Sustainable Energy Development Research Articles

Underground hydrogen storage in depleted gas reservoirs with hydraulic fractures: Numerical modeling and simulation

Underground hydrogen storage (UHS) is one of the key technological solutions for balancing energy systems and promoting sustainable energy development. In this study, we propose a novel UHS method, which involves injecting hydrogen generated from electrolyzing formation water into depleted gas reservoirs through hydraulic fractures during off-peak electricity periods, and then efficiently producing hydrogen for power generation during peak electricity periods. We establish a numerical simulation model for UHS in a depleted fractured gas reservoir, covering the entire process from injecting hydrogen gas into the reservoir through wells to shutting them down and subsequently producing hydrogen when required. We then investigate the impact of hydrogen injection rate, injection duration, and shut-in duration on the UHS process and storage efficiency. Our findings reveal that hydrogen production efficiency can exceed 85 % by optimizing hydrogen injection and production strategies. Our study provides an exemplary numerical simulation scheme to evaluate the feasibility of UHS in depleted gas reservoirs with hydraulic fractures.

Short-Term Prediction of Rural Photovoltaic Power Generation Based on Improved Dung Beetle Optimization Algorithm

Addressing the challenges of randomness, volatility, and low prediction accuracy in rural low-carbon photovoltaic (PV) power generation, along with its unique characteristics, is crucial for the sustainable development of rural energy. This paper presents a forecasting model that combines variational mode decomposition (VMD) and an improved dung beetle optimization algorithm (IDBO) with the kernel extreme learning machine (KELM). Initially, a Gaussian mixture model (GMM) is used to categorize PV power data, separating analogous samples during different weather conditions. Afterwards, VMD is applied to stabilize the initial power sequence and extract numerous consistent subsequences. These subsequences are then employed to develop individual KELM prediction models, with their nuclear and regularization parameters optimized by IDBO. Finally, the predictions from the various subsequences are aggregated to produce the overall forecast. Empirical evidence via a case study indicates that the proposed VMD-IDBO-KELM model achieves commendable prediction accuracy across diverse weather conditions, surpassing existing models and affirming its efficacy and superiority. Compared with traditional VMD-DBO-KELM algorithms, the mean absolute percentage error of the VMD-IDBO-KELM model forecasting on sunny days, cloudy days and rainy days is reduced by 2.66%, 1.98% and 6.46%, respectively.

Preparation of copper-based carbon composites for adsorption of iodine gas

Exploring efficient, cheap and scalable adsorbents to capture the radioactive iodine gas is crucial for the sustainable development of nuclear energy. Herein, novel copper-based carbon (Cu–C) adsorbents were facilely prepared and applied for iodine adsorption. C supports were prepared by a simple sintering method using rice husk as carbon source. Cu–C adsorbents were synthesized by an improved impregnation reduction method. The adsorption behaviors impacted by Cu content and contact time were investigated. The adsorption mechanism was elucidated through XRD, SEM, XPS and TGA analyses. The results demonstrated that the adsorption capacity of 30%Cu–C sample reached up to 1282 mg/g due to the synergistic effect of copper and carbon. In addition, the obtained Cu–C adsorbents and adsorbed iodine exhibited high thermal stability. The dominant mechanism for the adsorption of iodine gas on Cu–C materials were chemical reaction (2Cu + I2 = 2CuI) and C–I interaction. Thanks to the excellent properties, the obtained Cu–C materials could be added into the list of potential iodine gas adsorbents.

Can the new energy demonstration city policy promote corporate green innovation capability?

New energy demonstration city (NEDC) projects are significantly driving changes in energy structures and sustainable economic development. Corporate green technology innovation is key to achieving an alternative to our current energy use. To explore whether the NEDC project can help to improve companies' green innovation capabilities, this paper presents the dual differential model (DID) to conduct empirical research on panel data in 285 prefecture-level and above cities in China from 2008 to 2020. The DID model is arguably the most adequate for addressing the issue of endogenous problems. The research shows that the NEDC project has improved corporate green innovation capability; the findings of mechanism analysis show that NEDC projects have incited an upgrading of the local urban industrial structure and thereby improved corporate green innovation. The policy effect of NEDC projects obviously shows “insufficient momentum”; the heterogeneity test found that the promotion effect of NEDC projects for corporate green innovation in eastern cities, major cities, large-scale companies and energy-dependent industries is more significant. Based on the conclusions and heterogeneous characteristics for promoting corporate green innovation capability, the government should roll out further NEDC projects in a systematic way and more closely monitor the quality of such new energy demonstration cities.

