Techno-Ecological Synergy as a Path Toward Sustainability of a North

Jan 8, 2013 - The resulting techno-ecological network mimics nature at the systems level, can stay within ecological constraints, and can identify nov...
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Techno-Ecological Synergy as a Path Toward Sustainability of a North American Residential System Robert A. Urban and Bhavik R. Bakshi* William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States S Supporting Information *

ABSTRACT: For any human-designed system to be sustainable, ecosystem services that support it must be readily available. This work explicitly accounts for this dependence by designing synergies between technological and ecological systems. The resulting techno-ecological network mimics nature at the systems level, can stay within ecological constraints, and can identify novel designs that are economically and environmentally attractive that may not be found by the traditional design focus on technological options. This approach is showcased by designing synergies for a typical American suburban home at local and life cycle scales. The objectives considered are carbon emissions, water withdrawal, and cost savings. Systems included in the design optimization include typical ecosystems in suburban yards: lawn, trees, water reservoirs, and a vegetable garden; technological systems: heating, air conditioning, faucets, solar panels, etc.; and behavioral variables: heating and cooling set points. The ecological and behavioral design variables are found to have a significant effect on the three objectives, in some cases rivaling and exceeding the effect of traditional technological options. These results indicate the importance and benefits of explicitly including ecosystems in the design of sustainable systems, something that is rarely done in existing methods.

1. INTRODUCTION

The buildings community has made efforts in including ecological design in conventional architecture and building construction. Vegetated roofs for building cooling and stormwater retention,8 rain capture systems for freshwater use,9 and use of biodegradable building materials10 are examples of improving the sustainability of buildings by borrowing design cues from nature. However, this is often done in an ad-hoc manner based on the qualitative assumption that “more is better”, that simply adding on these design changes will result in a more environmentally friendly building. This point is supported by the LEED standard,11 which awards scores based on the number of “green” design elements in the building. A high LEED score may not necessarily indicate a sustainable building design if the design elements were not chosen in a quantitatively rigorous manner and without considering the dependence and impact on ecosystems. In fact, studies12 indicate that LEED certified buildings may not necessarily be better than their non-LEED counterparts. Although LEED is a positive effort in improving the sustainability of buildings, this indicates that a more rigorous and ecologically inclusive approach should be applied to design for sustainable buildings.

Many of the efforts for reducing the impact of human activities focus on technological solutions: higher fuel efficiency, green chemistry, waste integration, etc. Other efforts such as life cycle assessment and design and byproduct synergy take a systems view by considering a network of technological systems. While these efforts may play an important role in reducing the environmental impact of many processes, sustainability requires that the ecosystem services (such as carbon and nitrogen cycles, freshwater, and biodiversity) that support any activity are adequate and not deteriorated; that is, engineering design needs to be done within ecological constraints.1 Unfortunately, despite reports of enormous deterioration of ecosystem services2 and the development of ecologically based solutions3 such as wetlands,4 Biolytix,5 and Ecomachines6 it is still rare to account for the role of ecosystems in sustaining technological systems. This includes methods that look to nature for design structure such as industrial ecology and biomimicry.7 This disconnect between technological and ecological design is hindering the promises of both approaches by excluding the vast array of ecological processes that can constrain human activities and assist in achieving both economic and sustainability goals. This research aims to demonstrate the benefits of establishing techno-ecological synergies in a quantitatively rigorous manner with the goal of this type of approach being used in a wide variety of sustainable design problems. © 2013 American Chemical Society

Received: Revised: Accepted: Published: 1985

July 26, 2012 November 29, 2012 January 7, 2013 January 8, 2013 dx.doi.org/10.1021/es303025c | Environ. Sci. Technol. 2013, 47, 1985−1993

