Energy-saving Strategy Research Articles

Energy-saving Behavior and Households’ Practices in Prevention of Climate Change

The relevance of the problem of energy-saving behavior and the negative contribution of households to climate change is dictated by the need to reduce the burden on the environment. Based on a meta-analysis of scientific publications, an overview of modern behavioral approaches in this area is provided. Results of a survey of experts in the energy sector are presented. The conclusion is that the energy saving strategy is being implemented due to savings, with increasing dynamics of involvement, and the main barriers in this case are the technical limitations of households and economic investments in energy-efficient equipment. Waste management in households is associated with increased demands of citizens for comfortable infrastructure and the reluctance to create additional time costs. The same can be said about the use of personal transport, here the impediments are the desire to maintain mobility and save time. Key energy-saving strategies include reducing resource use, avoiding unsustainable practices and increasing environmental awareness. Institutional barriers to energy-saving behavior are identified: household infrastructure, which does not allow energy efficiency, lack of environmental culture and insufficient interaction between the authorities and the population. Knowledge about environmental problems and understanding the contribution of anthropogenic influence on climate change do not always guarantee the implementation of pro-environmental energy-saving practices. In-depth work to raise knowledge of environmental problems, introduce incentives, spread values and strengthen motivation is necessary.

Antipredator responses of bats during short boreal nights with variable climatic conditions

Abstract The threat of predation can influence the behavior of animals. To minimize the impact of predation, animals rely on antipredatory responses and effectively balance these responses with other activities to maximize survival. Boreal bats are nocturnal animals that must forage within a narrow time frame during short, light summer nights with unpredictable weather. Despite having no specialized predators, boreal bats are still subject to predation. However, whether they express antipredatory responses has not been established. We studied antipredatory responses and responses to climatic conditions in boreal bats in 2 settings: 1) during roost emergence; and 2) during foraging within Tawny Owl territories and at locations with no Tawny Owl sightings. Acoustic data were collected at 23 roosts and 10 foraging grounds. Two controlled predation threats were used—playbacks of Tawny Owl calls and fledgling calls. Fledgling calls were only played during roost emergence. In both experiments, music and no treatment were used as controls. We also incorporated weather variables in the model. According to our results, bats tend to delay their emergence by 16 min when Tawny Owl calls were played outside the roost, but this effect was not noticeable when weather variables were included. There was no difference in exit time when music or fledgling sounds were played. While foraging, bats reduced their activity in Tawny Owl territories when calls of owls or music were played compared to no treatment. These results suggest that bats might display variable antipredatory responses, but weather influences behavior of bats more than predation risk, highlighting the importance of energy-saving strategies at northerly latitudes.

Greenhouse gas emissions and net energy production of dark fermentation from food waste followed by anaerobic digestion

There are diverse estimations regarding the carbon reduction effects of alternative hydrogen production technologies. This study assessed the greenhouse gas (GHG) emissions and energy balance of a two-stage process combining dark fermentative hydrogen production and anaerobic digestion from food waste using the cradle-to-gate life cycle assessment (LCA). The system boundary included collection and transportation, pretreatment and feedstock storage, dark fermentation, H2 purification, anaerobic digestion and heat and power generation and the estimated GHG emission was compared with a single anaerobic digestion of food waste. GHG emission of the biohydrogen production was estimated as 2.48 kg CO₂-eq per kg H₂ without considering avoided emissions from heat and power generation. The environmental impacts were majorly influenced by electricity use. The net energy ratio of the two-stage process was calculated to be 8.18, confirming a net energy gain and the potential GHG emission avoidance for electricity and heat use. Given that the single-stage anaerobic digestion of 16 tons of food waste is replaced by the two-stage dark fermentation process, 76.6 kg CO₂-eq would be avoided. Sensitivity analysis revealed that energy-saving strategies are the most sensitive factors for achieving positive net energy production and low global warming potential.

