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Keywords:

  • carbon dioxide (CO2);
  • design for environment;
  • industrial ecology;
  • information and communication technology;
  • life cycle thinking;
  • water consumption

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Design for Environment Method: Meeting Multiple Performance Goals under Variable Operating Conditions
  5. Case Study of Design Constraints to Realize Life Cycle Economic and Carbon Dioxide Reduction Benefits
  6. Implications
  7. Acknowledgements
  8. References
  9. About the Authors
  10. Supporting Information

Smart irrigation controllers (SICs) can save water by adapting watering schedules to climate and soil conditions. The potential benefit of SICs is particularly high in southwestern U.S. states, where the arid climate makes water scarcer and increases watering needs of landscapes. A number of studies have tested the ability of SICs to save water in residential and small commercial settings. Results generally show overall savings, but there is substantial variability, including cases of increased water use. Though there are many controllers on the market, we argue there is a further need for optimization of design and field performance. To inform the technology development process, we develop a design for environment method, which overlays economic and environmental performance parameters under different operating conditions. This method is applied to characterize design goals for controller price and water savings that SICs must meet to yield life cycle carbon dioxide reductions and economic savings in southwestern U.S. states, accounting for regional variability in electricity and water prices and carbon overhead. Results from applying the model to SICs in the Southwest suggest that some areas are significantly easier to design for. One concept to realize improved design in practice is to build out the controller market in a staged set of niches, starting from a more favorable area then moving toward more challenging conditions.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Design for Environment Method: Meeting Multiple Performance Goals under Variable Operating Conditions
  5. Case Study of Design Constraints to Realize Life Cycle Economic and Carbon Dioxide Reduction Benefits
  6. Implications
  7. Acknowledgements
  8. References
  9. About the Authors
  10. Supporting Information

Water Scarcity and Water Use in the United States

The southwestern United States is the driest part of the country, and much of it was developed in conjunction with large water works projects intended to support an increasing population (Reisner 1986). In the recent past, Arizona and Nevada have been two of the fastest growing parts of the United States, but also the driest (Day and Conway 2009). The West is also the largest user of water for landscaping and agriculture in the nation. Eighty-five percent of irrigation withdrawals are used in 17 western states, with California, Idaho, Montana, Oregon, Colorado, Nebraska, Texas, and Arkansas being the largest users in all of the United States (Kenny et al. 2009). On the level of home water use, it is estimated that in Las Vegas 70% of residential drinking water is for exterior uses, which is mostly landscaping (Devitt et al. 2008). Compare this to Pennsylvania, where only 7% of household water is for outdoor use (U.S. Environmental Protection Agency [EPA] 2004). These factors, in combination with the possibility that climate change will make this part of the world even dryer, may result in a major water crisis in the future (Gertner 2007). Looking at the energy-water nexus, the Southwest also uses more energy to treat and transport water than the average in the United States. The EPA estimates that it takes 1.5 kilowatt-hour/1,000 gallon (kWh/gal)1 of energy to treat and transport drinking water in the United States (EPA 2010). For Phoenix this value is estimated to be 6.47 kwh/1,000 gal (Hallin and Holton 2008) and for the southern Los Angeles basin the estimate is 9.9 kwh/1,000 gal (Cohen et al. 2004). These factors make it increasingly important to look for solutions that deal with the use of water for landscapes and agriculture in the Southwest.

Water Conservation and Information Technology

Information technology can potentially play an important role in water conservation. Smart water meters installed at homes and businesses can monitor water flows in a system on a real-time basis. When these data are transmitted to a computer, pipe leaks can be detected early and fixed. As it is now, many homes and businesses will never know they have a leak unless it reaches the surface or their water bill increases significantly (Hauber-Davidson and Idris 2006). Some home water sensing systems can determine how much water is being used and by what water fixture. HydroSense is a sensor that can be attached to a single pipe on the home and uses pressure differentials to find the “signature” of each water fixture in the house. These data can be transmitted to a computer, and consumers can then track their water usage, over time and by fixture (Froehlich et al. 2009). Another information technology that can be used in homes and businesses is the smart irrigation controller (SIC). These controllers use local evapotranspiration (ET) rates and/or environmental data to determine the watering schedule for a landscape and have been found to save water when compared with traditional controllers (U.S. Bureau of Reclamation [USBR] 2008).

