A Taxonomic Framework for Assessing Governance Challenges and

Dec 11, 2014 - Predominant forms of food and energy systems pose multiple challenges to the environment as current configurations tend to be structure...
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A Taxonomic Framework for Assessing Governance Challenges and Environmental Effects of Integrated Food-Energy Systems Michael D. Gerst,† Michael E. Cox,‡ Kim A. Locke,‡ Mark Laser,§ and Anne R. Kapuscinski*,‡ †

Earth System Science Interdisciplinary Center, University of Maryland, 5825 University Research Court, College Park, Maryland 20740, United States ‡ Environmental Studies Program, Dartmouth College, 6182 Steele Hall, Hanover, New Hampshire 03755, United States § Thayer School of Engineering, Dartmouth College, 14 Engineering Drive, Hanover, New Hampshire 03755, United States S Supporting Information *

ABSTRACT: Predominant forms of food and energy systems pose multiple challenges to the environment as current configurations tend to be structured around centralized one-way through-put of materials and energy. In addition, these configurations can introduce vulnerability to input material price and supply shocks as well as contribute to localized food insecurity and lost opportunities for less environmentally harmful forms of local economic development. One proposed form of system transformation involves locally integrating “unclosed” material and energy loops from food and energy systems. Such systems, which have been termed integrated food-energy systems (IFES), have existed in diverse niche forms but have not been systematically studied with respect to technological, governance, and environmental differences. As a first step in this process, we have constructed a taxonomy of IFES archetypes by using exploratory data analysis on a collection of IFES cases. We find that IFES may be classified hierarchically first by their primary purposefood or energy productionand subsequently by degree and direction of vertical supply chain coordination. We then use this taxonomy to delineate potential governance challenges and pose a research agenda aimed at understanding what role IFES may play in food and energy system transformation and ultimately what policies may encourage IFES adoption.

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INTRODUCTION The predominant forms of food and energy systems pose multiple challenges to the environment as current configurations tend to be structured around one-way through-put of materials and energy. Globally, 30% of energy is consumed in the agri-food chain. As most energy is primarily sourced from burning fossil fuels, the global agri-food chain produces 20% of greenhouse gas emissions.1 Similarly, modern high-yield agriculture is largely dependent on irrigated water and on fertilizers composed of synthesized nitrogen and mined phosphate and potash. Poor management of water and fertilizer applications results in unnecessary drainage of aquifers and excessive phosphorus- and nitrogen-laden runoff that degrades water quality.2 Furthermore, the centralized, one-way structure of industrial food and energy systems often trade cost efficiency for other socio-economic effects. For example, heavy reliance on fossil fuels and synthesized nutrients can leave food production vulnerable to price and supply shocks without recourse to alternatives. At the local level, loss of local food systems may contribute to food insecurity and lost opportunities for local economic development.3,4 Thus, transforming food and energy production systems to mitigate these problems is a critical goal on the path to creating a more sustainable society and environmenta defining challenge in the 21st century.5 One proposed form of system transformation adopts the concept of “loop-closing” (i.e., finding environmentally © 2014 American Chemical Society

beneficial uses for waste streams), which is a central concept in the field of industrial ecology. These transformed configurations, called integrated food-energy systems (IFES), purposefully link “unclosed loops” from food and energy systems to one another through locally integrated systems. Although integrated food energy systems (IFES) have existed for awhile in diverse niche forms,6 it was only recently that the United Nations Food and Agriculture Organization (FAO) drew attention to their diversity and emergence in both developing and developed nations.7,8 This is likely because the wide variety of IFES has tended to lead to in-depth study of particular configurations, such as agroforestry or manure-based biogas production, instead of more systematic study of technological and institutional commonalities and differences. When broader synthesis of a topic is not well-established, it is often useful to delineate a taxonomy of archetypal cases in order to provide a point of departure for further analysis, such as Chertow’s seminal article on industrial symbiosis.9 As discussed in Cox,10 the usefulness of taxonomies stems from their ability to reveal “limited diversity” among seemingly unlimited potential system configurations.11 Limited diversity often emerges in coupled human-natural systems because Received: Revised: Accepted: Published: 734