Elevated CO2 enhances growth and cyanide assimilation in nitrogen-deficient rice: A transcriptome and metabolomic perspective

Plants face multiple challenges from environmental pollutants and higher emissions of atmospheric CO2. Therefore, a hydroponic-based experiment was used to explore the combined effects of elevated [CO2] (700 ppm) and exogenous cyanide (CN−) (3.0 mg CN/L) on rice seedlings under nitrogen deficiency, utilizing metabonomic and transcriptomic analysis. Elevated [CO2] significantly improved the growth of CN−-treated rice seedlings compared to those with ambient [CO2] (350 ppm), and it also significantly affected CN− assimilation. Transcriptome analysis revealed distinct impacts on differentially expressed genes (DEGs) across treatments and tissues. KEGG analysis showed variability in DEGs enriched in amino acid (AA) and energy metabolism pathways due to elevated [CO2] and CN−. Metabonomic indicated that higher input of [CO2] and exogenous CN− more severely impacted energy metabolism elements than the individual species of AAs. Positive synergistic effects of elevated [CO2] and CN− were observed for glutamine and asparagine in shoots, and methionine in roots, wherein negative effects were noted for phenylalanine in shoots, and phenylalanine, valine, and alanine in roots. Meanwhile, positive effects on fumarate in shoots and α-ketoglutarate and succinate in roots were also found. Overall, elevated [CO2] enhanced growth in CN−-treated rice seedlings under nitrogen deficiency by altering AA and energy metabolism. This is the first attempt to provide new evidence of [CO2]-based gaseous fertilization as an energy-saving strategy for rice plants fed with biodegradable N-containing pollutants as a supporting N source under N deficient conditions.

Constructing Bionic Perovskite Smart Photovoltaic Windows with Switchable Colors and High Cycling Stability.

Smart photovoltaic windows (SPWs) provide a high-efficiency and energy-saving strategy owing to the dual capabilities of electricity generation and sunlight modulation achieved by tunable colors and transmittances. Due to the deterioration of chromic process on photovoltaic layers, SPWs usually suffer from poor cycling stability. Moreover, thermochromic SPWs with a multilayer structure usually change transmittance without reversible color transitions. To address these issues, inspired by chameleon skin, bionic SPWs are designed and constructed by integrating hydrogel, CsPbBr3 semitransparent perovskite solar cells (ST-PSCs), and transparent polymer film. The SPWs realize reversible transitions between transparent green (25°C) and opaque yellow (45°C) states in a short duration (2min) under natural conditions. By optimizing perovskite film and ultrathin-metal electrodes, CsPbBr3 ST-PSCs achieve a good trade-off between transmittance and efficiency, delivering the highest photovoltaic efficiency (8.35%) and a record light utilization efficiency (4.43). Ultimately, the multilayer SPWs maintain stable optical properties and more than 88% initial conversion efficiency after 100 transition cycles, presenting excellent cycling stability. This study proposes a novel approach and device structure for SPWs with high cycling stability, switchable colors, and switchable transmittances. It also paves the way for smart photovoltaic deployment in buildings and many other fields.

Green Campus Transformation in Smart City Development: A Study on Low-Carbon and Energy-Saving Design for the Renovation of School Buildings

In the context of increasingly deteriorating global ecological conditions and rising carbon emissions from buildings, campus architecture, as the primary environment for youth learning and living, plays a crucial role in low-carbon energy-efficient design, and green environments. This paper takes the case of Yezhai Middle School in Qianshan, Anhui Province, to explore wind environment optimization and facade energy-saving strategies for mountainous campus buildings under existing building stock renovation. In the context of smart city development, integrating advanced technologies and sustainable practices into public infrastructure has become a key objective. Through wind environment simulations and facade energy retrofitting, this study reveals nonlinear increases in wind speed with building height and significant effects of ground roughness on wind speed variations. Adopting EPS panels and insulation layers in facade energy retrofitting reduces energy consumption for winter heating and summer cooling. The renovated facade effectively prevents cold air intrusion and reduces external heat gain, achieving approximately 24% energy savings. This research provides a scientific basis and practical experience for low-carbon energy retrofitting of other campus and public buildings, advancing the construction industry towards green and low-carbon development goals within the framework of smart city initiatives.

An Optimization Method for Green Permutation Flow Shop Scheduling Based on Deep Reinforcement Learning and MOEA/D

This paper addresses the green permutation flow shop scheduling problem (GPFSP) with energy consumption consideration, aiming to minimize the maximum completion time and total energy consumption as optimization objectives, and proposes a new method that integrates end-to-end deep reinforcement learning (DRL) with the multi-objective evolutionary algorithm based on decomposition (MOEA/D), termed GDRL-MOEA/D. To improve the quality of solutions, the study first employs DRL to model the PFSP as a sequence-to-sequence model (DRL-PFSP) to obtain relatively better solutions. Subsequently, the solutions generated by the DRL-PFSP model are used as the initial population for the MOEA/D, and the proposed job postponement energy-saving strategy is incorporated to enhance the solution effectiveness of the MOEA/D. Finally, by comparing the GDRL-MOEA/D with the MOEA/D, NSGA-II, the marine predators algorithm (MPA), the sparrow search algorithm (SSA), the artificial hummingbird algorithm (AHA), and the seagull optimization algorithm (SOA) through experimental tests, the results demonstrate that the GDRL-MOEA/D has a significant advantage in terms of solution quality.

