Scholarly Activity: Project Management Overview

Evaluating planning strategies for prioritizing projects in sustainability improvement 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 Civil Engineering, Texas A&M University, College Station, USA; cDepartment of Civil and Environmental Engineering, University of Washington, Seattle, USA

ABSTRACT Programs to improve the sustainability of building infrastructures often consist of project portfolios that need to be prioritized in an appropriate chronological fashion to maximize the program’s benefits. This is particularly important when a revolving-fund approach is used to leverage savings from the initial projects to pay for later improvements. The success of the revolving-fund approach is dependent on the appropriate prioritization of projects. Competing performance measures and scarce resources make this task of project prioritization during the planning stage a complex and challenging endeavour. The current study examined the impact of different project prioritization strategies for revolving-fund sustainability program performance. A novel modeling approach for sustainability decision-analysis was developed using the system dynamics method, and the model was calibrated using a campus sustainability improvement program at a major university. The model was applied to evaluate the effects of five common project-prioritization strategies on three program-performance measures, across a wide range of initial investment levels. For the university case study, we found that the strategy of prioritizing projects according to decreasing benefit/cost ratio performed best. The research demonstrated that using a system dynamics model can allow sustainability program managers to make better-informed sequencing decisions, leading to a financially and environmentally successful program implementations.

ARTICLE HISTORY Received 15 August 2018 Accepted 11 April 2019

KEYWORDS Project prioritization; system dynamics; sustainability improvement; revolving fund; energy efficiency

Introduction

The development of sustainable infrastructure is of vital concern in a world of limited resources. Currently, in the United States, the residential and commercial sectors account for about 40% of the country’s total consumed energy (U.S. Energy Information Administration 2016). Meanwhile, electricity generation, the industrial sector, and the residential sector generate over 45% of the country’s CO2 emissions (U.S. Environmental Protection Agency 2016a). Reducing energy consumption in these sectors through sustainability improvement programs can provide great benefits – both in the form of imme- diate monetary savings for owners and in the overall context of a better living environment for the public.

A large amount of existing infrastructure was built prior to the adoption of current sustainability design and construction practices. For example, in the United States, the electricity consumed in energy-efficient

buildings accounts for only 30% of the country’s total building electricity consumption (Syal et al. 2013). Upgrading older buildings to current energy standards can thus help tremendously in reducing energy use. The impact of energy efficiency improvements on the economy has been thoroughly quantified, and these numbers can be used as a basis for public policies (Hartwig and Kockat 2016). In recent years there has been 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 upgrade process and has been supported through programs such as the United States Department of Energy’s Better Buildings Challenge (DoE 2018). In a similar fashion, the Connecticut Energy Efficiency Program facilitated the access of low-income households to efficiency improvement opportunities by bundling

CONTACT Amir R. Hessami [email protected] Department of Civil and Architectural Engineering, Texas A&M University–Kingsville, 700 University 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 ECONOMICS 2020, VOL. 38, NO. 8, 726–738 https://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 for a building is essential to the success of sustainability programs. One meta-analysis of the research literature on building upgrades determined that these programs could benefit from better energy-use modelling, eco- nomic evaluation, and risk assessment to help select the most cost-effective retrofit measures (Ma et al. 2012). These concerns become even more important when considering collective portfolios. The limited available studies in this area have focused on develop- ing tools to quantify energy saving opportunities for a portfolio of buildings (Lee et al. 2011) and to identify buildings heterogeneities across portfolios (Pacheco- Torres et al. 2016). The majority of research on the cost-effectiveness of energy upgrades for a portfolio of buildings is focused on identifying end-of-life positive net present value opportunities (Granade et al. 2009), rather than attempting to maximize the performance in different dimensions. Carli et al. (2017) indicated that there is a clear gap in the research literature for defin- ing optimal energy retrofit strategies for a portfolio of buildings based on performance outcomes. The current research study contributed to filling this gap by analyz- ing strategies for the optimal allocation of sustainability upgrade resources in a portfolio of buildings based on both 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 to capital has been identified as a key barrier to initiating energy 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 programs focused 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 program are used to fund subsequent improvements, leading to even greater savings. Thus, a relatively small initial investment can leverage savings from energy-effi- ciency improvements to fund many more projects than the initial funding could support alone. Revolving funds allow sustainability programs to be initiated with far less than the total anticipated investment that will be needed to complete their mission (Peckinpaugh 1999). This approach has been adopted by many university systems, as well as a variety of

