Pub Date : 2024-07-16DOI: 10.1088/2634-4505/ad63c9
Joël Jean-François Gabriel De Plaen, E. Koks, Philip J. Ward
Critical infrastructure (CI) are at risk of failure due to the increased frequency and magnitude of climate extremes related to climate change. It is thus essential to include them in a risk management framework to identify risk hotspots, develop risk management policies and support adaptation strategies to enhance their resilience. However, the lack of information on the exposure of CI to natural hazards prevents their incorporation in large-scale risk assessments. This study sets out to improve the representation of CI for risk assessment studies by building a neural network model to detect CI assets from optical remote sensing imagery. We present a pipeline that extracts CI from OpenStreetMap (OSM), processes the imagery and assets’ masks, and trains a Mask R-CNN model that allows for instance segmentation of CI at the asset level. This study provides an overview of the pipeline and tests it with the detection of electrical substations assets in the Netherlands. Several experiments are presented for different under-sampling percentages of the majority class (25 %, 50 % and 100 %) and hyperparameters settings (batch size and learning rate). The highest scoring experiment achieved an Average Precision at an Intersection over Union of 50 % of 30.93 and a tile F-score of 89.88 %. This allows us to confirm the feasibility of the method and invite disaster risk researchers to use this pipeline for other infrastructure types. We conclude by exploring the different avenues to improve the pipeline by addressing the class imbalance, Transfer Learning and Explainable Artificial Intelligence.
由于与气候变化相关的极端气候发生频率和规模的增加，关键基础设施（CI）面临着失灵的风险。因此，必须将其纳入风险管理框架，以确定风险热点、制定风险管理政策和支持适应战略，从而增强其抗灾能力。然而，由于缺乏有关沿海地区暴露于自然灾害的信息，因此无法将其纳入大规模风险评估。本研究旨在通过建立一个神经网络模型，从光学遥感图像中检测 CI 资产，从而改进 CI 在风险评估研究中的代表性。我们介绍了一个从 OpenStreetMap (OSM) 中提取 CI、处理图像和资产掩码并训练掩码 R-CNN 模型的管道，该模型允许在资产级别对 CI 进行实例分割。本研究概述了该管道，并通过荷兰变电站资产的检测对其进行了测试。针对多数类的不同低采样率（25%、50% 和 100%）和超参数设置（批量大小和学习率）进行了多次实验。得分最高的实验取得了 30.93% 的平均精确度和 89.88% 的瓦片 F 分数。这使我们能够确认该方法的可行性，并邀请灾害风险研究人员将该管道用于其他类型的基础设施。最后，我们探讨了通过解决类不平衡、迁移学习和可解释人工智能来改进管道的不同途径。
{"title":"Towards an open pipeline for the detection of Critical Infrastructure from satellite imagery – a case study on electrical substations in the Netherlands","authors":"Joël Jean-François Gabriel De Plaen, E. Koks, Philip J. Ward","doi":"10.1088/2634-4505/ad63c9","DOIUrl":"https://doi.org/10.1088/2634-4505/ad63c9","url":null,"abstract":"\u0000 Critical infrastructure (CI) are at risk of failure due to the increased frequency and magnitude of climate extremes related to climate change. It is thus essential to include them in a risk management framework to identify risk hotspots, develop risk management policies and support adaptation strategies to enhance their resilience. However, the lack of information on the exposure of CI to natural hazards prevents their incorporation in large-scale risk assessments. This study sets out to improve the representation of CI for risk assessment studies by building a neural network model to detect CI assets from optical remote sensing imagery. We present a pipeline that extracts CI from OpenStreetMap (OSM), processes the imagery and assets’ masks, and trains a Mask R-CNN model that allows for instance segmentation of CI at the asset level. This study provides an overview of the pipeline and tests it with the detection of electrical substations assets in the Netherlands. Several experiments are presented for different under-sampling percentages of the majority class (25 %, 50 % and 100 %) and hyperparameters settings (batch size and learning rate). The highest scoring experiment achieved an Average Precision at an Intersection over Union of 50 % of 30.93 and a tile F-score of 89.88 %. This allows us to confirm the feasibility of the method and invite disaster risk researchers to use this pipeline for other infrastructure types. We conclude by exploring the different avenues to improve the pipeline by addressing the class imbalance, Transfer Learning and Explainable Artificial Intelligence.","PeriodicalId":476263,"journal":{"name":"Environmental research: infrastructure and sustainability","volume":"85 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643246","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}
Pub Date : 2024-07-11DOI: 10.1088/2634-4505/ad620b
Serena Patel, D. Mallapragada, Karthik Ganesan, R. Stoner
