IV. In addition to processes for generation of heat, other environmental hotspots in the product systems are: production of timber and paint for the W window; the injection moulding process of PVC and production of steel in the PVC window. V. The use of glass fibre-based composite has some contribution (up to 12%) to total impacts, depending on the impact category, but cannot be considered a hot spot given that the composite substantially improves insulation properties causing an overall reduction in environmental impacts. VI. Similarly, the use of 3-layered glass instead of 2-layered improves insulation properties resulting in an overall reduction in environmental impacts with the respective heating mix. VII. The trade-off between impacts from the material used and the improved insulation properties that the material may give the window has to be considered when assessing environmental performance of windows


Recommendations are given to the commissioner to support eco-design of the new window and greening of the whole value chain: A. The design of windows to ensure better environmental performance should focus on optimising insulation properties of windows. This can be done by introducing a 3-layered pane, or improving the design of the frame. If the latter is considered, the choice of frame material is important and in each case where new frame material is used in the design of a frame we recommend evaluating (using tools like LCA) whether environmental benefits achieved by improved insulation properties are really sufficient to outweigh potential environmental burden from the use of novel materials. Indeed, if the heat mix changes substantially within the lifetime of the window this could potentially move the hot spots from the use stage to manufacturing and end-of-life stages in which case our recommendations for design of the windows might not hold. B

39 Illustrative Case Study: Life Cycle Assessment of Four Window Alternatives

Hauschild et al., LCA: Theory and Practice


B. Selection of new materials for frame design should consider functional properties of materials in a window design context, i.e. the focus should not be on selection of materials that perform environmentally best per unit mass of the materials, but on selection of materials that perform best considering insulation properties and the amount applied when used in the frame. C. For the existing W-based windows, improvement potentials lie in selection of paints with lower environmental impact. For the paint applied for maintenance in the use stage, this may be outside the influence of the producer, because it is the window users who will select the type of paint. Our recommendation is to provide information to the users about recommended types of paint. D. Finally, we recommend to phase-out the PVC window as the option with likely the highest environmental burden overall


D. Finally, we recommend to phase-out the PVC window as the option with likely the highest environmental burden overall. If this is not possible, we recommend its redesign through the introduction of a 3-layered pane to improve its insulation properties. Further improvement potentials for the PVC window system lie mainly in selection of cleaner technology for production of PVC frame elements. Illustrative Case Study: Life Cycle Assessment ...


The major limitations of the LCA are: 1. Our findings about major drivers of environmental impacts apply to windows where crystal glass is used in the panes with a relatively large (>0.6) visible light transmittance coefficient. They are not thought to be applicable for windows, which change their transparency in response to light intensity (e.g. photochromic windows) where the need for electricity to provide lighting indoor may become an important factor contributing to impacts in the use stage. 2. The disregard of changes in heat mix and heat demand in the future and potential development of more efficient heat supply technologies is another potential limitation. It is uncertain to what extent these will become effective within the time frame of the study (25–30 years)


It is uncertain to what extent these will become effective within the time frame of the study (25–30 years). If such is the case, impacts from the manufacturing stage or disposal will become more important in the future (if there is no development of cleaner manufacturing and waste management technologies, which also is uncertain). They may change both the ranking of window alternatives and recommendations given to the commissioner. We expect, however, that in a 20-year time horizon, the use stage will likely remain the most important contributor to total impacts from the window product system, and efforts to design windows with low U-values should continue. 39.3 Main Report Nor-win, a Danish-based windows manufacturer, produces windows for use in residential buildings in Scandinavia (mainly Denmark and Sweden), and some Western European countries (mainly Germany)


Existing Nor-win windows on the market are dominated by windows made of wood, a combination of wood and aluminium, or polyvinyl chloride (PVC). Nor-win is currently designing a new type of window made of wood and a composite (glass fibre-reinforced polyamide) to be introduced to the market in 1–2 years, starting with the home market in Denmark. The new window is expected to gain a share of 20–30% of the total current market share of Nor-win. It differs from the existing windows with respect to heat insulation properties, which are improved by combining wood with glass fibre-reinforced plastic in the construction of window frame. The new window is thus expected to have a lower overall environmental impact compared to earlier products from the company. However, a quantitative, life cycle based assessment has not been done yet


The new window is thus expected to have a lower overall environmental impact compared to earlier products from the company. However, a quantitative, life cycle based assessment has not been done yet. 39.3.1 Goal Definition 39.3.1.1 Intended Applications The study aims to perform a benchmarking for internal use of a wood/composite (W/C) window against three window types made of wood (W), wood/aluminium


(W/ALU), and PVC (PVC) used in Danish residences. With this regard, this study is a comparative case study. However, given that Nor-win will use the results as guidance for the ongoing design of the new window, environmental hotspots for each window system will also be identified. One of the aims of this study is to quantify the trade-off that may occur when potentially reduced environmental impacts from better insulation properties are achieved at the expense of increased impacts from higher demand for materials needed for manufacturing. Overall, the results of this LCA are intended to be used to initiate a greening of the value chains of the four window alternatives. 39.3.1.2 Method Assumptions and Impact Limitations We follow best, EU-recommended practice for characterisation modelling to quantify life cycle impacts of four window alternatives, referred to as ILCD (EC-JRC 2010; Hauschild et al. 2013). However, even best practice has limitations


2013). However, even best practice has limitations. The recommended methods currently do not allow for consistent spatially explicit impact assessment. Given that Nor-win operates in Northern Europe, those impacts which occur within this region may be subject to bias, if global-generic characterisation factors are used (Scandinavian soils are for instance quite sensitive to acidification compared to an average European soil). Further, in ILCD, no method has been recommended for dealing with terrestrial and marine ecotoxicity. Thus, impacts on terrestrial and marine ecosystems stemming from emissions of some important stressors, like metals, are not considered in this assessment. In addition, due to insufficient quality of the inventory data, we had to exclude the impact category water use from the set of ILCD methods. Normalisation was done to relate impact scores to background activities of the society in Europe


Normalisation was done to relate impact scores to background activities of the society in Europe. However, normalisation references are thought to be underestimated for the toxicity-related impact categories due to the insufficient knowledge of total emissions of the thousands of different chemicals with toxicity potentials in Europe, resulting in overestimation of normalised impact scores for the freshwater ecotoxicity and human toxicity (both cancer and non-cancer effects) impact categories (Laurent et al. 2011). 39.3.1.3 Reasons for Carrying Out the LCA Study and Decision Context Nor-win wishes to position itself as a proactive company on the market in terms of environmental sustainability, with the ambition to attract customers demanding more environmentally friendly products. For that reason, Nor-win is considering to apply for a Nordic Ecolabel for selected windows in its portfolio, to be used for marketing purposes


For that reason, Nor-win is considering to apply for a Nordic Ecolabel for selected windows in its portfolio, to be used for marketing purposes. Furthermore, the company expects that the LCA study will provide valuable information to be incorporated at the early stages in the development of the new composite window. Illustrative Case Study: Life Cycle Assessment ...


The decision context is situation A as the decisions taken by Nor-win stakeholders will primarily have an internal influence (i.e. in supporting the eco-design of the new type of window) and will not result in structural consequences on the market because the share of Nor-win of total window market in Europe is small (ca. 3%). This is mainly because Nor-win is a relatively minor customer for its suppliers and the market production capacity will not be influenced if changes in the choice of suppliers are introduced based on the LCA results. 39.3.1.4 Target Audience The target audience is environmental and design departments at Nor-win. The company has limited knowledge about life cycle concepts and has neither conducted nor commissioned an LCA before. However, the company has recently employed a designer who is familiar with eco-design principles


However, the company has recently employed a designer who is familiar with eco-design principles. 39.3.1.5 Comparisons Intended to Be Disclosed to the Public This comparative LCA study is not intended to be a comparative assertion disclosed to the public. 39.3.1.6 Commissioner of the LCA Study and Other Influential Actors This study is commissioned and fully financed by Nor-win. The team carrying out the LCA includes an employee from Ecolabelling Denmark at Danish Standard (Dansk Standard, DS), an organisation that is monitoring and developing ecolabel standards for various products, including windows. Ecolabelling Denmark is thus an influential actor. 39.3.2 Scope Definition 39.3.2.1 Deliverables This is both a comparative LCA study (for internal use) and an environmental hotspot analysis carried out for each window alternative (see Sect. 2.1)


39.3.2 Scope Definition 39.3.2.1 Deliverables This is both a comparative LCA study (for internal use) and an environmental hotspot analysis carried out for each window alternative (see Sect. 2.1). The deliverables include (i) detailed life cycle inventory model of the compared systems, including unit process data; and (ii) life cycle impact assessment results (in both characterised and normalised forms).


39.3.2.2 Function, Functional Unit, and Reference Flows Function. The four window types are made of different materials and are compared on the basis of their main function, which is allowing light inside the building. The fraction of light that enters through the window into the building is characterised by a parameter called visible light transmittance (Tvis). Tvis can vary between windows depending on the type and properties of the windowpane, but for windows with panes made of crystal glass it is generally not lower than 0.6. The visible light transmittance is a relevant parameter to consider because light transmittance properties of some windows (like the photochromic or electrochromic ones) may vary, depending on other factors (e.g. light intensity), in which case increased need for indoor lighting should be considered when modelling life cycle inventories


light intensity), in which case increased need for indoor lighting should be considered when modelling life cycle inventories. This was not considered relevant in our case study where all four windows have pane made out of crystal glass of constant (and relatively high, Tvis > 0.6) light transmittance properties. The secondary function of the window (i.e. its ability to transfer heat) was considered by crediting the system for the heat loss in the use stage (as will be explained in detail in Sect. 39.2.3). While all windows must allow daylight into a building (obligatory property), they may differ in some of the positioning properties (Table 39.3). The window’s ability to conduct heat is described by its heat transfer coefficient, the U-value (W m−2 K−1). The U-value is a measure of how well a window prevents heat from escaping the building. The lower the U-value the lower is the heat loss


The U-value is a measure of how well a window prevents heat from escaping the building. The lower the U-value the lower is the heat loss. The U-value depends on the type of material and design of the window frame, and properties (thickness, coating, and number of layers) of the windowpane and on the properties of the material between the panes. The U-value is usually measured and provided by the manufacturers. The U-value is thus an important parameter because any heat loss through the window must be compensated by providing extra heating to the indoor environment that the window shields. It is estimated that losses through windows can account for 25% of the total heat loss in a residential building (Natural Resources Canada 2015). Note that both window frame and windowpane can be characterised by a U-value. The U-value used here refers the window as a whole. Major properties of the four window alternatives are presented in Table 39.4


The U-value used here refers the window as a whole. Major properties of the four window alternatives are presented in Table 39.4. Table 39.3 Obligatory and positioning properties of windows in this case study Obligatory property Positioning properties • Allow daylight into a building through a physical barrier • Thermal and noise insulation • Allow ventilation between indoor and outdoor • Provide aesthetic functionality to the building • Protection against breaking into the building Illustrative Case Study: Life Cycle Assessment ...