Clean energy transition and energy security in metropolises: A case study of Beijing under the “coal-to-gas” movement

The transition to clean energy is one of the most important trends of development in the contemporary world, and ensuring energy security during the energy transition has naturally become an issue that urgently needs to be studied. Unlike most studies that analyze this issue at the global and sovereign national levels, this article delves back to the subnational level. It focuses on exploring energy security guarantees in metropolitan areas while considering differences in urban hierarchies. Considering that Beijing is not only the most influential metropolis in China but also a pioneer in promoting clean energy transition globally, this article analyzes the energy security issues in Beijing through the framework of Energy Security and Sustainable Development and explores the policy actions taken during the 2017 “coal-to-gas” movement by the city. Our research shows that the transition to clean energy may reduce the energy security of metropolitan areas. However, the big cities can receive prioritized protection from the central government and state-owned enterprises due to their prominent political status. However, the cost of ensuring energy security in urban areas may be passed on to their surrounding areas, where their energy security may be additionally impacted. These findings indicate that it is necessary to distinguish and examine the energy security of the metropolitan areas and surrounding regions. Only when energy justice is protected can urban areas truly achieve energy security.

Spatiotemporal analysis and forecasting of PV systems, battery storage, and EV charging diffusion in California: A graph network approach

Understanding the dynamics of clean energy adoption is crucial for shaping effective energy policies and strategies. To understand the spatiotemporal variability and bottom-up spontaneity of the diffusion of clean energy technologies, this study examined the spatiotemporal diffusion of Photovoltaic (PV) systems, Battery Energy Storage Systems (BESS), and Electric Vehicle Charging Stations (EVCS) across 1773 postcode areas in California over the past 35 years. The Bayesian change point detection algorithm (BCDA) was initially utilized to identify temporal change points in the evolution of these technologies, revealing their diffusion trends over time. Then, the Louvain community detection algorithm (LCDA) was employed to elucidate spatial diffusion patterns across regions, offering insights into the geographic proliferation of clean energy solutions. Furthermore, to forecast future distributions, the spatiotemporal graph convolutional network (STGCN) model was applied, adeptly capturing the multi-stage, nonlinear characteristics of clean energy diffusion marked by significant spatiotemporal interactions. With historical data and spatial proximity, the STGCN model demonstrated fine forecasting precision (R^2 ≥ 0.78). This study validates the effectiveness of a graph network-based approach for analyzing the diffusion of clean energy technologies. It recommends policies tailored to account for technological maturity, geographic disparities, and diffusion stages, aiming for equitable and sustainable clean energy development in the region.

Numerical analysis on the effects of hydrothermal development on the permeability of porous reservoir based on coupled THMC modeling

As a clean and renewable energy, geothermal is gaining widespread attention worldwide. The development of hydrothermal resources is accompanied by the coupling processes of thermo-hydro-mechanical-chemical (THMC) caused by production and injection, which have a complex effect on the porosity and permeability of reservoir. Exploring the effects of these processes on the changes in various physical fields within the reservoir is of great significance for the efficient and sustainable development of geothermal energy. Based on the fully coupled framework of fluid flow and transfer in TOUGH2, a method that considered both mechanical and chemical processes was proposed and taken in this study, to analyze the effects of long-term injection and production on reservoir porosity and permeability. The results show that the effective stress changes under fluid pressure and thermal stress, as well as mineral dissolution and precipitation under chemical action, jointly led to the variation of reservoir porosity and permeability. The overall variation trend was mainly controlled by the mechanical action, as the influence of fluid pressure was more obvious and rapid, which dominated the mechanical changes of the whole reservoir in the early stage of injection (within 3.5 years). With the continuous injection, thermal stress played a more obvious role around the cooling area. By 30 years, the significant change in effective stress has extended to the entire reservoir. In the later stage of operation (after 20 years), as the temperature field of the reservoir gradually stopped changing, porosity began to increase under the dominance of fluid pressure. The effect of chemical action was long-term and lag in time. Mineral precipitation only occurred near the injection well, and the precipitation range and amount increased gradually with the continuous injection. By 30 years, the precipitation of dolomite injected into the grid reached 0.06%.

