/public services (0.227 Mt, -5.42% growth), demonstrated minor contributions with varying trends. The residential sector\u0026rsquo;s growth may reflect increased household energy consumption, whereas the decline in commercial emissions suggests improved energy management practices. Agriculture and forestry emissions (0.540 Mt, -4.93% growth) along with the non-specified \u0026ldquo;other\u0026rdquo; sector (0.540 Mt, -4.93% growth) showed moderate reductions, potentially due to sustainable land-use practices and better energy management in miscellaneous activities.\u003c/p\u003e \u003cp\u003eData gaps in the fishing sector, other energy industries, and natural gas limit a full assessment but highlight areas for improved monitoring in the future. Overall, most sectors demonstrated stable or declining year-on-year growth rates, which may indicate the effectiveness of energy efficiency measures and policy interventions. These findings suggest that targeted mitigation strategies should focus on transport, industry, and coal-based activities, while continued promotion of renewable energy, energy-efficient practices, and comprehensive data collection will be crucial for reducing national CO₂ emissions and supporting sustainable energy transition efforts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.1.6. Energy Import and Export Trends\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of fuel imports from 2011 to 2022 reveals significant and distinct trends for coal and oil products (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Coal imports increased from 3,947 TJ to 15,364 TJ, though with substantial year-on-year volatility. This included periods of strong growth (e.g., increases of 58.85% and 56.55%) alongside minor declines, suggesting sensitivity to domestic demand fluctuations, policy shifts, or supply constraints. In contrast, oil product imports were consistently higher, rising from 99,743 TJ to 205,051 TJ. Their generally positive growth rates with peaks exceeding 14% annually reflect steady consumption growth driven by industrial expansion and transport fuel demand, while occasional minor declines indicate periods of moderated use or inventory adjustments.\u003c/p\u003e \u003cp\u003eEnergy export patterns present a contrasting picture, with negative absolute values throughout the period, reflecting net energy outflows or accounting conventions. The year-on-year growth rates of energy export were highly volatile, with sharp increases, such as 86.11% and 69.54%, and substantial declines, including a -30.18% decrease. This volatility suggests irregularities in energy production, export capacity, or international market demand, highlighting the sensitivity of energy export dynamics to external factors. Overall, the combined analysis of fuel imports and energy exports illustrates that the country relies heavily on imported energy, particularly oil products, while energy exports are unstable. Strategic measures to stabilize imports, improve domestic production, and enhance export efficiency could support energy security and reduce vulnerability to international market fluctuations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.1.7. Population, GDP and Energy Use per Capital Overview\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eYear-on-year GDP growth rates exhibited strong fluctuations throughout the period (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The highest growth reached 35.55% early in the series, with several other years maintaining double digit increases, indicating phases of rapid economic expansion likely driven by industrial growth, investment, or stimulative policies. However, growth slowed markedly in other years, such as to 2.78% in year 10, reflecting periods of economic stabilization or the impact of external constraints on output.