Relative Energy Efficiency Research Articles

Sustainable energy practices and cryptocurrency market behavior

We examine the effects of energy usage on the return, volatility and jump processes in the Bitcoin (BTC) and Ethereum (ETH) markets. Our main finding indicates that while BTC returns respond significantly to changes in electricity consumption, the effect of electricity consumption on ETH returns is negligible. We attribute this discrepancy to BTC's relative energy inefficiency, which contrasts with ETH's commitment to transitioning to a more energy-efficient mining protocol over our sample period. Additionally, we show that the effect of electricity consumption on cryptocurrencies is mitigated by media coverage and policy uncertainty regarding the state of the economy, environment, and cryptocurrency markets. Our results suggest that when trading cryptocurrencies, investors consider their relative energy efficiency and price electricity consumption differently, highlighting the influence of the transition towards more sustainable energy practices on investor preferences and investment decisions in cryptocurrency markets.

Benefits and challenges in deployment of low global warming potential R290 refrigerant for room air conditioning equipment in California

High global warming potential gases (“high GWP”) are the fastest growing sector of greenhouse gas emissions in the world and in California and are primarily used as refrigerant gases in refrigeration and cooling equipment. Hydrofluorocarbons (HFCs) refrigerants are the dominant type of high GWP gases with GWP values thousands of times larger than CO2 on a 100-year timescale. Refrigerant-grade propane (“R290”) has a very low GWP (GWP = 3.3) with good thermodynamic properties and good cooling equipment performance but the flammability of any leaked refrigerant makes equipment design, handling, and maintenance critical factors to manage. This paper focuses on the potential climate benefits and costs of transitioning to R290 refrigerant in small room air conditioning (AC) units, specifically window AC, packaged terminal AC/heat pumps (PTAC/PTHP), and mini-split heat pumps. Overall climate impact for a transition to all three types of air conditioning units in the 2022–2051 timeframe is found to be from 15 to 64 million metric tons of greenhouse gas (GHG) savings in California with a cost of saved CO2eq that ranges from $14.50 per ton of CO2eq saved to −$50.30 per ton of CO2eq saved (net savings) depending on whether the baseline refrigerant is R32 or R410A and depending on the relative energy efficiency for R290 units compared to baseline units.

Numerical investigation of a THz-driven dielectric accelerator with a Bragg-reflector for accelerating sub-relativistic electron beams

Structure-based novel accelerators exhibit significant potential for substantial reduction in size and associated costs of future accelerators. Utilizing high-power THz sources in dielectric accelerator structures presents a favorable compromise for achieving elevated gradients and alleviating beam injection requirements. We conducted numerical investigations on an energy-efficient dielectric single grating structure accompanied by a Bragg-reflector, employing THz pulses to generate a phase-modified field for accelerating sub-relativistic electron beams. The structural parameters were optimized to enhance the strength of the acceleration field. The simulation results demonstrate that the side-coupling single grating structure, accompanied by a Bragg-reflector, designed for sub-relativistic electron beam acceleration, can increase the relative structural energy efficiency by more than 50% compared to a dual-grating accelerator structure. Moreover, it offers an available maximum acceleration gradient of up to 400 MeV m−1.

Stochastic Energy Performance Evaluation Using a Bayesian Approach

In the past two decades, stochastic frontier analysis (SFA) has been extensively employed to assess energy efficiency. However, the use of the Bayesian approach in SFA for energy performance evaluation has not received significant attention. This study aims to address this gap by measuring the energy-based development performance of 29 OECD countries using stochastic frontier analysis with a Bayesian approach. In the existing literature, there is no apparent method for selecting the distribution of the inefficiency term, which represents the unexplained deviation from the production frontier. To address this issue, we propose different models with various inefficiency components, namely, the half normal, truncated normal, exponential distribution, and gamma distribution. Our analysis utilizes a panel dataset covering the period from 2004 to 2010. The Bayesian implementation of the proposed models is conducted using the WinBUGS package, employing the Markov chain Monte Carlo (MCMC) method. The primary objective of our study is to compare these models, each assuming a different distribution for the inefficiency term, using the deviance information criterion (DIC). The DIC serves as a reliable measure for model comparison and enables us to identify the most suitable model that accurately captures the energy efficiency scores of the countries. Based on the comparison of models with different distributional assumptions using the DIC, we find that the model with a half-normal inefficiency distribution yields the lowest DIC score. Consequently, this model is employed to rank the energy efficiency scores of the countries. In summary, our study fills a research gap by applying the Bayesian approach to SFA in the context of energy efficiency analysis. By proposing and comparing models with different inefficiency components, we contribute to the literature and offer insights into the relative energy efficiency performance of 29 OECD countries. The findings of our study not only inform the selection of an appropriate model but also facilitate the ranking of countries based on their energy efficiency using the identified best model.