April 28, 2014 December 2, 2014 December 11, 2014 December 11, 2014 DOI: 10.1021/es504090u Environ. Sci. Technol. 2015, 49, 734−741

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Figure 1. Schematic of the potential flows among IFES plant, animal, and energy subsystems and primary and secondary sites. Dashed box indicates the IFES boundary. Dotted boxes delineate sites within an IFES. If more than one site exists, then the primary site takes in materials from the secondary site and possibly returns energy and materials. Solid arrows represent material and energy flows, where black arrows are products sold or obtained from off-site and gray arrows are on-site exchanges.

governance implications for this finding and outline avenues of future research needed to better understand the potential for IFES to contribute to sustainable energy and food production.

potential system permutations are constrained by system purposes, technologies, and institutions. Thus, a useful taxonomy can help highlight where these constraints occur and can be especially important in forming prescriptions about system governance and in understanding environmental effects. The FAO report “Making Integrated Food-Energy Systems Work for People and Climate” presented a first attempt at creating an IFES taxonomy, defining IFES in terms of different technological configurations, scales, and purposes.7 Along the technological dimension, it identified two types of systems. Type 1 systems are characterized by cropping patterns that produce food and energy feedstock, such as agroforestry, while Type 2 systems consist of more technologically complex arrangements that maximize use of byproducts, such as anaerobic digestion of cow manure. In terms of scale, FAO characterized IFES as small- or large-scale, which captures a range from farming for self-sufficiency to growing mainly cash crops. Finally, purpose was divided into three categories. Farmcentered IFES typically approach energy production as a spinoff of an existing agricultural operation while the energy farm IFES switches the design and operational focus to energy production. Community-centered systems have high multifunctionality and are more embedded in the surrounding community. All together the three axes yield 12 possible IFES types. While establishing a starting point for thinking about IFES, FAO’s taxonomy does not necessarily establish the existence of limited diversity or a ranking of the importance of axes in delineating IFES. Furthermore, classification based on dichotomies (e.g., Type 1 or 2, large or small) are often too coarse to aid in understanding the governance and environmental implications of evolving socio-technical systems.12 For example, it is hard to research how typology affects governance and environmental effects when a given IFES might have both Type 1 and 2 configurations; and when small- and large- scale have ambiguous definitions, leaving no option for medium-sized operations. In this paper we introduce a new IFES taxonomy. Instead of being defined by a priori selection of axes and categories, our taxonomy is guided by an exploratory data analysis of material and energy flows of 87 IFES cases. Focusing on flows allows for the exploration of emergent system properties for use in a taxonomy and has proved useful in the sociological investigation of biogas production.12 We find that this method reveals limited diversity among IFES: purpose is the most important distinguishing factor followed by degree and direction of vertical supply chain coordination. Importantly, our purpose and configuration categories substantially differ from FAO’s taxonomy. In the Discussion section, we describe the environmental and

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MATERIALS AND METHODS

In order to build our sample, we searched the relevant scientific literatures, nongovernment and governmental reports, and company Web sites. While our goal is to include as much diversity as possible in terms of location, institutional arrangement, and technology, the paucity of published data on IFES cases likely results in oversampling of certain welldocumented cases, such as anaerobic digestion of cow manure. However, our choice of statistical technique, hierarchical clustering, somewhat mediates this problem. Characterization of IFES Cases. In choosing the variables to describe IFES, our strategy is to use variables that require minimal, easy to obtain information. This is necessary to achieve our goal of including diverse cases because the information currently available on IFES varies from brief descriptions to detailed engineering-economic studies. After initial review of available case descriptions, we found that, at a minimum, each case can be described in terms of the existence of a prespecified set of energy and matter flows. Setting a higher threshold for more information on IFES operational parameters drastically reduced the potential sample size. Defining the set of possible energy and matter flows entails a tension between minimizing the number possible flows, which improves interpretability, and preserving detail necessary to distinguish among IFES configurations. To create a balance between these two goals, IFES processes were aggregated into three subsystems: plant, animal, and energy (Figure 1). For example, on a dairy farm, the plant, animal, and energy subsystems would correspond, respectively, to the growing of feed, dairy operations, and anaerobic digestion and energy production. Possible flows among subsystems include food, feed, nutrient, fuel, heat, electricity, and other. In examples with linked local sites, such as farms sending manure to a community anaerobic digester, exchanges among sites are also accounted for. Flows are recorded in an n-by-m matrix, where n = number of cases (87) and m = number of flows (59). Element (i, j) of the matrix, which corresponds to case i and flow j, takes the value 0 if the flow is not present and 1 if the flow is present. For linked local systems, on-site flows are differentiated by site. Flows occurring at the site that primarily produces energy are recorded using the same attributes as a single site system while flows occurring at secondary sites are recorded separately as secondary on-site flows. Supporting Information (SI) (Table 735