Functional unit. All four windows are compared on the basis for the following functional unit: “Allow daylight into a residential building through a physical barrier, equivalent to light being transmitted through an area of 1.82 m2 with visible light transmittance of at least 0.6, for 20 years”. This definition allows for a fair comparison between windows with different U-values. Note, however, that it does not allow for a comparison with an empty hole in the house (which although allows daylight into a building, is not a physical barrier). Reference flows. Considering the different life times for window frames and panes, the reference flows in Table 39.5 are derived as needed to provide the service defined in the functional unit. It is assumed that the pane will be changed once in windows’ lifetime (specifically, after 20 years) and that the window is used for the time equal to the lifetime of the frame


Please note that reference flows for Table 39.4 Major properties of the four window alternatives Properties Window type W W/ALU PVC W/C Frame material Mainly wood Mainly wood and aluminium Mainly polyvinyl chloride and galvanised steel Mainly wood and polyamide/glass fibre composite Glass material 2-layered, coated, sealed with silicone, filled with argon 2-layered, coated, sealed with silicone, filled with argon 2-layered, coated, sealed with silicone, filled with argon 2-layered, coated, sealed with silicone, filled with argon U-value (W m−2 K−1) 1.29 1.31 1.36 1.08 Tvis (fraction) 0.8 0.8 0.8 0.8 Glass dimensions (m) 1.23  1.48 1.23  1.48 1.23  1.48 1.23  1.48 Wood (W), wood/aluminium (W/ALU), PVC or wood/composite (W/C) Table 39.5 Life times and reference flows needed to fulfil the functional unit for the four window alternatives Property/reference flow Window type W W/ALU PVC W/C Life time of the window frame (years) Life time of the window pane (years) Reference flows


the functional unit for the four window alternatives Property/reference flow Window type W W/ALU PVC W/C Life time of the window frame (years) Life time of the window pane (years) Reference flows (numbers) Window frame 0.5 0.5 0.67 0.5 Window pane Packaging Paint for window frame


pane and frame would be equal if the whole window is to be replaced after 20 years equal to the expected life time of the pane (and irrespective of the life time of the frame). Whether this is the case will depend on factors like trends in design, the overall state of the building, or the frequency of change in the ownership of the apartment where the window is installed. These factors were not considered here. 39.3.2.3 LCI Modelling Framework Nor-win’s introduction of a new window and improvements in heat insulation properties of existing windows are not expected to have large structural changes on the market (like installation of new composite factories or decommissioning of existing heat pumps). Thus, the decision context is micro-level, product or process-related decision support studies, i.e. situation A in the ILCD Guideline (EC-JRC 2010), suggesting that the attributional principle be chosen as LCI modelling framework


situation A in the ILCD Guideline (EC-JRC 2010), suggesting that the attributional principle be chosen as LCI modelling framework. This implies that the systems are modelled depicting existing value chains, i.e. using current Danish electricity and heating mix, and Danish recycling rates for end-of-life scenarios. Consistently with the micro-level decision context, system expansion was done to credit for the heat loss in the use stage using average Danish data. Note, that we applied system expansion (through crediting) using average processes in this attributional approach, consistently with both ILCD and the ISO hierarchy to solving multifunctionality, although system expansion using marginal processes has traditionally been considered for the consequential approach to inventory modelling (allocation has traditionally been used for the attributional approach). Apart from the secondary function of the window (i.e


Apart from the secondary function of the window (i.e. its ability to transfer heat), other processes downstream can also have secondary functions or co-products, namely recycling operations (producing recycled steel, aluminium, glass, or recycled PVC). As system expansion is the preferred approach to solving multifunctionality, materials produced from recycled content are credited using virgin materials, i.e. aluminium ingots, steel billet, glass cullets (for all windows), and additionally PVC granulate mix (for PVC window), where all virgin materials and PVC granulate are produced using average technologies. Similarly, incineration of some materials in the end-of-life stage produces heat and power, which is credited using average Danish heat and power mixes. Secondary functions in upstream processes also exist (e.g. naphtha cracking, waste incineration), but these secondary functions had to be handled according to how the database (i.e. ecoinvent v.2.1) did it, so using allocation


naphtha cracking, waste incineration), but these secondary functions had to be handled according to how the database (i.e. ecoinvent v.2.1) did it, so using allocation. 39.3.2.4 System Boundaries and Completeness Requirements System boundaries. The analysis includes all life cycle stages from cradle to grave (Fig. 39.2). Processes include for all product systems raw material extraction, primary and secondary material production and upstream processes such as mining Illustrative Case Study: Life Cycle Assessment ...


of metal ores and extraction of crude oil within the system boundaries. Raw materials are transported to metal smelters or refineries to produce virgin metals, fuels and plastics. Similarly, forestry is included to produce wood. These materials are used to produce specific parts from which window components (window frame and windowpane) are made. Assembled windows are packaged and transported to retailers, and from there to residential buildings, in which they are mounted and provide their function. The use stage includes maintenance painting of the wood window (W), and cleaning of all windows. At the end of life, windows are dismantled and transported to a waste handling facility for disassembly. Steel, aluminium and glass are mainly recycled. Landfilling and incineration with heat recovery apply to cardboard, wood, and plastics, and to the remaining, non-recycled fractions of steel, aluminium and glass. Completeness requirements


Landfilling and incineration with heat recovery apply to cardboard, wood, and plastics, and to the remaining, non-recycled fractions of steel, aluminium and glass. Completeness requirements. As the LCA includes a hotspot analysis, no processes should ideally be excluded from the system boundaries based on their similarity between the four window systems. Yet, we excluded: (i) Cleaning of windowpane. Although potentially interesting for the hot spot analysis, Nor-win has little control of the cleaning process (e.g. frequency of the cleaning, detergent type). Thus, the inclusion of cleaning is not that important for the goal of the study (that is, to support guidance for the ongoing design of the new window and green supply chain for the existing windows)


Extraction of resources: crude oil metal ores wood sand other Production of materials and semi-products: aluminium profiles wood profiles steel profiles glass other Production of window pane Production of window frame Assembly of window Use of window Dissasembly of window Incineration Landfilling Production of heat and power Production of virgin materials: aluminium ingot glass cullets secondary steel avoided products Window packaging Painting of window Recycling foreground processes background processes Provision of heat Fig. 39.2 System boundaries for the product systems of the four window alternatives. Transportation and provision of utilities other than heat (e.g. electricity) are included inside the system boundaries but not shown in the figure in order to make it more legible


(ii) Capital equipment such as buildings or machines, unless already integrated in aggregated unit processes of the background system, is excluded. This is common practice in process-based LCA. (iii) Materials contributing to less than 5% of the total mass of the window are cut-off, excepting substances for surface treatment of the window frame, which are expected to be toxic and hence potentially contribute significantly to some impact scores even with much smaller quantities applied. 39.3.2.5 Representativeness of LCI Data Technological representativeness. The manufacturing technology for all four windows, including the elements composing the pane and the frame, should (ideally) represent the technology that is currently used by Nor-win and its primary suppliers


This technology is characterised by relatively high efficiency (in terms of material output per day), mainly due to the use of modern (<5 years old) machines and production lines and the employment of relatively new (<7 years old) technological solutions (like those used for impregnating and painting wooden frame, or painting aluminium). Thus, the data for window manufacturing should primarily come from Nor-win and its suppliers. Alternatively, other Scandinavian or European window manufacturers (and European suppliers) that use relatively modern technology can be used as source of data for manufacturing to compensate for missing data. Data for background processes, like extraction of metal ores and production of raw metals, or extraction of fossils, should ideally represent the average technology currently used globally. It is sufficient that this data comes from generic databases. Geographical representativeness. Geographical coverage is similar for all four windows (Table 39.6)


It is sufficient that this data comes from generic databases. Geographical representativeness. Geographical coverage is similar for all four windows (Table 39.6). Wood for the frame originates from forest in Finland, while the pane together with the glass is produced in Sweden. Polyvinyl chloride for the PVC window is produced in Germany. Thus, in the absence of Nor-win and supplier-specific data, they should originate from associations and companies located in Europe. End-of-life data should be the average for the main market, which is either Denmark (W, W/ALU and W/C) or Germany (PVC). Temporal representativeness. The data for manufacturing processes should be representative for windows produced from 2015 to 2020, i.e. a 5-year time horizon for window manufacturing (the product development takes about 1 year and is not considered important). The average window lifetime is assumed to be 30 (PVC) or 40 years (W, W/ALU and W/C)


The average window lifetime is assumed to be 30 (PVC) or 40 years (W, W/ALU and W/C). However, to comply with the definition of the functional unit, the use stage and end-of-life processes should (ideally) be representative for the 25–30 year time horizon, over which the products will be in use or disposed of. Figure 39.3 shows temporal frames of the windows. Illustrative Case Study: Life Cycle Assessment ...