Hybrid Relay and Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems

Reconfigurable intelligent surfaces (RISs) are viewed as key enablers for next-generation wireless communications. This paper investigates a multiuser downlink multiple-input single-output (MISO) system in which a multiantenna base station (BS) transmits information to multiple single-antenna users with the aid of both a half-duplex decode-and-forward (DF) relay and a full-duplex RIS. Active beamforming at the BS and the DF relay, as well as passive beamforming at the RIS, are jointly designed for system sum-rate maximization. The design problem is challenging to solve due to coupled beamforming variables. An alternating optimization (AO) based algorithm is proposed to tackle this complex co-design problem. Numerical results demonstrate the superior performance of the proposed hybrid relay–RIS system with a judicious joint beamforming design. Convergence and complexity analysis shows that the convergence rate of the proposed algorithm is dominated by the numbers of users and RIS elements, and the proposed scheme can converge in a few iterations even in the configuration of large numbers of users and RIS elements. Interesting tradeoffs posed in the joint design are discussed. An extension of the proposed design method to a related energy efficiency (EE) optimization problem is also outlined and implemented.

Are SNNs Really More Energy-Efficient Than ANNs? an In-Depth Hardware-Aware Study

Spiking Neural Networks (SNNs) hold the promise of lower energy consumption in embedded hardware due to their spike-based computations compared to traditional Artificial Neural Networks (ANNs). The relative energy efficiency of this emerging technology compared to traditional digital hardware has not been fully explored. Many studies do not consider memory accesses, which account for an important fraction of the energy consumption, use naive ANN hardware implementations, or lack generality. In this paper, we compare the relative energy efficiency of classical digital implementations of ANNs with novel event-based SNN implementations based on variants of the Integrate and Fire (IF) model. We provide a theoretical upper bound on the relative energy efficiency of ANNs, by computing the maximum possible benefit from ANN data reuse and sparsity. We also use the Eyeriss ANN accelerator as a case study. We show that the simpler IF model is more energy-efficient than the Leaky IF and temporal continuous synapse models. Moreover, SNNs with the IF model can compete with efficient ANN implementations when there is a very high spike sparsity, i.e. between 0.15 and 1.38 spikes per synapse per inference, depending on the ANN implementation. Our analysis shows that hybrid ANN-SNN architectures, leveraging a SNN event-based approach in layers with high sparsity and ANN parallel processing for the others, are a promising new path for further energy savings.

PRIMARY OIL REFINING AND HEAT EXCHANGER APPARATUS

Different technological processes are applied in the field of modern oil refining. Each of the processes in turn is implemented by many technological schemes and methods. In petroleum refining and chemical production, these technological processes provide the basis for many simple processes. In this way, the learning of the processes is facilitated. They process oil and its products by applying various processes. Oil refining and its main organic synthesis plants are composed of a collection of various technological processes and general plant farms. Depending on the number and quality of the products received from the factories, great attention is paid to the supply of hardware and equipment. It is through these equipment and devices that technological processes are carried out. Heat exchangers are among the most commonly used devices in the oil refining industry. The most common type of heat exchangers are shell-and-tube heat exchangers. Shell and tube heat exchangers are technological in the oil and gas industry pretreatment to improve properties and reduce viscosity of streams stages are widely used. Distribution of shell-and-tube heat exchanger design reliability and due to the variety of types and versions to work in a wide range of temperature, pressure and corrosive environment. At the current stage of production development, an important trend is increasing the efficiency of the used technological equipment, ensuring high energy efficiency in relation to heat exchange equipment. Flat cross-sections of different configurations are used to organize the cross current in the inter-tube space of the heat exchanger and to increase the cooling speed. The presence of such partitions significantly complicates the flow pattern of the cooling liquid, and is reliable for the flow around the tube bundle. Currently used methods of heat exchanger heat calculation based on criterion dependencies do not take into account all characteristics of the flow. Keywords: Boiler, reactor, heater, furnace, filter, air cooler, electrodehydrator, gasoline, diesel, catalyst, injector, pump, piping, hot flow, cold flow.