other organizations (Indvik et al. 2013). According to the Association for the Advancement of Sustainability in Higher Education, over 80 higher-education institu- tions now use a revolving-fund approach to promote energy conservation, with a total investment of over 118 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 portfolios makes it difficult to implement this approach effect- ively. In some cases, there may be trade-offs between the goal of maximizing early financial returns (and thus having more funding to implement further proj- ects) versus the goal of quickly implementing projects that will maximize building performance (which may require larger investments with slower returns, thus reducing the amount of available capital). Analyzing these factors to maximize the overall energy perform- ance of the entire revolving-fund program over time can be a daunting task. Project managers will need to determine the best order in which to implement desired projects to ensure that the maximum benefits are obtained. If loan interest rates are low enough, then it may be feasible to use the maximum amount of capital possible from loans and improve all of the facilities as soon as possible. However, moving too quickly can also overwhelm the capital assets with debt if the rate of financial savings cannot keep up. Many sustainability improvement programs start with very limited resources, which makes project sequenc- ing a critical driver of performance.

Choosing an optimal project implementation sequence is thus a complex program-design challenge. In many cases, the data needed for a complete opti- mization analysis is not available. Nonetheless, during the project planning stages, managers must make decisions about program sequencing. There is a sig- nificant need for rapid, practical and reasonably accur- ate methods to evaluate the feasibility of investments and the sequencing of projects. The objective of the current study was to evaluate common sequencing heuristic strategies and identify their effect on the overall performance of revolving-fund sustainability improvement programs, considering a variety of differ- ent program sizes and initial funding levels for a port- folio of buildings. To achieve this objective a system dynamics model was developed for sustainability pro- gram decision-analysis, and several commonly used heuristic strategies were tested to evaluate the effects of the sequencing choices on the overall success of the sustainability programs. The analysis carried out in the current paper and the scope of research was

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

Methods

This section discusses the research approach that was used to analyze sustainability project sequencing. The general method for solving sequencing problems is defined, the applicability of the system dynamics model is explained, and the specific design of the model is described in detail.

Project sequencing strategies for sustainability improvement programs

Project sequencing in a sustainability improvement program can be viewed as a scheduling problem. Methods of determining the optimum sequence of activities in scheduling problems are categorized into three major classes: (a) exact solutions, (b) approxima- tions, and (c) heuristic algorithms (Shakhlevich 2004). The best method for use in a particular context depends on the level of accuracy that is needed and the input parameters of the specific problem. Exact solutions give more precise answers, but these meth- ods also require more precise inputs, and the analysis can often be very resource-intensive. Linear program- ming (Mingozzi et al. 1998) and branch-and-bound analyses (Lomnicki 1965) are examples of methods used in exact mathematical scheduling solutions. In contrast, approximation methods are designed to find solutions that may not be the perfect optimum, but that can be shown to be within an acceptable range from the actual optimum. This approach can also be complex, but it allows more flexibility and ease of application compared to finding exact solutions. Approximate methods have been successfully applied in a variety of complex problems such as pavement rehabilitation scheduling (Ouyang and Madanat 2004), resource-constrained construction project scheduling (Liu and Wang 2008), and vehicle routing (Novoa and Storer 2009).

Finally, heuristic algorithms are designed to find a good solution, but they do not necessarily guarantee that it is within a specific range of accuracy. With the right set of knowledge and experience, heuristic analy- ses can provide viable solutions for complex problems in a very short amount of time. Heuristic approaches are particularly useful during the earliest phases of program development, when precise design-level data

about the projects may not yet be available. The rela- tive simplicity of heuristic algorithms also makes them particularly suitable for supporting decisions at higher levels of management. Examples of commonly used heuristic methods include the Bottleneck Dynamics approach (prioritizing in order of decreasing benefit–cost ratio) (Morton et al. 1995), and the Tabu Search (solution neighbourhood searches with worsen- ing moves permission) (Glover and Laguna 1998). In the current work, the researchers examined the most applicable heuristic strategies for sequencing projects in sustainability programs and assessed their effects on 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. System dynamics is one of several established and successful approaches to systems analysis and design (Flood and Jackson 1991, Lane and Jackson 1995, Jackson 2003). It shares many fundamental concepts with other sys- tems approaches, including emergence, control, and layered structures, which are intended to help the model address issues such as risk in large, complex systems (Lane et al. 2004). The system dynamics method uses a control-theory approach to study the non-linear behaviour of complex systems. Since this approach represents systems using interacting feed- back loops, it is suitable for and widely used in policy analysis (Flood and Jackson 1991, Lane and Jackson 1995, Jackson 2003). Forrester (1961) described the original philosophy behind the system dynamics method, and Sterman (2000) developed the modelling process in detail and described several practical appli- cations. When applied to engineered systems such as improvements in building infrastructure, system dynamics simulates the interactions within the causal structure 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 predicts how the system performance will change as various parameters are adjusted.

