Systematically incorporating environmental objectives into shale gas

For example, 10 percent of the habitat impacts can be avoided at less than a .... Binary variables are used to represent whether a pipeline is present...
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Article Cite This: Environ. Sci. Technol. 2019, 53, 7155−7162

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Systematically Incorporating Environmental Objectives into Shale Gas Pipeline Development: A Binary Integer, Multiobjective Spatial Optimization Model Kailin Kroetz,*,† Jhih-Shyang Shih,† Juha V. Siikamäki,‡ Vladimir Marianov,§ Alan Krupnick,† and Ziyan Chu∥ †

Resources for the Future, 1616 P St. NW, Washington, D.C. 20036, United States International Union for Conservation of Nature, 1630 Connecticut Ave. NW, Suite 300, Washington, D.C. 20009, United States § Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Av. Vicuna Mackenna 4860, Santiago, Chile ∥ First Street Foundation, Brooklyn, New York 11201, United States

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ABSTRACT: Shale gas pipeline development can have negative environmental impacts, including adverse effects on species and ecosystems through habitat degradation and loss. From a societal perspective, pipeline development planning processes should account for such externalities. We develop a multiobjective binary integer-programming model, called the Multi Objective Pipeline Siting (MOPS) model, to incorporate habitat externalities into pipeline development and to estimate the trade-offs between pipeline development costs and habitat impacts. We demonstrate the utility of the model using an application from Bradford and Susquehanna counties in northeastern Pennsylvania. We find that significant habitat impacts can be avoided for relatively low cost, but the avoidance of the additional habitat impacts becomes gradually and increasingly costly. For example, 10% of the habitat impacts can be avoided at less than a two percent pipeline cost increase relative to a configuration that ignores habitat impacts. MOPS or a similar model could be integrated into the pipeline siting and permitting process so oil and gas companies, communities, and states can identify cost-effective options for habitat conservation near shale gas development. and carbon sequestration.12 Natural gas development can also adversely affects surface water systems by reducing streamflow and introducing chemical and sediment runoff.13 Reduced streamflow may inhibit wetland productivity, and pipelines pose a risk of rupture. Approximately half of the global wetland area has already been lost, and thus ecosystem services provided by wetlands (e.g., flood abatement, water quality benefits, and carbon sequestration) are highly valued and remain threatened by irreversible damage.14 Recent gas development in Pennsylvania and West Virginia intersects with forested areas, which has spurred a significant body of literature focused on how development has contributed to core forest loss.6−9 Studies within and outside of Pennsylvania have found that shale gas pipeline infrastructure development is a primary contributor to rural landuse change and forest fragmentation. For example, Abrahams, Griffin, and Matthews5 found gathering lines accounts for 94% of incremental fragmentation of core forest in Bradford

1. INTRODUCTION Advances in drilling technologies have led to a boom in oil and gas development in the United States. This growth has provided a number of benefits, including lower energy prices, displaced coal in electricity production (and related health and climate policy benefits1,2), and increased energy independence. However, shale gas extraction requires intense development (possibly less intense than conventional well development), including the construction of well pads, the gathering of pipelines, larger inter- and intrastate pipelines, compressor stations, and roads to transport materials. These infrastructure needs can lead to trade-offs between shale gas development and environmental impacts.3 Environmental impacts of shale gas development depend on the ecosystem in which the development is occurring. They also depend on the distribution of pipelines, wellpads, and wells within the ecosystem. Documented impacts include ecosystem disruptions, habitat loss, core forest loss, and forest fragmentation.4−10 These impacts are associated with a decline in forest biodiversity, at both local and regional scales.11 Moreover, forest biodiversity is critical for the stable provision of ecosystem services, including natural resource availability © 2019 American Chemical Society

