Resources, conservation & recycling advances最新文献

{"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}

{"title":"Measuring public support towards different clean air targets in China","authors":"Yining Huang ,&nbsp;Miaomiao Liu ,&nbsp;Jianxun Yang ,&nbsp;Wen Fang ,&nbsp;Zongwei Ma ,&nbsp;Jun Bi","doi":"10.1016/j.rcradv.2025.200260","DOIUrl":"10.1016/j.rcradv.2025.200260","url":null,"abstract":"<div><div>Following remarkable achievements in air quality improvement, China has transitioned into a period of relatively low pollution levels, promoting discussions on further upgrading air quality standards. Effective policy-making depends not only on cost-effectiveness but also heavily on the public’s willingness to support such initiatives. Previous studies on willingness-to-pay (WTP) inadequately distinguished between the impacts of objective pollution levels and subjective cognitions, as well as how the cognitive relationships formed by these two factors affect WTP decisions. This study examines public willingness to support two different air quality improvement scenarios and investigates the underlying mechanisms influencing WTP decisions. Utilizing nationwide survey data from 7457 Chinese respondents and employing a two-part regression model, we found the average WTP to reduce PM_2.5 concentrations by 10 % (below 30 µg/m³) and to achieve levels below 5 µg/m³ were 277 and 295 CNY, respectively. Residents demonstrated significantly greater participation willingness and contribute higher amounts when presented with stricter air quality targets (<em>p</em> &lt; 0.05). Subjective cognitions, rather than objective pollution levels, primarily shape WTP through two distinct pathways: external factors (e.g., trust in government) influence initial participation willingness, while internal factors (e.g., perceived risk control) determine payment amounts. Additionally, pessimistic cognitive biases- low acceptance of air quality despite low pollution levels- significantly reduced individuals' likelihood of contributing (<em>p</em> &lt; 0.05). Our findings highlight distinct cognitive mechanisms underlying WTP decisions, suggesting tailored strategies to encourage collective action to further improve air quality and overcome pessimistic biases.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"27 ","pages":"Article 200260"},"PeriodicalIF":5.4,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124447","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":"Utilization of fish sludge and aquaculture effluent water from Norway for nutrient and energy recovery","authors":"Hanna Böpple ,&nbsp;Gregorio Brussino ,&nbsp;Anton Engel ,&nbsp;Peter Breuhaus ,&nbsp;Nicole Dopffel ,&nbsp;Biwen Annie An-Stepec ,&nbsp;Dorinde M.M. Kleinegris ,&nbsp;Petronella Margaretha Slegers","doi":"10.1016/j.rcradv.2025.200256","DOIUrl":"10.1016/j.rcradv.2025.200256","url":null,"abstract":"<div><div>The Norwegian aquaculture industry is experiencing rapid growth. At the same time the demand for suitable aquaculture waste treatment is rising. This study aims to evaluate the environmental impacts of a waste-treatment process through anaerobic digestion and microalgae cultivation in Norway. This circular economy approach aims to recover nutrients and energy from an average land-based recirculating aquaculture system (RAS) with a production of 5000 tonnes of salmon yearly. In comparison, one of today's common solutions, the shipment of 50 % of the RAS fish sludge to Denmark for biogas production, was assessed as a baseline. A life cycle assessment showed that shipping 100 % of the fish sludge to Denmark for anaerobic digestion is more environmentally sustainable than releasing half of the fish sludge into the environment and only shipping 50 % to Denmark. Anaerobic co-digestion of fish sludge in Norway showed a lower environmental impact potential than the anaerobic digestion with fish sludge as the sole substrate (in Norway). The cultivation of microalgae in northern latitudes is highly energy demanding, which is one of the highest impact contributions of the value chain. National differences in the electricity grid composition (hydropower, other renewable energy, fossil energy) that is used for the respective scenarios in either Norway or Denmark, had a major impact on the assessment. A preliminary economic assessment showed that all scenarios had a decrease in operational costs compared to the baseline scenario due to the recycling of nutrients and produced energy from the RAS waste streams.