Remote communities such as rural villages, post-disaster housing camps, and military forward operating bases (FOB) are often detached from established infrastructure grids and require a constant resupply of resources. This resource dependence presents sustainability challenges such as a significant logistical burden, negative environmental impacts, and increased costs [1
]. In one example, a 2000-member isolated village in northern Canada relying solely on diesel generators required 2.95 million liters of fuel per year to support its power requirement [3
]. The fuel cost $
8.6M USD and emitted 8500 tons of CO2
—the annual equivalent of nearly 1700 passenger vehicles.
For the purposes of this research effort, sustainability refers to the planning and implementation of conservation measures and infrastructure alternatives that reduce reliance on fossil fuels, conserve water, minimize waste streams, abate negative environmental impacts, and promote self-sufficient operations [4
]. While this definition addresses only one portion of a broader sustainability challenge at remote communities, it enables the quantification and mitigation of negative environmental impacts and costs resulting directly from infrastructure decisions. Planners may choose to enhance the proposed objective function by adding measures of sustainability or adapting the function to be more reflective of the community in question. In the present application, power production, water production, and waste management systems are of primary concern, due to their direct impact on sustainability objectives and logistical requirements for resources such as fuel, water, and waste [5
]. Remote community planners have the opportunity to select technologies that will reduce the negative sustainability impacts [7
], but such alternatives are often bulky to transport and expensive to procure [8
]. Both the environmental impact and the cost involved with mobilizing equipment-based components can negatively impact sustainability objectives based on the item’s size, weight, and mode of delivery. Accordingly, planners are faced with the challenging task of selecting infrastructure alternatives that optimize initial and operational tradeoffs between environmental and economic performance.
A number of studies have been conducted that (1) quantify the environmental impact of infrastructure; (2) identify tradeoffs between the environmental and economic impact of infrastructure alternatives; and (3) optimize tradeoffs between sustainability objectives for remote communities. First, various research efforts have quantified the environmental sustainability of infrastructure, including power production methods [9
]; water production methods [12
]; wastewater management systems [14
]; and solid-waste management systems [16
]. These efforts quantified environmental sustainability through various indicators, such as greenhouse gas (GHG) emissions, pollution emissions, energy consumption, embodied emissions, and global warming potential. These indicators can be quantified at a static point in time, such as embedded emissions of materials, or over the infrastructure’s lifetime via a life-cycle assessment.
Second, additional studies identified tradeoffs between the environmental and economic sustainability of infrastructure alternatives. For example, Karatas and El-Rayes [19
] utilized GHG emissions, water consumption, and initial cost metrics to assess the integration of green building measures and fixtures into housing units, generating optimal tradeoffs between environmental impact and cost. Alternatively, Ozcan-Deniz et al. [20
] utilized a global warming potential metric to optimize the selection of construction activities, thereby minimizing project time, cost, and environmental impact. Additional economic metrics include energy consumption, transportation requirements, operating costs, and life-cycle costs [21
Third, other research efforts optimized tradeoffs between sustainability objectives for remote communities. Optimization is the process by which one determines the best solution to a problem based on a set of constraints [22
]. When this process includes just one objective, the intent is to determine one ideal solution. A multi-objective optimization problem, however, occurs when two or more objectives must be enhanced simultaneously. Often, these objectives are in direct conflict with each other, requiring the researcher to identify optimal tradeoffs between objectives. For example, El-Anwar et al. [23
] identified infrastructure decision impacts on the prolonged use of isolated, post-disaster housing camps. The authors produced optimal housing construction decisions, minimizing environmental, social welfare, economic, and public safety impacts. Conversely, Poreddy and Daniels [24
] and Putnam et al. [8
] analyzed military sites, investigating resource requirements as a proxy for sustainability. The first effort utilized a comprehensive systems-based approach to quantify a site’s resource requirements, such as electricity, fuel, water, maintenance hours, and geographical footprint. The authors proposed optimal site layouts that maximized operational efficiency and minimized logistical requirements. The second effort optimized the selection of infrastructure technologies to minimize mobilization investments and daily resupply. By quantifying the logistical impact of equipment and the volume of fuel, water, and waste transported on- and off-site each day, the work identified infrastructure alternatives that improved personnel safety and minimized transportation expenses. Filer and Schuldt [25
] expanded Putnam’s approach to quantify the impact of an infrastructure alternative’s resource consumption and logistics on the environment. While the authors computed GHG emissions and total cost for various infrastructure systems, they failed to fully consider the impact of transportation requirements or establish optimal tradeoffs between competing objectives. This paper is a follow-on effort that expands transportation considerations, enhances emissions calculations, incorporates decision-maker priorities, and optimizes sustainability tradeoffs over time.
