Estimating the Additional Greenhouse Gas Emissions in Korea: Focused on Demolition of Asbestos Containing Materials in Building

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When asbestos containing materials (ACM) must be removed from the building before demolition, additional greenhouse gas (GHG) emissions are generated. However, precedent studies have not considered the removal of ACM from the building. The present study aimed to develop a model for estimating GHG emissions created by the ACM removal processes, specifically the removal of asbestos cement slates (ACS). The second objective was to use the new model to predict the total GHG emission produced by ACM removal in the entire country of Korea. First, an input-equipment inventory was established for each step of the ACS removal process. Second, an energy consumption database for each equipment type was established. Third, the total GHG emission contributed by each step of the process was calculated. The GHG emissions generated from the 1,142,688 ACS-containing buildings in Korea was estimated to total 23,778 tonCO2eq to 132,141 tonCO2eq. This study was meaningful in that the emissions generated by ACS removal have not been studied before. Furthermore, the study deals with additional problems that can be triggered by the presence of asbestos in building materials. The method provided in this study is expected to contribute greatly to the calculation of GHG emissions caused by ACM worldwide.

Highlights

  • greenhouse gas (GHG) emissions occur during the dismantling of general building materials

  • When asbestos containing materials (ACM) must be removed from the building before demolition, additional GHG

  • The GHG emission generated by the asbestos cement slates (ACS) removal stage per one day of ACS was found to range from 1.0436 kgCO2 eq to 2.7997 kgCO2 eq, while the GHG emission generated by transporting 1 m2 of ACS for 1 km by a single cargo truck ranged from 0.000646 kgCO2 eq to

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The Intergovernmental Panel on Climate Change (IPCC) has warned that without global efforts to reduce additional greenhouse gas (GHG) emissions, the mean global temperature may rise by up to. 3–5 degrees by 2100 [1,2]. The global society is adopting extensive practices and policies toward reducing GHG emissions [3]. Various studies conducted over the past couple of years have focused on the reduction of GHG emissions from the building sector [4,5,6,7], as this sector accounts for approximately 30% of total global GHG emission [8]. GHGs are generated by buildings directly and indirectly over the course of a building’s life cycle, from the construction stage through the operation. Public Health 2016, 13, 902; doi:10.3390/ijerph13090902 www.mdpi.com/journal/ijerph

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CitationsShowing 5 of 5 papers
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Life Cycle Assessment of Asbestos Waste Management Scenarios: Case Study in Argentina
  • Oct 1, 2021
  • Journal of Hazardous, Toxic, and Radioactive Waste
  • Irma Mercante + 2 more

Abstract The aim of this research was to compare the environmental impacts of applying thermal vitrification, recycling in clinker furnace, or final disposal of asbestos in a hazardous waste landfi...

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  • 10.1016/j.jclepro.2022.132032
Methods for assessing asbestos-containing roofing slate distribution in an area with poor dwelling conditions
  • Jul 1, 2022
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  • Yuan-Long Zhang + 2 more

Methods for assessing asbestos-containing roofing slate distribution in an area with poor dwelling conditions

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  • 10.1101/2025.06.04.25328959
Enhancing Fairness in Diabetes Prediction Systems through Smart User Interface Design
  • Jun 5, 2025
  • Asma Tavangar + 2 more

