As China accelerates its transition toward carbon neutrality, evaluating the life-cycle carbon and energy performance of photovoltaic systems is critical to defining the real pathway toward a low-carbon energy future. Previous studies have faced three major limitations: they failed to integrate temporal dynamics and spatial heterogeneity, lacked systematic assessments at a national scale with city-level resolution, and overlooked the impact of actual grid absorption on carbon accounting. This study develops a spatio-temporal dynamic life-cycle assessment (ST-DLCA) model that integrates multi-source data across temporal, spatial, and process dimensions to quantify the evolving carbon, energy, and techno-economic performance of utility-scale photovoltaic systems in China. The ST-DLCA results reveal clear spatio-temporal patterns and continuous improvement in both environmental and economic performance. From 2015 to 2024, the average life-cycle carbon intensity (CI) declined from 45.7 to 25.4 g CO2e/kWh, the energy payback time (EPBT) shortened from 4.36 to 2.86 years, the energy return on investment (EROI) rose from about 5.5 to 8.0, and the levelized cost of electricity (LCOE) decreased from 0.465 to 0.182 CNY/kWh. Overall, this study provides a comprehensive evaluation of the spatio-temporal evolution of carbon intensity, energy performance, and techno-economic indicators of China's utility-scale PV systems. The findings deepen understanding of how technological progress, grid decarbonization, and regional resource endowment jointly shape PV decarbonization potential, offering scientific guidance for optimizing deployment strategies and supporting China's long-term carbon neutrality transition.
随着中国加速向碳中和转型，评估光伏系统的生命周期碳和能源性能对于确定通往低碳能源未来的真正途径至关重要。以往的研究存在三个主要的局限性：未能整合时间动态和空间异质性，缺乏在国家尺度上具有城市分辨率的系统评估，忽略了实际网格吸收对碳核算的影响。本研究开发了一个时空动态生命周期评估（ST-DLCA）模型，该模型集成了跨时间、空间和过程维度的多源数据，以量化中国公用事业规模光伏系统的碳、能源和技术经济绩效的演变。ST-DLCA结果揭示了清晰的时空格局和环境和经济绩效的持续改善。从2015年到2024年，平均生命周期碳强度（CI）从45.7 g CO2e/kWh下降到25.4 g CO2e/kWh，能源回收期（EPBT）从4.36年缩短到2.86年，能源投资回报率（EROI）从5.5左右上升到8.0，平准化电力成本（LCOE）从0.465元/kWh下降到0.182元/kWh。总体而言，本研究对中国公用事业规模光伏发电系统的碳强度、能源性能和技术经济指标的时空演变进行了综合评估。研究结果加深了对技术进步、电网脱碳和区域资源禀赋如何共同影响光伏脱碳潜力的理解，为优化部署策略和支持中国长期碳中和转型提供了科学指导。
{"title":"Utility-scale photovoltaic carbon footprint evaluation from a spatio-temporal dynamic life cycle perspective: A case study of China","authors":"Zhuoyang Xie, Xueying Bao, Jingle Liu, Haiwen Li, Zhongshuai Shen","doi":"10.1016/j.jclepro.2026.148002","DOIUrl":"https://doi.org/10.1016/j.jclepro.2026.148002","url":null,"abstract":"As China accelerates its transition toward carbon neutrality, evaluating the life-cycle carbon and energy performance of photovoltaic systems is critical to defining the real pathway toward a low-carbon energy future. Previous studies have faced three major limitations: they failed to integrate temporal dynamics and spatial heterogeneity, lacked systematic assessments at a national scale with city-level resolution, and overlooked the impact of actual grid absorption on carbon accounting. This study develops a spatio-temporal dynamic life-cycle assessment (ST-DLCA) model that integrates multi-source data across temporal, spatial, and process dimensions to quantify the evolving carbon, energy, and techno-economic performance of utility-scale photovoltaic systems in China. The ST-DLCA results reveal clear spatio-temporal patterns and continuous improvement in both environmental and economic performance. From 2015 to 2024, the average life-cycle carbon intensity (CI) declined from 45.7 to 25.4 g CO<sub>2</sub>e/kWh, the energy payback time (EPBT) shortened from 4.36 to 2.86 years, the energy return on investment (EROI) rose from about 5.5 to 8.0, and the levelized cost of electricity (LCOE) decreased from 0.465 to 0.182 CNY/kWh. Overall, this study provides a comprehensive evaluation of the spatio-temporal evolution of carbon intensity, energy performance, and techno-economic indicators of China's utility-scale PV systems. The findings deepen understanding of how technological progress, grid decarbonization, and regional resource endowment jointly shape PV decarbonization potential, offering scientific guidance for optimizing deployment strategies and supporting China's long-term carbon neutrality transition.","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":"12 1","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147489524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-19DOI: 10.1016/j.jclepro.2026.148036
Milena Maredmi Corrêa Teixeira, Clarissa Stefani Teixeira, Deoclécio Junior Cardoso da Silva, Luis Felipe Dias Lopes
