Nano/Micro-Structural Supramolecular Biopolymers: Innovative Networks with the Boundless Potential in Sustainable Agriculture

Sustainable agriculture plays a crucial role in meeting the growing global demand for food while minimizing adverse environmental impacts from the overuse of synthetic pesticides and conventional fertilizers.In this context,renewable biopolymers being more sustainable offer a viable solution to improve agricultural sustainability and production.Nano/micro-structural supramolecular biopolymers are among these innovative biopolymers that are much sought after for their unique features.These biomaterials have complex hierarchical structures,great stability,adjustable mechanical strength,stimuli-responsiveness,and self-healing attributes.Functional molecules may be added to their flexible structure,for enabling novel agricultural uses.This overview scrutinizes how nano/micro-structural supramolecular biopolymers may radically alter farming practices and solve lingering problems in agricultural sector namely improve agricultural production,soil health,and resource efficiency.Controlled bioactive ingredient released from biopolymers allows the tailored administration of agrochemicals,bioactive agents,and biostimulators as they enhance nutrient absorption,moisture retention,and root growth.Nano/micro-structural supramolecular biopolymers may protect crops by appending antimicrobials and biosensing entities while their eco-friendliness supports sustainable agriculture.Despite their potential,further studies are warranted to understand and optimize their usage in agricultural domain.This effort seeks to bridge the knowledge gap by investigating their applications,challenges,and future prospects in the agricultural sector.Through experimental investigations and theoretical modeling,this overview aims to provide valuable insights into the practical implementation and optimization of supramolecular biopolymers in sustainable agriculture,ultimately contributing to the development of innovative and eco-friendly solutions to enhance agricultural productivity while minimizing environmental impact.

Sustainable Mining in the Era of Artificial Intelligence

The mining sector historically drove the global economy but at the expense of severe environmental and health repercussions,posing sustainability challenges[1]-[3].Recent advancements on artificial intelligence(AI)are revolutionizing mining through robotic and data-driven innovations[4]-[7].While AI offers mining industry advantages,it is crucial to acknowledge the potential risks associated with its widespread use.Over-reliance on AI may lead to a loss of human control over mining operations in the future,resulting in unpredictable consequences.

‘Jelly to Joule’:Direct laser writing of sustainable jellyfish-based ‘graphenic silicon’ anodes for environmentally remediating high-performance lithium-ion batteries

The ramifications of global climate change and resource scarcities have made it imperative to re-examine the definition of sustainable energy-storage systems.It is crucial to recognize that not all renewable resources are inherently sustainable,and their full impact on the environment must be assessed.With the proliferation of invasive jellyfish species wreaking havoc on marine ecosystems and economies worldwide,utilizing overabundant jellyfish as a carbon source presents an opportunity to create energy-storage systems that are both financially beneficial and environmentally remediating.Accordingly,a comprehensive approach to sustainability also requires eco-friendly solutions throughout the entire lifecycle,from material sourcing to battery production,without compromising highperformance requirements.Currently,most electrode syntheses for lithium-ion batteries(LIBs) employed are energy-intensive,multiple-steps,complex,and additive-heavy.In response,this work pioneers the straightforward use of low-energy laser irradiation of a jellyfish biomass/silicon nanoparticle blend to encapsulate the silicon nanoparticles in-situ within the as-forming conductive carbonized matrix,creating sustainable and additive-free composite anodes.The self-standing anode is directly synthesized under ambient conditions and requires no post-processing.Here,a laser-synthesized conductive threedimensional porous carbon/silicon composite anode from raw jellyfish biomass for LIBs is presented,displaying outstanding cyclic stability(>1000 cycles),excellent capacity retention(>50% retention after1000 cycles),exceptional coulombic efficiency(>99%),superb reversible gravimetric capacity(>2000 mAh/g),and high rate performance capability(>1.6 A/g),paving a new path to future sustainable energy production.

