eberle, annika - final report - tiny house...

Annika Eberle University of Washington

ME 515: Winter 2015

Life Cycle Assessment of a Tiny House

ME 515 Final Report – Tiny House LCA

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Table of Contents Goal & Scope .......................................................................................................................... 3 Motivation ............................................................................................................................................................................................... 3 Literature Review ................................................................................................................................................................................ 4 Goal ............................................................................................................................................................................................................ 5 Scope ......................................................................................................................................................................................................... 5 Geographic Specificity ........................................................................................................................................................................ 5 Function & Functional Unit ............................................................................................................................................................. 5 Description of Core Production & System Boundaries ......................................................................................................... 5 Criteria for Inclusion of Inputs & Outputs ................................................................................................................................. 6 Reference Flows .................................................................................................................................................................................... 7 Impact Categories & Assessment Methodology ....................................................................................................................... 7

Inventory Analysis .................................................................................................................. 7 Data Quality Requirements ............................................................................................................................................................. 7 Data Collection Procedures & Validation of Data .................................................................................................................. 8 Qualitative Descriptions of the Unit Processes .................................................................................................................... 11 Identification & Allocation of Co-‐Products ............................................................................................................................ 15 Description of LCA Methodology ............................................................................................................................................... 15 Sensitivity Analysis for Refining System Boundary ........................................................................................................... 16 Assumptions & Limitations .......................................................................................................................................................... 16 Type of Critical Review ................................................................................................................................................................... 16 Impact Assessment ............................................................................................................... 16 Classification ....................................................................................................................................................................................... 16 Characterization ................................................................................................................................................................................ 16 Normalization ..................................................................................................................................................................................... 17 Interpretation ....................................................................................................................... 18 Evaluation of Findings .................................................................................................................................................................... 18 Comparison to Prior Studies ........................................................................................................................................................ 20 Identification & Evaluation of Significant Issues ................................................................................................................ 22 Data quality ......................................................................................................................................................................................... 22 Completeness ....................................................................................................................................................................................... 22 Sensitivity .............................................................................................................................................................................................. 23 Consistency ........................................................................................................................................................................................... 24

Recommendations ................................................................................................................ 24 References ............................................................................................................................ 26 Appendices ........................................................................................................................... 29 Appendix 1: Estimated List of Building Materials .............................................................................................................. 29 Appendix 2: Tiny House Energy Requirements ................................................................................................................... 35 Appendix 3: Hierarchical List of Unit Processes ................................................................................................................. 35 Appendix 4: Characterization Factors ..................................................................................................................................... 36 Appendix 5: Technology Matrix for Denver Tiny House ................................................................................................. 37 Appendix 5: Technology Matrix for Seattle Tiny House .................................................................................................. 39 Appendix 6: Intervention Matrix ............................................................................................................................................... 41 Appendix 7: Denver Demand Vector, Scaling Vector, and Result Vector ................................................................. 43 Appendix 8: Seattle Demand Vector, Scaling Vector, and Result Vector .................................................................. 46


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Goal & Scope

Motivation

In 2010, energy used by residential buildings accounted for approximately 20% of the primary energy consumed by the U.S. (U.S. Energy Information Administration [US EIA], 2011). To reduce consumption, the tiny house movement advocates living in small spaces – often composed of only a few hundred square feet (see Figure 1). The movement has gained traction recently and has received a surprising amount of media attention in the past few years (Sheam, 2011; Wilkinson, 2011; Wax, 2012; Manetti, 2013; Speak Thunder Films, 2013; Tortorello, 2014; Lundahl, 2014). While tiny homes are often admired for their ability to reduce the financial burden associated with home ownership, they are also promoted for environmentally conscious living. However, the actual environmental impact of a tiny house is unknown. Thus, this study attempts to assess the life-cycle impact of a tiny house, specifically with regard to its contribution to climate change, acidification, and ozone depletion.

Figure 1. The Tumbleweed Cypress 24 tiny house (http://www.tumbleweedhouses.com/).


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Literature Review

The average size of single-family homes in the U.S. has doubled since the 1950s (Wilson & Boehland, 2005) and numerous life cycle assessment (LCA) tools have been developed to quantify the impact of residential structures spanning from North America to Europe (Adalberth, 1997a; Peuportier, 2001; Zabalza Bibian, Aranda Uson, & Scarpellini, 2009; Blengini & Di Carlo, 2010). Adalberth was one of the first to implement a cradle-to-grave LCA of residential buildings (1997a) and used this method to compare the environmental impact of three different homes in Sweden (1997b). However, since this work was performed in the early years of LCA it did not clearly identify a functional unit or give a detailed description of the scope.

Since the early 1990s, numerous LCA studies have been performed on residential buildings around the world. For example, Blanchard and Reppe (1998) evaluated both the life-cycle global warming potential and the life-cycle cost of two comparable homes in Michigan: traditional and energy-efficient. They employed a functional unit that included a list of over ten qualifications, including usable floor area, occupancy, life span, style, thermal comfort, and indoor air quality. Their results showed that the energy-efficient house had a life-cycle greenhouse warming potential that was three times smaller than the traditional home (Keoleian, Blanchard, & Reppe, 2001). In addition, they found that the use phase accounted for more than 80% of the global warming potential associated with both houses.

Similar results were also found when 60 cases from nine countries were compared to evaluate the performance of traditional, self-sufficient solar, and energy-efficient houses (Sartori & Hestnes, 2007). For a traditional home, building use and maintenance play a large role in the environmental impact of the structure (Sartori & Hestnes, 2007). However, in contrast to the study by Keoleian, Blanchard, & Reppe, for energy-efficient homes the embodied energy of the construction materials dominated because the energy consumed during the use phase decreased substantially. For example, a study of low energy homes in Italy revealed that the shell-embedded materials represented the largest contribution to global warming potential (Blengini & Di Carlo, 2010). The functional unit employed in this study was 1 m2 of usable floor area per year and the scope included pre-use and maintenance, use, and end-of-life. The main components included in the inventory were shell components (e.g. windows, roof, walls, insulation), plants (e.g. heating, ventilation, water), and use phase (e.g. cooking, washing, heating). Similar functional units and project scopes were consistently used across several other studies (Lippke, Wilson, Perez-Garcia, Bowyer, & Meil, 2004; Monahan & Powell, 2011; Dahlstrom, Sornes, Eriksen, & Hertwich, 2012; Nässén, Hedenus, Karlsson, & Holmberg, 2012).

While geographic specificity, even within the U.S., appeared to have a substantial impact on some subsystems, it often had little impact on the overall life cycle of residential buildings. For example, Lippke et al. (2004) showed that substituting a wood frame for a concrete frame resulted in an 80% decrease in global warming potential for the above-grade walls in a house in Atlanta, while the same change only resulted in a 33% reduction in Minneapolis. However, when the overall differences were compared for the wood-framed and steel-framed structures as a whole, they showed comparable amounts of energy savings in both locations, approximately 15% overall. This result is likely a consequence of the relatively large importance of the use phase as compared to the manufacture of the shell components. Nevertheless, it also highlights the possibility that climate may play a significant role in the relative importance of building materials in the production phase as compared to energy during the use phase.


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Goal

The goal of this project is to use LCA methodologies to evaluate the cradle-to-grave environmental impact of a prototypical tiny house. The intended audience includes private citizens looking to purchase or build tiny houses and tiny house manufacturers looking to design and promote tiny house products. The intended application is to provide these audiences with information about the environmental impact of living in a tiny house and improve the development of future tiny houses. Thus, the results of this study will serve to inform the public and may also be used for marketing purposes.

Scope

The product of this study is a building that is used for year-round dwelling purposes and consists of one or more rooms along with a cooking area. The scope of this project will be limited according to the following criteria.

Geographic Specificity

The geographic region of this study will be focused on two locations: a severe winter climate similar to Denver, Colorado and a more temperate location, like Seattle, Washington.

Function & Functional Unit

The function of the product is to house people (i.e. to provide shelter from the elements, to allow for the preparation of food, to permit comfortable sleeping quarters, and to provide toilet facilities). The functional unit for this study is a comfortable individual residential dwelling of 170 square feet with a lifespan of 50 years. According to the Product Category Rules for buildings, I will report the overall environmental impact and the environmental impact per square meter of temperature-controlled space, called Atemp (Environmental Product Declarations [EPD], 2014).

Description of Core Production & System Boundaries

In order to determine the amount and type of materials needed for the construction of a prototypical tiny house, I contacted several tiny house manufactuers. The Tumbleweed Tiny House Company was kind enough to provide a bill of materials for their Cypress 24 model (Tumbleweed, personal communication, February 6, 2015; see Appendix 1). The mass of each material on the list was estimated using information compiled from online building suppliers (e.g. http://www.homedepot.com) and was incorporated into the production model.

The system boundaries for the model of a tiny house life cycle include the extraction of raw materials, manufacture of building components, transportation of materials to the site, occupation and maintenance of the house, and disposal/recycling of the product (see Figure 2). The co-products include recyclable material at end-of-life. Core production does not include the


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labor and tools required for the construction and deconstruction of the tiny house, nor the transportation to the landfill.

Figure 2. A process flow chart showing the system boundary used in this project.

Criteria for Inclusion of Inputs & Outputs

Due to the time constraints associated with completing this study within the 10-week course period, the inputs and outputs to the production model were limited according to a mass-based cut-off rule. Inclusion was required for those inputs that contributed to more than 1% of the mass input of the product system being modeled. If an input did not contribute to more than 1% of the total mass of the system, then the input was excluded. In future work, the environmental impacts associated with the excluded inputs should be examined to ensure none of these items have a large impact on the results.