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Figure 2. Dendrogram of IFES cases resulting from cluster analysis (panel a). Dashed boxes delineate the area of dendrogram shown in panels b−d. The y-axis of panels b−d show a property’s relative frequency for each archetype: a value of zero (one) means that none (all) of the cases in the cluster have the specified property. Properties are grouped and labeled by subsystem: plant, animal, or energy. P refers to whether a product is sold from the labeled subsystem, F is whether the subsystem is multifunctional (i.e., produces food/feed and fuel/energy or multiple types of energy), and D indicates if the subsystem is distributed across two or more sites.

SI-S1) presents the database of the 87 cases and matrix of flows for these cases. Hierarchical Clustering. Delineation of our IFES taxonomy is guided by applying a hierarchical clustering algorithm13 to the matrix of cases and flows, where element (i,j) denotes case i and flow j. As opposed to more ad hoc methods, hierarchical clustering provides a systematic way to group the cases into meaningful clusters that might comprise a taxonomy. Although it is one of many available techniques to cluster data, one of its distinctive advantages is that cluster construction is nested, which provides additional useful information, such as what variables more strongly distinguish clusters in the data. Additional detail on the methodology and our choice of algorithm is described in the SI. A major assumption underlying our choice of variables is that the distribution of primary and secondary sites and structure of material and energy flows may be used to inform a taxonomy meant to provide guidance on assessing governance challenges and environmental effects of IFES. The first step in this process is to define a small set of IFES properties that are easily calculated from the i × j matrix of biophysical flows and are relevant to institutional analysis of IFES governance and environmental effects. The properties are not used as input to the clustering algorithm; we use them only for succinctly communicating the character of the resulting clusters. For the first property, we indicate whether the system is designed to sell plant, animal, and/or energy products. The second property is whether each of the IFES subsystems

plant, animal, and energyis multifunctional. Plant and animal subsystems are considered multifunctional if they produce both food/feed and fuel/energy. Energy subsystems are considered multifunctional if multiple forms of energy are produced and used by the energy or other subsystems or, if in addition to energy production, materials are transferred to other subsystems (e.g., manure digestate to fields). The final IFES property measures whether each subsystem is distributed across two or more sites. We measured each property for each IFES subsystem, yielding nine properties, all of which are binary variables.

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RESULTS Applying the hierarchical clustering algorithm to our cases yields the dendrogram shown in Figure 2(a). A dendrogram visually displays the building up of clusters from individual cases (distance = 0) to iteratively larger clusters: vertical lines represent clusters and horizontal lines (nodes) link two smaller clusters. The height (y-axis) at which a link occurs indicates the distance of the two constituent clusters. Thus, smaller initial clusters at a lower height are more similar, but as groups are iteratively joined into bigger groups, the cluster becomes increasingly dissimilar. The trend in the error sum of squares (defined in SI) indicates that little information is added after seven clusters and that moving from two to three clusters adds a relatively large amount of information. Furthermore, three clusters has a low misspecification rate as well as high interpretability. Therefore, 736

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Figure 3. Schematics of IFES configurations. The numbered boxes indicate a food or energy production process, with 1 indicating a process producing a primary product and 2 indicating a process producing a secondary product. Black and red arrows represent primary and secondary products, respectively, which are sold offsite or exchanged between subsystems.

