Achieve Energy Efficiency Research Articles

Performance evaluation and multi-objective optimization of hydrogen-based integrated energy systems driven by renewable energy sources

This study proposes an integrated energy system using hydrogen storage to realize the efficient utilization of renewable energy sources and reduce the fluctuation when renewable energy is connected to grid. The system utilizes solar and wind energy to realize hydrogen production, desalination, and CCHP. First, the energy, exergy, and economic evaluations for the proposed system are carried out, and then an in-depth analysis of the key operating parameters is performed. The system achieves energy efficiency, exergy efficiency, and cost rate of 48.49%, 19.98%, and 7.969$/h, respectively. And the exergy analysis shows that the main exergy destructions are caused by the parabolic trough solar collector and the transcritical CO2 power cycle. The parametric analysis demonstrates when solar radiation flux and wind speed increase, the exergy efficiency and hydrogen production are increased, but the cost rate is increased accordingly. Finally, two sets of multi-objective optimization schemes are performed combining the artificial neural network with the non-dominant genetic algorithm-Ⅱ(NSGA-II). For the optimized fresh water output, cost rate, and exergy efficiency, it is achieving improvements of 51.73%, 8.4%, and 3.6%, and for the optimized hydrogen production, cost rate, and exergy efficiency, it is increased by 12.53%, 0.564%, and 0.75%, respectively.

Innovative passive techniques and PCM integration in building envelopes for enhanced energy efficiency in desert regions

Rising energy demands in buildings for heating, cooling and operating appliances underscore the importance of implementing energy-efficient solutions. Passive cooling techniques provide a sustainable alternative to conventional, energy-intensive cooling systems, promising reduced energy consumption in buildings. Using a dynamic heat transfer model validated with ANSYS Fluent, this study examined three configurations: integrating phase change materials at the brick's centerline with cavities filled with air, EPS insulation, and emissivity coating. Additionally, the benefits of combining all three passive methods were evaluated. Results indicated that among the evaluated PCMs, capric acid demonstrated superior performance. While each passive method studied offers distinct advantages in enhancing thermal performance and achieving energy efficiency, assessing their combined effects revealed significant insights. Specifically, integrating capric acid at the brick's centerline, while simultaneously configuring EPS insulation in the upper cavities and emissivity coating in the lower cavities, yielded superior performance. This configuration significantly reduced inner surface temperature by approximately 6.6 °C and minimized heat flux by about 58.4 % compared to traditional bricks. Achieving energy savings of 56.24 %, this configuration shows potential to enhance both thermal comfort and energy efficiency in building applications.

Awareness of Campus Building Users to Achieve Energy Efficiency in Facing the Implementation of Green Buildings (Case Study: Politeknik Negeri Pontianak Integrated Lecture Building)

Abstract Building users’ awareness is a very crucial factor in efforts to achieve efficient energy use in buildings, especially in the green building concept. However, research that examines the awareness of building users specifically in the campus environment is still very rarely conducted, especially in Indonesia. This research, which uses a case study at Politeknik Negeri Pontianak integrated lecture building, investigates the criteria for awareness of building users and their behavior in using energy in the building. This research tries to identify social parameters in achieving building energy efficiency using a quantitative approach. The series of statistical tools used includes ranking of average values. The results of this research show that the focus of improvement can be done by increasing awareness of several things, namely recognizing the problem of decreasing electrical resources, dividing resource use in order to reduce electricity consumption, and understanding efforts and making preparations in the face of reduced electrical energy sources.

Enhancing Knowledge on Energy Refurbishment of Buildings and Green Procurement through Living Labs

Buildings account for a significant portion of energy consumption and greenhouse gas emissions, underscoring the urgent need for energy refurbishment and green procurement strategies. This study explores the potential of Living Labs, a collaborative and user-centered approach, to bridge knowledge gaps and foster innovation in these areas. The research employed a comprehensive methodology, including stakeholder surveys and statistical analyses, to evaluate knowledge enhancement in various domains such as green policies, mentoring, funding possibilities, and green public procurement. The results demonstrated statistically significant improvements in knowledge across different fields and stakeholder groups. This study contributes to the objectives of the European Green Deal by highlighting the role of Living Labs in advancing knowledge critical to achieving energy efficiency and sustainability targets. Based on these findings, several recommendations are proposed to further leverage the effectiveness of Living Labs in promoting the energy refurbishment of buildings and green public procurement.