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Reference Flows

The reference flows include all materials that comprise more than 1% of the mass required to construct a tiny house, all energy required for house use (e.g. heating, cooling, cooking), and all materials needed to maintain the house. Energy requirements for the house were obtained from two tiny house owners and one manufacturer (see Appendix 2). By compiling the information from the mass-based assessment of material contribution and the energy requirements, the life cycle of a tiny house was found to include the following reference flows (Table 1). When subject to the 1% cut-off rule, the material flows for tiny house construction accounted for 92% of the mass of materials needed to construct the house. The corresponding hierarchical list of unit processes is provided in Appendix 3.

Table 1. The reference flows for a prototypical tiny house over a 50 lifespan.

Material Units Quantity Steel girts and purlins (for trailer foundation) lb 8000 Plywood ft2 1150 Softwood m3 1.40 Window m2 6.32 Cork flooring ft2 1.75 Cedar siding ft2 9.28 Wood framing ft2 8.33 Electricity for appliances kWh Varies Propane for heating and cooking lb Varies Transport of all materials to site tkm Varies

Impact Categories & Assessment Methodology

As per the Product Category Rules for Buildings, emissions of greenhouse gases and ozone-depleting gases appear to be the top two default environmental impact categories for building life cycles (EPD, 2014). In order to quantify the impacts on climate change and ozone depletion, I will use global warming potentials (in kg CO2 equiv.) and ozone depletion potentials (in kg CFC-11 equiv.), respectively, to scale inventory flows for each impact of interest. I will also examine the impact of the house on acidification via acidification potentials (in kg H+

equiv.). The characterization factors for each of these three impact categories were extracted from the Institute of Environmental Sciences, Leiden University (2015) (see Appendix 4).

Inventory Analysis

Data Quality Requirements

The data used in this project was evaluated using a two-tiered quality analysis method (Cooper and Kahn, 2012). This method uses seven categories, including reliability, completeness, uncertainty, and precision, to designate data as either of quality A or B. To


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achieve a score of A, the data must adhere to the requirements outlined in Table 2. Otherwise, the data are of quality B.

Table 2. Requirements for data to achieve a quality score of A (reproduced from Cooper and Kahn (2012)).

Data Collection Procedures & Validation of Data Due to the time constraints for this project, the data for unit processes were gathered from a variety of sources. A summary of the data quality scores for each unit process is provided in Table 3. While most of the data only achieve a score of B, there are a few exceptions where the reproducibility, geographical coverage or technical coverage of a few data sources is of higher quality. Specifically, flow data obtained from the Building for Economic and Environmental Sustainability (BEES) software, developed by the National Institute of Standards and Technology, were estimated using a specified and standardized measurement method. While some of these data were compiled specifically for proprietary materials (like Natural Cork used in this study), generic building materials, like wood framing, Cedar siding, and plywood were also available. As a result they are technologically relevant. Furthermore, data obtained from BEES are also geographically relevant for the United States. Similarly, data for softwood, obtained from the Consortium for Research on Renewable Industrial Materials (CORRIM) were estimated using specified and standardized measurement methods and are technologically and geographically relevant to the study. Finally, data obtained for wind (Dolan & Heath, 2012) and hydropower (Kumar et al., 2011) were also evaluated using specified and standardized methods, are technology-specific, and include uncertainty information.


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Additionally, the data flows associated with product transport are geographically specific. They were calculated for each product type using data from the US Census Bureau (http://factfinder.census.gov) with specific distance ranges for transport from the geographic region of manufacture to the site of tiny house construction (Denver, CO or Seattle, WA) approximated via Google Maps (https://www.google.com/maps) (Tables 4 and 5). As suggested by Cooper, Woods, and Lee (2008), backhaul factors for truck and rail transport were assumed to be 100% and 30%, respectively.

Table 3. Data quality scores for tiny house unit processes according to criteria from Cooper and Kahn (2012) (see Table 1). Data sources include: Tumbleweed (http://www.tumbleweedhouses.com), USDB (https://www.lcacommons.gov/nrel/search), CORRIM (http://www.corrim.org), GREET (https://greet.es.anl.gov), BEES (http://www.nist.gov/el/economics/BEESSoftware.cfm), and US Census Bureau (http://factfinder.census.gov/).

QUALITY CATEGORY

Unit Process Data Source 1 2 3 4 5 6 7 tiny house, geographic region of interest Tumbleweed, 2014 B B B A A B B steel girts and purlins USDB B B B A B B B plywood BEES A B B A A B B softwood CORRIM A B B A A B B windows Salazar & Sowlati, 2008 B B B A A B B cork flooring BEES A B B A A B B cedar siding BEES A B B A A B B wood framing BEES A B B A A B B tiny house foundation (trailer) Assumed 100% steel B B B A B B B propane, at local store GREET B B B A B B B propane, residential use US EPA, 1995 B B B A B B B electricity mix, geographic region of interest US EIA, 2014 B B B A A B B energy for heat, geographic region of interest Personal communication B B B A B B B electricity, natural gas, at plant USDB B B B A A B B electricity, bituminous coal, at plant USDB B B B A A B B electricity, wind Dolan & Heath, 2012 A B B B A A B electricity, hydropower Kumar et al., 2011 A B B B A A B steel transport, geographic region of interest US Census Bureau B B B A B B B plywood transport, geographic region of interest US Census Bureau B B B A B B B softwood transport, geographic region of interest US Census Bureau B B B A B B B window transport, geographic region of interest US Census Bureau B B B A B B B cork transport, geographic region of interest US Census Bureau B B B A B B B cedar siding transport, geographic region of interest US Census Bureau B B B A B B B wood framing transport, geographic region of interest US Census Bureau B B B A B B B transport, truck, diesel powered USDB B B B A A B B transport, rail, diesel powered USDB B B B A A B B softwood waste in landfill US EPA, 2014 B B B A A B B


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Table 4. Transport distances for Denver, CO tiny house components.

Table 5. Transport distances for Seattle, WA tiny house components.

In order to get an approximate sense for the energy needs of a tiny house, I contacted

several tiny house experts. A summary of the responses regarding tiny house energy requirements is provided in Appendix 2. Most tiny house dwellers use a combination of propane and electricity to supply for their energy needs: propane for space heating, cooking, and water heating, and electricity for other appliances and lighting. While some tiny house dwellers use solar arrays for their electrical needs, most connect their houses directly to the grid instead. As expected, the energy requirements for a tiny house vary by geographic region and are correlated with the climate zone in which the house resides. In order to compare tiny houses in Denver, CO and Seattle, WA, I used the average values for fuel and electricity that corresponded to the data that I obtained for their respective climate zones (Table 6). It is important to note that these data do not correspond to these exact locations. Instead, they were computed using surrogate data from other places with similar climate zones (see Appendix 2).

Table 6. Tiny house energy requirements in two geographic regions: Seattle, WA and Denver, CO.

Seattle, WA Denver, CO Climate zone Moderate winter Severe winter Propane (lb./sq. ft./person) 0.22 0.55 Electric grid (kWh/sq. ft./person) 0.62 1.84 Propane (mmBtu/functional unit) 820 2,000 Electric grid (kWh/functional unit) 5,200 16,000

Unit Process Weight

per process unit (t)

Avg. transport distance Transport method Transport total

Truck (mi) Rail (mi) Truck (%) Rail (%) Truck (t-km) Rail (t-km) Steel transport 0.000454 562 180 25 75 0.20 0.13 Plywood transport 0.000556 200 350 40 60 0.14 0.24 Softwood transport 0.4 200 350 40 60 103 176 Window transport 0.00971 308 0 100 0 5.94 0.00 Cork flooring transport 0.000227 200 350 40 60 0.06 0.10 Cedar siding transport 0.000529 200 350 40 60 0.14 0.23 Wood framing transport 0.00859 200 350 40 60 2.21 3.77

Unit Process Weight

per process unit (t)

Avg. transport distance Transport method Transport total

Truck (mi) Rail (mi) Truck (%) Rail (%) Truck (t-km) Rail (t-km) Steel transport 0.000454 562 180 25 75 0.20 0.13 Plywood transport 0.000556 750 650 40 60 0.54 0.45 Softwood transport 0.4 750 650 40 60 386 326 Window transport 0.00971 308 0 100 0 5.94 0.00 Cork flooring transport 0.000227 750 650 40 60 0.22 0.19 Cedar siding transport 0.000529 750 650 40 60 0.51 0.43 Wood framing transport 0.00859 750 650 40 60 8.29 7.01


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Qualitative Descriptions of the Unit Processes

Reference flows for the construction, operation, and maintenance of the tiny house are outlined in the Goal & Scope (see Table 1). The foundation of the tiny house was assumed to be a 24-foot steel trailer composed of 100% steel girts and purlins (secondary structural steel products). The unit process for steel girts and purlins production was obtained from the USDB and was collected from the “cradle-to-gate” LCI from the World Steel Association, which excludes end of life recycling of the steel. Thus, the steel LCI data used in this study only represent the extraction of resources and the production of steel products to the steelworks’ gate (World Steel Association, 2014). These data were aggregated from ten Metal Building Manufacturers Association members (25% of steel producing establishments).

