Resources, conservation & recycling advances最新文献

{"title":"Evaluating the development of macro infrastructural stocks in the Netherlands: Assessing factors driving historical material stocks growth","authors":"Md Faysal Tareq,&nbsp;Sebastiaan Deetman,&nbsp;Arnold Tukker","doi":"10.1016/j.rcradv.2025.200269","DOIUrl":"10.1016/j.rcradv.2025.200269","url":null,"abstract":"<div><div>Maintaining a constant macro-infrastructure stock requires significant material resources, which incurs an environmental cost. This study presents a detailed database of material stocks (steel, concrete, and asphalt) for macro-infrastructure (transport, energy, water, and utilities infrastructure) in The Netherlands. Using a bottom-up material stock accounting approach at high (25m² grid) resolution, we analyze the macro-infrastructure distribution across spatial and temporal scales. We examine 39 detailed infrastructure types in highway, railway, oil and gas, water, and utility aggregated infrastructure categories. Since 1950, material stocks have increase over fourteenfold, driven mainly by asphalt use in highways. Urban areas exhibit 3–4 times lower per capita stocks than rural areas, while material density per km² is 4–5 times higher in urban areas. Decomposition analysis reveals strong economic influences on historical stock growth. This research supports the sustainable transition of macro-infrastructure by quantifying existing material stocks, identifying growth factors, anticipate future needs, promoting circularity, and guiding investments.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"27 ","pages":"Article 200269"},"PeriodicalIF":5.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471381","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":"Comprehensive characterization of solid waste incineration fly ash before and after salt washing: evaluating analytical methods for sustainable waste management","authors":"Edzhe Soylu ,&nbsp;Stefanie Lode ,&nbsp;Camilo M. Silva ,&nbsp;Kristina Ryeng Simavik ,&nbsp;Kai Erik Ekstrøm ,&nbsp;Inge Johansson ,&nbsp;Bjørn Eske Sørensen ,&nbsp;Przemyslaw B. Kowalczuk ,&nbsp;Gabriella Tranell","doi":"10.1016/j.rcradv.2025.200268","DOIUrl":"10.1016/j.rcradv.2025.200268","url":null,"abstract":"<div><div>Despite extensive research on municipal solid waste incineration (MSWI) fly ash (FA) and air pollution control ash (APCA), comprehensive studies employing multiple characterization techniques remain limited. This study evaluates an extensive suite of analytical methods to characterize MSWI FA and APCA before and after water washing. Washing effectively removed soluble salts (e.g., NaCl, KCl), increasing stable phases like CaCO₃ and silicates, with variations dependent on ash origin and phase solubility. Washing also reduced particle size variability, yielding more uniform distributions. Chemical analyses demonstrated portable X-ray fluorescence (PXRF) as a reliable, cost-effective alternative to ICP-MS for elements including Al, Ca, Cu, Fe, P, and Zn, though weaker correlations for Pb, Si, and S suggest the need for more precise methods. Automated mineralogy showed sample-specific phase distributions: rotary kiln (RK) ash contained abundant ultrafine, unclassified matrix that increased post-washing, while grate furnace (GF) samples were dominated by lime, gypsum, and Ca-Si-Al phases, with washing reducing Cl-bearing and gypsum phases and slightly increasing reacted silicates. The circular fluidized bed (CFB) sample predominantly contained lime, and Ca-Cl-O and related reacted phases, showing minimal changes after washing. Minor metals such as Zn and Cu appeared heterogeneously distributed, bound within discrete or embedded phases, whereas bulk elements (Al, Ca, Mg, Si, Na) were uniformly integrated into the matrix. The findings emphasize the importance of combining multiple analytical techniques to achieve comprehensive characterization of MSWI ashes, bridging rapid screening with detailed analysis.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"27 ","pages":"Article 200268"},"PeriodicalIF":5.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365865","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":"Towards a circular economy: Identifying key driving factors for waste-to-energy across EESG dimensions","authors":"Siraprapa Yosthongngam ,&nbsp;Sittichok Plaiphum ,&nbsp;Roengchai Tansuchat","doi":"10.1016/j.rcradv.2025.200267","DOIUrl":"10.1016/j.rcradv.2025.200267","url":null,"abstract":"<div><div>Waste-to-energy (WtE) systems are increasingly recognized as a strategic component of the circular economy (CE), offering dual benefits in waste reduction and renewable energy generation. This study investigates the key Environmental, Economic, Social, and Governance (EESG) drivers influencing WtE production across nine EU countries from 2000 to 2020, with particular attention to long-run dynamics, institutional readiness, and structural conditions. Using the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model and Dumitrescu-Hurlin panel causality tests, the analysis captures both short- and long-run interdependencies between EESG indicators and