39.3.2.6 Basis for Impact Assessment ILCD’s recommended practice for characterisation modelling is employed as life cycle impact assessment method (EC-JRC 2012; Hauschild et al. 2013). The ILCD is a combination of state of the art methods for LCIA (as of 2009). Analysis of the sensitivity of the results to alternative LCIA methods was not deemed necessary since the results are for internal use only


Analysis of the sensitivity of the results to alternative LCIA methods was not deemed necessary since the results are for internal use only. Modelling impacts at midpoint is considered sufficient given the goal of the study (comparative assessment and Table 39.6 Geographical scope for life cycle stages and central unit processes in the window frames case study Stage Window type Wood Wood/aluminium PVC Wood/composite Materials Metal ores: not known Crude oil: Norway, Russia, Middle East Forestry: Finland – – Manufacturing Glass pane: Sweden Wood frame: Scandinavia Wood/aluminium frame: Scandinavia PVC frame: Scandinavia Composite frame: Scandinavia Other elements: mainly Europe Other elements: mainly Europe Other elements: mainly Europe Other elements: mainly Europe Assembly: Denmark Use (heat supply) Mainly Scandinavia, Germany Mainly Scandinavia, Germany Mainly Germany Mainly Scandinavia Disposal The same as the use stage Development Manufacturing Use Disposal Fig


39.3 Temporal scope of the W, W/ALU and W/C windows expressed for different life cycle stages. Manufacturing starts in 2015 and continues for 5 years, thus the overall time horizon for the use stage is 5 years longer than the 20-year duration of the use stage for individual window (indicated with a black-white pattern). Similarly, the temporal horizon for the disposal stage may start right after the first window had been produced, and end 2 years after end of the use stage (as indicated with the black-white pattern). The temporal scope for the use and disposal of the PVC window is 10 years shorter compared to the three other windows (not shown). Note that the temporal scope looks at the time horizon of the window life cycle (i.e. 40 years), regardless of the duration considered in the functional unit (in our study equal to 20 years)


identification of hotspots). Normalisation was performed using the set of normalisation references presented for year 2010. The following impact categories are included in the ILCD: climate change (unit: kg CO2 eq.), ozone depletion (kg CFC-11 eq.) photochemical ozone formation (kg NMVOC eq.), terrestrial acidification (AE, accumulated exceedance), terrestrial eutrophication (AE, accumulated exceedance), freshwater eutrophication (kg P eq.), marine eutrophication (kg N eq.), freshwater ecotoxicity (CTUe, comparative toxic unit for ecosystems), ionising radiation (human health, in kBq U235 eq.), particulate matter/(kg PM2.5 eq. to air), human toxicity (cancer effects, in CTUh for human health), human toxicity (non-cancer effects, in CTUh for human health), land use (kg C year), and resource depletion (mineral and fossils, in kg Sb eq.). Product systems were modelled in GaBi, version 4.3 (PE International, Germany; renamed to thinkstep)


Product systems were modelled in GaBi, version 4.3 (PE International, Germany; renamed to thinkstep). Because the ILCD LCIA method was not implemented in GaBi at the time of the study, characterisation factors for the ILCD methods (version 1.0.3, 01 March 2012) were downloaded from the Life Cycle website of the European Commission (http://lct.jrc.ec.europa.eu/assessment) and were imported into the software. For those impact categories where ReCiPe 2008 (Goedkoop et al. 2009) is the recommended method (6 categories in total), impact scores were calculated using the original set of ReCiPe (version 1.05) characterisation factors as implemented in GaBi. Normalisation references are for the EU27 in the reference year 2010 as presented in Benini et al. (2014). The LCIA methods and normalisation factors are presented in Annex, Sect. 39.4.1


Normalisation references are for the EU27 in the reference year 2010 as presented in Benini et al. (2014). The LCIA methods and normalisation factors are presented in Annex, Sect. 39.4.1. 39.3.2.7 Requirements for Comparative Studies Although requirements for a comparative study (like quality requirements, exclusion of identical processes and interpretation in light of affected stakeholders) are only applicable to studies reported at level 3 (that is, comparative studies to be disclosed to the public), we note that in our case study comparison has been made using the same functional unit, system boundaries omit common processes only (i.e. window washing), and data quality is the same between the compared windows (e.g. primary data come from manufacturer). Thus, the comparison between the four window systems is fair and requirements for comparative studies are met


primary data come from manufacturer). Thus, the comparison between the four window systems is fair and requirements for comparative studies are met. The readers should note, however, that although we quantified inventory uncertainties, comparison could not be made taking into account correlation between inventory uncertainties of those inventory processes which are the same for the compared window systems (as will be explained in detail in Sect. 39.3.5). Thus, in some cases there may be statistically significant difference in impact scores between window systems, even if that is not apparent in our analysis. On the other hand, uncertainties in background processes were not considered, which may, at least partially, outweigh a potential decrease in uncertainty due to correlations. Illustrative Case Study: Life Cycle Assessment ...


39.3.2.8 Critical Review Needs This is a comparative study but since it is not intended for disclosure to the public, there is no obligation for a critical review by a third-party panel. 39.3.3 Life Cycle Inventory Analysis 39.3.3.1 LCI Model at System Level Flow diagrams show the product systems of the four windows in Figs. 39.4, 39.5, 39.6 and 39.7. Comparison between the flow diagrams shows that many processes are the same for the four windows (e.g. mining of silica and production of glass, or mining of iron and production of chromium steel). Yet, magnitude of flows often varies between the systems (not shown). Extraction of crude oil Extraction of bauxite Refining Chemicals & polymers Forestry Production of aluminium Aluminium parts Production of wood Wooden parts Mining of silica Mining of iron Surface treatment Production of chrom


steel Steel parts Production of glass Extraction of argon Window frame Window assembly Use (heat loss & maintenance) Dissasembly Recycling of glass Recycling of aluminium Recycling of steel Landfilling Incineration Window pane Heat and power Glass cullets Steel billet Aluminium ingot Packaging Production of cardboard foreground processes background processes Fig. 39.4 Product system of the wood window (W). Red line indicates foreground processes. Grey boxes indicate avoided processes


39.3.3.2 Data Collection Data used to model life cycle inventories for the foreground systems were collected from two sources: (i) Nor-win, who provided primary data related mainly to energy use in the manufacturing and bills of materials and (ii) ecoinvent and Plastics Europe databases for foreground processes where primary data could not be achieved (PlasticsEurope Database 2016; Ecoinvent 2010). The primary data from Nor-win meet the quality requirements given in Sect. 39.3.5. The data are synthesised in Table 39.7. 39.3.3.3 System Modelling Per Life Cycle Stage Below, we present details of the system modelling, the data collected and treatment, and major assumptions. The full list of major and minor assumptions is given in the report Annex, Sect. 39.4.3. Materials stage. Bills of activities required to produce one window are given in Table 39.7, with details on the bill of materials presented in Annex, Sect. 39.4.2 (Table 39.12)


39.4.3. Materials stage. Bills of activities required to produce one window are given in Table 39.7, with details on the bill of materials presented in Annex, Sect. 39.4.2 (Table 39.12). Amounts of materials in each window are provided by Nor-win. Extraction of crude oil Extraction of bauxite Refining Chemicals & polymers Forestry Production of aluminium Aluminium parts Production of wood Wooden parts Mining of silica Mining of iron Surface treatment Production of chrom. steel Steel parts Production of glass Extraction of argon Window frame Window assembly Use (heat loss) Dissasembly Recycling of glass Recycling of aluminium Recycling of steel Landfilling Incineration Window pane Packaging Production of cardboard Heat and power Glass cullets Steel billet Aluminium ingot foreground processes background processes Fig. 39.5 Product system of the wood/aluminium window (W/ALU). Red line indicates foreground processes


39.5 Product system of the wood/aluminium window (W/ALU). Red line indicates foreground processes. Grey boxes indicate avoided processes Illustrative Case Study: Life Cycle Assessment ...


Data for W, W/ALU and PVC windows are precise, because these windows are already on the market and detailed information is available. Data for the W/C window are considered sufficiently accurate to be used in modelling, because the prototype of the window has been produced. Note that based on the outcome of this study, the W/C window may be redesigned, bringing about a change in amounts of some materials in which case the LCA may have to be updated with the new numbers. It is not expected that this change will be higher than 5% for any window frame material. No major assumptions were made for the materials stage. Manufacturing stage. Data on electricity use for production come from measurements of the actual processes and are provided by Nor-win. These data are of high quality and are considered certain. Data on electricity requirements for assembly of the W/C window frame are less certain, and are initial estimates provided by Nor-win


These data are of high quality and are considered certain. Data on electricity requirements for assembly of the W/C window frame are less certain, and are initial estimates provided by Nor-win. The three major assumptions made in the production stage are (i) losses of materials during production are not considered, (ii) energy used for operation of the manufacturing and window disassembly facilities for the W/C window is assumed equal to numbers for other windows, and (iii) energy requirements for window disassembly are assumed equal to 1 MJ per 1 kg of dismounted window. Extraction of crude oil Extraction of bauxite Refining Chemicals & polymers Production of aluminium Aluminium parts Mining of silica Mining of iron Production of chrom


Extraction of crude oil Extraction of bauxite Refining Chemicals & polymers Production of aluminium Aluminium parts Mining of silica Mining of iron Production of chrom. steel Steel parts Production of glass Extraction of argon Window frame Window assembly Use (heat loss) Dissasembly Recycling of glass Recycling of aluminium Recycling of steel Landfilling Incineration Window pane Packaging Production of cardboard Production of galv. steel Production of zinc PCV profiles Recycling of PVC PVC granulate mix Forestry Production of wood Heat and power Glass cullets Steel billet Aluminium ingot foreground processes background processes Fig. 39.6 Product system of the polyvinyl chloride window (PVC). Red line indicates foreground processes. Grey boxes indicate avoided processes


Use stage. Data on heat use during the use stage are calculated using the U-values and average temperature difference between outdoor and indoor environment (Table 39.7). Several assumptions were made for the use stage. First, we modelled the heat loss based on the annual average temperatures indoor and outdoor, without considering the temperature dynamics during the year. Second, we assumed that there is no shift in the source of heat (e.g. towards wind-driven electricity) over the lifetime of the window. Third, we assumed that the windows are used only in buildings to which heat is provided by district heating. In Denmark, district heating was estimated to deliver 55% of the total heat demand for buildings in 2010 (Dyrelund and Lund 2009)


In Denmark, district heating was estimated to deliver 55% of the total heat demand for buildings in 2010 (Dyrelund and Lund 2009). Fourth, processes used to model the district heat mix technologies were representative for Switzerland, or were based on European processes for the generation of heat from the sources included in the study, as no Danish processes were available in ecoinvent 2.2. The fifth assumption is that processes for generation of heat from incineration of straw and non-renewable waste in the Danish heat mix were modelled as incineration of bio-waste, while heat generation from biomass in the EU27 heat mix was modelled as incineration of bio-waste combined with combustion of wood pellets (50:50). Disposal stage. Waste treatment options are based on the data retrieved from Eurostat (2016). Glass, aluminium and steel are mainly recycled, and wood is Fig. 39.7 Product system of the wood/composite window (W/C). Red line indicates foreground processes


Glass, aluminium and steel are mainly recycled, and wood is Fig. 39.7 Product system of the wood/composite window (W/C). Red line indicates foreground processes. Grey boxes indicate avoided processes Illustrative Case Study: Life Cycle Assessment ...