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Table 1. Design Variables for the Residential Systema variable name

variable type

values considered

central air coefficient of performance furnace gas burner thermal efficiency photovoltaic panels for electricity generation roof construction type

continuous continuous integer integer

HWSYS WALL CEIL TOILET SINK SHOWER CLTHSWSHR DSHWSHR CORN POND TREE C_TREE HSP CSP SHADE

hot water system type wall insulation grade ceiling insulation grade toilet type sink faucet type shower head type clotheswasher type dishwasher type fraction of available land planted with corn fraction of water taken from external water reservoir mature trees close to the home for shading purposes number of new trees planted for sequestration purposes thermostat set point when furnace is on thermostat set point when air conditioner is on shading windows to block solar insulation when the outside temperature is above 72 °F (22.2 °C) building orientation relative to true north

integer integer integer integer integer integer integer integer continuous continuous integer discrete continuous continuous integer

3.2−3.9 0.80−0.96 no PV, PV conventional (dark), cool (white) conventional, Solar R13, R21 R30, R49 conventional, dual flush conventional, low flow conventional, low flow conventional, EnergyStar conventional, EnergyStar 0−1 0−1 0, 9 0−20 65−72 °F (18.3−22.2 °C) 72−80 °F (22.2−26.7 °C) off, on

continuous

0−359

AXIS a

description

COP FURN PV ROOF

Values in bold indicate base case value.

Figure 1. Residential system with flows between technological and ecological components.

are often left to technological solutions or simply ignored. For example, trees can be planted for building shading/cooling, carbon sequestration, and pollution reduction; soil can sequester carbon; vegetation can be grown and used as a food source or sold for profit. Also, there are many technological design alternatives, such as high-efficiency heating, ventilation, and air conditioning (HVAC) equipment, photovoltaic generators, and solar hot water generation. Instead of using a piece-wise or ad-hoc design approach, our goal is to push toward quantitative simulation and optimization of the system as a whole to determine the designs by using a

In this article, the concept of techno-ecological synergy is applied to the design of a residential home. Households account for 15−20% of the total primary energy consumption13 and 11% of the total water consumption14 in developed countries. Therefore, households are prime candidates for sustainability improvements by exploiting the synergy between technological and ecological components. This system also presents an accessible case study that people are familiar with and can therefore connect with the concept and findings. Furthermore, American suburban homes have available to them ecological design solutions for achieving sustainability goals that 1986

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then be harvested and sold for money. The carbon sequestration rate is assumed to be a constant.20 Trees. Carbon sequestration in trees is simulated using a dynamic tree growth model that predicts the carbon sequestration in tree biomass.21 Water. It is assumed that the house has access to a naturally occurring or man-made reservoir, such as a pond, lake, or well, that the home can extract water from at the cost of installing piping and pumping equipment as well as the operation of this equipment. Rain capture and storage was quite common in ancient societies and is again gaining interest due to water scarcity issues.22,23 2.4. Life Cycle Accounting. To account for life cycle aspects, the analysis boundary of the residential system is expanded to include the entire supply chain. This expansion is enabled by using ecologically based life cycle assessment (EcoLCA)24 to capture indirect effects (water withdrawal and carbon emissions) of the system. The resulting model is analogous to a tiered hybrid LCA.25 Additional details are in the Supporting Information. 2.5. Optimization. The resulting design problem can be written as follows and solved by mixed integer optimization.

combination of technological and ecological design alternatives.15 Despite the specificity and limited scope of the case study considered, this work is meant to illustrate a more general approach to sustainable design, which has the following features: (1) Inclusion of ecosystems into the design space to take advantage of the capabilities of natural systems and mimic them at the systems level; (2) consideration of an expanded process boundary to include life cycle or indirect effects; and (3) quantitatively rigorous multiobjective optimization of the design space to meet economic and environmental goals. Therefore, this work serves as both a demonstration and a template of this type of design approach. An important finding is that accounting for ecosystems can expand the design space and result in solutions that may not be found by a conventional technological approach. Additionally, this study helps expand upon similar studies of coupled socio-ecological systems, such as the analysis of an integrated coffee bean production/forest system16 and the effect of hydropower production on local ecosystem services.17 These studies focus on quantifying the benefit of natural systems to man-made systems; the present study aims to shift such studies from analysis to design. The rest of the paper is organized as follows. First, the system of interest along with the design variables considered is discussed, followed by a description of the ecological systems included in the study. Next, the optimization problem is presented which describes the objective functions and the solution approach. Results are presented in terms of the design problem and with respect to potential improvements, followed by a discussion of the opportunities for the future.