Smart Irrigation Controllers

An SIC is similar to a traditional irrigation controller, insofar as it controls a landscape's sprinkler or drip system. The difference, however, lies in how efficiently it does the job. The functionality of a traditional irrigation controller includes setting the days and amount of time to run the sprinkler or drip system. It is up to the user to determine and adjust the watering schedule for their landscape. The SIC, on the other hand, has the goal of giving the landscape exactly the amount of water it needs, at any given time, without much user interaction. Two different strategies that SICs employ to meet this goal exist: soil moisture sensing and ET tracking.

Soil-moisture-based SICs include one or more soil moisture probes that are installed in the root zone of the landscaping. Information from these probes is transmitted to the controller, and the controller determines the water schedule based on this information. Weather-based SICs use meteorological data to determine the landscape's watering schedule. These controllers vary in how many parameters they measure and whether on-site sensors or area weather stations with remote data transmission are used. Some of these controllers also use historical weather data, in addition to or instead of real-time data. Figure 1 shows three basic types of residential SICs. SICs are also known to use other data to determine schedules, such as landscape type, sprinkler type, and slope factors.

image

Figure 1. (a) A soil-based smart irrigation controller (SIC); (b) an on-site sensor, weather-based SIC; (c) an off-site weather station weather-based SIC. Source:Frisco Public Works (2010).

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SIC Studies

A number of studies have been conducted on SICs, with most concentrating on their potential water savings for the residential and small commercial sectors. Some studies have been implemented by educational institutions and some by government agencies and water utilities. The studies were conducted mostly in western states, including California, Colorado, Washington, Nevada, Florida, Oregon, Utah, and Arizona. There have also been studies in western Australia (USBR 2008).

The largest study on SICs to date was done for the State of California. This study assessed promotion programs in different parts of the state; it was not an experimental study aimed at identifying the effect of different variables on system performance. Water districts across the state implemented their own SIC programs, with the data being collected and analyzed from these programs. This one-year study included 2,294 sites, 14 controller brands, residential and commercial applications, volunteer and targeted high use participants, and both professional and self-installation. An overall water savings (adjusted for weather) of 6.1% was found compared with the prestudy year, with 41.8% of sites increasing their water consumption, 56.7% decreasing their consumption, and 1.5% having no change in water consumption (Mayer et al. 2009).

Two scientifically-controlled studies include one by Devitt and colleagues (2008) in Las Vegas, Nevada, and one by Quanrud and France in Tucson, Arizona (USBR 2008). The study by Devitt and colleagues (2008) included the installation of Hydropoint SICs (Hydropoint Data Systems, Inc., Petaluma, CA) at residential households. They compared the water savings and plant health of the group receiving SICs to a group of households receiving only landscape watering education, and a control group. They found an average 20% savings of outdoor water, compared with a slight increase in water use in the other two groups. This study not only indicated water savings from the use of an SIC, but it also showed that depending on a homeowner to use educational information solely to save water may not be an effective strategy. The study by Quanrud and France was also applied to a residential setting and compared brands. The brands compared were Hydropoint, WeatherMiser (WeatherMiser Energy Efficiency, Inc., Albuquerque, NM), and Rain Bird MS-100 (Rain Bird Corporation, Azusa, CA). Water savings were 25%, 3.2% and 4.3%, respectively (USBR 2008).

Scope of Analysis

The popularity of SICs in the municipal sector has been increasing in recent years. Between 2004 and 2007, the number of available brands has increased 400% (USBR 2004, 2007). Many water utilities have been promoting SICs to their customers as well. SICs remain expensive, however, and according to the case studies discussed in the previous section, exhibit considerable variability in water savings. If SICs are to be diffused via market forces, it is important that they deliver net economic benefits as well as substantial water savings to consumers. It is not clear how beneficial current designs and practices are to consumers in different circumstances. In addition, it is important to be careful to ensure that SICs do not induce unintended environmental externalities. In particular, SICs are more electronically complex than traditional irrigation controllers and require a degree of additional energy use in their manufacture and operation. Figure 2 illustrates a life cycle for an SIC system.

image

Figure 2. Life cycle of a smart irrigation controller system. The dotted line represents the main boundary of the study; other components are included in this study as well.

