Green Technologies and Sustainability最新文献

{"title":"Financial inclusion and green technological innovation as drivers of human capital: Evidence from E7 countries","authors":"Husam N. Yasin ,&nbsp;Ahmed Samour ,&nbsp;Mohammad W. Alomari ,&nbsp;Samer Abdelhadi","doi":"10.1016/j.grets.2025.100277","DOIUrl":"10.1016/j.grets.2025.100277","url":null,"abstract":"<div><div>The acceleration of green technological innovation is an important strategy to sustain economic and human development. Prior studies focus on the financial inclusion-human capital linkage but overlook the role of green technological innovation within the financial inclusion-human capital framework. This study bridges this gap by examining the influence of financial inclusion on human capital by considering the role of green technological innovation. We use panel data from seven emerging (E7) countries, namely Brazil, China, India, Indonesia, Mexico, Russia, and Turkey, from 2004 to 2021. We selected these nations due to their rapidly evolving financial ecosystems and urgent sustainability challenges. We adopted the Method of Moments Quantile Regression (MMQR) approach, which captures heterogeneous effects across quantiles to analyze the heterogeneous relationship among the selected variables. The outcomes state that financial inclusion promotes human capital across all quantiles. Likewise, the outcomes show that green technological innovation positively and significantly affects human capital across low, medium, and high quantiles. Additionally, the robustness check study is supported by parametric methods, including FMOLS and CCR regression. Furthermore, using the Dumitrescu–Hurlin technique, the study finds a bidirectional causality between technological innovation and human capital and a bidirectional causality between technological innovation and financial inclusion. Based on these findings, the study suggests helpful lessons for top emerging economies in stimulating human capital via green technological innovation and financial inclusion channels. Key policy recommendations include investing in green technology education and skills training; and promoting green bonds and green technology entrepreneurship by providing support and resources, which can all strengthen human capital.</div></div>","PeriodicalId":100598,"journal":{"name":"Green Technologies and Sustainability","volume":"4 2","pages":"Article 100277"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel and hybrid model of pelican optimization algorithm with light gradient boosting model for smart infrastructure: A high-precision approach to occupancy and failure prediction","authors":"Chao Pan","doi":"10.1016/j.grets.2025.100323","DOIUrl":"10.1016/j.grets.2025.100323","url":null,"abstract":"<div><div>Prediction of building occupancy is a very important element of structural health monitoring (SHM), smart building systems, and energy management. In this study, a number of visualization tools are used to analyze the performance of models, which would allow engineers and operations personnel of a system to interpret forecasts and review them to make informed decisions based on the data on the structural health of the building. It also introduces a new machine learning (ML) system, coupled with optimization schemes, to improve the efficiency and accuracy of prediction. This paper employs a set of state-of-the-art ML models, such as Light Gradient Boosting Machine (LightGBM), LightGBM Logistic Regression (LR), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting (GBM), and Random Forest (RF), to come up with a model to predict occupancy. The model is applicable in structural health monitoring, smart building applications, and energy management. The proposed POA-LightGBM approach has a high F1-score of training (0.9373) and test (0.9267), respectively. In addition, the model has enhanced AUC–ROC values of 0.9995 and 0.9990 during training and testing stages, respectively, meaning that the model has a high degree of classification. These findings underline the efficiency, effectiveness, and dependability of the POA-LightGBM approach, solving the practical issues of structural health and failure prediction. By implementing the proposed approach, based on the optimization of performance with the help of ML, to enhance the resiliency of infrastructure and energy efficiency of smart buildings, an efficient and effective solution can be provided. The proposed solution would promote energy through streamlined predictive modeling, smart building efficiency, and resilience of infrastructure.