Received: March 15, 2019 Accepted: May 3, 2019 Published: May 3, 2019 7155

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Environmental Science & Technology County, Pennsylvania. Langlois, Drohan, and Brittingham4 determined that core forest decreased by 4% in Lycoming County, Pennsylvania due to shale development, with pipelines and roads responsible for around 80% of that loss. Focusing on seven counties in the Barnett shale in Texas, Jordaan et al.15 found that 74% of total land use change was related to midstream infrastructure (mainly, pipelines). Despite frequent calls for the assessment of habitat externalities and for their incorporation into systematic shale gas development planning across larger spatial scales (see, e.g., Drohan, Brittingham, Bishop, and Yoder,6 Northrup and Wittemyer,16 Mauter et al.,17 Middleton and Brandt18), the availability of research on how to integrate these concepts into models that can be used for policy-making is limited. Studies focusing on larger spatial scales, such as the state- or countylevel, have mainly focused on retrospective analyses characterizing past land-use change related to shale development or pipelines or have projected future impacts based on status quo policies and technologies.4,6−9,19 Only one study that we are aware of, Abrahams, Griffin, and Matthews,5 conducts an analysis of a counterfactual shale gas development plan at the county-scale or larger. The Abrahams, Griffin, and Matthews5 model assesses forest impacts under two predetermined policies: requiring pipelines to follow roads and reducing well pad density. To our knowledge, ours is the first quantitative model to systematically enumerate trade-offs between habitat impacts and pipeline siting costs on a large spatial scale. Other recent studies have incorporated environmental externalities into modeling shale gas supply chains,20−22 but these studies do not model pipeline siting. Doing so requires a quantitative approach to optimizing pipeline configuration under economic and environmental objectives, including evaluating trade-offs. Here, we address this gap in the literature by developing a multiobjective binary integer-programming model, the Multi Objective Pipeline Siting (MOPS) model, which incorporates habitat considerations into pipeline development and estimates the trade-offs between pipeline development costs and habitat impacts. The model takes well locations as fixed and then solves for the least-cost pipeline configuration, given constraints on habitat impacts and the requirement that each well be connected to a larger intra- or interstate pipeline via gathering lines. The model formulation is novel in that it accounts for pipeline connectivity, flow direction, and allows merging of upstream pipelines. To demonstrate an application of MOPS to pipeline siting and permitting processes, we apply it using data from the Bradford and Susquehanna counties in northeastern Pennsylvania. We find that a large number of habitat impacts can be avoided for a relatively low cost, but the avoidance of additional habitat impacts becomes gradually and increasingly costly. For example, 10% of habitat impacts can be avoided at less than a two percent cost increase over the privately optimal pipeline configuration.

cost and habitat impacts of building a pipeline network. We treat these two objectives as incommensurable, as is often the case in multiobjective optimizations. Each component is assigned a weight such that the weights add up to one and varying the weight on each term allows us to develop a tradeoff curve between cost and habitat impacts. An equivalent representation is a function minimizing habitat impact subject to a cost constraint. Under this formulation, a nonbinding cost constraint is equivalent to a weight of zero on cost and positive weight on habitat impacts; a cost constraint equal to the cost of a solution associated with the minimum cost pipeline configuration is equivalent to a weight of zero on the habitat term. An alternative approach would be to minimize social costs, which consist of private costs and the external damage to habitat. The challenging nature of the valuation of habitat favors starting with a weighting/trade-off approach. We measure habitat impacts relative to the cost of pipeline construction using MOPS to derive a trade-off curve for the marginal cost of avoiding habitat impacts, in terms of the cost of pipeline configuration. There are two end points, or corner solutions, that arise from the weighting scheme: full weight on pipeline construction costs (minimum cost solution that disregards habitat impacts) and full weight on habitat impacts (minimum impact solution that disregards pipeline construction costs). The model is formulated such that shale gas is collected from all origin wells through gathering pipelines, which then connect to compression stations on the transmission pipeline. A conceptual diagram of the problem and two extreme solutions (minimum cost and minimum impact, respectively) are presented in Figure 1; a full mathematical formulation of the problem is available in the Supporting Information (SI).

Figure 1. Conceptual model.