Table 39.7 Metadata including model parameters and data sources for foreground processes for the four window alternatives Parameter Value Unit Note Source W W/ALU PVC W/C Materials Frame materials See Annex, Sect. 39.4.2, Table 39.12 kg In addition to materials presented in Table 39.12, other materials used to produce the frames include acrylic binder, triethylene glycol and wood preservative which are used in small amounts (<1% of total frame mass). Bills of materials are retrieved from the producer Measured Pane materials See Annex, Sect. 39.4.2, Table 39.12 kg In addition to materials presented in Table 39.12, synthetic rubber (ethylene propylene diene monomer, EDPM is used in smaller amounts (<2–6% of total pane mass). Bills of materials are retrieved from the producer Measured Thickness of glass underlying window pane mm Nominal thickness as provided by the producer Measured Packaging See Annex, Sect. 39.4.2, Table 39.12 kg Packaging is made of polyethylene and recycled cardboard


Bills of materials are retrieved from the producer and are presented in Table 39.12 Measured Manufacturing Electricity for production of window frame MJ The data come from measurements of the actual processes and are provided by the producer, apart from the W/C window for which the producer assumed that electricity required for assembly of the frame for the W/C window is 2 times higher than for the W window Measured or assumed Electricity for production of window pane MJ The data come from measurements of the actual processes and are provided by the producer Measured Electricity for mounting window frame and window pane into a window (MJ) MJ The data come from measurements of the actual processes and are provided by the producer, apart from the W/C window for which the producer assumed that electricity required for mounting of thee W/C window is 1.3 times higher than for the W window Measured or assumed (continued)


Table 39.7 (continued) Parameter Value Unit Note Source W W/ALU PVC W/C Electricity for operation of the manufacturing facility MJ The data is provided by the producer for the plant and is scaled to one window basing on the electricity bills and production capacity while taking into account electricity used for production and mounting Measured Heat demand Heat loss per year MJ/year Heat loss is directly proportional to the U-value and the window area and to the temperature difference between indoor and outdoor environment, and is calculated using the formula Ut 1⁄4 U  A  hi  he ð Þ where Ut [W] is the heat loss, U [W m−2 K−1] is the U-value, A [m2] is the heat exchange area; hi is the indoor temperature (K), and he is the outdoor temperature (K) Calculated Indoor temperature °C Annual average indoor temperature in residential buildings Assumed Outdoor temperature 7.7 7.7 7.7 7.7 °C Annual average outdoor temperature in Denmark, calculated based on the temperature data retrieved for


indoor temperature in residential buildings Assumed Outdoor temperature 7.7 7.7 7.7 7.7 °C Annual average outdoor temperature in Denmark, calculated based on the temperature data retrieved for year 2014 from DMI (2016) Calculated Maintenance of window Painting needed for painting of 1 window 0.6 kg Approximately 1 L of paint is needed for a window frame area of 1 m2 (based on data from a single paint producer) Assumed Transportation distances and means Elements composing window frame and window pane km Distance between suppliers of elements underlying window frame and windowpane and location of the producer


Calculated using Google maps. Transport by truck 34–40 t, EURO4 Calculated (continued) Illustrative Case Study: Life Cycle Assessment ...


Table 39.7 (continued) Parameter Value Unit Note Source W W/ALU PVC W/C Window from producer to warehouse km Distance between producer and warehouse. Calculated using Google maps. Transport by truck 34–40 t, EURO4 Calculated Window from warehouse to residential building km Distance between the warehouse and residential building including retail. The location of final user is unknown and distance had to be assumed. Transport by truck 12–14 t, EURO3 Assumed Window from residential building to disposal/ recycling site km Distance between the residential building and disposal/ recycling site. The locations are unknown and thus the distance had to be assumed. Transport by truck 12–14 t, EURO3 Assumed Packaging from residential building to the disposal site km Distance between the warehouse and residential building including retail. The location of final user is unknown and distance had to be assumed


The location of final user is unknown and distance had to be assumed. Transport by truck 12–14 t, EURO3 Assumed Disassembly and disposal Electricity for disassembly of window MJ It is assumed that electricity consumption is equal to 1 MJ per 1 kg of dismounted window Assumed Waste treatment options See Sect. 39.4.2, Table 39.13 % Disposal according to the Danish waste policy (Eurostat 2016). Treatment rates are presented in Annex, Sect. 39.4.2, Table 39.12 Measured Wood (W), wood/aluminium (W/ALU), PVC or wood/composite (W/C) Note that the values are scaled to one window used for one year, not to the functional unit of the window systems


incinerated. Other materials are mainly incinerated, or landfilled. PVC is technically recyclable but not to the extent as for other plastics (30%). The remaining part of PVC is landfilled. The composite (glass fibre/polyamide) is technically difficult to recycle, and is assumed 100% incinerated. Details of end-of-life options are presented in Annex, Sect. 39.4.2 (Table 39.13). The two major assumptions are (i) all recycled materials replace virgin materials in the market, i.e. glass cullets, aluminium ingot, steel billet, and PVC granulate mix, at a 1:1 ratio, i.e. without considering any loss of material functionality in the recycling; and (ii) although the wood-based windows are sold mainly in Scandinavian countries and Germany, the use and disposal stages for all windows are modelled using data from processes representative for Denmark, e.g. Danish heating and electricity mixes and waste management systems. Transportation


Danish heating and electricity mixes and waste management systems. Transportation. Transportation distances and means are either provided by Nor-win or assumed. The data provided by Nor-win are considered sufficiently accurate, whereas the assumed data are considered uncertain. 39.3.3.4 Basis for Sensitivity and Uncertainty Analyses To test the influence of the assumptions made on the results of the LCA, sensitivity analyses were performed, followed by uncertainty and variability analyses. Sensitivity analyses. First, to identify which of the parameters influence impact scores the most, and to provide a basis for uncertainty and variability analysis, we calculated normalised sensitivity coefficients (XIS,k), according to Eq. 39.1 (e.g. Prommer et al


39.1 (e.g. Prommer et al. 2006): XIS;k 1⁄4 DIS=IS Dak=ak ð39:1Þ where XIS,k is the normalised sensitivity coefficient of impact score (IS) for perturbance of a parameter k, ak is the default value of parameter k, Dak is the perturbation of parameter ak, IS is the calculated impact score for parameter value ak, and DIS is the change of the impact score that results from the perturbation of parameter ak. The following parameters were tested: amount of wood, aluminium, steel composite (W/C window only), and PVC (PVC window only) in the window frame, the amount of glass in the pane, amount of paint for manufacturing, electricity needed for assembly, U-value, and transportation distance from Nor-win to retailers. All input parameters were perturbed by 10%, which is a realistic range around the expected values. XIS,k equal to 1 means that a 10% increase in parameter value brings about a 10% increase in the impact score


XIS,k equal to 1 means that a 10% increase in parameter value brings about a 10% increase in the impact score. Generally, a parameter is considered to have medium sensitivity if XIS,k > 0.3, and large sensitivity if XIS, k > 0.5. In this study, a parameter is considered important when XIS,k > 0.3. Second, in addition to testing sensitivity to individual parameters through computation of normalised sensitivity coefficients, perturbing each parameter at once, a separate sensitivity check was done, where several parameters expected to Illustrative Case Study: Life Cycle Assessment ...


be important were perturbed at once. The overview of the two sensitivity scenarios considered is given in Table 39.8. Scenario 1 reflects a situation where the window is used by an average European residence rather than a Danish residence. Scenario 2 reflects the situation where a 3-layered windowpane is used instead of a 2-layered one, which improves insulation properties of the whole window (without any considerable influence on visible light transmission properties). This scenario was included to identify potential for improvements of existing and new windows. Note, that over the coming 20 years we may see a shift in the heat source (e.g. towards wind-driven electricity) but it is uncertain to what extent these will become effective within the time frame of the study (25–30 years). On the other hand, we may also witness the development of cleaner manufacturing and waste management technologies in 20 years (which is also uncertain)


On the other hand, we may also witness the development of cleaner manufacturing and waste management technologies in 20 years (which is also uncertain). Thus, the potential change in heat mix and change in manufacturing and waste management systems were not considered in the sensitivity analysis. Uncertainty and variability analysis. Parameter uncertainties stem from the imprecision in knowledge about the actual value of a parameter, e.g. electricity use during window assembly. By contrast, variability is the inherent variance that will exist between similar processes depending on technological level and spatial location, e.g. transportation distance from factory to retail Steinmann et al. (2014)


transportation distance from factory to retail Steinmann et al. (2014). Table 39.8 Sensitivity scenarios and corresponding model parameters Sensitivity parameters Baseline scenario Sensitivity scenario Scenario 1 Scenario 2 Use locationa DK EU27 DK Disposal routeb,e Heat mixc,f Electricity mixd,g Pane design 2-layered 2-layered 3-layeredh aDK Denmark; EU27 European Union’s 27 member states bPlease see Annex, Sect


39.4.2 (Table 39.13) for details of end-of-life options in DK and EU27 cDanish heating mix in 2010 was based on: natural gas (24%), coal (23%), straw (8%), wood chips (12%), wood pellets (10%), non-renewable waste (17%), oil (2%), and other sources (4%) (Energynet 2012) dDanish electricity mix in 2010 as based on: hard coal (36%), natural gas (14%), wind power (15%), oil (2%), import from Sweden (14%), Norway (10%), Germany (3%), and other sources (6%) (Ecoinvent 2010) eCompared to Danish disposal routes the EU27 disposal routes in 2010 is characterised by lower frequency of recycling and/or incineration, and increased frequency of landfilling (Eurostat 2016). The disposal options are summarised in Annex, Sect. 39.4.2 (Table 39.13) fEU27 heat mix in 2010 was based on: natural gas (57%), oil (21%), biomass (13%), and coal (9%) (Connolly et al


