A PM2.5 pollution-level adaptive air filtration system based on elastic filters for reducing energy consumption.

Exacerbated by human activities and natural events, air pollution poses severe health risks, requiring effective control measures to ensure healthy living environments. Traditional filtration systems that employ high-efficiency particulate air (HEPA) filters are capable of effectively removing particulate matter (PM) in indoor environments. However, these systems often work without considering the fluctuations in air pollution levels, leading to high energy consumption. This study proposed a novel PM2.5 pollution-level adaptive air filtration system that combined elastic thermoplastic polyurethane (TPU) filters and an Internet of Things (IoT) system. The developed system can effectively adjust its filtration performance (i.e., pressure drop and PM2.5 filtration efficiency) in response to real-time air quality conditions by mechanically altering the structures of TPU filters. Furthermore, while operating in varied pollution conditions, the proposed system demonstrated remarkable reductions in pressure drop without notably compromising the pollution control capability. Finally, the energy consumption of the pollution-level adaptive air filtration system was estimated when applied in mechanical ventilation systems in different cities (Hong Kong, Beijing, and Xi'an) with various pollution conditions. The results revealed that, compared to a traditional fixed system, the annual energy consumption could be reduced by up to ∼26.4 % in Hong Kong.

Wind gradient exploitation during foraging flights by black skimmers (Rynchops niger).

Birds commonly exploit environmental features such as columns of rising air and vertical windspeed gradients to lower the cost of flight. These environmental subsidies may be especially important for birds that forage via continuous flight, as seen in black skimmers. These birds forage through a unique behavior, called skimming, where they fly above the water surface with their mandible lowered into the water, catching fish on contact. Thus, their foraging flight incurs costs of moving through both air and water. Prior studies of black skimmer flight behavior have focused on reductions in flight cost due to ground effect, but ignored potential beneficial interactions with the surrounding air. We hypothesized a halfpipe skimming strategy for skimmers to reduce the foraging cost by taking advantage of the wind gradient, where the skimmers perform a wind gradient energy extraction maneuver at the end of a skimming bout through a foraging patch. Using video recordings, wind speed and wind direction measurements, we recorded 70 bird tracks over 4 days at two field sites on the North Carolina coast. We found that while ascending, the skimmers flew more upwind and then flew more downwind when descending, a pattern consistent with harvesting energy from the wind gradient. The strength of the wind gradient and flight behavior of the skimmers indicate that the halfpipe skimming strategy could reduce foraging cost by up to 2.5%.

Boosting urea-assisted water splitting over P-MoO2@CoNiP through Mo leaching/reabsorption coupling CoNiP reconstruction.

Combining the urea oxidation reaction (UOR) with the hydrogen evolution reaction (HER) is an effective technology for energy-saving hydrogen production. Herein, a bifunctional electrocatalyst with CoNiP nanosheet coating on P-doped MoO2 nanorods (P-MoO2@CoNiP) is obtained via a two-step hydrothermal followed a phosphorization process. The catalyst demonstrates exceptional alkaline HER performance due to the formation of MoO2 and the dissolution/absorption of Mo. Meanwhile, the inclusion of Co and P in the P-MoO2@CoNiP catalyst facilitated the formation of NiOOH, enhancing UOR performance. Density functional theory calculations reveal that the hydrogen adsorption Gibbs free energy (ΔGH*) of P-MoO2@CoNiP is closer to 0 eV than CoNiP, favoring the HER. The catalyst only needs -0.08 and 1.38 V to reach 100 mA cm-2 for catalyzing the HER and UOR, respectively. The full urea electrolysis system driven by P-MoO2@CoNiP requires 1.51 V to achieve 100 mA cm-2, 120 mV lower than the traditional water electrolysis.

New Perspective to Evaluate the Carbon Offsetting by Urban Blue-Green Infrastructure: Direct Carbon Sequestration and Indirect Carbon Reduction.