</div></div>","PeriodicalId":100598,"journal":{"name":"Green Technologies and Sustainability","volume":"4 2","pages":"Article 100323"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High efficiency and sustainability of PVDF membrane in wastewater treatment and reuse","authors":"Fuxing Lin ,&nbsp;Xiaoyan Lin ,&nbsp;Jiaye Li ,&nbsp;Jiaxin Zeng ,&nbsp;Weifan Lin ,&nbsp;Jianhua Wang","doi":"10.1016/j.grets.2025.100317","DOIUrl":"10.1016/j.grets.2025.100317","url":null,"abstract":"<div><div>To address the challenges of membrane fouling, short service life, and unstable performance associated with PVDF membranes in wastewater treatment and reuse, this study proposes a multi-dimensional collaborative optimization approach integrating four core strategies: material modification, structural optimization, process synergy, and intelligent operation. At the material level, a TiO ₂ nanoparticle-composite PVDF membrane is combined with low-temperature plasma surface treatment and polyethylene glycol coating to enhance its hydrophilicity and anti-fouling capabilities. Structurally, a double-layer membrane with a gradient pore structure is designed, and porosity and pore size distribution are manipulated through a template-based method and thermally induced phase separation. Pretreatment and photocatalytic self-cleaning technologies are integrated into the process to reduce membrane fouling. Intelligent operation utilizes real-time sensor monitoring and machine learning models to dynamically optimize operating parameters. Results show that the optimized PVDF membrane achieves a COD removal rate of 93%, 2.5 times that of conventional membranes, extends its service life to 8 years, reduces system energy consumption to 1.0 kWh/m³, and demonstrates excellent treatment performance for industrial, municipal, agricultural, and domestic wastewater. This study provides a feasible solution for the large-scale application of membrane technology for efficient and sustainable wastewater treatment, while also providing technical support for water resource management and environmental sustainability.</div></div>","PeriodicalId":100598,"journal":{"name":"Green Technologies and Sustainability","volume":"4 2","pages":"Article 100317"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Eco-efficient concrete with various treatment methods of coal bottom ash: A review on mechanical and durability performance","authors":"Mohammad I. Al Biajawi ,&nbsp;Rahimah Embong ,&nbsp;Bassam A. Tayeh ,&nbsp;Andri Kusbiantoro","doi":"10.1016/j.grets.2025.100258","DOIUrl":"10.1016/j.grets.2025.100258","url":null,"abstract":"<div><div>The energy sector produces large amounts of coal ash waste to generate power. To meet increasing energy needs, thermal power plants often generate vast quantities of coal bottom ash (CBA) and contribute to further environmental degradation. To maintain a greener environment and create a more sustainable ecosystem, researchers have been gradually implementing an effective and sustainable approach for reusing or recycling industrial waste materials, such as CBA in production concrete mixes. (CBA) possesses favorable properties, making it a suitable partial replacement for primary materials in construction applications. Previous experiments studies have demonstrated that utilizing a sufficient quantity of CBA can enhance the strength and durability of concrete properties. This study primarily aims to investigate the effect of CBA on the properties of concrete, particularly the mechanical and durability properties using various treatment methods, based on previously published data. Furthermore, this study was reviewed to provide a comprehensive literature review on the durability and sustainable production of CBA in the construction industry. This study also aims to investigate the potential of recycling CBA in the construction industry. Numerous previous researchers have advocated the recycling of a variety of CBA wastes to conserve valuable green space. Therefore, to reduce reliance on river sand mining and ensure the sustainability of green rivers globally, we recommend more productive uses instead of dumping. This review paper supports and promotes more efficient reuse of CBA, as well as providing details on its many uses in the building sector.