This study investigates the role of business incubators in promoting sustainability within innovation ecosystems by proposing and validating monitoring indicators aimed at strengthening management practices and supporting public policy formulation. The fuzzy Delphi and random forest importance techniques were applied to assess the relevance and prioritization of indicators across three dimensions: Legal, Organizational, and Managerial Structure, Services, and Strategic Interactions. The findings show that strategic interactions are the most decisive factor for the long-term sustainability of incubators, followed by the services dimension, which includes mentoring, consultancy, and technical support that are essential for the consolidation of incubated startups. The Legal, Organizational, and Managerial dimension had a relatively lower impact but remains a fundamental support base. These results highlight the need for monitoring tools that capture the interdependence between institutional, operational, and relational factors. The study advances the literature by proposing a robust evaluation model applicable to different contexts and aligned with the Sustainable Development Goals 8, 9, and 17, while offering practical insights for incubator managers and policymakers to foster innovation and sustainable competitiveness.
{"title":"Evaluating sustainability in innovation ecosystems: monitoring indicators and the role of business incubatos","authors":"Milena Maredmi Corrêa Teixeira, Clarissa Stefani Teixeira, Deoclécio Junior Cardoso da Silva, Luis Felipe Dias Lopes","doi":"10.1016/j.jclepro.2026.148036","DOIUrl":"https://doi.org/10.1016/j.jclepro.2026.148036","url":null,"abstract":"This study investigates the role of business incubators in promoting sustainability within innovation ecosystems by proposing and validating monitoring indicators aimed at strengthening management practices and supporting public policy formulation. The fuzzy Delphi and random forest importance techniques were applied to assess the relevance and prioritization of indicators across three dimensions: Legal, Organizational, and Managerial Structure, Services, and Strategic Interactions. The findings show that strategic interactions are the most decisive factor for the long-term sustainability of incubators, followed by the services dimension, which includes mentoring, consultancy, and technical support that are essential for the consolidation of incubated startups. The Legal, Organizational, and Managerial dimension had a relatively lower impact but remains a fundamental support base. These results highlight the need for monitoring tools that capture the interdependence between institutional, operational, and relational factors. The study advances the literature by proposing a robust evaluation model applicable to different contexts and aligned with the Sustainable Development Goals 8, 9, and 17, while offering practical insights for incubator managers and policymakers to foster innovation and sustainable competitiveness.","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":"20 1","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147489457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-19DOI: 10.1016/j.jclepro.2026.148024
Ying-yu Li, Pei Wang, Guiyun Chen, Lin Lin, Xiao-yan Li
The slow enrichment of autotrophic nitrifying biofilms remains a critical bottleneck restricting the rapid start-up of biofilm technologies for low-strength wastewater treatment. In this study, quartz crystal microbalance with dissipation monitoring (QCM-D) and Bayesian SourceTracker analysis were integrated to establish a microscale, process-oriented framework for deciphering how hydrodynamic conditions and carrier packing patterns regulate the biofilm formation in three distinct reactor systems: moving-bed biofilm reactor (MBBR), packed-bed biofilm reactor (PBBR), and trickling filter (TF). Among these, PBBR, characterized by reduced fluid turbulence, exhibited markedly enhanced biofilm development. During the initial cultivation phase, QCM-D revealed that microbial communities adhered more rapidly to the carrier surfaces and formed highly viscoelastic biolayers (|ΔD/Δf| = 0.379) in the PBBR. Within 7 days, the biofilm density reached 231.1 mg SS/m2, and the specific growth rate was more than 10 times higher than that in the MBBR and TF. SourceTracker analysis further revealed that more than 70% of biofilm accumulation was driven by the self-proliferation of pre-existing colonies rather than continuous planktonic adhesion, thereby enabling a semi-quantitative assessment of suspended sludge contribution and providing mechanistic support for commonly applied sludge reduction strategies. Furthermore, biofilms pre-cultivated in the PBBR maintained high ammonium oxidation rates (>98%) after its transition to a more dynamic MBBR condition. These findings validate the feasibility of employing PBBR as an effective means of biofilm pre-cultivation and offer a practical strategy for achieving rapid start-up and stable operation of high-performance biofilm reactors for wastewater treatment.