\u003c/p\u003e \u003cp\u003ePopulation growth remained relatively stable throughout the period, ranging from approximately 2.66% to 2.84% annually. This consistent growth reflects a steady demographic increase, which could impact demand for energy, infrastructure, and services. Despite the relatively high GDP growth in some years, population growth shows minimal variation, indicating that economic expansion is likely outpacing population increase, contributing to improve per capita metrics. Energy use per capita exhibited low and fluctuating growth, with some years experiencing slight negative growth (e.g., -1.77% and \u0026minus;\u0026thinsp;1.44%) and others modest positive increases around 1.13%. The relatively small changes in energy use per capita, compared to the stronger GDP growth, suggest improvements in energy efficiency or shifts in economic structure toward less energy-intensive activities. The negative growth in certain years may indicate temporary reductions in energy consumption due to efficiency measures, fuel substitution, or economic adjustments. Overall, the data suggest a decoupling of energy use from economic growth, where GDP continues to grow at a faster rate than per capita energy consumption. This highlights potential progress toward a more energy-efficient economy, but also underscores the need to monitor energy demand to ensure sustainable development. The combination of strong economic growth, steady population increase, and modest per capita energy use growth points toward economic expansion without a proportionate increase in energy consumption, which is a positive indicator for energy policy and sustainability planning.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Machine Learning Models Result\u003c/h2\u003e \u003cp\u003eThe machine learning analysis of energy security drivers demonstrates the predictive power of statistical methods while quantifying the contributions of different dimensions, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMachine Learning Models, Performance and Feature\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKey Feature Importance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRidge Regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.1762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAvailability (0.0708), Accessibility (0.5274), Affordability (0.5434), Acceptability (0.5940), Energy Security Index(57.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLasso Regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.1899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAvailability (0.0705), Accessibility (0.5265), Affordability (0.5420), Acceptability (0.5901), Energy Security Index(57.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.4256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAvailability (0.1957), Accessibility (0.2052), Affordability (0.1820), Acceptability (0.4171), Energy Security Index(58.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecision Tree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.8031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAvailability (0.0170), Accessibility (0.1267), Affordability (0.1538), Acceptability (0.7026), Energy Security Index(57.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.9426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAvailability (0.1382), Accessibility (0.4716), Affordability (0.4335), Acceptability (0.0776) and Energy Security Index(58.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe findings of the machine learning analysis are visualized in the accompanying figures. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e showcases the performance metrics of the developed predictive models, establishing their validity. Subsequently, Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e compares the actual and predicted values of the Energy Security Index, illustrating the models' accuracy. A detailed breakdown of the predictions generated by each individual model is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e. Finally, to interpret the drivers behind these predictions, Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e summarizes the contribution of key features across the different energy security dimensions, providing critical insight into the factors that most significantly influence the index.