Sustainable Energy Planning and Management with PV, Waste heat, positive energy districts and CO2 accounting

This 41st volume of the International Journal of Sustainable Energy Planning and Management includes three categories of papers: Ordinary submissions, submissions from the 2023 Smart Energy Systems Conference held in Copenhagen, Denmark and a submission from the 2023 Sustainable Development of Energy, Water and Environmental Systems (SDEWES) conference series. The work presented address photo voltaics (PV) in the arctic and in Sweden and electricity system scenarios of Indonesia based on, e.g. PV. Staying with PV a study investigated the interplay between PV and electric vehicles in Denmark. Moving to district heating, two papers address waste heat utilisation and positive energy districts respectively, and finally a study proves into unavoidable carbon dioxide emissions in Austria accounts for carbon capture and storage (CCS) and carbon capture and utilisation (CCUS) needs and possibilities.

Investigating the Energy Potential and Degradation Kinetics of Nine Organic Substrates: Promulgating Sustainability in Developing Economies

To standardize, systematize, and improve the efficiency of the evaluation of biodegradable materials for large-scale biogas projects to support clean and sustainable energy development in emerging economies from a sub-Saharan African perspective, this paper analyzes and fits the potential for methane production (biochemical methane potential, BMP) and degradation kinetics of materials based on the gas production and degradation dynamics obtained from methane potential experiments. The first-order, modified first-order, and Gompertz models are used for analysis and fitting. The Gompertz model shows higher accuracy in fitting the methane production potential curve of screened materials, and the fitted methane potential values are close to the experimental values. When using BMP1% (cumulative gas production reaching 1% of cumulative gas production per day) as a quantitative indicator for the methane production potential of materials, the cumulative methane production reaches over 85% of the cumulative methane production at the end of the experiment. The BMP test time is shortened by 26.98% to 72.06%. Among the screened materials, the methane production potential (calculated using BMP1%) of dry rice straw, maize leaves, fresh rice, soybean straw, maize stalks, chicken manure hydrolysate, chicken feathers, kitchen/food waste, and chicken offal are 234.14, 241.01, 253.34, 331.40, 305.80, 508.41, 510.10, 630.7, and 621.32 mL/g, respectively. The kinetic parameters show that among the nine materials, cellulose materials (except for maize stalks and soybean straw), chicken manure, and kitchen waste are easily degradable materials. In contrast, chicken feathers and offal are slowly degradable materials. The study posits that comparing standardized methane production potential and methane production kinetic parameters among materials improves the efficiency of screening materials and is critical for biogas projects.

A CROSS COUNTRY (AZERBAIJAN AND TURKEY) COMPARISON ON THE RENEWABLE ENERGY POTENTIAL OF AZERBAIJAN AND TURKEY AND THE JOB SATISFACTION OF EMPLOYEES IN THE ENERGY SECTOR

This research conducts a comparative analysis of the renewable energy potential and job satisfaction within the energy sectors of Azerbaijan and Turkey. Drawing upon insights from discussions within this thread, the research explores the unique characteristics, challenges, and opportunities inherent in each country's energy landscape. It examines factors such as resource availability, technological advancements, policy frameworks, and workforce dynamics to provide comprehensive insights into the complex relationship between sustainable energy development and employee well-being. The research reveals that Azerbaijan and Turkey both exhibit significant renewable energy potential, encompassing diverse sources such as solar, wind, hydroelectric, geothermal, and biomass energy. Both countries are investing in various projects to increase their future potential in the field of renewable energy. Azerbaijan plans to increase its renewable energy capacity by focusing on solar and wind energy projects. Türkiye, on the other hand, focuses on sustainable energy production with solar, wind and hydroelectric energy projects. Cooperation and joint projects between Azerbaijan and Turkey can enable more efficient use of the renewable energy potential in the region. For example, through energy trade and joint infrastructure projects, both countries can increase energy supplies and support sustainable development. While Azerbaijan's transition to renewable energy is relatively nascent, its rich fossil fuel reserves necessitate a strategic shift towards sustainable alternatives. In contrast, Turkey, amid rapid industrialization and urbanization, has prioritized the development of renewable energy sources to meet growing demands and address environmental concerns. Furthermore, the research highlights the importance of job satisfaction in fostering a resilient and motivated workforce within the energy sectors of both countries. Factors influencing job satisfaction, including compensation, career advancement opportunities, organizational culture, and work-life balance, are analyzed to identify areas for improvement and strategic interventions. By understanding and addressing these factors, energy companies in Azerbaijan and Turkey can enhance employee engagement, retention, and overall organizational performance amidst the evolving energy landscape. Keywords: Renewable energy, Azerbaijan, Turkey, Energy sector, Job satisfaction, Sustainable development.