Short process for Li2CO3 synthesis and spent LiCoO2 remediation via Glycine-LiOH slurry electrolysis

Effectively recycling spent lithium-ion batteries (S-LIBs) has considerable economic and environmental benefits. In this study, a novel approach for simultaneously synthesizing Li2CO3 and remediating LiCoO2 from S-LIBs via slurry electrolysis is proposed, employing glycine-LiOH as the electrolyte. The operating conditions were optimized to a solid-to-liquid ratio of 20 g/L, slurry electrolysis time of 8 h, current density of 50 mA/cm2, reaction temperature of 80 °C, and glycine concentration of 1.0 mol/L. Li2CO3 was directly synthesized in the anode region, with a purity of up to 99.52 %. Additionally, LiCoO2 was partially remediated in the cathode, increasing Li-Co molar ratio from 0.659 to 0.93. The obtained Li2CO3 and the electrolysis residue LiCoO2 then underwent additional calcination, yielding a layered LiCoO2. At a Li-Co molar ratio of 1.1, the initial specific capacity of the calcined LiCoO2 material was 129.9 mAh/g, exhibiting a capacity retention rate of 50.48 % over 100 cycles. Therefore, a novel, cost-effective, and energy-saving strategy for recycling S-LIBs is proposed.

Rapid conversion of biomass to hierarchical porous carbons via one-step microwave carbonization/activation for long cycle-stable supercapacitor and zinc-ion capacitor

Biomass derived carbons as electrode materials for supercapacitors have drawn great attention owing to their low cost, high specific areas and abundant surface functional groups, but most of their preparation processes are time-consuming and power-wasting. In this work, we report a rapid and energy-saving strategy for preparing hierarchical porous carbons derived from loofah sponges for supercapacitors via one-step microwave carbonization/activation. Utilizing KOH as the microwave absorption medium and activator, the microwave carbonization/activation process can be completed within 6–10 min. The resulting loofah sponges derived porous carbons demonstrate a high specific capacitance of 294 F g−1 at 0.5 A g−1 and an outstanding retention rate of 74.8 % at 20 A g−1 in three-electrode setup for supercapacitor. The fabricated zinc-ion capacitor based on the as-prepared carbon materials displays a comparable energy density of 62.3 Wh∙kg−1 at the power density of 160 W kg−1. Notably, a remarkable long-term cyclic stability by retaining of 93.6 % of its initial capacitance is acquired after 30,000 cycles. Our research not only fabricates sustainable electrode materials for supercapacitors and zinc-ion capacitors but also provides a fast and energy efficient route for producing biomass derived carbon materials with high capacitive performances for commercial applications.

Modeling daily energy use in British homes amidst the electricity market crisis: Insights from smart meter and socio-technical data

This study examines the factors influencing daily electricity use in UK homes during the electricity market crisis. Using data from smart meters and socio-technical sources, the research identifies key drivers of electricity consumption, such as household size, weather conditions, and appliance use. Results from a sample of British homes, analyzed through linear mixed effects modeling, show that households with more occupants, greater number of adults, higher heating settings, and fewer energy-saving efforts consume more energy. Weather, housing type, and air conditioning also play significant roles. The findings highlight the importance of integrating smart meter data with other sources to develop effective energy-saving strategies and inform targeted conservation policies.

Photothermal Catalytic Removal of 1,2-DCE with High HCl Selectivity over the Brønsted Acid-Enriched Sulfur-Doped MOFs.