Dynamic Modeling and Control Strategy Optimization of a Volkswagen Crafter Hybrid Electrified Powertrain

This article studies the transformation and assembly process of the Volkswagen (VW) Crafter from conventional to hybrid vehicle of the department of vehicles engineering, University of Debrecen, and uses a computer-aided simulation (CAS) to design the vehicle based on the real measurement data (hardware-in-the-loop, HIL method) obtained from an online CAN bus data measurement platform using MATLAB/Simulink/Simscape and LabVIEW software. The conventional vehicle powered by a 6-speed manual transmission and a 4-stroke, 2.0 Turbocharged Direct Injection Common Rail (TDI CR) Diesel engine and the transformed hybrid electrified powertrain are designed to compare performance. A novel methodology is introduced using Netcan plus 110 devices for the CAN bus analysis of the vehicle’s hybrid version. The acquired raw CAN data is analyzed using LabVIEW and decoded with the help of the database (DBC) file into physical values. A classical proportional integral derivative (PID) controller is utilized in the hybrid powertrain system to manage the vehicle consumption and CO2 emissions. However, the intricate nonlinearities and other external environments could make its performance unsatisfactory. This study develops the energy management strategies (EMSs) on the basis of enhanced proportional integral derivative-based genetic algorithm (GA-PID), and compares with proportional integral-based particle swarm optimization (PSO-PI) and fractional order proportional integral derivative (FOPID) controllers, regulating the vehicle speed, allocating optimal torque and speed to the motor and engine and reducing the fuel and energy consumption and the CO2 emissions. The integral time absolute error (ITAE) is proposed as a fitness function for the optimization. The GA-PID demonstrates superior performance, achieving energy efficiency of 90%, extending the battery pack range from 128.75 km to 185.3281 km and reducing the emissions to 74.79 gCO2/km. It outperforms the PSO-PI and FOPID strategies by consuming less battery and motor energy and achieving higher system efficiency.

Economic development through sustainable energy transition: the impact of machine learning-driven energy efficiency in industrial sectors

The transition to sustainable energy systems is a critical component in addressing global environmental challenges and fostering economic development. Industrial sectors, which are major consumers of energy and contributors to greenhouse gas emissions, play a pivotal role in this transition. Machine learning (ML) technologies have emerged as transformative tools for enhancing energy efficiency in these sectors, offering significant potential for economic and environmental benefits. This review explores the impact of ML-driven energy efficiency on economic development, focusing on its application within various industrial contexts. The integration of machine learning into energy management systems allows for advanced predictive analytics, real-time monitoring, and process optimization. ML algorithms can analyze large datasets to forecast energy demand, detect inefficiencies, and optimize operational processes, leading to substantial cost savings and energy reductions. For example, in manufacturing industries, ML-driven solutions have been employed to enhance predictive maintenance, optimize production processes, and reduce energy consumption, resulting in lower operational costs and increased productivity. Similarly, in industrial facilities such as chemical plants and data centers, ML has been used to streamline energy management and minimize waste, demonstrating its versatility and effectiveness across diverse industrial applications. The economic impacts of ML-driven energy efficiency are profound. By reducing energy costs and improving operational performance, industries can achieve significant financial savings, which in turn can be reinvested into further innovations and growth. Additionally, the adoption of ML technologies fosters job creation and skill development in the tech and energy sectors, contributing to broader economic growth. Industries that leverage ML for energy efficiency gain a competitive advantage, positioning themselves favorably in the market and tapping into new opportunities for innovation and expansion. However, the transition to ML-driven energy efficiency is not without challenges. Issues related to data availability and quality, high implementation costs, and industry-specific constraints must be addressed to fully realize the benefits. Policymakers play a crucial role in facilitating this transition by developing supportive policies and regulations that encourage the adoption of ML technologies in energy management. Machine learning offers a transformative approach to achieving energy efficiency in industrial sectors, with significant implications for economic development. By addressing the challenges and leveraging emerging opportunities, industries and policymakers can drive sustainable energy transitions that benefit both the environment and the economy. This review provides a comprehensive analysis of these impacts and offers strategic recommendations for advancing ML-driven energy efficiency in the industrial sector. Keywords: Economic Development, Sustainable Energy, Machine Learning, Industrial Sectors.

