Operational Energy Use Research Articles (Page 1)

Construction cannot be called as an environment friendly process, hence many solutions are being developed to define the negative interactions of the buildings, determine the extent of environmental impact and find alternatives to improve design performance. The paper examines environment impacts of two warehouses using LCA methodology that has been widely applied in the construction sector, since 1990, taking into consideration life cycle stages from cradle to grave with separate summary for product stage, construction process, use stage and end of life. Phase of the building operational energy use is not discussed in the article. Paper focuses on evaluation of building materials instead of operations of facilities. Analysis takes into account following environment impacts: GlobalWarming Potential (GWP), Acidification Potential (AP), Eutrophication Potential (EP), Ozone Depletion Potential (ODP), Photochemical Ozone Formation Potential (POFP) and Non-Hazardous Waste Disposed (NHWD). The main conclusion derived from the received results of the warehouse buildings case study is that the product stage is a particularly important phase of the life cycle, as it reveals the highest levels of emissivity impacts among the analyzed stages. The paper indicates materials that are responsible for the greatest impacts.

With India’s rapid urbanization and growing population, residential energy demand is surging, positioning the sector as the country’s second-largest energy consumer. Traditional buildings, with poor insulation and ventilation, contribute substantially to operational energy use and carbon emissions. This study introduces a novel, integrated framework combining life cycle energy analysis, dynamic energy simulation, and statistical design optimization applied across four residential building envelope scenarios—Conventional, Base, ECBC-Compliant, and Net Zero Aspiring—in a composite Indian climate. Unlike previous studies that focus solely on operational energy, this research holistically evaluates both embodied and operational energy across a building’s lifecycle, guided by International Organization for Standardization 14040 (ISO). Uniquely, this study incorporates experimental characterization of building materials e.g., scanning electron microscope, thermal conductivity testing of ordinary Portland cement-fly ash mixes, and employs Taguchi orthogonal design with regression modeling to optimize and validate simulation outcomes. Simulations conducted using eQUEST software and localized Patiala climate data demonstrate that optimized envelopes can reduce operational energy by up to 46% and whole-life carbon by 50%. The results align with Energy Conservation Building Code guidelines and India’s low-carbon development goals, offering actionable insights into sustainable urban housing design under net-zero targets.

Energy efficiency and carbon sequestration are fundamental components of sustainable urban development, as cities account for majority of global energy consumption and carbon emissions. This research explores Urban Digital Twins (UDTs) as an innovative approach to optimizing operational energy use and enhancing carbon sequestration in urban environments. UDTs integrate real-time data processing, simulation models, and public participation to drive sustainable energy management and emissions reduction. The study examines two pilot buildings in Thailand and Vietnam, using energy data to evaluate the feasibility of UDTs. Key implementation challenges, including regulatory barriers, setup costs, and limited public engagement, are analyzed, with proposed strategies to address them. A digital twin-based energy framework enables cities to integrate renewable energy systems, intelligent controls, and carbon sequestration strategies, optimizing energy use while reducing emissions and costs. This research aligns with UN Sustainable Development Goals (SDGs) 7 and 11, supporting clean energy adoption and sustainable urban development.

This study evaluates the carbon footprints of two traditional Mongolian dwellings—the portable Ger and the self-built Baishin—through a comprehensive life cycle assessment (LCA). The Ger, prevalent among nomadic communities, relies on wood and coal-fired stoves for heating, leading to high operational emissions despite its minimal embodied carbon. Conversely, the Baishin, a permanent structure constructed from timber, bricks, or concrete, incurs higher embodied emissions but can achieve reduced operational emissions with effective insulation. Utilising OpenLCA and the Ecoinvent v3.11 database, the analysis spans material extraction, construction, operational energy use, and end-of-life disposal under typical Mongolian conditions. Results highlight the Ger’s lower cumulative energy demand (880,446,708 MJEq) and ecological footprint (59,409 m²a) compared to the Baishin’s higher impacts (934,217,037 MJEq and 279,801,890 m²a). These findings underscore the potential for energy-efficient retrofitting and sustainable material choices to mitigate emissions. The study provides actionable insights for developing low-carbon housing strategies tailored to Mongolia’s cold climate, contributing to climate change mitigation while preserving cultural heritage.