Data for four of the other main materials used to construct the tiny house – plywood, wood framing, cork flooring, and Cedar siding – were obtained from BEES. As such, they include transport for all material production requirements, the life cycle of nails needed for construction, the preliminary and maintenance requirements for primers and stains, the electricity production needed for producing nails, and the life cycle of harvesting wood products (see Figures 3-6 for detailed system diagrams used by BEES). Except Cedar siding, which was assumed to only have a 40-year lifespan, all other products obtained from BEES achieved the 50-year lifespan of the functional unit. To account for the additional Cedar siding needed, this reference flow was increased by 25% (assuming open loop recycling at the end of life).

The reference flows associated with the life cycle of softwood and the life cycle of residential windows were obtained from literature sources. The system boundary and data sources associated with a 75-year life cycle of a residential window are reproduced from Salazar and Sowlati (2008) in Figure 7. As shown, the unit process data include milling and finishing of the wood inputs, sealing and assembling the unit, maintaining the unit, and demolishing the unit. The life cycle of softwood was obtained from the cradle-to-gate LCA of Pacific Northwest softwood in the CORRIM database (Puettmann et al., 2013). This softwood LCA includes the regeneration of forest, harvest and transportation of logs, and lumber manufacturing (see Figure 8). Unlike the data obtained from BEES, CORRIM, data for these three unit processes do not include construction materials (e.g. nails) or end of life disposal.

Finally, the reference flows for the use phase include the life cycle of electricity in the geographic region of interest, the life cycle of propane available for purchase at a local store, and the life cycle of propane combusted for heating and cooking. The mix of energy sources used to generate electricity in Seattle and Denver were evaluated according to data from the Energy Information Administration (US EIA, 2014). Data for the life cycle of each associated energy source were acquired from several sources. The life cycles of electricity generated at power plants using natural gas and coal were acquired from the US LCI Database (https://www.lcacommons.gov/nrel/search). The life cycle of wind power was evaluated according to the National Renewable Energy Laboratory harmonization study performed by Dolan & Heath (2012) and the life cycle of hydropower was based on the work done by Kumar et al. (2011). The life cycle of propane at local stores was determined using the Greenhouse Gasses, Regulated Emissions, and Energy Use in Transportation model (GREET, https://greet.es.anl.gov). Finally, the life cycle of propane combusted at residential homes was based on work done by the US EPA (1995), which assumes an 85% conversion efficiency.


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Figure 3. A process flow chart showing the system boundary used for Cedar siding production (see BEES documentation, http://www.nist.gov/el/economics/BEESSoftware.cfm).

Figure 4. A process flow chart showing the system boundary used for cork flooring production (see BEES documentation, http://www.nist.gov/el/economics/BEESSoftware.cfm).


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Figure 5. A process flow chart showing the system boundary used for plywood production (see BEES documentation, http://www.nist.gov/el/economics/BEESSoftware.cfm).

Figure 6. A process flow chart showing the system boundary used for wood framing production (see BEES documentation, http://www.nist.gov/el/economics/BEESSoftware.cfm).


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Figure 7. A process flow chart showing the system boundary used for the production of an aluminum-‐clad wood window (from Salazar & Sowlati, 2008).

Figure 8. A process flow chart showing the system boundary used for the production of softwood (from Puettmann et al., 2013).


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Identification & Allocation of Co-‐Products Most of the unit process data used in this analysis deal with co-products internally evaluated by implementing system expansion, mass allocation, or closed loop recycling. As a result, I assume that there are only two co-products associated with the tiny house system: softwood waste from deconstruction and trailer recycling after use. For softwood waste, I assumed that it would be sent to a landfill after the deconstruction. I chose this approach so as to be consistent with the other data that I obtained from BEES, where the wood waste is assumed to go into a landfill after use. For the trailer recycling, I assumed 60% of the steel could be used in closed-loop recycling (i.e. 60% of the steel would be of high enough quality at the time of disposal that it could replace virgin steel girts and purlins) and the remainder would go to open loop recycling.

I modeled the landfilling of the wood using the EPA’s Waste Reduction Model (WARM) for wood products (US Environmental Protection Agency [US EPA], 2014). Using this method, the end-of-life emissions include: 1) the emissions associated with transport to the landfill, 2) the emissions associated with the landfill machinery, 3) the offsets due to carbon storage potential, and 4) the offsets due to energy recovery from landfill methane capture. While WARM does not account for the CO2 emissions from the wood as it degrades in a landfill, it does account for the methane and it assumes that the landfill is able to capture the methane and use it to offset energy production. While I do not completely agree with all of these assumptions, the results from WARM are sufficient for this study and indicate that the main component of the net emissions of wood products in landfills is the wood’s carbon storage capacity. Thus, according to WARM, the result of putting wood into a landfill is a negative emission (carbon sink) due to the carbon storage capacity of the wood.

Description of LCA Methodology

To perform the life cycle analysis, I followed the methodology of computational life cycle assessment described by Heijungs & Sangwon (2002). This approach models inventory analysis using the tools of linear algebra. A process matrix, P, describes the resource flows associated with all unit processes in a product’s life cycle, from materials extraction through disposal and recycling. The process matrix is subdivided into two separate matrices, A and B, where A represents the resource flows within the technosphere (or economic systems) and B represents flows to and from the environment (see Appendices 5 and 6 for the resource flows within the technosphere (the technology matrix, A) and the flows to and from the environment (the intervention matrix, B)).

Due to the allocation of co-products with closed-loop recycling, the pseudoinverse must be used to solve the system. Once the desired performance of the system is specified by a demand vector, f, the scaling, s, of the resource flows needed to achieve such performance is calculated using the pseudo-inverse of A, such that s = (ATA)-1ATf. The resulting inventory of environmental flows, g, associated with a given demand vector, f, can then be calculated from the matric of environmental flows via g = Bs. The demand vector, scaling vector, and result vector can all be found in Appendix 7.


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Sensitivity Analysis for Refining System Boundary

Based on my preliminary analysis, propane refining accounts for ~18% of the tiny house’s contribution to climate change. As a result, it would be interesting to consider how changes to the propane model might impact the system. In particular, I would like to understand how sensitive the system boundary is to aspects of propane production. Details about the influence of propane refining is provided in the Sensitivity subsection in the Interpretation section of this report.

Assumptions & Limitations Due to time constraints, the following system components are not included in this study:

• Capital equipment and labor needed for construction • Land use requirements • Furniture or large household appliances • Miscellaneous materials and additives (pigments, etc.) • Transport to landfill

Type of Critical Review This project was completed as part of a 10-week graduate course (ME 515) at the University of Washington. Critical review of this project included feedback from the course instructor, Dr. J.S. Cooper, on interim and final reports, along with comments from peers on an oral presentation.

Impact Assessment

Classification

As described in the Goal & Scope, the emissions associated with the life cycle of a tiny house will be classified according to their contribution to climate change, ozone depletion, and acidification. The climate change and ozone depletion categories were chosen based on the Product Category Rules for Buildings (EPD, 2014), and the assessment was extended to include acidification.

Characterization

The inventory flows corresponding to each impact of interest were scaled according to the characterization factors and converted into the common unit of each category indicator: global warming potential (impact on climate change in kg CO2 equiv.), ozone depletion potential


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(impact on ozone depletion in kg CFC-11 equiv.), or acidification potential (impact on acidification in kg H+ equiv.). The characterization factors for each of these three impact categories were extracted from the Institute of Environmental Sciences, Leiden University (2015) and are provided in Appendix 4.

Due to time constraints and the need to compile data from multiple sources, not all of the substances in each impact category are included in this analysis. The impact on climate change is limited to methane, carbon dioxide, nitrous oxide, trichloro- and trifluoro-ethane, and dichlorodifluoromethane. The impact on acidification is limited to sulfur and nitrogen oxides. And, finally, the impact on ozone depletion is limited to tetrachloromethane, Dichlorodifluoromethane, trichloro- and trifluoro-ethane, and dichloro- and tetrafluoro-ethane. The global warming potentials are based 100-year factors from the Intergovernmental Panel on Climate Change (IPCC, 2007). The ozone depletion potentials are based on the steady state total from the World Meteorological Organisation (WMO, 2002), and the acidification potentials are based on the total European average (Huijbregts, 1999a; Huijbregts, 1999b).

Results from the characterization of inventory flows to impact category indicators for both the Denver and Seattle models are shown in Table 7. As per the Product Category Rules for buildings, the impact is also reported per Atemp (i.e. square-foot of temperature controlled space).

Table 7. Environmental impact of tiny houses in Denver and Seattle for three impact categories.

Denver Tiny House Seattle Tiny House Category Unit Value Value per Atemp Value Value per Atemp Climate change kg CO2 equiv. 173,000 1020 68,900 410 Ozone depletion kg CFC-11 equiv. 0.000419 0.00000246 0.000419 0.00000246 Acidification kg H+ equiv. 11,500 68 4,480 26

Normalization

In order to compare the results across impact categories, the category indicator results were normalized according to annual emissions from U.S. sources. The normalization parameters used in this analysis (Table 8) correspond with those outlined by Bare, Gloria, and Norris (2006). They were computed using a U.S. dataset and include the total national emissions associated with each impact category per capita per year.

Table 8. Normalization parameters used to compare environmental impacts across categories.

The normalization results indicate that the contribution to climate change is the most substantial; the 50-year life cycle of a tiny house is about 3-7 times larger than the total U.S. emissions per year per capita (see Figure 9). The impact of the life cycle of a tiny house on ozone depletion and acidification is much smaller, approximately 1 and <0.1 of U.S. emissions per year per capita, respectively.

Impact Category Normalization Unit Normalization Factor Climate change kg CO2 equiv./yr./capita 24,500

Ozone depletion kg CFC-11 equiv./yr./capita 0.311 Acidification kg H+ equiv./yr./capita 7,440


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Figure 9. Environmental impact of Seattle and Denver tiny houses for three impact categories, normalized by the total U.S. emissions per year per capita for each category indicatory.