Role of outdoor trees on pedestrian wind and thermal conditions around a pre-education building for sustainable energy management

Finding sustainable energy resources is essential to face the increasing energy demand.Trees are an important part of ancient architecture but are becoming rare in urban areas.Trees can control and tune the pedestrian-level wind velocity and thermal condition.In this study,a numerical investigation is employed to assess the role of trees planted in the windward direction of the building complex on the thermal and pedestrian wind velocity conditions around/inside a pre-education building located in the center of the complex.Compared to the previous studies(which considered only outside buildings),this work considers the effects of trees on microclimate change both inside/outside buildings.Effects of different parameters including the leaf area density and number of trees,number of rows,far-field velocity magnitude,and thermal condition around the main building are assessed.The results show that the flow velocity in the spacing between the first-row buildings is reduced by 30%-40% when the one-row trees with 2 m height are planted 15 m farther than the buildings.Furthermore,two rows of trees are more effective in higher velocities and reduce the maximum velocity by about 50%.The investigation shows that trees also could reduce the temperature by about 1℃around the building.

Recycled graphite for more sustainable lithium-ion batteries

The demand for lithium-ion batteries(LIBs)is driven largely by their use in electric vehicles,which is projected to increase dramatically in the future.This great success,however,urgently calls for the efficient recycling of LIBs at the end of their life.Herein,we describe a froth flotation-based process to recycle graphite—the predominant active material for the negative electrode—from spent LIBs and investigate its reuse in newly assembled LIBs.It has been found that the structure and morphology of the recycled graphite are essentially unchanged compared to pristine commercial anode-grade graphite,and despite some minor impurities from the recycling process,the recycled graphite provides a remarkable reversible specific capacity of more than 350 mAh g^(−1).Even more importantly,newly assembled graphite‖NMC532 cells show excellent cycling stability with a capacity retention of 80%after 1000 cycles,that is,comparable to the performance of reference full cells comprising pristine commercial graphite.

Towards Sustainable Grinding of Difficult-to-Cut Alloys-A Holistic Review and Trends

Grinding,a critical precision machining process for difficult-to-cut alloys,has undergone continual technological advancements to improve machining efficiency.However,the sustainability of this process is gaining heightened attention due to significant challenges associated with the substantial specific grinding energy and the extensive heat generated when working with difficult-to-cut alloys,renowned for their exceptional physical and mechanical properties.In response to these challenges,the widespread application of massive coolant in manufacturing industries to dissipate grinding heat has led to complex post-cleaning and disposal processes.This,in turn,has resulted in issues such as large energy consumption,a considerable carbon footprint,and concerns related to worker health and safety,which have become the main factors that restrict the development of grinding technology.This paper provides a holistic review of sustainability in grinding difficult-to-cut alloys,encompassing current trends and future directions.The examination extends to developing grinding technologies explicitly tailored for these alloys,comprehensively evaluating their sustainability performance.Additionally,the exploration delves into innovative sustainable technologies,such as heat pipe/oscillating heat pipe grinding wheels,minimum quantity lubrication,cryogenic cooling,and others.These groundbreaking technologies aim to reduce dependence on hazardous coolants,minimizing energy and resource consumption and carbon emissions associated with coolant-related or subsequent disposal processes.The essence of these technologies lies in their potential to revolutionize traditional grinding practices,presenting environmentally friendly alternatives.Finally,future development trends and research directions are put forward to pursue the current limitation of sustainable grinding for difficult-to-cut alloys.This paper can guide future research and development efforts toward more environmentally friendly grinding operations by understanding the current state of sustainable grinding and identifying emerging trends.

Application of Stork Optimization Algorithm for Solving Sustainable Lot Size Optimization

The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and transparency.To address these challenges and improve operations in green manufacturing,optimization algorithms play a crucial role in supporting decision-making processes.In this study,we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms,notably the Stork Optimization Algorithm(SOA).The SOA draws inspiration from the hunting and winter migration strategies employed by storks in nature.The theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases:exploration,based on migration simulation,and exploitation,based on hunting strategy simulation.To tackle the green lot size optimization issue,our methodology involved gathering real-world data,which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO_(2) emissions.This function served as input for the SOA model.Subsequently,the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and sustainability.Through extensive experimentation,we compared the performance of SOA with twelve established metaheuristic algorithms,consistently demonstrating that SOA outperformed the others.This study’s contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma,thereby reducing environmental impact and enhancing supply chain efficiency.The simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies,making it a promising approach for green manufacturing and sustainable supply chain management.