As the population increases, the growing demand for residential housing escalates construction activities, significantly impacting global warming by contributing 42% of primary energy use and 39% of global greenhouse gas (GHG) emissions. This study addresses a gap in research on lifecycle assessment (LCA) for Indian residential buildings by evaluating the full cradle-to-grave carbon footprint of a typical single-family house in Northern India. A BIM-based LCA framework was applied to a 110 m2 single-family dwelling over a 60-year life span. Operational use performance and climate analysis was evaluated via cove tool. The total carbon footprint over a 60-year lifespan was approximately 5884 kg CO2e, with operational energy use accounting for about 87% and embodied carbon approximately 11%. Additional impacts came from maintenance and replacements. Energy usage was calculated as 71.76 kWh/m2/year and water usage as 232.2 m3/year. Energy consumption was the biggest driver of emissions, but substantial impacts also stemmed from material production. Cement-based components and steel were the largest embodied carbon contributors. Under the business-as-usual (BAU) scenario, the operational emissions reach approximately 668,000 kg CO2e with HVAC and 482,000 kg CO2e without HVAC. The findings highlight the necessity of integrating embodied carbon considerations alongside operational energy efficiency in India’s building codes, emphasizing reductions in energy consumption and the adoption of low-carbon materials to mitigate the environmental impact of residential buildings. Future work should focus on the dynamic modeling of electricity decarbonization, improved regional datasets, and scenario-based LCA to better support India’s transition to net-zero emissions by 2070.

The agricultural sector is one of the most significant sectors of the global economy, yet it is concurrently a highly energy-intensive industry. The issue of optimizing field operations in terms of energy consumption is therefore a key consideration for sustainable agriculture, and the solution to this issue leads to both environmental and financial benefits. The aim of this study was to estimate energy consumption during soil cultivation using geophysical scanning data and machine learning (ML) algorithms. This included determining the optimal set of independent variables and the most suitable ML method. Soil parameters such as electrical conductivity, magnetic susceptibility, and soil reflectance in infrared spectra were mapped using data from Geonics EM-38 and Veris 3100 scanners. These data, along with soil texture, served as inputs for predicting fuel consumption and field productivity. Three machine learning algorithms were tested: support vector machines (SVMs), multilayer perceptron (MLP), and radial basis function (RBF) neural networks. Among these, SVM achieved the best performance, showing a MAPE of 4% and a strong correlation (R = 0.97) between predicted and actual productivity values. For fuel consumption, the optimal method was MLP (MAPE = 4% and R = 0.63). The findings demonstrate the viability of geophysical scanning and machine learning for accurately predicting energy use in tillage operations. This approach supports more sustainable agriculture by enabling optimized fuel use and reducing environmental impact through data-driven field management. Further research is needed to obtain training data for different soil parameters and agrotechnical treatments in order to develop more universal models.

To study the resource utilization and environmental impacts over the life cycle of plug-in hybrid electric vehicles （PHEV）， this study employs a life cycle assessment （LCA） approach. It focuses on the Toyota Levin PHEV for inventory analysis， assessing material and fossil fuel consumption and the overall environmental footprint. Additionally， the study compares these impacts with those of battery electric vehicles （BEV） and hybrid electric vehicles （HEV）， analyzing various factors such as operational conditions， battery wear， and mileage increments during usage. The research found that the extraction and utilization of metals like lithium and copper led to considerable consumption of material resources， with operational energy use being the major contributor to the vehicle's lifetime fossil fuel consumption. Scenarios of urban， highway， and aggressive driving suggest that BEVs maintain the lowest environmental and resource burden. The study further investigated the effect of battery capacity degradation to 90% and 85%， noting a heightened sensitivity in BEVs. Mileage increments of 10 000， 50 000， 100 000， and 150 000 km were analyzed under private and taxi usage scenarios， indicating a higher environmental and resource impact in BEVs and PHEVs under private usage when exceeding 50 000 km compared to that of HEVs and PHEVs used in taxis.