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To inform the design and operation of future generations of SICs, we undertake analysis to establish design/operating parameters needed to realize economic and environmental performance targets under different operating conditions. In the next section, we develop a general method to scope the design space needed to meet the target life cycle impacts.2 We then implement the method for residential SICs using the design parameters of controller price and water savings achieved. The parameters of each operating condition studied include the water price, electricity price, and grid carbon intensity associated with six different urban areas in the southwestern United States. In the final section we discuss how these results relate to strategies for developing improved controllers.

Design for Environment Method: Meeting Multiple Performance Goals under Variable Operating Conditions

  1. Top of page
  2. Summary
  3. Introduction
  4. Design for Environment Method: Meeting Multiple Performance Goals under Variable Operating Conditions
  5. Case Study of Design Constraints to Realize Life Cycle Economic and Carbon Dioxide Reduction Benefits
  6. Implications
  7. Acknowledgements
  8. References
  9. About the Authors
  10. Supporting Information

In this section, we develop a method that maps out the multicriteria design space for a product to meet economic and environmental objectives. This work is part of design for environment (DfE), an umbrella label for concepts and methods aimed at integrating environmental considerations into product design (Graedel and Allenby 2003). Environmental considerations can be considered from a life cycle assessment perspective (Keoleian 1993). The central challenge is the understanding of how multiple design parameters affect multiple environmental issues, as well as economic performance. Allenby (1991, 2000) developed a matrix method to aid designers in understanding and navigating this multiparameter space. Another stream of work aims to characterize the functional relationships between design attributes and their environmental and economic performance, and then develop optimization approaches. Ishii and collaborators (1994), for example, developed a model linking design attributes of electronics with the efficiency of disassembly to identify designs that enhance recyclability. Azapagic and Clift (1999) framed the multicriteria design problem as a linear programming model and explored trade-offs between objectives using methods such as the Pareto optimum. Michalek and collaborators (2004) embedded design parameters into larger system models, aiming to maximize profit to producers, while meeting external environmental design constraints.

Here we take a different track on the use of functional relationships between design parameters and environmental and economic performance. The method is targeted at technology still in development; attainable performance is assumed to be unknown (e.g., how much water could be saved with an SIC). The intent is to formulate specific goals (e.g., zero emissions) for product characteristics that meet multiple environmental and economic objectives. Designers then use these goals as targets for developing the next generation of products. The method is also designed to address how variability in operating conditions (i.e., local conditions—economic, social, mechanical, environmental, etc.) affects environmental and economic performance. The purpose is to identify performance goals robust enough to deliver benefits under different operating conditions. Though not all technologies will display variability in operation that significantly affects design, this is clearly relevant for SICs, and there are many other examples.

To develop this method, we first recap the basic life cycle impact equation that applies to manufactured goods:

  • image(1)

The life cycle impacts chosen can be based on economics, greenhouse gases, energy, or water, for example. Life cycle impacts (LC_impacts)can be written as function of design/performance attributes of the technology being considered and operating conditions to which the technology is subjected (e.g., different geographical locations, which have differing local environmental conditions):

  • image(2)

where D is a design parameter and O is an operating condition. The index l denotes the life cycle impact of concern, n is the number of design parameters considered, and m is the number of operating conditions. Each operating condition can have a number of different variables.

Given a set of functions relating design parameters and operating conditions to a set of life cycle impacts, the next step is to establish target performance for each impact:

  • image(3)

where T denotes the target life cycle impact value. For example, T can be chosen to be zero, meaning zero life cycle impacts.

The design space is n-dimensional. The method uses the equations above to find the design space that meets all target life cycle impacts, which in mathematical terms is the intersection of all hypersurfaces defined in equation (3). Assuming that the life cycle impact functions are monotonic as a function of design parameters, a specific design space emerges that is defined by all impacts being less than the threshold or baseline that results from solving and graphing the equation based on the chosen target life cycle impact values.

