Resources, conservation & recycling advances最新文献_第6页

Towards a circular economy: Identifying key driving factors for waste-to-energy across EESG dimensions 迈向循环经济：在EESG各维度上确定废物转化为能源的关键驱动因素

IF 5.4 Q1 ENVIRONMENTAL SCIENCES

Resources, conservation & recycling advances

Pub Date : 2025-06-16 DOI: 10.1016/j.rcradv.2025.200267

Siraprapa Yosthongngam , Sittichok Plaiphum , Roengchai Tansuchat

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.

废物转化为能源（WtE）系统日益被认为是循环经济（CE）的一个战略组成部分，在减少废物和产生可再生能源方面具有双重效益。本研究调查了2000年至2020年间影响9个欧盟国家WtE生产的主要环境、经济、社会和治理（EESG）驱动因素，特别关注长期动态、制度准备和结构条件。利用横截面增强自回归分布滞后（CS-ARDL）模型和dumitrescui - hurlin面板因果关系检验，该分析捕捉了EESG指标与循环经济绩效之间的短期和长期相互依赖关系，以总WtE产出为代表。结果表明，循环效率、能源自给、循环创新和WtE消耗在长期内显著提高循环经济绩效。因果检验证实了循环经济绩效与循环效率和能源自给之间的双向关系，强调了前端恢复与能源弹性之间的相互加强。在直接能源需求和废物转移的驱动下，垃圾消费对循环经济绩效表现出单向影响。循环经济绩效在循环创新和环境监管方面也有格兰杰原因的改善，这表明强劲的循环成果可能催化下游创新和政策制定。然而，环境监管本身并没有对WtE产生显著的直接影响。这些发现突出表明，需要制定有针对性的系统级政策，平衡环境完整性、能源自主性和技术能力，以增强WtE对欧盟循环经济转型的贡献。

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引用次数: 0

Evaluating multi-dimensional risks embodied in the global supply chain of lithium-ion batteries 锂离子电池全球供应链多维度风险评估

IF 5.4 Q1 ENVIRONMENTAL SCIENCES

Resources, conservation & recycling advances

Pub Date : 2025-06-16 DOI: 10.1016/j.rcradv.2025.200266

Yiran Yang , Xin Sun

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.

在电动汽车和可再生能源发展的推动下，近年来对电池材料的需求激增，同时对供应链稳定性的担忧也在加剧。以前的研究从多个角度评估了这些风险，但整体情况仍不清楚。本研究评估了全球锂离子电池（LIB）行业的供应链风险，涵盖238个国家和地区以及15种与锂离子电池相关的关键商品。我们整合了三个维度来开发风险评估模型：生产中断的可能性、竞争风险和脆弱性，提供了供给侧和需求侧风险的整体视图。结果表明，氢氧化锂和LIB的供应链风险最高，主要是由于生产集中，需求分散。日本、韩国和美国等国面临供应链风险上升。低钴技术的早期采用者通过减少资源竞争，在一定程度上减轻了风险。基于这些发现，该研究强调了供应链多样化和加速技术转型的重要性。进一步强调了在高地理和技术粒度上评估和提高复原力的努力。

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引用次数: 0

Drivers for the adoption of circular eco-innovations in agriculture: insights from a field experiment on olive growers 农业采用循环生态创新的驱动因素：来自橄榄种植者田间试验的见解

IF 5.4 Q1 ENVIRONMENTAL SCIENCES

Resources, conservation & recycling advances

Pub Date : 2025-06-07 DOI: 10.1016/j.rcradv.2025.200265

Alessia Lombardi , Domenico Carlucci , Luigi Cembalo , Antonio Paparella , Luigi Roselli , Sarah Stempfle , Riccardo Vecchio , Giacomo Giannoccaro

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.