Interpretation

This section presents the interpretation of the results of the life cycle assessment of a tiny house with a 50-year lifespan. It is important to note that these results are based on a relative approach and only provide information about potential environmental effects on category midpoints (e.g. climate change). As a result, they do not correspond to actual environmental impacts on category endpoints, like human health, ecosystem health, or biodiversity.

Evaluation of Findings In order to determine which processes contribute most to the environmental impact of a tiny house, I performed a contribution analysis. The life cycle stages of the tiny house include 1) production (i.e. materials extraction and manufacture of the components required to build the house, 2) operation (i.e. the fuel and electricity needed to operate the house, 3) the transport of all of the materials to the site of construction, and 4) the end-of-life disposal of the components. After breaking down the life cycle of the tiny houses (Denver and Seattle) into these four life cycle stages, I then computed the percentage contribution of each stage to the overall impact for each impact category (Tables 9 and 10).

Table 9. Percentage contribution of LCI outputs to life cycle stage, Denver Tiny House.

LCI Outputs Production Operation Transport End of Life Climate change 2.5% 96.6% 0.6% 0.3% Acidification 4.6% 91.7% 3.7% 0.0% Ozone depletion 100.0% 0.0% 0.0% 0.0%

0

2

4

6

8

Denver Seattle Denver Seattle Denver Seattle

Normalized im

pact

(U.S. resident equiv. per year)

Climate Change Ozone Depletion Acidi<ication

End of life

Transportation

Electricity

Propane combustion

Propane, at reainery

Housing materials


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Table 10. Percentage contribution of LCI outputs to life cycle stage, Seattle Tiny House.

LCI Outputs Production Operation Transport End of Life Climate change 6.3% 92.4% 0.5% 0.3% Acidification 11.8% 84.2% 4.0% 0.0% Ozone depletion 100.0% 0.0% 0.0% 0.0%

These results indicate that even though there are differences in climate between Denver

and Seattle, the operation of the house is the major contributor to climate change and acidification in both locations. The large contribution of the use phase is not surprising given the results of prior residential housing LCA studies and the large amount of energy associated with operation of the house over a 50-year period. Conversely, this analysis shows that the production of the house is the primary contributor to ozone depletion. However, this result may not be very reliable because of inconsistent data quality for ozone depleting emissions associated with the other life cycle stages.

Still, there are several processes that reside within each of these four life cycle stages. Since the main stages of interest are production and operation, I chose to expand my contribution analysis on these two areas (see Tables 11-14). The percentage contribution of the LCI components to the production of the Denver and Seattle houses is provided in Tables 11-12. In both cases, the contribution of the production materials is quite small for climate change and acidification and very large for ozone depletion. However, there are several differences between the houses that manifest in the operation phase (Tables 13 and 14). Due to climate and electricity differences in Denver and Seattle, the operation of the houses has different component contributions for each of these two locations.

Table 11. Percentage contribution of LCI inputs/outputs in manufacturing life cycle stage, Denver.

Production, Denver Tiny House LCI Outputs Steel Plywood Softwood Windows Cork

flooring Cedar siding

Wood framing

Climate change 0.0% 0.3% 0.0% 1.7% 0.0% -0.2% 0.6% Acidification 0.0% 1.0% 0.0% 0.2% 0.3% 2.1% 1.0% Ozone depletion 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0%

Table 12. Percentage contribution of LCI inputs/outputs in manufacturing life cycle stage, Seattle.

Production, Seattle Tiny House LCI Outputs Steel Plywood Softwood Windows Cork

flooring Cedar siding

Wood framing

Climate change 0.0% 0.7% 0.0% 4.3% 0.1% -0.4% 1.6% Acidification 0.0% 2.6% 0.0% 0.6% 0.7% 5.3% 2.6% Ozone depletion 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0%


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Table 13. Percentage contribution of LCI inputs/outputs in operation life cycle stage, Denver.

Operation, Denver Tiny House LCI Outputs Propane, at store Propane,

residential use Natural gas, at power plant

Coal, at power plant Wind power Hydropower

Climate change 15.5% 73.3% 1.3% 6.4% 0.0% 0.0% Acidification 35.0% 44.9% 0.7% 11.1% 0.0% 0.0% Ozone depletion 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Table 14. Percentage contribution of LCI inputs/outputs in operation life cycle stage, Seattle.

Operation, Seattle Tiny House LCI Outputs Propane, at store Propane,

residential use Natural gas, at power plant

Coal, at power plant Wind power Hydropower

Climate change 16.0% 75.3% 0.0% 0.1% 0.0% 1.0% Acidification 36.8% 47.3% 0.0% 0.1% 0.0% 0.0% Ozone depletion 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

These results illustrate how important each of the unit processes is to the life cycle stage.

For instance, windows account for 100% of the tiny house’s impact on acidification. However, breaking these impacts up by life cycle state is slightly misleading in this analysis because the unit process data used here often includes the entire life cycle rather than just manufacturing (i.e. also maintenance and end of life). Still, it provides a starting point for examining the significance of various unit processes to the environmental impact of the functional unit.

For example, in the operation phase in both Denver and Seattle, the combined impact of propane refining and combustion accounts for more than 80% of the house’s impact on climate change and acidification. The contribution due to propane refining accounts for almost 16% of the global warming potential and 35% of the acidification potential, while the remaining emissions result from the combustion of propane for residential heating and cooking. These results indicate that even though the electricity mixes differ between Denver and Seattle, the primary contributor to the operation phase (and the overall life cycle of the tiny house) is propane refining and combustion. Thus, the heating requirements of a tiny house appear to dominate its life-cycle environmental impact, particularly with regard to climate change and acidification.

Comparison to Prior Studies

To understand how the climate change impact of a tiny house competed with traditional residential housing, I compared my results with those from Blanchard & Reppe (1998). While the study by Blanchard & Reppe (1998) is a bit old, it is still one of the most thorough U.S. residential housing studies to date and it was performed in Michigan, a severe winter climate. Before I discuss my comparative analysis, it is important to note that there are several inconsistencies between the Blanchard & Reppe study and the tiny house study performed here. As compared to this study, the functional unit employed by Blanchard & Reppe includes:


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• Four times more people (4 people as compared to 1 person in tiny house) • Fourteen times more area (2,450 sq. ft. as compared to 170 sq. ft. tiny house) • Entertainment space • Garage (though not included in heated area)

In addition, Blanchard & Reppe also include construction within the scope of their study. As a result, the results reported in this study are not directly comparable to Blanchard & Reppe. However, it seems useful to provide a rough comparison to this study in order to understand where the environmental impact of a tiny house falls within the residential housing industry.

The magnitudes of the global warming potentials associated with the Denver and Seattle tiny houses are much larger than the standard house (SH) or energy efficient house (EEH) from Blanchard & Reppe (see Figure 10a). However, as discussed previously, the tiny houses are not functionally equivalent to the traditional houses. However, when compared to the global warming potential per square foot of a standard house in Michigan, the Seattle tiny house was similar and the Denver tiny house had more than double the global warming potential (see Figure 10b). Additionally, the energy efficient house was about three times smaller than the standard house. Thus, in a severe winter climate, even a standard house appears to perform as well per square foot as a tiny house in a moderate winter climate. Furthermore, an energy efficient house in a severe winter climate performs better per square foot than a tiny house in a moderate or severe winter climate.

Figure 10. The global warming potentials (a) of the Denver and Seattle tiny houses compared to a standard house (SH) or energy efficient house (EEH) (Blanchard & Reppe, 1998). When compared per square foot (b) or per square foot per person (c), the standard and energy efficient houses perform the same or better than either of the tiny houses. Coloring indicates the contribution from each phase of the life cycle: construction (blue), use (red), and end of life (green).


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When further normalized by the number of people housed by each functional unit, the standard house was four times better and the energy efficient house twelve times better than the Seattle house (Figure 10c). Moreover, both the standard and energy efficient houses were more than ten times better than the Denver tiny house when compared via global warming potential per area per person. The improved performances per square foot of the standard and energy efficient houses are likely due to increased energy savings associated with less heat being lost via wall sharing within these traditional houses. The tiny house, on the other hand, does not have the ability to take advantage of such savings per square foot because it is just a single room with insulated walls on all sides. In addition, the tiny house will likely perform worse per square foot with regard to heating because the house is built on a trailer so there is additional heat lost through the floor. Even if all of the walls of the tiny house are well insulated, the structure does not have a concrete foundation so it will lose heat from one additional surface as compared to a traditional house. Given the results of this study, additional analysis, including thermal modeling, should be performed to understand the root causes behind these differences.

Identification & Evaluation of Significant Issues

Through the use of contribution analysis, I have identified three significant issues relating to the unit processes: windows, propane refining, and propane use. I will now evaluate these three significant issues in greater detail by considering their data quality and examining these data with regard to completeness, sensitivity, and consistency.

Data quality A description of the quality of all of the data used in this study can be found in the Inventory Analysis section under “Data Collection Procedures & Validation of Data.” It is important to note that, as for most of the data used in this study, the three signifcant issues are all largely of data quality B. As a result, pariculary for these three areas, it would be very important for any future analysis to go back and acquire better data sets that are more reliable, complete, and representative of these unit processes.

Completeness

One big problem with the data for propane (both for refining and residential use) is that the data source does not include emissions associated with ozone depletion. Similarly, in addition to lacking data for ozone depletion, the life cycle data for wind energy and hydropower also do not have information about acidification. As a result, it is very difficult to draw any conclusions relating to ozone depletion or acidification without this information.