The basis of the optimization model is the representation of a continuous landscape as a grid, allowing pipeline placement to be modeled as a binary integer program. Integer programming has been used to solve pipeline siting models before (see, e.g., Brimberg et al.25), and in our case, allows for inclusion of both pipeline siting constraints as well as habitat impacts, while still being able to solve the problem. The centroid of each grid cell is a node in the integer programming problem. Within the grid, we specifically identify two set of nodes: (1) origins (in this case, wells) and (2) destinations (in this case, all nodes through which transmission lines pass). Binary variables are used to represent whether a pipeline is present on the path connecting the nodes of two adjacent grid

2. METHODS We discuss the optimization model and our methods in applying it to a case study below. 2.1. Optimization Model. While most pipeline design models focus on cost and construction details,23,24 the MOPS model is designed to calculate the trade-off between pipeline development costs and environmental outcomes. The optimization problem minimizes a weighted combination of 7156

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Figure 2. (a) MOPS cost minimizing solution. The pipeline configuration represents optimal pipeline placement to minimize pipeline construction costs, with no limit on the quantity of previously undeveloped habitat traversed. Pennsylvania state and county spatial data are from the U.S. Census Bureau. Pipeline and well spatial data are f rom Rextag. Maps were generated using ArcGIS 10.3 (ESRI, Redlands, CA, U.S.A.:http://www.esri.com/software/ arcgis). (b) MOPS habitat impact reduction versus minimum cost solutions. Red translucent coloring is used to represent the pipeline in the cost minimization solution; the gray pipeline configuration represents pipeline placement in the solution with f ull weight on wetland and forest habitat. Pennsylvania state and county data f rom the U.S. Census Bureau and land cover is derived f rom the 2001 NLCD. Maps were generated using ArcGIS 10.3 (ESRI, Redlands, CA, U.S.A.:http://www.esri.com/software/arcgis).

cells (called an arc). Attributes can be associated with an arc, for example, cost, distance, population exposure, habitat area, and types of land use. The objective function of the integer program is the weighted sum of: (1) cost of pipeline

construction for each arc where a pipeline is present and (2) the area of forest and wetland along the arc where pipeline is present (with area along the arc being a proxy for habitat impact). 7157

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The region, as shown in pink in Figure 2, includes 117 wells across an area of about 364 km2. The existing gathering (brown) pipeline and transmission (black) pipeline are based on 2016 production well data from the Pennsylvania Department of Environmental Protection and pipeline data from Hart Energy Mapping & Data Services (Figure 2). There are eight connectors to connect gathering and transmission pipelines. Within the study area, approximately 53% of land cover is forest or wetlands, which we identify as previously undeveloped habitat, and is the focus of our modeling effort. Of this previously undeveloped habitat area, 95% is forest and 5% is wetland. To apply the model to this region, we represent the area as a set of nodes and arcs. There is a trade-off between computational burden and incorporation of heterogeneity of the land cover. We find that the use of 1010 evenly spaced grid cells across the study area, each about 600 m by 600 m, provides adequate treatment of landscape heterogeneity while remaining computationally feasible to solve. We assume that a pipeline, if constructed, will go through and connect the center of a grid cell. For each potential connection (arc), we calculate several attributes including distance, forest and wetland area, slope of pipeline connection, and the length of any water crossings. We include the latter two elements because, although data is not easily accessible, literature has claimed that the cost of materials and construction of shale gas pipelines increases if pipelines go through difficult terrain such as sloped land (see, e.g., Marcoulaki et al.28) or over/under river channels (see, e.g., Marcoulaki, Tsoutsias, and Papazoglou,28 Oil and Gas Journal,29 Racicot et al.30). We define the slope of an arc to be the difference in elevation between the two centroids of the grid cells the arc connects. If the slope is greater than a prespecified number, we assume construction along this arc would be cost-prohibitive and constrain the arc to be an infeasible connection. Given that we only measure the difference in slope between nodes and not the maximum slope at any point along the connection route, the measured slope of the arc may underestimate the maximum slope. We also account for water crossings in our model. Through correspondence with multiple shale gas industry contacts we established “ballpark” estimates of the increased cost of routing shale gas gathering pipelines across water crosings as a function of three main categories of crossing width: small (less than 10 feet), medium (10−100 feet), and large (greater than 100 feet). For small crossings, we assume a cost increase of 10% for the arc (which is around 600 m or .4 miles); for medium crossings, we assume an increase of 20%; and for large crossings, we assume an increase of 150%. We follow previous models and use distance as a proxy for private pipeline infrastructure cost to the developer (see, e.g., Abrahams, Griffin, and Matthews5). Following Lade and Rudik,31 we assume that gathering lines are 4 in. in diameter. Then, using an estimate of the cost per inch-mile from the Interstate National Gas Association of America32 and the PPI to convert the estimate to $2016, we estimate the cost per mile of gathering line as $105936 ($2016). Forest and wetland area affected is used to approximate the habitat impact as an environmental externality. Coverage is calculated as the average percentage of the area of the two connected grid cells covered by forest or wetland. There are no protected areas in our selected study area, so, as an important