{"title":"Pre-cultivation of nitrifying biofilms for rapid start-up of biofilm reactors: Manipulation by the biocarrier packing mode and fluid conditions","authors":"Ying-yu Li, Pei Wang, Guiyun Chen, Lin Lin, Xiao-yan Li","doi":"10.1016/j.jclepro.2026.148024","DOIUrl":"https://doi.org/10.1016/j.jclepro.2026.148024","url":null,"abstract":"The slow enrichment of autotrophic nitrifying biofilms remains a critical bottleneck restricting the rapid start-up of biofilm technologies for low-strength wastewater treatment. In this study, quartz crystal microbalance with dissipation monitoring (QCM-D) and Bayesian SourceTracker analysis were integrated to establish a microscale, process-oriented framework for deciphering how hydrodynamic conditions and carrier packing patterns regulate the biofilm formation in three distinct reactor systems: moving-bed biofilm reactor (MBBR), packed-bed biofilm reactor (PBBR), and trickling filter (TF). Among these, PBBR, characterized by reduced fluid turbulence, exhibited markedly enhanced biofilm development. During the initial cultivation phase, QCM-D revealed that microbial communities adhered more rapidly to the carrier surfaces and formed highly viscoelastic biolayers (|<em>ΔD/Δf</em>| = 0.379) in the PBBR. Within 7 days, the biofilm density reached 231.1 mg SS/m<sup>2</sup>, and the specific growth rate was more than 10 times higher than that in the MBBR and TF. SourceTracker analysis further revealed that more than 70% of biofilm accumulation was driven by the self-proliferation of pre-existing colonies rather than continuous planktonic adhesion, thereby enabling a semi-quantitative assessment of suspended sludge contribution and providing mechanistic support for commonly applied sludge reduction strategies. Furthermore, biofilms pre-cultivated in the PBBR maintained high ammonium oxidation rates (>98%) after its transition to a more dynamic MBBR condition. These findings validate the feasibility of employing PBBR as an effective means of biofilm pre-cultivation and offer a practical strategy for achieving rapid start-up and stable operation of high-performance biofilm reactors for wastewater treatment.","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":"99 1","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147492976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-19DOI: 10.1016/j.jclepro.2026.148067
Zifei Wang, Huaqing Qi, Song Hu, Xiaoyu Wu, Yulin Han, Yi Man
As environmental protection standards become increasingly stringent, wastewater treatment plants (WWTPs) must precisely control aeration volumes and chemical additions to achieve improved effluent quality. The accurate and rapid prediction of water pollutant loads is becoming increasingly urgent. An efficient multi-input, multi-output water-quality prediction model has become a practical necessity for WWTPs. However, the multi-input multi-output model needs to capture the complex interactions between input and output variables, as well as the hidden temporal characteristics of the water quality sequence. It often requires a complex model design and a large amount of training data to achieve good prediction results. The complex model design and extensive data training entail high time and computational resource costs, which will limit the model's applicability. Based on this, this study proposes an active deep learning framework. This framework first queries high-value samples in the data using an active learning module, and then learns the hidden, complex relationships within them using a multi-module fusion deep learning architecture. While ensuring the accuracy of the model's predictions, it significantly reduces the cost of training and the model's computational resource usage. This study uses a municipal WWTP as a case study to predict influent COD and NH3-N loads. The results show that, compared with the traditional passive deep learning model, the active deep learning framework proposed in this study can achieve a prediction effect similar to that of passive learning while reducing the model's time cost by 39.8% and the model's computational resource usage by 18.4%.