Substantial coal phase out initiatives have been growing as the world mobilizes to meet the Paris climate goals. However, the stranded asset risk associated with this critical transition could fall disproportionately on Asian economies with younger coal fleets, like India. Here, we undertake plant-level techno-economic analysis to explore the value of installing commercially available, molten-salt thermal energy storage (TES) systems for repurposing existing coal power plants in the Indian context. We combine process simulation and an economic optimization model to evaluate design and operations of TES systems for a variety of technology assumptions, coal plant archetypes, and electricity price scenarios. Key drivers of economic viability identified include longer remaining plant lifetime, increasing peak TES temperature, lower TES energy capacity cost, co-production of waste heat for end-uses, and increasing temporal variability of electricity prices. The plant-level analysis was then extended to screen for the potential of TES retrofits within the coal power fleet in Uttar Pradesh, the most populous Indian state with a significant share of India's coal capacity. Analysis for a single electricity price scenario indicates that over 82% of the coal units in the state can be retrofitted and recover the installed costs of TES retrofits, provided that fixed operating and maintenance costs (FOM) are excluded. These results reinforce the opportunity for decision-makers to consider TES retrofits of coal plants into cost-effective grid decarbonization strategies.
{"title":"Repurposing coal plants into thermal energy storage - a techno-economic assessment in the Indian context","authors":"Serena Patel, D. Mallapragada, Karthik Ganesan, R. Stoner","doi":"10.1088/2634-4505/ad620b","DOIUrl":"https://doi.org/10.1088/2634-4505/ad620b","url":null,"abstract":"\u0000 Substantial coal phase out initiatives have been growing as the world mobilizes to meet the Paris climate goals. However, the stranded asset risk associated with this critical transition could fall disproportionately on Asian economies with younger coal fleets, like India. Here, we undertake plant-level techno-economic analysis to explore the value of installing commercially available, molten-salt thermal energy storage (TES) systems for repurposing existing coal power plants in the Indian context. We combine process simulation and an economic optimization model to evaluate design and operations of TES systems for a variety of technology assumptions, coal plant archetypes, and electricity price scenarios. Key drivers of economic viability identified include longer remaining plant lifetime, increasing peak TES temperature, lower TES energy capacity cost, co-production of waste heat for end-uses, and increasing temporal variability of electricity prices. The plant-level analysis was then extended to screen for the potential of TES retrofits within the coal power fleet in Uttar Pradesh, the most populous Indian state with a significant share of India's coal capacity. Analysis for a single electricity price scenario indicates that over 82% of the coal units in the state can be retrofitted and recover the installed costs of TES retrofits, provided that fixed operating and maintenance costs (FOM) are excluded. These results reinforce the opportunity for decision-makers to consider TES retrofits of coal plants into cost-effective grid decarbonization strategies.","PeriodicalId":476263,"journal":{"name":"Environmental research: infrastructure and sustainability","volume":"46 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141658345","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}
Pub Date : 2024-07-08DOI: 10.1088/2634-4505/ad54ed
Eleanor M. Hennessy, Madalsa Singh, Sarah Saltzer, Inês M L Azevedo
California contributes 0.75% of global greenhouse gas (GHG) emissions and has a target of reaching economy-wide net zero emissions by 2045, requiring all sectors to rapidly reduce emissions. Nearly 8% of California’s GHG emissions are from the heavy-duty transportation sector. In this work, we simulate decarbonization strategies for the heavy-duty vehicle (HDV) fleet using detailed fleet turnover and air quality models to track evolution of the fleet, GHG and criteria air pollutant emissions, and resulting air quality and health impacts across sociodemographic groups. We assess the effectiveness of two types of policies: zero emission vehicle sales mandates, and accelerated retirement policies. For policies including early retirements, we estimate the cost of early retirements and the cost-effectiveness of each policy. We find even a policy mandating all HDV sales to be zero emission vehicles by 2025 would not achieve fleetwide zero emissions by 2045. For California to achieve its goal of carbon neutrality, early retirement policies are needed. We find that a combination of early retirement policies and zero emission vehicle sales mandates could reduce cumulative CO2 emissions by up to 64%. Furthermore, we find that decarbonization policies will significantly reduce air pollution-related mortality, and that Black, Latino, and low-income communities will benefit most. We find that policies targeting long-haul heavy-heavy duty trucks would have the greatest benefits and be most cost-effective.