Advances in hierarchically porous materials: Fundamentals, preparation and applications

Hierarchically porous materials have enormous potential in energy storage, thermal management, desalination, biomedicine, and catalytic reactions for their superior thermophysical properties, high specific surface area, low density, and excellent accommodation capability with volume and thermal variation. As the demand for sustainable energy solutions continues to grow, it is crucial to optimize and fabricate hierarchically porous materials that can cater to diverse application scenarios. Based on the significant progress made in recent years, this study aims to review advanced hierarchically porous materials preparation methods and engineering applications. This study first introduces the fluid flow and heat transfer mechanism in hierarchical porous materials, providing a theoretical basis for the structural design and preparation methods to improve the evaporation performance of porous materials. The study focuses on the design and preparation methods of hierarchically porous materials, including metallic materials, ceramic materials, and carbon-based materials. In addition, the application scenarios of hierarchically porous materials are introduced, and the existing challenges and prospects of the design, preparation, and application of hierarchically porous materials are explained. The review provides an attempt to investigate the potential of hierarchically porous materials to become important contributors to global renewable and sustainable energy development.

Advancing Sustainable Energy Development: A Study of the Factors Influencing Distributed Photovoltaic Industry in Heilongjiang Province

In today’s energy industry, the development of clean energy has become a global concern. As an important clean energy technology, photovoltaic power generation (PV) is widely used in various countries and regions, helping to reduce the dependence on traditional fossil energy sources and to reduce greenhouse gas emissions. Existing studies have some shortcomings in terms of factors affecting distributed PV grid-connected capacity. First, previous studies tend to focus on the influence of a single factor on the grid-connected capacity of distributed PV, ignoring the interaction effects among multiple factors. Second, the sample scope in existing studies is relatively narrow, often limited to specific regions or countries, and lacks comprehensive regional comparative analysis. In addition, previous studies often do not fully consider the moderating effect of carbon emission intensity on the influencing factors. Based on the above deficiencies, this study aims to build a regression model by analyzing panel data of central municipalities and provinces including Heilongjiang, Shandong and Beijing over the past three years, and construct an interaction model based on the interaction term. The empirical results show that effective sunshine hours, average feed-in tariff and annual electricity consumption are the main factors affecting the grid-connected capacity of distributed PV with a positive effect. The regional heterogeneity analysis also finds that carbon emission intensity enhances the impact of each factor on distributed PV grid-connected capacity. The regulation effect analysis confirms that the average feed-in tariff and annual electricity consumption have a regulation effect. With the results of this study, we fill the knowledge gaps left by previous studies and provide a new perspective to explore the influencing factors of distributed PV. A deeper understanding of the impact of these factors on distributed PV capacity helps us to better understand and optimize the potential of PV generation. In addition, our findings provide policy makers with recommendations and guidance on how to promote distributed PV generation, thereby contributing to the sustainable development of the clean energy sector.

Enhanced capture of uranyl in simulated seawater by supramolecular PSA@PAO hydrogel through carboxyl synergistic effect

Efficient capture of uranium from seawater is of great significance for promoting the sustainable development of nuclear energy and environmental protection. The preparation of highly efficient adsorbent materials plays a key role in this process. In this study, a composite gel PSA@PAO with ultra-high uranium adsorption capacity and excellent desorption efficiency was successfully designed through the supramolecular crosslinking and synergistic effect of glutaraldehyde, hydrazine oxime, sodium alginate, and polyvinyl alcohol. The gel achieved a uranium adsorption capacity of 348 mg·g−1 in a solution with an extremely low uranium concentration (8 ppm) and exhibited ultra-high adsorption selectivity for uranium oxyanions in simulated seawater containing various metal ions. XPS analysis showed that the acylhydrazone groups and carboxylic acid ligands in the PSA@PAO gel could form more stable synergistic uranium complexes. In the desorption solution, the recovery rate of uranium by the PSA@PAO gel reached 98 %, providing a certain strategy for designing efficient adsorbent materials with multi-functional group synergistic effect.