Today’s economic and social environment faces several problems and challenges (e.g. energy crisis, inflation, environmental protection), most of which interact with the transport system in two directions. Researchers and relevant organisations have developed several proposals and action plans to mitigate the ‘problem cloud’ for each mobility subsystem, but these tend to focus on a technological, economic or industrial solution rather than a complex one. This includes subsidising the purchase and operation of electric vehicles, encouraging the use of public transport, and developing soft modes of transport. This study develops a multi-layered, complex, cost-oriented methodology to increase the sustainability and economic stability of local and intercity bus and coach public transport. The methodology based on the main technical and operational (maintenance, energy use and storage) parameters of different conventional and alternative propulsion vehicles, as well as on the available forms of financing, taking into account discount rates. The procedure developed will be illustrated with examples from Hungarian cities. The unit costs per kilometre of the different propulsion systems will be examined. The method can be used to determine the most economically efficient and sustainable choice of vehicle propulsion for the public transport service provider, and to obtain a realistic picture of unit costs.

There is a scientific consensus that further delay to preventive and mitigative action addressing human-induced climate change represents costly and existential danger, with no inhabited region of the earth unaffected. The UK must transition to net zero by 2050. It is estimated that 35–40 per cent of UK emissions result from building use, with 5–10 per cent from the construction industry. A majority of buildings designed or in use today are not sufficiently prepared for this transition. This affords those working in building design and construction unique and critical opportunities for positive change and damage limitation. There is a consensus of expertise that prior to 2050, almost every new and existing UK building will need to be heated with an electric heat pump and have a greatly reduced energy demand. This represents a significant retrofit challenge, which there are insufficient plans or progress to address. More stringent building regulations for proposed buildings’ operational energy use were introduced in 2022. More may imminently follow, though there is concern from the industry that these plans lack the scope and ambition required. Despite broad support, no building regulations limiting embodied carbon exist or are proposed. Projected increases in the frequency, length and severity of heatwaves mean that UK buildings designed for past conditions are unlikely to remain useful or habitable throughout their lifespan. Existing methodologies to assess and mitigate this risk, sometimes a requirement of planning policy, now feature within the new Building Regulations Part O. This article is also included in The Business & Management Collection which can be accessed at https:// hstalks.com/business/.

The paper explores the pressing issue of energy consumption in machine learning (ML) models and their environmental footprint. As ML technologies, especially large-scale models, continue to surge in popularity, their escalating energy demands and corresponding CO2 emissions are drawing critical attention. The article dives into innovative strategies to curb energy use in ML applications without compromising—and often even enhancing—model performance. Key techniques, such as model compression, pruning, quantization, and cutting-edge hardware design, take center stage in the discussion. Beyond operational energy use, the paper spotlights a pivotal yet often overlooked factor: the substantial emissions tied to the production of ML hardware. In many cases, these emissions eclipse those from operational activities, underscoring the immense potential of optimizing manufacturing processes to drive meaningful environmental impact. The narrative reinforces the urgency of relentless advancements in energy efficiency across the IT sector, with machine learning and data science leading the charge. Furthermore, deploying ML to streamline energy use in other domains like industry and transportation amplifies these benefits, creating a ripple effect of positive environmental outcomes. The paper culminates in a compelling call to action: adopt a dual-pronged strategy that tackles both operational energy efficiency and the carbon intensity of hardware production. By embracing this holistic approach, the artificial intelligence (AI) sector can play a transformative role in global sustainability efforts, slashing its carbon footprint and driving momentum toward a greener future.