Despite the significant contributions of the aforementioned research studies, there has been no known research that has optimized sustainability in remote communities. That is, there lacks a detailed investigation that optimizes tradeoffs between the environmental and economic performance of remote community infrastructure alternatives while considering initial and recurring logistical requirements. To address this limitation, this paper presents the development of an innovative model that is capable of optimizing tradeoffs between the environmental and economic sustainability of remote community infrastructure.
The objective of this paper is to present an infrastructure sustainability assessment model that quantifies the tradeoffs between environmental impacts and life-cycle costs of remote communities. The model is intended to assist planners in the difficult task of analyzing and comparing all feasible combinations of infrastructure alternatives in order to construct sites with reduced costs, emissions, and resupply requirements. The following sections of this paper describe: (1) developing metrics to measure the performance of the model’s two competing objectives; (2) formulating the model’s objective function; (3) identifying the model’s required input data; and (4) testing the model’s performance via a case study.
4. Case Study
In order to demonstrate the model, a theoretical military forward operating base (FOB) was designed as a reasonable representation of a remote community, and infrastructure alternatives were considered. A military application was chosen for the following example due to the abundance of bases with remote community characteristics and the breadth of data on sustainable base initiatives. For this case study, a baseline FOB was first modelled as a typical example of deployed military assets. Next, a set of equipment alternatives were modelled to demonstrate potential improvements as a result of investing in sustainable technologies. Then, a set of procedural alternatives were applied to demonstrate potential performance improvements based on currently fielded infrastructure.
For this case study, the required input data included community features, planning factors, and infrastructure alternative characteristics. First, community features were dictated based on the FOB’s design to accommodate 300 personnel in an arid region of Southwest Asia for an anticipated duration of up to 7 years. All equipment technologies (such as generators, solar panels, and water purifiers) had to be delivered via aircraft from suppliers located in Central Europe, 5150 km away. Common services (such as purchasing bottled water or contracting solid waste disposal) could be sourced from local vendors ranging from 40–80 km from the site. The community feature data and assumptions are summarized in Table 2
. Second, planning factors were identified for power, water, wastewater, and solid waste through U.S. Army design manuals and historical data [6
]. Third, infrastructure alternative data were sourced from a collection of U.S. Army reports published as a result of an initiative to identify fuel, water, and waste (FWW) mitigation measures [30
]. Objectives were computed in R version 3.6.0 [33
] and figures were produced with the ggplot2 package [34
First, a set of baseline FWW values, summarized in Table 3
, was established through experimental testing of a baseline camp setup [38
]. This baseline established a standard by which all other alternatives could be compared. The baseline setup represented commonly deployed assets for billeting, food preparation and dining facilities, hygiene services, waste management, water storage and distribution, and power generation, as shown in Table 4
. The identified fuel demand included fuel for infrastructure sustainment only—fuel required for transportation outside of the FOB must be accounted for separately. Historical data and subject matter expertise ensured that the baseline infrastructure met U.S. Army requirements for the sustainment of a 300-person contingency site.
The FOB’s baseline environmental impact and cost were calculated using Equations (1)–(3). The initial environmental impact was found to be 2350 tons CO2e, increasing at a rate of 14.3 tons/day. The capital procurement and mobilization cost was $3.1M, with operating costs of $134,000/day. These values provided a standard by which further infrastructure alternatives may be compared.
4.2. Equipment Alternatives
Next, the performance of a set of equipment alternatives was modelled. The alternatives and their FWW consumption and production values are detailed in Table 4
as compared to the baseline. The results of this analysis are shown in Figure 2
. Each alternative required some material equipment in addition to, or in place of, a baseline equipment set with the potential to conserve resources. Coincidentally, many of these technologies required a substantial investment in terms of the purchase cost and delivery. For example, a photovoltaic array and lithium ion battery system, as a power production alternative, was compared against a baseline of 60 kW generators. While the solar alternative saved the site nearly 3560 liters of fuel per day, the equipment itself weighed over 900,000 kg more than its generator competitor [39
]. This extra weight imposed additional delivery costs and transportation emissions.
illustrates the tradeoffs between initial and operating requirements for 864 potential equipment portfolios. Each line represents the cumulative EI and C of one portfolio, with the baseline signified in red. While the baseline equipment set imposed a low IEI and IC, it led to one of the highest possible cumulative EI and C values due to its operating requirements. Other alternatives imposed higher IEI and IC values but lower operating requirements. For example, portfolio #807, shown in Figure 2
as a blue line, was comprised nearly exclusively of sustainable technologies outlined in Table 5
. While this portfolio’s IEI and IC were 1.5 and 4.2 times higher than the baseline’s, its operating requirements were 1.6 and 10.3 times lower, respectively. These sustainability tradeoffs resulted in the IC being offset after 81 days and the IEI being offset after 231 days, at which time portfolio #807 became more sustainable than the baseline. Similarly, each interaction in Figure 2
designates the time at which a portfolio became a more environmentally or financially sustainable choice.