{"title":"Multi-input and multi-output prediction of influent water quality in wastewater treatment plants based on active deep learning","authors":"Zifei Wang, Huaqing Qi, Song Hu, Xiaoyu Wu, Yulin Han, Yi Man","doi":"10.1016/j.jclepro.2026.148067","DOIUrl":"https://doi.org/10.1016/j.jclepro.2026.148067","url":null,"abstract":"As environmental protection standards become increasingly stringent, wastewater treatment plants (WWTPs) must precisely control aeration volumes and chemical additions to achieve improved effluent quality. The accurate and rapid prediction of water pollutant loads is becoming increasingly urgent. An efficient multi-input, multi-output water-quality prediction model has become a practical necessity for WWTPs. However, the multi-input multi-output model needs to capture the complex interactions between input and output variables, as well as the hidden temporal characteristics of the water quality sequence. It often requires a complex model design and a large amount of training data to achieve good prediction results. The complex model design and extensive data training entail high time and computational resource costs, which will limit the model's applicability. Based on this, this study proposes an active deep learning framework. This framework first queries high-value samples in the data using an active learning module, and then learns the hidden, complex relationships within them using a multi-module fusion deep learning architecture. While ensuring the accuracy of the model's predictions, it significantly reduces the cost of training and the model's computational resource usage. This study uses a municipal WWTP as a case study to predict influent COD and NH<sub>3</sub>-N loads. The results show that, compared with the traditional passive deep learning model, the active deep learning framework proposed in this study can achieve a prediction effect similar to that of passive learning while reducing the model's time cost by 39.8% and the model's computational resource usage by 18.4%.","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":"79 1","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147489519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accelerating the clean energy transition in vast rural areas still heavily reliant on traditional energy sources is critical for achieving carbon neutrality. To address regional heterogeneity and avoid one-size-fits-all solutions, this study proposes a unified multi-objective optimization framework integrating spatial clustering and Mixed-Integer Linear Programming (MILP) to coordinate provincial-level transition pathways. By dynamically coupling coal-to-gas and county-wide photovoltaic (PV) policies, the model achieves the synergistic optimization of regional transition strategies and natural gas pipeline network expansion. A case study of Liaoning Province demonstrates that under the baseline scenario, the coordinated approach yields a clean energy share of 44.92% and reduces annual emissions by 28.62%. Multi-scenario analyses with varying emission reduction targets and subsidy schemes identify the 40% emission reduction target as the strategic inflection point for the transition from a gas-dominant approach to a gas-PV complementary framework. However, overlapping aggressive subsidies under deep decarbonization push government abatement costs up to 2.25 kCNY/t, cautioning against fiscally unsustainable dual-incentive policies. Furthermore, sensitivity analysis on natural gas prices and PV penetration rates explicitly quantifies the trade-offs between household economic burdens, government transition costs, and environmental performance. The results show that raising the PV penetration limit from 20% to 60% decreases the government's average abatement cost by 30.1%, while a 20% reduction in natural gas prices lowers household transition burdens by 18.5%. These quantitative insights provide a robust decision-support tool for formulating differentiated policies and coordinating infrastructure planning across large-scale rural regions.