To illustrate the method, consider the case of two life cycle impact types, two design parameters, and three operating condition types (l= 2, n= 2, m= 3). For the sake of illustration, assume that the life cycle impact functions are linear. Figure 3 shows hypothetical results for LC_impact1 in the two-dimensional design space. Assuming that LC_impact1 < T1 for all spaces to the right of the lines, the design space that meets T1 under all operating conditions is shown by the cross-hatched lines.

image

Figure 3. Hypothetical results for a two-dimensional design space meeting first target life cycle impact: LC_impact1(D1, D2; O1,2,3) = T1.

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Figure 4 shows hypothetical results for the second impact category, with the favorable design space again denoted by cross-hatched lines. Note that for LC_impact2, operating condition 2 solely constrains the design space.

image

Figure 4. Hypothetical results for a two-dimensional design space meeting second target life cycle impact: LC_impact2(D1, D2; O1,2,3) = T2.

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The design space that meets both target life cycle impacts, T1 and T2, is defined by the intersection of the two spaces in Figures 3 and 4, shown in figure 5. Note that for part of the design space in figure 5, operating condition 3 from LC_impact1 is constraining but elsewhere the design space is determined by operating condition 2 from LC_impact2.

image

Figure 5. The design space that meets both target life cycle impacts under all operating conditions.

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Case Study of Design Constraints to Realize Life Cycle Economic and Carbon Dioxide Reduction Benefits

  1. Top of page
  2. Summary
  3. Introduction
  4. Design for Environment Method: Meeting Multiple Performance Goals under Variable Operating Conditions
  5. Case Study of Design Constraints to Realize Life Cycle Economic and Carbon Dioxide Reduction Benefits
  6. Implications
  7. Acknowledgements
  8. References
  9. About the Authors
  10. Supporting Information

We next apply the DfE methodology developed in the previous section to SICs in residential settings in the southwestern United States, where SICs have the potential to make positive economic and environmental impact. In this study we do not explore the particulars of how to design better controllers for the Southwest; our focus instead is to clarify the design goals that SICs must meet. To expand on the motivation, it is not clear from the review of studies of SICs how the manufacturer design goal of applying only enough water to match local landscape ET rates compares with controller brands/strategy in the same location, or when a brand/strategy is moved geographically. It also seems that the advertised goal of water savings by manufacturers and utilities depends on many different variables, including the climate of the location, type of landscape, previous water application rate, self- or professional installation and maintenance, and proper installation and maintenance of the sprinkler system. Further work is needed to understand how to design SICs and adoption programs to realize maximum economic and environmental benefits of the technology. Higher economic benefits in particular ease the promotion of any technology and, indeed, if benefits are sufficiently high, market forces alone can lead to widespread diffusion.

One impact to consider is economic; at the very least an SIC should generate an economic life cycle impact benefit for consumers. A second impact to consider is related to carbon dioxide impact.3 Though the ostensible purpose of an irrigation controller is to save water, it is preferable that these water savings do not induce negative environmental externalities such as increased energy use or carbon emissions. Given that SICs require both additional energy to produce and consume more electricity to use as compared with a conventional controller, it is worth ensuring that energy and thus carbon dioxide savings embodied in the water savings exceed the additional energy investment and carbon dioxide emissions in controllers. Many other important goals exist, such as ease of set up, use, and maintenance. In this study we only consider the design goals that irrigation controllers must meet so as to realize net economic and carbon benefits.

We therefore construct models of life cycle economic and carbon characteristics of SICs in residential settings. One key issue to consider is that the design goals to realize net economic and carbon emission benefits will change depending on where the controller is used. Water and electricity prices affect the life cycle impact and vary significantly in different areas in the Southwest. The carbon dioxide embodied in electricity and water also varies. We therefore construct a model accounting for geographical variability with two goals in mind. One goal is to identify if there are certain areas in the Southwest that appear particularly attractive for early adoption of controllers. A second goal is to work toward long-term design goals for a controller that will realize benefits wherever it is used in the Southwest. Realizing inexpensive controllers will require mass-produced standardized designs, therefore, a controller that will work anywhere can achieve better economies of scale.