Chlorinated volatile organic compounds come from a wide range of sources and are highly toxic, posing a serious threat to biological health and the environment. Herein, a high-efficiency and energy-saving photothermal synergistic catalytic oxidation method was developed for the removal of 1,2-dichloroethane (1,2-DCE). Compared to traditional thermocatalysis, the 1,2-DCE conversion over Ru-U6S in photothermal synergistic catalysis at 340 °C increased by approximately 44% not only reducing energy consumption but also avoiding the instability of MOF structure caused by high reaction temperature. The excellent photothermal catalytic oxidation activity was derived from the synergistic effect of photo- and thermocatalysis. Ru-U6S demonstrated excellent 1,2-DCE adsorption capacity and stronger light utilization and could produce more reactive oxygen species (•OH and •O2-) after light illumination, which participated in the oxidation reaction, promoting the release of the active site of the catalyst. The results of H2O-TPD and NH3-DRIFTS exhibited that the use of S-containing ligands in the synthesis process increased the hydroxyl groups and Brønsted acid sites, significantly improved the selectivity of CO2 and HCl in the oxidation process, and reduced the release of chlorine-containing byproducts. This work provides a high-efficiency and energy-saving strategy for removing chlorinated volatile organic compounds and increasing the selectivity of ideal products directly with MOFs directly.

Challenges of Salinity Intrusion and Drought Stress on Olive Tree Cultivation on Mljet Island.

Understanding genotype-specific responses to environmental stressors is vital for developing resilience strategies that ensure sustainable olive cultivation and productivity. In this work, cultivar 'Oblica' and several olive genotypes from the island of Mljet (Croatia) were exposed to short-term (21 days) salinity and drought treatments. In contrast to other olive genotypes, genotype M29 as well as cultivar 'Oblica' managed to maintain growth and chlorophyll a levels under salinity stress to the same level as the control. Drought, however, significantly reduced the growth parameters in all olive trees. Cultivar 'Oblica' accumulated the greatest amount of Na+ ions in the leaves compared to olive genotypes from the island of Mljet, demonstrating superior resistance by translocating Na+ to leaf vacuoles. The observed reduction in K+ content in the roots of olive trees under all treatments suggests a generalized stress response. On the other hand, effective Ca2+ uptake has been identified as a crucial energy-saving strategy that olive trees use to cope with brief periods of salinity and drought. The proline content and activities of superoxide dismutase (SOD) and guaiacol peroxidase (GPOX) varied among the olive trees, highlighting the importance of antioxidative capacities and stress adaptation mechanisms. According to the obtained results, stress-resistant olive genotypes like 'Oblica' and M29 show potential for breeding resilient varieties.

Building energy consumption analysis and measures: a case study from an administration building in Chengdu, China

With the peak of carbon dioxide emissions and carbon neutrality, China is placing greater emphasis on energy expenditure. Office buildings occupy a prominent position in building energy consumption, which is one of the main energy consumption areas. Taking an administration building in Chengdu as an example, this article simulates the building energy consumption based on Design Builder software, examines the variables influencing energy consumption, and suggests energy-saving strategies combined with fresh ideas for sustainable architectural design. The results showed that the modeling building was a high-energy-consuming building, with an energy consumption of 724,857.59 kWh, and a unit area energy consumption of 288.17 kWh/m2 in Chengdu. For energy conservation and emission reduction, this article proposes the following three energy-saving measures. The first is to apply heat recovery technology for air conditioning systems. The second is photovoltaic glass, which provides partial electricity demand for buildings and reduces dependence on traditional energy sources. The third is roof greening, which utilizes the plants to purify the air and beautify the environment. The results showed that the heat recovery technology in air conditioning systems reduced the total energy consumption of buildings from 642144.04 kWh/m2 to 502937.83 kWh/m2, photovoltaic glass reduced 552243.87 kWh/m2, and roof greening reduced to 635947.35 kWh/m2. All of these have good energy-saving and emission reduction effects. The above three strategies not only help reduce building energy consumption, but also provide substantial support for China to achieve carbon neutrality.

Environmental assessment of industrial aquaponics in arid zones using an integrated dynamic model

Land desertification, water scarcity, and food security challenges in arid zones are intensifying, driving the need for sustainable agricultural solutions like aquaponics. This study investigated innovative water and energy-saving strategies using an integrated dynamic model for an on-demand industrial aquaponics system in Israel. The model evaluated the performance of a recirculating aquaculture system (RAS), hydroponics system (HPS), and desalination unit (DU) by adjusting physical and operational parameters to optimize water and nutrient use efficiency, energy consumption, and yield. Optimizing the system design resulted in an aquaponics system with approximately 420 m3 RAS, 6.85 ha HPS and 40 m3/d DU, achieving phosphorus use efficiency of 96 %, a water use efficiency of 97 %, freshwater input of 1.5 L/day/m2, and energy consumption of 0.56 kWh/day/m2. To mitigate the challenges of extreme arid climates, evaporative cooling combined with outdoor shading and mechanical cooling was found to be a feasible option to control temperature and humidity in the greenhouse. Dehumidification technologies further improved system performance by recovering 22 % freshwater from seawater and increasing nitrogen use efficiency by 18 %. Achieving daily energy self-sufficiency required 4500 m2 photovoltaic panels and 5000 m2 solar heating system. While the system model was initially devised with a specific focus on conditions in Israel, it has been designed with scalability, allowing it to be adapted and applied extensively across diverse peri-urban regions and arid zones globally.

