Energy Efficiency最新文献

{"title":"Turning Green into Gold: Evidence from a Quasi-Experiment on Energy Rights Trading and Firm-Level Decarbonization","authors":"Baiyu Wu,&nbsp;Benhong Peng,&nbsp;Yue Zhu,&nbsp;Ehsan Elahi","doi":"10.1007/s12053-026-10441-6","DOIUrl":"10.1007/s12053-026-10441-6","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148173581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy efficiency and resilience transitions: empirical evidence on the roles of energy productivity and environmental innovation","authors":"Zeeshan Khan","doi":"10.1007/s12053-026-10437-2","DOIUrl":"10.1007/s12053-026-10437-2","url":null,"abstract":"<div><p>Recently, climate-resilient disruptions and geopolitical shocks in energy markets have made resilience a pressing policy issue. In this research, the effects of environmental innovation (linear and quadratic), energy productivity, natural resources, and research and development (R&amp;D) expenditure on the energy resilience index (ERI) are analyzed. The ERI is constructed of the losses in transmission and distribution, net energy imports, per capita energy use, consumption of renewable energy sources and CO₂ emissions via the entropy method. Using a balanced panel of 10 economies (Canada, China, France, Germany, India, Japan, Norway, Spain, and the UK) for 1996–2023, this study estimates a nonlinear resilience model through moment quantile regression. The results are verified using panel-corrected standard error and Driscoll–Kraay estimators. According to the results, linear environmental innovation is negative and significant for all quantiles (Q0.25–Q0.90), whereas the quadratic term is positive and significant. Taken together, these results imply a U-shaped relationship between innovation and resilience. In contrast, energy productivity, natural resources, and R&amp;D are favorable and considerably beyond the resilience distribution, with compelling confirmation in various specifications. These results are consistent with quantile-specific policy sequencing: low-resilience countries should lower grid losses, import risks, flexibility and storage and increase energy efficiency programs to avoid transition friction costs. Countries with higher resilience should scale up mission-oriented green innovation and R&amp;D to enhance resilience.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Achieving carbon reduction amidst China’s provinces: an energy security perspective","authors":"Kai-Hua Wang,&nbsp;Shu-Mei Li,&nbsp;Xin Li","doi":"10.1007/s12053-026-10438-1","DOIUrl":"10.1007/s12053-026-10438-1","url":null,"abstract":"<div><p>This study discusses the influence of energy security on carbon dioxide (CO₂), using China’s 30 provincial data and panel fixed-effect model. The empirical results identify the potential mechanism of impact and draw several important conclusions. First, energy security can reduce CO₂ emissions, which means that China’s energy security strategy does not contradict its carbon targets. Second, energy security reduces CO₂ emissions through optimization of energy structure and enhancement of energy efficiency. Third, energy security in southern regions and provinces with light environmental regulations significantly inhibits CO₂ emissions. The results of this study go beyond simple causation and reveal specific mechanisms through which energy security affects CO₂ emissions. This work provides a thorough analytical framework and practical policies for addressing energy and environmental challenges. Thus, China ought to advance the exploitation of renewable energy sources, diversifying energy utilization structure, and enhance the national total energy system efficiency.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147938008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of residential rooftop PV configurations for maximum energy efficiency and economic viability","authors":"Ahmed Attia","doi":"10.1007/s12053-026-10435-4","DOIUrl":"10.1007/s12053-026-10435-4","url":null,"abstract":"<div><p>This paper proposes an integrated optimization model for residential rooftop photovoltaic (PV) systems, aiming to balance economic viability and operational performance. The problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) model that captures seasonal variations and incorporates reliability indicators such as the Loss of Power Supply Probability (LPSP) and energy density. To efficiently solve the model, a hybrid approach is used: the Relax-and-Fix (R&amp;F) heuristic generates an initial feasible layout, which is subsequently refined using the Branch-and-Bound (B&amp;B) algorithm. In addition, variability in energy density is addressed by constructing an empirical probability distribution, which guides the determination of optimal inter-row spacing across different seasonal conditions. To enhance performance, the model determines the optimal number of PV panels, tilt angle, and inter-row spacing. A case study of a residential building in eastern Saudi Arabia assesses the model's practicality. Sensitivity analysis examines the impact of economic and design factors, such as inflation dynamics and tilt angles, on system viability. Findings reveal that at low interest rates (0%–5%), larger PV systems achieve a lower levelized cost of energy (LCOE) and greater grid independence, whereas interest rates above 7% lead to system downsizing, making solar investments unviable. An optimal tilt angle range of 20°–40° balances energy generation, cost, and land-use efficiency across seasonal variability, while extreme angles degrade economic and operational performance.