The disposal options are summarised in Annex, Sect. 39.4.2 (Table 39.13) fEU27 heat mix in 2010 was based on: natural gas (57%), oil (21%), biomass (13%), and coal (9%) (Connolly et al. 2012) gEU27 electricity mix was 2010 is based on: nuclear power (28%), coal and peat (27%), natural gas (27%), hydropower (11%), wind power (4%), oil (3%), biofuels (3%), and non-renewable waste (7%) (Ecoinvent 2010) h3-layered windows have improved insulation properties thanks to smaller U-values, which were reduced by 25% for the W, W/ALU, and PVC windows, and by 30% for the W/C window


Here, parameter uncertainty was assessed together with variability by means of a Monte Carlo simulation. Only parameters that were found important (XIS,k > 0.3) in the sensitivity analysis, for any of the considered impact categories for either window design option, were considered. In total, four parameters where considered (Table 39.9). They were assigned relative standard deviations derived from the expected range of parameter values. The uncertainty ranges and number of uncertain parameters is higher for the W/C window, because this window is still in under development and very accurate bills of materials and performance parameters (U-value) are not known. We assumed normal distributions of all parameters mainly because this is one of two types of distribution implemented in our version of the software, GaBi v. 4.3 (the other being equal distribution). Other distribution types (e.g


4.3 (the other being equal distribution). Other distribution types (e.g. lognormal) can be used if found more appropriate, provided that such is possible in the modelling software employed. Uncertainties in the background processes were not considered as they were not known and the unit process database did not include them at the time of the study. Differences in impact scores between the compared systems were considered significant if the calculated 95% probability ranges of the impact scores from 1000 iterations did not overlap. Although not deemed necessary in this case study, all other flows and parameters could be ascribed to standard deviations, supporting a more comprehensive uncertainty analysis


Although not deemed necessary in this case study, all other flows and parameters could be ascribed to standard deviations, supporting a more comprehensive uncertainty analysis. In such cases, standard deviations for each flow in foreground Table 39.9 Uncertain or variable parameters included in the Monte Carlo simulation and the associated relative standard deviation, expressed in percentage Uncertain or variable parameter Mean (relative standard deviation)a W W/ALU PVC W/C Amount of wood in the frameb 30 (1%) 9.2 (1%) 0 (0%) 9.2 (2.5%) Amount of steel in the frameb 0.5 (1%) 1.2 (1%) 15.1 (1%) 1.2 (2.5%) Amount of glass in the panec 56 (0.5%) 56 (0.5%) 56 (0.5%) 56 (0.5%) U-value of the windowd 1.29 (1.5%) 1.31 (1.5%) 1.36 (1.5%) 1.08 (3%) aRelative standard deviation (also known as coefficient of variation, CV) is equal to sample standard deviation divided by sample mean, expressed in percentage


Sample standard deviation was estimated using an empirical rule that the sample standard deviation is equal to one fourth of the whole parameter range (equal to the difference between maximum and minimum value) bChange in amounts of wood and steel in the frame depend mainly on losses in the production, and are expected to be maximum 2% for W, W/ALU and PVC windows, and 5% for the W/C window, because of the ongoing development of the latter. These values are realistic values provided by Nor-win based on the information retrieved from suppliers CChange in the amount of glass is expected to be by maximum 1%. Again, this value was provided by Nor-win dAlthough the U-value is considered as an inherent property of a window, the actual amount of heat exchanged depends on other factors, like the quality of the work during window installation, type and quality of insulation used to install the window in the wall, or type and properties of walls


To account for this variability, a maximum change in the U-value of 3% was used for W, W/ALU and PVC windows, based on the information from Nor-win. For the W/C window, 6% was used to calculate minimum and maximum U-values (again, because it is ongoing product development) Illustrative Case Study: Life Cycle Assessment ...


processes could be computed using the Pedigree matrix approach (Ciroth 2013). Uncertainties in the background processes should be considered based on standard deviations already assigned to flows in processes of the considered unit process database. Newer versions of the database offer such features. The calculated probability ranges represent the modelled inventory uncertainty, but we did not account for covariation between processes that occur in some or all of the compared window systems (e.g. production of heat for the use stage), leading to correlations between uncertainties of those inventory processes. The employed modelling software (GaBi v. 4.3) did not allow taking this into account, but it would have reduced the uncertainty in comparison between the systems (see Sect. 11.4.2). Thus, in some cases there may be statistically significant difference in impact scores, even though that is not revealed by our analysis


11.4.2). Thus, in some cases there may be statistically significant difference in impact scores, even though that is not revealed by our analysis. On the other hand, uncertainties in background processes were also not considered in our case study, which would increase the uncertainty in the results and may, to some extent, counterbalance this effect. In addition, the characterisation and normalisation factors applied in the impact assessment are accompanied by uncertainties but these were not known to us and we were therefore unable to take them into account in our uncertainty analysis. They are expected to be equal to or higher than the inventory uncertainties. 39.3.3.5 Calculated LCI Results Unit processes and life cycle inventories showing elementary flows for each window product system are documented in Annex, Sect. 39.4.4 (Tables 39.15, 39.16, 39.17, 39.18, 39.19, 39.20, 39.21, 39.22, 39.23, 39.24, 39.25, 39.26, 39.27, 39.28, 39.29, 39.30, 39.31, 39.32, 39.33 and 39.34)


39.4.4 (Tables 39.15, 39.16, 39.17, 39.18, 39.19, 39.20, 39.21, 39.22, 39.23, 39.24, 39.25, 39.26, 39.27, 39.28, 39.29, 39.30, 39.31, 39.32, 39.33 and 39.34). 39.3.4 Life Cycle Impact Assessment Characterised results. The life cycle impacts are listed in characterised form in Table 39.10. All four window alternatives have impacts within the same order of magnitude. For most impact categories the impact scores follow the order W/C < W = W/ALU < PVC. Ranking of window systems normalised internally to the W window (equal to 100% of impact) is presented in Fig. 39.8. The W/C window has the lowest environmental impact in all 14 impact categories, while the PVC window system has the highest impact scores for 11 impact categories. For these 11 impact categories, the differences in impact scores between the W/C and PVC windows are statistically significant (the calculated 95% probability ranges of the impact scores do not overlap)


The PVC window performs better in land use impacts with a significantly lower impact compared to the W and W/ALU window systems, but still slightly higher compared to the W/C window system. By contrast, the W window system performs significantly worse than the other window systems


Table 39.10 Characterised impacts and accompanying 95% probability ranges from Monte Carlo simulations for each window alternative Impact category Unit Impact score (95% probability range) W W/ALU PVC W/C Climate change kg CO2 eq. 1162 (1134–1189) 1158 (1129–1188) 1232 (1203–1260) 978 (933–1023) Stratospheric ozone depletion kg CFC-11 eq. 1.9e−5 (1.8e−5–1.9e.5) 1.6e−5 (1.5e−5–1.6e−5) 1.6e−5 (1.5e−5–1.6e−5) 1.4e−5 (1.4e−5–1.4e−5) Photochemical ozone formation kg NMVOC eq. 1.59 (1.55–1.63) 1.57 (1.53–1.61) 1.72 (1.67–1.76) 1.33 (1.26–1.4) Terrestrial acidification AE 2.00 (1.95–2.04) 2.00 (1.95–2.05) 2.31 (2.26–2.36) 1.67 (1.6–1.75) Terrestrial eutrophication AE 7.14 (6.94–7.35) 7.04 (6.83–7.24) 7.79 (7.56–8.02) 5.96 (5.61–6.32) Freshwater eutrophication kg P eq. 0.042 (0.041–0.043) 0.043 (0.041–0.044) 0.046 (0.044–0.047) 0.035 (0.033–0.037) Marine eutrophication kg N eq


0.042 (0.041–0.043) 0.043 (0.041–0.044) 0.046 (0.044–0.047) 0.035 (0.033–0.037) Marine eutrophication kg N eq. 0.65 (0.63–0.67) 0.62 (0.60–0.64) 0.68 (0.66–0.7) 0.54 (0.51–0.57) Freshwater ecotoxicity CTUe 2675 (2605–2745) 1755 (1706–1805) 1852 (1809–1895) 1545 (1461–1630) Human toxicity (cancer) CTUh 2e−5 (1.9e−5–2e−5) 1.8e−5 (1.8e−5–1.9e−5) 3.4e−5 (3.3e−5–3.5e−5) 1.6e−5 (1.5e−5–1.7e−5) Human toxicity (non-cancer) CTUh 1.5e−4 (1.5e−4–1.6e−4) 1.3e−4 (1.2e−4–1.3e−4) 1.3e−4 (1.3e−4–1.3e−4) 1.0e−4 (9.9e−5–1.1e−4) Particulate matter formation kg PM2.5 eq. to air 0.085 (0.083–0.087) 0.082 (0.080–0.084) 0.116 (0.114–0.119) 0.070 (0.067–0.073) Ionising radiation (human health) kBq U235 eq. 7.69 (7.56–7.81) 7.99 (7.86–8.12) 8.63 (8.49–8.77) 6.26 (6.07–6.45) Land use kg C year 657 (646–668) 405 (399–410) 386 (384–387) 364 (351–377) Resource depletion (minerals, fossils) kg Sb eq


7.69 (7.56–7.81) 7.99 (7.86–8.12) 8.63 (8.49–8.77) 6.26 (6.07–6.45) Land use kg C year 657 (646–668) 405 (399–410) 386 (384–387) 364 (351–377) Resource depletion (minerals, fossils) kg Sb eq. 0.0072 (0.0070–0.0073) 0.0074 (0.0072–0.0076) 0.0081 (0.0080–0.0083) 0.0063 (0.0060–0.0066) W/ALU Wood/aluminium, PVC polyvinyl chloride, W/C wood/composite Illustrative Case Study: Life Cycle Assessment ...


for the impact categories freshwater ecotoxicity, human toxicity (non-cancer), stratospheric ozone depletion, and land use. The W and W/ALU window systems rank as second or third for 10 out of 14 impact categories, but for these alternatives, differences between impact scores are only statistically significant in the ionising radiation impact category. Normalised results. Figure 39.9 shows the normalised results. The common unit for indicator scores is person equivalents (pe) representing the annual impact of an average person in the European Union (EU27) in 2010. For nearly all the non-toxicity impact categories, like climate change, the life cycle impacts of the four windows correspond to approximately 10% of the total annual average impacts of an average EU27 citizen in the year 2010. Much smaller normalised impact scores are seen for stratospheric ozone depletion