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Sensitivity To quantify the sensitivity of environmental interventions to small changes in each of these three unit processes, I performed a perturbation analysis on both Denver and Seattle tiny house models. Since data relating windows represented the complete life cycle, a 25% change in the environmental interventions associated with this unit process was examined. For the other two significant areas, particular aspects of the unit process were considered. For example, a 25% change in heading and cooking energy requirements would change both the demand for propane at the refinery and amount of propane combusted for residential use. In addition, a 10% change in the efficiency of the propane combustion process would change both the amount of propane at the refinery and the emissions during residential use. The results of this analysis are summarized in Tables 15 and 16.

Table 15. Sensitivity analysis, Denver Tiny House, for three significant issues: windows, propane refining, and appliance efficiency (i.e. efficiency of combustion at residence).

Table 16. Sensitivity analysis, Seattle Tiny House, for three significant issues: windows, propane refining, and appliance efficiency (i.e. efficiency of combustion at residence).

The results from this sensitivity analysis reveal that a 25% change in the life cycle of the

windows has some influence over the ozone depletion impacts. However, changes in the propane refining and appliance efficiency appear to have little to no influence on the overall life cycle impact of the tiny house, at least for the three impact categories considered here. This lack of influence is likely a result of the limited ability to change appliance efficiency (e.g. even a 10% change in combustion efficiency is quite large) and the reduced importance of propane refining has as compared to combustion of propane onsite at the residence.

Sensitivity to Significant Issues, Denver Tiny House

Change in g by Impact Category

Window (Δ=25%)

Propane refining (Δ=25%)

Appliance efficiency (Δ=10%)

Climate change 0.3% 3.1% 1.6% Acidification 0.0% 6.9% 3.5% Ozone depletion 19.9% 0.0% 0.0%

Sensitivity to Significant Issues, Seattle Tiny House

Change in g by Impact Category

Window (Δ=25%)

Propane refining (Δ=25%)

Appliance efficiency (Δ=10%)

Climate change 0.9% 3.2% 1.6% Acidification 0.1% 7.4% 3.7% Ozone depletion 19.9% 0.0% 0.0%


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Consistency With regard to consistency, the data relating to each of these three significant issues is rather poor. All four data sources are different (e.g. propane at refinery is based on GREET while windows are based on literature values). Moreover, the accuracies of the data are also different (e.g. the LCI of propane at refinery is a cumulated black-box system while the LCI of windows has a detailed process flow chart). Furthermore, the data age, time-related coverage, and technology coverage also vary. The only consistent aspect across all four data sets is the geographical coverage, which is of the U.S.

Recommendations

According to the results outlined in the Interpretation section, several conclusions about each of the three impact categories may be drawn. First, in order to reduce the house’s impact on climate change and acidification, alternative propane refining methods or fuel options should be considered. Second, given the current data, the life cycle of residential windows should be considered the most important contributor to ozone depletion. However, when compared to relative U.S. emissions per capita per year as in the normalization analysis in the previous section, results show that the emissions associated with ozone depletion have relatively little impact whereas climate change and acidification have a relatively larger impact. Still, at least with regard to these three impact categories, the normalization results indicate that the life cycle of a tiny house has about five times larger impact on climate change than on acidification.

While the tiny house impacts on climate change and acidification do depend geographic location, the impact on ozone depletion does not. This difference is a result of the ozone depletion potential being entirely dependent on the construction materials, specifically the windows. Since tiny house construction is assumed to be the same for both locations, the impact of construction materials does not change across the two locations. However, the energy mix of the electricity grid and the severity of the climate do differ between the two locations. Seattle electricity is primarily composed of hydropower and the climate is characterized by a moderate winter. On the other hand, Denver experiences a severe winter and generates electricity primarily from natural gas and coal. As are result, it is not surprising that the acidification and climate change impacts of the Seattle tiny house are consistently about 2.5 times smaller than the Denver tiny house.

Despite these differences in magnitude, contribution analyses on both the Denver and Seattle tiny houses indicate that the use phase accounts for more than 80% of the emissions associated with climate change and acidification in both locations. More specifically, about 70% of the tiny houses’ impact on climate change is due to propane refining and 15% are due to propane combustion. Similarly, about 45% of the tiny houses’ impact on acidification is due to propane refining and 35% is due to propane combustion. Since the percentage contribution from propane remains the same for both locations, and propane is used for space heating, it appears that the differences in emissions between the two locations are primarily a result of climate. Specifically, while the lower climate change and acidification emissions in Seattle are due to a combination of both the difference in the electric grid and the climate, Seattle’s temperate


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climate contributes the most to the reduction in global warming and acidification potentials for the Seattle tiny house.

In order to examine possible pollution prevention strategies, sensitivity analysis allows us to understand how changes in a unit process might impact the environmental flows. The sensitivity analysis performed in this study showed that even a 25% change in propane refining efficiency or a 10% change in the combustion efficiency at the residence resulted in less than 4% change in the overall environmental interventions associated with climate change. As a result, a more fruitful pollution prevention strategy might be to examine alternative heating and cooking technologies or increase the thermal insulation and energy efficiency of the tiny house.

To understand how the climate change impact of a tiny house compares to traditional residential housing, I compared my results with those from Blanchard & Reppe (1998). When compared to the global warming potential per square foot associated with a standard house, the Seattle tiny house was comparable to the standard house and the Denver tiny house had more than double the global warming potential. Moreover, both the standard and energy efficient houses were more than ten times better than the Denver tiny house when compared via global warming potential per area per person.

Since there are several inconsistencies between this study and the study by Blanchard & Reppe (1998), future work should examine these two types of houses in greater detail. Special attention should be given to modeling the heating and cooling requirements since this aspect of the life cycle appears to contribute to a large portion of a house’s impact on climate change and acidification. It would be interesting to see if increased insulation of the tiny house walls or floor would decrease its global warming potential per square foot or if the increased efficiency of heating and cooling appliances and/or wall sharing in traditional housing overshadow these benefits. It is important to reiterate that this analysis assumes a cut-off criterion for inputs less than 1% mass of the system. This criterion is arbitrary and it is possible that a small mass of material might result in a large environmental impact. However, due to the comparatively large impact of the use phase, it is unlikely that the additional construction materials would have much influence on the overall result. Still, for the sake of completeness, it would be prudent to examine the impacts of these additional materials.

Finally, and most pertinently, the data used in this analysis are not of very high quality. Thus, I would highly recommend that future work include the development of better data sets, particularly for the three areas with significant issues: windows, propane refining, and propane combustion. In addition, I would recommend improving the data quality for incomplete data sets (i.e. data sets that do not have environmental flows associated with all three impact categories).


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Appendices

Appendix 1: Estimated List of Building Materials This list was copied directly from the bill of materials provided by the Tumbleweed Tiny House Company for the Cypress 24 tiny house of 170 square feet (Tumbleweed, personal communication, February 6, 2015). The total mass for each line item was estimated using information from online building suppliers. Materials comprising more than 1% of the total mass are highlighted in yellow. Mass (lb) % mass

FRAMING QUANTITY DESCRIPTION

77 2x4x8 1129 6.5 28 2x3x8 308 1.8 38 2x4x12 836 4.8 10 2x6x10 275 1.6 7 4x4x8 LOFT BEAMS - HEM FIR S4S 205 1.2

17 7/16" ZIP BOARD PANELS 772 4.4 11 5/8" CDX PLYWOOD 623 3.6 5 3/4" OSB TONGUE & GROOVE PLYWOOD 340.8 2.0

1 ROLL 3" SILL SEALER 0.52083 0.0 11 4'x8' - 1 12" POLYSTYRENE (FLOOR) 77 0.4 2 514x514x14 PARALLAM 2.0 POSTS (FRONT WALL) 0.81845 0.0 1 312x514x10 PARALLAM 2.0 POSTS (FRONT WALL) 0.34375 0.0 1 13 4x512x22 MICRALLAM BEAM (RIDGE BEAM) 2.10069 0.0

24 28OZ DAP SUBFLOOR ADHESIVE 42 0.2 27 1x6x10 WHITE PINE TONGUE AND GROOVE 371 2.1

WINDOWS & DOORS

QUANTITY DESCRIPTION 0.0

8 A. JELD-WEN AWNING WINDOW (SEE SHEET N2) FRAME SIZE: 24"x36"

256 1.5

1 B. JELD-WEN AWNING WINDOW (SEE SHEET N2) FRAME SIZE: 30"x24" - ROUGH OPENING: 30 3 4"x243 4" R.O.

32 0.2

4 E. JELD-WEN WINDOW SASH (SEE SHEET N3) FRAME SIZE: 15"x36"

280 1.6

1 G. JELD-WEN AWNING WINDOW (SEE SHEET N2) FRAME SIZE: 30"x40" - ROUGH OPENING: 30 3 4"x403 4" R.O.

32 0.2

1 A-2. 1'6" 3 PANEL PINE INTERIOR DOOR (BATHROOM)

12 0.1

2 A-3. 1'6" 3 PANEL PINE INTERIOR DOOR (CLOSET) 24 0.1

1 EXTERIOR DOOR FRAME TO FIT 2412x7412 ROUGH OPENING (ALSO SUPPLY 3 HINGES)

0 0.0

2 2x8x7 PINE (FOR EXTERIOR DOOR) 51 0.3


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1 2X10X8 PINE (FOR EXTERIOR DOOR) 37 0.2 HALF 20"X48" PINE PANEL (FOR EXTERIOR DOOR) 20.5 0.1