{"title":"Pathways to zero emissions in California’s heavy-duty transportation sector","authors":"Eleanor M. Hennessy, Madalsa Singh, Sarah Saltzer, Inês M L Azevedo","doi":"10.1088/2634-4505/ad54ed","DOIUrl":"https://doi.org/10.1088/2634-4505/ad54ed","url":null,"abstract":"\u0000 California contributes 0.75% of global greenhouse gas (GHG) emissions and has a target of reaching economy-wide net zero emissions by 2045, requiring all sectors to rapidly reduce emissions. Nearly 8% of California’s GHG emissions are from the heavy-duty transportation sector. In this work, we simulate decarbonization strategies for the heavy-duty vehicle (HDV) fleet using detailed fleet turnover and air quality models to track evolution of the fleet, GHG and criteria air pollutant emissions, and resulting air quality and health impacts across sociodemographic groups. We assess the effectiveness of two types of policies: zero emission vehicle sales mandates, and accelerated retirement policies. For policies including early retirements, we estimate the cost of early retirements and the cost-effectiveness of each policy. We find even a policy mandating all HDV sales to be zero emission vehicles by 2025 would not achieve fleetwide zero emissions by 2045. For California to achieve its goal of carbon neutrality, early retirement policies are needed. We find that a combination of early retirement policies and zero emission vehicle sales mandates could reduce cumulative CO2 emissions by up to 64%. Furthermore, we find that decarbonization policies will significantly reduce air pollution-related mortality, and that Black, Latino, and low-income communities will benefit most. We find that policies targeting long-haul heavy-heavy duty trucks would have the greatest benefits and be most cost-effective.","PeriodicalId":476263,"journal":{"name":"Environmental research: infrastructure and sustainability","volume":"10 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141667950","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}
Pub Date : 2024-07-05DOI: 10.1088/2634-4505/ad5fb4
Macie S Joines, Madison D. Horgan, Rui Li, Alysha M. Helmrich, A. Dirks, Kayla Tarr, Ryan Sparks, Ryan Hoff, Mindy Kimball, Mikhail Chester
Extreme weather-related events are showing how infrastructure disruptions in hinterlands can affect cities. This paper explores the risks to city infrastructure services including transportation, electricity, communication, fuel supply, water distribution, stormwater drainage, and food supply from hinterland hazards of fire, precipitation, post-fire debris flow (PFDF), smoke, and flooding. There is a large and growing body of research that describes the vulnerabilities of infrastructures to climate hazards, yet this work has not systematically acknowledged the relationships and cross-governance challenges of protecting cities from remote disruptions. An evidence base is developed through a structured literature review that identifies city infrastructure vulnerabilities to hinterland hazards. Findings highlight diverse pathways from the initial hazard to the final impact on an infrastructure, demonstrating that impacts to hinterland infrastructure assets from hazards can cascade to city infrastructure. Beyond the value of describing the impact of hinterland hazards on urban infrastructure, the identified pathways can assist in informing cross-governance mitigation strategies. It may be the case that to protect cities, local governments invest in mitigating hazards in their hinterlands and supply chains.