Efficient CO2 Desorption Catalysts: from Material Design to Kinetics Analysis and Application Evaluation

Catalytic amine-solvent regeneration has been validated as an energy-saving strategy for CO2 chemisorption by boosting reaction kinetics under mild conditions. The upscale performance evaluation and long-term durability are indispensable steps for industrial application but have been scarcely reported thus far. Here, we report a ZrO2/Al2O3 pack catalyst that possesses strong metal oxide-support interactions, a porous structure, active and stable Zr–O–Al coordination, promoted proton transfer and a 40.7% decrease in the energy activation of carbamate decomposition, which significantly accelerates CO2 desorption kinetics. The upscale experiment and cost evaluation based on industrial flue gas revealed that the use of packing catalysts can reduce energy consumption by 27.56% and optimize the overall cost by 10.49%. The active sites present excellent stability in alkaline solvents. This work is the first to investigate the ability of high-technology readiness (technology readiness level at 6 (TRL 6)) for catalytic amine-solvent regeneration, providing valuable insights for potential applications involving efficient CO2 capture with catalyst assistance.

Developing Energy Conservation Strategies in Agricultural Enterprises

The article emphasizes that agriculture has specific aspects, particularly regarding energy consumption. The research reveals that some agricultural enterprises are capable of generating their own energy. These characteristics are associated with various types of energy used in the production process: fossil energy, solar energy, and soil energy. The authors highlight the need for special attention to the development of energy-saving strategies in agriculture, as these enterprises often face high energy costs and have significant potential for enhancing energy efficiency. It has been established that reducing the use of fossil energy sources and maximizing the use of renewable sources, such as solar energy and soil energy potential, can substantially contribute to energy conservation in the agricultural sector. The considerable dispersion of agricultural enterprises leads to high costs and differences in energy consumption principles: fossil energy is primary for industry, while in agriculture, it serves a supplementary role, where natural energy forms biological mass (crops, livestock). The importance of management aspects in developing an effective energy-saving strategy, which promotes sustainable development and economic benefit in the agricultural sector, is highlighted. The authors propose key steps for ensuring energy conservation, which should form the basis for creating a comprehensive energy-saving strategy in agriculture.

A multi-objective genetic algorithm based on two-stage reinforcement learning for green flexible shop scheduling problem considering machine speed

The consumption of energy and resources in the manufacturing industry has garnered significant attention due to the increasingly severe environmental issues. Green shop scheduling research is focused on optimizing economic and environmental indicators within the current manufacturing model. This paper specifically addresses the flexibility of job-shop scheduling problem considering machine speed (CMS-FJSP), as different machine speeds during the production process can impact energy and resource consumption. The objectives of this study include minimizing the makespan, total energy consumption, and tool wear. To tackle this problem, a multi-objective genetic algorithm that incorporates a two-stage reinforcement learning approach is proposed. In light of the characteristics of the problem, a three-layer encoding approach is suggested, which encompasses machine allocation, operation sequencing, and machine speed selection. Additionally, a decoding method that integrates energy-saving strategies is proposed to enhance the optimization process. To improve the quality of the population, three distinct initialization methods have been developed. Furthermore, a parameter adjustment strategy informed by two-stage reinforcement learning is introduced. This strategy incorporates a state set and action set tailored to the unique characteristics of two-stage reinforcement learning, alongside corresponding reward mechanisms. In 30 test cases, the proposed algorithm demonstrates superior uniformity and convergence compared to five classical algorithms. In a practical case within a hydraulic component production workshop conducted at a hydraulic component company, the proposed algorithm generates 26 scheduling schemes with different focuses, achieving a 14.39% reduction in makespan, a 2.13% decrease in energy consumption, and a 10.65% reduction in tool wear.

