A Review of Swedish Residential Building Stock Research

Energy renovation rates in the Netherlands – comparing long and short term prediction methods

Energy renovation rates in the Netherlands – comparing long and short term prediction methods

In Europe, the final energy demand and greenhouse gas (GHG) emissions of residential and commercial building stocks account for approximately 40% of energy and emissions. A building stock model (BSM) is a method of assessing the energy demand and GHG emissions of building stocks and developing pathways for energy and GHG emission reduction. The most common approach to building stock modeling is to construct archetypes that are taken to representing large segments of the stock. This paper introduces a new method of building stock modeling based on the generation of synthetic building stocks. By drawing on relevant research, the developed methodology uses aggregate national data and combines it with various data sources to generate a disaggregated synthetic building stock. The methodology is implemented and validated for the residential building stock of Switzerland. The results demonstrate that the energy demand and GHG emissions can vary greatly across the stock. These and other indicators vary significantly within common building stock segments that consider only few attributes such as building type and construction period. Furthermore, the results indicate a separation of the stock in terms of GHG emissions between old fossil fuel-heated buildings and new and refurbished buildings that are heated by renewable energy. Generating a disaggregated synthetic building stock allows for a discrete representation of various building states. This enables a more realistic representation of past building stock alterations, such as refurbishment, compared with commonly used archetypes, and not relying on more extensive data sources and being able to accommodate a wide variation of data types. The developed methodology can be extended in numerous manners and lays groundwork for future studies.

Energy performance certificate (EPC) databases are crucial for analysing building stocks and informing relevant policy interventions across Europe. Initially designed for building-to-building energy efficiency assessments, EPCs and resultant EPC databases, are now used to inter alia, evaluate stock energy efficiency, monitor impacts of national policies, and estimate financial viability of interventions. Consumers use EPCs when seeking financial incentives like “green” mortgages, while financial institutions use EPCs to consider renovation costs in mortgage assessments. The range of stakeholders using EPC data thus extends beyond those who are expert in building energy rating.Errors in manually inputting data from on-site surveys into calculations can undermine EPC results. Currently, no standardised method exists for validating EPC datasets. This research introduces the first automated, data-driven validation of an EPC dataset. It adapts as the database evolves. Seventeen unique filters were developed, revealing that 30% of EPC entries were erroneous and/or outlier data, with 80% related to misassessment of geometrical features. Errors in one field often correlated with errors in others, indicating some Assessors’ low responsibility towards data quality.The validation method, automated through Python and R language scripts, align the Irish EPC database taxonomy with Irish and European Building Stock Observatories, facilitating consistent future reporting. Recommendations are provided to enhance EPC data quality.

Evaluating the energy performance of existing buildings is critical for improving the efficiency and resilience of the building stock as a whole. The importance of this information holds at different scales, both locally and at the national and international levels. A major problem arises from the difficulty in obtaining information from existing buildings; often, the only available data are the yearly consumption per unit area, typically corresponding to the energy performance certificate (EPC). This paper shows how to address concerns of practical relevance with a limited number of variables by examining an EPC national database (including the major cities of Tallinn, Pärnu, Tartu, and others) that provides only EPCs, construction/renovation year and heated area. Through a systematic statistical investigation of nearly 35 000 EPCs of educational, office, commercial and other building typologies, we i) characterise the time evolution of EPC classes, ii) evaluate the impact of incentives pre/post-renovations, and iii) create benchmarking tables that allow comparisons of a specific building with the existing stock to identify representative buildings for detailed auditing. The readiness of the Estonian building stock could thus be evaluated by linear fitting. All new and renovated buildings are estimated to achieve the zero-energy building (ZEB) status by 2050; remarkably, for some categories, this will occur already in the present decade if the identified linear trends persist. Additionally, we investigated whether the COVID-19 pandemic has affected building stock readiness by comparing pre- and post-2020 ZEB year fit estimations. Contrary to what was expected, the change in working habits affected some building types only marginally, while the national regulations played a prominent role. Detached private houses exhibited a pronounced worsening in readiness, while the educational and entertainment sectors benefited from specific energy labelling remodulations.