Examples of system dynamics applications for pro- ject planning and management issues can be found throughout the research literature, including project fast-tracking failure (Ford and Sterman 1998), undesir- able schedule performance (Abdel-Hamid 1988), change impacts (Cooper 1980, Rodrigues and Williams 1997), and assessing rework impacts on project

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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 risk management (Nasirzadeh et al. 2008), project cash- flow management (Cui et al. 2010), market fluctuation analysis (Mbiti et al. 2011) and managing the complex- ity of information flow (Khan et al. 2016). The wide- spread applicability of system dynamics modelling in these fields provides strong support for the use of this method in the current research.

In the system dynamics method causal feedback and the accumulations and flows of materials, people, and information are combined with behaviour-based representations of managerial decision-making. This approach is unique in its integrated use of stocks and flows, causal feedback, and time delays to model sys- tem processes. Stocks represent accumulations that change over time, and flows represent the movement of commodities into, between, and out of stocks. The system components are linked with causal arrows that indicate the direction of influence, helping to identify feedback loops and cascading effects. Initial condi- tions, time/speed factors, and managerial decisions affect the overall balance of the system, allowing for a model that has a strong predictive capability. System dynamics is an ideal approach for modelling the impacts of project sequencing on sustainability improvement 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 over time (e.g. the total sustainability fund and the total energy savings), with significant program implications; these factors are well suited to modelling with the stocks and flows of a system dynamics approach.

In this paper, project prioritization was investigated by building a system dynamic model of a sustainabil- ity improvement program. The model was based on an actual building retrofit program (the case study) at a major university campus. The validated model was then used as an experimental tool to simulate the per- formance of five project-sequencing strategies in terms of monetary, temporal, and environmental objectives (performance dimensions), using a wide range of initial funding levels. The results were ana- lyzed to identify preferred strategies in the case study and to demonstrate how program managers can use a system dynamics approach to draw conclusions about program design.

The case of a sustainability improvement program

To demonstrate the impact of program managers’ decisions on the success of sustainability improvement programs, a system dynamics model of such programs was developed. The model was then calibrated based on the specific case of a sustainability program carried out at Texas A&M University (TAMU). The data used for 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 Utilities and Energy Management Department oversaw the sustainability improvement program, which mainly involved increasing lighting efficiencies, improving building automation systems (BAS), and improving the heating, ventilation and air-conditioning (HVAC) sys- tems. The total area covered under the program, including all of the buildings, was slightly more than four million square feet. The individual parking garages had the largest areas, ranging from about 200,000 ft2 to about 1 million ft2. The 13 research and teaching buildings had a much smaller square foot- age, less than 200,000 ft2 each. Lighting retrofits, which comprised the bulk of the work, involved switching inefficient light bulbs and lamps with more efficient equivalents. The BAS optimization consisted of installing better automated climate-control equip- ment for HVAC systems in each facility. For example, sensors for detecting occupancy were mounted and wired to HVAC controllers to reduce airflow while an area is unoccupied. The installation of these sensors allowed for automatically turning off lighting and cli- mate conditioning when the areas were not being used. Facility reset and hold up/setback plans were also applied to further decrease energy usage. These plans involved programming building environment technology according to anticipated usage, for example by adjusting temperatures in such a way as to maintaining users’ comfort while minimizing cool- ing and heating energy charges. The enhancement of the parking garages involved only lighting retrofits, while the 13 research and teaching buildings had a combination of different types of improvements.