Much smaller normalised impact scores are seen for stratospheric ozone depletion. Normalised results are somewhat higher for freshwater ecotoxicity and human toxicity impact categories (scoring up to 1 PE for cancer effects), but are smaller for respiratory effects and ionising radiation impacts on human health (around or below 0.1 PE). Normalised impact scores are the highest for human toxicity (cancer), equal to ca. 0.5 PE, but are small for land use (below 0.001 PE). 39.3.5 Interpretation Before providing final recommendations to the commissioner of the study, it is necessary to interpret the results of the LCA


39.3.5 Interpretation Before providing final recommendations to the commissioner of the study, it is necessary to interpret the results of the LCA. Climate change Stratospheric ozone depletion Photochemical ozone formation Terrestrial acidification Terrestrial eutrophication Freshwater eutrophication Marine eutrophication Freshwater ecotoxicity Human toxicity (cancer) Human toxicity (non-cancer) Particulate matter formation Ionising radiation (human health) Land use Resource depletion (mineral, fossils) % of total impact score W W/ALU PVC W/C Fig. 39.8 Ranking of the four window options with impact scores scaled to those of the W window (equal to 100% of total impact). Whiskers represent inventory uncertainty stemming from uncertainty and variability in model parameters presented in Table 39.8


39.3.5.1 Significant Issues Process contribution analysis. To explain differences in window ranking and identify hot spots, a process contribution analysis was conducted, i.e. identifying the processes with the largest environmental burden. Figure 39.10 shows that the main driver of environmental impacts is the production of residential heating to compensate for heat losses through the window. The contribution of this process to total impact is around 90% for climate change, freshwater eutrophication, or resource depletion, and is above 50% for most other impact categories (apart from ozone depletion and ionising radiation, where the contribution is smaller). This trend is consistent across all four window systems. Across all window systems, climate change impacts from the use stage due to combustion of fossil coal and natural gas, which constitute 25 and 31% of total Danish heating mix, respectively


Across all window systems, climate change impacts from the use stage due to combustion of fossil coal and natural gas, which constitute 25 and 31% of total Danish heating mix, respectively. The use of fossil fuels in the use stage is also the major driver of impacts related to depletion of resources. For other impact categories where the use stage is important (>50% of total impact score), however, the major driver of environmental impact is the use of other fuels like wood, straw and bio-waste. These processes are important for the impact categories terrestrial and freshwater eutrophication, and all the toxicity related impact categories. Although the use stage is the main driver for the above-mentioned impact categories, for some impact categories the differences between window systems can sometimes be attributed to differences in material composition of the window


The manufacturing stage is important (>50% of total impacts) for impacts on Climate change Stratospheric ozone depletion Photochemical ozone formation Terrestrial acidification Terrestrial eutrophication Freshwater eutrophication Marine eutrophication Freshwater ecotoxicity Human toxicity (cancer) Human toxicity (non-cancer) Particulate matter formation Ionising radiation (human health) Land use Resource depletion (minerals, fossils) Normalized impact score (PE) 0.0001 0.001 0.01 0.1 W W/ALU PVC WC Fig. 39.9 Normalised impacts and accompanying 95% confidence intervals (log10-scale) in person equivalents (pe) for each window system (W wood, W/ALU wood/aluminium, PVC polyvinyl chloride, W/C wood/composite). Whiskers represent inventory uncertainty stemming from uncertainty and variability in model parameters presented in Table 39.8 Illustrative Case Study: Life Cycle Assessment ...


stratospheric ozone depletion, and ionising radiation (human health) across all windows, and for impacts on freshwater ecotoxicity and human toxicity. In addition, the manufacturing processes overall contribute to impacts on land use (around 40% of total impact) and to some extent also to the remaining impact categories (with contributions from 10 to 30%), reflecting that the materials used in the windows are considered part of the manufacturing stage. Substantial contribution to land use impacts in the W window is thus from the production of glue laminated timber. Impacts in these categories are also caused by production of alkyd paint (18 and 13% of total impact, respectively). In addition, the alkyd paint shows contribution of the same order of magnitude for four other impact categories, i.e. aquatic acidification, ionising radiation, ozone layer depletion and photochemical ozone formation. For the W/ALU window system, considerable impacts are caused by production of aluminium


aquatic acidification, ionising radiation, ozone layer depletion and photochemical ozone formation. For the W/ALU window system, considerable impacts are caused by production of aluminium. This process contributes substantially to terrestrial acidification and stratospheric ozone depletion (20–23% of total impacts)


Note that the introduction of aluminium has negative influence on the window performance in those impact categories that are determined by the use stage, because insulating % of total impact score -100 -50 Climate change Stratospheric ozone depletion Photochemical ozone formation Terrestrial acidification Terrestrial eutrophication Freshwater eutrophication Marine eutrophication Freshwater ecotoxicity Human toxicity (cancer) Human toxicity (non-cancer) Particulate matter formation Ionising radiation (human health) Land use Resource depletion (mineral, fossils) % of total impact score -100 -50 Climate change Stratospheric ozone depletion Photochemical ozone formation Terrestrial acidification Terrestrial eutrophication Freshwater eutrophication Marine eutrophication Freshwater ecotoxicity Human toxicity (cancer) Human toxicity (non-cancer) Particulate matter formation Ionising radiation (human health) Land use Resource depletion (mineral, fossils) Materials and manufacturing Transport


Human toxicity (cancer) Human toxicity (non-cancer) Particulate matter formation Ionising radiation (human health) Land use Resource depletion (mineral, fossils) Materials and manufacturing Transport Use Disposal W PVC W/C W/ALU Fig


39.10 Contribution of individual life cycle stages to total impact for each impact category for the four window systems (W wood, W/ALU wood/aluminium, PVC polyvinyl chloride, W/C wood/composite)


properties are slightly worse than for the W window. Yet, the overall differences in impact scores are not statistically significant. Environmental impacts in the PVC window system in the manufacturing stage originate mainly from the PVC injection moulding process and production of steel. Injection moulding contributes substantially to impacts on human health (42 and 34% for carcinogens and non-carcinogens, respectively) while 94% of total impacts on mineral depletion is caused by the need for chromium; this, however, is not apparent in Fig. 39.10 because the resource depletion impact category is driven by the use of fossils. Given that insulation properties of the PVC window are not improved when PVC and steel are used in the window frame (they even decrease), the PVC window performs the worst among considered alternatives


An exception is an impact on land use, in which the PVC windows performs nearly as good as the best W/C window, which is mainly due to no use of wood in the PVC window. For the W/C window, the composite contributes to some extent (up to 12%) to some impact categories, but environmental benefits are obtained due to improved insulation properties. Across all windows, production of flat glass is a considerable contributor (>25% of total impact) to impacts on ionising radiation, stratospheric ozone depletion, and respiratory effects. In addition, silicone used as insulating material in the pane contributes substantially to ionising radiation and ozone layer depletion (15 and 32%, respectively). The disposal stage is less important across all windows and impact categories, with contribution from 1 to 20% of the total impacts, depending on the impact category. Benefits are mainly due to recycling of materials, like aluminium in the W/ALU window system


Benefits are mainly due to recycling of materials, like aluminium in the W/ALU window system. Transportation is not seen as substantial for any impact category, irrespective of the window system. Substance contribution analysis. To provide further insights into the causes of environmental impacts from the window product systems, the contribution analysis was also conducted at the level of elementary flows, identifying the individual substances that cause the largest environmental burden. The analysis was carried out for the W window system only, because for most impact categories the drivers of environmental impacts are expected to the same across windows. However, differences in contributing substances between the W window and the alternative design options are also discussed, when found important for the interpretation of results. Climate change impacts are mainly driven by emissions of CO2, which contributes to 99% of the total impacts


Climate change impacts are mainly driven by emissions of CO2, which contributes to 99% of the total impacts. This contribution is mainly due to emissions from processes associated with generation of heat. Emissions of other substances from the generation of heat drive impact scores for several other impact categories. Potential impacts of photochemical ozone formation on human health are mainly due to emissions of nitrogen oxides (NOx), which account for 95% of the total impact. Note, that the current implementation of characterisation factors into the modelling software employed omits potential contribution from unspecified emissions of non-methane volatile organic compounds (NMVOC), which are also reported in life cycle inventories (see Annex, Sect. 39.4.4, Table 39.34) and would be expected to contribute to photochemical ozone formation. Ammonia (NH3), Illustrative Case Study: Life Cycle Assessment ...


nitrogen oxides (NOx) and sulphur dioxide (SO2) are the substances that dominate the acidification and eutrophication impacts in terrestrial ecosystems, whereas eutrophication impacts in freshwater and marine ecosystems are mainly due to emissions of NOx and phosphorus (P). By contrast, toxic impacts in freshwater ecosystems are dominated by emissions of metals (again, stemming mainly from processes associated with generation of heat), namely zinc (II) and copper (II). For all window systems except the PVC system, the use stage is also the main contributor to the human health impact categories (carcinogens and non-carcinogens). Again, production of heat from incineration of fossil fuels and biomass, and the associated emissions of metals, are the major contributors to human health impacts; arsenic (V) and zinc (II) emitted to freshwater drive toxic impact scores for non-cancer effects, while chromium (VI) emitted to freshwater is the major driver of cancer effects


By contrast, for the PVC window, human health impacts (cancer and non-cancer effects) are mainly driven by substances associated with production of steel in the manufacturing stage. Potential impacts on depletion of resources also vary between windows when only mineral resources are considered (e.g. impacts of the PVC window are dominated by the need for chromium in production of the PVC window frame), but altogether (combining impact scores from depletion of fossils and minerals) this impact category is dominated by the depletion of fossils. 39.3.5.2 Sensitivity and Uncertainty Checks The assumptions and choices that had to be made when modelling window systems can potentially influence conclusions from the study and they were systematically compiled in Table 39.14 of the Annex, Sect. 39.4.3. To determine the extent of this potential influence, we first identified individual parameters that are important for the results. Annex, Sect


39.4.3. To determine the extent of this potential influence, we first identified individual parameters that are important for the results. Annex, Sect. 39.4.5, Tables 39.35, 39.36, 39.37, and 39.38 gives details of normalised sensitivity coefficients. Next, we compared the baseline and the two sensitivity scenarios with all uncertain parameters perturbed at once. Thereby, we found that many of the assumptions presented in Table 39.34 did not influence the results in terms of ranking or identification of hot spots to the extent that would change our conclusions. The influence of heat loss. The parameters involved in the modelling of the heat loss compensation are important because their uncertainty can potentially change the results of the comparative part of the LCA (which window performs best?) and the results of the weak point analysis (what are the most environmentally harmful parts of the product life cycle?)


