Energy Services Research Articles

ABSTRACT Many households utilize a combination of energy sources to meet their diverse energy service needs; however, the extent to which households with different energy source combinations respond to fluctuations in energy prices remains insufficiently understood. This study categorizes households into six distinct groups based on their use of four energy sources – electricity, city gas, liquefied petroleum (LP) gas, and kerosene. Given the focus on how households combine multiple energy sources, we employ Seemingly Unrelated Regression Equations (SURE) models to jointly estimate demand functions for different energy types. The analysis reveals that price elasticity of energy demand is lowest for kerosene, followed by electricity and gas. Moreover, elasticity estimates differ across household types, depending on their specific energy combinations. While instances of positive cross-price elasticity were observed, the magnitude of these estimates was generally small, indicating that households tend not to substitute between energy sources in response to short-term price changes.

Synergistic home energy management system with building-to-vehicle-to-building operation for enhancing energy service accessibility in disaster affected communities

Energy poverty—defined by inadequate access to reliable and affordable energy services—continues to pose a major barrier to economic development, agricultural productivity, and environmental sustainability in Sub-Saharan Africa (SSA). This study explores the complex interrelationship between energy poverty, environmental degradation, and agricultural productivity in SSA, with the aim of extracting policy insights relevant to Vietnam, a developing country facing similar rural energy access challenges. Drawing on a comprehensive literature review and empirical evidence from SSA, the study identifies that rural electrification significantly boosts agricultural productivity, while environmental degradation exerts a detrimental effect. The impact of renewable energy adoption is found to be context-dependent, with both enabling and constraining factors. Using qualitative synthesis and comparative case study analysis, the research contextualizes these findings within Vietnam’s rural development landscape. The results suggest that Vietnam can strengthen energy access and agricultural outcomes by expanding rural electrification programs, investing in decentralized renewable energy systems, and enforcing environmental regulations to prevent resource degradation. Policy recommendations include promoting public-private partnerships, supporting digital innovations for precision agriculture, and tailoring energy strategies to local socio-economic conditions. By leveraging lessons from SSA’s experiences, Vietnam can make strategic progress toward achieving energy equity and sustainable development in its rural sectors.

Solid-state batteries (SSBs) replace the liquid electrolyte in liquid batteries with solid-state electrolytes, which effectively improve energy density, safety, and service life. Currently, solid-state electrolytes mainly include three types: oxide-based electrolytes, sulfide-based electrolytes, and polymer-based electrolytes. The challenges they face include interface impedance, dendrite growth, and cost issues. In the future, it is expected that by enhancing material performance, improving interface stability, and optimizing preparation processes to reduce costs, solid-state batteries will be widely applied in fields such as electric vehicles and energy storage systems. This paper adopts a literature review method to study the principle of solid-state batteries and the development status and challenges of the three major technical routes (oxide, sulfide, and polymer). By analyzing material properties and interface issues, it proposes directions for improving performance and reducing costs, which holds guiding significance for the future development of solid-state batteries.

As energy conservation and sustainability become key priorities in public transportation, optimising bus scheduling with an emphasis on energy efficiency offers a practical pathway toward greener transit systems. However, existing studies often treat energy consumption and service quality in isolation, lacking an integrated framework for regional bus dispatch optimisation. This study proposes a multi-objective energy-saving dispatch model that jointly minimises passenger waiting time and vehicle energy consumption while considering vehicle allocation and passenger transfers. A vehicle-specific power (VSP)-based energy estimation method is introduced to enhance the accuracy of energy consumption assessments under real-world operating conditions. To solve the proposed model, we develop a two-phase optimisation algorithm that balances computational efficiency and solution quality. A case study on the Lanzhou regional transit network validates the model’s feasibility, demonstrating improvements in both service levels and energy consumption. The findings contribute to the development of more sustainable and efficient regional bus scheduling strategies.