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

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project (Siemens Industry US 2011). This means that a specific total of confirmed savings was guaranteed for the 17 buildings annually over the 10-year term of the contract. To help achieve these guaranteed savings, an approach called the “cyclical process of action” was used (Gottsche et al. 2016). In this process, first, the existing condition of each building was reviewed to identify areas for improvement. The possible improve- ments were then prioritized using a hierarchical strat- egy. As the sequence of improvements was carried out, the performance of the program was periodically evaluated to ensure that it was on course to achieving the anticipated savings. The cyclical nature of this pro- cess can be appropriately represented with a system dynamics model.

Data from 2009 building energy consumption records were used as the baseline to calculate future energy savings. To determine the Actual Realized Savings this baseline energy usage was considered as the reference point and was compared against the actual energy consumption during the Performance Guarantee Period. Heating water, chilling water, and electricity were the three basic energy consumption sources that were identified for determining the total energy consumption affected by the sustainability improvements. Heating and chilling water were meas- ured in millions of British Thermal Units (MMBTU) and electricity was measured in kilowatt-hours (kWh). The total annual energy consumption was calculated by converting the kWhs to MMBTUs (1 kWh ¼ 0.0034 MMBTU).

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

Model structure

The conceptual basis of the system dynamics model was the revolving fund structure (Like 2009). In this structure, the costs of initial improvement projects are covered by taking out loans from the revolving fund.

As a result of those improvement projects, the system uses less energy and generates savings, which are then used to repay the loan back into the revolving fund. The system dynamics model was developed to simulate the accumulations and flows of money and the causal feedback that drive program behaviour and performance (Figure 1). This general conceptual model was extended to simulate the specific TAMU sustain- ability improvement program, specifying the 17 TAMU buildings and their particular characteristics (energy usage, improvement cost, etc.) (Kim et al. 2012). The model was developed in VensimV

R DSS software and

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

R Excel file.

The three main stocks in the system dynamics model are the Sustainability Fund, Savings, and Investment. External funds, as well as the monetary savings of the program, gradually pool in the Sustainability Fund over time. When the available Sustainability Fund reaches the amount needed to start the next project (the next building’s improve- ment), as determined by the sequencing strategy, the model triggers the project’s start and removes funds equal to the defined project budget from the Sustainability Fund (loop B2 in Figure 1). As a result of implementing the projects, the amount of energy and operating expenditure decreases in a manner defined by the guaranteed contract, resulting in savings that are added back into the Sustainability Fund (loop B1 in Figure 1). Loan payments are also processed by removing them from the Sustainability Fund (loop B3 in 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 Sustainability Fund and then eventually increases it after all of the projects have been completed. A more detailed description of this model structure was published by Faghihi 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 to actual 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 to develop confidence in the model’s fidelity with the system being modelled. For example, the major rein- forcing loop of investment in energy efficiency and generating savings (R1) was isolated from the rest of

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the model, so that it could be tested and calibrated independently.

The model was calibrated to the TAMU case study using data from the project’s Utility Assessment Report, Texas A&M University utility records for each building, the details of the contract between TAMU and Siemens, and informal discussions with represen- tatives of the involved parties. The behaviour of the calibrated model was used to further validate its applicability. After the model was tested and cali- brated to the case study conditions, a few adjustments were made so that the calibration would be more realistic for a wide range of sustainability programs. These changes included the addition of increases in utility prices (assumed to be 2% per year). A negative Sustainability Fund was allowed in the model as long as it subsequently became positive again within one fiscal year. The researchers assumed that in such a case the owners would borrow funds to cover these temporary deficits, paying an additional 2% interest per year on the extra funds. This version of the model is hereafter referred to as the “base case”. More details

about the model are available from the authors upon request.

Simulation design

The most applicable heuristic strategies for sequencing projects in sustainability programs were evaluated using the system dynamics model. First, two heuristics were set as benchmarks for comparative purposes (H1 and H2). Then an exhaustive list of heuristic schedul- ing rules from the literature (Panwalkar and Iskander 1977) was carefully examined to select the approaches that are most applicable for use in sustainability improvement programs. Three common heuristic strat- egies (H3, H4 and H5) were identified based on Panwalker’s approaches of the highest dollar value and shortest implementation time.

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

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

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

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

� Heuristic 3 (H3): Projects are initiated in order of decreasing improvement cost. This strategy reflects a risk-management perspective based on the view that delayed projects have a lower chance of being successfully completed. Many factors can combine to 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 near future are almost always clearer than the prospects of the far future. Therefore, program managers may try to mitigate risks by prioritizing the most expensive projects. This strategy is referred to as “H3: Decreasing Cost.”