Economic Analysis—Consumer Impact

In this section we find the price and water savings characteristics a controller must meet in order to realize net economic benefits for a resident in different southwestern cities:

  • image(4)

where LC_impact1 refers to economic performance over the life cycle of the controller; D1 is the percent outdoor water savings of the controller; D2 is the annual cost of controller; O1 is the operating condition for Tucson, Arizona; O2 is the operating condition for Phoenix, Arizona; O3 is the operating condition for Las Vegas, Nevada; O4 is the operating condition for San Diego, California; O5 is the operating condition for Los Angeles, California; and O6 is the operating condition for Riverside, California.

The life cycle impact equation we used takes into consideration the cost of the controller, the money saved on a water bill due to a water savings, and the extra electricity cost to run an SIC. In addition, the economic analysis takes into consideration net present value of an SIC with an assumed ten-year lifespan (Mayer et al. 2009). To conform to our conventions, we specifically use net present cost:

  • image(5)

where D1 is the retail cost of the SIC, including the yearly service fees that some companies charge; N is the lifetime of the controller; r is the discount rate; K1 is the average water rate as of April 2010 (only charges based on consumption are included); K2 is the average household water consumption per year; K3 is the average fraction of that water demand for outdoor uses; D2 is the fraction of outdoor water that is saved by an SIC; K4 is the yearly electricity required to run an SIC; K5 is the yearly electricity required to run a traditional irrigation controller; and K6 is the average cost of electricity in 2008 (total electric industry). The constants, K, represent the parameters for an operating condition (see tables S1 and S2 in supporting information on the Journal Web site). We solved for D1 and graphed the baseline for each city (Figure 6).

image

Figure 6. Baselines representing a life cycle cost of zero for residential smart irrigation controllers in different cities in the Southwest. The cost of the controller is the retail cost, plus ten years of service fees for brands that charge annual service fees.

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Carbon Dioxide Analysis—Global Impact

Our goal for life cycle carbon is for the SIC to be, at the very least, carbon neutral; the additional carbon dioxide emitted in manufacture and electricity generation for use by the controller must at least be balanced by the carbon dioxide reductions from reduced water use. The impact function takes the form:

  • image(6)

where LC_impact2 refers to carbon dioxide emission over the life cycle of the controller and the other variables are the same as in equation (4).

The carbon life cycle impact equation we used takes into consideration the manufacturing process, the carbon dioxide emitted from the generation of extra electricity needed to run an SIC compared with a traditional controller, and the carbon dioxide emissions avoided due to the decreased need to transport and treat drinking water for landscaping:

  • image(7)

where K7 is the kilograms of carbon dioxide emitted per 2002 producer dollar of manufacturing in the U.S. small electrical appliance manufacturing sector (NAICS #33521, 335211, and 335212), C1 is the 2004 producer price to 2007 producer price ratio (2002 data were not available), C2 is the 2002 producer price to 2002 consumer price ratio (2007 data were not available), K8 is the average kilograms (kg)4 of carbon dioxide emitted per kilowatt-hour of electricity produced from 1998 to 2000, and K9 is the kilowatt-hours required to treat and transport one liter (L)5 of drinking water. The other constants and variables are the same as in equation (5). The constants, K, represent the parameters for an operating condition (see tables S1 and S2 in the supporting information on the Journal Web site). C1 and C2 convert the 2002 producer price used in K7 to a 2007 consumer price, so that both LC_impact1 and LC_impact2 are based on the 2007 consumer prices that are used in table S3 in the supporting information. We solved for D1 and graphed the baseline for each city (Figure 7).

image

Figure 7. Baselines representing a carbon dioxide life cycle emissions of zero for residential smart irrigation controller in different cities in the Southwest. The cost of the controller is the retail cost, plus ten years of service fees for brands that charge annual service fees.

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Data

Data for the SIC case study can be found in the supporting information on the Journal Web site. This includes the values for constants and variables in equations 5 and 7 (see tables S1 and S2). It also includes information about six SIC studies conducted by various entities (see table S3). The information in table S3 was used in Figures 8 and 9.

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Figure 8. The results of six smart irrigation controller studies compared with the least favorable economic and carbon dioxide conditions from Figures 6 and 7. The cost of the controller is the retail cost, plus ten years of service fees for brands that charge annual service fees.