IFES that only have a primary process (Figure 3(a)) are labeled joint. As shown in Table 1, the four configurations shown in Figure 3 provide further refinement of our identified upper-level Table 1. Summary of IFES Archetypes and Sub-Typesa archetype plant

subtype joint forward backward circular

animal

energy

joint forward backward circular

joint forward backward circular

example

FAO taxonomy

maize-eucalyptus intercropping (El Salvador)

farm-centered; type 1; small or largescale

Schramsberg Vineyards (Calistoga, California) Gill’s Onions (Oxnard, California)

farm-centered; type 1; small or largescale farm-centered; type 2; large-scale

Audet’s Blue Spruce Farm (Bridport, Vermont)

farm-centered; type 2; large-scale

Keene Energy & Agriculture Project (Keene, New Hampshire) Willow-pig system (Denmark) Farmer’s Ethanol (Cadiz, Ohio)

energy farm; type 2; large-scale energy farm; type 2; large-scale energy farm; type 2; large-scale

a

Blank cells indicate that we have not yet identified a case for the subtype.

archetypes and display the “limited diversity” that was absent in FAO’s taxonomy. Furthermore, Table 1 illustrates that the coarseness and lack of hierarchy of the FAO taxonomy leads to much less precision in classifying IFES, as the same FAO types may be used to describe many of our archetypes. This is similar to a taxonomy of organisms. We know that differences are larger among organisms in separate kingdoms than differences among organisms in the same kingdom but different families. Managing, for example, a resource from the plant versus animal kingdom entails much larger differences than managing two different types of plants. The type of taxonomy used by FAO would not make these hierarchical distinctions. The Plant archetype is populated by three of the four configurations. Joint Plant IFES are exemplified by small-scale agroforestry. For example, in El Salvador intercropping of maize with eucalyptus trees provides food and fuel wood from the same land; fuel wood is a supplement to food production. Our sample does not contain a forward Plant IFES. We believe this 737

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Backward Energy IFES tend to be large-scale agroforestry or other multipurpose systems that produce fuel and food. The distinction between this subtype and joint Plant IFES is that backward Energy IFES are at a scale where plant-based fuel needs to be locally converted to a more energy-dense form in order to reduce transportation costs. In order justify the cost of conversion equipment, the agricultural scheme would need to favor production of fuel over food. For example, a system in Denmark grows willow trees to be used in large-scale combined heat and power generation. The willow trees are fertilized by pigs, which are raised for their meat. As the pig meat is a secondary coproduct, the manure input into the willow tree operation is backward integration. Circular Energy IFES usually involve all three subsystems and are highly integrated. An example is a design by Farmer’s Ethanol LLC in Ohio, which meshes aspects of grain-based fuel production with raising of livestock. In the setup under construction in Ohio, corn is used to produce ethanol and a coproduct that are fed to cattle. Manure from the cattle is anaerobically digested to produce methane, which is used to produce heat for ethanol distillation. The nutrient-rich coproducts of the anaerobic digestion are then applied to the corn fields. Other combinations of grain and livestock are under design for the Banat agricultural region of Romania.