{"title":"Pathways for clean energy transition in rural China: Natural gas vs. photovoltaic power","authors":"Chunying Liu, Qi Liao, Renfu Tu, Xiaomeng Bai, Kaikai Lu, Likun Peng, Yongtu Liang, Haoran Zhang","doi":"10.1016/j.jclepro.2026.148013","DOIUrl":"https://doi.org/10.1016/j.jclepro.2026.148013","url":null,"abstract":"Accelerating the clean energy transition in vast rural areas still heavily reliant on traditional energy sources is critical for achieving carbon neutrality. To address regional heterogeneity and avoid one-size-fits-all solutions, this study proposes a unified multi-objective optimization framework integrating spatial clustering and Mixed-Integer Linear Programming (MILP) to coordinate provincial-level transition pathways. By dynamically coupling coal-to-gas and county-wide photovoltaic (PV) policies, the model achieves the synergistic optimization of regional transition strategies and natural gas pipeline network expansion. A case study of Liaoning Province demonstrates that under the baseline scenario, the coordinated approach yields a clean energy share of 44.92% and reduces annual emissions by 28.62%. Multi-scenario analyses with varying emission reduction targets and subsidy schemes identify the 40% emission reduction target as the strategic inflection point for the transition from a gas-dominant approach to a gas-PV complementary framework. However, overlapping aggressive subsidies under deep decarbonization push government abatement costs up to 2.25 kCNY/t, cautioning against fiscally unsustainable dual-incentive policies. Furthermore, sensitivity analysis on natural gas prices and PV penetration rates explicitly quantifies the trade-offs between household economic burdens, government transition costs, and environmental performance. The results show that raising the PV penetration limit from 20% to 60% decreases the government's average abatement cost by 30.1%, while a 20% reduction in natural gas prices lowers household transition burdens by 18.5%. These quantitative insights provide a robust decision-support tool for formulating differentiated policies and coordinating infrastructure planning across large-scale rural regions.","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":"14 1","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147492483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-19DOI: 10.1016/j.jclepro.2026.148053
Kangli Yan, Yuntao Guo, Xinwu Qian, Shuai Zhang, Can Liu, Xinghua Li, Jie Yang
Battery swapping services (BSS) reduce downtime, alleviate range anxiety, and enhance operational efficiency for electric taxi and ridesourcing service (ETRS) drivers. However, drivers' preferences and the key factors shaping their continuance intention to use BSS remain underexplored. Previous studies have largely relied on stated preference surveys constrained by restricted analytical scope and by BSS-inexperienced participants, reducing findings' reliability. To address this gap, we propose an extended Technology Acceptance Model that incorporates social influence, battery quality assessment, service expectation, BSS experience, shift patterns, and sociodemographic attributes, and estimate it using a Multiple Indicators Multiple Causes framework. Based on offline survey data from approximately 1000 ETRS drivers with BSS experience in Shanghai, China, the results show that the proposed model demonstrates improved model fit and explanatory power compared to baseline models (over 10% increase compared to the base model in terms of explained variance). Among the examined factors, battery quality assessment and perceived usefulness emerge as the primary determinants of drivers’ intention to continue using BSS. Perceived ease of use and service expectation also significantly influence attitudes and intentions. In contrast, social influence exerts limited direct effects. Multi-group analysis further demonstrates heterogeneity across time sensitivity and operational shift patterns: double-shift and high time-pressure drivers are more sensitive to service reliability and convenience. The findings offer empirical evidence to support BSS policy and service design.
{"title":"Understanding taxi and ridesourcing drivers’ continuance intention to use battery swapping services","authors":"Kangli Yan, Yuntao Guo, Xinwu Qian, Shuai Zhang, Can Liu, Xinghua Li, Jie Yang","doi":"10.1016/j.jclepro.2026.148053","DOIUrl":"https://doi.org/10.1016/j.jclepro.2026.148053","url":null,"abstract":"Battery swapping services (BSS) reduce downtime, alleviate range anxiety, and enhance operational efficiency for electric taxi and ridesourcing service (ETRS) drivers. However, drivers' preferences and the key factors shaping their continuance intention to use BSS remain underexplored. Previous studies have largely relied on stated preference surveys constrained by restricted analytical scope and by BSS-inexperienced participants, reducing findings' reliability. To address this gap, we propose an extended Technology Acceptance Model that incorporates social influence, battery quality assessment, service expectation, BSS experience, shift patterns, and sociodemographic attributes, and estimate it using a Multiple Indicators Multiple Causes framework. Based on offline survey data from approximately 1000 ETRS drivers with BSS experience in Shanghai, China, the results show that the proposed model demonstrates improved model fit and explanatory power compared to baseline models (over 10% increase compared to the base model in terms of explained variance). Among the examined factors, battery quality assessment and perceived usefulness emerge as the primary determinants of drivers’ intention to continue using BSS. Perceived ease of use and service expectation also significantly influence attitudes and intentions. In contrast, social influence exerts limited direct effects. Multi-group analysis further demonstrates heterogeneity across time sensitivity and operational shift patterns: double-shift and high time-pressure drivers are more sensitive to service reliability and convenience. The findings offer empirical evidence to support BSS policy and service design.","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":"78 1","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147492487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-19DOI: 10.1016/j.jclepro.2026.148031
Qingfeng Luo, Fenfen Wang, Can Wang, Xinyang Dong
In the face of escalating climate change and environmental pollution, this study examines whether the combination of national big data pilot zones and carbon emissions trading can generate synergistic effects on pollution and carbon reduction. Using city-level panel data and a difference-in-differences model, the results show that the dual pilot programs significantly reduce pollution and carbon emissions, with stronger effects than single-policy implementations. These policies enhance environmental outcomes through intercity collaboration, green innovation, improved productivity, and cleaner production. Heterogeneity analysis shows stronger effects in high-emission cities, those with stringent regulations, developed green finance, and larger urban scales. Spatial analysis reveals spillover benefits in neighboring cities, while border cities experience weaker effects due to the gray border effect. This study provides theoretical insights for local governments seeking to leverage the complementary effects of data-driven and market-based policies to achieve coordinated pollution reduction, carbon mitigation, and sustainable development.
