Demand-Side Management (DSM) plays an important role in stabilizing energy systems and enhancing energy efficiency. DSM technologies optimize residential energy consumption patterns through smart devices, real-time monitoring, and automated control systems. While factors influencing consumer DSM adoption have been extensively studied, less attention has been paid to the role of business models (BMs) in shaping consumer willingness. This study uses survey-based literature to identify the key factors influencing consumers' intention to adopt DSM technologies and analyzes how different BMs incorporate such factors into their offerings. Focusing on Finland, the study categorizes DSM companies into six BM archetypes based on their value propositions and revenue mechanisms. A consumer-centric evaluation is applied to evaluate BM adoption potential, enabling standardized scoring and cross-case comparison. Results indicate that consumers’ willingness to adopt is primarily driven by perceived ease of use and usefulness, while environmental considerations and saving potential are gaining importance. Product-oriented BMs receive slightly higher scores than service-oriented BMs, particularly for features such as remote and automatic control. A geographical sensitivity analysis for the evidence base reveals that Finnish consumers prefer offerings from traditional utilities and smart device providers, instead of software providers. These insights offer valuable guidance for designing consumer-centric DSM BM strategies, emphasizing the necessity of innovation to ensure these BMs effectively support energy transition and sustainability goals.
This study investigated building energy efficiency from two complementary perspectives: a passive approach involving systematic variation of building envelope parameters, including external wall composition, insulation thickness, and window and glazing type, across 72 design alternatives, and an active approach based on solar energy integration. The passive evaluation led to the selection of Alt.53, characterized by 5 cm of expanded polystyrene (EPS) insulation in the external walls and UPVC double-glazed windows with low-emissivity glazing, resulting in a 31.5
Switching to electric vehicles (EVs) is essential for reducing carbon emissions and mitigating climate change, but their share in the total car market remains small, especially in emerging economies. In response to this issue, this study investigates the determinants of consumers’ purchase intentions for EVs in China, accounting for the removal of incentive policies, thereby contributing to the field of sustainable consumption. Integrating psychological and environmental variables, this study conceptualizes the effects of EV cognition, environmental knowledge, and environmental concern, which draws on the reasoned action model to investigate consumers’ purchase intentions for EVs. The concept of attitudes without incentives is introduced to capture the effects of removing incentive policies on individual purchase intention. Survey evidence from Beijing, China, validates the model, confirming that attitudes without incentives are influenced by cognition of EVs and environmental knowledge and that attitudes without incentives both directly and indirectly affect purchase intention. The model provides a framework for understanding consumers’ purchase intentions for more environmentally sustainable products, yielding specific, practical insights for promoting the switch to EVs.
Energy communities (ECs) are increasingly promoted as a tool for decarbonisation, local participation, and social inclusion. Meanwhile, the EU Renewable Energy Directive requires ECs to provide support in reducing energy poverty by lowering grid consumption and electricity supply tariffs. Yet, their potential to mitigate energy poverty remains insufficiently operationalised in spatially explicit decision-support frameworks. This paper develops a conceptual georeferenced framework informed by a focused literature review on energy poverty, energy communities, urban building energy modelling and multi-objective optimization, to prioritise the deployment of inclusive solar energy communities in urban areas, according to household vulnerability to energy poverty. The proposed framework integrates spatial data, vulnerability indicators, building energy performance, renewable generation potential, and policy-relevant prioritisation criteria, using a multi-criteria decision analysis approach, to help local authorities identify where ECs are most likely to deliver both energy and social benefits. This conceptual framework classifies intervention areas into different priority levels to support decision-makers on where to focus inclusive solar ECs implementation efforts, providing additional economic, social and environmental outputs, such as estimated EC investment and payback times, CO2 emission reduction or the burden reduction of the energy bills in the overall households’ income. Main limitations refer to irregular data availability and the lack of validation close to vulnerable populations, and policy and governance structures. Nevertheless, the framework can help local authorities identify where solar EC deployment may generate the strongest energy poverty mitigation and clarify what data is needed to support its implementation.