is because a food-driven IFES that uses a primary coproduct to produce energy is likely to use the energy on-site, which would classify as a circular system. Backward Plant IFES are largely composed of colocation of solar or wind production with crops, wherein the generated electricity (secondary product) is typically sold into the grid, both to offset onsite electricity use and generate additional revenue. An increasingly common type involves the siting of photovoltaic cells on marginal vineyard land, of which Shramsberg Vineyards in Calistoga, California is an example. Circular Plant IFES are probably the most complicated of Plant IFES. An example of this setup is Gill’s Onions in Oxnard, California. Here, waste from the processing of onions is used to produce biogas, which is then combusted for heat and electricity that offsets the need for purchased power. Fewer types of multifunctionality were found in our sample of Animal archetype IFES. We hypothesize this is because a joint configuration is less likely as coproducts from animal systems are difficult to use directly as energy. The forward configuration is unlikely for the same reasons as it is for the Plant archetype: if an IFES produces energy from animal coproducts then it is likely the energy produced would be used on-site. Although we did not find an example with sufficient information on flows to include in our sample, the backward configuration is conceptually more likely, as this would involve colocated generation of solar or wind energy. For example, an aquaculture operation might use photovoltaic arrays to provide power for aeration and pumps of fish tanks. The predominant configuration observed for the Animal archetype is circular. A prominent example of this is Audet’s Blue Spruce Farm, a dairy farm in Bridport, Vermont. Here, through the process of anaerobic digestion cow manure is converted to methane, carbon dioxide, and sulfide gases as well as a slurry of liquid and solid nutrient-rich material. The gases are burned to provide heat and electricity for the dairy operation as well as electricity for over 300 homes. The slurry is separated into solid and liquid fractions, with part of the solids being used as bedding for cows and the remainder sold as gardening fertilizer. The liquids, containing valuable phosphorus and nitrogen nutrients, are applied as fertilizer to the dairy’s land, which grows feed for the cows. For the Energy archetype, three of four configurations were found in our sample. Joint Energy IFES were not found and are unlikely to exist. A joint operation would entail food and energy coproducts from a single subsystem, with the energy coproduct driving system behavior. An energy subsystem is unlikely to directly produce food. A plant subsystem generating energy as a primary product is unlikely because it would need to be at a large enough scale to permit local conversion of the plant-based fuel into adequate quantities of a more energy-dense form for the IFES to be financially viable. Furthermore, this conversion of the plant-based fuel means that the configuration would no longer be a joint IFES. Forward Energy IFES tend to involve highly engineered food production that is sited to take advantage of waste heat from existing electricity production. An especially integrated example is Keene, New Hampshire’s recently approved Keene Energy and Agriculture Project, which will be situated next to a landfill that produces heat and power from captured landfill gas. The IFES uses the heat and power to run a greenhouse that encloses a recirculating aquaponics system, producing vegetables and fish.

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DISCUSSION The promise of IFESto “close loops” and thus mitigate environmental harms while providing social benefitsis possible because they inherently couple natural and human systems.16,17 Whether the different cases of IFES yield these benefits depends on dynamic interactions between internally and externally coupled natural and human systems. Within an IFES, internal coupling or feedback exists among human (e.g., farm workers, various technologies) and natural systems (e.g., land, plants, animals). External coupling occurs between an IFES and surrounding institutions, economy and natural environment. Our IFES taxonomy can guide the development and testing of hypotheses about the dynamic interactions among technological configurations, social institutions, and natural systems and about the environmental and social consequences of these interactions. The process of developing this taxonomy led us to identify a few initial key issues, which we elaborate below. Transaction Cost Economics and Vertical Supply Chain Coordination. The observed intersection of IFES purpose and configuration has important implications for governance because “closing loops” at the local scale requires devising a vertical supply chain coordination strategy.15 Vertical supply chain coordination may take a wide variety of forms. In the simplest sense it may be the purchasing of inputs on a spot market, where repeated interactions among buyers and sellers lead to market prices that contain all relevant information about goods being sold. This ensures efficient prices in the sense that neither buyer nor seller can improve their position without making the other worse off. Deviation from the conditions under which supply chain spot markets work well introduces so-called transaction costs. Minimizing these costs requires alternative governance structures, such as long-term contracts or, in the strongest form of vertical integration, internalizing previous external parts of a supply chain within a firm. Economic transactions, whether through a spot market or otherwise, have three critical characteristics: (i) uncertainty with respect to prices, quality, 738