By formulating the gathering pipeline layout design as a multiple origins (wells) and multiple destinations (transmission pipeline connectors) (MOMD) problem, we are able to draw from the solution algorithm for other MOMD network analysis problems, such as logistics planning in optimizing package delivery subject to shipping cost26, to solve the problem. The MOMD problem resembles the well-known minimum Steiner tree problem.27 The Steiner tree problem in graphs can be seen as a generalization of two other famous combinatorial optimization problems: the (non-negative) shortest path problem and the minimum spanning tree problem. If a Steiner tree problem in graphs contains exactly two terminals, it reduces to finding a shortest path. If all vertices are terminals, the Steiner tree problem in graphs is equivalent to the minimum spanning tree. Conventional network analysis models focused on connecting sources and destinations would require all the sources and destinations in the final layout. However, for the gathering pipeline problem, not all the potential transmission pipeline connectors (destinations) must be in the optimal layout. Therefore, the optimization model must determine how many and which connectors should be in the optimal layout. To allow such flexibility, we augment the network by adding a pseudo (destination) node which connects all the transmission pipeline connectors and treats connectors as intermediate nodes. We also assign zero value to all the attributes for the arcs between transmission line connectors and pseudo destination nodes. This approach provides the flexibility for the optimal pipeline layout to include or exclude potential transmission connectors. We solve the model using a commercial mixed integer programming solver, specifically the CPLEX solver in GAMS (GAMS Development Corporation 2018). To validate the model, we compare the observed configuration of pipelines to the modeled optimal configuration without accounting for habitat impact. To empirically measure fit, we calculate, for each grid cell, whether there is a model pipeline, an observed pipeline, or both. Recognize, however, that gathering line siting may, to an unknown extent, already account for the avoidance of habitat impacts in the process of meeting stakeholder concerns that arise during the siting process. Thus, any validation of the model by comparing model results to existing pipeline locations may be of limited utility. 2.2. Estimation of Trade-offs in Bradford and Susquehanna, PA. We apply the model to data from the Bradford and Susquehanna counties in northeastern Pennsylvania. We choose northeastern Pennsylvania because there has been considerable shale gas pipeline construction over the past decade. Additionally, the counties were selected for study based on data availability, pipeline characteristics, and environmental characteristics. We use two data sets in the study: pipeline location data from Rextag, current as of 2016, and 2001 land cover data from the National Land Cover Database (NLCD). We use the 2001 NLCD land cover data to match the land cover in the region at the beginning of the pipeline construction boom in the region. We searched within Pennsylvania for a study area with a pipeline network relatively dense within the area and relatively disconnected to nearby areas to reduce the impact of edge-effects on our results (i.e., modeled pipeline design being influenced by a pipeline network outside the study area). 7158

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represent the optimal pipeline layout with a full weight on habitat impact. As is evident from the figure, the gray optimal pipeline layout of this scenario avoids many areas covered by forest and wetland. In addition to exploring the two bounding cases, we develop a trade-off curve depicting the relationship between pipeline cost and habitat impacts as we vary the weights in the model’s objective function. The weights are associated with the decision makers’ preference over the two objectives. To develop the curve, we run our multiobjective optimization model using different sets of weights on pipeline cost and area of forest and wetland lost and report the model output as points in Figure 3. Specifically, we report (x,y) pairs where the

simplification, we treat all forest and wetland areas with equal weight.