{"title":"Cross-boundary risks of hinterland hazards to city infrastructure","authors":"Macie S Joines, Madison D. Horgan, Rui Li, Alysha M. Helmrich, A. Dirks, Kayla Tarr, Ryan Sparks, Ryan Hoff, Mindy Kimball, Mikhail Chester","doi":"10.1088/2634-4505/ad5fb4","DOIUrl":"https://doi.org/10.1088/2634-4505/ad5fb4","url":null,"abstract":"\u0000 Extreme weather-related events are showing how infrastructure disruptions in hinterlands can affect cities. This paper explores the risks to city infrastructure services including transportation, electricity, communication, fuel supply, water distribution, stormwater drainage, and food supply from hinterland hazards of fire, precipitation, post-fire debris flow (PFDF), smoke, and flooding. There is a large and growing body of research that describes the vulnerabilities of infrastructures to climate hazards, yet this work has not systematically acknowledged the relationships and cross-governance challenges of protecting cities from remote disruptions. An evidence base is developed through a structured literature review that identifies city infrastructure vulnerabilities to hinterland hazards. Findings highlight diverse pathways from the initial hazard to the final impact on an infrastructure, demonstrating that impacts to hinterland infrastructure assets from hazards can cascade to city infrastructure. Beyond the value of describing the impact of hinterland hazards on urban infrastructure, the identified pathways can assist in informing cross-governance mitigation strategies. It may be the case that to protect cities, local governments invest in mitigating hazards in their hinterlands and supply chains.","PeriodicalId":476263,"journal":{"name":"Environmental research: infrastructure and sustainability","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675897","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}
Pub Date : 2024-07-02DOI: 10.1088/2634-4505/ad5e1d
Wenjin Hao, Andrea Cominola, Andrea Castelletti
Urban water demand (UWD) forecasting is essential for water supply network optimization and management, both in business-as-usual scenarios, as well as under external climate and socio-economic stressors. Different machine learning and deep learning models have shown promising forecasting skills in various areas of application. However, their potential to forecast multi-step ahead UWD has not been fully explored. Modelling uncertain UWD patterns and accounting for variations in water demand behaviors require techniques that can extract time-varying information and multi-scale changes. In this research, we comparatively investigate different state-of-the-art machine learning- and deep learning-based predictive models on 1-day- and 7-day-ahead UWD forecasting, using daily demand data from the city of Milan, Italy. The contribution of this paper is two-fold. First, we compare the forecasting performance of different machine learning and deep learning models on single- and multi-step daily UWD forecasting. These models include Artificial Neural Network (ANN), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM), and Long Short-Term Memory network with and without an attention mechanism (LSTM and AM-LSTM). We benchmark their prediction accuracy against autoregressive time series models. Second, we investigate the potential enhancement in predictive accuracy by incorporating the wavelet transform and feature selection performed by LightGBM into these models. Results show that, overall, wavelet-enhanced feature selection improves the model predictive performance. The hybrid model combining wavelet-enhanced feature selection via LightGBM with LSTM (WT-LightGBM-(AM)-LSTM) can achieve high levels of accuracy with Nash-Sutcliffe Efficiency larger than 0.95 and Kling–Gupta Efficiency higher than 0.93 for both 1-day- and 7-day-ahead UWD forecasts. Furthermore, performance is shown to be robust under the influence of external stressors causing sudden changes in UWD.
{"title":"Combining wavelet-enhanced feature selection and deep learning techniques for multi-step forecasting of urban water demand","authors":"Wenjin Hao, Andrea Cominola, Andrea Castelletti","doi":"10.1088/2634-4505/ad5e1d","DOIUrl":"https://doi.org/10.1088/2634-4505/ad5e1d","url":null,"abstract":"\u0000 Urban water demand (UWD) forecasting is essential for water supply network optimization and management, both in business-as-usual scenarios, as well as under external climate and socio-economic stressors. Different machine learning and deep learning models have shown promising forecasting skills in various areas of application. However, their potential to forecast multi-step ahead UWD has not been fully explored. Modelling uncertain UWD patterns and accounting for variations in water demand behaviors require techniques that can extract time-varying information and multi-scale changes. In this research, we comparatively investigate different state-of-the-art machine learning- and deep learning-based predictive models on 1-day- and 7-day-ahead UWD forecasting, using daily demand data from the city of Milan, Italy. The contribution of this paper is two-fold. First, we compare the forecasting performance of different machine learning and deep learning models on single- and multi-step daily UWD forecasting. These models include Artificial Neural Network (ANN), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM), and Long Short-Term Memory network with and without an attention mechanism (LSTM and AM-LSTM). We benchmark their prediction accuracy against autoregressive time series models. Second, we investigate the potential enhancement in predictive accuracy by incorporating the wavelet transform and feature selection performed by LightGBM into these models. Results show that, overall, wavelet-enhanced feature selection improves the model predictive performance. The hybrid model combining wavelet-enhanced feature selection via LightGBM with LSTM (WT-LightGBM-(AM)-LSTM) can achieve high levels of accuracy with Nash-Sutcliffe Efficiency larger than 0.95 and Kling–Gupta Efficiency higher than 0.93 for both 1-day- and 7-day-ahead UWD forecasts. Furthermore, performance is shown to be robust under the influence of external stressors causing sudden changes in UWD.","PeriodicalId":476263,"journal":{"name":"Environmental research: infrastructure and sustainability","volume":"9 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141687589","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}