Building construction and use are major drivers of climate change. To avoid these impacts, research is exploring the potential of narrowing material and energy cycles (“narrow” strategies) by reducing floor area per capita. Yet, integrated assessment models (IAM) have mostly overlooked material stocks and flow, service levels, and geospatial location of buildings. Thus, they have only limited capacity in modelling such floor area dynamics which are dependent on urbanization trends and changes in dwelling preferences. Current modelling of narrow strategies in IAMs, where available, is therefore focused on normative targets of overall reduction in floor area per capita without consideration of local building stock limitations. Inspired by recent advances in urban metabolism studies of representing urban form in material and energy flow modelling, we propose an integration of detailed geospatial building and material stock accounts with IAMs. Specifically, we demonstrate how the open-source building stock database EUBUCCO can inform the modelling of narrow strategies in the building sector model MESSAGEix-Buildings by adding subnational detail and urban density parameters. EUBUCCO is the first near-complete 3D model of individual buildings including building footprints, heights, usage types and material content in Europe. The database is a collection of government, volunteered, and satellite-derived data, and missing attribute values are inferred with machine learning. Material content is based on the globally harmonized material intensity database RASMI. MESSAGEix-Buildings is a framework to model the material stocks and flows and energy demand of buildings at national or larger scales in future scenarios. Embedding material flow analysis for stock turnover accounting and energy demand modelling at sectoral level, the framework is soft-linked to the MESSAGEix-GLOBIOM IAM to account for demand and supply sides interactions and greenhouse gas (GHG) emissions under different climate policies. EUBUCCO is integrated in MESSAGEix-Buildings by differentiating building and material stocks, as well as demographic trends and climate, at NUTS2 level and degree of urbanicity. This enables for improved representation of floor area and urban form-related aspects, and their regional distributions. With this geospatially informed IAM module, we can model the effect of regionally differentiated floor area reduction pathways, consider population-trend-dependent urban mining potentials, and effects of reverse urbanization such as revitalization of rural building stocks. Ultimately, this integrated module can inform prioritization of such reuse and reduce strategies for climate change mitigation in the housing sector. 

This paper aims to share the journey of Carbon Footprint reduction in Exploration and Production (E&P) in reducing carbon emissions on operated assets through the implementation of a Real-Time tool for Monitoring Green House Gases (GHG) Emissions and Energy Efficiency (EE). The objectives are to improve the processes efficiencies, track and aid to reduce the total emissions. The solution was designed to monitor GHG key sources using available real time data within facilities. It starts by tracking emissions coming from energy usage through fuel gas combustion and then addresses emissions resulting from energy loss, such as flaring, venting and liquid fuels. The tool has been successfully deployed across 34 operated sites, allowing the tracking of approximately 85% of GHG emissions from Scope 1 & 2 within the E&P Operated Assets’ perimeter. The implemented solution follows a highly standardized and scalable approach. A model is built once and then applied to hundreds of equipment and assets. Once deployed, the model continuously provides real-time data derived from calculations, analyses, and Key Performance Indicator (KPIs). These products increase operational efficiency and help decision-making process regarding emissions reduction. Additionally, transparency and traceability are ensured by granting users access to all data and calculation. The tool uses different inputs such as fluid composition, process design data and operating parameters. It delivers a comprehensive overview of emissions and performance, ranging from individual equipment to site and country levels. The solution also eases benchmarking carbon footprint in different systems and identifies corrective and mitigation actions for both design and operational philosophy. Furthermore, it allows to establish a relationship between GHG and Energy efficiency based on potential optimization to be carried-out on significant energy users. Headquarters and operating centers consistently rely on the solution's displays, which present performance indicators for daily discussions and used in operational decision-making. This solution enables operators to identify poorly performing equipment and capture undesired process deviations as well as metering inaccuracies that contribute to GHG emissions. As a result, these indicators improve operational excellence and revenues, while minimizing the environmental impact of our activities. The paper discusses the implementation process, highlights the capabilities of the digital tool, and demonstrates its value through use cases where a positive impact both GHG reduction and improved energy efficiency was measured. By leveraging existing resources and valorizing field data and software, we have successfully transformed our operational practices and brought them to higher standards in terms of environmental performance and lower carbon intensity.

Towards agent-based building stock modeling: Bottom-up modeling of long-term stock dynamics affecting the energy and climate impact of building stocks

Building stock models can provide information on the current and future environmental impacts of buildings. Therefore, these models are useful tools for identifying trajectories that are compatible with the objectives of the Paris Agreement. However, the models often lack detail, which can lead to underestimations of the actual impacts of national building stocks, resulting in misinformed decision‐making. This study presents the steps needed to create an archetype‐based bottom‐up building stock model that uses Python and Brightway2. Prospective environmental assessments, including circularity assessments, can be performed by combining life cycle assessment (LCA) with material flow analysis (MFA). An important facet of this model is that it supports the development of a practical and easily reproducible method for the high‐precision modeling of a building stock. This model is open source, is readily adaptable to other countries, and does not require programming knowledge. This combined LCA‐MFA method can be used to assess the potential to reduce greenhouse gas (GHG) emissions from the Austrian building stock in five future scenarios involving sufficiency, energy, material, and design‐related measures. The results show different reduction potentials for embodied and operational GHG emissions depending on the set of measures taken. In all scenarios, mineral and synthetic materials contribute the most to embodied GHG emissions. Finally, the issue of validating building stock models is addressed, and numerous cross‐evaluations are proposed to ensure the reliability of results.