3. RESULTS 3.1. Model Validation. We first estimate and validate the model by running it with the objective of minimizing the total pipeline construction cost and compare the optimal pipeline network from the model results with the existing pipeline network. Figure 2a shows the cost minimizing pipeline network in red and existing gathering pipeline network in brown for our preferred model specification. In evaluating model performance, we focus on the extent to which the placement of pipelines by the model overlaps with the observed pipeline configuration. To quantify the overlap, we calculate the total number of grid cells where the model places a pipeline and then calculate the number of these grid cells that also contain an existing pipeline within a one-cell radius of the model pipeline. We view this as a rough estimate of model fit, as it abstracts from important pipeline properties such as direction (we do not have data on pipeline direction) and influence of the pipeline network outside of the study area on study-area pipeline siting. Note that we do not measure whether the existing pipeline matches the model pipeline. As evident in Figure 2, some existing pipelines do not connect to a well. We use a snapshot of pipeline placement, and therefore do not have information on wells that are expected to be drilled in the future. Furthermore, it is possible that some pipeline placement is to facilitate network robustness. We leave the dynamics of pipeline and well siting for future work. Our preferred model avoids steeply sloped l and, specifically, any connections with an estimated slope greater than 20%, and assigns a higher cost to cells that require water crossings. It places a pipeline in the same grid cell as existing pipeline for 94% of model-pipeline grid cells. By and large, the model replicates actual pipeline development. However, in some regions, especially in the northwest region and particularly around the edge of the modeled area, the actual and modeled pipeline network differ from each other. This indicates that factors in addition to the shortest path (cost), slope, and water crossings determine the pipeline layout. For example, topography, soils, connections needed to pipelines outside the study area, pipeline and well ownerships, and even habitat concerns may factor into the observed layout. The reasonable possibility that habitat concerns may factor into the pipeline layout, albeit not with the use of a model such as ours, reduces the accuracy of our validation approach. But with the high fit statistic for the layout minimizing private costs and with a variety of factors omitted from our model that affect such costs, it is likely that habitat concerns had little influence. See the SI for detailed statistics on slope characteristics of grid cells with pipelines. 3.2. Model Results. We bound the model results by two cases: the previously described cost minimization solution and a second case where all wells must be connected, but the area of previously undeveloped habitat traversed is minimized. The second bounding case is solved by putting a weight of zero on the pipeline cost term and a weight of one on habitat value in the objective function. In Figure 2b, we contrast results of the scenario that places a full weight on habitat impact with the cost minimization solution previously described. In Figure 2b, the green color represents forest and wetland land cover. The red line represents the least cost pipeline layout, while the gray lines

Figure 3. Per-acre cost of avoided forest and wetland impact.

x-coordinate is the avoided habitat impact relative to the habitat impact in the cost minimizing solution and the ycoordinate is the cost per acre of habitat impact avoided, calculated as the change in pipeline construction costs (proxied by pipeline distance) relative to the cost-minimization case divided by the acres of habitat impacted. We interpolate between points to develop a curve representing the marginal pipeline cost of avoiding an additional acre of habitat impact using Matlab’s Piecewise Cubic Hermite Interpolating Polynomial (PCHIP). The slope of the curve is relatively flat for low quantities of avoided habitat impact but increases sharply with more avoided habitat. This suggests that relatively large amounts of habitat impact can be avoided at a relatively low cost, but the avoidance of additional units of habitat impact becomes costlier as more habitat is avoided. For example, 10% of the habitat impact can be avoided at less than 2% increase in costs over the privately optimal pipeline configuration. Exploring the relationship between costs and habitat impact for additional habitat avoidancefurther to the right on the curvewe find that approximately 20% of habitat impact can be avoided for about a 11% increase in cost. The avoidance of additional habitat impacts becomes gradually and increasingly costly.