Buildings are significant consumers of natural and manufactured resources, especially in rapidly urbanizing regions. This study presents the first comprehensive analysis of residential building stocks in 19 countries across these Western Asia and Northern Africa (WANA), utilizing an archetype-based, bottom-up, stock-modeling approach. By integrating life-cycle assessment (LCA) with building energy models via BuildME, the study estimates the material, energy, and annualized life-cycle greenhouse gas emissions (ALCGHGE) intensities associated with various residential building types. These findings provide critical insights into the resource use and environmental impact of residential buildings in WANA, considering the region’s diverse climatic, architectural, and economic contexts. Significant variations are found in both total residential emissions and the proportion attributed to material-related versus use-phase-related sources. The analysis reveals that 64% of total annualized life-cycle emissions from residential buildings stem from operational energy use, e.g. 90% in Qatar and 38% in Georgia. Oman has the highest per capita annualized material use at 3 t/capita/year, while Kuwait shows the highest total per capita emissions at 5 tCO2eq/capita/year, significantly outpacing countries such as Morocco and Syria (0.7 and 0.5 tCO2eq/capita/year, respectively). Although single-family houses are the least emission-intensive per m2, they exhibit the highest emissions per dwelling unit. Policy relevance The findings hold significant implications for policymakers in WANA as they address the environmental impacts of rapid urbanization and growing residential building stocks. The findings emphasize the urgent need for targeted policies that focus on reducing energy use in residential buildings and for incorporating targets into nationally determined contributions, particularly in energy-intensive countries such as Qatar and Kuwait. Policymakers should prioritize the development of energy-efficient building codes, incentivize the adoption of low-emission technologies, and promote sustainable building practices that minimize material use. The study also underscores the importance of considering both per dwelling unit and per m2 emissions in policy frameworks, especially in countries such as Oman and Kuwait, where large, energy-intensive homes dominate. By leveraging the region-specific insights provided by this research, governments can design more effective strategies for reducing ALCGHGE and resource use in residential sectors, contributing to broader climate change-mitigation efforts.

The building sector significantly contributes to global resource depletion and greenhouse gas emissions, necessitating integrated approaches to evaluate both environmental and economic performance. This study developed a sustainability-oriented assessment framework—applied in a Chinese context—that integrates life cycle assessment (LCA), life cycle costing (LCC), and carbon financial optimization to evaluate the life cycle performance of prefabricated steel buildings. Using publicly available databases (CEADs, Ecoinvent, and the Chinese Life Cycle Database), the framework quantified cradle-to-grave environmental impacts across raw material extraction, prefabrication, transport, on-site assembly, operation, and end-of-life stages. Emissions were monetized using standardized emission factors and official cost coefficients, enabling environmental costs to be expressed in financial terms. A dynamic financial simulation module was incorporated to assess the effects of carbon price fluctuations and quota allocation schemes. Sensitivity analyses were performed to examine the influence of key variables such as retrofit investment costs, emission reduction efficiency, and carbon policy scenarios on financial returns. The results show that material production and operational energy use dominate life cycle carbon emissions, jointly contributing more than 90% of the total impacts. Moderate decarbonization investments—such as HVAC upgrades and improved insulation—can achieve positive net economic returns under baseline carbon pricing. This integrated, data-driven framework serves as a practical decision-support tool for policymakers and industry stakeholders. It is adaptable across different regions and material systems, supporting the global transition toward low-carbon and financially viable construction practices.

Comparative life cycle assessment of alternative heating, ventilation and air-conditioning (HVAC) systems for poultry houses.

Enhancing Environmental Sustainability in Diagnostic Radiology: Focus on CT, MRI, and Nuclear Medicine.