Extreme rainfall and flooding severely impact urban systems by disrupting access to critical services, interrupting mobility, and posing challenges for emergency management. Accurate road network flood prediction remains challenging due to complex flow dynamics, coarse-resolution traditional models, and limited data. The main objective of this study is to enhance road-network flood prediction using ensemble machine learning models trained on crowd-sourced flood datasets. Our results for the Washington, D.C. area show that stacked super-ensemble learning improves road flood prediction compared to the voting algorithm and several other base learners, including random forest, support vector machine, bagging, and boosting. Stacking algorithm achieved an accuracy of 0.84, precision of 0.82, and F1-score of 0.82. Shapley additive explanations indicate that elevation strongly influences model prediction accuracy. Stacking ensemble classifies around 5% of road networks as having very high likelihood and 11% as having high likelihood of flooding. We find that over 40% of energy and emergency services are located within high hazard networks. The insights gained from this study can help improve urban flood prediction which is crucial for enhancing community resilience to extreme weather events.

Danantara Indonesia is the latest Sovereign Wealth Fund (SWF) established by the Indonesian government to optimize state assets through strategic investments. This study employs a qualitative descriptive approach with a case study method to analyze Danantara’s sustainable strategy in achieving global competitiveness. The study utilizes the Triple Bottom Line (TBL), VRIO Framework, and PESTEL Analysis to evaluate competitive advantages, external factors, and the balance between social, environmental, and economic aspects. The findings indicate that Danantara possesses sustainable competitive advantages in the form of access to strategic assets and government support, yet still faces challenges in governance, transparency, and global market volatility. External factors such as green investment regulations and urbanization trends create opportunities for Danantara to invest in renewable energy, infrastructure, and public services. To strengthen its position, Danantara must implement investment diversification strategies, optimize governance based on the Santiago Principles, and leverage digital technology for risk management and investment decision-making. By adopting sustainability-oriented strategies, Danantara can evolve into a global player in the investment industry while contributing to national economic development. Additionally, partnerships with international investors serve as a strategic step to accelerate Danantara’s global expansion.

Echoing the discussions about the role of the state in accelerating the transition to low-emission sources of energy, this article revisits the ideas developed in Brazil, from the 1930s to the 1950s, to accelerate the transition not away from but at that time further toward domestically produced oil, with the creation of the state-owned company Petrobras. It shows that two distinct issues were at stake: the public or private control over the company for matters of economic efficiency, and the use of domestic or foreign capital for questions of long-term energy sovereignty and economic independence. This article finally draws parallels for the twenty-first-century energy transition, especially in developing countries. Given today’s geopolitical uncertainty and the need to accelerate the transition to energy sources that are still in development, such as renewables and green hydrogen, the creation of sector-specific public companies could be a valuable strategy alongside traditional regulatory tools. JEL Classification: B20 History of Economic Thought since 1925: General; N76 Economic History: Transport, Trade, Energy, Technology, and Other Services: Latin America; Caribbean; Q40 Energy: General

Purpose The study aims to investigate the relationship between green energy sustainability and transport services in Middle East and North Africa (MENA) countries from 2012 to 2021. Design/methodology/approach The study used feasible generalized least squares (FGLS), instrumental variables of two-stage least squares (IV-2SLS) and instrumental variables of generalized method of moments (IV-GMM) estimators to increase robustness and overcome the panel data’s heterogeneity and endogeneity issues. Findings The study revealed a significant positive relationship between urban development and transport services (% of commercial service imports and exports). Furthermore, there were negative relationships between water footprint and transport services (% of commercial service imports and exports) and carbon footprint and transport services (% of commercial service imports). Additionally, there was a significant positive relationship between renewable energy and transport services (% of commercial service imports). Originality/value This study comprehensively analyses how to achieve sustainable trade transport services. It highlights the importance of investing in transport and renewable energy infrastructure, adopting water conservation technologies in the transport sector and introducing a carbon footprint to incentivize reductions in transport-related emissions.