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"27 ","pages":"Article 200256"},"PeriodicalIF":5.4,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098371","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":"Methodological insights of defining material criticality by assessing different electrolysis and fuel cell stacks","authors":"Andrea Schreiber,&nbsp;Petra Zapp,&nbsp;Lavinia Reitz","doi":"10.1016/j.rcradv.2025.200257","DOIUrl":"10.1016/j.rcradv.2025.200257","url":null,"abstract":"<div><div>Shifting economic sectors to a resource-efficient economy with zero net greenhouse gas emissions by 2050 faces major challenges for the European Union, which is highly dependent on material imports. Critical raw materials play a key role in a wide range of emerging technologies. In times of increasing demand, the assessment of critical raw materials is therefore of utmost importance. This study addresses methodological principles of various materials criticality indicators on product-level. Using the example of manufacturing different electrolysis and fuel cell stacks, these criticality indicators are applied, and the results are discussed. The case study demonstrated that alkaline electrolysis has the lowest criticality among the electrolyzers in seven out of nine criticality indicator evaluations. For fuel cells, the heavier stack concept shows lower criticality compared to the light-weight concept. One reason is the higher demand of rare earth elements and cobalt needed for manufacturing compared to heavier stack. Various rare earths are identified as critical in the manufacture of solid oxide electrolysis and fuel cell stacks. Iridium and nickel contribute most to criticality in the construction of proton exchange membrane electrolysis and alkaline electrolysis stacks, respectively. Five of nine indicators point to the same or similar criticality hotspots and can therefore set priorities for action in materials research for hydrogen and fuel cell systems. Nevertheless, when deciding for or against a material, one has to be aware that the criticality indicators use different sensitive sub-indicators which have an impact on the ranking of materials.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"27 ","pages":"Article 200257"},"PeriodicalIF":5.4,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154478","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":"The role of drivers for sustainable development in the electric vehicle adoption: A two-staged structural equation modelling-artificial neural network technique","authors":"Rohit Bansal ,&nbsp;Yasmeen Ansari","doi":"10.1016/j.rcradv.2025.200255","DOIUrl":"10.1016/j.rcradv.2025.200255","url":null,"abstract":"<div><div>Automobile technology is improving, enabling the development of electric vehicles, which are expected to replace traditional combustion-powered vehicles. The study explores the role of perceived benefits, policy interventions, public opinions, knowledge, and awareness toward using and buying electric vehicles. 434 random responses were analyzed about their intention. The study uses public opinion and awareness as mediating variables towards adopting electric vehicles with an advanced, “two-staged structural equation modelling-artificial neural network” technique. Findings suggest that the public's opinion, policy interventions, perceived benefits, and perceived risk are significantly related to buying electric vehicles. The sample includes 55.76 % male and 44.24 % female respondents. 30 % are postgraduate, 78 % are single, and 80 % live in urban. The findings will be essential for manufacturers and policymakers to formulate and implement strategies to boost electric vehicle market penetration. Based on the result, the study discussed the practical and managerial implications of adopting electric vehicles in an emerging market and provided suggestions for future directions.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"26 ","pages":"Article 200255"},"PeriodicalIF":5.4,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847645","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":"Optimising rainwater harvesting systems under uncertainty: A multi-objective stochastic approach with risk considerations","authors":"Alireza Shefaei ,&nbsp;Arash Maleki ,&nbsp;Jan Peter van der Hoek ,&nbsp;Nick van de Giesen ,&nbsp;Edo Abraham","doi":"10.1016/j.rcradv.2025.200254","DOIUrl":"10.1016/j.rcradv.2025.200254","url":null,"abstract":"<div><div>Optimising rainwater harvesting (RWH) systems’ design involves sizing the storage and catchment areas to enhance cost-effectiveness, self-sufficiency, and water quality indicators. This paper considers the design of RWH systems under long-term uncertainty in precipitation and demands. In this work, we formulate and solve a multi-objective stochastic optimisation problem that allows explicit trade-offs under uncertainty, maximising system efficiency and minimising deployment cost. We use the yield after spillage (YAS) approach to incorporate the physical and operational constraints and the big-M method to reformulate