{"title":"Synergistic effects of the dual pilot policy on pollution reduction and carbon mitigation: The role of data empowerment and market mechanisms","authors":"Qingfeng Luo, Fenfen Wang, Can Wang, Xinyang Dong","doi":"10.1016/j.jclepro.2026.148031","DOIUrl":"https://doi.org/10.1016/j.jclepro.2026.148031","url":null,"abstract":"In the face of escalating climate change and environmental pollution, this study examines whether the combination of national big data pilot zones and carbon emissions trading can generate synergistic effects on pollution and carbon reduction. Using city-level panel data and a difference-in-differences model, the results show that the dual pilot programs significantly reduce pollution and carbon emissions, with stronger effects than single-policy implementations. These policies enhance environmental outcomes through intercity collaboration, green innovation, improved productivity, and cleaner production. Heterogeneity analysis shows stronger effects in high-emission cities, those with stringent regulations, developed green finance, and larger urban scales. Spatial analysis reveals spillover benefits in neighboring cities, while border cities experience weaker effects due to the gray border effect. This study provides theoretical insights for local governments seeking to leverage the complementary effects of data-driven and market-based policies to achieve coordinated pollution reduction, carbon mitigation, and sustainable development.","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":"9 1","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147489458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The efficient removal of solid fines and metal impurities from fluid catalytic cracking (FCC) slurry oil remains a critical challenge for its high-value utilization in petroleum refining. Conventional separation techniques often suffer from high energy consumption, low fine-particle retention, secondary pollution, and elevated operational costs. Herein, we propose a green and tunable filtration strategy using chemically modified biomass as a sustainable filter medium. Biomass filter powder was treated with hydrochloric acid (HCl) and a NaOH/Na2SO3 mixed system to tailor its surface chemistry and pore structure. Systematic investigations revealed that acid-modified 180-mesh biomass (PA-180) exhibits superior performance under optimized conditions (1 wt% dosage, 135 °C, −100.13 kPa), reducing the ash content of slurry oil from 3599.9 μg/g to 37 μg/g (98.97% removal) while achieving >99% removal of Si and Al. Characterization studies demonstrate that acid treatment enhances surface polarity, creates a hierarchical porous structure, and introduces abundant oxygen-containing functional groups, which collectively promote the capture of sub-micron particles and polar asphaltenes via a synergistic “bridging effect” and chemisorption. This biomass-based approach operates without chemical additives, offering an energy-efficient, environmentally benign, and effective route for the ultra-cleaning of FCC slurry oil, aligning with the principles of cleaner production and sustainable resource utilization in the petrochemical industry.