Mapping upland crop–rice cropping systems for targeted sustainable intensification in South China

Upland crop-rice cropping systems(UCR)facilitate sustainable agricultural intensification.Accurate UCR cultivation mapping is needed to ensure food security,sustainable water management,and rural revitalization.However,datasets describing cropping systems are limited in spatial coverage and crop types.Mapping UCR is more challenging than crop identification and most existing approaches rely heavily on accurate phenology calendars and representative training samples,which limits its applications over large regions.We describe a novel algorithm(RRSS)for automatic mapping of upland crop-rice cropping systems using Sentinel-1 Synthetic Aperture Radar(SAR)and Sentinel-2 Multispectral Instrument(MSI)data.One indicator,the VV backscatter range,was proposed to discriminate UCR and another two indicators were designed by coupling greenness and pigment indices to further discriminate tobacco or oilseed UCR.The RRSS algorithm was applied to South China characterized by complex smallholder rice cropping systems and diverse topographic conditions.This study developed 10-m UCR maps of a major rice bowl in South China,the Xiang-Gan-Min(XGM)region.The performance of the RRSS algorithm was validated based on 5197 ground-truth reference sites,with an overall accuracy of 91.92%.There were7348 km^(2) areas of UCR,roughly one-half of them located in plains.The UCR was represented mainly by oilseed-UCR and tobacco-UCR,which contributed respectively 69%and 15%of UCR area.UCR patterns accounted for only one-tenth of rice production,which can be tripled by intensification from single rice cropping.Application to complex and fragmented subtropical regions suggested the spatiotemporal robustness of the RRSS algorithm,which could be further applied to generate 10-m UCR datasets for application at national or global scales.

Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part geopolymer.However,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported.Therefore,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part geopolymer.The database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing temperature.The performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed GEP.Sensitivity and parametric analysis were also performed to identify the significant contributor to strength.According to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 days.The water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.

Assessment of rural sustainable development and analysis and prediction of obstacles and coupled coordinated development:A case study of Zaozhuang City

In China,the Innovation Demonstration Zone for the National Sustainable Development Agenda is a key initiative for executing the United Nations(UN)2030 Agenda for Sustainable Development.The Zone also plays critical roles in addressing the developmental bottlenecks that China faces and sharing Chinese insights into global sustainable development efforts.On July 15,2022,the State Council endorsed the Zone’s establishment in Zaozhuang City,Shandong Province,where it focuses on innovation-led sustainable development in rural areas.Research into the levels,impediments,interdependencies,and evolutionary trends of rural sustainable development is crucial.Therefore,this research aimed to assist in comprehensively assessing developmental challenges and facilitating the harmonious advancement of social,economic,and environmental aspects in rural areas.In pursuit of the three fundamental dimensions of the UN’s Sustainable Development Goals(SDGs),namely development’s drivers,quality,and equity,this study was grounded in China’s national Rural Revitalization Strategy and the demands of sustainable development strategies.It also aligns with the UN 2030 Agenda for Sustainable Development and the associated SDG indicators.Focusing on four key areas,namely production elements,natural elements,social elements,and rural governance,this study developed an evaluation index system for assessing the level of rural sustainable development.It employed a range of analytical models,including the game theory-based combination empowerment method,barrier degree model,coupling degree model,coupling coordination degree model,and gray prediction GM(1,1)model,to analyze the status and evolving trends of rural sustainable development in Zaozhuang City from 2015 to 2022.The key findings were as follows:①Relative to the baseline year 2015,the sustainable development level in Zaozhuang’s rural areas has shifted toward an improved state overall.②The primary barrier to achieving rural sustainable development in Zaozhuang is the city’s rural governance system.③While the components of rural sustainable development in Zaozhuang are in the early stages of both basic and moderate coordination,an overall enhancement has occurred in their integrative coordination.④Between 2023 and 2025,the level of integrative coordination in Zaozhuang is expected to rise steadily.However,reaching a state of advanced coordination will require additional time for development.