This research explores the use of IoT-enabled infrastructure within the context of urban centers in Pakistan, measuring the implications such infrastructure has on urban sustainability. The research focused on the interest of the smart city technology available and the use of technology in transform municipal service, resource management and the quality of life in the urban cities in Pakistan. The research utilized a combination of qualitative and quantitative approaches, including surveys of 450 urban inhabitants, 35 municipal officer in-depth interviews, and the use of IoT sensors in the core cities of the Karachi, Lahore and Islamabad within the areas of transportation, water management and energy distribution systems. The results of resource efficiency were remarkable, with smart systems on integrated water management and wastage reduction of 23%; and intelligent traffic systems on management of time and reduction of time of average traffic by 18% in real time. The predominant challenges were limited technical human capital, poorly aligned and insufficient infrastructure investment, and apathetic behaviors towards provisions on data privacy and data protection. Even with the aforementioned challenges, the findings outlined evidence of urban sustainability and ranging improvements in energy use, pollution abatement, service, and smart city project implementation. The research proposes the minimum required smart city development in scope and sustain political will, public and private sector, and governance pan and partnerships. The provided research outlined the evidence based and recommended actions to urban planners and policy makers and offered the urban planners and policy makers the scope of IoT on urban development sustainability.

The extensive use of fossil fuels contributes to the emission of toxic pollutants, including sulfur oxides, nitrogen oxides, and greenhouse gases. Notably, a significant quantity of carbon dioxide is emitted, along with other energy forms. Consequently, transitioning to renewable or clean energy sources is crucial for long-term sustainability of energy development. Climate change has emerged as a major concern for human life in the 21st century, potentially leading to various problems, such as more frequent and intense heatwaves, increased occurrences of floods and droughts, alterations in seasonal patterns, impacts on disaster likelihood, and malnutrition. Achieving carbon neutralityby 2050 through adoption of clean and green energy sources is imperative. To attain net-zero emissions, the implementation of an energy-efficient technology known as a polygeneration energy system is recommended. This system offers high energy efficiency by simultaneously producing multiple energy carriers and services from one or more primary energy sources. Poly-generation systems are versatile, as they can operate using both fossil fuels and renewable energy resources. In this study, a sustainable solar-powered polygeneration energy system was proposed for a residential building located in Bengaluru (12.885138°N, 77.572538°E), Karnataka, India. The energy system integrates three main components: a Photovoltaic-Thermal (PVT) hybrid collector, Reverse Osmosis (RO) water purification unit, and Vapour Absorption chiller to assess the system’s suitability for residential applications. The daily energy demands were estimated on a typical housing cluster comprising seven 1 BHK and five 2 BHK houses totaling approximately 60 dwellings. The system is proposed to meet a comprehensive range of daily energy requirements of electrical demand, amounting to 1185 kWh/day for electricity (general use), freshwater, and space cooling, as well a thermal demand for hot water. Performave evalution revealed that the CHCP (Cooling, Heating and Power) system achieved an electrical efficiency, thermal efficiency and overall efficiency of the proposed CHCP system were found as 18.1%, 50% and 68.1% respectively. These results highlight the system's potential as a viable and sustainable energy solution for integrated residential needs, contributing to both energy efficiency and environmental conservation. Major Findings: Excessive use of fossil fuels contribute significant amount of green house gas emission leads to environmental degradation, thereby underscoring the inevitable shift toward clean and sustainable energy systems. In this context, a solardriven poly-generation configuration, integrating photovoltaic–thermal (PVT) technology with reverse osmosis (RO) and a vapor absorption chiller, was proposed to simultaneously satisfy the electricity, cooling, and freshwater requirements of a residential building. The system achieved an overall efficiency of 68.1%, highlighting its potential as a sustainable pathway for advancing carbon-neutral and energy-efficient residential applications.