circular economy performance, as proxied by total WtE output. The results indicate that recycling efficiency, energy self-sufficiency, recycling innovation, and WtE consumption significantly enhance circular economy performance in the long term. Causal tests confirm bidirectional relationships between circular economy performance and both recycling efficiency and energy self-sufficiency, underscoring mutual reinforcement between front-end recovery and energy resilience. WtE consumption shows a unidirectional effect on circular economy performance, driven by immediate energy needs and waste diversion. Circular economy performance also Granger-causes improvements in recycling innovation and environmental regulation, suggesting that strong circular outcomes may catalyze downstream innovation and policy development. However, environmental regulation itself does not show a significant direct impact on WtE. These findings highlight the need for targeted, system-level policies that balance environmental integrity, energy autonomy, and technological capability to enhance WtE’s contribution to circular economy transitions in the EU.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"27 ","pages":"Article 200267"},"PeriodicalIF":5.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534315","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":"Evaluating multi-dimensional risks embodied in the global supply chain of lithium-ion batteries","authors":"Yiran Yang ,&nbsp;Xin Sun","doi":"10.1016/j.rcradv.2025.200266","DOIUrl":"10.1016/j.rcradv.2025.200266","url":null,"abstract":"<div><div>Driven by the development of e-mobility and renewable power, recent years saw a surging demand for battery materials along with concerns about the supply chain stability. Previous studies have assessed such risks from multiple perspectives while the whole picture is still unclear. This study evaluated global supply chain risks in the lithium-ion battery (LIB) industry, covering 238 countries and regions and 15 key LIB-related commodities. We integrated three dimensions to develop a risk assessment model: the probability of production disruptions, competition risk, and vulnerability, offering a holistic view of both supply- and demand-side risks. The results reveal that lithium hydroxide and LIB exhibit the highest supply chain risks, primarily due to concentrated production and widely dispersed demand. Countries such as Japan, South Korea, and the United States face elevated supply chain risks. Early adopters of low-cobalt technologies have somewhat mitigated risks by reducing their exposure to resource competition. Based on these findings, the study underscores the importance of diversifying supply chains and accelerating technological transitions. The efforts to assess and improve resilience at high geographic and technical granularity are further highlighted.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"27 ","pages":"Article 200266"},"PeriodicalIF":5.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331186","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":"Drivers for the adoption of circular eco-innovations in agriculture: insights from a field experiment on olive growers","authors":"Alessia Lombardi ,&nbsp;Domenico Carlucci ,&nbsp;Luigi Cembalo ,&nbsp;Antonio Paparella ,&nbsp;Luigi Roselli ,&nbsp;Sarah Stempfle ,&nbsp;Riccardo Vecchio ,&nbsp;Giacomo Giannoccaro","doi":"10.1016/j.rcradv.2025.200265","DOIUrl":"10.1016/j.rcradv.2025.200265","url":null,"abstract":"<div><div>Circular economy is emerging as a key approach for transitioning to a more sustainable economic model. However, understanding how to facilitate the transition to a circular economy remains a critical challenge. This study analyses the drivers for the adoption of circular eco-innovations in the agricultural sector by considering both firm-level factors and cognitive characteristics of firms’ managers. Specifically, the willingness to adopt two selected circular eco-innovations was assessed through face-to-face interviews involving a sample of 211 olive growers in Apulia region (Italy). The two tested circular eco-innovations consist of the use of olive pomace digestate as soil conditioner/fertilizer and the use of reclaimed wastewater for irrigation. Data analysis was performed by estimating a bivariate ordered-response probit model. Findings revealed that farmer’s cognitive characteristics, particularly environmental risk awareness, play a key role in the path of adoption. Therefore, a full circular transition in agriculture can be achieved only through a significant shift in farmers’ mind-set, a process that takes time and needs to be supported by tailored policy measures.