Pub Date : 2024-06-05DOI: 10.1088/2634-4505/ad546a
Hatzav Yoffe, Keagan Rankin, Chris Bachmann, I. D. Posen, Shoshanna Saxe
This paper examines the tension between needing to build more infrastructure and housing and simultaneously reduce greenhouse gas emissions (GHG) to avoid the most catastrophic impacts of climate change. This study uses an Environmentally Extended Input-Output (EEIO) approach to conduct a high-resolution top-down analysis of Canada's national construction GHG emissions. Our findings highlight that Canada's current construction practices cannot accommodate the construction required to restore housing affordability by 2030 without substantial environmental consequences. On a consumption life cycle basis, the construction sector was responsible for approximately 90 Mt CO2e in 2018, equivalent to over 8% of Canada’s total GHG emissions, while delivering less than a third of Canada’s annual housing needs. Residential construction was responsible for the largest share (42%) of total construction emissions. Overall, 84% of emissions are from material manufacturing and 35% of construction emissions are imported, underscoring the need for a comprehensive regulatory framework addressing both domestic and imported emissions. Under current construction practices (i.e., current material use patterns and emissions intensities), meeting Canada’s 2030 housing affordability and climate commitments requires an 83% reduction in GHG emissions per construction product (i.e., per home) compared to the 40% economy-wide reduction promised in Canada’s international reduction commitments. Mitigating the GHG gap between emission caps and housing demand calls for changes in the ratio of housing to other infrastructure (e.g. fewer roads, less fossil fuel infrastructure), new construction approaches (e.g. increasing material efficiency) and/or disproportionally allocating climate budget to construction. The implications of our study extend beyond Canada, offering valuable insights for other growing countries with climate goals. The results emphasize the urgency in considering and establishing sectoral GHG budgets for construction and for transformative changes in the construction sector to meet national GHG emission reduction commitments.
{"title":"Mapping construction sector greenhouse gas emissions: a crucial step in sustainably meeting increasing housing demands","authors":"Hatzav Yoffe, Keagan Rankin, Chris Bachmann, I. D. Posen, Shoshanna Saxe","doi":"10.1088/2634-4505/ad546a","DOIUrl":"https://doi.org/10.1088/2634-4505/ad546a","url":null,"abstract":"\u0000 This paper examines the tension between needing to build more infrastructure and housing and simultaneously reduce greenhouse gas emissions (GHG) to avoid the most catastrophic impacts of climate change. This study uses an Environmentally Extended Input-Output (EEIO) approach to conduct a high-resolution top-down analysis of Canada's national construction GHG emissions. Our findings highlight that Canada's current construction practices cannot accommodate the construction required to restore housing affordability by 2030 without substantial environmental consequences. On a consumption life cycle basis, the construction sector was responsible for approximately 90 Mt CO2e in 2018, equivalent to over 8% of Canada’s total GHG emissions, while delivering less than a third of Canada’s annual housing needs. Residential construction was responsible for the largest share (42%) of total construction emissions. Overall, 84% of emissions are from material manufacturing and 35% of construction emissions are imported, underscoring the need for a comprehensive regulatory framework addressing both domestic and imported emissions. Under current construction practices (i.e., current material use patterns and emissions intensities), meeting Canada’s 2030 housing affordability and climate commitments requires an 83% reduction in GHG emissions per construction product (i.e., per home) compared to the 40% economy-wide reduction promised in Canada’s international reduction commitments. Mitigating the GHG gap between emission caps and housing demand calls for changes in the ratio of housing to other infrastructure (e.g. fewer roads, less fossil fuel infrastructure), new construction approaches (e.g. increasing material efficiency) and/or disproportionally allocating climate budget to construction. The implications of our study extend beyond Canada, offering valuable insights for other growing countries with climate goals. The results emphasize the urgency in considering and establishing sectoral GHG budgets for construction and for transformative changes in the construction sector to meet national GHG emission reduction commitments.","PeriodicalId":476263,"journal":{"name":"Environmental research: infrastructure and sustainability","volume":"344 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141385725","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}
Pub Date : 2024-03-19DOI: 10.1088/2634-4505/ad3577
F. Prideaux, K. Allacker, Robert H Crawford, A. Stephan