Urban blue-green infrastructure (BGI) offers a multitude of ecological advantages to residents, thereby playing a pivotal role in fortifying urban resilience and fostering the development of climate-resilient cities. Nonetheless, current research falls short of a comprehensive analysis of BGI's overall potential for carbon reduction and its indirect carbon reduction impact. To fill this research gap, we utilized the integrated valuation of ecosystem services and trade-offs model and remote sensing estimation algorithm to quantify the direct carbon sequestration and resultant indirect carbon reduction facilitated by the BGI within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) (China). To identify the regions that made noteworthy contributions to carbon offsets and outliers, spatial autocorrelation analysis was also employed. The findings of this study reveal that in 2019, the BGI within the study area contributed an overall carbon offset of 1.5 × 108 t·C/yr, of which 3.5 × 107 and 11.0 × 107 t·C/yr were the result of direct carbon sequestration and indirect carbon reduction, respectively. The GBA's total CO2 emissions were 1.1 × 108 t in 2019. While the direct carbon sequestration offset 32.0% of carbon emissions, the indirect carbon reduction mitigated 49.9% of potential carbon emissions. These results highlight the critical importance of evaluating BGI's indirect contribution to carbon reduction. The findings of this study provide a valuable reference for shaping management policies that prioritize the protection and restoration of specific areas, thereby facilitating the harmonized development of carbon offset capabilities within urban agglomerations.

Preparation of Thermochromic Vanadium Dioxide Films Assisted by Machine Learning.

In recent years, smart windows have attracted widespread attention due to their ability to respond to external stimuli such as light, heat, and electricity, thereby intelligently adjusting the ultraviolet, visible, and near-infrared light in solar radiation. VO2(M) undergoes a reversible phase transition from an insulating phase (monoclinic, M) to a metallic phase (rutile, R) at a critical temperature of 68 °C, resulting in a significant difference in near-infrared transmittance, which is particularly suitable for use in energy-saving smart windows. However, due to the multiple valence states of vanadium ions and the multiphase characteristics of VO2, there are still challenges in preparing pure-phase VO2(M). Machine learning (ML) can learn and generate models capable of predicting unknown data from vast datasets, thereby avoiding the wastage of experimental resources and reducing time costs associated with material preparation optimization. Hence, in this paper, four ML algorithms, namely multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), were employed to explore the parameters for the successful preparation of VO2(M) films via magnetron sputtering. A comprehensive performance evaluation was conducted on these four models. The results indicated that XGB was the top-performing model, achieving a prediction accuracy of up to 88.52%. A feature importance analysis using the SHAP method revealed that substrate temperature had an essential impact on the preparation of VO2(M). Furthermore, characteristic parameters such as sputtering power, substrate temperature, and substrate type were optimized to obtain pure-phase VO2(M) films. Finally, it was experimentally verified that VO2(M) films can be successfully prepared using optimized parameters. These findings suggest that ML-assisted material preparation is highly feasible, substantially reducing resource wastage resulting from experimental trial and error, thereby promoting research on material preparation optimization.

Artificial-intelligence-model to optimize biocide dosing in seawater-cooled industrial process applications considering environmental, technical, energetic, and economic aspects.

This research introduces an Artificial Intelligence (AI) based model designed to concurrently optimize energy supply management, biocide dosing, and maintenance scheduling for heat exchangers. This optimization considers energetic, technical, economic, and environmental considerations. The impact of biofilm on heat exchangers is assessed, revealing a 41% reduction in thermal efficiency and a 113% increase in flow frictional resistance of the fluid compared to the initial state. Consequently, the pump's power consumption, required to maintain hydraulic conditions, rises by 9%. The newly developed AI model detects the point at which the heat exchanger's performance begins to decline due to accumulating dirt, marking day 44 of experimentation as the threshold to commence the antifouling biocide dosing. Leveraging this AI model to monitor heat exchanger efficiency represents an innovative approach to optimizing antifouling biocide dosing and reduce the environmental impact stemming from industrial plants.

Coupling methanol oxidation with CO2 reduction: A feasible pathway to achieve carbon neutralization.

The energy consumption of up to 90 % of the total power input in the anodic oxygen evolution reaction (OER) slows down the implementation of electrochemical CO2 reduction reaction (CO2RR) to generate valuable chemicals. Herein, we present an alternative strategy that utilizes methanol oxidation reaction (MOR) to replace OER. The iron single atom anchored on nitrogen-doped carbon support (Fe-N-C) use as the cathode catalyst (CO2RR), low-loading platinum supported on the composites of tungsten phosphide and multiwalled carbon nanotube (Pt-WP/MWCNT) use as the anode catalyst (MOR). Our results show that the Fe-N-C exhibits a Faradaic selectivity as high as 94.93 % towards CO2RR to CO, and Pt-WP/MWCNT exhibits a peak mass activity of 544.24 mA mg-1Pt, which is 5.58 times greater than that of PtC (97.50 mA mg-1Pt). The well-established MOR||CO2RR reduces the electricity consumption up to 52.4 % compared to conventional OER||CO2RR. Moreover, a CO2 emission analysis shows that this strategy not only saves energy but also achieves carbon neutrality without changing the existing power grid structure. Our findings have crucial implications for advancing CO2 utilization and lay the foundation for developing more efficient and sustainable technologies to address the rising atmospheric CO2 levels.