\u003c/p\u003e \u003cp\u003eThe performance metrics of the evaluated models are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe comparison between actual and predicted energy security index values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePredicted Energy Security Index by Model\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eContribution of Features to Each Energy Security Dimension\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe predicted Energy Security Index (ESI) across the models ranges from 57.42 to 58.81. This reflects a moderate but stable energy security status for Ethiopia. The Ridge and Lasso regression models show an ESI of 57.42 and an R\u0026sup2; of about 0.556. These models assign moderate to high importance to Acceptability (0.5940 / 0.5901), Affordability (0.5434 / 0.5420), and Accessibility (0.5274 / 0.5265). In contrast, Availability (0.0708 / 0.0705) is relatively low. This suggests that, within linear modeling frameworks, social and environmental factors, along with cost and infrastructure access, are the main drivers of energy security. Meanwhile, raw energy supply plays a minor role. Industry 4.0 technologies, such as real-time monitoring and optimization platforms, can further improve these areas by allowing better management of energy distribution, cost efficiency, and social compliance.\u003c/p\u003e \u003cp\u003eThe Random Forest model (ESI\u0026thinsp;=\u0026thinsp;58.45, R\u0026sup2; = 0.659) distributes feature importance more evenly but still emphasizes Acceptability (0.4171), followed by Accessibility (0.2052), Availability (0.1957), and Affordability (0.1820). This suggests that non-linear interactions elevate the role of social and environmental acceptability, reflecting how community trust, regulatory compliance, and environmental considerations shape overall energy security. In Industry 4.0 terms, blockchain-based carbon tracking, IoT-enabled monitoring, and ESG compliance platforms can enhance acceptability by providing transparency and accountability. Meanwhile, the model also captures modest contributions from availability and accessibility, showing the importance of integrated digital platforms for distributed energy management.\u003c/p\u003e \u003cp\u003eThe Decision Tree model shows that Acceptability has the highest weight at 0.7026, while Affordability accounts for 0.1538, Accessibility for 0.1267, and Availability only for 0.0170. This focus on Acceptability highlights the dangers of oversimplifying issues by relying on a single dimension, even though acceptability plays a key role in social and environmental legitimacy. In this context, tools from Industry 4.0, such as digital ESG dashboards and community engagement platforms, could help balance acceptability with accessibility, affordability, and availability to improve overall energy security.\u003c/p\u003e \u003cp\u003eThe Support Vector Regression(SVR) model (ESI\u0026thinsp;=\u0026thinsp;58.81, R\u0026sup2; = 0.782) shows Accessibility (0.4716) and Affordability (0.4335) as dominant factors, with smaller contributions from Availability (0.1382) and Acceptability (0.0776). This underscores that physical access to energy infrastructure and cost efficiency are the most critical dimensions in Ethiopia. Industry 4.0 technologies, such as IoT-enabled smart grids, decentralized microgrids, predictive demand analytics, and automated cost optimization, directly support these dimensions. The slightly higher predicted ESI (58.81) compared to other models indicates that applying such technologies could incrementally improve Ethiopia\u0026rsquo;s overall energy security.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Limitation of study\u003c/h2\u003e \u003cp\u003eOverall, the analysis of predicted ESI values alongside the 4A dimension weights indicates that Ethiopia\u0026rsquo;s energy security is primarily driven by Accessibility and Affordability, while Acceptability gains importance in non-linear modeling approaches, and Availability is consistently lower but can be strengthened via digitalized supply and renewable integration. Leveraging Industry 4.0 innovations smart grids, IoT monitoring, AI-driven cost optimization, decentralized generation, and blockchain transparency can enhance all four dimensions, providing a pathway to elevate Ethiopia\u0026rsquo;s moderate energy security toward a more resilient, equitable, and sustainable system.\u003c/p\u003e \u003cp\u003eOverall, the results suggest that future policy efforts may benefit from placing greater emphasis on improving accessibility and affordability, alongside continued investments in generation capacity. However, this study is subject to limitations, including reliance on available secondary data, the specific energy security index construction and equal weighting of the energy security all dimensions, and the use of associational machine learning models rather than causal inference techniques. Consequently, the findings should be interpreted with appropriate caution.