Time-dependent factors and parameters of a building system
Before analyzing the temporal aspects in LCA applied to buildings, this section will present various aspects of a building system expected to vary in time. In order to facilitate our understanding of how a building and its surroundings are changing, a short story of the evolution of a family is presented below (Figure 2-1).
In 1985, NEGISHI family purchased a new single house in Tokyo, which is based on the wood structure. In several years, the family becomes from 2 to 5 persons including 3 children. As children grow very fast, they extend the house adding two more bedrooms and another salon for more comfort in 2000. Their consumption of energy and water increases in parallel. 10 years later, all children leave the house for the reason of their work, study, marriage, etc. In 2015, they notice that there are significant material degradations and product and equipment dysfunctions after 30 years of the building occupation. Insulation materials decay, door opening and closing do not work well, windows become to be drafty, wall paint peels off, etc. At the same time, they needed to repair and replace some construction products and materials. Also, for now, that only the parent lives, the house comes to be a little bit too large. Therefore, they decide to renovate the entire house and to relive with one of the children. The renovated house has now three stories and is designed as a low energy house. The house is equipped with the new technologies of better insulation systems and a high-performance heating system with mechanical ventilation.
Time-related aspects in conventional LCA
LCA is the common method for evaluating products, services, and activities in terms of their potential environmental impacts throughout their life cycle, supported by ISO 14040 – 14044 standards (ISO, 2006a, 2006b). According to the standards, the LCA methodology is divided into four steps. i) Goal and scope definition. ii) Life cycle inventory compilation calculating material and energy balances at the level of the processes and of the environmental interventions (substances emitted into the environment and natural resources consumed), throughout the system’s lifespan. While the LCI of background processes is generic and can be obtained from databases, the foreground process inventory is case-specific and must be obtained by data collection or specific modelling for the process/product under study. iii) Life cycle impact assessment based on methods for evaluating environmental impacts. iv) Interpretation of LCA results, as a step for uncertainty and sensitivity analysis of outcomes, and recommendations for decision makers. This phase includes the analysis of a consequence of time dimension included mainly in the first three steps of the methodology.
The analysis time span is the period used for each system studied, chosen in coherence with the lifetime of this system. It is specified in the first step of LCA, i.e., the definition of the goal and scope, and represents the period for which the inventory flows are considered to be present and the LCA results are considered valid (e.g., 50 years, the lifespan of a building). In the same manner, the time boundaries of a study situate the period of interest in time (e.g., a past or a future new system).
The conventional LCA approach considers stationary conditions, static LCI being a list of environmental emissions without their occurrence time or spatial location. Environmental interventions, occurring in reality at different moments on the timescale and over different periods, are all considered equivalent with respect to the period of analysis. In the context of rapid changes of the system properties, it is important to indicate the temporality of data used, as they reflect specific conditions of the system under evaluation at a chosen moment. However, the practice of LCA is often limited by a lack of consistent and relevant data, especially for studies including prospective analysis.
Instead of being based on instantaneous and simultaneous inventory flows as in current LCA practice, the assessment can include a time dimension, integrated as temporally segmented inventories over the whole system life cycle. A time differentiated inventory, defined over distinct periods, takes account of the prospective evolution of systems over time. For the environmental evaluation of buildings having long lifetimes, time-dependent analysis with changes at different levels of systems, e.g. technologies, economy, occupant behaviours, and political rules, can be implemented within conventional LCA by defining distinct scenarios over each time period of interest.
Various LCIA methods exist for calculating environmental impacts. The basic principle of all conventional methods is to provide a characterization factor (CF) for a given combination of substance/environmental compartment/effect (or impact). Then the substance amount (i.e., LCI) is multiplied by its CF to obtain the impact result. This is the basic principle of LCA. In such approach, assumptions and simplifications have been operated at the level of CF calculation, notably by considering the environmental mechanisms in steady state (static) conditions, or considering a fixed time horizon when an integral over time is required to calculate CF for some impact indicators (e.g., GWP – global warming potential). Conventional characterization factors are thus dependent on arbitrarily fixed time horizons (e.g., 20, 50, 100 years), which constitutes a substantial limitation of the current LCIA methods. It is entirely legitimate to raise questions like, “Does today’s 1 kg of CO2 emission have the same environmental impact as 1 kg emitted after 50 years?” Or, “Between 10 kg of CO2 emission in a year and 1 kg of CO2 emissions per year during ten years, which case has the most impact at a precise date?” It is not possible to answer such questions within conventional LCA, and a new approach with fully dynamic modelling of environmental mechanisms is needed. The relevance of taking account of the temporal dimension in the LCA, at both LCI and LCIA levels, has already been discussed in past studies (Beloin-Saint-Pierre et al., 2013; Collet et al., 2014; Pinsonnault et al., 2014). Based on the previous LCA developments, we identify five aspects in LCA where time dimension intervenes.