4. DISCUSSION Since the beginning of the boom in the mid-2000s, shale gas production has quickly become the main source of natural gas production in the U.S. Shale accounted for 60% of total U.S. gas production in 2017 and resulted in the U.S. becoming a net 7159

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Environmental Science & Technology exporter of natural gas in 2017 for the first time in at least 60 years. Furthermore, U.S. shale gas production is expected to nearly double by 2040.33,34 This development has been accompanied by a large increase in pipeline construction, with over 0.4 million miles of gathering, transmission, and distribution natural gas pipelines added to the U.S. network, totaling 2.2 million miles in 2015.35 Of these added pipelines and associated natural gas infrastructure, gathering pipelines, which bring produced natural gas from the wellhead to midstream infrastructure, are responsible for nearly 80% of total land use over the entire life cycle of natural gas-fired electricity.15 Our results contribute to growing research showing that state regulations and policies have not led to efficient land use and that energy development could occur with fewer environmental impacts at relatively low cost.36−38 Klaiber, Gopalakrishnan, and Hasan36 used satellite land cover data to find that policies encouraging consolidation on well pads in Pennsylvania would have conserved almost 113000 acres over a nine-year period. Abrahams, Griffin, and Matthews5 find that requiring pipelines to follow existing roads in forested areas would prevent additional fragmentation at a cost of $0.005 to $0.02 per Mcf of natural gas. Milt, Gagnolet, and Armsworth,37 using 20 case study sites, found that conservation-oriented policies could reduce surface impacts from gathering pipelines and access roads without excessively altering current industry practices. Our work augments studies that have assessed alternative policies and/or more comprehensive or systematic planning that have thus far generally focused on small spatial scales. For example, Milt, Gagnolet, and Armsworth38 and Milt and Armsworth39 assessed the costs and benefits of planning to reduce habitat impacts of pipelines but only within leaseholds. Our model allows for quantification of trade-offs between shale gas pipeline development costs and habitat, a particular contribution to policymaking aimed at coordinating shale gas development across spatial scales. Although a number of studies call for such trade-offs to be estimated, this paper is the first to do so, particularly on a large spatial scale.6,16,17 The trade-off curve could be used in conjunction with ecosystem service values to guide policy-making, aiding in the selection of optimal habitat impact avoidance. Specifically, given a value of mitigating forest and wetland impact, the value could be compared with the cost (the y-axis in Figure 3) to determine the optimal and additional expenditure on pipeline construction to avoid disturbing habitat. The cost of avoiding habitat impact ranges between roughly $2000 and $14000 ha−1 (Figure 3), which corresponds to $100-$700 ha−1 year−1 when annualized (5% discount rate, perpetuity). As long as the economic benefits of avoiding habitat impact exceed its costs, from a societal perspective, investing in habitat protection is economically desirable. Depending on the source, the literature includes estimates of ecosystem service values for forests and wetlands that fall within or go beyond or below the above cost range (see, e.g., Petrolia et al.40 and Jenkins et al.41). This is intuitive, as the value of ecosystem services supported by specific habitat area varies depending on the location and type of habitat (see, e.g., de Groot et al.42 who screened 300 case studies on the value of ecosystem services, many of them in tropical and temperate forests). Accordingly, further work is needed to locally determine the magnitude of benefits from avoided habitat impacts on average and to account for the