Enhancing CO2 conversion in a temperature-controlled DBD plasma reactor with HKUST-1 catalyst: Water removal and CuO/Cu2O-derived approach

Due to a synergy effect, using dielectric barrier discharge (DBD) plasma technology combined with catalysts for CO2 decomposition, a major contributor to global warming, is recognized as an effective approach to transforming CO2 into valuable products such as CO, which is a crucial feedstock for chemical synthesis. Herein, HKUST-1 was synthesized using the hydrothermal method for CO2 conversion in the DBD reactor. To enhance catalyst performance in the plasma region, HKUST-1 was treated with O2 plasma to increase its specific surface area. Additionally, HKUST-1 was calcined at 300 °C to produce the CuO/Cu2O catalyst. Since the recombination reaction in the CO2 conversion is increased at higher temperatures, the reactor heat is removed using fan cooling and a water circulation system. In the presence of the HKUST-1 catalyst, the CO2 conversion rate significantly increased by 132 %, 82 %, and 12 % in reactors operating without cooling, with fan cooling, and with water cooling circulation, respectively compared to the reactors without catalyst, at a flow rate of 50 ml/min and maximum input power. The catalysts have been characterized using a comprehensive suite of analytical techniques, including FTIR, XRD,TEM, SEM, EDS, and BET analysis. The BET analysis indicates that the specific surface area of HKUST-1 after O2 plasma treatment is increased by 52 %, which causes an increasing conversion rate of up to 18 %. The CuO/Cu2O catalyst demonstrated maximum CO2 conversion of 21 % at an input power of 140 W and achieved energy efficiency of 8.6 % at 40 W. The presence of oxygen vacancies within this catalyst enhances the process of CO2 decomposition.

Maximizing flow battery membrane performance via pseudo-nanophase separation enhanced by polymer supramolecular sidechain

Pseudo-nanophase separation, enabled by noncovalently grafted sidechains, offers a promising approach for constructing high-performance membranes, featuring rapid ion transport, robust chemical stability, and simplified manufacturing. However, striking a balance between ionic conductivity and mechanical/chemical stability proves challenging since excessive hydrophilic grafting leads to overswelling and compromised integrity of the membranes, rendering them unsuitable for demanding applications like vanadium redox flow batteries (VRFBs). In this study, we describe a new approach for achieving high-performance VRFB membranes via employing polymer as supramolecular sidechains, rather than small molecules. This strategy achieves remarkable pseudo-nanophase separation while minimizing the utilization of functional (hydrophilic) sites. As a result, the resulting membranes exhibit exceptional robustness and proton conductivity, with an extraordinarily low area resistance of merely 0.11 Ω cm2, thus circumventing the prevailing trade-off between ionic conductivity and mechanical/chemical stability. Ultimately, VRFBs integrated with these membranes achieve energy efficiencies up to 80 % even at high current densities of 240 mA cm−2, accompanied by a remarkably low capacity decay rate of 0.064 % per cycle during long-cycle tests. This work not only achieves ultra-high conductivity with minimal functional groups, but also advances pseudo-nanophase separation strategies and provides valuable insights into optimized utilization of limited functional groups in membrane design.

Investigating daylighting quality in educational spaces following building renovation

Abstract Daylighting is essential to the well-being of occupants in educational spaces. It increases students’ performance and provides an opportunity for achieving energy efficiency, as well as indoor visual and thermal comfort. However, daylight performance is rarely accounted for when taking decisions attributed to building renovation. This results in increased or decreased daylighting lighting levels and glare which jeopardizes the functional requirements of the space. Hence, this study adopted a mixed qualitative/quantitative approach to compare two recently renovated educational spaces (architectural design studio drawing halls) in terms of the effect of renovation activities on daylighting and visual comfort, noting that both spaces were located in the same building and subject to similar prevailing climatic conditions. This included a sequence of site measurements, surveys and simulations. The results showed great variation between both studied spaces in terms of daylight efficiency and visual comfort. Some design proposals were tested in order to deduct learnt lessons for proper accounting of daylight during building renovation plans.