{"title":"Green purification of FCC slurry oil to ultra-low ash via acid-modified biomass filter","authors":"Jinxuan Wu, Libo Zhang, Songtao Liu, Xiaoli He, Zhenjiang Chen, Hui Wang, Qinzhen Fan","doi":"10.1016/j.jclepro.2026.148040","DOIUrl":"https://doi.org/10.1016/j.jclepro.2026.148040","url":null,"abstract":"The efficient removal of solid fines and metal impurities from fluid catalytic cracking (FCC) slurry oil remains a critical challenge for its high-value utilization in petroleum refining. Conventional separation techniques often suffer from high energy consumption, low fine-particle retention, secondary pollution, and elevated operational costs. Herein, we propose a green and tunable filtration strategy using chemically modified biomass as a sustainable filter medium. Biomass filter powder was treated with hydrochloric acid (HCl) and a NaOH/Na<sub>2</sub>SO<sub>3</sub> mixed system to tailor its surface chemistry and pore structure. Systematic investigations revealed that acid-modified 180-mesh biomass (PA-180) exhibits superior performance under optimized conditions (1 wt% dosage, 135 °C, −100.13 kPa), reducing the ash content of slurry oil from 3599.9 μg/g to 37 μg/g (98.97% removal) while achieving >99% removal of Si and Al. Characterization studies demonstrate that acid treatment enhances surface polarity, creates a hierarchical porous structure, and introduces abundant oxygen-containing functional groups, which collectively promote the capture of sub-micron particles and polar asphaltenes via a synergistic “bridging effect” and chemisorption. This biomass-based approach operates without chemical additives, offering an energy-efficient, environmentally benign, and effective route for the ultra-cleaning of FCC slurry oil, aligning with the principles of cleaner production and sustainable resource utilization in the petrochemical industry.","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":"67 1","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147489459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-18DOI: 10.1016/j.jclepro.2026.148042
Manouchehr Shokri, Marzia Traverso, Rose Nangah Mankaa
The decarbonization of transport infrastructure is pivotal for achieving climate neutrality and advancing Circular Economy (CE) goals. Conventional asphalt production, heavily reliant on energy-intensive methods and virgin materials, contributes significantly to greenhouse gas emissions. Integrating alternative materials and low-emission energy sources could offer a viable pathway to minimizing environmental impacts while preserving pavement performance and optimizing Life Cycle Costs (LCC). This study developed a multi-objective optimization model that combines Monte Carlo simulation with Pareto front analysis to identify optimal asphalt mixtures by jointly evaluating LCC, Global Warming Potential (GWP), and product quality. The model incorporated various parameters including mix design, Reclaimed Asphalt Pavement (RAP) content, bitumen, fuel types, heating energy, transport distance, and other influencing factors. The results reveal substantial variability in environmental and economic performance, with GWP ranging from 9.17 to 97.72 kg CO2-eq per ton and LCC between 2.75 and 13.67 €/ton, primarily driven by the type and amount of fuel consumed, with green hydrogen playing a particularly notable role despite its higher cost. Pareto-optimal solutions achieved average reductions of 35.7% in GWP and 11.7% in LCC, respectively. Analysis of Pareto-optimal solutions demonstrates that achieving low GWP does not inherently require high costs or reduced quality, nor does cost minimization necessarily lead to increased emissions or compromised performance. Overall, this research establishes a practical framework for simultaneously balancing economic, environmental, and technical criteria in asphalt production, thereby enabling more informed and sustainable decision-making in real-world applications.
交通基础设施的脱碳对于实现气候中和和推进循环经济（CE）目标至关重要。传统的沥青生产严重依赖能源密集型方法和原始材料，对温室气体排放有很大贡献。整合替代材料和低排放能源可以提供可行的途径，以尽量减少对环境的影响，同时保持路面性能和优化生命周期成本（LCC）。本研究开发了一个多目标优化模型，将蒙特卡罗模拟与帕累托前分析相结合，通过联合评估LCC、全球变暖潜势（GWP）和产品质量来确定最佳沥青混合物。该模型考虑了混合料设计、再生沥青路面（RAP）含量、沥青、燃料类型、加热能量、运输距离等多种影响因素。结果显示，在环境和经济绩效方面存在显著差异，GWP在9.17 - 97.72 kg co2当量/吨之间，LCC在2.75 - 13.67欧元/吨之间，主要受燃料消耗类型和数量的影响，其中绿色氢的作用尤为显著，尽管其成本较高。帕累托最优方案实现了GWP和LCC分别平均降低35.7%和11.7%。对帕累托最优解的分析表明，实现低全球升温潜能值并不必然需要高成本或降低质量，成本最小化也不一定会导致排放增加或性能降低。总体而言，本研究为同时平衡沥青生产中的经济、环境和技术标准建立了一个实用框架，从而在实际应用中实现更明智和可持续的决策。
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Pub Date : 2026-03-18DOI: 10.1016/j.jclepro.2026.147988
Rui Hu, Hao-Tong Zheng, Boxi Tang, Jialing Tu, Yu-Hong Cui, Xuedong Zhai, Gang Wen, Zheng-Qian Liu