heterogeneity of habitat vulnerability and protection value across space. Our results also expand the literature related to mitigation of shale gas impacts more broadly. For example, a growing body of work examines the potential gains from mitigating waterrelated shale development impacts by using more systematic, forward-looking planning and by planning across space. There have been both qualitative and quantitative studies examining environmental and health benefits as well as private costs from more systematic planning of shale development to minimize water impacts. Rahm and Riha43 assess several water management policy options and argue in favor of regional impact analysis to ensure the most efficient use of water resources. Bartholomew and Mauter44 study several well pads to assess the trade-offs between water management strategy costs (e.g., acquisition, transportation, storage, and treatment) and the benefits of minimizing impacts on human and environmental health. Gao and You45 developed a model that covers the life cycle of electricity generated from shale gas, consisting of a number stages including freshwater acquisition, shale well drilling, hydraulic fracturing and completion, shale gas production, wastewater management, shale gas processing, electricity generation, transportation, and storage. Shih et al.46 developed a multiobjective programming model for shale gas water and wastewater management that incorporates the objectives of four types of decision makers: oil and gas well developers and operators, centralized wastewater treatment facility planners and operators, environmental regulators, and social planners. Our main findingthat there are gains from coordination of land use changes across larger spatial scalesarises in many contexts in the systematic conservation literature focused on the incorporation of biodiversity and cost attributes into the planning process,47−49 and further work could be done to better understand conservation outcomes and integrate this understanding with systematic conservation planning literature. Potential extensions to the representation of habitat include the consideration of habitat fragmentation (e.g., Saunders et al.50), size of reserves relative to species habitat needs (e.g., Marianov et al.51), and complementarity of the sites left undisturbed (see, e.g., Margules and Pressey52). In the extreme, connectivity in the form of habitat corridors could be considered using model elements similar to those used in the model now to ensure pipeline connectivity (see, e.g., Conrad et al.53). Although the model developed in this paper captures the essential elements necessary to identify the trade-off curve, additional work could be done to develop a more realistic representation of both pipeline cost and placement and habitat impacts. Potential modifications to the pipeline model include allowing for diagonal connections across grid squares, inclusion of flow constraints, and extension of the geographic reach. Other extensions include the consideration of ownership of wells and pipelines, allowing for estimation of gains for coordination between firms, and building in constraints and/or cost modifications to allow for pipelines following an existing transportation network. Habitat values may also be heterogeneous across space, and therefore the ecosystem service valuation methods previously discussed could be applied to develop arc-specific values. Additionally, there may be uncertainty over one or more inputs and/or the future. Although our model is deterministic, the conduction of multiple runs by varying uncertain inputs could be used to 7160

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B. Risks and Risk Governance in Unconventional Shale Gas Development. Environ. Sci. Technol. 2014, 48 (15), 8289−8297. (4) Langlois, L. A.; Drohan, P. J.; Brittingham, M. C. Linear infrastructure drives habitat conversion and forest fragmentation associated with Marcellus shale gas development in a forested landscape. J. Environ. Manage. 2017, 197, 167−176. (5) Abrahams, L. S.; Griffin, W. M.; Matthews, H. S. Assessment of policies to reduce core forest fragmentation from Marcellus shale development in Pennsylvania. Ecol. Indic. 2015, 52, 153−160. (6) Drohan, P. J.; Brittingham, M.; Bishop, J.; Yoder, K. Early trends in landcover change and forest fragmentation due to shale-gas development in Pennsylvania: a potential outcome for the Northcentral Appalachians. Environ. Manage. 2012, 49 (5), 1061−1075. (7) Slonecker, E.; Milheim, L.; Roig-Silva, C.; Malizia, A.; Marr, D.; Fisher, G. Landscape Consequences of Natural Gas Extraction in Bradford and Washington Counties, Pennsylvania. US Geological Survey Open-File Report. 2012, 1154. (8) Slonecker, E. T.; Milheim, L.; Roig-Silva, C.; Malizia, A.; Gillenwater, B. Landscape consequences of natural gas extraction in Fayette and Lycoming Counties, Pennsylvania, 2004−2010; U.S. Geological Survey: 2013; 2331−1258. (9) McGunegle, M. L. effects of oil and gas development on forest fragmentation and breeding bird populations in the Allegheny National Forest. Master’s Thesis, Pennsylvania State University 2009. (10) Donnelly, S.; Cobbinah Wilson, I.; Oduro Appiah, J. Comparing land change from shale gas infrastructure development in neighboring Utica and Marcellus regions, 2006−2015. Journal of Land Use Science. 2017, 12 (5), 338−350. (11) Fahrig, L. Effects of Habitat Fragmentation on Biodiversity. Annual Review of Ecology, Evolution, and Systematics. 2003, 34, 487− 515. (12) Balvanera, P.; Pfisterer, A. B.; Buchmann, N.; He, J.-S.; Nakashizuka, T.; Raffaelli, D.; Schmid, B. Quantifying the Evidence for Biodiversity Effects on Ecosystem Function and Services. Ecology Letters. 2006, 9, 1146−1156. (13) Entrekin, S.; Evans-White, M.; Johnson, B.; Hagenbuch, E. Rapid Expansion of Natural Gas Development Poses a Threat to Surface Waters. Frontiers in Ecology and the Environment. 2011, 9 (9), 503−511. (14) Zedler, J. B.; Kercher, S. Wetland Resources: Status, Trends, Ecosystem Services, and Restorability. Annual Review of Environment & Resources. 2005, 30, 39−74. (15) Jordaan, S. M.; Heath, G. A.; Macknick, J.; Bush, B. W.; Mohammadi, E.; Ben-Horin, D.; Urrea, V.; Marceau, D. Understanding the life cycle surface land requirements of natural gas-fired electricity. Nature Energy. 2017, 2 (10), 804. (16) Northrup, J. M.; Wittemyer, G. Characterising the impacts of emerging energy development on wildlife, with an eye towards mitigation. Ecology Letters. 2013, 16 (1), 112−125. (17) Mauter, M. S.; Palmer, V. R.; Tang, Y.; Behrer, A. P. The next frontier in United States shale gas and tight oil extraction: Strategic reduction of environmental impacts. Energy Technology Innovation Policy (ETIP) Research Group: Belfer Center for Science and International Affairs. 2013, 4. (18) Middleton, R. S.; Brandt, A. R. Using Infrastructure Optimization to Reduce Greenhouse Gas Emissions from Oil Sands Extraction and Processing. Environ. Sci. Technol. 2013, 47 (3), 1735− 1744. (19) Johnson, N.; Gagnolet, T.; Ralls, R.; Zimmerman, E.; Eichelberger, B.; Tracey, C.; Kreitler, G.; Orndorff, S.; Tomlinson, J.; Bearer, S. Pennsylvania energy impacts assessment report 1: Marcellus Shale natural gas and wind. The Nature ConservancyPennsylvania Chapter: Harrisburg, PA, 2010. (20) He, L.; Chen, Y.; Li, J. A three-level framework for balancing the tradeoffs among the energy, water, and air-emission implications within the life-cycle shale gas supply chains. Resources, Conservation and Recycling. 2018, 133, 206−228. (21) He, L.; Chen, Y.; Zhao, H.; Tian, P.; Xue, Y.; Chen, L. Gamebased analysis of energy-water nexus for identifying environmental