The increasing integration of renewable energy sources into power systems has accelerated the deployment and usage of microgrids. However, the stochastic nature of renewable energy generation and fluctuating power demand pose significant challenges to maintaining grid stability. To address these challenges, this paper proposes an intelligent Model Predictive Control (MPC) framework for optimal power flow management in microgrids, with the objective of enhancing operational resilience, reducing diesel fuel consumption, and preventing blackouts through coordinated electric vehicle (EV) charging and discharging. The proposed MPC is formulated as a nonlinear optimization problem that minimizes operational costs while satisfying dynamic microgrid constraints. A Passive MPC strategy, which relies on predefined EV availability schedules without predictive forecasting, is first investigated to assess the impact of EV participation on microgrid performance. Simulation results show that allowing EV support outside critical hours (9 AM to 7 PM) achieves fuel and CO _2 emission reductions of approximately 9.11 _2 emissions are further reduced by up to 33.39
This present review article examined the yield improvement of the phase change materials (PCMs), nanophase change material (nPCM) in the solar still. Relevant literature published between 2003 and 2025 was collected from major scientific databases, encompassing experimental, numerical, and hybrid investigations. It has been observed that PCM significantly improve the overall yield of the solar still. However, in some cases it marginally improves the day yield while it significantly improves the nocturnal yield. Based on the literature paraffin wax is readily used with solar still. It might be due to several important factor including their availability, favourable operating temperatures (40–60 °C), chemical stability, and strong compatibility with container materials. Moreover, it has been observed that copper oxide (CuO) and aluminum oxide (Al₂O₃) are most frequently employed with the PCM to improve its thermophysical properties. The choice may be fairly based on their low cost, favourable thermophysical properties, and wide availability. Finding suggests a bit modification in the solar still involving paraffin wax PCM, copper oxide (CuO) nanoparticles, and an electric heater can improve the yield up to 196
Energy efficiency is a key component of energy policies worldwide. Corresponding measures tend to be considered in isolation and assessed by quantifying obtained energy savings. In contrast, so-called energy efficiency programmes can involve wider activity portfolios across sectors and usages, which present both operational complexities and synergies that are understudied. Accordingly, there is a lack of comprehensive tools for the monitoring and scaling of such programmes. This gap is addressed through participatory research within a regional, utility-led energy efficiency programme. Programme activities (e.g. subsidies) and effects (e.g. cost-effectiveness improvement) were inventoried from a round of workshops with programme staff, co-constructed with programme managers. They were then articulated into a system dynamics model, following a causality-mapping plenary workshop. This approach allows to assess and compare short- and long-term effects of otherwise incommensurable activities, as well as synergistic effects. Simulation results of this generic model suggest a significant long-term impact for learning and collaboration structures as well as networking activities, fostering actor engagement. Importantly, these activities have marginal, if at all quantifiable, short-term effects. Lastly, relevant activity component types (e.g. online subsidy application platform) were identified along the activity lifecycle. These components can be shared across activities to unlock synergistic potential. Key development principles are further suggested to achieve and enhance such synergies.
Small-scale photovoltaic (PV) installations purchased by private investors (households) play a key role in efforts toward carbon neutrality. Understanding their specific motivations and different decision-making processes is critical for supporting their participation and enabling better-targeted policymaking. Based on a sample of 2,565 responses in Czechia, this paper employs Latent Class Analysis to uncover distinct subgroups among private PV investors. Six unique investor profiles emerged: Environmentalists (6
Rapid economic growth and increasing energy demand have intensified environmental issues, especially carbon dioxide emissions, in energy-dependent economies. This paper investigates how energy consumption and technological innovation influence carbon dioxide emissions in Saudi Arabia from 1988 to 2023, while accounting for key macroeconomic variables, including economic growth, domestic and foreign investment, financial development, and urbanization. The study employs the Autoregressive Distributed Lag (ARDL) model to analyze short- and long-term relationships. Results show that energy consumption significantly contributes to carbon dioxide emissions in both the short and long runs, highlighting the need to adopt renewable energy and enhance energy efficiency. Conversely, technological innovation, measured by patents filed by country residents, reduces carbon dioxide emissions in the short-run and long-run, with significant long-term effects, illustrating how innovation-driven efficiency can mitigate environmental pollution in the long run. The analysis also indicates that economic growth initially accelerates emissions but slows over time. Domestic investment and urbanization are associated with reduced emissions, reflecting advancements in infrastructure and investment quality. Meanwhile, foreign investment appears to increase pollution, implying a need for sustainable investment flows. Overall, the findings underscore the importance of sustainable growth and decarbonization in Saudi Arabia, necessitating policies for the energy transition, innovation-led efficiency improvements, higher investment quality, and sustainable urban development.
