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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsEconomists and energy-efficiency practitioners produce more credible, usable cost/benefit studies when they design and review the work together. Economists bring statistical methods, causal-inference skills and disciplined accounting. Practitioners know how programs are marketed, installed and maintained, which customers can realistically participate, and which outcomes matter beyond a meter reading. Combining those perspectives makes it less likely that a study will choose an impractical design, count the wrong costs or benefits, or generalize one unusual project to an entire sector.
The case was made by Steven Nadel in an ACEEE article published December 7, 2015, and in the International Association for Energy Economics (IAEE) Third Quarter 2016 Energy Forum. Its lessons still apply to weatherization, building retrofits, commercial efficiency programs and public construction initiatives.
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Why collaboration changes the quality of an efficiency study
An evaluation has two jobs: establish what a program actually achieved and explain what should change next. A statistical model can estimate savings, but it cannot by itself tell you whether installers followed the specification, whether participants replaced equipment anyway, or whether a maintenance policy undermined expected performance. Conversely, a program team may understand every operational detail yet lack the tools to separate program effects from weather, occupancy, prices or changes that would have happened without assistance.
Nadel’s central point is that each discipline corrects the other’s blind spots. Economists can test assumptions, construct comparison groups and quantify uncertainty. Practitioners can identify representative projects, explain implementation choices and spot interpretations that do not fit field conditions. As Nadel puts it, “By coupling economists with knowledgeable practitioners, many of these problems can be avoided.”
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Evaluation finds successes as well as failures
Measurement is not an exercise in proving that a program worked. It should reveal where a design delivered value and where its assumptions were too optimistic. Nadel cites weatherization energy audits that overestimated achievable savings. An evaluation that detects that gap gives managers a chance to revise audit procedures, installation packages, quality control or savings claims. Without the evaluation, the same error can be repeated across many buildings.
For construction and retrofit leaders, this means treating evaluation as part of project delivery rather than an audit performed after the budget is spent. A baseline, data plan and feedback loop should be agreed before procurement and installation begin.
Choose a method that fits how the program operates
No single evaluation method is best for every efficiency program. The practical choice depends on enrollment, eligibility rules, when measures are installed, data quality and whether withholding service from a control group is ethical or lawful. Three broad approaches recur in efficiency work.
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In a randomized controlled trial (RCT), eligible participants are assigned by chance to receive an intervention now or later, or to a control condition. Randomization can balance observed and unobserved differences between groups, making a clear estimate of the program’s incremental effect possible.
That design becomes difficult when a program is broadly available and applicants reasonably expect service. A utility or public agency may not be able to deny an eligible household an audit or safety repair simply to create a control group. Construction schedules can also make delayed treatment impractical: a building may be vacant only briefly, or a failed boiler may require immediate replacement. These constraints do not make RCT evidence useless; they mean the design must be negotiated with the people operating the program.
Quasi-experimental studies
Quasi-experimental analysis estimates a program effect without random assignment. A carefully selected comparison group can be built from similar customers, buildings or projects that did not participate, with statistical adjustments for pre-program use, weather, occupancy and other measurable differences. Difference-in-differences, matched comparison and regression-based designs are examples of this family, although the specific technique should follow the available data.
Nadel suggests more frequent use of quasi-experimental studies with carefully selected comparison groups as a possible compromise when RCTs are hard to implement. This is a proposal, not a universal standard. A weak comparison group can be more misleading than a transparent deemed-savings calculation, so practitioners must help researchers determine whether the candidates are genuinely comparable.
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Deemed savings and engineering estimates
Some programs use deemed savings: an approved value is assigned to a measure based on engineering calculations, prior studies and operating assumptions. This approach can reduce data collection costs and speed incentive payments, especially for standardized measures such as insulation or efficient lighting. It is less able to capture unusual building behavior, installation quality, interactive effects or changes in maintenance unless those factors are built into the assumptions.
Deemed values should therefore be reviewed against field evidence where feasible. A practitioner can identify installation or operating conditions that invalidate a standard value; an economist can test whether observed results differ systematically from the assumption and quantify the uncertainty.
Design a fair cost/benefit accounting
A fair study counts the costs and benefits that belong to the decision being analyzed—no fewer and no more. The boundary should be written down before results are calculated, because changing it after seeing the outcome can make an attractive program look better or worse for reasons unrelated to performance.
Include relevant costs
Depending on the question, relevant costs may include incentives, program administration, outreach, design and verification, participant contributions, installation labor, equipment, financing and incremental construction costs. If a retrofit requires a planned maintenance change or creates recurring inspection work, those effects belong in the analysis when they affect the decision-maker’s budget or the resource use being measured.
Do not add unrelated costs merely because they are easy to find. A cost that would have occurred without the program, or that belongs to a different policy objective, can distort the comparison. The analysts and delivery team should document exclusions and explain who bears each included cost.
Count benefits beyond the energy meter
Energy savings are often the headline benefit, but programs may also improve comfort, indoor air quality, safety, resilience, equipment life, maintenance, health or compliance with a building requirement. Whether those effects are monetized depends on the study’s purpose and the evidence available. They should at least be identified so decision-makers do not mistake an energy-only result for a complete assessment of program value.
Maintenance deserves special attention. An efficient system that requires more frequent service can reduce net benefits; a measure that eliminates repairs can increase them. Practitioners can supply realistic maintenance schedules and failure modes, while economists can value those changes consistently with the chosen perspective.