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image

Figure 9. The Tucson economic and carbon dioxide baselines compared with Tucson empirical results. The cost of the controller is the retail cost, plus ten years of service fees for brands that charge annual service fees.

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Results

Figures 6 and 7 show baselines for the economic and carbon dioxide life cycle impacts of residential SICs for different cities in the Southwest. Data points to the right of and below a line reflect economic or carbon dioxide savings, and data points to the left of and above a line represent net economic costs or carbon dioxide emissions. Figures 6 and 7 demonstrate that there is variability in the economic conditions and life cycle carbon emissions resulting from SICs in different parts of the Southwest. Phoenix has favorable economic conditions for SICs because of higher water rates based on consumption and higher outdoor water use (high total consumption and outdoor consumption). Riverside, on the other hand, faces more severe economic constraints because water rates are based less on consumption and more on a flat fee. With regard to carbon dioxide emissions, higher water consumption made Phoenix, San Diego, and Riverside the best places to implement SICs because higher water consumption means more potential for water savings. Los Angeles came out on the bottom due to low water consumption. Comparing Figures 6 and 7, we also see that SICs may have a slightly greater carbon dioxide benefit than an economic benefit because the slope of the baselines in Figure 7 are steeper than in Figure 6.

In Figure 8 we plotted the two least favorable conditions for economics and carbon dioxide emissions. We can see what controller price and water savings manufacturers might want to strive for in their products. The area to the right of and below the Riverside economic baseline is effectively the design space because there is little overlap between the two baselines. This space is the template for producing a controller that is both economical and carbon neutral in all cities studied. We also plotted the six studies described in table S3 on the same graph. Comparing individual studies against the two least favorable conditions, only Study 6 (Inland Empire) is within the range of both economic and carbon dioxide savings. Study 5 (Glendale) is in the range of carbon dioxide savings, but not economic savings. Studies 1 through 4 (Tucson, Las Vegas, and Foothill) are out of range of both types of savings. Although we cannot say whether the results of these studies would be the same when moved geographically, they at least give an idea of how SICs might be falling short of realizing their maximum environmental and economic benefit over the region of the Southwest. Alternatively, changes in other variables such as water pricing and amount of energy to treat and deliver water may also change the position of the baselines and thus the economic ability or sustainability of SICs.

Another perspective can come from focusing on one area in order to assess how water savings and controller price, under the given local operating condition, compare with the life cycle impact targets. Figure 9 shows sample results for Tucson; other areas are shown in the supporting information. In Tucson, controllers on the borderline of failing one or the other life cycle impact targets indicate a need for improved designs.

Implications

  1. Top of page
  2. Summary
  3. Introduction
  4. Design for Environment Method: Meeting Multiple Performance Goals under Variable Operating Conditions
  5. Case Study of Design Constraints to Realize Life Cycle Economic and Carbon Dioxide Reduction Benefits
  6. Implications
  7. Acknowledgements
  8. References
  9. About the Authors
  10. Supporting Information

What do these results imply for future efforts to improve SICs? One conclusion is that there is clearly a need to lower prices and increase water savings to make SICs broadly attractive to consumers in the Southwest. Partly, these are design issues, but design issues also interface with systems aspects. Price is related to economies of scale. Like many new technology products, SICs face a “chicken and egg” dilemma: at the beginning they are expensive, which limits demand, but without demand, economies of scale do not come into play to reduce the price. A niche market structure is often the solution to this dilemma; even when the product is expensive, there is an initial set of consumers willing to pay. Purchases from this niche support building capacity to bring the price down low enough to be attractive to the next niche, and so on. In the case of SICs, it is not clear whether there is a viable path through niche markets. This analysis suggests that geographical area is one way to conceptualize the niche markets: at the beginning, focus on areas such as Phoenix, where the product delivers higher benefits, and use experiences and capacity in these areas to improve the product in order to become viable in other areas. More work is needed to determine an effective niche strategy.