min −S(x , y), W (x , y), C(x , y) x ,y

subject to

x ∈ X,

y ∈ {0, 1}m

(1)

S(x, y), W(x, y), and C(x, y) are cost savings, water withdrawal, and carbon emissions, respectively. x and y are noninteger and integer variables, respectively. In order to run the optimization, the objective functions values must be known. Due to the complex nature of this problem, there are no explicit objective functions, i.e., there is no explicit function describing C(x, y) = f(x, y). Rather, objective function values can be determined through simulation. 2.5.1. Objective Functions. The objective functions are calculated using the results of the simulation as well as the values of the design variables, as summarized below and described in more detail in the Supporting Information. Carbon Emissions. This is defined as emissions at the household level plus carbon emitted due to electricity generation. When including the life cycle scale, carbon emissions over the life cycle are included. Water Withdrawal. This is defined as water withdrawn from public utility. When including the life cycle scale, water withdrawal over the life cycle is included. Cost Savings. This objective is defined as the cost difference between the design and the base case design. Time value of money is not considered since that would have also required consideration of the dynamics of ecological processes, which is quite complicated. 2.5.2. Simulation and Optimization Algorithm. The system is simulated to determine the objective function values by combining the building simulation software EnergyPlus26 with external calculations that handle effects of certain variables that cannot be captured by EnergyPlus, such as the ecological models. EnergyPlus is a building simulation software package which calculates heating and cooling loads necessary to maintain thermal set points, such as ambient air temperature and hot water demand, and the energy required by the HVAC equipment to maintain these set points. EnergyPlus can also determine water consumption based on specified water demands for various equipment, as well as simulate photo-

2. MATERIALS AND METHODS 2.1. System Description. This study considers a singlestory, 2000 ft2 (185 m2) residential home with slab-on-grade (no basement) construction. The space within the home is allocated to a garage, an attic, and a climate-controlled living space. The home resides on a parcel of land of which 0.25 acre (0.1 ha) is not occupied by the house itself. A model of the home and additional information are in the Supporting Information. 2.2. Design Variables. The design variables considered can be categorized as technological, ecological, or behavioral, as summarized in Table 1. Behavioral variables are named as such because they require a modification in occupant behavior. Details about the variables and the costs that they incur are in the Supporting Information. Ecological variables are further discussed in Section 2.3. The flows of energy, water, and carbon emissions for the system and their interactions with processes within the system are shown in Figure 1. 2.3. Ecological Systems and Models. Three ecological systems are incorporated in this study: the soil system corresponding to the unoccupied land, the trees that reside on the unoccupied land, and the water reservoir. Details are included in the Supporting Information. Turf and Soil. Various models exist for modeling turfgrass systems. For this study, results from a detailed CENTURY turfgrass simulation18 were used to dynamically model the carbon sequestration in the lawn.19 Garden and Soil. Another option for the soil is to replace part of the turf by a vegetable garden. In this work, such a garden is approximated as the planting of corn for computational simplicity. Corn can sequester carbon into the soil and 1987

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Figure 2. Multiobjective optimization results: extreme point designs at life cycle scale. See Table 1 for variable definitions.

voltaics, solar hot water systems, and external shading surfaces. EnergyPlus performs simulations under a specified climate, which accounts for all of the environmental conditions of a specific geographical location, such as temperature and sun position, which change dynamically through the year. The system is simulated over a period of 10 years for a Columbus, Ohio, USA, climate. Other climates and time periods were

considered and are presented in the Supporting Information. The search method used is a genetic algorithm (GA),27 which can handle multiobjective optimization and works well with the nonexplicit nature of the objective functions. However, a GA does not guarantee optimality; rather it gives an approximate solution to the optimization problem.28 The GA is coupled to the system simulation using the optimization package 1988