Effluent organic matter (EfOM) from municipal wastewater treatment plants is an emerging concern because of its refractory and potential risk to ecological environment. Herein, this investigation comprehensively evaluated the effects of HO• and O3 exposures on EfOM transformation and micropollutant degradation under different pre-coagulation operating conditions during ozonation. Compared to coagulation and ozonation alone, coupling two processes could effectively reduce total organic carbon, color and UV254 of effluent. Fe-based coagulant was easier to combine with OH− to generate floc in bulk than Al-based coagulant for EfOM capture, which could decrease total scavenging capacities of effluents as well as increase the exposures of O3 and HO• during ozonation, enhancing ozonation efficiency for EfOM degradation. EfOM from membrane bioreactor treatment was more difficult to capture during pre-coagulation but more easily degraded during ozonation than that from conventional activated sludge treatment due to its lower molecular weight distribution and higher contents of humic-like and tryptophan-like substances. Parallel factor analysis shows that coagulation combined with ozonation breaks the fluorescent groups of humic-like and tryptophan-like substances effectively. Molecular weight distribution determination indicates that coupling two processes could more effectively remove humic-like substances, organic colloids and polysaccharides compared to coagulation or ozonation alone. Furthermore, micropollutants with higher energy of highest occupied molecular orbital were degraded more efficiently during ozonation. The investigation reveals that O3 utilization efficiency for micropollutant degradation can be significantly improved by pre-coagulation, offering new ideas for reducing energy consumption during advanced ozonation treatment and ensuring the safety of effluent and sustainable water reuse.
{"title":"Revisit the improvement of coagulation pretreatment on ozonation performance: The critical roles of HO• and O3 for effluent organic matter transformation and micropollutant degradation","authors":"Rui Hu, Hao-Tong Zheng, Boxi Tang, Jialing Tu, Yu-Hong Cui, Xuedong Zhai, Gang Wen, Zheng-Qian Liu","doi":"10.1016/j.jclepro.2026.147988","DOIUrl":"https://doi.org/10.1016/j.jclepro.2026.147988","url":null,"abstract":"Effluent organic matter (EfOM) from municipal wastewater treatment plants is an emerging concern because of its refractory and potential risk to ecological environment. Herein, this investigation comprehensively evaluated the effects of HO<sup>•</sup> and O<sub>3</sub> exposures on EfOM transformation and micropollutant degradation under different pre-coagulation operating conditions during ozonation. Compared to coagulation and ozonation alone, coupling two processes could effectively reduce total organic carbon, color and UV<sub>254</sub> of effluent. Fe-based coagulant was easier to combine with OH<sup>−</sup> to generate floc in bulk than Al-based coagulant for EfOM capture, which could decrease total scavenging capacities of effluents as well as increase the exposures of O<sub>3</sub> and HO<sup>•</sup> during ozonation, enhancing ozonation efficiency for EfOM degradation. EfOM from membrane bioreactor treatment was more difficult to capture during pre-coagulation but more easily degraded during ozonation than that from conventional activated sludge treatment due to its lower molecular weight distribution and higher contents of humic-like and tryptophan-like substances. Parallel factor analysis shows that coagulation combined with ozonation breaks the fluorescent groups of humic-like and tryptophan-like substances effectively. Molecular weight distribution determination indicates that coupling two processes could more effectively remove humic-like substances, organic colloids and polysaccharides compared to coagulation or ozonation alone. Furthermore, micropollutants with higher energy of highest occupied molecular orbital were degraded more efficiently during ozonation. The investigation reveals that O<sub>3</sub> utilization efficiency for micropollutant degradation can be significantly improved by pre-coagulation, offering new ideas for reducing energy consumption during advanced ozonation treatment and ensuring the safety of effluent and sustainable water reuse.","PeriodicalId":349,"journal":{"name":"Journal of Cleaner Production","volume":"6 1","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147492485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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