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis study examined Ethiopia\u0026rsquo;s energy security from 2011 to 2022 using the 4A framework Availability, Accessibility, Affordability, and Acceptability and explored the potential role of machine learning methods, informed by an Industry 4.0 perspective, in identifying key drivers and possible pathways for improvement. The findings provide a data-driven assessment of the country\u0026rsquo;s energy security profile over the study period.\u003c/p\u003e\n\u003cp\u003eThe results indicate that Ethiopia demonstrates a moderate level of overall energy security (ESI = 52.65), with uneven performance across the four dimensions. Relative strength is observed in Availability (mean = 59.52), reflecting sustained investment in hydropower, and in Acceptability (mean = 64.18), which shows gradual improvement over time. In contrast, Accessibility (mean = 52.11) and Affordability (mean = 58.82) remain key areas of concern, both exhibiting substantial variability. These patterns suggest that energy security challenges in Ethiopia are influenced not only by energy supply but also by infrastructural, economic, and distributional constraints.\u003c/p\u003e\n\u003cp\u003eThe application of machine learning models enabled a comparative examination of the relative importance of the four dimensions. Across models, Accessibility and Affordability consistently emerged as influential contributors to variations in the energy security index. Linear models emphasized the role of cost and access-related factors, while non-linear approaches, such as Random Forest, highlighted the increasing relevance of Acceptability. The comparatively lower contribution of Availability across models suggests that, while necessary, supply expansion alone may be insufficient to achieve balanced energy security outcomes.\u003c/p\u003e\n\u003cp\u003eFrom an Industry 4.0 perspective, the findings suggest that digital technologies may offer supportive mechanisms for addressing several of the identified challenges. For example, smart grids and predictive analytics could improve system reliability and operational efficiency within Ethiopia\u0026rsquo;s hydropower-dominated energy mix. Similarly, IoT-enabled monitoring and decentralized renewable systems have the potential to support improved access in remote and underserved areas, while data-driven optimization in industrial and commercial energy use may contribute to cost efficiency. Blockchain-based platforms could further enhance transparency related to environmental and social performance, supporting acceptability objectives. These insights should be interpreted as indicative opportunities rather than direct causal effects.\u003c/p\u003e\n\u003cp\u003eFuture research could extend this work by incorporating longer time horizons and higher-resolution datasets, exploring alternative weighting schemes for the energy security index, and empirically evaluating the causal impacts of specific digital and Industry 4.0 technology interventions on energy security outcomes. Such efforts would further strengthen the evidence base for data-driven and technology-informed energy policy in Ethiopia.\u003c/p\u003e"},{"header":"Statements of Declarations","content":"\u003cp\u003e\u003cstrong\u003eHuman ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw datasets, cleaned datasets, variable definitions, data transformations, and machine learning scripts used for data preprocessing, model development, and analysis have been made available to ensure peer review and reproducibility. The datasets generated and analyzed during this study are available in the Zenodo repository at:\u003c/p\u003e\n\u003cp\u003ehttps://zenodo.org/records/18464010 raw dataset from World Bank\u003c/p\u003e\n\u003cp\u003ehttps://zenodo.org/records/18463978 raw datasets from International Energy Agency\u003c/p\u003e\n\u003cp\u003ehttps://zenodo.org/records/18463683 raw datasets from Energy Information Administration (USA) \u003c/p\u003e\n\u003cp\u003ehttps://zenodo.org/records/18464081 Processed and analyzed data of Ethiopia energy sector\u003c/p\u003e\n\u003cp\u003ehttps://zenodo.org/records/18464445 Energy Security Index and Prediction Models code\u003c/p\u003e\n\u003cp\u003eThe repository includes all data preprocessing steps, model training scripts, and documentation necessary to reproduce the results reported in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eB. K. Sovacool and M. A. Brown, \u0026ldquo;Competing dimensions of energy security: an international perspective,\u0026rdquo; \u003cem\u003eAnnu. Rev. Environ. Resour.