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This last point is related to the possibility that increasing vertical supply chain integration might impose strategic transaction costs associated with the trade-off between stability and flexibility.22 Alternatives to transactions in a spot market will lock-in, to varying degrees, the economic, social, and technological relationships. Thus, increasingly integrated vertical governance structures must plan for small changes in internal and external conditions as well as disruptive changes. For example, an IFES whose business model is dependent on state-level renewable energy portfolio standards and acceptance of renewable energy certificates across states would have to consider resilience of the IFES to small random fluctuations in operational parameters (e.g., machinery breakdown or animal sickness) across diverse processes and to disruptive changes to renewable energy policy. This implies that governance structures must carefully consider the irreversibility of moving from a food or energy business into an IFES. Production-based transaction costs are especially relevant to consider for colocated vertical coordination. Often there are mismatches in scale among processes along the supply chain, with some processes not achieving economies of scale due to small capacity or others producing more than can be economically handled on-site. For example, many dairy farms are too small to achieve the economies of scale necessary to install an anaerobic digester and a combined heat and power generator. Many smaller dairy farms might cooperate to build a centralized digester/generator, but this increases group size and hence bureaucratic transaction costs and, depending on the ownership arrangement of the digester/generator, might introduce principal-agent problems. Furthermore, strategic transactions costs might occur if the centralized digester/ generator is dependent on full participation by local farms or uncertain energy policy in order to remain viable. Avenues for Future Research. Integrated food-energy systems promise to reduce negative environmental effects of food and energy production via “loop-closing” configurations that recycle byproducts and wastes into valuable resources reused in the system. At the upstream end of production lifecycles, IFES may reduce dependence on fossil fuels and may even eliminate direct fossil-fuel use in the case of circular Energy configurations. In another example, circular Animal configurations can reduce purchases of fertilizer phosphorus, which can support global stewardship of this essential, nonsubstitutable, and limited resource; 26,27 and backward Animal configurations may reduce fossil fuel use or leapfrog directly to renewable energy use, including new systems in places without a developed grid.28 At the downstream end of production life-cycles, IFES can reduce harmful discharges to the natural environment. Most IFES configurations should mitigate greenhouse gas emissions in either or both the energy and food subsystems. IFES that involve animals may also mitigate eutrophication of natural waters by reusing nutrientladen animal manures. Finally, IFES may mitigate solid waste problems, for example, configurations with landfill-gas or organic biodigesters turn organic wastes into valuable energy feedstocks. If we only consider these kinds of biophysical couplings of IFES to surrounding natural environments, we can hypothesize that upstream and downstream environmental harms decrease as the “loop-closing” and complexity of IFES configurations increases from joint (Figure 3a) to circular (Figure 3d). However, our discussion of transaction cost economics suggests that governance challenges might increase with system

and availability, (ii) frequency of transaction recurrence, and (iii) asset specificity. Increasing uncertainty and asset specificity or decreasing frequency of occurrence tend to lead to less efficient prices, which provides opportunities for either buyer or seller to take advantage of the other. Of these, asset specificity is thought to be the most important motivator for implementing a more integrated vertical supply chain coordination strategy.18,19 Asset specificity refers to the degree to which the value of investments made by an organization are specific to its relationship to other organizations with whom it has contracts. High asset specificity indicates that a firm cannot redeploy its investments toward other contracts with other firms than those with which it is currently engaged. Four sources of asset specificity have been identified in the literature: site, physical, human, and dedicated assets.19,20 Site specificity, which describes material and energy flows that are dependent on their location, is especially relevant to IFES and other resourceoriented industries. In these highly site-specific cases, colocation of downstream processing tends to minimize inventory and transportation costs but also generate large sunk costs in the form of immobile physical assets. Possible Implications for Governance Challenges. In hypothesizing possible implications for IFES governance challenges, we draw on broad conclusions from over 30 years of theoretical and empirical work in transaction cost economics.18−24 As asset specificity increases, governance structures may respond by becoming increasingly integrated, spanning from less integrated arrangements such as short-term contracts, to long-term contracts, franchising, joint ventures, and finally to vertical financial ownership in the case of very high asset specificity. In general, the literature contends that increasing vertical supply chain integration in response to increasing asset specificity improves (i) coordination and control among units and divisions, (ii) audit and resource allocation, (iii) motivation among actors to align their interests, and (iv) communication efficiency and operational stability.21 These advantages, however, might be offset by increases in bureaucratic, strategic, and production transaction costs.21 With respect to bureaucratic transactions costs, as group size or participants’ heterogeneity increases, coordination may become more difficult and additional levels of hierarchy may be needed. This is to some extent related to the principal-agent problem, which describes challenges that might arise when a principal pays an agent to make decisions that affect the principal.25 Agency costs are incurred if the interests of the principal and agent are not aligned or if information is not fully shared. For IFES, such agency costs might emerge in systems that have diverse and knowledge-intensive processes that require different specialized employees. If governance structures are not carefully put in place, then agency costs might emerge if the interests of each employee or contractor (i.e., the agents) is in maximizing the importance each one’s respective process instead of overall IFES performance or if the IFES owner (i.e., the principal) does not adequately understand decisions made about each process. This problem might be especially acute for IFES, when these systems begin as more simple food or energy production firms. Integrating away from the original core business is likely to require skills different from those previously acquired by firms. Furthermore, the lack of spot market prices along the supply chain, which provide information on competitiveness of each production step, may undercut gains of increasing integration if governance plans are not appropriately structured. 739