Abstract ObjectivesArtificial intelligence (AI) in chronic disease prediction often exhibits algorithmic biases, hindering equitable healthcare delivery. This study aims to develop and evaluate a Smart User Interface (Smart UI) framework that enhances fairness in diabetes prediction systems by operationalizing fairness at the human-computer interaction level, a dimension frequently overlooked in AI fairness research.Materials and MethodsWe employed a nine-metric fairness evaluation framework across four demographically diverse diabetes datasets (Kaggle, Pima Indian, Azure Open, CDC Health Indicators). The Smart UI integrates contextual adjustment tools, dynamic visualizations, real-time alerts, and transparent reporting, combining structured EHR data, wearable sensor inputs, and unstructured clinical notes via natural language processing. The framework was evaluated on a clinical dataset to assess fairness and performance improvements.ResultsThe Smart UI significantly reduced disparities: for age, the equal opportunity difference (EOD) improved from 0.35 to 0.25, with accuracy rising from 90.52% to 91.83%; for BMI, EOD decreased from 0.56 to 0.38, with the F1-score increasing from 83.89% to 86.37%. These outcomes highlight the framework’s ability to enhance fairness without altering underlying algorithms.DiscussionWhile the Smart UI demonstrates promise as a model-agnostic, scalable solution for equitable AI deployment, challenges such as data privacy, usability, and real-time processing persist. The framework’s reliance on diverse data sources and user-centered design underscores its potential, though validation in broader clinical settings is needed.ConclusionThe Smart UI offers a replicable blueprint for embedding fairness in healthcare AI through interface design. Future research should focus on multicenter trials and applications to other chronic diseases to advance inclusive digital health solutions.

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  • Research Article
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  • 10.3390/s23198021
Construction of Asbestos Slate Deep-Learning Training-Data Model Based on Drone Images
  • Sep 22, 2023
  • Sensors (Basel, Switzerland)
  • Seung-Chan Baek + 4 more

The detection of asbestos roof slate by drone is necessary to avoid the safety risks and costs associated with visual inspection. Moreover, the use of deep-learning models increases the speed as well as reduces the cost of analyzing the images provided by the drone. In this study, we developed a comprehensive learning model using supervised and unsupervised classification techniques for the accurate classification of roof slate. We ensured the accuracy of our model using a low altitude of 100 m, which led to a ground sampling distance of 3 cm/pixel. Furthermore, we ensured that the model was comprehensive by including images captured under a variety of light and meteorological conditions and from a variety of angles. After applying the two classification methods to develop the learning dataset and employing the as-developed model for classification, 12 images were misclassified out of 475. Visual inspection and an adjustment of the classification system were performed, and the model was updated to precisely classify all 475 images. These results show that supervised and unsupervised classification can be used together to improve the accuracy of a deep-learning model for the detection of asbestos roof slate.

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Optimal management program for asbestos containing building materials to be available in the event of a disaster
  • Mar 31, 2017
  • Waste Management
  • Young-Chan Kim + 1 more

Optimal management program for asbestos containing building materials to be available in the event of a disaster

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BackgroundSocietal pressures exist to reduce greenhouse gas (GHG) emissions from farm animals, especially in beef cattle. Both total GHG and GHG emissions per unit of product decrease as productivity increases. Limitations of previous studies on GHG emissions are that they generally describe feed intake inadequately, assess the consequences of selection on particular traits only, or examine consequences for only part of the production chain. Here, we examine GHG emissions for the whole production chain, with the estimated cost of carbon included as an extra cost on traits in the breeding objective of the production system.MethodsWe examined an example beef production system where economic merit was measured from weaning to slaughter. The estimated cost of the carbon dioxide equivalent (CO2-e) associated with feed intake change is included in the economic values calculated for the breeding objective traits and comes in addition to the cost of the feed associated with trait change. GHG emission effects on the production system are accumulated over the breeding objective traits, and the reduction in GHG emissions is evaluated, for different carbon prices, both for the individual animal and the production system.ResultsMultiple-trait selection in beef cattle can reduce total GHG and GHG emissions per unit of product while increasing economic performance if the cost of feed in the breeding objective is high. When carbon price was $10, $20, $30 and $40/ton CO2-e, selection decreased total GHG emissions by 1.1, 1.6, 2.1 and 2.6% per generation, respectively. When the cost of feed for the breeding objective was low, selection reduced total GHG emissions only if carbon price was high (~ $80/ton CO2-e). Ignoring the costs of GHG emissions when feed cost was low substantially increased emissions (e.g. 4.4% per generation or ~ 8.8% in 10 years).ConclusionsThe ability to reduce GHG emissions in beef cattle depends on the cost of feed in the breeding objective of the production system. Multiple-trait selection will reduce emissions, while improving economic performance, if the cost of feed in the breeding objective is high. If it is low, greater growth will be favoured, leading to an increase in GHG emissions that may be undesirable.

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