</div></div>","PeriodicalId":100598,"journal":{"name":"Green Technologies and Sustainability","volume":"4 1","pages":"Article 100258"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven lightweight CNN model for sustainable vegetable classification in smart food systems","authors":"Akshat Gaurav ,&nbsp;Vincent Shin-Hung Pan ,&nbsp;Varsha Arya ,&nbsp;Ramakrishnan Raman ,&nbsp;Brij B. Gupta ,&nbsp;Kwok Tai Chui","doi":"10.1016/j.grets.2025.100257","DOIUrl":"10.1016/j.grets.2025.100257","url":null,"abstract":"<div><div>The increasing need for sustainable and environmentally friendly technological solutions in agriculture requires AI approaches that are both precise and computationally efficient. In this context, this study introduces a lightweight AI-driven convolutional neural network (CNN) that is designed for sustainable vegetable classification within intelligent food systems. The model is characterized by a compact architecture that incorporates convolutional layers for spatial feature extraction and Squeeze-and-Excitation (SE) blocks for adaptive channel-wise attention. It achieves a high classification accuracy of 98% while maintaining a minimal computational burden with only 0.39 million parameters and 15.63 GFLOPs. Compared to deeper models such as ResNet18 and GoogLeNet, the proposed model demonstrates superior efficiency and faster inference. Its low power consumption and real-time processing capabilities make it particularly suitable for implementation in resource-limited, environmentally friendly agricultural settings.</div></div>","PeriodicalId":100598,"journal":{"name":"Green Technologies and Sustainability","volume":"4 1","pages":"Article 100257"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable agriculture through environmental adaptation engineering for waste management","authors":"Debasmita Behera ,&nbsp;Jesna Fathima ,&nbsp;Noori M. Cata Saady ,&nbsp;Sohrab Zendehboudi ,&nbsp;Talib M. Albayati ,&nbsp;Abbas Al-nayili ,&nbsp;Pritha Chatterjee ,&nbsp;V. Ponnusami ,&nbsp;Brian Peach ,&nbsp;Juan E. Ruiz Espinoza","doi":"10.1016/j.grets.2025.100242","DOIUrl":"10.1016/j.grets.2025.100242","url":null,"abstract":"<div><div>Global climate change destabilizes ecosystems, weather, and human livelihoods. Because it uses the industrial farming model, agriculture generates 10% of the global greenhouse gas emissions. However, food production must increase by 70% by 2050; achieving this goal under the evolving and dynamic climate change and its impacts and repercussions is challenging. This review explores how environmental adaptation engineering can transform agriculture to a sustainable, resilient, low-carbon system that balances productivity with environmental stewardship, and describes policies and practices supporting this transformation. It uses a comprehensive bibliometric analysis, updated climate data (e.g., IPCC AR6), and an integrative literature review of agricultural practices, environmental engineering innovations, adaptive biotechnologies, socioeconomic aspects, community involvement, and policy implications. It introduces the novel ecological farm model that aligns climate resilience, resource efficiency, and circular economy principles. It innovatively bridges a gap in the literature by synthesizing advances in hydroponics, anaerobic digestion, and microalgae technologies as an integrated adaptation strategy to address agricultural vulnerabilities under climate change. It highlights the potential of these environmental engineering solutions to manage waste, reduce emissions, generate renewable biofuels, sequester and convert CO₂ into biomass, optimize water use, recover nutrients, enhance crop quality and yield, and restore the environment. We highlight how important community engagement, knowledge sharing, and capacity building are in adopting adaptation practices across diverse socioeconomic settings. By integrating these approaches, adaptation engineering can align agricultural productivity with ecological responsibility. The findings suggest that incorporating adaptive technologies in agriculture is crucial to mitigate climate impacts and build sustainable, inclusive, and resilient food systems, ensuring long-term environmental and societal well-being.</div></div>","PeriodicalId":100598,"journal":{"name":"Green Technologies and Sustainability","volume":"4 1","pages":"Article 100242"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing small and medium-sized enterprises’ sustainability in global value chains: The impact of multinational corporations on Bangladesh’s garment industry","authors":"Md Moniruzzaman Kiron ,&nbsp;Md Faysal ,&nbsp;Md Monaim Islam ,&nbsp;Md Estehad Chowdhury ,&nbsp;Md Kariul Islam","doi":"10.1016/j.grets.2025.100265","DOIUrl":"10.1016/j.grets.2025.100265","url":null,"abstract":"<div><div>This paper presents a detailed investigation into the influence of multinational corporations (MNCs) on the sustainability practices of small and medium-sized enterprises (SMEs) operating within Bangladesh’s garment industry, an integral part of global value chains (GVCs). Drawing on empirical data from 65 SMEs in Bangladesh and complemented by secondary data from the Asian Development Bank (ADB), the study employs advanced regression and correlation analyses to unravel the dual-edged effect of the MNC-SME relationship. The findings reveal that while MNC support positively affects SMEs’ sustainability initiatives, the stringent sustainability requirements exacerbate operational inefficiencies, creating a paradox where sustainability initiatives contribute to both growth and constraint. The research concludes that for MNC-driven sustainability efforts to be truly effective, a more balanced and collaborative approach is necessary, one that addresses the unique resource constraints faced by SMEs in emerging markets. This study offers valuable insights into the evolving discourse on sustainability within GVCs, highlighting the strategic role of MNCs and their influence on the sustainability and competitiveness of SMEs in developing economies.</div></div>","PeriodicalId":100598,"journal":{"name":"Green Technologies and Sustainability","volume":"4 1","pages":"Article 100265"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Process analysis and optimization of biocompatible diesel production from blended vegetable oils using KOH-impregnated eggshells derived CaO catalyst","authors":"Anietie O. Etim ,&nbsp;P. Musonge","doi":"10.1016/j.grets.2025.100262","DOIUrl":"10.1016/j.grets.2025.100262","url":null,"abstract":"<div><div>The approach of feedstock blending for biodiesel production is one of the viable and cost-effective ways to reduce costs and overcome the logjams in feedstock availability. The present study investigates biodiesel synthesis from blended linseed-baobab seed oil (LOBO) via a transesterification process using a KOH-impregnated eggshell-CaO nano-catalyst (KOH/es-CaO NC). The catalyst was prepared through <em>in-situ</em> impregnation and calcination methods to obtain a K-modified CaO, which was further characterized using scientific analytical equipment: SEM, EDS, XRD, TGA, and FT-IR. The results revealed a meso-porous structured composite catalyst with a high content of CaO, and potassium containing species; K<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>Ca (CO<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>)<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>, indicating its high basicity and potential activity in the transesterification of LOBO. A central composite design - response surface methodology (CCD-RSM) was used to examine the effects of independent variables on process efficiency. The model established an optimal yield of 94.04 wt% under the process conditions of molar ratio MeOH/LOBO of 18:1, KOH/es-CaO-900 loading of 5.5 wt%, and reaction 64 min, at a fixed temperature of 65 <span><math><msup><mrow></mrow><mrow><mi>o</mi></mrow></msup></math></span>C and agitation speed of 650 rpm. The statistical model, R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> (0.9758), RMSE (0.46%), SEP (0.51%), and AAD (0.005%), confirmed the high ability and accuracy of the model with minimal deviation between experimental and predicted data. P &lt; 0.0001 suggests a highly statistically significant regression model. Furthermore, the physicochemical properties of the synthesized catalyst revealed an enhanced durability and sustained activity potential for recycling across multiple applications in the biodiesel production process. The fuel characteristics of the biodiesel produced were found to conform to ASTM D6751, EN 14214, and SANS 1935:2011, making it a viable alternative fuel source to petroleum-derived diesel.</div></div>","PeriodicalId":100598,"journal":{"name":"Green Technologies and Sustainability","volume":"4 1","pages":"Article 100262"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bee Vectoring Technology (BVT) as a nature-based strategy for sustainable pest control management","authors":"Wasswa Shafik","doi":"10.1016/j.grets.2025.100267","DOIUrl":"10.1016/j.grets.2025.100267","url":null,"abstract":"<div><div>Global agriculture continues to face increasing abiotic and biotic stresses, with pest and disease outbreaks representing major constraints to crop productivity and sustainability. Conventional chemical-based control measures, while effective, pose environmental risks and compromise pollinator health. Bee Vectoring Technology (BVT) offers an innovative, nature-based strategy that leverages the natural foraging behavior of managed pollinators, principally <em>Apis mellifera</em> and <em>Bombus spp.