Tools for LCA of buildings and their limitations
LCA is usually performed with dedicated software (e.g., SimaPro, Umberto, GaBi) and inventory datasets (e.g., ecoinvent, ELCD, Agribalyse). There are many LCA tools, software, and databases adapted to the construction sector; several tools that were available for this work and were included in the new methodology are presented here. In France, national experimentation is presently on progress to assess the feasibility to support the future French energy and environmental regulation (called « RE2020 » – Réglementation Environnementale des bâtiments neufs, that should be available by 2020) by the LCA method with its public inventory dataset for the construction sector (http://www.batiment-energiecarbone.fr/fr/).
For example, ELODIE is one of the French LCA tools specific to buildings connected to INIES (http://www.base-inies.fr/iniesV4/dist/consultation.html), a public database of environmental and health data for construction products and building equipment. It includes the Environmental and Health Product Declaration (EHPD: FDES in French) and PEP ecopassport® (INIES, 2019). The data recorded in the FDES are values for environmental impact indicators declared in a voluntary way by the industrials. Data sets from INIES provide more realistic and precise information for construction products than other generic LCI such as data form ecoinvent. One constraint of FDES to effectuate dynamic LCA is that it does not contain inventory data (i.e., substance emission flows).
Another French LCA tool for buildings, named EQUER, can be linked to a dynamic thermal simulation tool named COMFIE. This software allows time-varying energy production mixes and energy consumptions to be taken into account when assessing environmental impacts of building energy use at each time step, with historical weather data. As mentioned by Lasvaux (2012) summarizing principal building-LCA software and its databases, many other examples of similar LCA calculation tools for buildings exist in different countries, e.g. One Click LCA (German), ATHENA (Canada), IMPACT (United Kingdom), which are based on different guidelines and contextual hypotheses (methodological choices, boundaries of an evaluated system, method of impact calculation, etc.). For example, LEGEP is a tool for LCA of buildings developed in Germany (LEGEP, 2019), and it has an inventory dataset of more than 6000 construction materials (consulted in April 2019) complying with the European standard EN15804 (CEN, 2012b) defining the method of an environmental assessment of construction products.
Ecoinvent is the most general and widely used in the European area (Wernet et al., 2016) and implemented in many of LCA software. The current version of econivent database is version 3, and it provides over 14,700 LCI datasets as background processes covering the wide range of human activities such as agriculture, forestry, manufacturing, transport, etc. Nowadays, ecoinvent is used for LCA, EPD, carbon footprinting, and other environmental studies.
Energy consumption during the service life can be calculated using models and software developed for thermal performances of buildings. COMETH, the calculation engine for the simulation of a whole building’s thermal performance, could be used to compute energy consumptions related to heating, cooling, domestic hot water, lighting and ventilation at an hourly time step (Haas and Corrales, 2014). The tool is compliant with the French thermal regulations. This engine is easily configurable and allows to set the simulation boundary conditions, such as weather or occupant behaviour, and the building description, such as geometry, level of thermal resistance of its envelope and energy efficiency of the equipment. The output of COMETH can be then used as an input in ELODIE – LCA software. The modelling can be effectuated by different interfaces connected to this engine. Some of these interfaces are complying with the future environmental regulation RE2020 (http://www.batiment-energiecarbone.fr/fr/evaluation/logiciels/) (Bâtiments à Énergie Positive et Réduction Carbone, 2019).
None of these tools, however, considers any time-varying characteristics of construction products and equipment in the LCA study, nor can they differentiate the environmental interventions (emissions, resources) over time. Moreover, these existing tools do not allow for dynamic impact evaluations, e.g., the evolution of radiative forcing with time for climate change impact evaluation. This is because the dynamic impact calculation needs data of substance emission flows that is hardly available in specific LCI databases of the building sector.