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"27 ","pages":"Article 200265"},"PeriodicalIF":5.4,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291096","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":"Blockchain and NFTs: Revolutionizing critical material recycling from end-of-life lithium-ion batteries","authors":"Karim Moawad ,&nbsp;Ammar Hummieda ,&nbsp;Ahmad Musamih ,&nbsp;Khaled Salah ,&nbsp;Ahmad Mayyas","doi":"10.1016/j.rcradv.2025.200258","DOIUrl":"10.1016/j.rcradv.2025.200258","url":null,"abstract":"<div><div>Lithium-ion batteries (LIBs) have become a cornerstone of modern technology, where they serve as the power source for a wide range of applications, including electric vehicles and renewable energy storage systems. However, rapid production growth has introduced challenges regarding end-of-life management, particularly with waste disposal, resource recovery, and environmental sustainability. Inefficient recycling often leads to valuable materials like cobalt, lithium, and nickel being discarded in landfills, which exacerbates resource scarcity and poses environmental and health risks. To address these issues, there is a critical need for more efficient, transparent, and accountable systems for the collection, recovery, and recycling of LIBs. In this paper,A blockchain and Non-Fungible Token (NFT)-based solution is proposed to enable circular recycling and material recovery. This system improves transparency, traceability, and accountability throughout the battery lifecycle. The smart contracts (SCs) source code is made publicly available on GitHub.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"27 ","pages":"Article 200258"},"PeriodicalIF":5.4,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255330","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":"Assessment of toxic polycyclic aromatic hydrocarbons (PAH) in recycled plastics: A comparative study of LDPE, HDPE, PET, and PP","authors":"José Carlos Martín ,&nbsp;Julia Moltó ,&nbsp;Nuria Ortuño ,&nbsp;Andrés Fullana ,&nbsp;Juan Antonio Conesa","doi":"10.1016/j.rcradv.2025.200262","DOIUrl":"10.1016/j.rcradv.2025.200262","url":null,"abstract":"<div><div>Polycyclic aromatic hydrocarbons (PAHs) are toxic, potentially carcinogenic compounds that can be present in recycled plastics, raising concerns about human health and environmental safety. Despite their widespread use, recycled polymers are not routinely monitored for these contaminants. This study analysed 25 recycled plastic samples from recycling companies—including LDPE, HDPE, PET, PP, and polymer mixtures—to assess the presence of PAHs and evaluate an extraction method optimised for LDPE. The method's applicability to other polymer types was also explored, along with potential correlations between PAH levels and factors such as polymer type, origin, and colour. PAHs were extracted by stirring the samples for 24 h at room temperature in a 1:1 acetone/dichloromethane mixture, followed by purification using C18 solid-phase extraction cartridges. Results showed that PET samples had the lowest total PAH concentrations, often below detection limits. In contrast, one black LDPE sample exhibited the highest total PAH content at 2200 ng/g and toxicity value (68 ng TEQ/g). The study reveals significant variability in PAH content depending on polymer characteristics and recycling processes. These findings highlight the limitations of current industrial washing methods in removing persistent organic contaminants and underscore the need for improved control strategies and regular monitoring to ensure the safety of recycled plastics.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"27 ","pages":"Article 200262"},"PeriodicalIF":5.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212133","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":"Applying random forest to forecast municipal solid waste generation from household fuel consumption","authors":"Luis Izquierdo-Horna ,&nbsp;Ramzy Kahhat ,&nbsp;Ian Vázquez-Rowe","doi":"10.1016/j.rcradv.2025.200264","DOIUrl":"10.1016/j.rcradv.2025.200264","url":null,"abstract":"<div><div>Accurately forecasting municipal solid waste (MSW) generation is essential for designing efficient waste management systems and promoting sustainable urban development. As cities expand and consumption patterns shift, reliable data-driven approaches are increasingly necessary to address the complexities of MSW generation. This study applied the random forest (RF) algorithm, a machine learning technique, to predict MSW generation at the household level. RF was selected for its capacity to handle non-linear relationships, imbalanced datasets, and outliers. The analysis focused on data from 2019, avoiding distortions associated with the COVID-19 pandemic. The model integrated per capita MSW data with household fuel consumption indicators (i.e., natural gas, electricity, and liquefied petroleum gas) and demographic variables such as age, education level, and monthly expenditure. The case study focused on the city of Lima, Peru, using 80 % of the data for training and 20 % for testing, with hyperparameters optimized via 5-fold cross-validation. The final model explained 55 % of the variance in MSW generation (R² = 0.55). This result reflects the model’s ability to capture significant drivers of variability, although it leaves room for refinement due to factors not included in the analysis, such as cultural practices or seasonality. Among the predictors, household monthly expenditure on cooking fuels emerged as the most influential variable, reinforcing the connection between resource consumption and waste generation. These findings highlight the potential of integrating socioeconomic indicators into predictive models to enhance their reliability. By improving forecasting capabilities, this study supports targeted policies for urban waste management and sustainable resource use.