the nonlinear minmax rules of this approach as a mixed-integer linear programming (MILP) problem. By posing a risk averseness measure on efficiency as a conditional value at risk (CVaR) formulation, we guarantee the designer against the highest demand and driest weather conditions. We then exploit the lexicographic method to effectively solve the multi-objective stochastic problem as a sequence of equivalent single-objective problems. A detailed case study of a botanical garden in Amsterdam demonstrates the framework’s practical application; we show significant improvements in system efficiency of up to 15.5% and 28.9% in the driest scenarios under risk-neutral and risk-averse conditions, respectively, compared to deterministic approaches. The findings highlight the importance of taking into account multiple objectives and uncertainties when designing RWH systems, allowing designers to optimise efficiency and costs based on their specific requirements without extensive parameterisation.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"26 ","pages":"Article 200254"},"PeriodicalIF":5.4,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844768","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":"Unlocking advanced waste management models: Machine learning integration of emerging technologies into regional systems","authors":"Nicolás Martínez-Ramón ,&nbsp;Robert Istrate ,&nbsp;Diego Iribarren ,&nbsp;Javier Dufour","doi":"10.1016/j.rcradv.2025.200253","DOIUrl":"10.1016/j.rcradv.2025.200253","url":null,"abstract":"<div><div>The waste management sector requires specialized systems analysis tools to facilitate decision-making and make waste management sustainable and efficient. While integrated systemic approaches exist for assessing conventional waste management systems, the integration of emerging technologies such as gasification, pyrolysis, and methane dry reforming remains largely overlooked. In this work, these three technologies have been integrated into a conventional regional waste management model by abstracting rigorous simulation models into machine-learning surrogate models. The resulting technology-rich waste management model incorporates material flow analysis and life-cycle assessment as tools for supporting policy and decision-making. The model was tested by assessing the environmental impacts and landfill rates for three technology implementation scenarios. Overall, the inclusion of these emerging technologies led to an environmental performance improvement compared to a reference system. For example, a 116.5 % reduction of the carbon footprint in the most optimistic scenario. Nevertheless, the mere addition of these technologies was not enough to achieve landfill rates below 10 %, reaching 37.6 % in the most optimistic scenario. Therefore, properly sizing capacity was found to be a key factor in minimizing both environmental impact and landfill rate.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"26 ","pages":"Article 200253"},"PeriodicalIF":5.4,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shedding light on decommissioning solar panel streams: A system dynamics model for volume estimation","authors":"Beatriz Pérez Horno,&nbsp;Andreas Feldmann,&nbsp;Cali Nuur","doi":"10.1016/j.rcradv.2025.200252","DOIUrl":"10.1016/j.rcradv.2025.200252","url":null,"abstract":"<div><div>The global expansion of solar energy presents a paradox: while it is a key sustainable technology, a comprehensive waste management strategy for decommissioned solar panels remains insufficient. Previous studies have examined this issue, yet waste volume estimations remain incomplete due to the exclusion of early waste streams and the failure to account for temporal fluctuations in key variables. This study addresses these gaps by employing System Dynamics Modelling (SDM) to capture a more nuanced understanding of the heterogeneity of decommissioned panels. The findings reveal significant discrepancies between projections from conventional static models and those generated by the developed model, underscoring the need for more adaptive forecasting methods that account for temporal variations and the evolving characteristics of decommissioned panels. Furthermore, this paper highlights the inefficiencies of uniform waste management approaches, emphasizing the need for differentiated strategies based on panel characteristics. Crucially, the findings challenge the recycling-centric paradigm by exposing the overlooked potential of functional discarded panels, advocating for circular strategies that prioritize reuse and secondary markets.</div></div>","PeriodicalId":74689,"journal":{"name":"Resources, conservation & recycling advances","volume":"26 ","pages":"Article 200252"},"PeriodicalIF":5.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}