Constructing a standard system of city sustainable development assessment technology based on the SDGs

Given the rapid development of China’s new urbanization,cities with different locations and varying functional positioning,resource endowments,and development stages have insufficient scientific and applicable technical tools for implementing the United Nations Sustainable Development Goals(SDGs).City managers and policymakers must urgently establish SDG benchmarks to diagnose city development.Moreover,successful experiences from similar cities regarding sustainable development and self-improvement must be learned from to promote diversified,sustainable development across the country.Furthermore,emerging technologies such as artificial intelligence,the Internet of Things,big data and 5G are widely used in smart cities.Therefore,there is a growing need for“knowledge-based,personalized and intelligent”technologies to support monitoring,evaluation,and decision-making processes facilitating sustainable development in cities.This paper uses standardization as the theoretical support and technical basis.This approach can help clarify the sustainable development processes in China and clarify the evaluation results of and provide data on horizontal city comparisons,which can be used to develop evaluation technology for sustainable development in cities and construct a standardized system.The results provide a standard framework for intelligent assessment and decision-making regarding cities’sustainable development capabilities in China.Evaluating major international standardization institutions reveals that the practices of Chinese national standards should be fully absorbed and integrated to guide the evaluation of smart,resilient,and low-carbon cities.To this end,an indicator library of city sustainable development is proposed to provide standard evaluation technology methods.Finally,analyzing the response relationship of the indicator library to SDGs reveals the need for a standardized knowledge map of sustainable development assessment techniques and methods from the perspective of integrated management for sustainable development in cities.

Sustainable Intensification and Large-scale Operation of Cultivated Land Use at the Farmers’ Scale:A Case Study of Shandong Province,China

Sustainable intensification of cultivated land use(SICLU) and large-scale operations(LSO) are widely acknowledged strategies for enhancing agricultural performance.However,the existing literature has faced challenges in precisely defining SICLU and constructing comprehensive indicators,which has hindered the exploration of factors influencing LSO within the SICLU framework.To address this gap,we integrated self-efficacy theory into the design of an index framework for evaluating SICLU.We subsequently employed econometric models to analyze the significant factors that impact LSO.Our findings reveal that SICLU can be divided into four key dimensions:intensive management,efficient output,resource conservation,and ecological environment optimization.Furthermore,it is crucial to incorporate belief-based cognitive factors into the index system,as farmers’ understanding of fertilizer and pesticide application significantly influences their willingness to engage in LSO.Moreover,we identify grain market turnover as the most influential factor in promoting LSO,with single-factor contribution rates reaching 70.9% for cultivated land transfer willingness and 62.5% for the total planting areas.Interestingly,unlike irrigation and agricultural machinery inputs,increased labor inputs correspond to larger planting areas for farmers.This trend may be attributed to reduced labor availability because of rural labor migration,whereas the reduction in irrigation and agricultural input is contingent on innovations in production practices and the transfer of cultivated land management rights.Importantly,SICLU dynamically influences LSO,with each index related to SICLU having an optimal range that fosters LSO.These insights offer valuable guidance for policymakers,emphasizing farmers as their central focus,with the adjustment of input and output factors as a means to achieve LSO as the ultimate goal.In conclusion,we propose research avenues for further enriching the SICLU framework to ensure that it aligns with the specific characteristics of regional agricultural development.

Sustainable land management in Mali

This study uses logistic and Poisson regression models to examine the factors influencing the adoption of sustain-able land management(SLM)practices in Mali using two rounds of the nationally representative survey Enquête Agricole de Conjoncture Intégrée aux Conditions de Vie des Ménages.The SLMs considered include the applica-tion of organic fertilizers,the application of inorganic fertilizers,the use of improved seeds,and the practice of intercropping.On average the application of organic fertilizers(39.2%),and inorganic fertilizers(28.7%)are the most frequent SLM practices among Malian farmers,and between 2014 and 2017,we observe a decline in the practice of intercropping.The regression results show that farmers’adoption of different SLMs is significantly associated with biophysical factors(average temperature,climate type,plot size,plot shape,and location),de-mographic factors(age,gender,education,household size),and socioeconomic factors(number of cultivated plots,livelihood diversification,type of crop grown,market access,credit access,economic shocks,and social capital).Our findings suggest that policymakers and agricultural development agencies in Mali need to adopt a multidimensional policy framework to unlock the untapped potential of SLM practices in promoting sustainable agriculture and food security.