� Heuristic 4 (H4): Projects are initiated in order of decreasing first-year benefit to cost ratio (B/C). In this approach projects that will generate the high- est first-year B/C are completed first. The first year B/C for a project is the sum of total savings antici- pated from improving the energy consumption of a 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 the most benefits in comparison to the cost of their implementation. This strategy is referred to as “H4: Decreasing B/C.”

� Heuristic 5 (H5): Projects are initiated in order of decreasing estimated savings. This strategy priori- tizes projects that have the greatest total energy saving potential (without concern for their relative implementation costs). This strategy is referred to as “H5: Decreasing Savings.”

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

The success of the tested heuristic strategies was evaluated using program performance measures over an anticipated 30-year life cycle. Choosing the

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

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

� Goal 1: Achieve a 50% reduction in greenhouse gas emissions 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 decommission high-performing facilities.

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

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

energy retrofit projects is NPV (DeCanio 1998, Jackson 2010, Morrissey and Horne 2011, Ma et al. 2012), and this approach was also selected as the economic per- formance measure in the current study. The basic engineering economics method was used to calculate the NPV, assuming a 5% interest rate. It was assumed that 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 economic performance measures discussed above, the research- ers also introduced a third, temporal performance measure. This measure was simply the total duration of the program implementation phase (in months), with shorter durations being preferable. University administrations are always concerned about the dur- ation of ongoing construction projects, and eager to see these improvements completed as quickly as pos- sible. Construction creates inconveniences and aes- thetic impacts for students and campus visitors, and may even jeopardize the quality of education if it interrupts classroom activities. Thus, chronological per- formance in the sense of minimizing implementation time was also considered as a relevant measurement.

Results and discussion

Using the system dynamics model, each project sequencing heuristic (with the exception of H2 as noted below) was simulated over a range of initial funding – from 15% of the total program costs to 100% of the total program costs, in 5% increments. Program performance, as measured in the environmen- tal, economic, and temporal dimensions, was plotted over 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 the context of a single performance measure. Strategy H2, which describes the actual case study as implemented at TAMU, is shown in the graphs as a single “X” rather than a series of points. This is because in the actual case study the improvements were all fully funded at the beginning of the program.

The sequence of improvement projects for Strategy H2–H5 are provided in Table 1. Projects in Strategy H1 were assumed to be homogenous, and are therefore indifferent to sequencing strategy. For this reason, H1 is not included in Table 1. H2 is the original case study, wherein projects were categorized into four groups, with the projects in each group implemented at the same time.

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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 up to 100% ($3.0M vs. $1.5M). The schedule performance varied up to 36% (160 months vs. 250 months), and the 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.

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performance variations is much larger than those pro- duced by many other program performance improve- ment means. This demonstrates that project sequencing is an important, high-leverage factor in sustainability improvement programs using a revolving fund approach and that such decisions should be made with care based on a good understanding of the program’s feedback structure.

Second, the financial returns and schedule perform- ance generally improved for all strategies as initial funding levels increased. The reason for this is that regardless of the project sequencing strategy chosen, partial funding will delay the start of some projects and thereby delay the capture of their benefits. In contrast, the environmental performance of the vari- ous strategies was generally worse when initial fund- ing was higher (i.e. the cost per unit of carbon reduction was higher with greater initial funding, in all but the baseline homogenous project sequence, H1). This is because programs with more initial funding do not exploit the maximum cost savings that can be obtained from the revolving fund financing approach.

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

Fourth, all of the competitive strategies (H3, H4 and H5) performed noticeably better than the Homogenous Projects strategy (H1) in all three per- formance dimensions. Strategy H1 is the only approach that generated a negative NPV (when initial investment was less than 75% of the total improve- ment costs). The relatively poor performance of H1 can 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 leveraged from the revolving fund approach, as it is no longer possible to prioritize more effective projects and then roll these benefits over to the less effective projects. Therefore, as expected, the resulting performance curves of H1 are much smoother than those of other strategies in all three performance dimensions.

Fifth, there is one major “kink” in the performance curves that occurs at about 85% initial funding. This is a result of a meaningful shortage of funds. When the available funding falls below a certain percentage of full funding (90% in the case study program), the lack of funds begins to delay the initiation of projects. This funding shortage pushes multiple improvement proj- ects later in time while the program managers wait to collect the needed funding from energy savings in previously 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 Decreasing Savings strategy, followed by the Decreasing Cost strategy, followed by the Homogenous strategy. This suggests that incorporating both benefits and costs in decision-making improves performance when com- pared to approaches that consider only benefits or only costs. If only one factor, benefits or costs, can be considered, then these results indicate that benefits (savings) should be used to prioritize the sustainabil- ity projects.

Seventh, the strategies diverge more extensively in effectiveness as the initial funding level decreases (i.e. the lines move further apart toward the left-hand side of the graphs). This is true for all three performance dimensions. When initial funding levels are low, any inefficiency in the prioritization strategy is amplified because this inefficiency creates a more significant drag on future funding levels. The slower accumula- tion of funds from energy savings when there is poor prioritization, combined with lower starting funding, leads to a slow-programs-become-slower behaviour mode. This divergence in strategies at very low initial funding levels can become quite significant. For example, the schedule performance difference between the Decreasing B/C strategy and the Decreasing Savings strategy exceeds 40% when the initial funding level is 25% of the total improvement costs. When the initial funding level is reduced to 15% of the total improvement costs, the difference in effectiveness between these two strategies exceeds 100%.

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

Building ID H2 H3 H4 H5

1501 1 2 1 1 1507 3 1 8 2 378 4 7 2 6 388 2 6 3 5 1559 3 4 4 3 1194 1 5 5 4 469 1 11 6 9 379 3 10 7 8 392 2 8 9 10 463 3 9 10 11 518 3 3 11 7 1508 3 12 12 12

CONSTRUCTION MANAGEMENT AND ECONOMICS 735

Conclusions

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

The system dynamics model developed in this research was calibrated and tested using a sustainabil- ity improvement program at a major university. Three program performance measures (net present value, program duration, and per-unit carbon dioxide reduc- tion) were evaluated to reflect the values of a diverse set 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 to defining optimal energy retrofit strategies for a port- folio of buildings based on performance outcomes. The combination of a revolving-fund financing approach with complex program performance meas- ures creates an extremely complex scheduling prob- lem. Identifying optimal heuristic approaches for tackling this scheduling program is vital to help pro- ject managers make reasonably good decisions. The use of the developed approach supports the use of dynamic planning of portfolios rather than static plan- ning. There are several secondary contributions of the paper. The paper demonstrated the application of a previously developed structured method for defining performance measures in energy retrofit programs. This study revealed that the program performance is more sensitive to the choice of sequencing strategies when the initial seed funding levels decreases. The paper also confirmed that the use of both cost and savings in the sequencing of projects will result in the best sequencing strategy.

The large variation in results among the scheduling heuristics verifies that project sequencing policies are a 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 the decreasing savings, decreasing cost, and the finally the homogenous project sequencing strategies. Additional applications of the model are needed to generalize these results to broader classes of projects and pro- grams, but the results of the current work can be used as hypotheses in future investigations of similar systems. The simulation model produced in this research provides a formal causal structure that is widely applicable to sustainability improvement pro- grams using revolving funds.

The results and conclusions of the current work are limited by the assumptions used in the analysis. The current work looks only at a single program and not its 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 various macro-economic factors that are not considered here. Different measures of program success may be used by some decision-makers. Broader issues such as the socio-environmental impact of construction activities on the local community may need to be included for some projects. To address these concerns, the model used in the current study can be extended and recali- brated to develop additional insights into program design and optimization. The model can potentially be adapted to investigate a much larger array of financ- ing approaches, as well as other types of infrastructure improvement programs (beyond sustainability improvements). More nuanced versions of the model may be developed that can incorporate different pro- gram conditions, such as particular kinds of infrastruc- ture or additional financial variables. The focus of the current work was on improving sustainability through physical changes to built infrastructures, but the model can potentially also be expanded to incorpor- ate the impacts of facility user behaviours, and the combined effects of infrastructure upgrades with behavioural energy-conservation efforts.

Increasing the sustainability of existing building infrastructure is, and will continue to be, an important part of responsible infrastructure ownership and man- agement. Improvements in our understanding of sus- tainability program design can tremendously enhance the programs’ effectiveness, efficiency, and thereby their attractiveness. The current research contributes to 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 University Utilities and Energy Management group for sharing valuable information that made this research possible.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

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

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