Large-Scale Engineerable Films Tailored with Cellulose Nanofibrils for Lighting Management and Thermal Insulation.

Fibrillated cellulose-based nanocomposites can improve energy efficiency of building envelopes, especially windows, but efficiently engineering them with a flexible ability of lighting and thermal management remains highly challenging. Herein, a scalable interfacial engineering strategy is developed to fabricate haze-tunable thermal barrier films tailored with phosphorylated cellulose nanofibrils (PCNFs). Clear films with an extremely low haze of 1.6% (glass-scale) are obtained by heat-assisted surface void packing without hydrophobization of nanocellulose. PCNF gel cakes serve here as templates for surface roughening, thereby resulting in a high haze (73.8%), and the roughened films can block heat transfer by increasing solar reflection in addition to a reduced thermal conduction. Additionally, obtained films can tune distribution of light from visible to near-infrared spectral range, enabling uniform colored lighting and inhibiting localized heating. Furthermore, an integrated simulation of lighting and cooling energy consumption in the case of office buildings shows that the film can reduce the total energy use by 19.2-38.1% under reduced lighting levels. Such a scalable and versatile engineering strategy provides an opportunity to endow nanocellulose-reinforced materials with tunable optical and thermal functionalities, moving their practical applications in green buildings forward.

Energy-saving hydrogen production from sulfion oxidation-hybrid seawater splitting enabled by superwettable corrosion-resistant NiFe layered double hydroxide/FeNi2S4 heterostructured nanoarrays.

Electrochemical seawater splitting is a sustainable pathway towards hydrogen production independent of scarce freshwater resources. However, the high energy consumption and harmful chlorine-chemistry interference still pose major technological challenges. Herein, thermodynamically more favorable sulfion oxidation reaction (SOR) is explored to replace energy-intensive oxygen evolution reaction (OER), enabling the dramatically reduced energy consumption and the avoidance of corrosive chlorine species in electrocatalytic systems of NiFe layered double hydroxide (LDH)/FeNi2S4 grown on iron foam (IF) substrate. The resulting NiFe-LDH/FeNi2S4/IF with superwettable surfaces and favorable heterointerfaces can effectively catalyze SOR and hydrogen evolution reaction (HER), which greatly reduces the operational voltage by 1.05 V at 50 mA cm-2 compared to pure seawater splitting and achieves impressively low electricity consumption of 2.33 kW h per cubic meter of H2 at 100 mA cm-2. Significantly, benefitting from the repulsive effect of surface sulfate anions to Cl-, the NiFe-LDH/FeNi2S4/IF exhibits outstanding long-term stability for SOR-coupled chlorine-free hydrogen production with sulfion upcycling into elemental sulfur. The present study uncovers the "killing two birds with one stone" effect of SOR for energy-efficient hydrogen generation and value-added elemental sulfur recovery in seawater electrolysis without detrimental chlorine chemistry.

Exploring online consumer behavior on fraudulent energy-saving products.

Purchasing energy-saving products is key for public participation in energy conservation and sustainable development. However, the sale of fraudulent energy-saving products has boomed through online shopping, with little research on these products and consumer demands. This study explored the underlying factors driving consumer purchases of fraudulent energy-saving products and measured their impact on environmental awareness. Sales data for such products from four major online shopping platforms were collected. Results suggested unique demand characteristics from consumers who unknowingly purchase fraudulent energy-saving products, referred to as "hidden energy savers", including a preference for moderately priced products, a desire for straightforward energy-saving explanations, and a tendency to seek multiple additional features, even if they conflict with the core functionality. Perceived installation and usage difficulty significantly influences purchasing behavior. A practical survey of freight companies and individual transporters' demand for freight energy-saving products was conducted as a case study to validate the practical application of this research. This study presents a novel perspective on public energy-saving behavior, aiding in creating true energy-saving products, boosting public energy conservation interest, and reducing the negative impact of fraudulent products on environmental awareness. It also sheds light on hidden consumer needs, guiding the development of authentic energy-saving products.