Large-scale energy cost optimization and performance analysis for dedicated outdoor air system: simulation results from ASHRAE RP-1865

This is the first journal article from the ASHRAE research project RP 1865, “Optimizing Supply Air Temperature Control for Dedicated Outdoor Air Systems,” focusing on the large-scale energy performance evaluation of the optimal control of dedicated outdoor air systems (DOAS). DOAS is a type of air handling system that is installed in buildings to ensure proper ventilation for the built environment. It is acknowledged that DOAS with proper operation strategies can provide sufficient ventilation and dehumidification while achieving energy efficiency. In this regard, there have been efforts to develop advanced control sequences (e.g., optimal control) for DOAS. Still, limited studies have demonstrated the effectiveness of such DOAS controls on a larger scale under varying climate zones with different DOAS configurations. To fill the research gap, this study aims to quantify the energy-saving potentials of applying an optimal supply air temperature (SAT) control strategy for DOAS considering diverse configurations of HVAC systems in various climate conditions across the United States. For this study, we first developed a total of 180 EnergyPlus models accommodating different types of buildings (i.e., detailed medium-sized office and primary school) and HVAC systems (i.e., DOAS types, DOAS sizes, and terminal units) under varying ASHRAE climate zones (i.e., 2A, 3B, 4A, 4C, 6A, and 6B). Next, genetic algorithm (GA)-based optimizations were conducted to determine the optimal DOAS SAT control that would minimize the energy cost of the entire HVAC system operation. Our study of comparing the optimal control to a typical rule-based control (i.e., outdoor air temperature-based reset control) revealed approximately 4–34% of energy cost saving potential by optimizing the DOAS SAT control sequence.

Enhancing corrosion resistance and heat transfer performance of fin-tube heat exchangers for closed-type heat-source towers by superhydrophobic coatings

Heat-source towers are essential components in heat-source tower heat pump systems, pivotal for achieving energy efficiency. Enhancing corrosion resistance and heat transfer performance for heat-source towers is important but challengeable. For solving this problem, this study aims to introduce a novel superhydrophobic (SHP) coating applied via a one-step electrodeposition method to enhance both corrosion resistance and heat transfer performance of fin-tube heat exchangers in closed-type heat-source towers. The superhydrophobic nature of the coating is validated by contact angle measurements, while its structural integrity and chemical composition are confirmed through various methods i.e., X-ray diffraction, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and scanning electron microscopy. Corrosion resistance tests indicate a significant improvement, with the coating showing a corrosion current density of 1.400 × 10−6 A/cm2 and a low-frequency modulus |Z|0.01 Hz of 1.50 × 10⁴ Ω cm2 after 30 days in a 15 wt% calcium chloride solution at 0 °C. Additionally, heat transfer experiments demonstrate a 42.5 % increase in heat transfer efficiency compared to uncoated fins under identical conditions, which present the significant heat transfer performance of the presently developed SHP coating. The key contribution of this research lies in the development and application of a novel SHP coating that significantly enhances the performance of heat-source towers. Moreover, the present findings suggest that implementing this SHP coating can substantially improve the durability and efficiency of heat-source towers, supporting the advancement of more sustainable building infrastructures.

THE IMPACT OF DIGITALIZATION ON THE IMPLEMENTATION OF CARBON-FREE ECONOMY PRINCIPLES IN UKRAINE

This study explores the role of digitalization in implementing carbon-free economy principles in Ukraine, with a particular focus on how digital technologies contribute to enhancing energy efficiency and reducing CO2 emissions. Digitalization serves as a critical enabler of sustainable development by providing tools for optimizing energy consumption, improving resource management, and minimizing the environmental footprint of economic activities. Through correlation and regression analyses, the research investigates the relationship between investments in digital technologies, environmental protection measures, and CO2 emissions for the period from 2007 to 2020. The results reveal that while digitalization has the potential to improve energy efficiency, its current impact on reducing CO2 emissions remains limited. The findings indicate that increased investments in renewable energy sources and more comprehensive integration of digital technologies into the energy sector are essential to achieve significant reductions in emissions. Additionally, the study highlights the positive effect of innovation-driven activities on energy efficiency but points to the need for further development and deployment of environmentally friendly digital solutions. The research also underscores the importance of continued financial support for green technologies and emphasizes the necessity of improving greenhouse gas monitoring systems. Practical recommendations include increasing investment in renewable energy, expanding the use of smart technologies across industries, and promoting long-term innovation strategies aimed at achieving carbon neutrality. Overall, the research concludes that digital innovation plays a supporting role in achieving energy efficiency but requires stronger policy frameworks, investment, and coordination to realize its full potential in contributing to environmental sustainability.