Such parameters are the modelled heat loss, the assumed heat mix, the LCI processes used to model the heat mix technologies and the relevant characterisation factors and normalisation references involved in the impact assessment. This was confirmed in sensitivity and uncertainty analyses; impact scores are the most sensitive to the U-value of the window, and furthermore this parameter is the dominant driver of difference in impact scores between the compared window systems. Indeed the differences in impact scores between W and


W/ALU windows are in most cases are not statistically significant when uncertainties in U-values are considered. The assumption about using average indoor and outdoor temperatures when calculating the heat loss was not tested in the sensitivity analysis but is not expected to change our conclusions about which window performs best as heat loss is a linear function of the temperature difference. Similarly, it would not change our conclusion about hot spots; if higher temperature difference was considered (e.g. corresponding to winter temperatures), the contribution of heat to total impact scores would increase due to higher demand for heat. The influence of materials and production. Out of all assumptions in the materials and production stages, the most important one is about modelling of chromium steel and galvanised steel using the same processes (for chromium steel)


Out of all assumptions in the materials and production stages, the most important one is about modelling of chromium steel and galvanised steel using the same processes (for chromium steel). This assumption may influence impact scores in human health (cancer effects) and freshwater ecotoxicity, where impact scores might be overestimated (because production of chromium steel is associated with toxic emissions of chromium (VI). In contrast, impacts in human health (non-cancer effects) are expected to increase if process for galvanised steel had been used, due to expected increase in emissions of toxic zinc (II). The contribution of electricity requirements in window manufacturing and disassembly is for most impact categories too small to influence our comparison, and the same is the case for assumptions on transportation distances in these life cycle stages


The exclusion of painting activity (but not production of paint) is also not expected to be important for the result, because impacts are mainly expected to stem from transportation of paint from retailer to the housing (which is small relative to other impacts from the window product systems). The influence of disposal. Assumptions about incineration and landfilling processes for some materials are not expected to influence our conclusions, given that the contribution of disposal to total impact is relatively small (10–15%, depending on the impact category). The inclusion of landfilling of copper and zinc used in window frames could potentially influence impact scores for the toxicity-related impact categories (where both copper and zinc are characterised as very toxic), but the amounts of these metals is very small compared to emissions from production of heat in the use stage


For the same reason, omitting of disposal of wood preservative and acrylic binder in the window frame is not expected to change impact scores. Comparison between the baseline and the two sensitivity scenarios. Figure 39.11 shows the comparison between the baseline scenario and the two sensitivity scenarios. When EU27 average heat mix is used (along with and EU27 electricity mix and EU27 average disposal scenarios), impact scores generally increase compared to the base scenario, apart from the three eutrophication impact categories, and freshwater ecotoxicity. This is because the European heating mix mainly relies on natural gas (57%), with smaller contribution from coal and biomass compared to the Danish mix. On the other hand, a larger proportion of natural gas and oil (57 and 21%, respectively), results in considerably higher impacts in other impact categories


On the other hand, a larger proportion of natural gas and oil (57 and 21%, respectively), results in considerably higher impacts in other impact categories. We also tested a scenario where windowpanes are changed into 3-layered ones, causing a decrease in U-value thereby improving insulation properties of the window. The results show that additional environmental impacts from the extra layer of glass are generally compensated for by the reduced heat loss in the use stage, and the Illustrative Case Study: Life Cycle Assessment ...


% of total impact score base EU27 3-layered pane W % of total impact score base EU27 3-layered pane W/ALU % of total impact score base EU27 3-layered pane PVC Climate change Stratospheric ozone depletion Photochemical ozone formation Terrestrial acidification Terrestrial eutrophication Freshwater eutrophication Marine eutrophication Freshwater ecotoxicity Human toxicity (cancer) Human toxicity (non-cancer) Particulate matter formation Ionising radiation (human health) Land use Resource depletion (mineral, fossils) % of total impact score base EU27 3-layered pane W/C


overall life cycle impact are smaller compared to the base scenario by up to 20%. High increase for stratospheric ozone depletion is most likely an artefact related to the use of relatively old processes for generation of heat from natural gas and oil in the EU27 system, since ozone-depleting substances have been largely banned for at least a decade. Despite these differences in impact scores, the ranking of window options generally does not change irrespective of the analysed scenarios (Fig. 39.12). As whiskers do not overlap, the results can be considered statistically significant, although we repeat that neither were uncertainties in background processes considered (which would increase the overall inventory uncertainty), nor could correlation between uncertainties in processes that are the same be addressed (which would have an opposite effect)


It is, however, clear that if the heat mix changes substantially within the lifetime of the window as a consequence of the decarbonisation of our energy systems, the hot spots may move from the use stage to manufacturing and end-of-life stages and this would change the ranking of the alternatives and also the recommendations for design of the windows. Uncertainties in characterisation factors and sensitivity to LCIA method chosen. All characterisation factors in ILCD (just as in any other LCIA method) are associated with uncertainties, meaning that the contribution to impacts of different modelled elementary flows and processes (such as heating) display varying uncertainties across impact categories


Although the uncertainties in characterisation factors were not considered in this study (they are rarely even known today), we expect that the uncertainty in characterisation factors will result in lack of statistical significance of difference in impact scores for freshwater ecotoxicity and human toxicity across all four windows. These are the impact categories where the uncertainties in individual characterisation factors are the highest (up to a few orders of magnitude) (Rosenbaum et al. 2008). Sensitivity of the results to the chosen LCIA methods is also not considered in this LCA report (because the results are for internal use only). Such a sensitivity analysis could reveal that window ranking generally does not change for most impact categories because it is a few processes, associated with the production of heat, that are driving the main environmental impacts and there is large difference in demand for heat between the compared windows


This is expected to be the case for climate change and acidifying and eutrophying emissions where the driving elementary flows are very similar between different impact assessment methods. However, this may not be the case for freshwater and human toxicity, where impact scores can be sensitive to the inclusion of one or few substances with high characterisation factors, depending on the method, as for these impact categories up to 12 orders of magnitude between characterisation factors are observed (Rosenbaum et al. 2008). JFig. 39.11 Comparison between the baseline and two sensitivity scenarios: (i) where EU27 electricity and heating mix and EU27 disposal options are used for each window, and (ii) where 3-layered windowpane used instead of 2-layered one. W wood, W/ALU wood/aluminium, PVC polyvinyl chloride, W/C wood/composite


W wood, W/ALU wood/aluminium, PVC polyvinyl chloride, W/C wood/composite. Whiskers represent inventory uncertainty stemming from uncertainty and variability in model parameters presented in Table 39.8 Illustrative Case Study: Life Cycle Assessment ...


39.3.5.3 Completeness and Consistency Checks Completeness check. The cut-off rules have been consistently applied across the whole life cycle for all four window alternatives in order to ensure the completeness of the study. However, two processes had to be left out when modelling life cycle inventories due either to difficulties in finding and approximating data, or they were not thought to be important initially


First, we did not include the coating of glass in the windowpane, where the current Nor-win technology uses nanomaterials because Climate change Stratospheric ozone depletion Photochemical ozone formation Terrestrial acidification Terrestrial eutrophication Freshwater eutrophication Marine eutrophication Freshwater ecotoxicity Human toxicity (cancer) Human toxicity (non-cancer) Particulate matter formation Ionising radiation (human health) Land use Resource depletion (mineral, fossils) % of total impact score W W/ALU PVC W/C Scenario 1: EU27, 2-layered pane Climate change Stratospheric ozone depletion Photochemical ozone formation Terrestrial acidification Terrestrial eutrophication Freshwater eutrophication Marine eutrophication Freshwater ecotoxicity Human toxicity (cancer) Human toxicity (non-cancer) Particulate matter formation Ionising radiation (human health) Land use Resource depletion (mineral, fossils) % of total impact score W W/ALU PVC W/C Scenario 2: DK, 3-layered pane


(non-cancer) Particulate matter formation Ionising radiation (human health) Land use Resource depletion (mineral, fossils) % of total impact score W W/ALU PVC W/C Scenario 2: DK, 3-layered pane Fig


39.12 Ranking of four window options where impact scores are scaled to those of W window (equal to 100% of total impact) for the two sensitivity scenarios presented in Fig. 39.11. W wood, W/ALU wood/aluminium, PVC polyvinyl chloride, W/C wood/composite. Whiskers represent inventory uncertainty stemming from uncertainty and variability in model parameters presented in Table 39.8


of limited information about input and output flows from nanomaterial production. This is expected to result in underestimation of human health and ecotoxicity impacts (some of the nanomaterials used by Nor-win are recognised to be toxic), and furthermore production of nanomaterials will to some extent contribute to total impact scores for other impact categories (Jolliet et al. 2014). We estimate that this contribution will not be larger than 1–2% of total impact scores for all impact categories, apart from the three toxicity-related impact categories where our rough estimate is 2.5–5% contribution. Second, we assumed no loss in material functionality in recycling of PVC (for metals and glass this assumption is expected to hold), nor did we assume material loss during recycling or production of the materials


Assuming that 10% increase in material is sufficient to cover this, total impact scores are expected to be higher by roughly 1–5%, depending on the impact category and contribution of manufacturing and disposal to total impacts. Finally, we did not include capital equipment for foreground processes. The contribution of capital equipment can be 10–30%, depending on the type of sector (Frischknecht et al. 2007). Given that contribution to overall impact from the materials and production stages is around 30% (although this number varies between windows and impact categories, see Fig. 39.10), the contribution of capital equipment is expected to be equal to ca. 10% to total impact score. Overall, we estimate that the calculated impact scores represent 75–85% of the actual total impacts. Consistency check


10% to total impact score. Overall, we estimate that the calculated impact scores represent 75–85% of the actual total impacts. Consistency check. The major source of inconsistency in data quality is the limited knowledge of performance parameters of the prototype W/C window (like the U-values), and we took this into account in the uncertainty and variability analysis. The major source of inconsistency in the applied life cycle impact assessment method is missing characterisation factors for some of the flows, due to incorrect implementation of life cycle impact assessment methods into the modelling software employed. This inconsistency is not expected to change impact scores to an extent that would change our conclusion about window ranking or major drivers of environmental impacts, since the majority of input and output flows are the same for all four windows (see Annex, Sect. 39.4.4, Table 39.34)


39.4.4, Table 39.34). Cut-off criteria were applied consistently across the four window product systems and the same processes were omitted. Other assumptions, methods and data (like the attributional principle with credits given to recycling, or the sources and quality of primary and secondary data) have also been applied consistently to all four window options. 39.3.6 Conclusions, Limitations and Recommendations Conclusions: I. The W/C window performs significantly better compared to its alternatives in all 14 impact categories. The W/C window is thus the preferable option from an environmental perspective. Illustrative Case Study: Life Cycle Assessment ...