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovation and urban energy efficiency: a case study of China’ s innovative city pilot policy","authors":"Feifei Xu,&nbsp;Wei Shao,&nbsp;Yixuan Tang","doi":"10.1007/s12053-026-10436-3","DOIUrl":"10.1007/s12053-026-10436-3","url":null,"abstract":"<div><p>Sustainable economic development under energy constraints remains a critical challenge for China. As a new mode of urban development, innovation strategies have become a solution in Chinese practices. Motivated by this context, this study treats the phased rollout of the Innovative City Pilot Policy (ICPP) that started in 2008 as an exogenous policy shock and adopts a staggered difference-in-differences (DID) framework to evaluate its impact on urban energy efficiency. Using a three-stage data envelopment analysis (DEA) model to measure urban energy efficiency, our findings indicate that ICPP has a significant positive effect on energy efficiency in pilot cities. Mechanism analysis from the perspective of factor agglomeration shows that ICPP improves urban energy efficiency by promoting the agglomeration of talent, technology and capital, as well as the synergistic agglomeration of them. Heterogeneity analysis reveals that the ICPP has a greater effect on energy efficiency for non-resource cities, non-industrial cities, cities with a higher administrative hierarchy, and eastern and western cities. These findings have significant policy relevance for countries with backgrounds similar to China’s in alleviating energy constraints and achieving sustainable economic growth.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of thermal properties of lightweight concrete from mechanical parameters using GMDH","authors":"Davut Sevim,&nbsp;Yılmaz Kaya,&nbsp;Hasan Oktay","doi":"10.1007/s12053-026-10434-5","DOIUrl":"10.1007/s12053-026-10434-5","url":null,"abstract":"<div><p>Growing concerns about the energy consumption of heating and cooling systems in buildings have led to an increased demand for construction materials with enhanced insulation performance. Accurate determination of the thermal properties of lightweight concrete (LCW) is vital for evaluating its energy performance; however, conventional high-precision tests are both time-intensive and expensive. In this study, the Group Method of Data Handling (GMDH) model is proposed as a tool for directly predicting the thermal properties of LCW from its mechanical properties. For the experimental study, LCWs were produced using waste rubber, pumice, and expanded perlite aggregates as substitutes. Three prediction scenarios were established to accurately estimate thermal conductivity (λ), specific heat (c_p), and thermal diffusivity (α) from key mechanical properties, including compressive strength (σ_c), tensile strength (σ_t), porosity (ϕ), bulk density (ρ), and ultrasonic pulse velocity (upv). Using the GMDH model, mathematical relationships were established between mechanical inputs and thermal outputs, with the most influential parameters identified through layer-by-layer transformations. A topology consisting of two hidden layers, each with three neurons, was adopted, and the model was benchmarked against five machine learning algorithms. The results showed that GMDH provided high predictive accuracy, particularly for thermal conductivity, with an R² of 0.9957 and low error metrics. These findings indicate that GMDH can serve as an effective and cost-efficient approach for predicting the thermal behavior of LCW, while supporting the sustainable use of alternative aggregates in energy-efficient construction.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12053-026-10434-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can tax credit schemes support energy efficiency measures in residential buildings? A case study for Greece","authors":"Vlasios Oikonomou,&nbsp;Christos Tourkolias,&nbsp;Amaryllis Perotti,&nbsp;Julia Luca Szabo,&nbsp;Stavros Spyridakos,&nbsp;Shima Ebrahimigharehbaghi","doi":"10.1007/s12053-026-10429-2","DOIUrl":"10.1007/s12053-026-10429-2","url":null,"abstract":"<div><p>Alongside subsidies and grants, a standard policy instrument for supporting building energy efficiency upgrades is tax credits. In this study, we assess the case of Greece, which uses both subsidies and tax credits for energy efficiency improvement interventions in residential buildings. We conducted both a socio-economic and a cost–benefit analysis including environmental, multiple benefits, macroeconomic, and private perspectives. Drawing upon data from the Hellenic Statistical Authority including the Households Budget Survey, Eurostat and the Greek National Energy and Climate Plan (NECP), we built a model that evaluates how different tax credits scenarios interact with households’ characteristics. This way, we divided households using a clustering method, based on income, size, and energy use patterns. We explored how clusters and tax credits interact within a baseline, a low macroeconomic return and a high macroeconomic return scenario. The paper concludes that the optimal tax credit rate for middle and high-income households ranges from 40 to 52%, depending on economic and budgetary conditions. We recommend, however, that vulnerable households (below the €1,100 monthly income threshold) receive direct support such as grants, zero-interest loans, or refundable tax credits. This study informs suitable and fair policies adapted to the Greek context and aligned with the building renovation goals set by the Greek NECP.