Experimental Investigation of R404A Indirect Refrigeration System Applied Internal Heat Exchanger: Part 1—Coefficient of Performance Characteristics

In this study, the performance characteristics of an R404A indirect refrigeration system (IRS) applied to an internal heat exchanger (IHX) is evaluated for supermarkets and hypermarkets. In a direct expansion system, R404A is the primary refrigerant and R744, a brine, is the secondary fluid. While there are abundant studies analyzing the theoretical performance of IRS, experimental research on IRS is lacking, and there are no papers that address the results of changes in the IHX in detail. In this study, the results achieved by modifying various parameters are experimentally evaluated to provide fundamental data for designing the optimal IRS. In the main results, looking at the trend of the increase in IHX efficiency, the change is very minimal when the efficiency is above 50%, so it is ideal to apply an IHX efficiency of about 50% considering economics and COP, etc. Applying the results in this study enables the operation and maintenance of IRSs as an eco-friendly system by achieving energy efficiency through optimizing the system coefficient of performance and securing economic feasibility by minimizing the R404A charging amount of the refrigeration cycle. To serve supermarkets and hypermarkets, R744 as a secondary fluid may help to realize an ecologically friendly, compact IRS system with a high heat transfer coefficient that can operate at low temperatures (−35 to 5 °C).

Measuring effects of insulation renewal on heating energy and indoor thermal environment in urban residence of hot-summer/cold-winter region, China

Optimizing thermal insulation in residential buildings is crucial for energy efficiency, especially in Hot Summer and Cold Winter (HSCW) region. Current research often neglects retrofitting and real-life measurements, relying instead on simulations. This study conducted insulation retrofitting on a Chengdu high-rise residential unit, focusing on the flexible heating habits locally. The insulation renewal work involved transforming the thermal insulation structure by adding insulation to all internal surfaces of the rooms' opaque structure, including external walls, internal walls, ceilings, and floors. Experiments based on residents' routines quantified energy savings and improved indoor thermal conditions under intermittent heating. Results showed a significant decrease in daily heating energy consumption by 45.10 % on average, with a maximum of 55.53 %. The retrofitted room's energy-saving rate stabilized between 50 % and 55 % after the second hour. Indoor temperatures in the retrofitted bedroom increased by 0.7 °C on average and up to 1.4 °C. Raising the heating set temperature by 2 °C resulted in a 1.1 °C increase in the retrofitted room, higher than in the non-retrofitted one. These findings provide practical insights for renovating existing residences to achieve energy efficiency in the HSCW region.

A comparative study of DQN and D3QN for HVAC system optimization control

Ensuring the optimal performance of Heating, Ventilation, and Air Conditioning (HVAC) systems is paramount for achieving energy efficiency. This paper investigates the application of deep reinforcement learning algorithms in HVAC system control, aiming to identify the most suitable Q-network structure for optimizing HVAC systems and comparing the performance of Deep Q-learning (DQN) and Double Dueling Deep Q-learning (D3QN) algorithms. Initially, this paper evaluates and analyses existing literature to perform a normalization treatment on the state space. Through systematic simulation and rigorous data analysis, the impact of the Q-network structure on the efficacy of the DQN and D3QN algorithms is evaluated, resulting in the proposal of specific values for the Q-network structures within these two algorithms. Subsequently, comparisons are drawn on the optimization effectiveness, stability and reliability of these algorithms across diverse engineering projects. Results highlight the superiority of the D3QN algorithm over the DQN algorithm regarding both optimization effectiveness and stability across all evaluated projects. The proposed efficient Q-network structure comprises two hidden layers, with 64 and 12 neurons respectively in each layer. The findings of this paper are crucial in providing insights for HVAC systems control optimization using reinforcement learning and pave the way for advanced research and applications in the future.