gain insight into the sensitivity of the model results to these inputs. Well siting is now a given in the model, and although it would add significant complexity, well siting could be made endogenous to the model. The inclusion of well siting could be more realistic in some settings as shale gas drilling methods do have the potential to mitigate surface environmental impacts. New drilling technologies allow more production from a smaller area of land than that from conventional technology, as well as flexibility in well and pipeline siting decisions. Companies generally drill four to six wells (potentially up to several dozen) and have also increased the lateral length of these wells by thousands of feet, with this efficiency having increased over time throughout the shale development boom.54 In conclusion, the incorporation of habitat impacts associated with both well and pipeline siting encourages more efficient land use in shale gas development. MOPS or a similar model could be integrated into the pipeline siting and permitting process so that oil and gas companies, communities, and states can identify cost-effective options for habitat conservation near shale gas development.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.9b01583.

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Key attributes of the MOPS model, model definition and problem statement, formulation of a solution to the multiobjective constrained optimization, information on study site slopes and water crossings (PDF)

AUTHOR INFORMATION

Corresponding Author

*E-mail: kroetz@rff.org. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the Alfred P. Sloan Foundation and the Stephen D. Bechtel Foundation, Jr. Foundation for R.F.F.’s work on a range of issues related to shale gas development, including this research. Marianov acknowledges support from the Complex Engineering Systems Institute through grant CONICYT PIA FB0816 and grant FONDECYT 1160025. The authors thank Paul Armsworth, Michael Griffin, and Jan Mares for helpful insight and feedback and Matthew Ashenfarb, Jessica Blakely, Isabel Echarte, and Justine Huetteman for research assistance. The authors also thank anonymous shale gas industry stakeholders for sharing their insights on pipeline construction cost determinants.

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