1 LOWE'S ITEM #161742 DOOR BOTTOM VINYL FINS 1 0.0 CABINETRY

0 QUANTITY DESCRIPTION 0 0.0

SEE INTERIOR ELEVATIONS FOR CABINETRY QUANTITY AND SIZE 0 0.0 SELECT ADEQUATE QUANTITY OF HARDWARE PULLS 0 0.0

SELECT ADEQUATE QUANTITY OF HINGES 0 0.0 SELECT ADEQUATE QUANTITY OF LATCHES 0 0.0

1 24" MAPLE BUTCHER BLOCK COUNTERTOP 0 0.0 19 6912" MAXRIB (3' ROOFING PANELS) 0 0.0

ROOF

3 10' RIDGECAP 12.75 0.1 1 10' HIP CAP 4 0.0 6 10' EAVES TRIM 12 0.1 1 10' L FLASHING 2 0.0 3 10' GABLE TRIM 18 0.1

18 INSIDE FOAM CLOSURES 9 0.1 24 OUTSIDE FOAM CLOSURES 12 0.1

150 2" SCREWS 1.2 0.0 400 1" SCREWS 2 0.0

1 ROLL 15 LB FELT PAPER 15 0.1 EXTERIOR TRIM

QUANTITY DESCRIPTION

0.0 40 2x4x8 CEDAR 587 3.4 5 1x8x8 CEDAR 73 0.4

20 1x6x12 CEDAR 330 1.9 1590 LFT. 7" CEDAR SIDING 957 5.5

5 GAL EXTERIOR SILL SEALER 40 0.2 1 ROLL 9" FORTIFLASHING (BLACK) 9 0.1

2 ROLLS 6" FORTIFLASHING (BLACK) 12 0.1 1 TURNED PORCH POST 10 0.1 5 2x4x8 S4S CEDAR 73 0.4

16 2x2x36 CEDAR SPINDLES 44 0.3 FASTENERS


0.0 2 CASES CLEAR SILICONE, ITEM #08641 20 0.1 1 CASE 10.3 OZ. SUBFLOOR ADHESIVE, ITEM #25028 8 0.0 1 BOX BRAD 13 4" X 18 GAUGE SMOOTH CHISEL PX-134 2 0.0 1 BOX BRAD 3/4" X 18 GAUGE SMOOTH CHISEL PX-34 1.5 0.0 1 BOX 2"x0.099 GALVANIZED RINGSHANK COIL 2 0.0 1 BOX 3"x0.120 BRIGHT SMOOTH COIL HWCF10D120 2 0.0


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1/2 BOX 15 8" STAINLESS STEEL SCREWS X1QSS 1.25 0.0 1/2 BOX 7x3 STAINLESS STEEL SCREW X3QSS 1.25 0.0 1/2 BOX 7x214" STAINLESS SCREW X2QSS 1.25 0.0 1/2 BOX 9x3 STAR FLATHEAD XT930W 1.25 0.0 1/2 BOX 13 4" SQUARE FLATHEAD SCREWS X1828NZ 1.25 0.0 1 BOX JOIST HANGER NAILS 50 0.3

1 5/8"x60" ALL THREAD ROD 1 0.0 12 5/8" WASHERS 1.2 0.0 12 5/8" NUTS 1.2 0.0 24 LSTA12 RAFTER STRAP 2.4 0.0 1 SC14 STRAPPING 1 0.0

22 HURRICANE TIES H25A 2.2 0.0 6 HDU4 HOLDOWNS 15 0.1 2 HDU5 HOLDOWNS 5 0.0

60 PACKS 5 1/16x3 2x8' TONGUE & GROOVE PINE ECONOMY 120 0.7 140 SQ FT CORK FLOORING, NATURAL 229 1.3

1 ROLLS OF UNDERLAYMENT 54 0.3 8 1x4x8 WHITE PINE 19 0.1

10 1x6x10 WHITE PINE 45 0.3 5 1x10x8 WHITE PINE 30 0.2 3 4x8x3/4" PINE PLYWOOD 204 1.2 1 ROLLING LADDER HARDWARE 10 0.1

MISCELLANEOUS

QUANTITY DESCRIPTION 0 0.0 1 PACK WOODEN SHIMS 144 0.8

1 QT BLACK PAINT TO PAINT UNDERSIDE OF SHEATHING

2.5 0.0

2 CANS KRYLON SPRAY PAINT 0.26 0.0 1 KEYED DOOR LATCH AND DEADBOLT COMBO 1 0.0 1 HALL & CLOSET LOCKSET 1 0.0 1 INTERIOR BED & BATH LOCK SET 1 0.0 1 30" CLOSET ROD 3 0.0 1 SET OF CHROME CLOSET ROD BRACKETS 1 0.0 1 FIRE EXTINGUISHER 3.6 0.0

APPLIANCES, FIXTURES

QUANTITY DESCRIPTION 0 0.0 1 STAINLESS STEEL BAR SINK - FRANKE BMSK802 20 0.1 1 MOEN WETHERLY 87999SRS 6 0.0

1 TRUE INDUCTION S2F3 DOUBLE BURNER COOKTOP - UPC 837654962947

11.2 0.1

1 MOEN ADLER L82691SRN 3 0.0 1 MOEN BOARDWALK #84805SRN, BRUSH NICKEL 5 0.0 1 KSWS009-H113 HEAT PUMP, WHITE 140 0.8


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1 COIL INSTALLATION KIT - IKT1438F25 0.1 0.0

1 SECURE HOME ITEM #51680, MODEL #SH-4114-HB (EXTERIOR LIGHT)

4 0.0

1 PORTFOLIO ITEM #394131, MODEL #34620 (CHANDELIER)

12 0.1

4 PORTFOLIO ITEM #93027, MODEL #37263 (SCONCES)

8 0.0

PLUMBING

QUANTITY DESCRIPTION 0 0.0 1 BARKER 26 GALLON WATER TANK 88-1024 1 0.0

1 SUBURBAN 10 GALLON SW10DE ELECTRIC WATER HEATER

30 0.2

1 THETFORD AQUA MAGIC STYLE 2 RV TOILET 83-1679

35 0.2

1 SET 5 16"x214" CL BOLT NUT & WASHER 1 0.0 1 3" TOILET FLANGE 1 0.0 1 24"X32" FIBERGLASS SHOWER 70 0.4 1 SHOWER VALVE 2 0.0 1 SHOWER DRAIN 0.1 0.0 1 BARCLAY 4-551 WHITE 14 0.1 1 FLOW JET WATER PUMP 86-8327 3 0.0 1 WATER REGULATOR 0.1 0.0 1 VALTERRA WATER HATCH INLET 88-9700 0.5 0.0 1 3" WASTE VALVE 89-8282 1 0.0 1 112" WASTE VALVE 89-8419 0.375 0.0 1 3" WASTE VALVE CAP 89-8294 0.125 0.0 1 SHOWER P-TRAP 0 0.0 1 MARINE TECHNOLOGIES CO DETECTOR 66-8839 1 0.0

1 MARINE TECHNOLOGIES SMOKE DETECTOR 14-8891

1 0.0

2 112" FIT VALVE SPIGOTS 0 0.0 2 3" FIT VALVE SPIGOTS 0 0.0 1 PIPE ADAPTER 0.1 0.0 1 PLUMBING ROOF VENT 0.5 0.0

30 FT 1/2" BLUE PEX 1.65 0.0 22 FT 1/2" RED PEX 2.684 0.0

4 1/2" TEES FOR PEX 0.25 0.0 6 1/2" 90 FOR PEX 0.3 0.0 8 1/2" STRAIGHTS FOR PEX 0.2 0.0

40 PEX RINGS 0.6 0.0 1 3" ABS 90 1 0.0 4 112" ABS 40 4 0.0

2 FT 2" BLACK ABS 1.5 0.0 2 FT 3" BLACK ABS 0.75 0.0


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9 FT 112" BLACK ABS 2.7 0.0 1 CAN ABS GLUE 0.3 0.0

1 112" KITCHEN SINK DRAIN 4 0.0 1 112" Y 0.0625 0.0

10 FT PLUMBING STRAP 0.3 0.0 4 METAL PLATES 2 0.0 1 114" P-TRAP 2 0.0 1 112" P-TRAP 2 0.0 4 3/8" STRAIGHT VALVE 0.6 0.0 1 3/8" ANGLE VALVE 0.25 0.0 1 1/2"X 1/2" FEMALE BALL VALVE 0.45 0.0 4 1/2" PEX BALL VALVES 4.8 0.0 1 3/8" TO 1/2"x12" WATER SUPPLY 0.05 0.0

ELECTRICAL

QUANTITY DESCRIPTION 0 0.0 180 FT 14-2 WIRE 10.8 0.1 23 FT 14-3 WIRE 1.702 0.0 3 FT 6-3 WIRE 1.614 0.0

18 FT 12-2 WIRE 1.476 0.0 1 BOX WIRE STAPLES 1.6 0.0 1 BAG RED WIRE NUTS 3 0.0 1 BAG GREEN WIRE NUTS 3 0.0

1 PROGRESSIVE DYNAMICS 50 AMP POWER CENTER 55-0468

5 0.0

1 MARINCO 50 AMP 25 FT CORD 55-7248 23 0.1 1 MARINCO 50 AMP POWER INLET 55-8905 2 0.0 6 15 AMP BREAKERS 0.6 0.0

17 FT 3/4" METAL FLEXIBLE CONDUIT 1.632 0.0 1 WATERTIGHT SWITCH SET & COVER 0.35 0.0 1 EXTERIOR JUNCTION BOX 0.5875 0.0 1 BONDING CLAMP 0.2 0.0