循环经济正在成为向更可持续的经济模式过渡的关键途径。然而，了解如何促进向循环经济的过渡仍然是一项重大挑战。本研究通过考虑企业层面因素和企业管理者的认知特征，分析了农业部门采用循环生态创新的驱动因素。具体来说，采用两种选定的循环生态创新的意愿是通过面对面的访谈来评估的，其中包括意大利普利亚地区的211名橄榄种植者。经过测试的两项循环生态创新包括使用橄榄渣消化液作为土壤改良剂/肥料和使用再生废水进行灌溉。通过估计二元有序响应概率模型进行数据分析。研究结果表明，农民的认知特征，特别是环境风险意识，在采用路径中起着关键作用。因此，农业的全面循环转型只能通过农民思维方式的重大转变来实现，这一过程需要时间，需要有针对性的政策措施支持。

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引用次数: 0

Blockchain and NFTs: Revolutionizing critical material recycling from end-of-life lithium-ion batteries 区块链和nft：彻底改变报废锂离子电池的关键材料回收

IF 5.4 Q1 ENVIRONMENTAL SCIENCES

Resources, conservation & recycling advances

Pub Date : 2025-06-07 DOI: 10.1016/j.rcradv.2025.200258

Karim Moawad , Ammar Hummieda , Ahmad Musamih , Khaled Salah , Ahmad Mayyas

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.

锂离子电池（LIBs）已经成为现代技术的基石，它们是包括电动汽车和可再生能源存储系统在内的广泛应用的电源。然而，产量的快速增长也带来了报废管理方面的挑战，特别是在废物处理、资源回收和环境可持续性方面。低效的回收往往导致钴、锂和镍等有价值的材料被丢弃在垃圾填埋场，这加剧了资源短缺，并构成环境和健康风险。为了解决这些问题，迫切需要建立更高效、透明和负责任的lib收集、回收和再循环系统。本文提出了一种基于区块链和不可替代令牌（NFT）的解决方案，以实现循环回收和材料回收。该系统提高了整个电池生命周期的透明度、可追溯性和问责性。智能合约（SCs）源代码在GitHub上公开提供。

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引用次数: 0

Assessment of toxic polycyclic aromatic hydrocarbons (PAH) in recycled plastics: A comparative study of LDPE, HDPE, PET, and PP 再生塑料中有毒多环芳烃（PAH）的评估：LDPE、HDPE、PET和PP的比较研究

IF 5.4 Q1 ENVIRONMENTAL SCIENCES

Resources, conservation & recycling advances

Pub Date : 2025-06-05 DOI: 10.1016/j.rcradv.2025.200262

José Carlos Martín , Julia Moltó , Nuria Ortuño , Andrés Fullana , Juan Antonio Conesa

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.

多环芳烃（PAHs）是一种有毒的、潜在致癌的化合物，可能存在于回收塑料中，引发了对人类健康和环境安全的担忧。尽管它们被广泛使用，但回收聚合物并没有定期监测这些污染物。本研究分析了来自回收公司的25个回收塑料样品——包括LDPE、HDPE、PET、PP和聚合物混合物——以评估多环芳烃的存在，并评估了一种针对LDPE优化的提取方法。该方法对其他聚合物类型的适用性也进行了探讨，以及多环芳烃水平与聚合物类型、来源和颜色等因素之间的潜在相关性。在丙酮/二氯甲烷1:1的混合物中，室温搅拌24 h提取多环芳烃，然后用C18固相萃取筒纯化。结果表明，PET样品的总多环芳烃浓度最低，通常低于检测限。相比之下，一个黑色LDPE样品显示出最高的总多环芳烃含量为2200 ng/g和毒性值（68 ng TEQ/g）。该研究揭示了多环芳烃含量的显著变化取决于聚合物的特性和回收过程。这些发现突出了当前工业洗涤方法在去除持久性有机污染物方面的局限性，并强调了改进控制策略和定期监测以确保再生塑料安全的必要性。

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引用次数: 0

Applying random forest to forecast municipal solid waste generation from household fuel consumption 应用随机森林预测家庭燃料消费产生的城市固体废物

IF 5.4 Q1 ENVIRONMENTAL SCIENCES

Resources, conservation & recycling advances

Pub Date : 2025-06-03 DOI: 10.1016/j.rcradv.2025.200264

Luis Izquierdo-Horna , Ramzy Kahhat , Ian Vázquez-Rowe

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.