Three-dimensional numerical simulation, energy efficiency and economic benefit estimation of oil shale in situ pyrolysis process

Oil shale in situ conversion processing by the downhole burner heating technology (DBHT) has been technically proven as a field pilot project in the Songliao Basin, Northeast China. In this study, the three-dimensional numerical simulations of oil shale in situ pyrolysis with the hexagonal well pattern were conducted based on the idea of DBHT and the actual oil shale reservoir in Songliao Basin to examine the temperature field and thermal processes. The effects of different well spacings (30, 35, 40, and 45 m), gas injection rates (5, 10, 15, and 20 m s−1), and fracture spacings (2.5 and 3.5 m) on the thermal evaluation process were analyzed. The relative energy efficiency and economic viability of operating an oil shale in situ retort in various cases were evaluated. The results showed that increasing well spacing could enlarge the thermal field and improve the ultimate hydrocarbon production of oil shale, but the excessive well spacing caused the heating time to be too long. An increase in the gas injection rate can significantly boost heat transmission efficiency. The energy efficiency comparison of various cases showed that smaller well spacing was conducive to making full use of energy. The results of economic efficiency estimation indicated that a high recovery of shale oil (>80%) would be very difficult to be made with economy, but optimal conditions can be developed in some cases, determined mainly by the price of shale oil. We anticipate that this study can provide new ideas for designing the oil shale in situ conversion processing.

Assessment of space heating consumption efficiency based on a household survey in the hot summer and cold winter climate zone in China

Space heating demand in the hot summer and cold winter (HSCW) climate zone in China has increased rapidly over the past decade. It poses significant challenges for China's electricity supply infrastructure and building decarbonization. This study aims to understand the relative energy efficiency of household space heating and identify potential opportunities to reduce electricity usage at the household level. A stochastic frontier analysis approach is used to quantify the energy efficiency of households' space heating by forming an efficient frontier (minimum requirement of consumption given the characteristics of the household) and by comparing all households against that frontier. The results indicate that the average household space heating efficiency is 0.69. This indicates that households in the HSCW area have the ability to reduce their heating electricity consumption by 31% while still keeping the current level of indoor thermal comfort in the winter. The level of building energy efficiency standards (BEES) is the key factor influencing the space heating savings potential. Thus, Policy choices should focus on revising and optimizing superior BEES, and accelerating energy efficiency retrofits of existing residential buildings in this region.

Clustering and analysis of air source heat pump air heater usage patterns of inhabitants in Qinghai-Tibet Plateau areas

Occupant behavior is one of the most important factors influencing the level of energy consumption in the operational phase of buildings. The unique climatic conditions of the Qinghai-Tibetan plateau differ from those of lower altitude areas. Therefore, to specifically investigate the characteristics of the active heating behavior of the inhabitants of these areas, four households are selected for this study. Each is equipped with a corresponding Air Source Heat Pump Air Heater (ASHP-AH) as their only heating equipment. The occupants' usage patterns and active regulating patterns are analyzed explicitly by clustering the ASHP-AH operational data of four households during the heating season using the K-means algorithm. The use of different time steps to reduce the dimensionality of the data and reduce the time and space complexity of the K-means algorithm in clustering is also discussed, and the Preference Cumulative Value (PCV) and Change Direction Frequency (CDF) are proposed to evaluate changes in the clustering results. These results are potentially relevant to improving energy efficiency in relation to occupant behavior.

Energy Demand and Energy Efficiency in Developing Countries

This paper investigates relative aggregate energy efficiency for a panel of 39 developing countries by econometrically estimating an energy-demand function (EDF) using the stochastic frontier analysis (SFA) approach to provide relative energy efficiency scores over the period 1989 to 2008. Energy efficiency is arguably difficult to define or even conceptualise with several interpretations in the literature but here it is based on an economists’ perspective of efficiency. Hence, the estimates of ‘true’ energy efficiency found in the paper using this approach approximate the economically efficient use of energy capturing both technical and allocative efficiency and the results confirm that energy intensity should not be considered as a de facto standard indicator of energy efficiency. While, by controlling for a range of socio-economic factors, the measurements of energy efficiency obtained by the analysis are deemed more appropriate and hence it is argued that this analysis should be undertaken to avoid potentially misleading advice to policy makers. This study contributes to the literature since it is, as far as is known, the first attempt to apply the benchmarking parametric stochastic frontier technique to econometrically estimate energy efficiency for a large panel of only developing counties around the world. Moreover, the results from such analysis are arguably particularly relevant in a world dominated by environmental concerns, especially in the aftermath of energy price increase as a result of the unrest in Ukraine.