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Figure 3. Relative significance of design variables on the optimization objectives, life cycle scale. Values in blue indicate an improvement of the objective; those in red indicate a worsening. A larger t-ratio indicates a larger effect on the objective. The bars representing continuous and discrete variables describe the effect of increasing the value of these variables; the bars representing integer variables show the effect of having that variable present, denoted by the [1] in the variable name. The effect of having the integer variable absent and the effect of decreasing the continuous and discrete variables are opposite the value shown. An asterisk indicates a statistically insignificant variable.

grade, and tree shading are “win-win” variables. Such variables may be key to sustainable design because they have a large impact but a zero or relatively low monetary cost. This highlights the fact that behavioral issues are highly related to sustainability and should not be ignored, an important conclusion that has been extensively promoted in the social sciences but mostly ignored in conventional engineering analysis.31−33 3.2. Effect of Individual Variables on the Objective Functions. A statistical analysis can be applied to the data points in which the algorithm visited to determine the relative significance of each variable on the three objectives. This indicates which variables have statistically significant effects on the objectives, as well as determines the relative impact of each of the variables on the objectives. This is done by fitting a linear regression to the data and performing a t test for each parameter in the model to determine statistical significance as well as computing the t-ratio to determine the relative significance among the variables. Figure 3a−c shows bar plots with the size of the bar being proportional to the t-ratio, or relative significance, of each variable in the regression model for the life cycle scale. Process scale results are in the Supporting Information. For the carbon

GenOpt. 29 Details about setting up and running the optimization are in the Supporting Information.

3. RESULTS AND DISCUSSION 3.1. Analysis of the Optimal Design Space. The output of the optimization gives a set of Pareto-optimal (nondominated) solutions,30 which are depicted in the Supporting Information. For the process scale (i.e., without life cycle considerations) optimization, this resulted in 79 optimal designs, while for the life cycle scale the algorithm yielded 120 optimal designs. When faced with such a set of optimal solutions, it is difficult for decision makers to pull meaningful conclusions about which is the “best” solution from the data due to the large number of data points. However, the set of optimal solutions can be reduced by looking at extreme points, which are defined as the points that have the lowest water withdrawal, lowest carbon emissions, and the highest cost savings. These points are summarized in Figure 2 for the life cycle scale. Process scale results are in the Supporting Information. As shown in Figure 2, certain variables are common in the optimal designs at the extreme points. Keeping the temperature cooler in the winter and warmer in the summer, high insulation 1989

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objective values for carbon emissions and cost savings attainable through technological changes alone. The combination of behavioral and ecological variables, with no technological changes, further improves the objective values, especially in water savings. These results also highlight that technological changes are key to making large improvements in the sustainability of anthropogenic activities as ecosystems can only do so much due to their carrying capacity limits; in other words, many modern technological systems can easily overwhelm supporting ecosystem services, and ecosystems cannot take on the mitigation of undesirable environmental impacts by themselves. The previous results indicate that behavioral variables are “win-win”, due to the fact that they incur no costs but result in significant improvements in the three objective functions. To analyze the specific effect of including ecological variables, the Pareto-optimal set of solutions for the process scale system under technological-only design changes was compared to the Pareto-optimal set of solutions for the system under technological and ecological design changes. Each point of the latter Pareto surface was compared to each point in the former Pareto surface to determine if including ecosystems resulted in improvements or degradations in the objective functions. Out of the 27 optimal points in the technological Pareto set, 25 (93%) are dominated by a point in the technological + ecological Pareto optimal set. This “win-win” situation in the majority of optimal points further indicates that including the ecological design variables results in an improved overall design. Furthermore, it helps reinforce that ecological systems can and should be included in design problems aiming for sustainability. 3.4. Process versus Life Cycle Scale Results. Optimization results for the process and life cycle scale show similar trends in the optimal “extreme points” described in Section 3.1. Additionally, it does not change the magnitude of the effect of the individual variables described in Figure 3 significantly in that the relative effect of the variables on the objective functions remains similar, aside from a few exceptions. Most significant of these are the variables CORN, which describes the fraction of lawn planted for corn, and the variable C_TREE, which describes the number of trees planted for carbon sequestration. When accounting for supply chain effects, the corn displaces corn that would have been grown through large-scale agriculture. This results in a life cycle water savings, because it is assumed that the corn grown in the residential system does not require water, but large-scale farms need to irrigate crops, on top of the water requirement for the multitude of inputs needed in industrial farming operations. Conversely, this supply chain displacement results in an increase in carbon emissions. The Eco-LCA model results in a larger amount of carbon sequestered through indirect factors than what is assumed to be the sequestration amount in the residential system. This result, of course, is dependent on complex carbon sequestration models that can be highly variable. For C_TREE, it is assumed that the carbon tree saplings are purchased from a nursery, which inputs water to ensure growth of the trees during their early life. Therefore, purchasing trees results in a life cycle water withdrawal increase, hence making this variable less appealing from a life cycle perspective. Installing photovoltaics is also appealing from a life cycle water perspective, since the electricity displaced from the grid means that process water used in traditional electricity generation plants is not needed.