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complexity (e.g., from joint to circular configurations) due to likely increases in bureaucratic, strategic and production transaction costs. Furthermore, governance challenges might differ depending on the IFES’s primary product because plant, animal, and energy products differ from each other in properties such as supply chain flexibility and price volatility. These apparent impediments to viability raise the following kinds of questions about the potential for IFES to contribute at a larger scale to the transformation of food and energy systems: 1. Under what conditions does the capacity to resolve governance challenges make or break the realization of environmental benefits from a particular IFES configuration? 2. Can certain coupled natural-human system dynamics such as reducing nutrient pollution in jurisdictions with strong pollution regulationslower governance challenges to the point that a more complex IFES configuration is financially viable, fully realizing possible environmental benefits? 3. Do the three archetypes (Plant, Energy, Animal) have equal or different capacities to resolve governance challenges and realize potential environmental benefits? 4. How does the evolution of multifunctional cases from simpler configurations (an evolution indicated in some cases in our IFES database), compared to starting out with a complex configuration, affect the dynamic interactions between governance challenges and environmental effects? 5. Finally, how does IFES archetype and configuration affect system resilience to future technological, social, and environmental changes, particularly climate change? Answering these questions will require multidisciplinary efforts, as seeds to the answers are likely scattered among many disciplinary traditions. Filling this knowledge gap should matter to researchers, policy makers, entrepreneurs, and practitioners interested in advancing transitions away from high throughput food and energy production to more sustainable food and energy production systems. Our IFES taxonomy provides a framework for developing hypotheses and testing them via analysis of appropriately sampled cases within and among the Plant, Energy, and Animal archetypes, and across simpler to complex configurations (joint to circular). In addition, the validity of our taxonomy may be tested in the future as new IFES cases are identified and compared to the archetypes we have presented. We hope that this taxonomy will provide a common foundation for research by diverse teams, in addition to our own research program, and thus facilitate comparisons across research efforts and future meta-analyses.

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Policy Analysis

AUTHOR INFORMATION

Corresponding Author

*Phone: 603-646-2668, fax: 603-646-1682; e-mail: anne.r. [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was supported by the Sherman Fairchild Professorship in Sustainability Science (to ARK) and the Dean of the Faculty of Arts and Sciences, Dartmouth College. We thank Michael Perlstein (Dartmouth class of 2014) for starting the IFES case database, which we have expanded prior to use in this research.