Abstract Many residential prosumers exhibit a high price tolerance for household electricity bills and a low response to price incentives. This is because household electricity bills are not inherently high, and the potential for saving electricity bills through participation in conventional shared energy storage (SES) is limited, which diminishes their motivation to actively engage in SES. Additionally, existing SES models often require prosumers to take additional actions, such as optimising rental capacity and bidding prices, which happen to be capabilities that typical household prosumers do not possess. To incentivise these high‐price‐tolerance residential prosumers to participate in SES, a novel SES aggregation framework is proposed, which does not require prosumers to take additional actions and allows them to maintain existing energy storage patterns. Compared to the conventional long‐term operation of SES, the proposed framework introduces an additional short‐term construction step during which the energy service provider (ESP) acquires control of the energy storage systems (ESS) and offers electricity deposit and withdrawal services (DWS) with dynamic coefficients, enabling prosumers to withdraw more electricity than they deposit without additional actions. Additionally, a matching mechanism is proposed to align prosumers’ electricity consumption behaviours with ESP optimisation strategies. Finally, the dynamic coefficients in the DWS and SES trading strategies are jointly optimised using a modified deep reinforcement learning algorithm. Combining neighbouring experience pool replay modifies the twin delay deep deterministic policy gradient (CNEPR‐TD3), which introduces a multilabel neighbouring experience replay mechanism to improve learning efficiency and convergence stability. Simulation studies based on one‐year real‐world data validated the proposed approach. Ablation experiments showed that the inclusion of dynamic DWS and the matching mechanism increased the overall SES profit by 42.87%, confirming the effectiveness and economic value of the proposed framework.

ABSTRACTThis study evaluates the commonly cited claim that 85% of U.S. critical infrastructure is privately owned, exploring its accuracy and implications for policy. Through sector‐specific analysis and a criticality lens, the findings demonstrate that ownership patterns vary widely, with foundational sectors like water, energy, and emergency services exhibiting substantial public or mixed ownership of their most critical assets. The study identifies the “85%” statistic as a mythical number, unreflective of sectoral diversity and criticality variations. By revealing the complexity of ownership structures, including public–private partnerships and regulated utilities, this study challenges oversimplified narratives and emphasizes the need for evidence‐based policies that align resources and regulations with genuine ownership dynamics to enhance infrastructure resilience and security.

Introduction & BackgroundTraditional poverty classifications rely on relatively small samples; for example, the main resource used to model fuel poverty (i.e., an inability to afford sufficient energy services) in England is the English Housing Survey (~30,000 homes). The superior coverage of financial datasets, e.g., aggregate banking data, may allow more precise estimations. Further, the current approach to measuring fuel poverty in England, Low Income Low Energy Efficiency (LILEE), has been shown to significantly underestimate the true rate of fuel poverty. To reconcile these matters, the current study explores the merits of financial data for fuel poverty classifications and also provides evidence for a set of fuel poverty measurement criteria that remedy the shortcomings of LILEE while also being compatible with adjacent modern agendas. Objectives & ApproachThis study analyses financial data, specifically an income volatility dataset provided by Smart Data Foundry that was derived from NatWest accounts (drawn from an original sample of ~5 million customers), to explore alternative fuel poverty measurement approaches in England. The analysis involves the estimation of fuel poverty according to a range of existing definitions, e.g., LILEE, 10% and Low Income High Costs, as well as emerging methods, e.g., the Minimum Income Standard (MIS)-based approach, which is able to account for living standards and was recently adopted as the official fuel poverty definition in Scotland. Following this, the merits of using financial data for poverty classifications are discussed, and the modern appropriateness of competing fuel poverty definitions is evaluated. Relevance to Digital FootprintsThe relevance to digital footprints is inherent to the study’s purpose: that is, to improve the identification of fuel poor homes through the utilisation of financial sector digital footprints. ResultsInitial results provide further evidence that the English (LILEE) approach to measuring fuel poverty underestimates fuel poverty in many low-income homes. We suggest that using financial data for fuel poverty classifications can provide more granular and accurate estimates. In addition, we suggest that MIS-based approaches to measuring fuel poverty are more accurate and better suited to modern needs. Conclusions & ImplicationsThe results can be used to inform a more effective approach to measuring fuel poverty in England: first, in terms of the data used for fuel poverty modelling; and second, concerning the most appropriate fuel poverty definition.