Urban water cycle systems(UWCS), including water treatment facilities, distribution facilities, sewers, and wastewater treatment facilities, are energy intensive and significant source of greenhouse gas (GHG) emissions, making the reduction of GHG emissions and the transition to eco-friendly energy essential. This study identifies specific GHG emission sources at each stage of the UWCS and proposes detailed methods to achieve a 40% reduction in GHG emissions, implement RE100, and attain Net Zero by employing insets and offsets. This study develops scenarios for insets and offsets based on the baseline process of the UWCS, and investigates potential pathways to reduce GHG emissions by quantifying emissions from each process. Internal insets, which are self-implemented and technical measures, are prioritized, while external offsets are applied to compensate for the remaining emissions. Internal insets include the application of anaerobic digesters and combined heat and power(CHP), improvements in energy efficiency of equipment, reduction in water pipe leakage, implementation of water footprint labeling, and installation of on-site photovoltaic system. External offsets comprise renewable energy certificates(REC), power purchase agreements(PPA), green hydrogen fuel for vehicles, natural sequestration improvement, and emission trading system. GHG emissions at each stage within the UWCS are quantified using modeling software. Based on these results, the effectiveness of insets and offsets in achieving a 40% GHG emissions reduction, Net Zero, and RE100 goal is analyzed. The baseline total GHG emissions for the UWCS are estimated at 4,732.8 tCO2eq/yr, of which 56.8% is identified as targets for internal insets, and the remaining 43.2% is reduced through external offsets. A 40% GHG reduction can be achieved through internal insets, and Net Zero can be attained by incorporating additionally applying external offsets. The total power demand of UWCS facilities and equipment is calculated as 572.8 kW. Renewable energy is generated through anaerobic digesters and CHP(116.1kW) as well as on-site PV(395.0 kW), while RE100 compliance is achieved by securing an aditional 61.7 kW through REC/PPA. Achieving Net Zero and RE100 requires prioritizing strategies for insets, offsets and efficient resource allocation. For this, the technical feasibility and self-implementation potential of reduction efforts and the external conditions for offsets, should be carefully reviewed to optimize implementation strategies. GHG reduction and renewable energy utilization in the UWCS are key priorities for addressing the climate crisis and achieving sustainable water resource management, requiring technological innovation and institutional support. The comprehensive and systematic application of GHG insets and offsets is the optimal approach to achieving these goals. Furthermore, modeling software serves as a key tool for quantifying GHG emissions and formulating concrete, viable GHG reduction strategies. In addition to the technical and institutional approaches proposed in this study, achieving Net Zero and implementing RE100 requires the integrated consideration of economic factors in the future.

Assessing building energy performance and energy policy impact through the combined analysis of EPC data – The Italian case study of SIAPE

This work is about the software named BEP-TR (Building Energy Performance -Turkey), which is developed upon request from Turkish Ministry of Environment and Urbanization to apply to build energy performance directive nationwide to improve the energy efficiency of buildings in Turkey. The directive requires that each building in the country must have an energy performance certificate (EPC) indicating the ratings of energy consumption and greenhouse gas (GHG) emission of the related building. BEP-TR software is designed mainly to issue EPC for buildings considering each phase of EPC preparation process and tasks of associated institutions and individuals. BEP-TR is mainly composed of two software applications called BEP-BUY and BEP-IS. BEP-BUY is a desktop application, which allows the user to design a building stored in XML project file and calculate its energy consumption and greenhouse gas emission to determine its corresponding ratings. EPC application for the designed building, which meets standards required by government is done by sending the project file of the building through BEP-BUY to the BEP-IS. BEP-IS is an internet based software which is under the control of the ministry. BEP-IS confirms the calculations made for the building using the received project file and initiates the process of preparing EPC. BEP-IS provides users who take part in this process interfaces designed as web pages. Users and their authorizations are managed by fully authorized users defined for the ministry through BEP-IS. Besides BEP-IS stores and reports data related to processes and users. There are two other modules called library and calculation which are embedded in both BEP-BUY and BEP-IS. Library module stores the data related to structural materials, which are determined to be usable in building construction. Calculation module contains the national calculation method developed to calculate greenhouse emission and energy consumption of a building using properties of materials selected from library module. In this study, as an example, a building is designed and its energy consumption is calculated using BEP-BUY. Article DOI: http://dx.doi.org/10.20319/mijst.2016.23.167182 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Pursuing a net-zero carbon future for all: Challenges for commercial real estate

Towards harmonising energy performance certificate indicators in Europe

Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers