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EvaluatingPlanningStrategies.pdf

EvaluatingPlanningStrategies.pdf

Evaluating planning strategies for prioritizing projects in sustainabilityimprovement programs

Amir R. Hessamia , Vahid Faghihib , Amy Kimc and David N. Fordb

aDepartment of Civil and Architectural Engineering, Texas A&M University–Kingsville, Kingsville, USA; bZachry Department of CivilEngineering, Texas A&M University, College Station, USA; cDepartment of Civil and Environmental Engineering, University ofWashington, Seattle, USA

ABSTRACTPrograms to improve the sustainability of building infrastructures often consist of project portfoliosthat need to be prioritized in an appropriate chronological fashion to maximize the program’sbenefits. This is particularly important when a revolving-fund approach is used to leverage savingsfrom the initial projects to pay for later improvements. The success of the revolving-fund approachis dependent on the appropriate prioritization of projects. Competing performance measures andscarce resources make this task of project prioritization during the planning stage a complex andchallenging endeavour. The current study examined the impact of different project prioritizationstrategies for revolving-fund sustainability program performance. A novel modeling approach forsustainability decision-analysis was developed using the system dynamics method, and the modelwas calibrated using a campus sustainability improvement program at a major university. Themodel was applied to evaluate the effects of five common project-prioritization strategies on threeprogram-performance measures, across a wide range of initial investment levels. For the universitycase study, we found that the strategy of prioritizing projects according to decreasing benefit/costratio performed best. The research demonstrated that using a system dynamics model can allowsustainability program managers to make better-informed sequencing decisions, leading to afinancially and environmentally successful program implementations.

ARTICLE HISTORYReceived 15 August 2018Accepted 11 April 2019

KEYWORDSProject prioritization; systemdynamics; sustainabilityimprovement; revolvingfund; energy efficiency

Introduction

The development of sustainable infrastructure is of vitalconcern in a world of limited resources. Currently, in theUnited States, the residential and commercial sectorsaccount for about 40% of the country’s total consumedenergy (U.S. Energy Information Administration 2016).Meanwhile, electricity generation, the industrial sector,and the residential sector generate over 45% of thecountry’s CO2 emissions (U.S. Environmental ProtectionAgency 2016a). Reducing energy consumption in thesesectors through sustainability improvement programscan provide great benefits – both in the form of imme-diate monetary savings for owners and in the overallcontext of a better living environment for the public.

A large amount of existing infrastructure was builtprior to the adoption of current sustainability designand construction practices. For example, in the UnitedStates, the electricity consumed in energy-efficient

buildings accounts for only 30% of the country’s totalbuilding electricity consumption (Syal et al. 2013).Upgrading older buildings to current energy standardscan thus help tremendously in reducing energy use.The impact of energy efficiency improvements on theeconomy has been thoroughly quantified, and thesenumbers can be used as a basis for public policies(Hartwig and Kockat 2016). In recent years there hasbeen a shift in policy and practice toward implement-ing retrofit projects for broad portfolios of buildings,rather than upgrading single buildings individually.This approach creates a more efficient overall upgradeprocess and has been supported through programssuch as the United States Department of Energy’sBetter Buildings Challenge (DoE 2018). In a similarfashion, the Connecticut Energy Efficiency Programfacilitated the access of low-income households toefficiency improvement opportunities by bundling

CONTACT Amir R. Hessami hessami_amir@tamu.edu Department of Civil and Architectural Engineering, Texas A&M University–Kingsville, 700University Blvd, Kingsville 78363, Texas, USA� 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed,or built upon in any way.

CONSTRUCTION MANAGEMENT AND ECONOMICS2020, VOL. 38, NO. 8, 726–738https://doi.org/10.1080/01446193.2019.1608369

similar retrofit components together across many indi-vidual houses and forming collective project portfolios(Cluett et al. 2016).

Identifying the optimal energy retrofit measures fora building is essential to the success of sustainabilityprograms. One meta-analysis of the research literatureon building upgrades determined that these programscould benefit from better energy-use modelling, eco-nomic evaluation, and risk assessment to help selectthe most cost-effective retrofit measures (Ma et al.2012). These concerns become even more importantwhen considering collective portfolios. The limitedavailable studies in this area have focused on develop-ing tools to quantify energy saving opportunities for aportfolio of buildings (Lee et al. 2011) and to identifybuildings heterogeneities across portfolios (Pacheco-Torres et al. 2016). The majority of research on thecost-effectiveness of energy upgrades for a portfolio ofbuildings is focused on identifying end-of-life positivenet present value opportunities (Granade et al. 2009),rather than attempting to maximize the performance indifferent dimensions. Carli et al. (2017) indicated thatthere is a clear gap in the research literature for defin-ing optimal energy retrofit strategies for a portfolio ofbuildings based on performance outcomes. The currentresearch study contributed to filling this gap by analyz-ing strategies for the optimal allocation of sustainabilityupgrade resources in a portfolio of buildings based onboth financial and environmental performance goals.

Improving energy efficiency in collections of build-ings requires major capital investments when trad-itional financing approaches are used. Access tocapital has been identified as a key barrier to initiatingenergy efficiency retrofits (Hiller et al. 2011).Innovative financing strategies such as the revolving-fund mechanism can ameliorate the concern of inad-equate capital. The revolving-fund financing mechan-ism has gained widespread popularity in programsfocused on retrofitting existing structures and promot-ing energy-conservation practices. In the revolving-fund approach, the savings from the reduced operat-ing costs achieved early in the sustainability programare used to fund subsequent improvements, leadingto even greater savings. Thus, a relatively small initialinvestment can leverage savings from energy-effi-ciency improvements to fund many more projectsthan the initial funding could support alone. Revolvingfunds allow sustainability programs to be initiatedwith far less than the total anticipated investment thatwill be needed to complete their mission(Peckinpaugh 1999). This approach has been adoptedby many university systems, as well as a variety of

other organizations (Indvik et al. 2013). According tothe Association for the Advancement of Sustainabilityin Higher Education, over 80 higher-education institu-tions now use a revolving-fund approach to promoteenergy conservation, with a total investment of over118 million dollars (AASHE 2016).

Despite the demonstrated value of revolving funds,the lack of research on strategies to maximize the per-formance of energy retrofits in building portfoliosmakes it difficult to implement this approach effect-ively. In some cases, there may be trade-offs betweenthe goal of maximizing early financial returns (andthus having more funding to implement further proj-ects) versus the goal of quickly implementing projectsthat will maximize building performance (which mayrequire larger investments with slower returns, thusreducing the amount of available capital). Analyzingthese factors to maximize the overall energy perform-ance of the entire revolving-fund program over timecan be a daunting task. Project managers will need todetermine the best order in which to implementdesired projects to ensure that the maximum benefitsare obtained. If loan interest rates are low enough,then it may be feasible to use the maximum amountof capital possible from loans and improve all of thefacilities as soon as possible. However, moving tooquickly can also overwhelm the capital assets withdebt if the rate of financial savings cannot keep up.Many sustainability improvement programs start withvery limited resources, which makes project sequenc-ing a critical driver of performance.

Choosing an optimal project implementationsequence is thus a complex program-design challenge.In many cases, the data needed for a complete opti-mization analysis is not available. Nonetheless, duringthe project planning stages, managers must makedecisions about program sequencing. There is a sig-nificant need for rapid, practical and reasonably accur-ate methods to evaluate the feasibility of investmentsand the sequencing of projects. The objective of thecurrent study was to evaluate common sequencingheuristic strategies and identify their effect on theoverall performance of revolving-fund sustainabilityimprovement programs, considering a variety of differ-ent program sizes and initial funding levels for a port-folio of buildings. To achieve this objective a systemdynamics model was developed for sustainability pro-gram decision-analysis, and several commonly usedheuristic strategies were tested to evaluate the effectsof the sequencing choices on the overall success ofthe sustainability programs. The analysis carried out inthe current paper and the scope of research was

CONSTRUCTION MANAGEMENT AND ECONOMICS 727

limited to university campus building retrofit pro-grams, but the methods described here can also begeneralized and applied to evaluate a variety of differ-ent types of infrastructure.

Methods

This section discusses the research approach that wasused to analyze sustainability project sequencing. Thegeneral method for solving sequencing problems isdefined, the applicability of the system dynamicsmodel is explained, and the specific design of themodel is described in detail.

Project sequencing strategies for sustainabilityimprovement programs

Project sequencing in a sustainability improvementprogram can be viewed as a scheduling problem.Methods of determining the optimum sequence ofactivities in scheduling problems are categorized intothree major classes: (a) exact solutions, (b) approxima-tions, and (c) heuristic algorithms (Shakhlevich 2004).The best method for use in a particular contextdepends on the level of accuracy that is needed andthe input parameters of the specific problem. Exactsolutions give more precise answers, but these meth-ods also require more precise inputs, and the analysiscan often be very resource-intensive. Linear program-ming (Mingozzi et al. 1998) and branch-and-boundanalyses (Lomnicki 1965) are examples of methodsused in exact mathematical scheduling solutions. Incontrast, approximation methods are designed to findsolutions that may not be the perfect optimum, butthat can be shown to be within an acceptable rangefrom the actual optimum. This approach can also becomplex, but it allows more flexibility and ease ofapplication compared to finding exact solutions.Approximate methods have been successfully appliedin a variety of complex problems such as pavementrehabilitation scheduling (Ouyang and Madanat 2004),resource-constrained construction project scheduling(Liu and Wang 2008), and vehicle routing (Novoa andStorer 2009).

Finally, heuristic algorithms are designed to find agood solution, but they do not necessarily guaranteethat it is within a specific range of accuracy. With theright set of knowledge and experience, heuristic analy-ses can provide viable solutions for complex problemsin a very short amount of time. Heuristic approachesare particularly useful during the earliest phases ofprogram development, when precise design-level data

about the projects may not yet be available. The rela-tive simplicity of heuristic algorithms also makesthem particularly suitable for supporting decisions athigher levels of management. Examples of commonlyused heuristic methods include the BottleneckDynamics approach (prioritizing in order of decreasingbenefit–cost ratio) (Morton et al. 1995), and the TabuSearch (solution neighbourhood searches with worsen-ing moves permission) (Glover and Laguna 1998). Inthe current work, the researchers examined the mostapplicable heuristic strategies for sequencing projectsin sustainability programs and assessed their effectson program performance.

System dynamics

Critical decisions in planning sustainability improve-ment programs can be evaluated by developing a sys-tem dynamics model of the program. Systemdynamics is one of several established and successfulapproaches to systems analysis and design (Flood andJackson 1991, Lane and Jackson 1995, Jackson 2003).It shares many fundamental concepts with other sys-tems approaches, including emergence, control, andlayered structures, which are intended to help themodel address issues such as risk in large, complexsystems (Lane et al. 2004). The system dynamicsmethod uses a control-theory approach to study thenon-linear behaviour of complex systems. Since thisapproach represents systems using interacting feed-back loops, it is suitable for and widely used in policyanalysis (Flood and Jackson 1991, Lane and Jackson1995, Jackson 2003). Forrester (1961) described theoriginal philosophy behind the system dynamicsmethod, and Sterman (2000) developed the modellingprocess in detail and described several practical appli-cations. When applied to engineered systems such asimprovements in building infrastructure, systemdynamics simulates the interactions within the causalstructure of the system (e.g. project progress rates),along with system design and management strategies(e.g. different project sequences), and base conditions(e.g. the initial funding level). The model then predictshow the system performance will change as variousparameters are adjusted.

Examples of system dynamics applications for pro-ject planning and management issues can be foundthroughout the research literature, including projectfast-tracking failure (Ford and Sterman 1998), undesir-able schedule performance (Abdel-Hamid 1988),change impacts (Cooper 1980, Rodrigues and Williams1997), and assessing rework impacts on project

728 A. R. HESSAMI ET AL.

performance (Ford and Sterman 2003). System dynam-ics has also been applied in the construction industry,to topics including engineering economics and invest-ment analysis (Senge 1980), bidding competition ana-lysis (Kim and Reinschmidt 2006), project riskmanagement (Nasirzadeh et al. 2008), project cash-flow management (Cui et al. 2010), market fluctuationanalysis (Mbiti et al. 2011) and managing the complex-ity of information flow (Khan et al. 2016). The wide-spread applicability of system dynamics modelling inthese fields provides strong support for the use of thismethod in the current research.

In the system dynamics method causal feedbackand the accumulations and flows of materials, people,and information are combined with behaviour-basedrepresentations of managerial decision-making. Thisapproach is unique in its integrated use of stocks andflows, causal feedback, and time delays to model sys-tem processes. Stocks represent accumulations thatchange over time, and flows represent the movementof commodities into, between, and out of stocks. Thesystem components are linked with causal arrows thatindicate the direction of influence, helping to identifyfeedback loops and cascading effects. Initial condi-tions, time/speed factors, and managerial decisionsaffect the overall balance of the system, allowing for amodel that has a strong predictive capability. Systemdynamics is an ideal approach for modelling theimpacts of project sequencing on sustainabilityimprovement program performance due to its capabil-ity to track the diverse set of features, characteristics,relationships, and strategies that may affect the pro-gram outcomes. Several core components of revolv-ing-fund sustainability programs grow and shrink overtime (e.g. the total sustainability fund and the totalenergy savings), with significant program implications;these factors are well suited to modelling with thestocks and flows of a system dynamics approach.

In this paper, project prioritization was investigatedby building a system dynamic model of a sustainabil-ity improvement program. The model was based onan actual building retrofit program (the case study) ata major university campus. The validated model wasthen used as an experimental tool to simulate the per-formance of five project-sequencing strategies interms of monetary, temporal, and environmentalobjectives (performance dimensions), using a widerange of initial funding levels. The results were ana-lyzed to identify preferred strategies in the case studyand to demonstrate how program managers can use asystem dynamics approach to draw conclusions aboutprogram design.

The case of a sustainability improvement program

To demonstrate the impact of program managers’decisions on the success of sustainability improvementprograms, a system dynamics model of such programswas developed. The model was then calibrated basedon the specific case of a sustainability program carriedout at Texas A&M University (TAMU). The data usedfor calibration were from the first phase of the pro-gram, carried out in 2011 (Siemens and TAMU 2011).This phase was a $10M upgrade for 17 existing facili-ties at the university, including 13 research and teach-ing facilities and 5 parking garages. The TAMU Utilitiesand Energy Management Department oversaw thesustainability improvement program, which mainlyinvolved increasing lighting efficiencies, improvingbuilding automation systems (BAS), and improving theheating, ventilation and air-conditioning (HVAC) sys-tems. The total area covered under the program,including all of the buildings, was slightly more thanfour million square feet. The individual parkinggarages had the largest areas, ranging from about200,000 ft2 to about 1 million ft2. The 13 research andteaching buildings had a much smaller square foot-age, less than 200,000 ft2 each. Lighting retrofits,which comprised the bulk of the work, involvedswitching inefficient light bulbs and lamps with moreefficient equivalents. The BAS optimization consistedof installing better automated climate-control equip-ment for HVAC systems in each facility. For example,sensors for detecting occupancy were mounted andwired to HVAC controllers to reduce airflow while anarea is unoccupied. The installation of these sensorsallowed for automatically turning off lighting and cli-mate conditioning when the areas were not beingused. Facility reset and hold up/setback plans werealso applied to further decrease energy usage. Theseplans involved programming building environmenttechnology according to anticipated usage, forexample by adjusting temperatures in such a way asto maintaining users’ comfort while minimizing cool-ing and heating energy charges. The enhancement ofthe parking garages involved only lighting retrofits,while the 13 research and teaching buildings had acombination of different types of improvements.

The funding required for this improvement pro-gram was made available under the federal AmericanRecovery and Reinvestment Act (State EnergyConservation Office 2010) and was supplied by theTexas State Energy Conservation Office (SECO) toTAMU at an annual interest rate of 2%. TAMU andSiemens, a large energy-service company, participatedin a guaranteed performance contract to complete the

CONSTRUCTION MANAGEMENT AND ECONOMICS 729

project (Siemens Industry US 2011). This means that aspecific total of confirmed savings was guaranteed forthe 17 buildings annually over the 10-year term of thecontract. To help achieve these guaranteed savings,an approach called the “cyclical process of action” wasused (Gottsche et al. 2016). In this process, first, theexisting condition of each building was reviewed toidentify areas for improvement. The possible improve-ments were then prioritized using a hierarchical strat-egy. As the sequence of improvements was carriedout, the performance of the program was periodicallyevaluated to ensure that it was on course to achievingthe anticipated savings. The cyclical nature of this pro-cess can be appropriately represented with a systemdynamics model.

Data from 2009 building energy consumptionrecords were used as the baseline to calculate futureenergy savings. To determine the Actual RealizedSavings this baseline energy usage was considered asthe reference point and was compared against theactual energy consumption during the PerformanceGuarantee Period. Heating water, chilling water, andelectricity were the three basic energy consumptionsources that were identified for determining the totalenergy consumption affected by the sustainabilityimprovements. Heating and chilling water were meas-ured in millions of British Thermal Units (MMBTU) andelectricity was measured in kilowatt-hours (kWh). Thetotal annual energy consumption was calculated byconverting the kWhs to MMBTUs (1 kWh¼ 0.0034 MMBTU).

Expected annual savings were defined in a UtilityAssessment Report, which was carried out and pro-vided by Siemens to TAMU. The greatest energy sav-ings were predicted for the parking garages; thesepredictions ranged from about 30% to almost 50%reduction compared to the baseline. All but one ofthe teaching and research buildings were expected tohave yearly savings ranging from 10–30%. One build-ing, the Zachry Engineering Center, was predicted tohave only about a 5% reduction in energy use.Overall, the total predicted (and guaranteed) annualcost savings for the project was about $1.126M. Thisincluded $45K in operational savings and $1.08M inutility savings.

Model structure

The conceptual basis of the system dynamics modelwas the revolving fund structure (Like 2009). In thisstructure, the costs of initial improvement projects arecovered by taking out loans from the revolving fund.

As a result of those improvement projects, the systemuses less energy and generates savings, which arethen used to repay the loan back into the revolvingfund. The system dynamics model was developed tosimulate the accumulations and flows of money andthe causal feedback that drive program behaviour andperformance (Figure 1). This general conceptual modelwas extended to simulate the specific TAMU sustain-ability improvement program, specifying the 17 TAMUbuildings and their particular characteristics (energyusage, improvement cost, etc.) (Kim et al. 2012). Themodel was developed in VensimV

RDSS software and

used an arraying function to reflect facility and projectdata that was stored in a MicrosoftV

RExcel file.

The three main stocks in the system dynamicsmodel are the Sustainability Fund, Savings, andInvestment. External funds, as well as the monetarysavings of the program, gradually pool in theSustainability Fund over time. When the availableSustainability Fund reaches the amount needed tostart the next project (the next building’s improve-ment), as determined by the sequencing strategy, themodel triggers the project’s start and removes fundsequal to the defined project budget from theSustainability Fund (loop B2 in Figure 1). As a result ofimplementing the projects, the amount of energy andoperating expenditure decreases in a manner definedby the guaranteed contract, resulting in savings thatare added back into the Sustainability Fund (loop B1in Figure 1). Loan payments are also processed byremoving them from the Sustainability Fund (loop B3in Figure 1). Taken all together, these interactions cre-ate the Revolving Fund Loop (R1 in Figure 1), a rein-forcing feedback loop that maintains the SustainabilityFund and then eventually increases it after all of theprojects have been completed. A more detaileddescription of this model structure was published byFaghihi et al. (2015).

Model testing and calibration

Standard model-testing methods for system dynamics(Sterman 2000) were applied to validate the model,including a comparison of the model structure toactual system structures, verifying unit consistency,testing behaviour under extreme conditions, and com-parison of model behaviour to known or expected sys-tem behaviour. Partial model testing was also used todevelop confidence in the model’s fidelity with thesystem being modelled. For example, the major rein-forcing loop of investment in energy efficiency andgenerating savings (R1) was isolated from the rest of

730 A. R. HESSAMI ET AL.

the model, so that it could be tested and calibratedindependently.

The model was calibrated to the TAMU case studyusing data from the project’s Utility AssessmentReport, Texas A&M University utility records for eachbuilding, the details of the contract between TAMUand Siemens, and informal discussions with represen-tatives of the involved parties. The behaviour of thecalibrated model was used to further validate itsapplicability. After the model was tested and cali-brated to the case study conditions, a few adjustmentswere made so that the calibration would be morerealistic for a wide range of sustainability programs.These changes included the addition of increases inutility prices (assumed to be 2% per year). A negativeSustainability Fund was allowed in the model as longas it subsequently became positive again within onefiscal year. The researchers assumed that in such acase the owners would borrow funds to cover thesetemporary deficits, paying an additional 2% interestper year on the extra funds. This version of the modelis hereafter referred to as the “base case”. More details

about the model are available from the authorsupon request.

Simulation design

The most applicable heuristic strategies for sequencingprojects in sustainability programs were evaluatedusing the system dynamics model. First, two heuristicswere set as benchmarks for comparative purposes (H1and H2). Then an exhaustive list of heuristic schedul-ing rules from the literature (Panwalkar and Iskander1977) was carefully examined to select the approachesthat are most applicable for use in sustainabilityimprovement programs. Three common heuristic strat-egies (H3, H4 and H5) were identified based onPanwalker’s approaches of the highest dollar valueand shortest implementation time.

� Benchmark Heuristic 1 (H1): Projects are regardedas a hypothetical set of homogenous projects, allof which have the same costs and generate thesame amount of savings (thus, the prioritization

Figure 1. The conceptual system dynamics model of revolving-fund sustainability improvement programs.

CONSTRUCTION MANAGEMENT AND ECONOMICS 731

order does not matter). This scenario provided abaseline against which the other strategies werecompared. This strategy is referred to as “H1:Homogenous Projects.”

� Benchmark Heuristic 2 (H2): Projects are initiated inthe order in which they were actually implementedduring the real-world program that was used asthe case study for this investigation. This strategy isreferred to as “H2: Case Study.”

� Heuristic 3 (H3): Projects are initiated in order ofdecreasing improvement cost. This strategy reflectsa risk-management perspective based on the viewthat delayed projects have a lower chance of beingsuccessfully completed. Many factors can combineto generate higher risk in postponed projects,including the possibility of internal program mis-steps and possible changes in external support.The prospects of available funding in the nearfuture are almost always clearer than the prospectsof the far future. Therefore, program managersmay try to mitigate risks by prioritizing the mostexpensive projects. This strategy is referred to as“H3: Decreasing Cost.”

� Heuristic 4 (H4): Projects are initiated in order ofdecreasing first-year benefit to cost ratio (B/C). Inthis approach projects that will generate the high-est first-year B/C are completed first. The first yearB/C for a project is the sum of total savings antici-pated from improving the energy consumption ofa building during the first year after the project

implementation, divided by the project’s cost. Thus,the first projects to be implemented are not neces-sarily those that will generate the greatest immedi-ate benefits, but rather those that will produce themost benefits in comparison to the cost of theirimplementation. This strategy is referred to as “H4:Decreasing B/C.”

� Heuristic 5 (H5): Projects are initiated in order ofdecreasing estimated savings. This strategy priori-tizes projects that have the greatest total energysaving potential (without concern for their relativeimplementation costs). This strategy is referred toas “H5: Decreasing Savings.”

The winnowing process for selecting these heuris-tics included developing scenarios to assess how eachstrategy would be applied in the context of a sustain-ability program, and in some cases running simula-tions to help eliminate strategies that consistentlyunderperformed in comparison to others. Examples ofstrategies that were eliminated due to their clearinapplicability include “increasing first-year B/C”(where projects with the lowest first-year B/C are pri-oritized) and “increasing savings” (where projects withthe smallest amount of savings are prioritized). Suchapproaches would be entirely unsuitable for maximiz-ing revolving fund returns.

The success of the tested heuristic strategies wasevaluated using program performance measures overan anticipated 30-year life cycle. Choosing the

Figure 2. Total monetary value (NPV) using different project sequencing strategies at different levels of initial funding.

732 A. R. HESSAMI ET AL.

performance measures was a delicate task. A system-atic approach to defining these measures begins withidentifying the agency’s sustainability goals andrelated objectives to achieve these goals. Then, preciseperformance measures need to be established toassess progress toward each of the objectives(Zietsman et al. 2011). In this case, the Texas A&MUniversity 2018 Sustainability Master Plan identified 16“Evergreen Goals” (TAMU Office of Sustainability 2018).Among these goals, only two were directly related tothe sustainability improvement program that wasexamined in the case study:

� Goal 1: Achieve a 50% reduction in greenhouse gasemissions per weighted campus user by 2030;achieve net-zero emissions by 2050.

� Goal 2: Deliver the lowest life-cycle-cost construc-tion to build, operate, maintain, and decommissionhigh-performing facilities.

To evaluate the progress toward achieving thesegoals, the researchers identified specific objectives andperformance measures. The first objective relates tothe program’s environmental performance, which wasquantified and measured as the per-unit cost of car-bon footprint reduction. Carbon footprint is a widelyaccepted and commonly used measure in environ-mental life-cycle assessment (Matthews et al. 2008). Tocalculate this environmental performance measure,the total cost of improvements was divided by thetotal decrease in energy use over the life-cycle of theprogram (defined in comparison to pre-improvementenergy use). Based on models from the U.S.Environmental Protection Agency (U.S. EnvironmentalProtection Agency 2016b), each kilowatt-hour of elec-trical energy saved reduces carbon dioxide by 0.0007metric tons, and each million British Thermal Units(MMBTU) of natural gas energy saved reduces carbondioxide by 0.005 metric tons.

The second performance measure focused on theeconomic efficiency objective of the program (asrelated to Goal 2) in terms of financial savings to theuniversity. There are several economic analysis meth-ods that can be used to assess the economic feasibil-ity of building-efficiency improvement projects. Themore credible methods are based on the concept ofthe time value of money (Park 2013). These methodsinclude net present value (NPV), internal rate of return(IRR), benefit-cost ratio (B/C), and discounted paybackperiod. A comparison of these economic analysismethods is beyond the scope of this paper. However,the most widely used economic analysis method in

energy retrofit projects is NPV (DeCanio 1998, Jackson2010, Morrissey and Horne 2011, Ma et al. 2012), andthis approach was also selected as the economic per-formance measure in the current study. The basicengineering economics method was used to calculatethe NPV, assuming a 5% interest rate. It was assumedthat the interest rate reflects the market interest rate(covering the earning power and effect of inflation),and cash flows were indicated in actual dollars (includ-ing inflation) (Park 2013).

In addition to the environmental and economicperformance measures discussed above, the research-ers also introduced a third, temporal performancemeasure. This measure was simply the total durationof the program implementation phase (in months),with shorter durations being preferable. Universityadministrations are always concerned about the dur-ation of ongoing construction projects, and eager tosee these improvements completed as quickly as pos-sible. Construction creates inconveniences and aes-thetic impacts for students and campus visitors, andmay even jeopardize the quality of education if itinterrupts classroom activities. Thus, chronological per-formance in the sense of minimizing implementationtime was also considered as a relevant measurement.

Results and discussion

Using the system dynamics model, each projectsequencing heuristic (with the exception of H2 asnoted below) was simulated over a range of initialfunding – from 15% of the total program costs to100% of the total program costs, in 5% increments.Program performance, as measured in the environmen-tal, economic, and temporal dimensions, was plottedover the range of initial funding levels (Figures 2–4).Each line in these graphs, therefore, represents the per-formance of a single project sequencing strategy in thecontext of a single performance measure. Strategy H2,which describes the actual case study as implementedat TAMU, is shown in the graphs as a single “X” ratherthan a series of points. This is because in the actualcase study the improvements were all fully funded atthe beginning of the program.

The sequence of improvement projects for StrategyH2–H5 are provided in Table 1. Projects in Strategy H1were assumed to be homogenous, and are thereforeindifferent to sequencing strategy. For this reason, H1is not included in Table 1. H2 is the original casestudy, wherein projects were categorized into fourgroups, with the projects in each group implementedat the same time.

CONSTRUCTION MANAGEMENT AND ECONOMICS 733

A number of important observations for the plan-ning of revolving-fund sustainability improvement pro-grams can be made on the basis of these results. First,performance varied widely across the sequencing heu-ristics, and this was true for all three of the perform-ance dimensions. Comparing the three non-

homogenous heuristics (H3, H4 and H5) with 50% ini-tial funding, the program net present value varied upto 100% ($3.0M vs. $1.5M). The schedule performancevaried up to 36% (160 months vs. 250 months), andthe environmental performance varied up to 25%($60/ton CO2 vs. $80/ton CO2). The scale of these

Figure 3. Total program duration using different project sequencing strategies at different levels of initial funding.

Figure 4. Per-unit cost of carbon footprint reduction using different project sequencing strategies at different levels of funding.

734 A. R. HESSAMI ET AL.

performance variations is much larger than those pro-duced by many other program performance improve-ment means. This demonstrates that projectsequencing is an important, high-leverage factor insustainability improvement programs using a revolvingfund approach and that such decisions should bemade with care based on a good understanding ofthe program’s feedback structure.

Second, the financial returns and schedule perform-ance generally improved for all strategies as initialfunding levels increased. The reason for this is thatregardless of the project sequencing strategy chosen,partial funding will delay the start of some projectsand thereby delay the capture of their benefits. Incontrast, the environmental performance of the vari-ous strategies was generally worse when initial fund-ing was higher (i.e. the cost per unit of carbonreduction was higher with greater initial funding, in allbut the baseline homogenous project sequence, H1).This is because programs with more initial funding donot exploit the maximum cost savings that can beobtained from the revolving fund financing approach.

A third general observation is that all of the com-petitive strategies (H3, H4 and H5) performed aboutthe same in all three performance dimensions if atleast 60% of the total improvement costs are providedas initial funding. This suggests that the program per-formance is fairly insensitive to the differences amongthese three sequencing variations when the initialfunding level equals or exceeds 60% of the totalimprovement costs.

Fourth, all of the competitive strategies (H3, H4 andH5) performed noticeably better than theHomogenous Projects strategy (H1) in all three per-formance dimensions. Strategy H1 is the onlyapproach that generated a negative NPV (when initialinvestment was less than 75% of the total improve-ment costs). The relatively poor performance of H1can be explained as a failure to take advantage of the

impacts of diversity in project characteristics.Assuming that all of the projects are the same elimi-nates most of the advantages that can be leveragedfrom the revolving fund approach, as it is no longerpossible to prioritize more effective projects and thenroll these benefits over to the less effective projects.Therefore, as expected, the resulting performancecurves of H1 are much smoother than those of otherstrategies in all three performance dimensions.

Fifth, there is one major “kink” in the performancecurves that occurs at about 85% initial funding. This isa result of a meaningful shortage of funds. When theavailable funding falls below a certain percentage offull funding (90% in the case study program), the lackof funds begins to delay the initiation of projects. Thisfunding shortage pushes multiple improvement proj-ects later in time while the program managers wait tocollect the needed funding from energy savings inpreviously improved buildings.

Sixth, the results indicate that with few exceptions,the Decreasing B/C strategy was best in all three per-formance dimensions, followed by the DecreasingSavings strategy, followed by the Decreasing Coststrategy, followed by the Homogenous strategy. Thissuggests that incorporating both benefits and costs indecision-making improves performance when com-pared to approaches that consider only benefits oronly costs. If only one factor, benefits or costs, can beconsidered, then these results indicate that benefits(savings) should be used to prioritize the sustainabil-ity projects.

Seventh, the strategies diverge more extensively ineffectiveness as the initial funding level decreases (i.e.the lines move further apart toward the left-hand sideof the graphs). This is true for all three performancedimensions. When initial funding levels are low, anyinefficiency in the prioritization strategy is amplifiedbecause this inefficiency creates a more significantdrag on future funding levels. The slower accumula-tion of funds from energy savings when there is poorprioritization, combined with lower starting funding,leads to a slow-programs-become-slower behaviourmode. This divergence in strategies at very low initialfunding levels can become quite significant. Forexample, the schedule performance differencebetween the Decreasing B/C strategy and theDecreasing Savings strategy exceeds 40% when theinitial funding level is 25% of the total improvementcosts. When the initial funding level is reduced to 15%of the total improvement costs, the difference ineffectiveness between these two strategiesexceeds 100%.

Table 1. Sequence of improvement projects for H2–H5.Heuristic

Building ID H2 H3 H4 H5

1501 1 2 1 11507 3 1 8 2378 4 7 2 6388 2 6 3 51559 3 4 4 31194 1 5 5 4469 1 11 6 9379 3 10 7 8392 2 8 9 10463 3 9 10 11518 3 3 11 71508 3 12 12 12

CONSTRUCTION MANAGEMENT AND ECONOMICS 735

Conclusions

This study demonstrates the application of systemdynamics models in successfully planning and manag-ing revolving-fund sustainability improvement pro-grams. Designing sustainability improvementprograms is a complex and challenging task due tothe interactions among diverse system components,the variety of potential performance measures, theeffects of limited funding, and the different trajectoriesthat the programs can take over time. Revolving fundfinancing can leverage relatively small initial invest-ments into large program benefits, but this approachcan only be used successfully when it is combinedwith careful program management and informed pro-ject-prioritization strategies.

The system dynamics model developed in thisresearch was calibrated and tested using a sustainabil-ity improvement program at a major university. Threeprogram performance measures (net present value,program duration, and per-unit carbon dioxide reduc-tion) were evaluated to reflect the values of a diverseset of program goals. Three program-sequencing heu-ristics, based on cost, savings, and benefit/cost ratio,were tested over a wide range of initial funding condi-tions and compared against two benchmark heuristics.As noted earlier, this approach addressed an import-ant gap in the existing research literature in regard todefining optimal energy retrofit strategies for a port-folio of buildings based on performance outcomes.The combination of a revolving-fund financingapproach with complex program performance meas-ures creates an extremely complex scheduling prob-lem. Identifying optimal heuristic approaches fortackling this scheduling program is vital to help pro-ject managers make reasonably good decisions. Theuse of the developed approach supports the use ofdynamic planning of portfolios rather than static plan-ning. There are several secondary contributions of thepaper. The paper demonstrated the application of apreviously developed structured method for definingperformance measures in energy retrofit programs.This study revealed that the program performance ismore sensitive to the choice of sequencing strategieswhen the initial seed funding levels decreases. Thepaper also confirmed that the use of both cost andsavings in the sequencing of projects will result in thebest sequencing strategy.

The large variation in results among the schedulingheuristics verifies that project sequencing policies area high-leverage component of the design and man-agement of revolving-fund sustainability programs.With lower initial funding levels, scheduling decisions

have increasingly pronounced effects on overall out-comes. The simulation results for the university cam-pus case study indicated that the decreasing benefit/cost ratio heuristic performed best, followed by thedecreasing savings, decreasing cost, and the finally thehomogenous project sequencing strategies. Additionalapplications of the model are needed to generalizethese results to broader classes of projects and pro-grams, but the results of the current work can beused as hypotheses in future investigations of similarsystems. The simulation model produced in thisresearch provides a formal causal structure that iswidely applicable to sustainability improvement pro-grams using revolving funds.

The results and conclusions of the current work arelimited by the assumptions used in the analysis. Thecurrent work looks only at a single program and notits environment. Some sustainability program contexts(e.g. those conducted by profit-driven organizations)may need to address competing uses of financial,managerial, and other resources, as well as variousmacro-economic factors that are not considered here.Different measures of program success may be usedby some decision-makers. Broader issues such as thesocio-environmental impact of construction activitieson the local community may need to be included forsome projects. To address these concerns, the modelused in the current study can be extended and recali-brated to develop additional insights into programdesign and optimization. The model can potentially beadapted to investigate a much larger array of financ-ing approaches, as well as other types of infrastructureimprovement programs (beyond sustainabilityimprovements). More nuanced versions of the modelmay be developed that can incorporate different pro-gram conditions, such as particular kinds of infrastruc-ture or additional financial variables. The focus of thecurrent work was on improving sustainability throughphysical changes to built infrastructures, but themodel can potentially also be expanded to incorpor-ate the impacts of facility user behaviours, and thecombined effects of infrastructure upgrades withbehavioural energy-conservation efforts.

Increasing the sustainability of existing buildinginfrastructure is, and will continue to be, an importantpart of responsible infrastructure ownership and man-agement. Improvements in our understanding of sus-tainability program design can tremendously enhancethe programs’ effectiveness, efficiency, and therebytheir attractiveness. The current research contributesto this goal by showing how a system dynamics

736 A. R. HESSAMI ET AL.

modelling approach can be used to analyze the effect-iveness of different project scheduling heuristics.

Acknowledgements

The authors are grateful to the Texas A&M UniversityUtilities and Energy Management group for sharing valuableinformation that made this research possible.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Amir R. Hessami http://orcid.org/0000-0001-7618-8159Vahid Faghihi http://orcid.org/0000-0002-6264-1378Amy Kim http://orcid.org/0000-0001-8877-3777David N. Ford http://orcid.org/0000-0003-3511-1360

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738 A. R. HESSAMI ET AL.

Copyright of Construction Management & Economics is the property of Routledge and itscontent may not be copied or emailed to multiple sites or posted to a listserv without thecopyright holder’s express written permission. However, users may print, download, or emailarticles for individual use.

  • Abstract
    • Introduction
    • Methods
      • Project sequencing strategies for sustainability improvement programs
      • System dynamics
      • The case of a sustainability improvement program
      • Model structure
      • Model testing and calibration
      • Simulation design
    • Results and discussion
    • Conclusions
    • Acknowledgements
    • Disclosure statement
    • References

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