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REFERENCES

(1) FAO. Energy-Smart Food at FAO: An Overview; United Nations Food and Agriculture Organization (FAO): Rome, 2012; p 71. (2) Foley, J. A.; Ramankutty, N.; Brauman, K. A.; Cassidy, E. S.; Gerber, J. S.; Johnston, M.; Mueller, N. D.; O’Connell, C.; Ray, D. K.; West, P. C.; et al. Solutions for a cultivated planet. Nature 2011, 478, 337−342. (3) Martinez, S.; Hand, M.; Pra, M. Da; Pollack, S.; Ralston, K.; Smith, T.; Vogel, S.; Clark, S.; Lohr, L.; Low, S. Local Food Systems: Concepts, Impacts, and Issues; United States Department of Agriculture, Economic Research Service, 2010; pp 1−77. (4) Pelletier, N.; Audsley, E.; Brodt, S.; Garnett, T.; Henriksson, P.; Kendall, A.; Kramer, K. J.; Murphy, D.; Nemecek, T.; Troell, M. Energy intensity of agriculture and food systems. Annu. Rev. Environ. Resour. 2011, 36, 223−246. (5) Gerst, M.; Raskin, P.; Rockström, J. Contours of a resilient global future. Sustainability 2014, 6, 123−135. (6) Mollison, B. C. Permaculture: A Designer’s Manual; Tagari Publications: Tyalgum, Australia, 1988; p 576. (7) Bogdanski, A.; Dubios, O.; Jamieson, C.; Krell, R. Making FoodEnergy Systems Work for People and Climate: An Overview; United Nations Food and Agriculture Organization (FAO): Rome, 2010; p 136. (8) Bogdanski, A. Integrated food−energy systems for climate-smart agriculture. Agric. Food Secur. 2012, 1, 9. (9) Chertow, M. R. Industrial symbiosis: Literature and taxonomy. Annu. Rev. Energy Environ. 2000, 25, 313−337. (10) Cox, M. Advancing the diagnostic analysis of environmental problems. Int. J. Commons 2011, 5, 346−363. (11) Ragin, C. C. Fuzzy-Set Social Science; University of Chicago Press, 2000; p 352. (12) Mol, A. P. J. Bounded biofuels? Sustainability of global biogas developments. Sociol. Ruralis 2014, 54, 1−20. (13) Murtagh, F. A survey of recent advances in hierarchical clustering algorithms. Comput. J. 1983, 26, 354−359. (14) Vatn, A. Multifunctional agriculture: some consequences for international trade regimes. Eur. Rev. Agric. Econ. 2002, 29, 309−327. (15) Andrews, C. J. Building a micro foundation for industrial ecology. J. Ind. Ecol. 2000, 4, 35−51. (16) Liu, J.; Dietz, T.; Carpenter, S. R.; Folke, C.; Alberti, M.; Redman, C. L.; Schneider, S. H.; Ostrom, E.; Pell, A. N.; Lubchenco, J.; et al. Coupled human and natural systems. Ambio 2007, 36, 639− 649. (17) Liu, J.; Dietz, T.; Carpenter, S. R.; Alberti, M.; Folke, C.; Moran, E.; Pell, A. N.; Deadman, P.; Kratz, T.; Lubchenco, J.; et al. Complexity of coupled human and natural systems. Science 2007, 317, 1513−1516. (18) Williamson, O. E. Transaction-cost economics: The governance of contractual relations. J. Law Econ. 1979, 22. (19) Williamson, O. E. The New Institutional Economics: Taking stock, looking ahead. J. Econ. Lit. 2000, 38, 595−613. (20) Williamson, O. E. Credible commitments: Using hostages to support exchange. Am. Econ. Rev. 1983, 73, 519−540.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information contains text describing the details of the clustering algorithm used and Table SI−S1, which is a spreadsheet providing for each IFES case: (i) name, (ii) location, (iii) matrix of flows, (iv) cluster assignment and (v) source. This material is available free of charge via the Internet at http://pubs.acs.org. 740

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Policy Analysis

Environmental Science & Technology (21) Mahoney, J. T. The choice of organizational form: Vertical financial ownership versus other methods of vertical integration. Strategic Manage. J. 1992, 13, 559−584. (22) Kessler, F.; Stern, R. H. Competition, contract, and vertical integration. Yale Law J. 1959, 69, 1−129. (23) McCann, L.; Colby, B.; Easter, K. W.; Kasterine, A.; Kuperan, K. V. Transaction cost measurement for evaluating environmental policies. Ecol. Econ. 2005, 52, 527−542. (24) Coggan, A.; Whitten, S. M.; Bennett, J. Influences of transaction costs in environmental policy. Ecol. Econ. 2010, 69, 1777−1784. (25) Rees, R. The theory of principal and agent Part I. Bull. Econ. Res. 1985, 37, 3−26. (26) Cordell, D.; Rosemarin, A.; Schröder, J. J.; Smit, A. L. Towards global phosphorus security: a systems framework for phosphorus recovery and reuse options. Chemosphere 2011, 84, 747−758. (27) Dawson, C. J.; Hilton, J. Fertiliser availability in a resourcelimited world: Production and recycling of nitrogen and phosphorus. Food Policy 2011, 36, S14−S22. (28) Hutchinson, I. Renewable Power Generation on Aquaculture Sites (SARF093); Scottish Aquaculture Research Forum: Pitlochry, Scotland, 2014; p 99.

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