\u003c/em\u003e, vol. 35, no. 1, pp. 77\u0026ndash;108, 2010.\u003c/li\u003e\n \u003cli\u003eE. A. Matsui and E. V Kryukova, \u0026ldquo;Energy security is an important component of national security,\u0026rdquo; in \u003cem\u003eSHS Web of Conferences\u003c/em\u003e, EDP Sciences, 2021, p. 1049.\u003c/li\u003e\n \u003cli\u003eA. Cherp and J. Jewell, \u0026ldquo;The concept of energy security: Beyond the four As,\u0026rdquo; \u003cem\u003eEnergy Policy\u003c/em\u003e, vol. 75, pp. 415\u0026ndash;421, 2014.\u003c/li\u003e\n \u003cli\u003eB. K. Sovacool and I. Mukherjee, \u0026ldquo;Conceptualizing and measuring energy security: A synthesized approach,\u0026rdquo; \u003cem\u003eEnergy\u003c/em\u003e, vol. 36, no. 8, pp. 5343\u0026ndash;5355, 2011.\u003c/li\u003e\n \u003cli\u003eJ. A. Paravantis, \u0026ldquo;Dimensions, components and metrics of energy security: Review and synthesis,\u0026rdquo; \u003cem\u003eSpoud. Econ. Bus.\u003c/em\u003e, vol. 69, no. 4, pp. 38\u0026ndash;52, 2019.\u003c/li\u003e\n \u003cli\u003eB. K. Sovacool and M. A. Brown, \u003cem\u003eCompeting dimensions of energy security: An international perspective\u003c/em\u003e, vol. 35. 2010. doi: 10.1146/annurev-environ-042509-143035.\u003c/li\u003e\n \u003cli\u003eB. W. Ang, W. L. Choong, and T. S. Ng, \u0026ldquo;Energy security: Definitions, dimensions and indexes,\u0026rdquo; \u003cem\u003eRenew. Sustain. energy Rev.\u003c/em\u003e, vol. 42, pp. 1077\u0026ndash;1093, 2015.\u003c/li\u003e\n \u003cli\u003eA. Paul, D. Sahoo, S. Mohapatra, and M. K. Behera, \u0026ldquo;How does the Energy Transition Shape Inclusive Green Growth in the European Union?,\u0026rdquo; \u003cem\u003eJahrb. Natl. Okon. Stat.\u003c/em\u003e, no. 0, 2025.\u003c/li\u003e\n \u003cli\u003eP. IEA, \u0026ldquo;World energy outlook 2022,\u0026rdquo; \u003cem\u003eParis, Fr. Int. Energy Agency\u003c/em\u003e, 2022.\u003c/li\u003e\n \u003cli\u003eA. Azzuni and C. Breyer, \u0026ldquo;Global energy security index and its application on national level,\u0026rdquo; \u003cem\u003eEnergies\u003c/em\u003e, vol. 13, no. 10, p. 2502, 2020.\u003c/li\u003e\n \u003cli\u003eV. D. Poturukhin, \u0026ldquo;PROSPECTS FOR THE FOURTH INDUSTRIAL REVOLUTION IN CONTEXT OF GLOBAL ENERGY SECURITY,\u0026rdquo; \u003cem\u003eЭкономика и управление проблемы, решения\u003c/em\u003e, vol. 1, no. 9, pp. 17\u0026ndash;25, 2020.\u003c/li\u003e\n \u003cli\u003eO. Peter and C. Mbohwa, \u0026ldquo;Correlation between future energy systems and industrial revolutions,\u0026rdquo; in \u003cem\u003eProceedings of the International Conference on Industrial Engineering and Operations Management\u003c/em\u003e, 2018, pp. 1953\u0026ndash;1961.\u003c/li\u003e\n \u003cli\u003eK. Anderson, \u0026ldquo;What Was the Industrial Revolution\u0026rsquo;s Environmental Impact?,\u0026rdquo; \u003cem\u003eGreenly, https//greenly. earth/en-gb/blog/ecology-news/what-was-the-industrial-revolutions-environmental-impact. Diakses pada\u003c/em\u003e, vol. 20, 2023.\u003c/li\u003e\n \u003cli\u003eG. Hu, J. Yang, and J. Li, \u0026ldquo;The dynamic evolution of global energy security and geopolitical games: 1995~ 2019,\u0026rdquo; \u003cem\u003eInt. J. Environ. Res. Public Health\u003c/em\u003e, vol. 19, no. 21, p. 14584, 2022.\u003c/li\u003e\n \u003cli\u003eC. O. Klingenberg, M. A. V. Borges, and J. A. do Vale Antunes Jr, \u0026ldquo;Industry 4.0: What makes it a revolution? A historical framework to understand the phenomenon,\u0026rdquo; \u003cem\u003eTechnol. Soc.\u003c/em\u003e, vol. 70, p. 102009, 2022.\u003c/li\u003e\n \u003cli\u003eZ. Suleiman, S. Shaikholla, D. Dikhanbayeva, E. Shehab, and A. Turkyilmaz, \u0026ldquo;Industry 4.0: Clustering of concepts and characteristics,\u0026rdquo; \u003cem\u003eCogent Eng.\u003c/em\u003e, vol. 9, no. 1, p. 2034264, 2022.\u003c/li\u003e\n \u003cli\u003eV. Pandey, A. Sircar, N. Bist, K. Solanki, and K. Yadav, \u0026ldquo;Accelerating the renewable energy sector through Industry 4.0: Optimization opportunities in the digital revolution,\u0026rdquo; \u003cem\u003eInt. J. Innov. Stud.\u003c/em\u003e, vol. 7, no. 2, pp. 171\u0026ndash;188, 2023.\u003c/li\u003e\n \u003cli\u003eP. F. Borowski, \u0026ldquo;Digitization, digital twins, blockchain, and industry 4.0 as elements of management process in enterprises in the energy sector,\u0026rdquo; \u003cem\u003eEnergies\u003c/em\u003e, vol. 14, no. 7, p. 1885, 2021.