Investigating and predicting the role of photovoltaic, wind, and hydrogen energies in sustainable global energy evolution

The global shift toward next-generation energy systems is propelled by the urgent need to combat climate change and the dwindling supply of fossil fuels.This review explores the intricate challenges and opportunities for transitioning to sustainable renewable energy sources such as solar,wind,and hydrogen.This transition economically challenges traditional energy sectors while fostering new industries,promoting job growth,and sustainable economic development.The transition to renewable energy demands social equity,ensuring universal access to affordable energy,and considering community impact.The environmental benefits include a significant reduction in greenhouse gas emissions and a lesser ecological footprint.This study highlights the rapid growth of the global wind power market,which is projected to increase from$112.23 billion in 2022 to$278.43 billion by 2030,with a compound annual growth rate of 13.67%.In addition,the demand for hydrogen is expected to increase,significantly impacting the market with potential cost reductions and making it a critical renewable energy source owing to its affordability and zero emissions.By 2028,renewables are predicted to account for 42%of global electricity generation,with significant contributions from wind and solar photovoltaic(PV)technology,particularly in China,the European Union,the United States,and India.These developments signify a global commitment to diversifying energy sources,reducing emissions,and moving toward cleaner and more sustainable energy solutions.This review offers stakeholders the insights required to smoothly transition to sustainable energy,setting the stage for a resilient future.

Minerals in edible insects:a review of content and potential for sustainable sourcing

In response to the rapid increase in world population and subsequent demands for food,edible insects represent an alternative food source for humans that is rich in proteins,amino acids and minerals.Entomophagy is a tradition in many countries including China and Thailand,and edible insects have attracted a lot of attention in Western World due to their suitable nutrient composition,high mineral content(e.g.,Fe,Zn,Ca,Mg)and potential use as a supplement in human diet.In this study,we surveyed mineral content in seven insect orders and 67 species of mass produced and wild-harvested edible insects.The total content of essential elements in edible insects was very high in Tenebrio molitor,Bombyx mori,and Zonocerus variegatus.The heavy metal content(summarized for eight species)was below the maximum limit allowed for safe consumption.Sustainable supply of minerals derived from insect biomass is complicated due to the high variations of mineral content in insects and the potential of its change due to processing.

Smart Farming for Sustainable Rice Production:An Insight into Application,Challenge,and Future Prospect

Rice has a huge impact on socio-economic growth,and ensuring its sustainability and optimal utilization is vital.This review provides an insight into the role of smart farming in enhancing rice productivity.The applications of smart farming in rice production including yield estimation,smart irrigation systems,monitoring disease and growth,and predicting rice quality and classifications are highlighted.The challenges of smart farming in sustainable rice production to enhance the understanding of researchers,policymakers,and stakeholders are discussed.Numerous efforts have been exerted to combat the issues in rice production in order to promote rice sector development.The effective implementation of smart farming in rice production has been facilitated by various technical advancements,particularly the integration of the Internet of Things and artificial intelligence.The future prospects of smart farming in transforming existing rice production practices are also elucidated.Through the utilization of smart farming,the rice industry can attain sustainable and resilient production systems that could mitigate environmental impact and safeguard food security.Thus,the rice industry holds a bright future in transforming current rice production practices into a new outlook in rice smart farming development.