To the best of our knowledge, there are no LCA tools specifically dedicated to buildings that include dynamic aspects. Only the “New Energy Externalities Development for Sustainability (NEEDS),” in the field of energy production at national and Europe level, includes a dynamic aspect in the form of future electricity supply systems (Frischknecht et al., 2009). Moreover, several scenarios are proposed for the European energy mix by 2025 and 2050, which could be considered in a temporal perspective in order to assess the influence of the grid mix evolution on LCA results.
Different ways of considering time in LCA of buildings
Scheuer et al. (2003) mentioned that future LCA studies should attach more and more importance to accounting for i) change in demand for materials and energy, ii) technical performances of energy services and equipment, and iii) material burdens due to changes in the foreground system e.g. choices of material, service lifetime of building components, as well as in the background system, e.g. production efficiencies of building materials. While authors have argued their proposals to integrate these dynamics in LCA, they did not have access to operational calculation tools to implement them in an appropriate LCA framework.
Frijia et al. (2012) investigated the contribution of manufacturing and construction phases to the life cycle energy use. They proposed different functional units for a residential building, including all domestic functionalities or only heating and cooling systems, to calculate the operational energy use. They also assumed, in their case study, annual improvements in the energy efficiency of heat pumps and air conditioning systems towards 2052, based on public data from the U.S. Department of Energy and periodic replacement of HVAC (Heating, Ventilation, and Air-Conditioning) systems by advanced technologies. Their LCA case study of residential houses revealed that, using an adapted functional unit, which only included heating and cooling systems with a consideration of technological improvement, the share of embodied energy use could be far higher than in previous studies, which were based on the conventional functional unit.
Collinge et al. (2013) proposed a dynamic LCA framework integrating the temporal variability of industrial processes for energy production and the influence of time on climate change impact calculations. The development of their dynamic LCA method was based on the conventional matrix-based computation of LCA (Heijungs and Suh, 2002) and consisted of calculating the inventory and impacts by time periods, i.e., a scenario approach. The functional units and reference flows were considered as parameters that could vary with time from one period to another. They exploited existing public data on dynamic energy productions (time variations of the grid electricity mix), and consumptions and related environmental interventions to obtain a distinct LCI per time period. Temporal variations of processes in the background system (energy production industry) were considered, as were different shares of energy resources over time (the supply chains). The building use phase was studied with particular attention due to the important environmental impact generated by energy consumption in this phase. Time-adjusted global warming potentials (TAWP) were used to calculate climate change impact following Kendall’s method of time horizon correction (Kendall, 2012) and using seasonal characterization factors for photochemical ozone formation (Shah and Ries, 2009). This work brought out the importance of considering time in LCA of buildings. However, access to the specific building data (e.g., energy consumption, emissions) and the separate collection of inventory data for many periods might be the main limitation of their work.
Fouquet et al. (2015) performed a comparative study of the static versus dynamic LCA for three low-energy buildings: 1) concrete cavity wall, 2) concrete double wall and 3) timber frame. This study also addressed the relevance of accounting for a temporal profile of CO2 balance through the uptake due to the growth of trees until the release into the atmosphere at their end of life (Fouquet et al., 2015). The scope of the CO2 balance should be well defined by considering forest management (i.e., timing of CO2 uptake) and waste management scenarios at the building end-of-life (i.e., landfilling or incineration of woods leading to emissions of biogenic carbon dioxide), which would lead to significant differences in the results calculated for climate change impact. Scenarios of grid mix were considered at time horizons of 2025 and 2050 based on given prospective LCI data sets from the ecoinvent database and the publicly available database, NEEDS. They also paid particular attention to possible technological innovation in the background system related to cement production using improved production technology. Time-dependent CFs were used for climate change impact calculations, following the method proposed by Levasseur et al. (2010). With this specific case study, it was acknowledged that dynamic LCA could alter the conventional interpretation of LCA results, thus providing a better understanding of the environmental behaviour of building systems.