准确预测城市固体废物的产生对于设计有效的废物管理系统和促进城市可持续发展至关重要。随着城市的扩张和消费模式的转变，越来越需要可靠的数据驱动方法来解决城市生活垃圾产生的复杂性。本研究应用随机森林（RF）算法（一种机器学习技术）来预测家庭生活垃圾的产生。选择射频是因为其处理非线性关系、不平衡数据集和异常值的能力。该分析侧重于2019年的数据，避免了与COVID-19大流行相关的扭曲。该模型将人均城市生活垃圾数据与家庭燃料消耗指标（即天然气、电力和液化石油气）以及年龄、教育水平和月支出等人口统计变量相结合。该案例研究集中在秘鲁利马市，使用80%的数据用于训练，20%用于测试，并通过5倍交叉验证优化了超参数。最终模型解释了55%的城市生活垃圾产生方差（R²= 0.55）。这一结果反映了模型捕捉可变性的重要驱动因素的能力，尽管由于分析中未包括的因素（如文化习俗或季节性），它留下了改进的空间。在预测指标中，家庭每月烹饪燃料支出成为影响最大的变量，加强了资源消耗与废物产生之间的联系。这些发现突出了将社会经济指标纳入预测模型以提高其可靠性的潜力。通过提高预测能力，本研究支持有针对性的城市废物管理和可持续资源利用政策。

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引用次数: 0

Machine learning applications for carbon emission estimation 机器学习在碳排放估算中的应用

IF 5.4 Q1 ENVIRONMENTAL SCIENCES

Resources, conservation & recycling advances

Pub Date : 2025-06-01 DOI: 10.1016/j.rcradv.2025.200263

Hala Salem Al Nuaimi , Adolf Acquaye , Ahmad Mayyas

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.

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.

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.

在全球气候变化问题日益严重的背景下，准确估算碳排放量至关重要。本文对机器学习（ML）技术在估计当前和未来碳排放中的应用进行了系统的文献综述（SLR）。该研究旨在评估不同行业的各种机器学习算法的有效性，确定特定行业的机会，并提出基于机器学习的碳排放估计的改进方案。该报告强调了交通运输领域的重大进展，重点关注汽车排放的研究。然而，它发现了能源和工业领域尚未开发的潜力，这些领域的数据可访问性和复杂性构成了挑战。本文讨论了常用的机器学习算法的适用性，包括人工神经网络、集成方法、支持向量机和极限学习机，强调了它们在不同环境下的优势和局限性。提高机器学习在碳排放估计中的性能的关键方法包括混合建模技术、优化算法、影响因素分析和数据估计方法。尽管取得了进步，但诸如计算复杂性、数据质量和模型可解释性等挑战仍然存在。本文建议加强优化技术，推进预测分析，改进数据收集实践，并专注于特定行业的应用来解决这些问题。通过综合现有知识和识别关键研究空白，本研究为推进基于ml的碳排放估算的未来研究提供了可操作的见解。这项工作的主要贡献在于它关注实际方面，而不是像许多现有研究那样强调模型的理论局限性。它突出了模型在现实场景中的性能，确定了在实践中限制某些ML模型有效实现的关键因素。此外，本研究提出了一个全面的指导框架，为机器学习在碳排放估算中的应用提供了一个领域概述和实践方向，为更有效的现实应用铺平了道路。

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引用次数: 0

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 消费者对海洋垃圾问题的认识，以及他们愿意购买来自无垃圾渔场的海产品：支持渔民回收海洋垃圾的途径

IF 5.4 Q1 ENVIRONMENTAL SCIENCES

Resources, conservation & recycling advances

Pub Date : 2025-05-19 DOI: 10.1016/j.rcradv.2025.200259

Zhaofei Lin , Takaaki Kato , Aiko Endo

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.