II. The PVC window is the least preferred option, as it performs the worst in 11 out of 14 impact categories. This conclusion, however, might change if land use, freshwater ecotoxicity and human health (non-cancer) (where the W window performs significantly worse) are given a higher weight than the rest of the impact categories. III. The overall environmental performance of the windows is mainly determined by the demand for heat to compensate for heat losses through the window during its use stage. This is true for nearly all impact categories. The U-value determines demand for heat, and can thus be considered a key environmental performance indicator of windows. IV. In addition to processes for generation of heat, other environmental hotspots in the product systems are: production of timber and paint for the W window; the injection moulding process of PVC and production of steel in the PVC window. V


V. The use of glass fiber based composite has some contribution (up to 12%) to total impacts, depending on the impact category, but cannot be considered a hotspot given that the composite substantially improves insulation properties causing an overall reduction in environmental impacts. VI. Similarly, the use of 3-layered glass instead of 2-layered improves insulation properties resulting in an overall reduction in environmental impacts with the respective heating mix. VII. The trade-off between impacts from the material used and the improved insulation properties that the material may give the window has to be considered when assessing environmental performance of windows. Limitations: The major limitations of the LCA are: 1. Our findings about major drivers of environmental impacts apply to windows where crystal glass is used in the panes with a relatively large (>0.6) visible light transmittance coefficient


Our findings about major drivers of environmental impacts apply to windows where crystal glass is used in the panes with a relatively large (>0.6) visible light transmittance coefficient. They are not thought to be applicable for windows, which change their transparency in response to light intensity (e.g. photochromic windows) where the need for electricity to provide lighting indoor may become an important factor contributing to impacts in the use stage. 2. The disregard of changes in heat mix and heat demand in the future and potential development of more efficient heat supply technologies is another potential limitation. It is uncertain to what extent these will become effective within the time frame of the study (25–30 years). If such is the case, impacts from the manufacturing stage or disposal will become more important in the future (if there is no development of cleaner manufacturing and waste management technologies, which also is uncertain)


They may change both the ranking of window alternatives and recommendations given to the commissioner. We expect, however, that in a 25–30 year time horizon the use stage will likely remain the most important contributor to total impacts from the window


product system, and efforts to design windows with low U-values should continue. Recommendations: Recommendations are given to the commissioner to support eco-design of the new window and greening of the whole value chain: A. The design of windows to ensure better environmental performance should focus on optimising insulation properties of windows. This can be done by introducing a 3-layered pane, or improving the design of the frame. If the latter is considered, the choice of frame material is important and in each case where new frame material is used in the design of a frame we recommend evaluating (using tools like LCA) whether environmental benefits achieved by improved insulation properties are really sufficient to outweigh potential environmental burden from the use of novel materials


Indeed, if the heat mix changes substantially within the lifetime of the window this could potentially move the hotspots from the use stage to manufacturing and end-of-life stages in which case our recommendations for design of the windows might not hold. B. Selection of new materials for frame design should consider functional properties of materials in a window design context, i.e. the focus should not be on selection of materials that perform environmentally best per unit mass of the materials, but on selection of materials that perform best considering insulation properties and the amount applied when used in the frame. C. For the existing W-based windows, improvement potentials lie in selection of paints with lower environmental impact. For the paint applied for maintenance in the use stage, this may be outside the influence of the producer, because it is the window users who will select the type of paint


For the paint applied for maintenance in the use stage, this may be outside the influence of the producer, because it is the window users who will select the type of paint. Our recommendation is to provide information to the users about recommended types of paint. D. Finally, we recommend to phase-out the PVC window as the option with likely the highest environmental burden overall. If this is not possible, we recommend its redesign through the introduction of a 3-layered pane to improve its insulation properties. Further improvement potentials for the PVC window system lie mainly in selection of cleaner technology for production of PVC frame elements. 39.4 Annex (Public) 39.4.1 Life Cycle Impact Assessment Methods and Normalisation Factors See Table 39.11. Illustrative Case Study: Life Cycle Assessment ...


Table 39.11 ILCD methods and normalisation factors for the impact categories considered in this study (EC-JRC 2011) Impact category Indicator Unit Model reference Normalisation factor [unit/person/year] Climate change Radiative forcing as Global Warming Potential, 100 years horizon (GWP100) kg CO2 eq. Baseline model of 100 years of the IPCC 9.10E+03 Ozone depletion Ozone Depletion Potential (ODP) kg CFC-11 eq. Steady-state ODPs 1999 as in WMOassessment 2.16E−02 Human toxicity, cancer effects Comparative Toxic Unit for humans CTUh USEtox model (Rosenbaum et al. 2008) 3.68E−05 Human toxicity, non-cancer effects Comparative Toxic Unit for humans CTUh USEtox model (Rosenbaum et al. 2008) 5.32E−04 Particulate matter Intake fraction for fine particles kg PM2.5 eq. RiskPoll model (Rabl and Spadaro 2004) and Greco et al. (2007) 4.82E+00 Ionising radiation (human health) Human exposure efficiency relative to U235 kg U235 eq. Human health effect model as developed by Dreicer et al


(2007) 4.82E+00 Ionising radiation (human health) Human exposure efficiency relative to U235 kg U235 eq. Human health effect model as developed by Dreicer et al. (1995), Frischknecht et al. (2000) 1.13E+03 Photochemical ozone formation Tropospheric ozone concentration increase kg NMVOC eq. LOTOS-EUROS (Van Zelm et al. 2008) as applied in ReCiPe 3.18E+01 Acidification Accumulated Exceedance mol H+ eq. Accumulated exceedance (Seppälä et al. 2006; Posch et al. 2008) 4.72E+01 Terrestrial eutrophication Accumulated Exceedance mol N eq. Accumulated exceedance (Seppälä et al. 2006; Posch et al, 2008) 1.74E+02 Freshwater eutrophication Residence time of nutrients in freshwater compartment (P) kg P eq. EUTREND model (Struijs et al. 2009) as implemented in ReCiPe 1.48E+00 Marine eutrophication Residence time of nutrients in marine compartment (N) kg N eq. EUTREND model (Struijs et al. 2009) as implemented in ReCiPe 1.68E+01 (continued)


Table 39.11 (continued) Impact category Indicator Unit Model reference Normalisation factor [unit/person/year] Freshwater ecotoxicity Comparative Toxic Unit for ecosystems CTUe USEtox model (Rosenbaum et al. 2008) 8.71E+03 Land use Soil Organic Matter kg C deficit Model based on Soil organic matter (SOM) (Milà i Canals et al. 2007) 6.30E+05 Water resource depletion Water use related to local scarcity of water kg water eq. Model for water consumption as in Swiss Ecoscarcity (Frischknecht et al. 2006) 7.89E+01 Mineral fossil and renewable resource depletion Scarcity kg Sb eq. CML 2002 (Guinée et al. 2002) 1.00E−01 Normalisation factors are for EU27 for the reference year 2010 as presented in Benini et al. (2014) Illustrative Case Study: Life Cycle Assessment ...


39.4.2 Bills of Materials and End-of-Life Options See Tables 39.12 and 39.13. Table 39.12 Amounts of materials (in kg) required to produce one window Material Window type W W/ALU PVC W/C Window frame Heartwood 9.2 – 9.2 Polyvinyl chloride (PVC) – – – Composite – – – 3.9 Aluminium 0.2 4.6 – – Galvanised steel – – – Chromium steel 0.5 1.2 5.1 1.2 Acrylic binder 0.168 0.056 – 0.056 Triethylene glycol 0.00427 0.00142 – 0.00142 Wood preservative 0.000525 0.000175 – 0.000175 Window pane Glass Aluminium 0.4 0.4 0.4 0.4 Argon 0.06 0.06 0.06 0.06 Synthetic rubber (EDPM) 3.6 3.6 3.6 Silicone 1.4 1.4 1.4 1.4 Window packaging Polyethylene 0.2 0.2 0.2 0.2 Cardboard Note, that the amounts are not scaled to the functional unit


Table 39.13 End-of-life options for window materials (percentage recycled/incinerated/landfilled) in Denmark and EU27 (given in brackets) ID Material Window type W W/ALU PVC W/C Window frame Heartwood DK: 90.5/9/0.5 EU27: 47/52/1 Not relevant DK: 90.5/9/0.5 EU27: 47/52/1 Polyvinyl chloride (PVC) Not relevant Not relevant DK: 93/5/2 EU27: 76/17/7 Not relevant Composite Not relevant Not relevant Not relevant DK: 93/5/2 EU27: 76/17/7 Aluminium DK: 99.987/0.01/0.003 EU27: 99.885/0/0.115 Not relevant Not relevant Steel (galvanised and chromium) DK: 99.987/0.01/0.003 EU27: 99.885/0/0.115 Window pane Glass 99.853/0.018/0.129 Aluminium DK: 99.987/0.01/0.003 EU27: 99.885/0/0.115 Synthetic rubber (EDPM) 99.39/0.604/0.06 Silicone 99.39/0.604/0.06 Window packaging Polyethylene DK: 93/5/2 EU27: 76/17/7 Cardboard DK: 99.32/0.59/0.09 EU27: 98.73/0.93/0.34 Data from Eurostat (2016) for the reference year 2014


The options are based on the data retrieved from Eurostat for the categories: metal wastes (mixed ferrous and non-ferrous), glass wastes, paper and cardboard wastes, rubber wastes, plastic wastes, and wood wastes Illustrative Case Study: Life Cycle Assessment ...


39.4.3 List of Assumptions See Table 39.14