A master-slave adaptive linear neuron-based approach for cost-effective use of battery energy storage systems in wind farms

Nowadays, clean and sustainable energy development is of essential concern, which justifies use of renewable energy sources. Wind energy is an important resource to provide such a demand. Due to the high costs of wind power generation compared to other renewable sources, the wind turbines should be designed in such a way that they usually operate at the highest point of their power. In this case, because of the random and alternating wind speed, the output power of the wind turbine generators and thus the windfarms fluctuate. When the capacity of windfarms is increased, the power injected from the windfarm into the grid can have significant negative effects on the power grid stability. In order to prevent these effects, it is necessary to use BESSs in windfarms. To this end, a waveform with acceptable fluctuations is considered for the windfarm output power. This waveform is compared with the output power waveform and then the difference between the two waveforms is compensated by a BESS. Proper tracking with the minimum delay reduces the required BESS capacity and thus initial investment cost for the windfarms is significantly reduced. In this paper, using real windfarm data, the conventional tracking methods for the windfarm output power waveform are analyzed and compared, first. Then, a novel tracking scheme, namely the MSA, is proposed, which is based on a two-fold master-slave adaptive linear neuron. Acquired results show that compared with the conventional methods, using the MSA scheme can reduce the costs of a 99 MW windfarm by 4.8 million dollars.

Integrating Ray Tracing with Single-Objective Optimization in Heliostat Field Design

Tower solar thermal power generation is a new type of low-carbon and environmentally friendly clean energy technology. Through continuous optimization of the optical efficiency of mirror field and other key technologies, the energy utilization efficiency and economic benefits of solar thermal power stations can be improved, and contribute to the sustainable development of clean energy. This paper establishes a geometric model based on the light tracing method to solve the daily average, average annual optical efficiency, thermal output power, and average optical efficiency of the heliostatic field. The cosine efficiency is the cosine of the incident light and the normal angle, which is the cosine of the incidence angle; the atmospheric transmission is only related to the distance between the mirror center and the collector center; the mirror reflection constant is 0.92. The shadow occlusion efficiency and the truncation efficiency of the collector are the difficulties in the process of calculating the optical efficiency: for the calculation of the shadow occlusion efficiency, the Monte Carlo light tracing method is adopted. Based on the energy density of the helioscope, the occlusion efficiency of the shadow can be expressed as the ratio between the amount of the outgoing light to the total generated amount; for the calculation of the collector truncation efficiency, it links the reflected light with the surface equation of the collector. According to the joint results, if there is a solution, it means that the reflected light can be reflected in the collector, and the collector truncation efficiency can represent the ratio of the number of lights reflected in the collector to the number of unblocked lights in all the reflected lines. Finally, the average annual optical efficiency and output power parameters are respectively: 0.6093, 0.7431, 0.9283, 0.9828, 35. 4430MW, 0.5642kW/m². Results for the remaining months are shown in the main text.

Energy Policy and Climate Change: Challenges for Public Sector Organizations in South Sulawesi

This paper aims to discuss about some issues and prospect that might faced by the power policies for public sector organizations in South Sulawesi. It celebrates the richness of the renewable power sources available within the region and the possibility of the enhanced opportunities brought about through technological advancement, at the same time as identifying strategic limitations including economic limitations, infrastructural challenges, regulatory issues, political considerations, social acceptance concerns, technological challenges amongst others. The paper offers recommendations, including reforms for coverage and financing as well as current funding mechanisms, building an institutional framework, partnership, and integration of climate resilience into the network’s capacity. Al these strategies are needed for providing solutions to the boundaries and for effective implementation of the sustainable energy development in the region which contributes for economic boom, social advancement and overall climatic trade system, mitigation process.

Innovative approach for efficient extraction of Nd from electrorefining waste salt into nuclear waste forms

Electrorefining of the high-burnup spent nuclear fuels is critical to the closed fuel cycle in fast reactors for the sustainable development of nuclear energy. However, a remaining challenge in the electrorefining process is the separation of radioactive rare earth fission products to reduce the geological waste volume. In this work, the extraction of a major radionuclide Nd from the molten waste salt into durable waste forms was investigated through a two-step approach consisting of electrolytic reduction and oxidative extraction. Liquid Bi was found to be effective in the electrolytic extraction of Nd, leading to an extraction efficiency of up to 98.6% and a current efficiency of 93.5%. The Nd target in the resulting Nd–Bi cathode alloy was then selectively extracted using specific oxidizers in a glass melting step. High loadings of Nd2O3 in the converted zinc-in-titania glass ceramic and aluminoborosilicate glass waste forms were achieved, demonstrating the prospect of the proposed method in terms of minimizing radioactive waste generation and recycling of the electrorefining waste salt.