Heat pumps are increasingly recognized as a key technology for decarbonizing heating and cooling, while natural gas systems remain widely used in current building applications. In this context, this study presents a techno-economic comparison of ground source heat pump (GSHP), air-source heat pump (ASHP), and natural gas (NG) systems within an EU-focused framework. To enable a harmonized comparison, the European Union was represented through three comparative climate-zone categories (warm, temperate, and cold), while EU-average economic values were used in the analysis. The comparative assessment was based on life-cycle cost (LCC) and related economic indicators under standardized assumptions. Under the adopted baseline assumptions, in heating applications, the GSHP system offers a cost advantage of up to 36
The Energy Efficiency First principle (EE1st principle) was defined in the 2018 EU Governance Regulation, and further elaborated in the 2023 recast of the Energy Efficiency Directive, which specifies concrete requirements for Member States in its articles 3 and 27. The application of the EE1st principle requires the identification of alternative cost-efficient energy efficiency measures optimising energy demand and supply. These measures must still achieve the objectives of the relevant sectoral policy, planning, and/or major investment decisions. In this paper, we present a methodology for estimating the cost-efficient potential for end-use energy savings and for other energy efficiency gains relevant to the energy system, which may particularly stem from demand-side response and storage but also targeted energy savings. The overarching methodology differentiates between the energy sector and the energy end-use sectors or areas. For the energy end-use sectors/areas, the methodology focuses on estimating energy savings potentials. The energy end-use sectors/areas included in the study were: transportation, industry, buildings, ICT services, water and wastewater management, and agriculture. A simplified methodology was applied to estimate the EU-wide energy savings potential for these energy end-use sectors/areas. For the energy sector—particularly in the electricity sector—the methodology incorporates additional steps to estimate the impact of energy efficiency on the load profile from energy savings, demand-side response and storage. These steps require a dynamic methodological approach, based on forecasting and modelling. We also discuss the proposed methodology’s limitations depending on its application in practice and currently available knowledge, using the results of an application for the transport sector as an illustration, and future research options to improve the assessment of the potentials of the EE1st principle in policy, planning and major investment decisions.
Energy poverty is a significant challenge facing sustainable human development. As the world’s largest developing country, China is confronted with particularly acute energy challenges. With the advancement of technology and the transformation of the financial sector, digital inclusive finance, as a new financial model, offers new possibilities for alleviating energy poverty. Consequently, digital inclusive finance has become vital in China’s efforts to mitigate energy poverty. Using balanced panel data from 31 provinces in China from 2011 to 2022, this paper theoretically analyzes and empirically tests whether digital inclusive finance can mitigate energy poverty and explores possible mechanisms of impact. The findings reveal that: (1) Digital inclusive finance can alleviate energy poverty, a conclusion that holds even after a series of robustness checks and handling endogeneity. (2) From a mechanistic perspective, digital inclusive finance reduces energy poverty by enhancing environmental regulation, narrowing the urban–rural income gap, promoting industrial structure optimization, and boosting technological innovation. (3) Regional examination shows that digital inclusive finance primarily mitigates energy poverty in the eastern and central regions. (4) Further studies indicate that the breadth of coverage, depth of use, and degree of digitization of digital inclusive finance all contribute to alleviating energy poverty.