Empirical analysis of machine learning-based energy efficient cloud load balancing architectures: a quantitative perspective

Design of energy efficient load balancing models in cloud environments requires in-depth analysis of the cloud architecture and nature of requests served by the cloud. Depending upon these parameters, machine learning models are designed which aim at assigning best possible resource combination to serve the given tasks. This assignment varies w.r.t. multiple task and cloud parameters; which include task time, virtual machine (VM) performance, task deadline, energy consumption, etc. In order to perform this task, a wide variety of algorithms are developed by researchers cloud designers. Each of these algorithms aim at optimizing certain load balancing related parameters; for instance, a Genetic Algorithm (GA) designed for optimization of VM utilization might not consider task deadline before task allocation to the VMs. While, algorithms aimed at performing deadline aware load balancing might not provide effective cloud-to-task-mapping before allocation of tasks. Thus, it becomes difficult for researchers to select the best possible algorithms for their cloud deployment. In order to reduce this ambiguity, the underlying text compares different energy efficient cloud load balancing algorithms; and evaluates their performance in terms of computational complexity, and relative energy efficiency. This performance evaluation is further extended via inter architecture comparison; in order to evaluate the most optimum load balancer implementation for a given energy efficient application. Thus, after referring this text, researchers and cloud system designers will be able to select optimum algorithmic implementations for their given deployment. This will assist in reducing cloud deployment delay, and improving application specific load balancer performance.

A hybrid approach for energy‐efficient routing in IoT using duty cycling and improved ant colony

AbstractThe promising technology of IoT in the fourth industrial revolution connects everything to the Internet in the digital world of the current era. Though the gigantic connectivity among things through the blend of multifarious technologies offers potential opportunities, it also increases the overhead to optimise the energy consumption in IoT networks. Energy optimisation has become the major concern in the IoT realm because of the continuous sensing of the constrained sensor nodes and data transmission to longer distances. The state‐of‐the‐art studies do not take into account the management of duty‐cycling and the process of efficient route discovery hand in hand. Also, the prevalent studies focus on the management of the sensor node's duty cycling (DC) solely instead of adaptive DC scheduling of the communication unit of the sensor node that consumes more energy during transmission. Therefore, keeping in view the existing tribulations regarding energy consumption, the authors attempt to devise an energy‐efficient routing approach using On‐Demand Duty Cycling and Ant‐Colony optimisation (DC‐ACO) for IoT. The ACO‐based routing approach is applicable in IoT networks because the ants’ environment is conceptualised as a distributed set of interconnected graph nodes. The proposed approach poses the empirical notion to manage the energy consumption of the IoT network by considering the key performance indicators (KPI) like energy consumption, packet delivery rate, average residual energy, mobility factor, distance, throughput, and network lifespan to accomplish the tangible outputs. The proposed approach is modelled using Data flow Diagrams (DFDs) and algorithms supported by results. Experimental results show significant improvement in relative throughput, network lifetime, and energy efficiency by 45%, 78%, and 68%, respectively, after simulating for successive iterations.

Global Multi-Regional Input-Output methodology reveals lower energy footprint in an alternative community project

Identifying the energy needs of citizens and taking into account different lifestyles and patterns of consumption is a first step for a global transformation towards renewable, fair and democratic energy systems. Currently, Total Primary Energy Supply (TPES) is the most widely used metric of energy consumption, which only includes the energy consumed within a country. This research addresses an alternative indicator, Total Primary Energy Footprint (TPEF), which also includes the energy embedded in imported goods and services. The research is innovative in its pioneering combination of a Global Multi-Regional Input-Output (GMRIO) methodology with household budget surveys (HBS) and consumption to production sectorial bridge matrices to calculate TPEF at a small community level. Errekaleor, the largest off-grid alternative intentional community located in Basque Country, Spain, was taken as a case study. The results show, firstly, that alternative communal living can reduce energy consumption. In terms of the specific case study, even if direct residential energy consumption (4.46 MWh·cap −1 ·yr −1 ) was shown to be 32 % and 15 % higher in Errekaleor as compared with Basque and Spanish averages, a TPEF of 31.10 MWh·cap −1 ·yr −1 per capita was determined for the community, 24 % and 14 % below the regional and national averages. Secondly, the relevance of indirect energy embedded in acquired goods and services in determining consumption-based energy use was shown. This accounts for 80.7 % of total consumption in Spain, 74.9 % in the Basque Country, and 66.3 % in Errekaleor. Within Errekaleor, individual arrangements impacted significantly, as people living in families have 33.5 % smaller energy footprints (28.45 MWh·cap −1 ·yr −1 ) than individuals living alone (42.79 MWh·cap −1 ·yr −1 ), who have a TPEF above the Basque average. Thus, the combination of GMRIO and HBS in the analyzed bottom-up case study made an important contribution in terms of clarifying the existing debate about the relative energy efficiency of alternative communities.