The environmental effects associated with buildings are significant, and include considerable contributions towards global greenhouse gas emissions, energy use, and waste generation. Until recently, mitigation efforts have concentrated on improving the operational energy efficiency of buildings, largely ignoring embodied environmental effects. However, focusing solely on increasing energy efficiency can inadvertently cause an rise in embodied effects. It is therefore critical that embodied effects are considered alongside operational effects and are actively integrated into design decisions throughout the building design process. Life cycle assessment (LCA) can be used to achieve this, however, it is often perceived as difficult to incorporate into design workflows, or requiring specialist knowledge. Additionally, it is not always clear how well aligned LCA approaches are with the building design process. To address this gap, this study aims to provide a detailed analysis of LCA approaches, to assess how well they align with building design stages, and to identify key characteristics, including LCA tools and environmental data used to conduct assessments. A review of academic and grey literature is conducted. Three primary approaches are identified for integrating LCA into the building design process: simplified, detailed and incremental LCA. Simplified LCA uses streamlined data inputs and typically targets a specific design stage. Detailed LCA follows a traditional approach with comprehensive user inputs and results. Incremental LCA progressively evolves the assessment based on design requirements and available building data at each design stage. An analysis of each approach is performed, and key user requirements are mapped against the early design, and detailed design stages. Results reveal that no single approach fully satisfies all design requirements. Findings also highlight a lack of incremental LCA approaches and challenges operationalising these techniques. These approaches often rely on complicated methods or tools not suitable for common design workflows, or they are in early development and require additional verification before implementation.
{"title":"Integrating life cycle assessment into the building design process - a review","authors":"F. Prideaux, K. Allacker, Robert H Crawford, A. Stephan","doi":"10.1088/2634-4505/ad3577","DOIUrl":"https://doi.org/10.1088/2634-4505/ad3577","url":null,"abstract":"\u0000 The environmental effects associated with buildings are significant, and include considerable contributions towards global greenhouse gas emissions, energy use, and waste generation. Until recently, mitigation efforts have concentrated on improving the operational energy efficiency of buildings, largely ignoring embodied environmental effects. However, focusing solely on increasing energy efficiency can inadvertently cause an rise in embodied effects. It is therefore critical that embodied effects are considered alongside operational effects and are actively integrated into design decisions throughout the building design process. Life cycle assessment (LCA) can be used to achieve this, however, it is often perceived as difficult to incorporate into design workflows, or requiring specialist knowledge. Additionally, it is not always clear how well aligned LCA approaches are with the building design process. To address this gap, this study aims to provide a detailed analysis of LCA approaches, to assess how well they align with building design stages, and to identify key characteristics, including LCA tools and environmental data used to conduct assessments. A review of academic and grey literature is conducted. Three primary approaches are identified for integrating LCA into the building design process: simplified, detailed and incremental LCA. Simplified LCA uses streamlined data inputs and typically targets a specific design stage. Detailed LCA follows a traditional approach with comprehensive user inputs and results. Incremental LCA progressively evolves the assessment based on design requirements and available building data at each design stage. An analysis of each approach is performed, and key user requirements are mapped against the early design, and detailed design stages. Results reveal that no single approach fully satisfies all design requirements. Findings also highlight a lack of incremental LCA approaches and challenges operationalising these techniques. These approaches often rely on complicated methods or tools not suitable for common design workflows, or they are in early development and require additional verification before implementation.","PeriodicalId":476263,"journal":{"name":"Environmental research: infrastructure and sustainability","volume":"61 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140228915","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}
Pub Date : 2024-03-19DOI: 10.1088/2634-4505/ad3579
Ulrika Uotila, A. Saari, T. Joensuu
Adoption of the design for disassembly (DfD) concept is suggested as a promising strategy to cope with the climate targets and increase circular economy in the construction sector. Yet, the concept is little used partially due to technical challenges, including inadequate information about demolition and the characteristics of components. This study aims to investigate the demands for information linked to new concrete components with the purpose of reuse. In the building phase, concrete components cause the majority of emissions. Thus, these components also have the greatest potential for CO2 emissions savings. A comprehensive list of information related to DfD concrete components and their characteristics was gathered in a workshop with experts of DfD concrete elements. Furthermore, the stakeholders of DfD components data processing were considered. The results of this study may support the adoption of DfD with concrete components as it provides information for designers and builders to implement in early phases of building projects.