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting energy use and calculating carbon emissions of higher education buildings with COVID-19 lockdown effect","authors":"Blessing, Ibukun Mafimisebi,&nbsp;Bahar Sennaroglu","doi":"10.1007/s12053-026-10433-6","DOIUrl":"10.1007/s12053-026-10433-6","url":null,"abstract":"<div><p>This study uses regression with Autoregressive Integrated Moving Average (ARIMA) errors method incorporating the COVID-19 lockdown effect to analyse the electricity consumption of a UK college’s facilities. The objective is to accurately predict the electricity consumption of three college campuses (labelled B, C, and T) for use in decision-making activities and planning. As benchmarking methods, Holt-Winters’ multiplicative and additive methods, ARIMA, Neural Network Autoregression (NNAR), and Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) methods are used to predict electricity consumption. The result identified regression with ARIMA errors as the best-performing model, as it considers the effects of the national lockdowns for Coronavirus based on error measures employed. The Mean Absolute Percentage Error (MAPE) values for forecast accuracy are 4.85% for Campus B, 5.31% for Campus C, and 10.76% for Campus T. The proposed method provides a single forecasting equation for all seasons, enables the inclusion of explanatory variables, including an intervention event, and shows good forecasting performance, even though the sample size is not large. The energy used and carbon emission intensities for each campus dropped significantly during the COVID-19 era compared to the pre-pandemic period. This result showed decreases in energy used intensity (EUI) and carbon footprint (CFP) across the three campuses. However, the significant basal energy use highlights a key opportunity for energy savings during low-occupancy periods. The NetZero carbon implication is that this result could be applied to predict carbon footprint of higher education buildings under similar circumstances.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing energy efficiency through building information modelling: a review of current insights and future prospects","authors":"Shubham Kumar Verma,&nbsp;Sahil Thappa,&nbsp;Yatheshth Anand,&nbsp;Sanjeev Anand","doi":"10.1007/s12053-026-10431-8","DOIUrl":"10.1007/s12053-026-10431-8","url":null,"abstract":"<div><p>In the current era of rapid urbanization, building development has increased significantly. This growth has placed increasing pressure on the architecture, engineering, and construction (AEC) industry to deliver energy-efficient buildings while maintaining quality. Building Information Modelling (BIM) has emerged as a transformative technique which is considered as having a potential to integrate energy performance considerations into architectural design decision-making. However, despite this potential, existing research synthesis remain fragmented and limited in their ability to present a coherent architectural perspective. This review critically examines the role of BIM in improving building energy efficiency from an architectural perspective. The study focuses on how energy-related tools and methods are embedded in design workflows across different project stages. The paper reviews BIM-enabled approaches, including Building Energy Modelling (BEM), artificial intelligence and machine learning, and data-driven digital twin frameworks, synthesising research published over the past two decades. From this synthesis, several key research gaps are identified, including fragmented application across the building lifecycle, limited feedback between operational performance and early design stages, and a persistent emphasis on tool-centric rather than workflow-integrated implementations. The findings show that BIM-enabled energy approaches can significantly improve energy performance when they support architect-led, performance-informed design processes. However, their effectiveness remains constrained by challenges related to workflow adaptation, interoperability, data integration, and professional skill development. By linking BIM-enabled energy approaches to architectural design workflows and decision-making processes, this review highlights strategic directions in which BIM can function as a central integration platform for energy-efficient design. The study underscores the need for lifecycle-oriented and architecturally embedded BIM workflows to support more reliable energy outcomes and contribute to the delivery of sustainable, high-quality built environments.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 5","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147727321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economic growth and environmental trade-offs: energy transition strategies for BRICS nations","authors":"Samir Ul Hassan","doi":"10.1007/s12053-026-10430-9","DOIUrl":"10.1007/s12053-026-10430-9","url":null,"abstract":"<div><p>This study examines the energy–growth–emissions nexus in the BRICS economies, which account for nearly 36% of global energy output and have experienced a 101% increase in CO₂ emissions since 2000. Using balanced panel data for the period 2000–2022, the study investigates the structural constraints and policy drivers shaping the renewable energy transition. The findings reveal that industrial scale generates substantial transition inertia in large economies such as China and India, where persistent coal dependence contributes to 12–18% higher annual emissions. Renewable energy is shown to have a limited short-term impact on economic growth, contributing less than 5% to GDP, due to a 3–5-year lag between investment and measurable economic returns. In contrast, fossil fuels continue to support industrial output and employment—accounting for about 8% of India’s workforce—highlighting the socio-economic trade-offs involved in rapid decarbonisation. Financial dynamics play a critical role: green foreign direct investment reduces emissions intensity by approximately 9%, while non-renewable investment increases it by 14%. Policy interventions, including China’s carbon neutrality commitment and India’s Perform, Achieve, and Trade (PAT) scheme, partially validate and refine the Environmental Kuznets Curve for BRICS nations by lowering emissions elasticity with respect to growth. Overall, the results suggest that effective decarbonisation in BRICS nations requires long-term renewable strategies, targeted green investment, and tailored policy frameworks to decouple economic growth from emissions while maintaining socio-economic stability.</p></div>","PeriodicalId":537,"journal":{"name":"Energy Efficiency","volume":"19 4","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}