objective, all of the variables are statistically significant in the linear regression model except for AXIS and TOILET. PV has the largest effect in the linear regression model, implying that it has the largest effect on the carbon savings for the system. It is worth noting that the behavioral variables HSP and CSP have a larger effect than technological variables such as FURN and COP in the regression model. This highlights that behavioral changes can have large impacts on environmental objectives. The ecological systems included for carbon reduction are also found to be significant, particularly TREE, which has a similar relative impact as furnace and air conditioner efficiency. Therefore, including ecological systems in the design and optimization of engineered systems may be important for improving the environmental objectives of such systems. Increasing the fraction of land for the vegetable garden is shown to have a negative effect on reducing carbon emissions. This implies that planting grass instead of corn is a better alternative for minimizing life cycle carbon emissions. However, strong conclusions cannot be made on the effect of planting corn due to the simplicity of the corn sequestration model used. Analysis of the cost savings objective shows that POND has the highest effect, albeit a negative one, on cost savings. It is more expensive to pump and purify water per gallon than it is to purchase water from the public supply. The next highest effect is HSP, with CSP nearby. This highlights that even with conflicting objectives, certain variables such as HSP and CSP are “win-win” in that they result in cost savings and decreased environmental impact. Most of the capitally expensive technological variables are shown to significantly decrease cost savings, due to their initial capital cost not being paid back over the 10 year period, but capitally inexpensive variables such as insulation have a short payback period. CORN is shown to increase cost savings due to the sale of the corn for profit. TREE increases cost savings, due to the decreased cooling demand during the summer. C_TREE decreases cost savings because no monetary cost savings are associated with saving carbon, so paying for the carbon trees only results in the decrease of cost savings. Therefore, ecological systems do have a positive effect on cost savings for the home based on the models and assumptions used; monetizing carbon sequestration may further improve the cost savings of the tree variables. The most significant variable in the linear regression model for the water withdrawal objective is POND, which corresponds to the fraction of the water for the base case design taken from the reservoir. This is expected, since this variable allows large reductions in the water withdrawal from public utilities, even allowing the system to achieve zero water withdrawal from public utility at the process scale. Other variables that are significant include the low-flow plumbing fixtures. CORN has the second largest effect, because growing corn locally is assumed to require zero additional irrigation water in addition to rainfall, whereas industrial corn farming is heavily irrigated. C_TREE has a large negative effect, because purchasing saplings from a tree nursery incurs the water costs associated with growing the trees. 3.3. Effect of Design Variable Types. The optimization was also run for the system for each possible combination of design variable types at the process scale. The results are summarized graphically in the Supporting Information. Although large improvements are had in technological design changes, ecological and behavioral design changes further improve the objectives beyond what technology can alone. Behavioral design changes rival and even exceed the best 1990