{"title":"Demands for DfD data characteristics: a step towards enabling reuse of prefabricated concrete components","authors":"Ulrika Uotila, A. Saari, T. Joensuu","doi":"10.1088/2634-4505/ad3579","DOIUrl":"https://doi.org/10.1088/2634-4505/ad3579","url":null,"abstract":"\u0000 Adoption of the design for disassembly (DfD) concept is suggested as a promising strategy to cope with the climate targets and increase circular economy in the construction sector. Yet, the concept is little used partially due to technical challenges, including inadequate information about demolition and the characteristics of components. This study aims to investigate the demands for information linked to new concrete components with the purpose of reuse. In the building phase, concrete components cause the majority of emissions. Thus, these components also have the greatest potential for CO2 emissions savings. A comprehensive list of information related to DfD concrete components and their characteristics was gathered in a workshop with experts of DfD concrete elements. Furthermore, the stakeholders of DfD components data processing were considered. The results of this study may support the adoption of DfD with concrete components as it provides information for designers and builders to implement in early phases of building projects.","PeriodicalId":476263,"journal":{"name":"Environmental research: infrastructure and sustainability","volume":"75 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229882","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}
Pub Date : 2024-03-19DOI: 10.1088/2634-4505/ad3576
P. Plötz, Matts Andersson, Aline Scherrer, Erik Johansson
Electrification of road transport is crucial to limit global warming. Battery electric vehicles (BEV) with stationary charging infrastructure have received considerable attention in the scientific literature for both cars and trucks, while dynamic charging via Electric Road Systems (ERS) has received much less attention and their future role in low-carbon road transport is uncertain. Here, we envision three potential scenarios for the future of ERS in European low-carbon transport. We sketch a potential European ERS network and discuss the political, technological, and market steps needed to realize these. We argue that existing field trials, tests, and research projects have collected sufficient evidence to make the next step: Decide and act. Decision-makers will never have perfect information about all aspects of ERS or competing technologies, but the urgency of the climate crisis requires a commitment one way or the other. A clear decision with respect to ERS would send a clear directive and would help focus time, effort, and money on the necessary infrastructure and policies to implement ambitious GHG abatement targets in road transport.
道路运输电气化对限制全球变暖至关重要。在科学文献中，带有固定充电基础设施的电池电动汽车（BEV）受到了汽车和卡车的广泛关注，而通过电动道路系统（ERS）进行动态充电的关注度要低得多，其未来在低碳道路运输中的作用也不确定。在此，我们设想了三种ERS在欧洲低碳交通中的潜在前景。我们勾勒了一个潜在的欧洲 ERS 网络，并讨论了实现这些网络所需的政治、技术和市场步骤。我们认为，现有的实地试验、测试和研究项目已经收集了足够的证据，可以采取下一步行动：做出决定并采取行动。决策者永远不可能掌握有关地球资源卫星或竞争技术各个方面的完美信息，但气候危机的紧迫性要求他们做出这样或那样的承诺。关于 ERS 的明确决定将发出一个清晰的指令，并有助于将时间、精力和资金集中在必要的基础设施和政策上，以在道路交通中实现雄心勃勃的温室气体减排目标。
{"title":"The possible future of electric road systems in Europe – time to decide and act","authors":"P. Plötz, Matts Andersson, Aline Scherrer, Erik Johansson","doi":"10.1088/2634-4505/ad3576","DOIUrl":"https://doi.org/10.1088/2634-4505/ad3576","url":null,"abstract":"\u0000 Electrification of road transport is crucial to limit global warming. Battery electric vehicles (BEV) with stationary charging infrastructure have received considerable attention in the scientific literature for both cars and trucks, while dynamic charging via Electric Road Systems (ERS) has received much less attention and their future role in low-carbon road transport is uncertain. Here, we envision three potential scenarios for the future of ERS in European low-carbon transport. We sketch a potential European ERS network and discuss the political, technological, and market steps needed to realize these. We argue that existing field trials, tests, and research projects have collected sufficient evidence to make the next step: Decide and act. Decision-makers will never have perfect information about all aspects of ERS or competing technologies, but the urgency of the climate crisis requires a commitment one way or the other. A clear decision with respect to ERS would send a clear directive and would help focus time, effort, and money on the necessary infrastructure and policies to implement ambitious GHG abatement targets in road transport.","PeriodicalId":476263,"journal":{"name":"Environmental research: infrastructure and sustainability","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140228379","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}
Pub Date : 2024-03-19DOI: 10.1088/2634-4505/ad3578