</em>, to deliver beneficial microbial control agents directly to floral targets. This targeted delivery system facilitates the deposition of antagonistic microorganisms, including <em>Clonostachys rosea CR-7</em>, <em>Aureobasidium pullulans, Bacillus thuringiensis, Pseudomonas fluorescens,</em> and <em>Trichoderma harzianum,</em> precisely at infection courts where pathogen ingress typically occurs. By reducing reliance on synthetic pesticides, BVT enhances ecological resilience, minimizes non-target contamination, and supports pollinator safety. This paper evaluates BVT mechanisms of action, hive-mounted dispenser deployment, and integration with digital agriculture platforms such as sensor-enabled smart hives and the Internet of Things (IoT)-based field monitoring for real-time decision support. Evidence from diverse cropping systems (for instance, berries, tomatoes, and sunflowers) demonstrates significant reductions in disease incidence, improved yield quality, and economic viability under large-scale implementation. The regulatory landscape, biosafety considerations, and compatibility with integrated pest management (IPM) frameworks are critically assessed. Therefore, BVT emerges as a scalable, climate-smart innovation that aligns with Sustainable Development Goals (SDGs) by advancing environmentally responsible, economically feasible, and pollinator-friendly pest management practices.</div></div>","PeriodicalId":100598,"journal":{"name":"Green Technologies and Sustainability","volume":"4 1","pages":"Article 100267"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors related to protective behaviors and interventions targeting PM2.5 exposure reduction: A systematic review","authors":"Weerawat Ounsaneha ,&nbsp;Jeevan Bhatta ,&nbsp;Cheerawit Rattanapan","doi":"10.1016/j.grets.2025.100256","DOIUrl":"10.1016/j.grets.2025.100256","url":null,"abstract":"<div><div>Fine particulate matter (PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span>) exposure presents a significant global health burden. While policy interventions remain essential for emission reduction, individual-level protective behaviors may mitigate immediate exposure. However, the effectiveness of these interventions and factors influencing their adoption remain incompletely characterized. This systematic review aims to (1) characterize protective behaviors and interventions targeting PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span> exposure reduction, (2) evaluate their effectiveness, (3) identify factors influencing their adoption, and (4) synthesize evidence across diverse exposure contexts. Following PRISMA guidelines, we searched three electronic databases (PubMed, Scopus, and Embase) for studies published between 2015 and 2024 that examined protective behaviors or interventions against air pollution exposure reduction. Quality assessment employed design-specific tools (Cochrane Risk of Bias 2.0, STROBE, MMAT, NOS). Data were synthesized using a PICOS framework (Population, Intervention, Comparison, Outcomes, Study design). Thirteen studies met the inclusion criteria. Interventions comprised technological solutions (air purifiers, masks), structural modifications (ventilation improvements), and behavioral adaptations (activity reduction). A randomized controlled trial demonstrated that multi-component interventions reduced PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span> exposure by <span><math><mrow><mn>5</mn><mo>.</mo><mn>7</mn><mspace></mspace><mi>μ</mi><mi>g</mi></mrow></math></span>/m³ with corresponding health improvements in asthma patients. Modeling evidence suggested that protective behaviors could reduce PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span>-associated mortality by 5.7% in highly polluted regions. Psychosocial factors, particularly risk perception, self-efficacy (<span><math><mrow><mi>β</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>47</mn></mrow></math></span>), and social norms (<span><math><mrow><mi>β</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>37</mn></mrow></math></span>), consistently predicted protective behavior adoption. Significant contextual differences existed across exposure settings, varying intervention effectiveness by pollution source and socioeconomic context. This review provides evidence supporting multi-component PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span> reduction interventions targeting exposure pathways and behavior’s psychosocial determinants. Contextual tailoring is essential for intervention effectiveness.</div></div>","PeriodicalId":100598,"journal":{"name":"Green Technologies and Sustainability","volume":"4 1","pages":"Article 100256"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}