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Water collection and solar energy capture exemplify a resilient system which does not depend on a single energy source but rather can adapt and use multiple energy sources. The inclusion of local ecological systems for carbon sequestration and building cooling utilizes locally available resources. Considering material flows within the home system and possible connections with other technological and ecological systems would highlight many of the opportunities for the home design to reflect ecological design cues. For example, considering the flow of waste would show the potential for closed autocatalytic feedback loops (i.e., waste to compost or energy) as well as the creation of interdependent networks. Also, the design method aims to optimize multiple objectives simultaneously, rather than maximizing a single objective, such as profit, which is often done in engineering design problems. These are likely to be steps toward sustainability because they allow the system to more closely mimic the structure of natural systems. 4.2. Expansion of the Design Boundary. Currently, the design problem only considers the house and some of its accompanying ecosystems. The design boundary can be expanded to include other processes (both industrial and ecological) that can form synergies with material and energy flows present in the residential system. This could identify opportunities to further reduce the carbon emissions and water withdrawal while also reducing cost. This would involve not only expanding the physical boundary but also expanding the variable space to include food, materials, and other resources that could affect the objective functions. Coupled with life cycle accounting, this could lead to the design of a large-scale integrated network with multiple synergies beyond what is considered here. Developing the proposed techno-ecological synergies at a regional scale could be particularly appealing. 4.3. Capturing Other Effects. Many of the design variables considered have effects not accounted for by the objective functions. For example, behavioral variables such as thermostat set point, manual window shading control, and building orientation have intangible “costs” associated with them that are not captured here. Comfort may be compromised when adjusting the temperature inside a home, manually shading windows could be an annoyance, and a nontraditional building orientation might look aesthetically unpleasing. These costs are hard to determine in traditional design and could be captured using an economic willingness-to-pay method, which could quantify the “cost” associated with variables that do not have a traditional market value. Some benefits of including ecosystem variables in the design are also not captured in this type of analysis. Growing a vegetable garden instead of the traditional grass not only creates the opportunity for cost savings to the homeowner in the sale or consumption of the crop but also creates a redundancy in the food supply network. This could make the network more resilient to disturbances in other portions of the food supply.34 This phenomenon is not being captured currently but could be included by applying more sophisticated network analysis methods such as creating robustness or resilience objectives. Ecosystems can also provide other benefits such as habitats for crucial organisms such as bees and other insects that are players in larger scale ecological networks. Improving such networks can result in increased overall resiliency to local disturbances such as colony collapse in the case of bees, since the network has been reinforced with redundancies in bee habitats.

It is also worth noting that HSP and FURN did not affect the water footprint in the process scale analysis but have a statistically significant impact on the life cycle water withdrawal. Both of these variables decrease natural gas consumption, which requires water directly and indirectly to produce. Their effect is small relative to the other variables specifically included for water savings, but it nonetheless highlights that expanding the analysis boundary to the life cycle can be significant. The observation that the rest of the design variables do not seem to change their relative impact on the objective functions when accounting for the life cycle can be explained in a few ways. First, the design variables that would incur life cycle increases in indirect carbon and water consumption, such as purchasing manufactured products like solar panels, are prorated over the time of the simulation (which is 10 years), so these effects are attenuated. In the case of photovoltaic panels or the solar hot water system, the life cycle penalties due to increased manufacturing are partially canceled by the life cycle benefits due to energy savings. Additionally, some of the design variables are not much different from the base case equivalent, such as a high-efficiency furnace versus a conventional furnace. Although the high efficiency furnace will realize large energy savings, the amount of additional manufacturing necessary is likely small, since they are fundamentally similar products. Finally, most of the energy saving variables use fossil fuels. Much of the energy is already present in these fuels, and the additional processing needed over the life cycle is small. So although there is processing needed directly and indirectly to deliver electricity and natural gas to the home, it is small relative to the combustion phase of the coal (for electricity) and natural gas. In other words, saving electricity and natural gas results in a direct reduction in carbon emissions (due to avoided combustion), as well as a life cycle reduction from indirect sources. However, the direct reduction is much larger than the indirect reduction, so including the life cycle does not significantly alter the results. Similarly for water, fossil fuels have a much smaller water footprint than water processing (a large amount of water is needed to generate clean water), so variables that save water will have a much larger life cycle water savings effect than fossil fuel saving variables. In general, including the life cycle in sustainable design problems is important, since these effects may be significant and would otherwise be ignored in a conventional design problem. Although including the life cycle in this specific design problem did not result in drastic changes in terms of optimal designs, this insight is still important as it answers doubt as to whether going to the broader life cycle scale would change the optimal design.