\u003c/li\u003e\n \u003cli\u003eM. Li, D. Hu, C. Lal, M. Conti, and Z. Zhang, \u0026ldquo;Blockchain-enabled secure energy trading with verifiable fairness in industrial Internet of Things,\u0026rdquo; \u003cem\u003eIEEE Trans. Ind. Informatics\u003c/em\u003e, vol. 16, no. 10, pp. 6564\u0026ndash;6574, 2020.\u003c/li\u003e\n \u003cli\u003eS. Otoum, I. Al Ridhawi, and H. Mouftah, \u0026ldquo;A federated learning and blockchain-enabled sustainable energy trade at the edge: A framework for industry 4.0,\u0026rdquo; \u003cem\u003eIEEE Internet Things J.\u003c/em\u003e, vol. 10, no. 4, pp. 3018\u0026ndash;3026, 2022.\u003c/li\u003e\n \u003cli\u003eG. Arana-Land\u0026iacute;n, N. Uriarte-Gallastegi, B. Landeta-Manzano, and I. Laskurain-Iturbe, \u0026ldquo;The contribution of lean management\u0026mdash;Industry 4.0 technologies to improving energy efficiency,\u0026rdquo; \u003cem\u003eEnergies\u003c/em\u003e, vol. 16, no. 5, p. 2124, 2023.\u003c/li\u003e\n \u003cli\u003eN. Mohamed, J. Al-Jaroodi, and S. Lazarova-Molnar, \u0026ldquo;Leveraging the capabilities of industry 4.0 for improving energy efficiency in smart factories,\u0026rdquo; \u003cem\u003eIeee Access\u003c/em\u003e, vol. 7, pp. 18008\u0026ndash;18020, 2019.\u003c/li\u003e\n \u003cli\u003eM. Ghobakhloo and M. Fathi, \u0026ldquo;Industry 4.0 and opportunities for energy sustainability,\u0026rdquo; \u003cem\u003eJ. Clean. Prod.\u003c/em\u003e, vol. 295, p. 126427, 2021.\u003c/li\u003e\n \u003cli\u003eE. Mastrocinque, F. J. Ram\u0026iacute;rez, A. Honrubia-Escribano, and D. T. Pham, \u0026ldquo;Industry 4.0 enabling sustainable supply chain development in the renewable energy sector: A multi-criteria intelligent approach,\u0026rdquo; \u003cem\u003eTechnol. Forecast. Soc. Change\u003c/em\u003e, vol. 182, p. 121813, 2022.\u003c/li\u003e\n \u003cli\u003eJ. Ol\u0026aacute;h, N. Aburumman, J. Popp, M. A. Khan, H. Haddad, and N. Kitukutha, \u0026ldquo;Impact of Industry 4.0 on environmental sustainability,\u0026rdquo; \u003cem\u003eSustainability\u003c/em\u003e, vol. 12, no. 11, p. 4674, 2020.\u003c/li\u003e\n \u003cli\u003eT. Nitlarp and S. Kiattisin, \u0026ldquo;The impact factors of industry 4.0 on ESG in the energy sector,\u0026rdquo; \u003cem\u003eSustainability\u003c/em\u003e, vol. 14, no. 15, p. 9198, 2022.\u003c/li\u003e\n \u003cli\u003eS. I. Vallarta-Serrano, E. Santoyo-Castelazo, E. Santoyo, E. O. Garc\u0026iacute;a-Mandujano, and H. V\u0026aacute;zquez-S\u0026aacute;nchez, \u0026ldquo;Integrated sustainability assessment framework of industry 4.0 from an energy systems thinking perspective: bibliometric analysis and systematic literature review,\u0026rdquo; \u003cem\u003eEnergies\u003c/em\u003e, vol. 16, no. 14, p. 5440, 2023.\u003c/li\u003e\n \u003cli\u003eJ. C. Kabugo, S.-L. J\u0026auml;ms\u0026auml;-Jounela, R. Schiemann, and C. Binder, \u0026ldquo;Industry 4.0 based process data analytics platform: A waste-to-energy plant case study,\u0026rdquo; \u003cem\u003eInt. J. Electr. power energy Syst.\u003c/em\u003e, vol. 115, p. 105508, 2020.\u003c/li\u003e\n \u003cli\u003eT. Ahmad \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Energetics Systems and artificial intelligence: Applications of industry 4.0,\u0026rdquo; \u003cem\u003eEnergy Reports\u003c/em\u003e, vol. 8, pp. 334\u0026ndash;361, 2022.\u003c/li\u003e\n \u003cli\u003eL. Gitelman, E. Magaril, and M. Kozhevnikov, \u0026ldquo;Energy security: new threats and solutions,\u0026rdquo; \u003cem\u003eEnergies\u003c/em\u003e, vol. 16, no. 6, p. 2869, 2023.\u003c/li\u003e\n \u003cli\u003eJ. Fouladvand, \u0026ldquo;Behavioural attributes towards collective energy security in thermal energy communities: Environmental-friendly behaviour matters,\u0026rdquo; \u003cem\u003eEnergy\u003c/em\u003e, vol. 261, p. 125353, 2022.\u003c/li\u003e\n \u003cli\u003eJ. Ren and B. K. Sovacool, \u0026ldquo;Quantifying, measuring, and strategizing energy security: Determining the most meaningful dimensions and metrics,\u0026rdquo; \u003cem\u003eEnergy\u003c/em\u003e, vol. 76, pp. 838\u0026ndash;849, 2014.\u003c/li\u003e\n \u003cli\u003eB. Kruyt, D. P. Van Vuuren, H. J. M. de Vries, and H. Groenenberg, \u0026ldquo;Indicators for energy security,\u0026rdquo; \u003cem\u003eEnergy Policy\u003c/em\u003e, vol. 37, no. 6, pp. 2166\u0026ndash;2181, 2009.\u003c/li\u003e\n \u003cli\u003eI. T. Jolliffe and J. Cadima, \u0026ldquo;Principal component analysis: a review and recent developments,\u0026rdquo; \u003cem\u003ePhilos. Trans. R. Soc. A Math. Phys. Eng. Sci.\u003c/em\u003e, vol. 374, no. 2065, p. 20150202, 2016.\u003c/li\u003e\n \u003cli\u003eZ. Yu, J. Li, and G. Yang, \u0026ldquo;A Review of Energy Security Index Dimensions and Organization,\u0026rdquo; \u003cem\u003eEnergy Res. Lett.\u003c/em\u003e, vol. 3, no. 1, 2022.\u003c/li\u003e\n \u003cli\u003eP. Gasser, \u0026ldquo;A review on energy security indices to compare country performances,\u0026rdquo; \u003cem\u003eEnergy Policy\u003c/em\u003e, vol. 139, p. 111339, 2020.\u003c/li\u003e\n \u003cli\u003eZ. Tajani, C. Tajani, and M. Sabbane, \u0026ldquo;Assessment of energy supply security using an analytic hierarchy process based approach,\u0026rdquo; \u003cem\u003eIndones. J. Electr. Eng. Comput. Sci.\u003c/em\u003e, vol. 32, no. 1, pp. 555\u0026ndash;562, 2023.\u003c/li\u003e\n \u003cli\u003eJ. Brodny and M. Tutak, \u0026ldquo;Assessing the energy and climate sustainability of European Union member states: an MCDM-based approach,\u0026rdquo; \u003cem\u003eSmart Cities\u003c/em\u003e, vol. 6, no. 1, pp. 339\u0026ndash;367, 2023.\u003c/li\u003e\n \u003cli\u003eE. Desogus, E. Bompard, and D. Grosso, \u0026ldquo;A Composite Index for Tracking the Evolution towards Energy Transition at Urban Scale: The Turin Case Study,\u0026rdquo; \u003cem\u003eEnergies\u003c/em\u003e, vol. 17, no. 6, p. 1281, 2024.\u003c/li\u003e\n \u003cli\u003eG. B. Bello, L. B. M. V. Viana, G. M. P. de Moraes, and D. Ferraz, \u0026ldquo;Renewable Energy Index: The Country-Group Performance Using Data Envelopment Analysis,\u0026rdquo; \u003cem\u003eEnergies\u003c/em\u003e, vol. 18, no. 14, p. 3803, 2025.\u003c/li\u003e\n \u003cli\u003eM. L. Mart\u0026iacute; Selva and R. M. Puertas Medina, \u0026ldquo;Sustainable energy development analysis: Energy Trilemma,\u0026rdquo; 2022.\u003c/li\u003e\n \u003cli\u003eW. E. Forum, \u0026ldquo;Fostering effective energy transition,\u0026rdquo; \u003cem\u003eInsight Rep.\u003c/em\u003e, 2021.\u003c/li\u003e\n \u003cli\u003eF. J. Hasanov, S. Mukhtarov, and E. Suleymanov, \u0026ldquo;The role of renewable energy and total factor productivity in reducing CO2 emissions in Azerbaijan. Fresh insights from a new theoretical framework coupled with Autometrics,\u0026rdquo; \u003cem\u003eEnergy Strateg. Rev.\u003c/em\u003e, vol. 47, p. 101079, 2023.\u003c/li\u003e\n \u003cli\u003eH. V. Singh, R. Bocca, P. Gomez, S. Dahlke, and M. Bazilian, \u0026ldquo;The energy transitions index: An analytic framework for understanding the evolving global energy system,\u0026rdquo; \u003cem\u003eEnergy Strateg. Rev.\u003c/em\u003e, vol. 26, p. 100382, 2019.\u003c/li\u003e\n \u003cli\u003eA. Stavytskyy, G. Kharlamova, O. Komendant, J. Andrzejczak, and J. Nakonieczny, \u0026ldquo;Methodology for calculating the energy security index of the state: Taking into account modern megatrends,\u0026rdquo; \u003cem\u003eEnergies\u003c/em\u003e, vol. 14, no. 12, pp. 1\u0026ndash;19, 2021, doi: 10.3390/en14123621.\u003c/li\u003e\n \u003cli\u003eS. Chakrabartty, \u0026ldquo;Geometric mean approach to measure Composite Index,\u0026rdquo; \u003cem\u003e[RMd] Rev.\u003c/em\u003e, vol. 6, no. 1, pp. 109\u0026ndash;131, 2024.\u003c/li\u003e\n \u003cli\u003eM. Mohsin \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Developing low carbon economies: an aggregated composite index based on carbon emissions,\u0026rdquo; \u003cem\u003eSustain. Energy Technol. Assessments\u003c/em\u003e, vol. 35, pp. 365\u0026ndash;374, 2019.\u003c/li\u003e\n \u003cli\u003eR. Sreedharan, J. Prajapati, P. Engineer, and D. Prajapati, \u0026ldquo;Leave-one-out cross-validation in machine learning,\u0026rdquo; in \u003cem\u003eEthical Issues in AI for Bioinformatics and Chemoinformatics\u003c/em\u003e, CRC Press, 2023, pp. 56\u0026ndash;71.\u003c/li\u003e\n \u003cli\u003eI. Citaristi, \u0026ldquo;International energy agency\u0026mdash;iea,\u0026rdquo; in \u003cem\u003eThe Europa directory of international organizations 2022\u003c/em\u003e, Routledge, 2022, pp. 701\u0026ndash;702.\u003c/li\u003e\n \u003cli\u003eE. Asmelash, G. Prakash, R. Gorini, and D. Gielen, \u0026ldquo;Role of IRENA for global transition to 100% renewable energy,\u0026rdquo; in \u003cem\u003eAccelerating the transition to a 100% renewable energy era\u003c/em\u003e, Springer, 2020, pp. 51\u0026ndash;71.