Achieving Energy Efficiency in AI: Balancing Performance in Machine Learning Models

Achieving Energy Efficiency in AI: Balancing Performance in Machine Learning Models

Study on energy-saving applications of vacuum ejectors across various operational demands

Vacuum suction technology is a pivotal innovation within the Industry 4.0 revolution. Ejectors serve as the core of vacuum systems with distinct advantages such as simple structure, low cost, and ease of operation. However, traditional fixed ejectors suffer from low efficiency, energy waste, and poor adaptability. This study aims to optimize the design of vacuum ejectors to meet various requirements, achieving energy efficiency and high performance in automated production. On this basis, an energy-saving vacuum system based on dual ejectors is proposed, which can switch between single and dual ejectors depending on different working stages. When the vacuum reaches 90 kPa, the required motive pressure and the entrainment ratio are selected as the screening indicators for the vacuum ejector. A two-dimensional axisymmetric model is established and verified through experimentation. The key parameters are identified through a single-factor analysis. A multi-factor analysis is employed to determine the recommended values for each parameter and their respective applications. The energy-saving effects of the optimized ejectors are evaluated in conjunction with the automated processes. The findings indicate that the vacuum system based on dual ejectors can achieve rapid response and low air consumption. This paper contributes to advancing the research on energy-saving applications of ejectors.

Energy efficiency in smart schools using renewable energy strategy

As smart schools increasingly rely on technology, achieving energy efficiency becomes crucial for cost reduction and sustainability. This study investigates energy efficiency strategies in smart schools, focusing on the integration of renewable energy technologies. A quantitative approach using numerical simulations and literature reviews establishes benchmarks for energy-efficient smart schools. The Design Builder software is employed to evaluate system performance, with validation achieved through analysis in the System Advisor Model software. The modeled smart school building in Design Builder consumes 75,385.63 kWh annually, based on the weather conditions of specific location. Further studies indicate that integrating photovoltaics and hot water collectors can generate approximately 86,635 kWh annually. This not only offsets the energy consumption of building but also produces an excess of 11,249 kWh, which can be transferred back to the grid for additional revenue. Validation using SAM software demonstrated a minimal difference (3.2%) in annual energy outputs, confirming the accuracy of the model. The findings suggest that photovoltaics and hot water collectors can significantly contribute to achieving net-zero energy consumption in smart school buildings. Additionally, a focus on rooftop installations promotes sustainability by minimizing land use.

Energy efficiency optimization for 6G multi-IRS multi-cell NOMA vehicle-to-infrastructure communication networks

Intelligent Reflecting Surfaces (IRS), software-controlled metasurfaces, have emerged as an upcoming sixth-generation (6G) wireless communication technology. IRS intelligently manipulates and optimizes signal propagation using a large-scale array of intelligent elements, enhancing signal coverage, increasing capacity, mitigating path loss, and combating multipath fading This work provides a new energy-efficiency model for multi-IRS-assisted multi-cell non-orthogonal multiple access (NOMA) vehicular to infrastructure communication networks. The objective is the joint optimization of the total power budget at the roadside unit (RSU), NOMA power allocation for the user equipment, and designing phase shifts for IRS in each cell to maximize the achievable energy efficiency of the system. Due to non-convexity, the original non-convex problem is first decoupled and transformed using block coordinate descent and successive convex approximation methods. Then, an efficient solution is achieved using Gradient-based and interior-point methods. We also consider two benchmark schemes: (1) NOMA power optimization at RSU with random phase shift design at IRS and (2) orthogonal multiple access power allocation with optimal phase shift design at IRS. Numerical results show the superiority of the proposed solution compared to the benchmark schemes. The proposed solution outperforms the benchmarks, demonstrating a 59.57% and 151.21% improvement over the NOMA and orthogonal schemes, respectively, at pct=2 dBm. Additionally, it shows up to a 10.43% better performance than OMA at 10 IRS elements.

The Green Trilemma: Energy Efficiency, Banking Stability and Climate Risk in the Environmental, Social and Governance Context at World Level

In the following article, we analyse the relationships among banking stability, the efficiency of the energy system and climate risks at a global level. We present a detailed analysis of the literature relating to the relationship between the banking system and Environmental, Social and Governance (ESG) models. In our research, we try to verify whether it is possible to achieve energy efficiency, stability of the banking system and reduction of climate risk together, i.e. the “Green Trilemma”. The econometric analysis is conducted through the following models: Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squared and Weighted Least Squared-WLS. To estimate the variables, we used World Bank data. The analysis shows that ESG growth is negatively associated with energy efficiency and positively associated with banking stability and climate risk. It therefore follows that the Green Trilemma hypothesis is rejected. Countries can only target banking stability and climate risk through ESG models.