Policies to decarbonize the Swiss residential building stock: An agent-based building stock modeling assessment

In light of the Swiss government\u27s reduction targets for greenhouse gas (GHG) emissions under the Paris Agreement, this article investigates how and with which policy measures these reduction targets can be met for the Swiss residential building sector. The paper applies an agent-based building stock model to simulate the development of the Swiss residential building stock under three different policy scenarios. The scenario results until 2050 are compared against the reduction targets set by the Swiss government and with each other. The results indicate that while the current state of Swiss climate policy is effective in reducing energy demand and GHG emissions, it will not be enough to reach the ambitious emission-reduction targets. These targets can be reached only through an almost complete phase-out of fossil-fuel heating systems by 2050, which can be achieved through the introduction of further financial and/or regulatory measures. The results indicate that while financial measures such as an increase in the CO2 tax as well as subsidies are effective in speeding up the transition in the beginning, a complete phase-out of oil and gas by 2050 is reached only through additional regulatory measures such as a CO2 limit for new and existing buildings

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

Buildings are responsible for a large share of the energy demand and greenhouse gas (GHG) emissions in Europe and Switzerland. Bottom-up building stock models (BSMs) can be used to assess policy measures and strategies based on a quantitative assessment of energy demand and GHG emissions in the building stock over time. Recent developments in BSM-related research have focused on modeling the status quo of the stock and comparatively little focus has been given to improving the modeling methods in terms of stock dynamics. This paper presents a BSM based on an agent-based modeling approach (ABBSM) that models stock development in terms of new construction, retrofit and replacement by modeling individual decisions on the building level. The model was implemented for the residential building stock of Switzerland and results show that it can effectively reproduce the past development of the stock from 2000 to 2017 based on the changes in policy, energy prices, and costs. ABBSM improves on current modeling practice by accounting for heterogeneity in the building stock and its effect on uptake of retrofit and renewable heating systems and by incorporating both regulatory or financial policy measures as well as other driving and restricting factors (costs, energy prices)

Synthetic building stocks as a way to assess the energy demand and greenhouse gas emissions of national building stocks

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

Challenges and Lessons Learned in Applying Sensitivity Analysis to Building Stock Energy Models

Uncertainty Analysis (UA) and Sensitivity Analysis (SA) offer essential tools to determine the limits of inference of a model and explore the factors which have the most effect on the model outputs. However, despite a well-established body of work applying UA and SA to models of individual buildings, a review of the literature relating to energy models for larger groups of buildings undertaken by Fennell et al. (2019) highlighted very limited application at larger scales. This contribution describes the efforts undertaken by a group of research teams in the context of IEA-EBC Annex 70 working with a diverse set of Building Stock Models (BSMs) to apply global sensitivity analysis methods and compare their results. Since BSMs are a class of model defined by their output and coverage rather than their structure and inputs, they represent a diverse set of modelling approaches. Key challenges for the application of SA are identified and explored, including the influence of model form, input data types and model outputs. This study combines results from 7 different modelling teams, each using different models across a range of urban areas to explore these challenges and begin the process of developing standardised workflows for SA of BSMs

Building Stock Modelling - A novel instrument for urban energy planning in the context of climate change