30 FT 12 GAUGE GROUNDING WIRE 0.648 0.0 18 SINGLE GANG BOXES 9 0.1 3 DOUBLE GANG BOXES 1.5 0.0 1 SINGLE GANG REMODEL 0.1 0.0 2 LIGHT BOXES 2 0.0 1 1/2" SHALLOW BOX 0.025 0.0 1 112" SHALLOW BOX 0.05 0.0 1 DOUBLE METAL SHALLOW BOX 0.1 0.0 6 GFI COVER & OUTLETS 0.6 0.0 2 SWITCH & GFI COVER 0.2 0.0 2 OUTSIDE GFI COVERS & OUTLETS 0.2 0.0


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2 3 WAY SWITCHES 0.0375 0.0 7 SINGLE GANG SWITCHES 0.7 0.0

12 SINGLE RECEPTACLES 1.2 0.0 11 SINGLE RECEPTACLE COVERS 1.1 0.0 3 SINGLE LIGHT SWITCH COVERS 0.3 0.0 1 SWITCH RECEPTACLE COMBO COVER 0.1 0.0 1 DOUBLE SWITCH COVER 0.1 0.0 5 1/2" CABLE CONNECTORS 4 0.0 2 3/4" METAL CLAMP 0.2 0.0 1 LAMP HOLDER 0.2875 0.0

33 METAL PLATES 33 0.2 6 FT 1/2" LIQUID TIGHT FLEX CONDUIT 0.768 0.0

26 FT LOW VOLTAGE WIRE 0.936 0.0 1 METAL LIGHT BOX 0.5 0.0 1 METAL DISCONNECT BOX 2.5 0.0 1 LIQUID 90 0.6 0.0 2 LIQUID STRAIGHT 1.15 0.0 4 60 WATT TEAR DROP BULBS 1 0.0 1 60 WATT SOFT WHITE BULBS 0.1625 0.0

TRAILER


1 24 FT X 8 FT UTILITY TRAILER 8000 45.8


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Appendix 2: Tiny House Energy Requirements In order to obtain data for the energy requirements of tiny houses in two different climate zones, I sent out surveys to several tiny house dwellers. Responses were received from two tiny house owners (A. Odom, personal communication, February 3, 2015; M. Miller, personal communication, February 10, 2015) and one manufacturer (Tumbleweed, personal communication, February 6, 2015). These data are summarized below.

Appendix 3: Hierarchical List of Unit Processes the life cycle of steel girts and purlins the life cycle of propane, at refinery (or local store) the life cycle of propane combustion, at residence the life cycle of softwood (Cedar, Douglas Fir, and Pine) the life cycle of wood framing the life cycle of plywood the life cycle of a residential window the life cycle of cork flooring the life cycle of Cedar siding the life cycle of truck transportation, diesel fueled the life cycle of rail transportation, diesel fueled the life cycle of diesel, at refinery the life cycle of barge transportation, diesel fueled the life cycle of electricity at location of interest the life cycle of steel girts and purlins transport to site the life cycle of plywood transport to site the life cycle of softwood transport to site the life cycle of window transport to site the life cycle of cork transport to site the life cycle of cedar siding transport to site the life cycle of wood framing transport to site

Andrew Odom (Tiny r(E)volution)

Cassie (Tumbleweed)

Macy Miller (Tiny House People)

Cassie (Tumbleweed)

Climate zone Moderate winter Moderate winter Severe winter Severe winter Location North Carolina Olympia Idaho Iowa Propane (lb./yr.) 149 48 209 113 Electric grid (kWh/yr.) 444 - 720 - Number people living in house 3 1 2 1 House size (sq. ft.) 240 200 196 200 Propane (lb./sq. ft./person/yr.) 0.21 0.24 0.53 0.57 Electric grid (kWh/sq. ft./person/yr.) 0.62 - 1.84 -


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Appendix 4: Characterization Factors The following characterization factors were excerpted from Institute of Environmental Sciences, Leiden University (2015). As described in the text, due to time constraints and the need to compile data from multiple sources, not all of the substances are included for each impact category. The impact on climate change is limited to methane, carbon dioxide, nitrous oxide, trichloro- and trifluoro-ethane, and dichlorodifluoromethane. The impact on acidification is limited to sulfur and nitrogen oxides. And, finally, the impact on ozone depletion is limited to tetrachloromethane, Dichlorodifluoromethane, trichloro- and trifluoro-ethane, and dichloro- and tetrafluoro-ethane. The global warming potentials are based 100-year factors from the Intergovernmental Panel on Climate Change (IPCC, 2007). The ozone depletion potentials are based on the steady state total from the World Meteorological Organisation (WMO, 2002), and the acidification potentials are based on the total European average (Huijbregts, 1999a; Huijbregts, 1999b).

Global warming potential

(kg CO2 equiv./kg)

Acidification potential

(kg H+ equiv./kg)

Ozone depletion potential

(kg CFC-11 equiv./kg)

Carbon dioxide (CO2) 1.00 0.00 0.00 Nitrous oxide (N2O) 298.00 0.00 0.00 Methane (CH4) 25.00 0.00 0.00 Trichloro- and trifluoro-ethane (CFC-113) 6130.00 0.00 1.00 Dichloro- and tetrafluoro-ethane (CFC-114) 0.00 0.00 0.94 Dichlorodifluoromethane (CFC-12) 10900.00 0.00 1.00 Tetrachloromethane (CFC-10) 0.00 0.00 0.73 Sulfur oxides (SOx) 0.00 50.79 0.00 Nitrogen oxides (NOx) 0.00 40.04 0.00


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Appendix 5: Technology Matrix for Denver Tiny House A detailed description of the solution methodology is described in the Inventory Analysis section. The technology matrix for the geographic location of Denver, CO is given by

A =


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where the rows of A are Technosphere Flow Units tiny house, Denver unit house steel girts and purlins lb plywood ft^2 softwood m^3 window m^2 cork flooring ft^2 cedar siding ft^2 wood framing ft^2 tiny house foundation (trailer) unit trailer propane, at local store (includes transport) mmBTU propane, residential use mmBTU electricity mix, Denver kWh energy for heat, Denver mmBTU electricity, natural gas, at power plant kWh electricity, bituminous coal, at power plant kWh electricity, wind kWh electricity, nuclear kWh electricity, hydropower kWh steel girts and purlins transport, geographic region of interest tkm plywood transport, Denver tkm softwood transport, Denver tkm window transport, Denver tkm cork transport, Denver tkm cedar siding transport, Denver tkm wood framing transport, Denver tkm transport, truck, diesel powered tkm transport, rail, diesel powered tkm softwood waste in landfill t steel waste: closed loop recycling lb steel waste: open loop recycling lb

and the columns of A correspond to the life cycles for the reference flows.


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Appendix 5: Technology Matrix for Seattle Tiny House A detailed description of the solution methodology is described in the Inventory Analysis section. The technology matrix for the geographic location of Seattle, WA is given by

A =


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where the rows of A are Technosphere Flow Units tiny house, Seattle unit house steel girts and purlins lb plywood ft^2 softwood m^3 window m^2 cork flooring ft^2 cedar siding ft^2 wood framing ft^2 tiny house foundation (trailer) unit trailer propane, at local store (includes transport) mmBTU propane, residential use mmBTU electricity mix, Seattle kWh energy for heat, Seattle mmBTU electricity, natural gas, at power plant kWh electricity, bituminous coal, at power plant kWh electricity, wind kWh electricity, nuclear kWh electricity, hydropower kWh steel girts and purlins transport, geographic region of interest tkm plywood transport, Seattle tkm softwood transport, Seattle tkm window transport, Seattle tkm cork transport, Seattle tkm cedar siding transport, Seattle tkm wood framing transport, Seattle tkm transport, truck, diesel powered tkm transport, rail, diesel powered tkm softwood waste in landfill t steel waste: closed loop recycling lb steel waste: open loop recycling lb

and the columns of A correspond to the life cycles for the reference flows.


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Appendix 6: Intervention Matrix A detailed description of the solution methodology is described in the Inventory Analysis section. The intervention matrix is provided here for reference. Please note that the intervention matrix is the same for both the Denver and Seattle models.