海洋垃圾影响海洋生态系统、食品安全和资源可持续性。让渔民参与是回收海洋垃圾的一个切实可行的解决方案。这项研究调查了日本消费者对海洋垃圾问题的认识，以及他们购买来自无垃圾渔场的海鲜的意愿。这项调查涉及日本西部郡县近根凯纳达海域的1000名居民，此前的研究报告称，那里有高密度的海洋垃圾。结果显示，61%的受访者更喜欢干净渔场的海鲜。我们进行了一项离散选择实验，调查对象平均愿意为来自无垃圾渔场的阿吉（马鲭鱼）菜单多支付至少77日元，相当于菜单正常价格的4 - 11%。对无垃圾渔场海产品的需求远远高于对渔业专家的初步预测。实现这一价格溢价将激励更多的渔民回收海洋垃圾。目前，日本零售市场没有提供有关渔场海洋垃圾状况的信息。如果可以改善海产品供应链的可追溯性，消费者的购买行为可能会改变，以支持渔民的海洋垃圾回收工作。

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引用次数: 0

Measuring public support towards different clean air targets in China 衡量公众对中国不同清洁空气目标的支持度

IF 5.4 Q1 ENVIRONMENTAL SCIENCES

Resources, conservation & recycling advances

Pub Date : 2025-05-17 DOI: 10.1016/j.rcradv.2025.200260

Yining Huang , Miaomiao Liu , Jianxun Yang , Wen Fang , Zongwei Ma , Jun Bi

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 (p < 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 (p < 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.

在空气质量改善取得显著成效之后，中国进入了相对低污染时期，推动了进一步提升空气质量标准的讨论。有效的政策制定不仅取决于成本效益，而且在很大程度上取决于公众是否愿意支持这些措施。以往关于支付意愿（WTP）的研究没有充分区分客观污染水平和主观认知的影响，以及这两个因素形成的认知关系如何影响WTP决策。本研究考察公众支持两种不同空气质素改善方案的意愿，并探讨影响WTP决策的潜在机制。利用7457名中国受访者的全国调查数据，并采用两部分回归模型，我们发现将PM2.5浓度降低10%（低于30微克/立方米）和达到低于5微克/立方米的平均WTP分别为277元和295元。当提出更严格的空气质量目标时，居民表现出更大的参与意愿和更高的捐款(p <；0.05)。主观认知，而不是客观污染水平，主要通过两种不同的途径塑造WTP：外部因素（如对政府的信任）影响初始参与意愿，而内部因素（如感知风险控制）决定支付金额。此外，悲观的认知偏见——尽管污染水平较低，但对空气质量的接受度较低——显著降低了个人贡献的可能性(p <；0.05)。我们的研究结果强调了WTP决策背后的独特认知机制，提出了量身定制的策略，以鼓励集体行动，进一步改善空气质量，克服悲观偏见。

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引用次数: 0

Utilization of fish sludge and aquaculture effluent water from Norway for nutrient and energy recovery 利用挪威的鱼泥和水产养殖废水进行养分和能量回收

IF 5.4 Q1 ENVIRONMENTAL SCIENCES

Resources, conservation & recycling advances

Pub Date : 2025-05-13 DOI: 10.1016/j.rcradv.2025.200256

Hanna Böpple , Gregorio Brussino , Anton Engel , Peter Breuhaus , Nicole Dopffel , Biwen Annie An-Stepec , Dorinde M.M. Kleinegris , Petronella Margaretha Slegers

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.

挪威水产养殖业正在经历快速增长。与此同时，对水产养殖废弃物适当处理的需求也在上升。本研究旨在评估挪威通过厌氧消化和微藻培养的废物处理过程对环境的影响。这种循环经济方法旨在从一个平均每年生产5000吨鲑鱼的陆基循环水产养殖系统（RAS）中回收养分和能源。相比之下，目前常见的解决方案之一，即将50%的RAS鱼污泥运往丹麦用于生产沼气，作为基线进行了评估。一项生命周期评估表明，将100%的鱼污泥运到丹麦进行厌氧消化，比将一半的鱼污泥排放到环境中，只将50%的鱼污泥运到丹麦更具有环境可持续性。挪威鱼类污泥厌氧共消化的环境影响潜力低于以鱼类污泥为唯一底物的厌氧消化（在挪威）。北纬地区微藻养殖能耗高，是价值链中影响贡献最大的环节之一。挪威或丹麦各自情景所使用的电网组成（水电、其他可再生能源、化石能源）的国家差异对评估产生了重大影响。初步经济评估表明，由于从RAS废物流中回收养分和产生的能量，所有方案的运营成本都比基线方案低。

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