Cities and their building stock represent one of the largest energy consumer groups and emitters of greenhouse gases (GHG). The urban building stock, including commercial buildings, offers a large and mostly untapped potential for energy efficiency improvement and GHG mitigation. Urban development, building stock alterations, and building technology measures therefore play a major role in setting the framework to exploit these potentials. A way to describe possible building stock development pathways is through building stock modelling. Although there have been many different bottom-up approaches that model energy demand and GHG emissions as well as other aspects of urban development, they have not been fully integrated and are not giving community energy planners, urban planners and decision makers enough information to influence the development in specific areas and technological fields. The building stock model presented in this paper gives the possibility to model energy supply and demand of both the residential and non-residential building stock at the scale of individual buildings, taking into account both heating and cooling demand (and other energy services) of buildings of various types and age classes. The model allows for tapping into spatially differentiated potentials and to balance demand and supply and renewable energy source (RES) potentials at a local scale (typically small-scale neighbourhoods and hectares) to guide the planning and development of sustainable cities. It is shown that commercial buildings in particular play a key role in initiating thermal energy network approaches (e.g. local low-temperature networks). Furthermore, the possibility to connect with other models e.g. through the Smart Urban Adapt (SUA) modelling platform, makes it possible to run a fully integrated, bottom-up simulation of different urban development scenarios and their impact on energy demand and GHG emissions taking into account all aspects of urban development. The SUA modelling platform provides a highly integrated model, taking into account energy, land use and urban design, in order to investigate the socio-economic drivers of energy consumption. The results of a concrete case study revealed the benefit of integrating energy efficient commercial buildings with district energy systems that allow for tapping local potentials of renewable energy sources, for both heating and cooling

A building specific economic building stock model to evaluate energy efficiency and renewable energy

In developed countries, the residential and commercial building stock account for a considerable share of final energy demand and greenhouse gas emissions. Building stock modeling is an established tool to assess different development paths of buildings on city, region or country level. Current building stock models (BSM) as well as previous works of the authors, however, lack a holistic approach that take technological, economic and ecological factors into account on an individual building scale. There are, therefore, limitations in the conclusions that can be drawn. In order to increase their significance, current research shows trends towards spatial differentiation, representation of individual building and owners as well as economic decision modeling. However, no model combines all three aspects in a more holistic approach. This paper describes a novel approach which combines spatial differentiation with building specific heat demand modeling and an economic decision simulation. The model developed combines a building specific engineering model with a micro-economic discrete choice approach. Using spatial building data, the engineering model calculates space heat and hot water energy demand on a building level. The alteration of the building refurbishment state is modeled using a discrete choice approach to simulate the decision process of building owners of building envelope refurbish and/or to substitute the heating system. Due to the building specific approach, the decision model is able to take into account building specific information such as size, geometry, room temperature, investment, maintenance and energy costs and achievable energy savings as well as other factors such as local potentials and restrictions on the use of renewable energy. In a case study of the city of Zürich we demonstrate the feasibility and strengths of the new model approach. The results demonstrate that modeling space heating demand on an individual building scale yields specific heat demand distribution across building clusters (and not simply in average values as in other models). The building level approach enables the model to deliver differentiated results of the heat demand development for the whole building stock, building types building periods or spatially distributed as shown in the results

A building specific economic building stock model to evaluate energy efficiency and renewable energy

In developed countries, the residential and commercial building stock account for a considerable share of final energy demand and greenhouse gas emissions. Building stock modeling is an established tool to assess different development paths of buildings on city, region or country level. Current building stock models (BSM) as well as previous works of the authors, however, lack a holistic approach that take technological, economic and ecological factors into account on an individual building scale. There are, therefore, limitations in the conclusions that can be drawn. In order to increase their significance, current research shows trends towards spatial differentiation, representation of individual building and owners as well as economic decision modeling. However, no model combines all three aspects in a more holistic approach. This paper describes a novel approach which combines spatial differentiation with building specific heat demand modeling and an economic decision simulation. The model developed combines a building specific engineering model with a micro-economic discrete choice approach. Using spatial building data, the engineering model calculates space heat and hot water energy demand on a building level. The alteration of the building refurbishment state is modeled using a discrete choice approach to simulate the decision process of building owners of building envelope refurbish and/or to substitute the heating system. Due to the building specific approach, the decision model is able to take into account building specific information such as size, geometry, room temperature, investment, maintenance and energy costs and achievable energy savings as well as other factors such as local potentials and restrictions on the use of renewable energy. In a case study of the city of Z\ufcrich we demonstrate the feasibility and strengths of the new model approach. The results demonstrate that modeling space heating demand on an individual building scale yields specific heat demand distribution across building clusters (and not simply in average values as in other models). The building level approach enables the model to deliver differentiated results of the heat demand development for the whole building stock, building types building periods or spatially distributed as shown in the results