\u003c/li\u003e\n \u003cli\u003eM. A. H. Mondal, E. Bryan, C. Ringler, D. Mekonnen, and M. Rosegrant, \u0026ldquo;Ethiopian energy status and demand scenarios: prospects to improve energy efficiency and mitigate GHG emissions,\u0026rdquo; \u003cem\u003eEnergy\u003c/em\u003e, vol. 149, pp. 161\u0026ndash;172, 2018.\u003c/li\u003e\n \u003cli\u003eW. Kruger, S. Fezeka, and A. Olakunle, \u0026ldquo;Ethiopia country report,\u0026rdquo; \u003cem\u003eRep. 5 Energy Econ. Growth Res. Program. (W01 W05) PO Number PO00022908\u003c/em\u003e, 2019.\u003c/li\u003e\n \u003cli\u003eP. Dechamps, \u0026ldquo;The IEA World Energy Outlook 2022\u0026ndash;a brief analysis and implications,\u0026rdquo; \u003cem\u003eEur. Energy Clim. J.\u003c/em\u003e, vol. 11, no. 3, pp. 100\u0026ndash;103, 2023.\u003c/li\u003e\n \u003cli\u003eE. Eludoyin \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Energy system development pathways for Ethiopia: Final project report,\u0026rdquo; 2021.\u003c/li\u003e\n \u003cli\u003eJ. Thorne and M. Gerhard, \u0026ldquo;Off-Grid Funding Strategy,\u0026rdquo; 2021.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Energy Security, Data Analytics, Industry 4.0, Machine Learning, Energy Security Dimension, Energy Security Index","lastPublishedDoi":"10.21203/rs.3.rs-8566045/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8566045/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnergy security comprising Availability, Affordability, Accessibility, and Acceptability is critical for Ethiopia’s industrial growth and sustainable development. However, leveraging this potential to achieve security across the 4A dimensions remains a critical challenge, particularly in the context of Industry 4.0. This study analyzes Ethiopia’s energy security from 2011 to 2022 using secondary data from the International Energy Agency, World Bank, U.S. and Energy Information Administration. Descriptive analytics revealed moderate overall energy security (ESI = 52.65), with stable Availability (mean = 59.52), variable Accessibility (mean = 52.11), fluctuating Affordability (mean = 58.82), and moderate Acceptability (mean = 64.18). The analysis indicates a heavy dependence on hydropower, while sectoral energy consumption is rising in industry and transport, with per-capita use remaining low. Machine learning models Ridge (R² = 0.556), Lasso (R² = 0.555), Decision Tree \u0026nbsp;(R² = 0.4484), and Random Forest (R² = 0.659), Support Vector Regression (R² = 0.782) \u0026nbsp;revealed that Accessibility (0.472) and Affordability (0.434) are dominant drivers, while Acceptability (0.594 in linear models, 0.417 in Random Forest) also significantly influences energy security. Availability contributes least across models but can be strengthened through renewable integration. Industry 4.0 technologies emerged as key enablers to directly enhance each dimension of the 4A framework: smart grids and predictive analytics bolster supply Availability and reliability; IoT-enabled monitoring and decentralized generation improve physical and equitable Accessibility; data-driven optimization and automation reduce costs, strengthening Affordability; and blockchain platforms ensure transparency for environmental and social Acceptability. The findings highlight the need for targeted policy interventions, renewable energy investment, digitalized energy management, and stakeholder engagement to enhance Ethiopia’s energy resilience, equity, affordability, and sustainability.\u003c/p\u003e","manuscriptTitle":"Evidence from data analytics and machine learning on Ethiopia’s energy security in the Industry 4.0 era","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 16:06:16","doi":"10.21203/rs.3.rs-8566045/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7bd874fb-b094-40d4-a569-bbef1900a3e2","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-14T06:19:16+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65701074,"name":"Physical sciences/Energy science and technology"},{"id":65701075,"name":"Physical sciences/Engineering"},{"id":65701076,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-05-14T06:25:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 16:06:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8566045","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8566045","identity":"rs-8566045","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}