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"27 ","pages":"Article 200264"},"PeriodicalIF":5.4,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288833","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":"Machine learning applications for carbon emission estimation","authors":"Hala Salem Al Nuaimi ,&nbsp;Adolf Acquaye ,&nbsp;Ahmad Mayyas","doi":"10.1016/j.rcradv.2025.200263","DOIUrl":"10.1016/j.rcradv.2025.200263","url":null,"abstract":"<div><div>In the context of escalating global climate change concerns, accurately estimating carbon emissions is crucial. This paper conducts a systematic literature review (SLR) on the application of machine learning (ML) techniques for estimating current and future carbon emissions. The study aims to evaluate the effectiveness of various ML algorithms across different sectors, identify sector-specific opportunities, and propose enhancements for ML-based carbon emission estimation.</div><div>The review highlights significant progress in the transportation sector, with notable research focusing on vehicle emissions. However, it identifies untapped potential in the energy and industrial sectors, where data accessibility and complexity pose challenges. The paper discusses the applicability of commonly used ML algorithms, including Artificial Neural Networks, Ensemble Methods, Support Vector Machines, and Extreme Learning Machines, emphasizing their strengths and limitations in different contexts. Key methodologies for improving ML performance in carbon emission estimation include hybrid modeling techniques, optimization algorithms, influential factor analysis, and data estimation methods. Despite advancements, challenges such as computational complexity, data quality, and model interpretability persist. The paper recommends enhancing optimization techniques, advancing predictor analysis, improving data collection practices, and focusing on sector-specific applications to address these issues.</div><div>By synthesizing existing knowledge and identifying critical research gaps, this study provides actionable insights to advance future research in ML-based carbon emission estimation. The main contribution of this work lies in its focus on practical aspects, rather than theoretical limitations of models, as emphasized in many existing studies. It highlights model performance in real-world scenarios, identifies key factors that restrict the efficient implementation of certain ML models in practice. Furthermore, the study presents a comprehensive guidance framework to provide an overview of the field and practical direction for application of machine learning in carbon emission estimation, paving the way for more effective real-world applications.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"27 ","pages":"Article 200263"},"PeriodicalIF":5.4,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212132","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":"Consumer awareness of marine debris issues and their willingness to pay for seafood from debris-free fishing grounds: A pathway for supporting marine debris recovery by fishers","authors":"Zhaofei Lin ,&nbsp;Takaaki Kato ,&nbsp;Aiko Endo","doi":"10.1016/j.rcradv.2025.200259","DOIUrl":"10.1016/j.rcradv.2025.200259","url":null,"abstract":"<div><div>Marine debris impacts marine ecosystems, food safety, and resource sustainability. Involving fishers is a practical solution for recovering marine debris. This study investigated Japanese consumer awareness of the issues surrounding marine debris and their willingness to pay for seafood from debris-free fishing grounds. The survey involved 1000 citizens from western prefectures in Japan near the Genkainada Sea area, where previous research reported a high density of marine debris. Results showed that 61 % of respondents preferred seafood from clean fishing grounds. A discrete choice experiment was conducted, and respondents were on average willing to pay at least 77 JPY more for an Aji (horse mackerel) menu if the seafood was from a debris-free fishing ground, equivalent to 4‒11 % of the typical prices of the menu. The demand for seafood from debris-free fishing grounds was much higher than the initial prediction based on interviews with fishery experts. Realization of this price premium would incentivize more fishers to recover marine debris. Currently, the Japanese retail market does not provide information on the status of marine debris in fishing grounds. If the traceability of the seafood supply chain can be improved, consumer purchasing behavior may change to support marine debris recovery efforts of fishers.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"27 ","pages":"Article 200259"},"PeriodicalIF":5.4,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116626","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}