Verification of Fuel Consumption and Carbon Dioxide Emissions under Sulfur Restriction Policy during Oceanographic Navigation

The paper presents a comparison of the fuel oil (FO) consumption and carbon dioxide (CO2) emissions of a container ship’s 8000 twenty-foot equivalent unit (TEU) during oceanographic navigation. The evaluation has two types of FOs: a 3.4% heavy fuel oil with desulfurization (HFOWD) and a 0.5% very-low-sulfur fuel oil (VLSFO), based on the sulfur cap policy of the International Maritime Organization (IMO). The results show the average FO consumption at 130 tons/day of HFOWD and 141 tons/day of VLSFO, which means shifting to VLSFO increases fuel consumption 8.4% more than the HFOWD. The average CO2 emissions are 429 tons/day of the HFOWD and 471 tons/day of the VLSFO, indicating an 9.5% increase in CO2 emissions when the IMO adopts the low-sulfur fuel policy. Moreover, the VLSFO blending of various chemicals further deteriorates and wears out the main engine of the ship. IMO’s low-sulfur fuel policy significantly reduced the emission of sulfur oxides (SOX) and particulate matter emissions. Still, we should not ignore the fact that adopting VLSFO may cause more CO2 emissions. Therefore, while switching to low-sulfur fuels, the maritime industry should improve the related energy efficiency to reduce fuel consumption and CO2 emissions.

Dream fusion in octahedral spherical hohlraum

The octahedral spherical hohlraum provides an ideal and practical approach for indirect-drive toward a dream fusion with predictable and reproducible gain and opens a route to the development of a laser drive system for multiple laser fusion schemes. This paper addresses a number of issues that have arisen with regard to octahedral spherical hohlraums, such as how to naturally generate a highly symmetric radiation drive at all times and for all spectra without the use of symmetry tuning technology, how to determine the three-dimensional, temporal, and spectral characteristics of the real radiation drive on a capsule in experiments, and the relative energy efficiency of an octahedral spherical hohlraum compared with a cylindrical hohlraum. A design island for an octahedral spherical hohlraum is presented. Finally, the challenges and future tasks for the path forward are presented.

Method for dynamic thermal insulation design revealing almost real heat transfer characteristics of building envelopes in different Chinese regions

The thermal insulation design for building envelopes in winter is traditionally based on steady-state heat transfer, which is the common ground of related building energy efficiency standards in the world. However, its adaptability may be limited to highly dynamic indoor and outdoor thermal environments, especially due to intense solar radiation. This study aims to explore the influence of climatic conditions on the heat transfer of building envelopes and the dynamic thermal insulation (DTI) design method responding to the local climate in China. The actual heat transfer characteristics of residential building envelopes and related factors were investigated based on field tests in two regions. The results showed that in contrast to Guanzhong Plain in Xi’an, strong alternate variations and differences occurred in the heat transfers of building envelopes at different orientations in Tibetan Plateau in Lhasa due to intense solar radiation and large diurnal temperature range. Hence, a DTI design method based on the dynamic heat transfer characteristics is recommended in these areas to maximize solar energy utilization. The outdoor heating supply degree-hours (OHSDH) and outdoor heating supply duration (OHSD) were developed to analyze DTI suitability zoning for 234 cities and towns in China. The optimal zone was identified as Sichuan-Tibet Plateau in Western China with high OHSD and OHSDH values (over 7 h and 95.64 °C·h, respectively). The study findings may provide references for the development of precise climate-responsive design.