Rahaf Hasan, Lauren McPhillips, Gordon P. Warn, Melissa Bilec
The study compared the life cycle environmental impacts of three coastal flood management strategies: grey infrastructure (levee), green-grey infrastructure (levee and oyster reef), and a do-nothing scenario, considering the flood damage of a single flooding event in the absence of protection infrastructure. A case study was adopted from a New Orleans, Louisiana residential area to facilitate the comparison. Hazus software, design guidelines, reports, existing projects, and literature were utilized as foreground data for modeling materials. A process-based Life Cycle Assessment (LCA) approach was used to assess environmental impacts. The life cycle environmental impacts included global warming, ozone depletion, acidification, eutrophication, smog formation, resource depletion, ecotoxicity, and various human health effects. The Ecoinvent database was used for the selected life cycle unit processes. The mean results show green-grey infrastructure as the most promising strategy across most impact categories, reducing 47% of the greenhouse gas (GHG) emissions compared to the do-nothing strategy. Compared to grey infrastructure, green-grey infrastructure mitigates 13% to 15% of the environmental impacts while providing equivalent flood protection. A flooding event with a 100-year recurrence interval in the study area is estimated at 34 million kg of CO2 equivalent per kilometer of shoreline, while grey and green-grey infrastructure mitigating such flooding is estimated to be 21 and 18 million kg, respectively. This study reinforced that coastal flooding environmental impacts are primarily caused by rebuilding damaged houses, especially concrete and structural timber replacement, accounting for 90% of GHG emissions, with only 10% associated with flood debris waste treatment. The asphalt cover of the levee was identified as the primary contributor to environmental impacts in grey infrastructure, accounting for over 75% of GHG emissions during construction. We found that there is an important interplay between grey and green infrastructure and optimizing their designs can offer solutions to sustainable coastal flood protection.
{"title":"Life cycle assessment of green–grey coastal flood protection infrastructure: a case study from New Orleans","authors":"Rahaf Hasan, Lauren McPhillips, Gordon P. Warn, Melissa Bilec","doi":"10.1088/2634-4505/ad3578","DOIUrl":"https://doi.org/10.1088/2634-4505/ad3578","url":null,"abstract":"\u0000 The study compared the life cycle environmental impacts of three coastal flood management strategies: grey infrastructure (levee), green-grey infrastructure (levee and oyster reef), and a do-nothing scenario, considering the flood damage of a single flooding event in the absence of protection infrastructure. A case study was adopted from a New Orleans, Louisiana residential area to facilitate the comparison. Hazus software, design guidelines, reports, existing projects, and literature were utilized as foreground data for modeling materials. A process-based Life Cycle Assessment (LCA) approach was used to assess environmental impacts. The life cycle environmental impacts included global warming, ozone depletion, acidification, eutrophication, smog formation, resource depletion, ecotoxicity, and various human health effects. The Ecoinvent database was used for the selected life cycle unit processes. The mean results show green-grey infrastructure as the most promising strategy across most impact categories, reducing 47% of the greenhouse gas (GHG) emissions compared to the do-nothing strategy. Compared to grey infrastructure, green-grey infrastructure mitigates 13% to 15% of the environmental impacts while providing equivalent flood protection. A flooding event with a 100-year recurrence interval in the study area is estimated at 34 million kg of CO2 equivalent per kilometer of shoreline, while grey and green-grey infrastructure mitigating such flooding is estimated to be 21 and 18 million kg, respectively. This study reinforced that coastal flooding environmental impacts are primarily caused by rebuilding damaged houses, especially concrete and structural timber replacement, accounting for 90% of GHG emissions, with only 10% associated with flood debris waste treatment. The asphalt cover of the levee was identified as the primary contributor to environmental impacts in grey infrastructure, accounting for over 75% of GHG emissions during construction. We found that there is an important interplay between grey and green infrastructure and optimizing their designs can offer solutions to sustainable coastal flood protection.","PeriodicalId":476263,"journal":{"name":"Environmental research: infrastructure and sustainability","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140230265","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}
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