Urban energy transition in renewable energy management considering policy and law challenges and opportunities in the pursuit of clean and sustainable urban environments

This research presents a novel approach to enhance sustainable energy development based on wind turbine (WT) output power prediction through the development of a hybrid deep learning model, considering policy and law challenges associated with the problem. The proposed model integrates a Self-Supervised Generative Adversarial Network (GAN) with Attention-based Recurrent Neural Networks (RNNs). The Self-Supervised GAN contributes to improved feature representation learning, while Attention-based RNNs capture temporal dependencies for accurate WT output power forecasting considering political challenges. Additionally, a Modified Adaptive Flower Pollination Algorithm (AFPA) is introduced to optimize the selection of the GAN model structure in view of the legal and political objectives. This adaptive approach refines the architecture of the GAN, enhancing its ability to generate realistic and informative features for the subsequent Attention-based RNNs. The efficacy of the hybrid model is demonstrated through real-world applications in an urban area, emphasizing sensitivity and stability analyses. A thorough investigation into the sensitivity of key model parameters, including the number of hidden units in the Attention-based RNNs and GAN structures, is conducted to fine-tune the model's performance. Stability analysis is employed to assess the robustness of the proposed method under dynamic conditions, ensuring its reliability in practical scenarios. Results indicate that the hybrid model, guided by the Modified AFPA, outperforms existing approaches in WT output power prediction. The proposed methodology showcases the significance of combining state-of-the-art deep learning techniques with adaptive optimization algorithms for accurate and stable predictions in urban energy management applications.

Multistage regulation of LiMn2O4 electrode for electrochemical lithium extraction from salt-lake

The sustainable development of future energy relies significantly on the abundant lithium resources present in salt-lake brines. LiMn2O4 (LMO) electrochemical Li+ pump has garnered substantial attention due to its capacity to efficiently extract lithium resources from aqueous solutions with minimal energy consumption. However, the industrial application of LMO electrodes is often limited by Mn dissolving during cycles, as well as the complex and variable brine environment. To address these challenges, this study conducted a multistage regulation of LMO electrodes. Originally, the sol-gel technique was used to prepare CePO4 loading LMO. Due to CePO4 rivet load reforming LMO surface charge, the best-performing 1 wt% CePO4-loaded LMO exhibited a remarkable capacity retention rate of 83.8 % after 30 cycles. Subsequently, a comprehensive hydrophilic modification was applied to the entire electrode. 20%PAA-1CP-LMO//Ag system displayed an admirably release capacity of 24.7 mg·g−1 in Zabuye real brine characterized by an ultra-low Li/Na ratio, with Li/Na ratio in recovery solution reaching as high as 0.62.

Aminated alkali lignin nanoparticles enabled formaldehyde-free biomass wood adhesive with high strength, toughness, and mildew resistance

The conversion of industrial lignin into wood adhesives offers a solution to issues related to formaldehyde pollution and the reliance on petrochemical resources of traditional formaldehyde-based resins, while promoting environmental and energy sustainability. However, conventional methods for preparing lignin-based adhesives generally involve the use of materials like formaldehyde, organic solvents, and costly epoxy, hindering their industrial application. This study introduces a novel approach for producing eco-friendly lignin-based wood adhesives using aminated lignin-Cu nanoparticles. The lignin-Cu nanoparticles were synthesized in water as a single solvent through a sedimentation method. Amination modification was achieved by grafting amino-terminated hyperbranched polyamide onto the nanoparticles via a Schiff base reaction. The prepared adhesive demonstrated a remarkable bonding strength of 1.51 MPa and debonding work of 0.272 J, which were 2.75 and 6.33 times higher than those of the pure lignin adhesive, respectively. Moreover, the adhesive exhibited improved water resistance with a wet shear strength of 1.15 MPa and sustained mildew resistance for approximately 100 days. Notably, this adhesive was synthesized without the use of formaldehyde, phenol, acids, or organic solvents, minimizing reliance on fossil resources and lowering costs compared to many formaldehyde-based and biomass adhesives. This research opens up new avenues for the efficient utilization of industrial lignin and the production of formaldehyde-free, environmentally friendly adhesives, which holds significant promise for energy conservation, emission reduction, and sustainable development within the wood-based panel industry.