Embodied GHG emissions of buildings – The hidden challenge for effective climate change mitigation

ISSN:0306-2619ISSN:1872-911

Embodied GHG emissions of buildings – The hidden challenge for effective climate change mitigation

Buildings are major sources of greenhouse gas (GHG) emissions and contributors to the climate crisis. To meet climate-change mitigation needs, one must go beyond operational energy consumption and related GHG emissions of buildings and address their full life cycle. This study investigates the global trends of GHG emissions arising across the life cycle of buildings by systematically compiling and analysing more than 650 life cycle assessment (LCA) case studies. The results, presented for different energy performance classes based on a final sample of 238 cases, show a clear reduction trend in life cycle GHG emissions due to improved operational energy performance. However, the analysis reveals an increase in relative and absolute contributions of so‐called ‘embodied’ GHG emissions, i.e., emissions arising from manufacturing and processing of building materials. While the average share of embodied GHG emissions from buildings following current energy performance regulations is approximately 20–25% of life cycle GHG emissions, this figure escalates to 45–50% for highly energy-efficient buildings and surpasses 90% in extreme cases. Furthermore, this study analyses GHG emissions at time of occurrence, highlighting the ‘carbon spike’ from building production. Relating the results to existing benchmarks for buildings’ GHG emissions in the Swiss SIA energy efficiency path shows that most cases exceed the target of 11.0 kgCO$^{2}$eq/m$^{2}$a. Considering global GHG reduction targets, these results emphasize the urgent need to reduce GHG emissions of buildings by optimizing both operational and embodied impacts. The analysis further confirmed a need for improving transparency and comparability of LCA studies

Embodied GHG emissions of buildings - Critical reflection of benchmark comparison and in-depth analysis of drivers

In the face of the unfolding climate crisis, the role and importance of reducing Greenhouse gas (GHG) emissions from the building sector is increasing. This study investigates the global trends of GHG emissions occurring across the life cycle of buildings by systematically compiling life cycle assessment (LCA) studies and analysing more than 650 building cases. Based on the data extracted from these LCA studies, the influence of features related to LCA methodology and building design is analysed. Results show that embodied GHG emissions, which mainly arise from manufacturing and processing of building materials, are dominating life cycle emissions of new, advanced buildings. Analysis of GHG emissions at the time of occurrence, shows the upfront \u27carbon spike\u27 and emphasises the need to address and reduce the GHG \u27investment\u27 for new buildings. Comparing the results with existing life cycle-related benchmarks, we find only a small number of cases meeting the benchmark. Critically reflecting on the benchmark comparison, an in-depth analysis reveals different reasons for cases achieving the benchmark. While one would expect that different building design strategies and material choices lead to high or low embodied GHG emissions, the results mainly correlate with decisions related to LCA methodology, i.e. the scope of the assessments. The results emphasize the strong need for transparency in the reporting of LCA studies as well as need for consistency when applying environmental benchmarks. Furthermore, the paper opens up the discussion on the potential of utilizing big data and machine learning for analysis and prediction of environmental performance of buildings

Biomarkers in asthma and allergic rhinitis

International audienceA biological marker (biomarker) is a physical sign or laboratory measurement that can serve as an indicator of biological or pathophysiological processes or as a response to a therapeutic intervention. An applicable biomarker possesses the characteristics of clinical relevance (sensitivity and specificity for the disease) and is responsive to treatment effects, in combination with simplicity, reliability and repeatability of the sampling technique. Presently, there are several biomarkers for asthma and allergic rhinitis that can be obtained by non-invasive or semi-invasive airway sampling methods meeting at least some of these criteria

Using Communicative Acts in High-Level Specifications of User Interfaces for Their Automated Synthesis

User interfaces are very important for the success of many computerbased applications these days. However, their development takes time, requires experts for user-interface design as well as experienced programmers and is very expensive. This problem becomes even more severe through the ubiquitous use of a variety of devices such as PCs, mobile phones, PDAs etc., since each of these devices has its own specifics that require a special user interface

Synthetische Trainingsdatengenerierung und Objekterkennung mit Deep Learning für Mixed Reality-Anwendungen mit Digitalen Zwillingen

Die Fähigkeit von Maschinen, Gegenstände über Kamerabildern und Videosequenzen wahrzunehmen, ermöglicht neue Formen der Mensch-Maschine-Interaktion. Bislang wird der breite Einsatz von Objekterkennung mit Deep Learning Algorithmen in der Automatisierungstechnik durch die aufwändige Datenakquise und den daraus resultierenden Mangel an Daten für das Training künstlicher neuronaler Netze gehemmt. Insbesondere die Kombination von Methoden der Objekterkennung mit Mixed Reality-Technologien und realdatengetriebenen Digitalen Zwillingen wird in Zukunft zahlreiche Anwendung im industriellen Kontext finden. Dieser Beitrag stellt ein Konzept für die Generierung synthetischer Trainingsdaten für die Objekterkennung mit Deep Learning auf Basis von Digitalen Modellen über das Training künstlicher neuronaler Netze bis hin zur Mixed Reality-Anwendung vor. Durch die automatisierte Generierung der synthetischen Trainingsdaten soll der Prozess der Trainingsdatenakquise bedeutend beschleunigt und die Qualität der Trainingsergebnisse gegenüber der Verwendung manuell erstellter Trainingsdaten erhöht werden. Die Tragfähigkeit des vorgestellten Konzeptes wird durch eine Realisierung in der am Virtual Automation Lab (VAL) der Hochschule Esslingen entwickelten Digital Twin as a Service Plattform (DTaaSP) sowie anhand von zwei Anwendungsbeispielen aufgezeigt

Process Planning in Special Machinery: Increasing Reliability in Volatile Surroundings

In Germany the growing demand for customized systems and integrated solutions in machinery enhance the importance of special machinery. Within this industry, the commissioning process represents a significant part in the product engineering process and forms the base for reliability and performance during future operation. However, there is little research focusing on this process for special machinery. In particular, there has been little discussion on methods to evaluate alternative test processes or arranging test processes along the commissioning process. Therefore, this paper develops an application-oriented simulation tool that allows an evaluation of test alternatives and an arrangement of test processes during the commissioning process in special machinery. The authors decided to use Bayesian Networks to model the commissioning process as they enable the connectivity of multiple modules and integrate the stochastic dependencies along the processes. In addition the paper reveals two concepts to deal with unknown processes and the lack of data. Applying the simulation tool in a laser system manufacturer reveals that the simulation tool allows an evaluation as well as the identification of risks and need for countermeasures