An agenda for future Social Sciences and Humanities research on energy efficiency: 100 priority research questions

Decades of techno-economic energy policymaking and research have meant evidence from the Social Sciences and Humanities (SSH)—including critical reflections on what changing a society’s relation to energy (efficiency) even means—have been underutilised. In particular, (i) the SSH have too often been sidelined and/or narrowly pigeonholed by policymakers, funders, and other decision-makers when driving research agendas, and (ii) the setting of SSH-focused research agendas has not historically embedded inclusive and deliberative processes. The aim of this paper is to address these gaps through the production of a research agenda outlining future SSH research priorities for energy efficiency. A Horizon Scanning exercise was run, which sought to identify 100 priority SSH questions for energy efficiency research. This exercise included 152 researchers with prior SSH expertise on energy efficiency, who together spanned 62 (sub-)disciplines of SSH, 23 countries, and a full range of career stages. The resultant questions were inductively clustered into seven themes as follows: (1) Citizenship, engagement and knowledge exchange in relation to energy efficiency; (2) Energy efficiency in relation to equity, justice, poverty and vulnerability; (3) Energy efficiency in relation to everyday life and practices of energy consumption and production; (4) Framing, defining and measuring energy efficiency; (5) Governance, policy and political issues around energy efficiency; (6) Roles of economic systems, supply chains and financial mechanisms in improving energy efficiency; and (7) The interactions, unintended consequences and rebound effects of energy efficiency interventions. Given the consistent centrality of energy efficiency in policy programmes, this paper highlights that well-developed SSH approaches are ready to be mobilised to contribute to the development, and/or to understand the implications, of energy efficiency measures and governance solutions. Implicitly, it also emphasises the heterogeneity of SSH policy evidence that can be produced. The agenda will be of use for both (1) those new to the energy-SSH field (including policyworkers), for learnings on the capabilities and capacities of energy-SSH, and (2) established energy-SSH researchers, for insights on the collectively held futures of energy-SSH research.

Neural Network Approach to Ranking Factors Affecting Energy Efficiency of Oil Production

The article describes the relevance of the topic, considers the features of approaches to factor analysis of the energy efficiency of the artificial oil lift. Comparative analysis of the approaches used in assessing the current and projected level of energy efficiency in relation to the planned values is carried out. An approach to the ranking of energy efficiency factors based on intelligent methods is proposed. An assessment of the modern possibilities of automating the factor analysis of the energy efficiency of artificial oil lift is given. To develop an effective methodology for the analysis and planning of energy efficiency based on a relevant factor model, an approach to ranking the factors influencing the energy efficiency of artificial lift using an artificial neural network is proposed. When solving the problem, the method of factor analysis of specific power consumption, the L-BFGS algorithm. Various sets of factors are considered, they were ranked according to the shares of significance, the procedure for excluding factors were carried out on the basis of paired correlation dependences between them. A correlation matrix is built for the adjusted set of factors. On the basis of an expert analysis of the results obtained, their relevance to the cause-and-effect relationships manifested in the practice of operating a mechanized well stock was assessed. The use of the proposed approach to factor analysis using an artificial neural network will improve the reliability of control over the energy efficiency of artificial oil lift. The developed model can be included in the algorithmic and software and hardware support of a promising automated energy efficiency control system and can be used in decision-making by specialists who plan, monitor and predict energy efficiency indicators and assess the results of the implementation of energy-saving measures.

Environment and Energy? The Impact of Environmental Management Systems on Energy Efficiency

Problem definition: Despite the widespread adoption of environmental management systems (EMS), their relationship with energy efficiency remains unclear. This study investigates whether and how EMS adoption lowers energy efficiency and, if so, how to mitigate the impact. Academic/practical relevance: Understanding the relationship between EMS and energy efficiency is important because any decrease in energy efficiency may eventually lead to undesired environmental outputs (e.g., CO2), potentially contributing to climate change. This study sheds light on energy as a necessary input required for environmental management, thereby elucidating the issue of environmental trade-offs in sustainable operations. Methodology: Using novel panel data on energy efficiency and EMS standards obtained from 2,690 South Korean plants operating between 2001 and 2014, this study empirically investigates the impact of EMS on energy efficiency at the plant level. Results: We provide evidence of a trade-off relationship between environmental performance and energy efficiency in relation to EMS adoption. We find that the adoption of ISO 14001, the representative EMS standard, results in approximately 6%–12% lower energy efficiency compared with nonadoption although it effectively reduces air, water, and waste pollution. More importantly, our results show that the trade-off, an unintended consequence of EMS implementation, can be moderated through quality management capability. Managerial implications: We suggest that sustainability managers take balancing actions to reduce trade-offs between environmental performance and energy efficiency. Specifically, when implementing EMS, managers are advised to (1) actively incorporate energy management activities, (2) employ more comprehensive measurements for energy efficiency, and (3) redesign incentive schemes to facilitate improvements in energy efficiency.