B =


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where the rows of B are given by

Environmental Flow Units Carbon dioxide kg Carbon dioxide, fossil kg Carbon dioxide, biogenic kg Carbon dioxide, land transformation kg N20 kg Methane kg Methane biogenic kg Ethane 112-trichloro-122-trifluoro- CFC-113 kg Ethane 12-dichloro-1122-tetrafluoro- CFC-114 kg Methane dichlorodifluoro- CFC-12 kg Methane tetrachloro- CFC-10 kg Sulfur oxides kg Nitrogen oxides kg H+ equiv kg C02 equiv kg CFC-11 equiv kg Dummy_Electricity, hydropower, at power plant, unspecified kWh Dummy_Electricity, at wind power plant, unspecified kWh Dummy_Electricity, photovoltaic, unspecified kWh Dummy_Electricity, geothermal, unspecified kWh Dummy_Electricity, fossil, unspecified, at power plant kWh Dummy_Disposal, solid waste, unspecified, to unspecified treatment kg Dummy_Disposal, solid waste, unspecified, to underground deposit kg Dummy_Disposal, solid waste, unspecified, to sanitary landfill kg Dummy_Disposal, ash and flue gas desulfurization sludge, to unspecified reuse kg Dummy_Disposal, lignite coal combustion byproducts, to unspecified reuse kg


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Appendix 7: Denver Demand Vector, Scaling Vector, and Result Vector A detailed description of the solution methodology is described in the Inventory Analysis section. The vectors obtained for the Denver, CO model are provided here for reference. The demand vector, f, for the Denver, CO model is given by f = 1 unit house tiny house, Denver 0 lb steel girts and purlins 0 ft^2 plywood 0 m^3 softwood 0 m^2 window 0 ft^2 cork flooring 0 ft^2 cedar siding 0 ft^2 wood framing 0 unit trailer tiny house foundation (trailer) 0 mmBTU propane, at local store (includes transport) 0 mmBTU propane, residential use

0 kWh electricity mix, Denver

0 mmBTU energy for heat, Denver 0 kWh electricity, natural gas, at power plant 0 kWh electricity, bituminous coal, at power plant 0 kWh electricity, wind 0 kWh electricity, nuclear 0 kWh electricity, hydropower 0 tkm steel girts and purlins transport, Denver 0 tkm plywood transport, Denver 0 tkm softwood transport, Denver 0 tkm window transport, Denver 0 tkm cork transport, Denver 0 tkm cedar siding transport, Denver 0 tkm wood framing transport, Denver 0 tkm transport, truck, diesel powered 0 tkm transport, rail, diesel powered 0 t softwood waste in landfill 0 lb steel waste: closed loop recycling 0 lb steel waste: open loop recycling


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The scaling vector, s, for the Denver, CO model is given by s = 1.00E+00 unit house tiny house, Denver

3.20E+03 lb steel girts and purlins

1.15E+03 ft^2 plywood

1.40E+00 m^3 softwood

6.32E+00 m^2 window

1.75E+02 ft^2 cork flooring

9.28E+02 ft^2 cedar siding

8.33E+02 ft^2 wood framing

1.00E+00 unit trailer tiny house foundation (trailer)

2.27E+03 mmBTU propane, at local store (includes transport)

2.00E+03 mmBTU propane, residential use

1.60E+04 kWh electricity mix, Denver area

2.00E+03 mmBTU energy for heat, Denver area

3.20E+03 kWh electricity, natural gas, at power plant

1.02E+04 kWh electricity, bituminous coal, at power plant

2.24E+03 kWh electricity, wind

0.00E+00 kWh electricity, nuclear

3.20E+02 kWh electricity, hydropower

3.20E+03 tkm steel girts and purlins transport, Denver area

1.15E+03 tkm plywood transport, Denver area

1.40E+00 tkm softwood transport, Denver area

6.32E+00 tkm window transport, Denver area

1.75E+02 tkm cork transport, Denver area

9.28E+02 tkm cedar siding transport, Denver area

8.33E+02 tkm wood framing transport, Denver area

9.25E+03 tkm transport, truck, diesel powered

7.66E+03 tkm transport, rail, diesel powered

-5.58E-01 t softwood waste in landfill

-3.20E+03 lb steel waste: open loop recycling


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And, finally, the result vector, g, for the Denver, CO model is given by g = 7.37E+03 kg Carbon dioxide

1.64E+05 kg Carbon dioxide, fossil

1.52E+02 kg Carbon dioxide, biogenic

2.20E-01 kg Carbon dioxide, land transformation

9.57E+00 kg N20

1.00E+02 kg Methane

2.91E+01 kg Methane biogenic

9.22E-12 kg Ethane 112-trichloro-122-trifluoro- CFC-113

7.52E-08 kg Ethane 12-dichloro-1122-tetrafluoro- CFC-114

1.11E-07 kg Methane dichlorodifluoro- CFC-12

2.59E-07 kg Methane tetrachloro- CFC-10

6.96E+01 kg Sulfur oxides

2.20E+02 kg Nitrogen oxides

1.15E+04 kg H+ equiv

1.73E+05 kg C02 equiv

4.19E-04 kg CFC-11 equiv

-2.28E+01 kWh Dummy_Electricity, hydropower, at power plant, unspecified

-4.70E-01 kWh Dummy_Electricity, at wind power plant, unspecified

-5.31E-02 kWh Dummy_Electricity, photovoltaic, unspecified

-1.18E+00 kWh Dummy_Electricity, geothermal, unspecified

-1.95E+00 kWh Dummy_Electricity, fossil, unspecified, at power plant

-4.78E+02 kg Dummy_Disposal, solid waste, unspecified, to unspecified treatment

-1.08E+03 kg Dummy_Disposal, solid waste, unspecified, to underground deposit

-1.18E+01 kg Dummy_Disposal, solid waste, unspecified, to sanitary landfill

-1.47E+02 kg Dummy_Disposal, ash and flue gas desulfurization sludge, to unspecified reuse

-1.90E-01 kg Dummy_Disposal, lignite coal combustion byproducts, to unspecified reuse


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Appendix 8: Seattle Demand Vector, Scaling Vector, and Result Vector A detailed description of the solution methodology is described in the Inventory Analysis section. The vectors obtained for the Seattle, WA model are provided here for reference. The demand vector, f, for the Seattle, WA model is given by f = 1 unit house tiny house, Seattle 0 lb steel girts and purlins 0 ft^2 plywood 0 m^3 softwood 0 m^2 window 0 ft^2 cork flooring 0 ft^2 cedar siding 0 ft^2 wood framing 0 unit trailer tiny house foundation (trailer) 0 mmBTU propane, at local store (includes transport) 0 mmBTU propane, residential use

0 kWh electricity mix, Seattle

0 mmBTU energy for heat, Seattle 0 kWh electricity, natural gas, at power plant 0 kWh electricity, bituminous coal, at power plant 0 kWh electricity, wind 0 kWh electricity, nuclear 0 kWh electricity, hydropower 0 tkm steel girts and purlins transport, Seattle 0 tkm plywood transport, Seattle 0 tkm softwood transport, Seattle 0 tkm window transport, Seattle 0 tkm cork transport, Seattle 0 tkm cedar siding transport, Seattle 0 tkm wood framing transport, Seattle 0 tkm transport, truck, diesel powered 0 tkm transport, rail, diesel powered 0 t softwood waste in landfill 0 lb steel waste: closed loop recycling 0 lb steel waste: open loop recycling


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The scaling vector, s, for the Seattle, WA model is given by s = 1.00E+00 unit house tiny house, Seattle

3.20E+03 lb steel girts and purlins

1.15E+03 ft^2 plywood

1.40E+00 m^3 softwood

6.32E+00 m^2 window

1.75E+02 ft^2 cork flooring

9.28E+02 ft^2 cedar siding

8.33E+02 ft^2 wood framing

1.00E+00 unit trailer tiny house foundation (trailer)

9.32E+02 mmBTU propane, at local store (includes transport)

8.20E+02 mmBTU propane, residential use

5.20E+03 kWh electricity mix, Seattle

8.20E+02 mmBTU energy for heat, Seattle

0.00E+00 kWh electricity, natural gas, at power plant

5.20E+01 kWh electricity, bituminous coal, at power plant

2.08E+02 kWh electricity, wind

2.60E+02 kWh electricity, nuclear

4.68E+03 kWh electricity, hydropower

3.20E+03 tkm steel girts and purlins transport, Seattle

1.15E+03 tkm plywood transport, Seattle

1.40E+00 tkm softwood transport, Seattle

6.32E+00 tkm window transport, Seattle

1.75E+02 tkm cork transport, Seattle

9.28E+02 tkm cedar siding transport, Seattle

8.33E+02 tkm wood framing transport, Seattle

2.96E+03 tkm transport, truck, diesel powered

4.31E+03 tkm transport, rail, diesel powered

-5.58E-01 t softwood waste in landfill

-3.20E+03 lb steel waste: open loop recycling


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And, finally, the result vector, g, for the Seattle, WA model is given by g = 7.37E+03 kg Carbon dioxide 6.27E+04 kg Carbon dioxide, fossil 1.49E+02 kg Carbon dioxide, biogenic 2.20E-01 kg Carbon dioxide, land transformation 3.99E+00 kg N20 5.17E+01 kg Methane 5.47E-01 kg Methane biogenic 9.22E-12 kg Ethane 112-trichloro-122-trifluoro- CFC-113 3.57E-08 kg Ethane 12-dichloro-1122-tetrafluoro- CFC-114 7.94E-08 kg Methane dichlorodifluoro- CFC-12 2.56E-07 kg Methane tetrachloro- CFC-10 3.19E+01 kg Sulfur oxides 9.24E+01 kg Nitrogen oxides 4.48E+03 kg H+ equiv 6.89E+04 kg C02 equiv 4.19E-04 kg CFC-11 equiv -1.50E+00 kWh Dummy_Electricity, hydropower, at power plant, unspecified -3.08E-02 kWh Dummy_Electricity, at wind power plant, unspecified -3.48E-03 kWh Dummy_Electricity, photovoltaic, unspecified -7.73E-02 kWh Dummy_Electricity, geothermal, unspecified -1.28E-01 kWh Dummy_Electricity, fossil, unspecified, at power plant -3.03E+00 kg Dummy_Disposal, solid waste, unspecified, to unspecified treatment -6.49E+00 kg Dummy_Disposal, solid waste, unspecified, to underground deposit -3.24E+00 kg Dummy_Disposal, solid waste, unspecified, to sanitary landfill -8.84E-01 kg Dummy_Disposal, ash and flue gas desulfurization sludge, to unspecified reuse -1.24E-02 kg Dummy_Disposal, lignite coal combustion byproducts, to unspecified reuse

eberle, annika - final report - tiny house...

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