Residual energy use and energy efficiency improvement of European supermarket facilities during the post-COVID and energy crisis period.

Supermarkets are significant consumers of electricity and contribute to the generation of associated pollutant emissions. This will help to mitigate the impact of increased energy costs on the prices of products sold in supermarkets. Therefore, it is essential to reduce energy consumption, starting with the equipment that consumes the most electricity, such as refrigeration, and using the residual thermal energy generated in supermarkets. This paper discusses the impact of rising energy costs in the post-Covid era and during the energy crisis. It evaluates the environmental and energy benefits of implementing energy improvements and utilizing residual energy in real supermarkets. The analysis takes into account the socio-economic characteristics of the EU-27 countries, which affect the economic feasibility of these measures. This would prevent the release of 122 tons of CO2 per year for each supermarket, resulting in energy savings of around 70 % or 305 kWh/m2. The required investments would have a payback period of 4 years.

Transforming waste cellulosic fabric dyed with Reactive Yellow C4GL into value textile by a microwave assisted energy efficient system of color stripping.

A million ton of cotton fabric is wasted during cutting process in garment industry as well as in textile dyeing industry due to faulty dyeing. Color stripping of cotton fabric has become a significant challenge in the textile industry because the harsh chemicals used in chemical stripping processes affects the quality of fabric very badly. Conventional stripping methods lead with severe effects due to prolonged treatment time and high chemical concentrations. Recently, microwave-assisted stripping techniques have been emerged as effective alternatives to improve stripping efficiency. In this research, the developed microwave assisted stripping system is improved by the application of Urea, which is utilized as a microwave absorber to further reduce stripping time, temperature, and chemical concentration kept focus on quality parameters of recycled cotton fabric. This study inspects the efficiency of microwave absorber-assisted alkali hydrolysis and reduction in terms of dye-fabric bond cleavage, chromophores removal, chemical consumption, and processing time and compared with sequential stripping, microwave assisted stripping without absorber and conventional methods. The results indicated that microwave absorber-assisted alkali hydrolysis and reduction achieved 90 % stripping efficiency by using lowest concentrations of chemicals, while sequential stripping yielded a stripping efficiency of 96 %. Similarly, microwave absorber assisted methods resulted in minor loss in tear strength and weight. These outputs highlight the superior performance of microwave absorber-assisted techniques, demonstrating their efficiency, novelty, time-saving nature, and reduced damage compared to other methods.

Hydrogen doping control method for gasoline engine acceleration transient air-fuel ratio.

One of the primary contributors to automobile exhaust pollution is the significant deviation between the actual and theoretical air-fuel ratios during transient conditions, leading to a decrease in the conversion efficiency of three-way catalytic converters. Therefore, it becomes imperative to enhance fuel economy, reduce pollutant emissions, and improve the accuracy of transient control over air-fuel ratio (AFR) in order to mitigate automobile exhaust pollution. In this study, we propose a Linear Active Disturbance Rejection Control (LADRC) Hydrogen Doping Compensation Controller (HDC) to achieve precise control over the acceleration transient AFR of gasoline engines. By analyzing the dynamic effects of oil film and its impact on AFR, we establish a dynamic effect model for oil film and utilize hydrogen's exceptional auxiliary combustion characteristics as compensation for fuel loss. Comparative experimental results demonstrate that our proposed algorithm can rapidly regulate the AFR close to its ideal value under three different transient conditions while exhibiting superior anti-interference capability and effectively enhancing fuel economy.

Stable Photo-Rechargeable Al Battery for Enhancing Energy Utilization.

Photovoltaic cells (PVs) are able to convert solar energy to electric energy, while energy storage devices are required to be equipped due to the fluctuations of sunlight. However, the electrical connection of PVs and energy storage devices leads to increased energy consumption, and thus energy storage ability and utilization efficiency are decreased. One of the solutions is to explore an integrated photoelectrochemical energy conversion-storage device. Up to date, the integrated photo-rechargeable Li-ion batteries often suffer from unstable photo-active materials and flammable electrolytes under illumination, with concerns in safety risks and limited lifetime. To address the critical issues, here a novel photo-rechargeable aluminum battery (PRAB) is designed with safe ionic liquid electrolytes and stable polyaniline photo-electrodes. The integrated PRAB presents stable operation with an enhanced reversible specific capacity ≈191% under illumination. Meanwhile, a simplified continuum model is established to provide rational guidance for designing electrode structures along with a charging/discharging strategy to meet the practical operation conditions. The as-designed PRAB presents an energy-saving efficiency ≈61.92% upon charging and an energy output increment ≈31.25% during discharging under illumination. The strategy of designing and fabricating stable and safe photo-rechargeable non-aqueous Al batteries highlights the pathway for substantially promoting the utilization efficiency of solar energy.