Abstract The problem at FMCG company is concentrated on waste that occurs during the internal logistics process in the raw material warehouse. The solution to the problem was solved by using the Value Stream Mapping (VSM) method, ABC Classification, and Order Picking Route Strategy. The purpose of this research is to propose a new layout and propose an optimal order picking route to reduce time, distance, material handling costs, and environmental impacts to implement sustainable warehouse management on FMCG Company. The processing results show that waste in the process of internal activities in the warehouse is caused by the process of implementing FIFO and the order picking process. In addition, the layout improvement had an impact on the addition of storage area by 104.1 m2 and the reduction of time in the flow of internal logistics processes by 3,020.6 seconds or equivalent to 50.3 minutes. The proposed order picking route strategy is also expected to help companies reduce material handling costs, as well as reduce carbon emissions from operational energy use, by optimizing forklift movements and reducing mileage. The implementation of a more efficient layout and picking strategy not only reduced material handling costs by Rp105,635.77/day, but also contributed to reducing the company’s carbon footprint, supporting sustainability initiatives.

The decarbonization of the building sector is essential to mitigate climate change, aligning with the EU’s Energy Performance of Buildings Directive (EPBD) and the transition from near-Zero-Energy Buildings (nZEBs) to Zero-Emission Buildings (ZEBs). This study introduces a novel and streamlined Life Cycle Assessment (LCA) methodology, in accordance with EN 15978, to holistically evaluate the Global Warming Potential (GWP) of buildings. Our approach integrates a calibrated dynamic simulation of operational energy use, performed with DesignBuilder, to determine precise operational CO2 emissions. This is combined with a comprehensive assessment of embodied emissions, encompassing construction materials and transportation phases, using detailed Environmental Product Declarations (EPDs). Applied to the IndUVa nZEB case study, the findings reveal that embodied emissions dominate the life cycle GWP, accounting for 69%, while operational emissions contribute just 31% over 50 years. The building’s use of 63.8% recycled materials highlights the transformative role of circular economy strategies in reducing embodied impacts. A comparative analysis of three energy-efficiency scenarios demonstrates the IndUVa building’s exceptional performance, achieving energy demand reductions of 78.4% and 85.6% compared to the ASHRAE and CTE benchmarks, respectively. This study underscores the growing significance of embodied emissions as operational energy demand declines. Achieving ZEBs requires prioritizing embodied carbon reduction through sustainable material selection, recycling, and reuse, targeting a minimum of 70% recycled content. By advancing the LCA framework, this study presents a pathway for achieving ZEBs, driving a substantial reduction in global energy consumption and carbon emissions, and contributing to climate change mitigation.

Significant advancements in Information Technology (IT) have improved work processes, efficiency, and business operations, but these advancements bring environmental sustainability issues. IT's role in climate change, resource management, waste production, and public health is critical yet often overlooked. Excessive energy use in IT operations contributes to negative environmental impacts. This research focuses on analyzing the behavioral determinants influencing information and communication technology (ICT) employees' participation in Green Information System (Green IS) practices in Malaysia. Additionally, it seeks to ascertain whether Malaysian ICT employees have a clear intention of incorporating Green IS in a bid to combat the aforementioned issues and guarantee the viability of a sustainable green environment. The study employed quantitative methods, and data was collected from 183 respondents from various ICT employees working in Malaysia Digital (MD) Status companies. The collected data was analyzed to examine the four Technology Readiness Index (TRI) constructs—Optimism, Innovativeness, Insecurity, and Discomfort. Results indicated that Optimism and Innovativeness significantly influenced attitudes towards Green IS, while Insecurity had a negative impact, and Discomfort was insignificant. The findings revealed that subjective norms significantly influence Malaysian ICT employees more than perceived behavioral control. Attitude was a full mediator between TRI components and the willingness to implement Green IS. The study provides new insights into the TRI’s application in Green IS and highlights attitude's mediating role. This emphasizes how crucial attitude is in determining users’ intentions to adopt Green IS, underscoring the necessity of taking proactive measures to alter attitudes and perceptions of Green IS prior to its broader acceptance. Ultimately, the research revealed that attitude indirectly influences the genuine intention to adopt Green IS among Malaysian ICT employees. The findings contribute to formulating new policies to adopt green IS in organizations and raise awareness about environmental sustainability among employees.