4.3. Procedural Alternatives
In addition to the 864 equipment portfolios, 48 procedural portfolios were also identified through the U.S. Army’s FWW initiative, shown in Table 4
and Figure 3
. While the equipment alternatives considered deviations from existing infrastructure, the procedural alternatives utilized only baseline camp equipment. The assessed procedures instead aimed to mitigate resource consumption by restricting personnel quality of life allowances, such as shortening shower times or limiting loads of self-help laundry. For this portion of the case study, each feasible portfolio was comprised of a unique combination of procedural alternatives and evaluated against the baseline. Portfolio #48, the most sustainable set of procedural alternatives, is designated in Figure 3
by a green line. Portfolio #48 was comprised exclusively of resource-saving measures such as billeting consolidation, limited laundry allowances, and reduced shower times and toilet flushes. While these alternatives were not considered in the final optimization function, they did highlight the model’s ability to quantify potential sustainability improvements with a limited equipment investment.
4.4. Optimal Alternatives
Finally, the equipment alternative data were normalized, and the negative sustainability impacts (SIp
) of all equipment-based portfolios were calculated. Then, the optimal solution with the lowest SI at each point in time was identified. Figure 4
shows the optimal portfolios for varying importance weights with the baseline in red for comparison.
In each scenario, the optimal site makeup transitioned rapidly in the first three years. After this point, the optimal site began to steady. In Figure 4
a,b, the importance weight applied to the environmental impact was set at 90% and 50%, respectively. In both scenarios, portfolio #816 was found to be the optimal infrastructure alternative combination from 3 years on, due to its low daily emissions of 1.1 CO2
e/day. This site’s makeup included sustainable technologies such as photovoltaic arrays and high efficiency refrigerators and incinerators, as described in Table 6
. Figure 4
c, however, illustrates optimal solutions when the environmental impact importance weight was set at just 10%. In this scenario, the optimal alternative combination changed twice in the fifth year before settling on portfolio #97. Rather than including pricey, environmentally conscious technologies, this site relied on less expensive, easily transportable alternatives that resulted in low procurement and operating costs.
5. Summary and Conclusions
This paper presented a novel infrastructure sustainability assessment model for the design and construction of remote communities. The model was developed in four main sections that included: (1) developing metrics to measure the environmental and economic performance of infrastructure alternatives; (2) formulating the model’s objective functions; (3) identifying the model’s required input data; and (4) testing the model’s performance via a case study. The case study modelled 864 portfolios of feasible infrastructure alternatives and 48 portfolios of procedural alternatives, highlighting that the model is capable of quantifying sustainability impacts for a wide range of decision-maker priorities and infrastructure alternatives. The results also display the model’s effectiveness at identifying the environmental and economic tradeoffs associated with more sustainable, yet more bulky and costly, alternatives. The model was able to generate optimal portfolio solutions according to the importance a planner assigns to the environmental impact and cost metrics. This model has the potential to assist planners by allowing them to identify optimal infrastructure alternatives according to the remote community’s mission, location, and personnel requirements.
This paper presents a model that may be utilized as a framework into which additional sustainability objectives can be incorporated. In this work, the objectives of environmental impact and cost assess the sustainability of infrastructure portfolio decisions, investigating impacts on resource consumption and transportation requirements. While the framework does provide a conduit through which the sustainability of infrastructure systems can be optimized for remote communities, the model presented here is not exhaustive, and future research is necessary. Areas of future research include the optimization of geographical citing according to resource locations, the ability to select multiple alternatives within each category in order to realize synergistic benefits, and the incorporation of additional sustainability objectives such as quality of life, social impact, and human health. Additionally, the present model assumed constant daily resource requirements and emissions factors. Further research should consider a more robust analysis of emissions and operating costs to account for equipment deterioration and irregular maintenance requirements. Additionally, while the presented objective function accounted for the environmental impact and cost from an infrastructure alternative’s purchase through operation, it disregarded production and demolition. Here, it was assumed that all infrastructure alternatives were previously manufactured, which classified their economic impacts as sunk costs. As the remote community’s duration was flexible, the impacts due to demolition or reconstitution were considered negligible. The present model may be adapted to account for these factors.