Identifying influencing factors and characterizing key issues in urban sustainable development capacity through machine learning

In response to the United Nations Sustainable Development Goals and China’s“Dual Carbon”Goals(DCGs means the goals of“Carbon Peak and carbon neutrality”),this paper from the perspective of the construction of China’s Innovation Demonstration Zones for Sustainable Development Agenda(IDZSDAs),combines carbon emission-related metrics to construct a comprehensive assessment system for Urban Sustainable Development Capacity(USDC).After obtaining USDC assessment results through the assessment system,an approach combining Least Absolute Shrinkage and Selection Operator(LASSO)regression and Random Forest(RF)based on machine learning is proposed for identifying influencing factors and characterizing key issues.Combining Coupling Coordination Degree(CCD)analysis,the study further summarizes the systemic patterns and future directions of urban sustainable development.A case study on the IDZSDAs from 2015 to 2022 reveals that:(1)the combined identification method based on machine learning and CCD models effectively quantifies influencing factors and key issues in the urban sustainable development process;(2)the correspondence between influencing factors and key subsystems identified by the LASSO-RF combination model is generally consistent with the development situations in various cities;and(3)the machine learning-based combined recognition method is scalable and dynamic.It enables decision-makers to accurately identify influencing factors and characterize key issues based on actual urban development needs.

Scientific principles for accelerating the Sustainable Development Goals

The Sustainable Development Goals(SDGs)are significantly off-course as we reach the midpoint of their 2030 deadline.From a scientific perspective,the critical challenge in achieving the SDGs lies in the need for more scientific principles to understand the complex socio-ecological systems(SES)and their interactions influencing the 17 SDGs.Here,we propose a scientific framework to clarify the common scientific principles and the rational treatment of diversity under these principles.The framework’s core is revealing the complex mechanisms underlying the achievement of each Sustainable Development Goal(SDG)and SDG interactions.Building upon the identified mechanisms,complex SES models can be established,and the implementation of SDGs can be formulated as a multi-objective optimization problem,seeking a compromise in competition between essential costs and desired benefits.Our framework can assist countries,and even the world in accelerating progress towards the SDGs.

Knowledge mapping of research on sustainable utilization of landscape resources:a case study of Guilin National Innovation Demonstration Zone for sustainable development in China

Worldwide interest has increasingly focused on the sustainable utilization of landscape as a resource in urban areas,emphasizing its ecological,cultural and social significance.This study examines Guilin City,China,as a representative case study due to its rich landscape resources and status as a national innovation demonstration zone for implementing the 2030 Agenda for Sustainable Development.This study uses bibliometric visualization tools like CiteSpace and VOSviewer to analyze research trends from 1980 to 2021 in the Chinese Academic Journal Network Publishing Database(CNKI).The results show increasing academic interest over three stages:initiation(1982-1997),exploration(1998-2004),and diversified development(2005-2021).Contributions are predominantly from local academic and tourism sectors,indicating a strong regional influence;however,relatively weak interinstitutional collaboration occurs,suggesting potential for more integrated research efforts.Primary research is also concentrated within economic disciplines,particularly tourism-related ones.The evolution of research frontiers reveals three main paths:urban development strategies,industrial economic theories and empirical validation,and ecosystem analysis and evaluation.A multidisciplinary approach and stronger collaborative efforts are crucial to enhance research on ecological values and empirical models while supporting evidence-based urban development strategies in Guilin City and comparable cities globally.

Promoting sustainable consumption and circular economy:the intention of Vietnamese youth consumers to purchase products made from recycled plastics

Recycling is viewed as a key component in a circular economy and serves as an ideal solution for promoting sustainability.During the global plastic crisis,plastic recycling practices have been adopted worldwide,leading to the production of various products made from recycled plastics(PRP).Nevertheless,a gap persists between consumption and demand for such products,which is primarily attributed to a lack of comprehension from the consumer perspective.Given the pivotal role consumers play in the adoption of these products,this study explores consumers’intentions to purchase PRP.This is particularly significant in Vietnam,which is an emerging economy aspiring to achieve the objectives of a circular economy and sustainable development.Utilizing an integrated cognitive-emotional framework comprising the Valence Theory and the Norm Activation Model,data from 564 Vietnamese students were gathered and analyzed using structural equation modeling.The results show that awareness of consequences is a major driver of consumer purchase intentions,followed by perceived ease of application and monetary incentives.The results also indicate that health concerns have the strongest effect on purchase intention and in the negative side,meaning that the health-related risk is the primary concern for consumers during the decision-making process.This research holds substantial value for academics and managers,as it aids in the theoretical exploration and the formulation of strategies to improve consumer acceptance of PRP.