4. DISCUSSION This research lays out a basic framework in the form of a case study for seeking synergies between technological and ecological systems using quantitatively rigorous methods. The application to a residential system demonstrates optimization of integrated technological and ecological systems and shows that this could lead to the identification of novel and “win-win” solutions that may not be discovered by a conventional technocentric approach. Expansion of this work in many directions is possible and needed. 4.1. Expanding the Ecological Design Component. The design presented here accounts for some local technoecological synergies, but more could be included to close more resource loops to bring the design closer to natural ecosystems. 1991

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These are only some of the myriad effects that are important but ignored or difficult to capture in traditional design problems. Although not quantitatively accounted for in the present case study, these effects are important and should not be overlooked as they can promote the use of ecosystems in ways that traditional analysis would not. 4.4. Decision Making. Even in relatively restricted design problems such as the one presented here, the consideration of multiple objectives can result in a large array of optimal solutions, each with its own trade-off, as discussed in Section 3.1. From a decision making point of view, it is difficult to pick the solution that will be the “best”, especially in problems that rely on models and simulations, such as the one presented here. As considered earlier, one can simply choose the extreme point corresponding to the most important objective to the decision maker, but this is nonrigorous and would not work in situations where the objectives are of equal importance, or at the very least, one is not clearly more important than the other. When faced with such a dilemma decision makers are left with few tools to rigorously analyze options and come up with a solution. Furthermore, capital investments made in systems such as the current residential system have a long life and are not easily changed. Methods such as adaptive management35 and control theory36 have been applied to dynamic systems such as natural resource management, but little work has been done in connecting these with sustainable design. Additionally, other methods such as goal programming are also available for multiobjective decision making.37,38 The challenge of finding weights for multiple objectives may be met by societal surveys, and methods such as analytic hierarchical process (AHP)39 can be used. Although this study only considered basic ecosystems and was applied to a system with a very specific niche, the principles can nonetheless be extended to any design problem. The results obtained from the case study highlight the importance of utilizing design ecological and behavioral variables, which are outside the scope of traditional design. Behavioral and ecological design changes can have a significant effect on monetary and environmental objectives and, when used in combination with technological design changes, can achieve better objective function values than technological solutions alone. The results of this study are not meant to be interpreted as a direct recommendation for the design of an actual residential home; rather, the study is meant to be a quantitatively rigorous proof-of-concept to motivate and demonstrate how a complicated design problem can be approached using a combination of technological, ecological, and behavioral design variables. With the aid of simulation and optimization tools, these complex design problems can be rigorously analyzed with meaningful results that push toward sustainability.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel: +1-614-292-4904. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was partially supported by the National Science Foundation via Grant CBET 0829026. Special thanks to Michael Wetter of Lawrence Berkeley National Laboratory and Matti Palonen of Aalto University in Finland for help with the optimization algorithm and execution.

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REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

Further details and graphics for the following topics: system description, cost data and additional details for design variables, detailed method for calculation of objective functions, life cycle accounting, results not included in main paper, relationship between objectives, utilizing cost savings to offset carbon, cost of carbon, and guide for setting up the optimization problem. This material is available free of charge via the Internet at http://pubs.acs.org. 1992

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