This study examines the impact of energy efficiency credit policy on corporate green innovation, using China’s policy as a quasi-natural experiment. Previous research has emphasized the relationship between energy efficiency and bank credit; however, few studies have focused on the economic consequences of energy efficiency credit from a policy analysis perspective. Using a difference-in-differences model and data from listed companies in China from 2008 to 2023, this study demonstrates that energy efficiency credit policy promotes corporate green innovation. The findings confirm that credit support, external financing, and green agency costs are the mechanisms through which energy efficiency credit policy fosters green innovation. Additionally, the effectiveness of the policy is more pronounced in regions with stringent environmental regulations, among firms with a higher propensity for risk-taking, and for state-owned enterprises. This study is the first to empirically substantiate the beneficial role of energy efficiency credit policy in advancing green innovation.
This study investigates South Korean households’ preferences for refrigerator attributes using a choice experiment with 1,000 respondents from across the nation, analyzed via a mixed logit model to account for preference heterogeneity. Five key attributes—energy efficiency, warranty period, storage capacity, upper cooling compartment, and price—yielded statistically significant coefficients consistent with economic theory. Energy efficiency grade emerged as the most influential attribute, with an estimated mean marginal willingness to pay (MWTP) of KRW 471.6 thousand (USD 339.8) for a one-level improvement in efficiency grade as indicated by the mandatory energy label. This suggests that households place greater value on the actual efficiency performance conveyed by the label rather than its mere presence. The MWTP for storage capacity was comparatively small, amounting to KRW 5.4 thousand (USD 3.9) per additional liter. In contrast, Upper cooling compartment was associated with a considerable MWTP of KRW 442.8 thousand (USD 319.0) when compared with a lower cooling compartment, while an additional year of warranty period received a relatively modest valuation. Simulations for hypothetical refrigerator profiles confirmed efficiency’s centrality amid usability trade-offs. The findings underscore efficiency’s dual economic-environmental appeal, advocating tiered incentives and refined labelling that better convey the economic value of higher efficiency grades revealed in this study, thereby aligning label design with consumers’ willingness to pay for efficiency improvements.
This study explores how the design of national policy mixes influenced the achievement of energy efficiency targets in the European Union (EU) during two programming periods (2014–2020 and 2021–2030). In particular, it examines the interplay between energy efficiency obligation schemes, financial and fiscal incentives, and taxation measures. Using descriptive statistics and comparative analysis across EU Member States, the study finds that greater diversity in the policy instruments generally contributed to better target achievement. The results suggest that while no universal policy mix exists, Member States relying exclusively on a single instrument type faced higher risks of non-achievement. Moreover, energy efficiency obligation schemes consistently proved to be more cost-effective (up to ten times) compared to financial and fiscal incentives. Nevertheless, financing schemes/fiscal incentives were by far the most numerous type of measures in the 2014–2020 period and experienced a significant increase in 2021–2030 in number and volume. Given the emphasis on cost-effectiveness and budgetary constraints in EU energy policy, future strategies may benefit from reinforcing the role of obligation schemes within a diversified policy mix.
Green energy technologies are crucial for sustainable development, particularly in small and medium-sized enterprises (SMEs). In SMEs, these investments predominantly involve energy efficiency (EE) and renewable energy (RE) technologies. Despite their importance, research on SMEs' environmental behaviour is limited, often focusing on large corporations instead. This study investigates the internal and external determinants influencing Slovenian SMEs' decisions to invest in EE and RE technologies. Using a cross-sectional sample of SMEs from the Slovenian Business Register and data from a self-administered survey conducted in 2020, we estimate separate probit models for investments in EE and RE, and a joint model for investments in green energy technologies (GEI), looking for the complementarities in determinants of both investment types. Key findings indicate that ownership of premises, investment in R D, and having a dedicated energy manager are significant positive predictors of GEI. Energy audits and advice also positively influence EE and RE investments. A novel finding is that higher indebtedness (debt ratio) is positively associated with RE investment, while not affecting EE, consistent with greater use of external financing for RE. The study underscores the critical role of SMEs in the EU's green energy transition and highlights the importance of asset ownership, innovativeness among technology push factors, and proactive energy management practices in driving GEI. These insights can inform policymakers and business leaders aiming to enhance the adoption of sustainable energy practices among SMEs by promoting voluntary energy networks for peer learning, encouraging the appointment of dedicated energy managers, through awareness campaigns, and considering financial incentives to support energy-related initiatives.