Dynamic predictions for the composition and efficiency of heating, ventilation and air conditioning systems in urban building energy modeling

The composition and efficiency of heating, ventilation and air conditioning (HVAC) systems are crucial inputs for urban building energy modeling (UBEM). However, in current studies, the heterogeneity of HVAC systems is often ignored, leading to huge modeling errors. To address these issues, this study developed a three-step prediction approach that can dynamically forecast the composition of HVAC systems (SystemID), the efficiency of heat sources and the efficiency of entire systems at the urban scale. The performance of the prediction models was evaluated through the ten-fold cross-validation. The results showed that the accuracy in predicting the composition could reach 0.87, and the coefficient of determination (R2) was greater than 0.93 in efficiency forecasts. The accuracy of the developed approach was further evaluated through the testing set in two situations, i.e., with the predicted and actual SystemID data. In the first situation, the overall accuracy in predicting the composition reached 78.2 %, and the R2 for forecasting the system efficiencies was over 0.6. Then, with the SystemID assumed to be accurate, the R2 increased to more than 0.87. To analyze the performance of the developed method in energy use predictions, two case regions in Changzhou and Nanjing were used, respectively. The simulation results were compared with the scenario in which the system efficiencies were determined based on the national standard. The results showed that the developed method could enhance the accuracy of total energy use predictions by approximately 10 % and HVAC energy use predictions by roughly 40 % at both urban and building scales. This helps policy-makers craft energy-saving strategies more reasonably.

Analysis of natural convection in a representative cavity of a room considering oscillatory boundary conditions: An experimental and numerical approach

Natural convection heat transfer in cavities commonly occurs in various engineering problems. Specifically, the problem of a differentially heated cavity with oscillatory boundary conditions arises in the design of energy-saving strategies or energy evaluations within the building sector. This work addressed numerically and experimentally the natural convection in a full-scale room subjected to diurnal heating and nocturnal cooling. The experiment was instrumented with temperature sensors and an air velocity sensor, inspired by works reported in the literature. The numerical model involved solving the equations governing natural convection, considering turbulent bi-dimensional and three-dimensional flow in a transient state. This research allowed the selection of an appropriate turbulence model and evaluated the performance of the two-dimensional and three-dimensional approaches in simulations. Results showed that the k-omega SST model performed best in predicting velocity, while the standard k-omega model performed best in predicting temperature for the two-dimensional simulation, and the RNG k-epsilon model performed best for the three-dimensional simulation. Additionally, the three-dimensional simulation more accurately reproduced the chaotic fluctuations observed in experimental velocity data, demonstrating up to 30 % better estimation during periods of high velocities. The study identified the reversal of convective cells in the cavity, with an approximate duration of 2 h and 40 min in a real case. This study contributes to improving the understanding of turbulent natural convection in a differentially heated cavity with oscillatory boundary conditions. These findings have significant implications for various environmental and engineering applications, including the design of thermally comfortable buildings.

Effects of dehydration time on performances of polyamidoamine-epichlorohydrin resin and its modified soybean-based adhesive

Soybean-based adhesive has been commercialized in the wood industry owing to its several advantages, such as no formaldehyde, good bonding properties, and renewability. However, the large-scale application of soybean-based adhesive is greatly limited by its significantly higher cost than urea-formaldehyde resin. Thus, in this study, a cost-effective polyamidoamine-epichlorohydrin (PAE) resin with improved crosslinking efficiency to soybean meal was prepared from branched polyamidoamine (PAA) with reduced dehydration time at high temperatures. Test results from GPC, FTIR and NMR analyses showed that traditional PAA synthesis involving 3-h dehydration time at 180 °C resulted in over-branching of PAA resin and poor crosslinking efficiency of PAE resin and decreased water resistance of obtained soybean-based adhesive. PAA resin synthesized at appropriate dehydration time (1 h) remained sufficient secondary amine groups for being grafted by epichlorohydrin to form effective PAE resin with more azetidinium groups. As a result, the water resistance of soybean-based adhesive crosslinked by the optimal PAE-1 resin prepared from 1 h-dehydrated PAA-1 significantly improved by 42.9 % compared with that of soybean-based adhesive crosslinked by traditional PAE resin, attributing to forming denser and stronger crosslinking networks after thermally cured. Consequently, this optimal PAE-1 could reduce the cost of soybean-based adhesive due to decreasing 16.7 wt% of PAE dosage without compromising bonding property and the energy consumption of PAA/PAE synthesis. Therefore, an appropriate decrease in the dehydration time of PAA resin at a high temperature provides an effective, economical, and energy-saving strategy to improve the bonding property of soybean-based adhesives.