In the aftermath of the global pandemic, the widespread embrace of flexible working models has led to suboptimal occupancy levels in office buildings. Despite this shift, traditional space management practices persist, contributing to increased energy consumption per person. This study investigates how integrating smart lock systems can enhance space utilization within flexible working environments, ultimately reducing energy use. A case study of an office building in Milan, Italy, is used to evaluate the proposed approach. The methodology includes a comprehensive assessment of building design and functionality, coupled with impact analyses using Building Energy Modeling and Life Cycle Assessment. The results indicate that innovative occupancy management strategies can achieve energy savings of from 9% up to 14% compared to baseline operational energy use, leading to a reduction in CO2 emissions of 7.5 to 17.6 kgCO2eq/m2 depending on occupancy scenarios. The life cycle assessment reveals that, while smart locks introduce an initial embodied carbon footprint of approximately 2 tons of CO2, that is recovered through the savings obtained after a few months of installation. The findings demonstrate that this methodology is effective in buildings that allow both functional and temporal flexibility, enabling partial shutdowns and the redirection of certain services when not in use, ultimately improving energy efficiency through lean interventions.

The design and configuration of buildings can play a major role in influencing the environmental impacts of the built environment. This paper explores the relation of building shape and it’s environmental impacts by employing a life cycle assessment (LCA) framework. The primary objective is to contribute to the ongoing discourse on sustainable construction practices by exploring alternatives in office building shapes and heights. The initial focus of our study centers on a set of plan shapes based on different combinations of a 12 × 14 square meter modular unit. This set introduces variations with and without courtyards, coupled with three distinct heights of 3, 6, and 12 m (1, 2, and 4 stories). Expanding our exploration, we introduce a second set of standard geometric shapes, namely square, pentagon, hexagon, heptagon, octagon, and circle. We assess the annual energy demand of buildings with these plan shapes and conduct an LCA analysis focused on the operational energy use stage in the eLCA tool to quantify their environmental implications, focusing on global warming potential (GWP) and primary energy non-renewable total (PENRT) indicators. Through calculations and comparisons of the LCA results, this paper provides insights into the environmental trade-offs and benefits associated with different building plan shapes and heights.

An urgent transition to environmentally sustainable healthcare is required. The purpose of this study was to identify key areas for environmental impact mitigation for a coronary artery bypass grafting trajectory. An ISO14040/44 standardized life cycle assessment was conducted for the functional unit of an individual patient trajectory of elective coronary artery bypass grafting surgery, from operating room admission until intensive care unit discharge. Data were collected for products, processes, and services required for care delivery in a Dutch academic hospital for 12 patients. The environmental impact was calculated using the ReCiPe 2016 method. A single patient trajectory caused 414 [IQR 383-461] kgCO2 equivalents of global warming, equal to 2753 km of driving an average Dutch petrol-fuelled car. Other notable environmental impacts were fine particulate matter, (non-)carcinogenic toxicity, land use, and terrestrial acidification. Operating room disposable products (162 kgCO2eq), energy use (48 kgCO2eq), and employee commute (36 kgCO2eq) contributed most to global warming. The extracorporeal circulation set, surgical drapes, intraoperative salvage set, surgical gowns, and cotton gauzes caused most of the disposables' environmental impact. Most energy use occurred in the operating room via heating, ventilation, and air conditioning. A coronary artery bypass grafting trajectory's environmental impact primarily contributed to global warming. Most impact mitigation could be achieved by avoiding/reducing disposable product use when possible or replacing these with reusables. Optimizing operating room energy systems, switching to renewable energy, and encouraging low-emission employee commute can further reduce the environmental impact.