Energy supply during nocturnal endurance flight of migrant birds: effect of energy stores and flight behaviour.

BACKGROUND: Migrating birds fly non-stop for hours or even for days. They rely mainly on fat as fuel complemented by a certain amount of protein. Studies on homing pigeons and birds flying in a wind-tunnel suggest that the shares of fat and protein on total energy expenditure vary with flight duration and body fat stores. Also, flight behaviour, such as descending flight, is expected to affect metabolism. However, studies on free flying migrant birds under natural conditions are lacking. METHODS: On a Swiss Alpine pass, we caught three species of nocturnal migrant passerines out of their natural migratory flight. Since most night migrants start soon after dusk, we used time since dusk as a measure of flight duration. We used plasma concentrations of metabolites of the fat, protein, and carbohydrate metabolism as indicators of relative fuel use. We used flight altitudes of birds tracked with radar and with atmospheric pressure loggers to characterize flight behaviour. RESULTS: The indicators of fat catabolism (triglycerides, very low-density lipoproteins, glycerol) were positively correlated with body energy stores, supporting earlier findings that birds with high fat stores have a higher fat catabolism. As expected, plasma levels of triglycerides, very low-density lipoproteins, glycerol and ß-hydroxy-butyrate increased at the beginning of the night, indicating that nocturnal migrants increased their fat metabolism directly after take-off. Surprisingly, fat catabolism as well as glucose levels decreased in the second half of the night. Data from radar observations showed that the number of birds aloft, their mean height above ground and vertical flight speed decreased after midnight. Together with the findings from atmospheric pressure-loggers put on three species, this shows that nocturnal migrants migrating over continental Europe descend slowly during about 1.5 h before final landfall at night, which results in 11-30% energy savings according to current flight models. CONCLUSIONS: We suggest that this slow descent reduces energy demands to an extent which is noticeable in the plasma concentration of lipid, protein, and carbohydrate metabolites. The slow descent may facilitate the search for a suitable resting habitat and serve to refill glycogen stores needed for foraging and predator escape when landed.

Sentiment analysis of online reviews of energy-saving products based on transfer learning and LBBA model.

With the exacerbation of global climate change and the growing environmental awareness among the general public, the concept of green consumption has gained significant attention across various sectors of society. As a representative example of green consumer products, energy-saving products play a crucial role in the timely realization of dual carbon goals. However, an analysis of online comments regarding energy-saving products reveals that the majority of these products still exhibit shortcomings in terms of efficacy, noise level, cost-effectiveness, and particularly, energy-saving appliances. This study focuses on the user-generated online comments data from the Taobao e-commerce platform for Grade 1 energy-saving refrigerators. By employing text mining techniques, the study aims to extract the essential information and sentiments expressed in the comments, in order to explore the consumption characteristics of Grade 1 energy-saving refrigerators. Moreover, the LBBA (LDA-Bert-BiLSTM-Attention) model is utilized to investigate the consumer topics of interest and emotional features. Initially, the LDA model is adopted to identify the attributes and weights of consumer concerns. Subsequently, the Bert model is pre-trained with the online comment data, and combined with the BiLSTM algorithm and Attention mechanism to predict sentiment categories. Finally, a transfer learning approach is utilized to determine the sentiment inclination of user-generated online comments and to identify the primary driving factors behind each sentiment category. This research employs sentiment analysis on online comments data regarding energy-saving products to uncover consumer sentiment attributes and emotional characteristics. It provides decision-makers with a comprehensive and systematic understanding of public consumption intentions, offering decision support for the efficient operation and management of the energy-saving product market.

Exploring a pathway to optimise the carbon tax policy in terms of the economy, the environment and health: A scenario-based system dynamics approach.