Increasing water savings are also needed. One layer of this challenge is choice of technology. Prior experience indicates that the ET tracking method results in the higher water savings throughout the Southwest. Work should be done to determine the robustness of this result, and if true, the technology could be standardized in order to reduce costs and increase average water savings. Another consideration is variability in operating conditions at the individual level. Wide variations in water savings, from considerable savings to increased water use, suggest that there is a substantial learning curve ahead in terms of how and when to implement the technology. Interfaces exist between controller design, landscape type, climate, and user behavior that significantly affect the performance of SICs. Research and development are needed to understand these better in order to optimize controller design and implementation programs. Given the potential social benefits of the technology, increased public investment should be considered.

It should also be determined if there are geographic areas that are not economically or environmentally suitable for SICs. If there are such regions, it should be determined if there are conditions that can change in order to make them more viable. The method presented may be able to indicate this. Lastly, it is worth noting that although this study focuses on the controller, the controller plays a role in a larger suite of options to reduce municipal landscape water use such as sprinkler system design and maintenance, low-water landscaping, and gray water reuse. Work is also needed to develop effective strategies that combine appropriate and effective options.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Design for Environment Method: Meeting Multiple Performance Goals under Variable Operating Conditions
  5. Case Study of Design Constraints to Realize Life Cycle Economic and Carbon Dioxide Reduction Benefits
  6. Implications
  7. Acknowledgements
  8. References
  9. About the Authors
  10. Supporting Information

This research was supported by the grant “Sustainable infrastructures for energy and water supply” (#0836046) from the National Science Foundation, Division of Emerging Frontiers in Research and Innovation (EFRI), Resilient and Sustainable Infrastructures (RESIN) program.

Notes
  • 1

    One kilowatt-hour (kWh) ≈ 3.6 × 106 joules (J, SI) ≈ 3.412 × 103 British Thermal Units (BTU). One gallon (gal) ≈ 3.79 liters.

  • 2

    We avoid using LCI as the abbreviation because LCI is widely used to refer to “life cycle inventory” in the life cycle assessment literature. We also avoid using “LCIA,” which stands for life cycle impact assessment, because that term refers to a type of method, rather than an outcome. “Impact” is frequently used in LCA and other environmental analysis domains to indicate quantifiable effect/damage that is associated with an emission. We use it here in order to have a term that can be applied to both environmental and economic outcomes.

  • 3

    One kilogram (kg, SI) ≈ 2.204 pounds (lb).

  • 4

    One liter (L) = 0.001 cubic meters (m3, SI) ≈ 0.264 gallons (gal).

  • 5

    Note that other green house gases (GHGs) were not included in this analysis.The model is capable of incorporating a broader range of GHGs, but only CO2 was addressed in this study.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Design for Environment Method: Meeting Multiple Performance Goals under Variable Operating Conditions
  5. Case Study of Design Constraints to Realize Life Cycle Economic and Carbon Dioxide Reduction Benefits
  6. Implications
  7. Acknowledgements
  8. References
  9. About the Authors
  10. Supporting Information
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About the Authors

  1. Top of page
  2. Summary
  3. Introduction
  4. Design for Environment Method: Meeting Multiple Performance Goals under Variable Operating Conditions
  5. Case Study of Design Constraints to Realize Life Cycle Economic and Carbon Dioxide Reduction Benefits
  6. Implications
  7. Acknowledgements
  8. References
  9. About the Authors
  10. Supporting Information

Michele A. Mutchek is an M.S. student at the Arizona State University School of Sustainable Engineering and the Built Environment in Tempe, AZ. Eric D. Williams is an assistant professor at the Arizona State University School of Sustainable Engineering and the Built Environment and School of Sustainability in Tempe, AZ.

Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Design for Environment Method: Meeting Multiple Performance Goals under Variable Operating Conditions
  5. Case Study of Design Constraints to Realize Life Cycle Economic and Carbon Dioxide Reduction Benefits
  6. Implications
  7. Acknowledgements
  8. References
  9. About the Authors
  10. Supporting Information

Supplement S1. This supplement contains descriptions of the assumptions and uncertainties, and additional information about the data used in the case study analyses. It presents analysis of whether smart irrigation controllers (SICs) rather than other strategies are appropriate for the American Southwest. The supplement also contains additional area-to-area SIC performance analyses, as well as sensitivity and economic analysis of the results.

FilenameFormatSizeDescription
JIEC_282_sm_SuppMat.pdf192KSupporting info item

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.