Residential building energy consumption is a complex interplay of geography, architecture, and human behavior. However, current models fail to adequately integrate these factors, leading to generic strategies that lack regional specificity. This study introduces a physics-based End-Use Energy Predictive Model (EUE-PM) that explicitly quantifies the sensitivity of energy use to regional climate conditions, main facade orientation, and HVAC operational behaviors. The scenario-based comparative simulation addresses these factors across diverse climates and represents a significant advancement over previous approaches. We developed a database of climatic conditions and building archetypes to simulate 80 energy models across 40 cities in Japan and Vietnam, spanning a wide range of latitudes and climate zones. Our analysis reveals two key findings. First, sensitivity to building orientation is highly latitude-dependent, with high-latitude Japanese cities showing up to 16
Municipal housing companies have been recognised for their potential in taking on a leadership role in terms of energy efficiency in buildings, raising the question of what the transaction cost barriers are in these companies’ internal operations for undertaking energy efficiency measures. In this study, we thus explore transaction costs barriers in municipal housing companies in Sweden. Data on qualitative transaction cost barriers in the planning and implementation of energy-efficiency renovations was collected through interviews with six companies, two of which were also able to present data and quantitative transaction costs. The study illustrates the characteristics of transaction costs barriers, and the results indicate these barriers result in conventional energy efficiency technologies and measures rather than more innovative measures. This is due to asset specificity, uncertainty, and bounded rationality. The study indicates that learning can be essential in reducing transaction cost barriers, this through capacity building, demonstration projects and interaction among different types of actors. Feedback and structured learning, as in demonstration projects, will be essential in reducing transaction costs and supporting the implementation of new (innovative) energy-efficiency measures. The study specifically highlights the need for local initiatives supporting (local) learning, by strengthening the role of municipalities and municipal housing companies.
Smart Home Energy Management Systems (SHEMS) play a vital role in improving residential energy efficiency, reducing costs, and supporting the integration of renewable energy sources. This review systematically examines recent advancements in Machine Learning (ML) and Deep Learning (DL) techniques for optimizing SHEMS, covering studies published between 2018 and 2024. Using the PRISMA-based structured review methodology, 80 high-quality research articles are analysed to evaluate algorithmic performance, technical challenges, security considerations, and economic viability. The findings indicate that traditional ML models, such as Support Vector Machines (SVM), and regression techniques, remain effective for structured data and resource-constrained environments. However, DL approaches, including Long Short-Term Memory (LSTM), CNN-based hybrid models, and Deep Reinforcement Learning, consistently outperform conventional methods in real-time energy demand forecasting, adaptive load scheduling, and handling renewable energy intermittency, achieving prediction accuracies above 95
Achieving low-carbon economic growth has become a pressing goal for emerging economies, particularly for China as the world's largest carbon emitter. The energy-use rights trading (EURT) system represents an important market-based instrument for advancing China's dual carbon targets, yet its micro-level effects on the low-carbon transition of high-carbon enterprises remain insufficiently understood. Using China's 2017 EURT pilot policy as a quasi-natural experiment, this study constructs a triple-differences (DDD) model and employs firm-level data from listed companies in high-carbon industries to empirically examine the effect of EURT policy on corporate carbon emission intensity in pilot regions. The results show that the EURT policy significantly reduces carbon emission intensity among high-carbon firms in pilot regions. Mechanism analysis identifies green technological innovation and green credit as two primary transmission channels through which the policy promotes low-carbon transformation. Heterogeneity analysis reveals that the emission-reduction effect is more pronounced among firms with higher business maturity, in industries with greater market competition, in regions with stronger environmental enforcement, and in areas with lower resource endowments. Further analysis demonstrates that perceived economic policy uncertainty and external attention significantly amplify the carbon-reduction effect of the EURT policy. This study provides micro-level empirical evidence on the role of market-based energy regulation in driving low-carbon transition in high-carbon industries and offers practical insights for policy optimization and targeted implementation.