The carbon tax, a pivotal policy instrument in tackling climate change, holds the potential to significantly influence the development of society. To comprehensively analyse the effectiveness of the policy on carbon taxation and explore its possible optimisation path, this study utilizes system dynamics theory to establish a simulation model. A detailed analysis and evaluation of this policy is then conducted from the perspectives of the economy, the environment, and health. To guarantee the precision of the simulation model, a new, comprehensive evaluation method is proposed, which can test the degree of fit of the relative trend and absolute data of the simulation model with reality. The findings reveal that, despite its negative economic implications, a carbon tax policy has positive ramifications for the environment, energy, health, industrial structure, and carbon intensity targets. Furthermore, the synergistic reinforcement effect of R&D and new energy support policies on carbon taxation surpasses the impact of any individual policy alone. Notably, the influence of auxiliary policies has a temporal difference on this policy. Based on these insights, the study concludes with practical policy recommendations.

Internet of things-based study on online monitoring system of building equipment energy saving optimization control using building information modeling.

Smart building equipment monitoring is a well-established field focused on enhancing contemporary building comfort. The proliferation of Internet connectivity, facilitated by the internet of things (IoT), has transformed buildings from static structures into interactive environments. IoT has witnessed substantial growth across various aspects of daily life, from monitoring environmental conditions to managing building systems and storing data in the cloud. One critical application is the intelligent monitoring and control of building equipment, such as air conditioners, to optimize energy efficiency-a matter of increasing concern for building owners, design experts, and system integrators. Achieving comprehensive energy savings demands a meticulous approach to energy-efficient design and control. This paper's primary objective is to explore and analyze IoT-based energy-saving optimization techniques for intelligent building equipment, integrating building information modeling (BIM) technology. It particularly delves into the energy conservation control algorithm for air-conditioning systems. The research presents a challenge rooted in energy-saving optimization, established upon specific objective functions, followed by a detailed explanation of the energy-saving control algorithm. To validate their approach, the paper outlines a comprehensive experimental design. Over three sessions in August, they conducted control experiments in two distinct areas. Area 1 implemented the energy-saving control methodology discussed in the paper, utilizing virtual parameter enhancement mechanisms, while Area 2 adhered to conventional control methods. The results were enlightening. Area 1 demonstrated superior energy efficiency, consuming 735 kWh compared to Area 2's 819 kWh, signifying an impressive 11.43% reduction in energy consumption thanks to the optimized control strategy. This research underscores the practicality and significance of implementing IoT-based energy-saving strategies, with a focus on smart thermostats, HVAC controllers, and daylight sensors, in intelligent building equipment management to achieve substantial energy conservation gains.

New Adsorption Materials for Deep Desulfurization of Fuel Oil.

In recent years, due to the rapid growth of mankind's demand for energy, harmful gases (SOx) produced by the combustion of sulfur-containing compounds in fuel oil have caused serious problems to the ecological environment and human health. Therefore, in order to solve this hidden danger from the source, countries around the world have created increasingly strict standards for the sulfur content in fuel. Adsorption desulfurization technology has attracted wide attention due to its advantages of energy saving and low operating cost. This paper reviewed the latest research progress on various porous adsorption materials. The future challenges and research directions of adsorption materials to meet the needs of clean fuels are proposed.

Capacity of waste heat recovery-based polygeneration to achieve sustainable development goals.

The objective of this study is twofold. First, it aims to bridge a significant gap in the existing literature by reviewing the integration of waste heat recovery (WHR) into polygeneration systems. Thus, it scrutinizes the methods and challenges of WHR-based polygeneration systems and explores their energy and economic potential based on data gathered from the literature and using key indicators. Second, it addresses the scarcity of existing studies assessing the environmental impact of these systems. Therefore, an environmental analysis is conducted to evaluate the potential mitigation of greenhouse gas emissions achievable through their implementation. The findings of the study reveal significant energy and exergy efficiencies, varying in the ranges of 20.8 %-96.9 % and 24.1 %-63.6 %, respectively, proving high performance of WHR-based polygeneration systems. Economically, these systems exhibit competitiveness, with short payback periods ranging from 1.4 to 6.7 years, along with average levelized costs of electricity, cooling, and heating of 0.17, 0.37 and 0.13 $/kWh, sequentially. Moreover, the environmental assessment confirmed substantial reductions in greenhouse gas emissions, reaching on average 2.45 kt of carbon dioxide per MW of installed capacity, annually. The study could contribute to raising awareness regarding the energy, economic, and environmental benefits of WHR-based polygeneration systems, thereby fostering the widespread adoption of these sustainable systems.