Electricity consumption and the method used for its production exhibits marked temporal variation in both the short term, e.g., due to daily occupant behaviour or weather conditions, and the long term, e.g., due to the increase in renewable energy technology in electricity mix production, and due to climate change. Integrating renewable energy and auto-consumption systems in buildings, e.g., a photovoltaic system on the roof, requires a more detailed analysis of LCI data over time (Fouquet, 2015). The high temporality of this energy aspect has always been considered in LCA studies. Peuportier and Herfray (2012) developed a dynamic and prospective LCA model to exploit historical data on public electricity (provided by Electricity Transmission Network) differentiated in given time steps from an hour to a year. Energy consumptions of testbed buildings were obtained at hourly or monthly time steps and then injected into the LCA study based on EQUER software in order to evaluate global warming potential (GWP). The impact was calculated by multiplying environmental emissions at each time step (from the French electricity grid) by the corresponding characterization factor (for instance, 𝐶𝐹=1 𝑘𝑔 𝐶𝑂2 𝑒𝑞 / 𝑘𝑔 𝐶𝑂2 for carbon dioxide). The dynamic electricity production mix model was based upon time-varying productions for each fuel type and based on a given atmospheric temperature. Following this study, Roux et al. (2016b) improved the approach by integrating a temporal variation of local energy production by the on-roof photovoltaic system. The improved methodology was applied to testbed cases of three low-energy buildings to evaluate their environmental performances and acknowledged the discrepancy between static and dynamic LCA results. The time step of the LCI model concerning the energy consumption and production was considered as a key element for the relevance of dynamic LCA results and should be adapted to each case study with respect to local conditions (e.g., climate, energy equipment, occupant behaviour). However, this study was limited to only one year for the energy use phase of buildings. Nevertheless, these studies have the virtue to include commonly used simulation tools in the field of energy efficiency of buildings in a temporal representation of the life cycle inventory, moving forward with respect to other studies (Babaizadeh et al., 2015; Fesanghary et al., 2012) in which the simulation results of dynamic energy demand are used in LCA in an integrated form, as a single total energy value over the building’s lifecycle.
Table of contents :
Chapter 1 Introduction
1.1 Context of the thesis work
1.2 Problem statement
1.2.1 Temporal evolution of a building system
1.2.2 Process and supply dynamics through technological and environmental flows
1.2.3 Time-dependent impacts
1.3 Research questions and thesis outline
Chapter 2 Development of modeling and simulation platform of a dynamic LCA methodology applied to buildings
2.2 Literature review
2.2.1 Time-dependent factors and parameters of a building system
2.2.2 Time-related aspects in conventional LCA
2.2.3 Tools for LCA of buildings and their limitations
2.2.4 Different ways of considering time in LCA of buildings
2.2.5 Existent operational tools for dynamic LCA
2.3 Presentation of the new approach
2.3.1 General trends from state of the art
2.3.2 Identification of key dynamic aspects
2.3.3 Proposed methodology for dynamic LCA of buildings
2.3.4 Dynamic methods for climate change impacts assessment
Chapter 3 Collection of time-varying building parameters and prospective scenario for dynamic LCI calculation
3.2 Classification of the building dynamic parameters
3.2.1 Foreground and background system
3.2.2 Physical scale of time-varying building parameters
3.2.3 Integration of time-varying building parameters into LCI calculation
3.2.4 Type of data collection
3.3 Time-varying parameters of building systems
3.3.1 Insulation material degradation
3.3.2 Recycling content materials/recycling process inputs and outputs
3.3.3 Future French energy mix
3.4 Temporal parameters of production and supply chain dynamics
Chapter 4 Climate change pathways with different applications of dynamic LCA: Case study – French low-energy single houses
4.2 Preliminary LCA of the test bed building using ELODIE software
4.3 Dynamic LCI and LCIA
4.3.1 General methodology of DLCA
4.3.2 Dynamic LCI modeling
4.3.3 Conventional and dynamic approaches for climate change impact
4.4 Prospective scenarios
4.4.1 Degradation of materials and energy system functioning
4.4.2 Technological progress
4.4.3 Change in family size
4.4.4 Evolution of the French electricity mix
4.4.5 Summary of temporal parameters and scenarios
4.5 Results and discussion
4.5.1 Results for the entire building
4.5.2 Contribution of construction products
4.5.3 Application to building scenarios
Chapter 5 Orientations for DLCA users
5.2 Dynamic LCA for buildings and construction products design
5.2.1 Role of the building sector within the global warming target
5.2.2 Identification of mitigation actions for impact contributors
5.2.3 New metrics and analysis at the building scale
5.2.4 Prospective scenario analysis by dynamic LCA
5.3 Dynamic LCA for building actors and regulation
5.3.1 Emission profile analysis
5.3.2 Carbon dioxide removal (CDR) by the building sector
Chapter 6 Conclusions and perspectives