Abstract Mechanical friction has always affected the energy loss and service life of equipment. The application of bearings has reduced the application loss of mechanical equipment. This paper proposes a method based on the Reynolds equation, using the finite difference method to decompose the Reynolds equation. Combined with the roughness parameter, the oil film thickness is measured to optimize the mechanical lubrication performance. By establishing the numerical equation of elastohydrodynamic lubrication simulation, the influence of different working conditions on the oil film distribution is analyzed. The effect of micro-polar fluid on the oil film thickness is verified in the experiment. The micro-polar fluid model ignores the deformation of micro-units and maintains the micro-motion of particles. Adding solid lubricants improves the coupling between linear momentum and angular momentum, increasing the oil film thickness.

Two-stage optimal scheduling strategy for electric-hydrogen integrated energy stations considering demand response in energy and reserve services markets

Structural racism contributes to energy insecurity via housing and income pathways. However, little is known about the role of energy pricing in (re)producing racialized energy inequities. This study explores the relationship between annual electricity rates and the resulting consumption and billing for the average customer based on demographic characteristics for different types of electric utilities in three states: Alabama, California, and New York. Results indicate that utility service customers in predominately non-white communities pay higher energy prices but consume less energy compared to those in white communities. The association between these utility schemas and the demography of service areas is likely influenced by numerous intermediating factors, such as urbanity, housing conditions, and household income variation—all of which are also shaped by historical racism. These findings call attention to the potential disparate impacts of energy service provision and costs across communities and add nuance to how energy insecurity may be powered.

An aggregate price for energy services: Useful exergy as an intermediate flow in a two-sector model of the economy

Addressing the contemporary climate crisis is the need of the hour to protect both people and the planet. As countries embark on green energy revolution, focussing on achieving the United Nations (UN) 2030 agenda for Sustainable Development, guaranteeing universal access to affordable, reliable, and modern energy services stands out as an important goal. As part of the implementation of this goal, solar panel installation scheme has been undertaken by the government of India to encourage widespread adoption of green energy. This research work proposes an effective method to assess the acceptance of this scheme among users and the broader audience. User comments/ feedback from various social networking sites are analysed in this research work using Machine Learning techniques along with Explainable Artificial Intelligence (XAI) to make the machine learning models’ predictions more transparent. OpenAI Generative Pre-trained Transformer (GPT) language model is also used to automatically identify key implementation challenges of the scheme by creating a concise summary of the feedback shared by the users. This insight, based on the pain points of the users, can further help in providing recommendations and suggestions to appropriate stakeholders to improve the success rate of this scheme. Five machine learning models- Logistic Regression, Random Forest, Decision Tree, Extreme Gradient Boosting, and Stochastic Gradient Descent- were compared to choose the right technique for sentiment analysis. Among them, Logistic Regression and Stochastic Gradient Descent achieved an accuracy of 93% in predicting the sentiment. Our analysis showed around 63% of user feedback was positive indicating the public acceptance of green energy projects in India despite higher initial investments. The methodology and framework developed during this research work have immense reusability across similar government schemes (where transparency in sentiment analysis and sensitivity of public data are critical) in assessing their effectiveness and identifying areas where improvements are required.

Purpose: Organizational culture is widely recognized as a fundamental element that can drive or force innovation, especially in dynamic economies such as the United Arab Emirates (UAE). This research is dedicated to examining the impact of organizational culture on innovative practices within UAE companies and providing actionable insights to business leaders and policymakers. This study investigates how specific cultural attributes—learning orientation, knowledge‐sharing, leadership backing, and openness to collaboration—shape innovation in Emirati firms. Methodology: A qualitative, multiple–case design was employed. Three innovation-leading companies (technology, renewable energy, and financial services) were selected purposively. Data came from corporate reports, semi-structured interviews (n = 27), and peer-reviewed literature (2020-2025). Thematic analysis, supported by NVivo 14, enabled triangulation across sources. Findings: Adaptive cultures marked by inclusivity, trust, risk tolerance, and open communication consistently generated higher innovation outputs, whereas rigid hierarchies curtailed experimentation. Unique Contribution to Theory, Practice, and Policy: The work extends Schein’s cultural model to a Gulf context, offers managers an evidence-based cultural roadmap, and supports UAE Vision 2031 by demonstrating how internal culture converts national policy into firm-level innovation.