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Use context-appropriate prices
The UK Government’s 2026 Green Book states that social cost and benefit valuation should start with relevant market prices and cautions that generic prices must be checked against the proposal’s context. That guidance is a useful current appraisal principle, not a replacement for the US examples in Nadel’s 2015 article. Energy tariffs, labor rates, carbon values, health assumptions and discounting rules vary by jurisdiction and decision-maker. A study should state the geography, price year, escalation assumptions and perspective—participant, program administrator, utility, taxpayer or society.
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Historical cost-of-saved-energy results: useful evidence, not a price list
The IAEE version reproduces a table attributed to Hoffman and co-authors that summarizes hundreds of programs using different evaluation approaches. The figures are historical study results, not present-day prices or universal estimates. They show why sector and program type matter when comparing efficiency investments.
| Sector or program type | Total cost of saved energy | How to interpret it |
|---|---|---|
| All sectors | 4.6 cents per kWh saved | Historical aggregate across the summarized programs; not a current benchmark. |
| Low-income programs | 14.2 cents per kWh saved | Historical result for this program category; higher cost can reflect deeper needs and delivery conditions. |
| Other residential programs | 3.3 cents per kWh saved | Historical result for the specified residential category. |
| Commercial and industrial programs | 5.5 cents per kWh saved | Historical result for the combined commercial and industrial category. |
The spread is a warning against ranking programs by a single headline number without checking what was included, how savings were estimated and which goals the program served. A low-income weatherization project may pursue health and safety improvements that an energy-only metric does not capture. A commercial project may have unusual operating hours or production constraints. The table can frame questions; it cannot determine whether a current building proposal is cost-effective.
Make the studied programs representative before generalizing
External validity is as important as statistical precision. A result from one atypical or poorly implemented program does not establish that every efficiency program fails. Nor does a successful pilot prove that a national rollout will achieve the same savings.
Before extending a conclusion, compare the studied projects with the intended portfolio:
- Building type, age, size, climate and occupancy pattern.
- Measure package, installation standard and commissioning practice.
- Participant eligibility, outreach channel and incentive level.
- Baseline energy use, fuel mix, tariff and operating schedule.
- Installer capability, inspection rules and post-installation maintenance.
- Program objectives, including health, safety, affordability or resilience goals.
Researchers should report which features limit transferability. Practitioners can help select a sample that reflects ordinary projects rather than only the easiest sites or the most motivated participants.
A joint workflow for credible, useful studies
The following workflow turns collaboration into specific decisions rather than a late-stage review meeting.
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- Define the decision. State whether the study will set an incentive, approve a measure, compare delivery models, justify expansion or improve construction practice. Name the decision-maker and the time horizon.
- Map the program. Document eligibility, enrollment, installation sequencing, contractors, inspection points, expected behavior and maintenance. Include a process diagram so the analyst understands where savings can be gained or lost.
- Set the evaluation boundary. List included and excluded costs, benefits, transfers, non-energy outcomes, discount rate, price year and geographic scope. Record the program goals that an energy-only test would miss.
- Test method feasibility. Ask whether random assignment is acceptable and operationally possible. If not, identify a defensible comparison group for a quasi-experimental design or establish transparent deemed-savings assumptions with a plan to validate them.
- Build the data plan before installation. Specify baseline periods, meter or billing data, weather normalization, occupancy information, equipment specifications, maintenance records and quality-control observations. Assign data ownership and retention periods.
- Check representativeness. Compare sampled buildings and participants with the full program. Investigate missing segments instead of silently treating a convenient sample as typical.
- Interpret results together. Have practitioners explain unexpected patterns and implementation changes. Have economists test alternative specifications, sensitivity to assumptions and uncertainty intervals. Disagreements should be recorded, not averaged away.
- Translate findings into action. Tie each material result to a change in design, installation, inspection, outreach, measure eligibility or future evaluation. Publish limitations alongside the estimate so later users do not overgeneralize it.
Common failure modes and practical fixes
Using an impossible control group
Symptom: The design assumes eligible customers can be denied service, but the program promises universal access or addresses urgent safety work.
Fix: Consider phased enrollment, a wait-list where ethically acceptable, or a carefully matched comparison group. Explain the trade-off between internal validity and operational feasibility.
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Counting only program-administered costs
Symptom: The analysis omits participant labor, financing, maintenance changes or incremental construction costs.
Fix: Revisit the perspective and include costs that change because of the intervention. Report separate totals when different decision-makers bear different shares.
Treating deemed savings as observed performance
Symptom: An engineering value is presented as if it were measured in the actual building stock.
Fix: Label the value as deemed, identify its operating assumptions and compare it with billing, interval-meter or inspection evidence where available.
Overgeneralizing from an atypical project
Symptom: A result from a pilot, failed contractor, unusual climate or narrow customer segment is applied to every program.
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Fix: Describe the sample and implementation context, test heterogeneous effects and limit the conclusion to sufficiently similar programs.
Delivering a technically correct report no one can use
Symptom: The report contains sophisticated estimates but no explanation of what project managers should change.
Fix: Involve practitioners during question selection, interim review and interpretation. Present uncertainty, practical implications and a prioritized action list in the same document.
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What this means for building and construction decisions
For a construction firm, owner or public agency, the collaboration principle changes procurement and delivery behavior. Require bidders to describe how installations will be verified, how commissioning and maintenance data will be captured, and how savings claims relate to actual operating conditions. Preserve baseline utility and occupancy data before work starts. Separate modeled savings from measured savings in project reporting. When a measure affects comfort, safety or indoor air quality, define those outcomes and their evidence requirements instead of forcing every benefit into a kilowatt-hour estimate.
When reviewing a cost/benefit study, ask four questions: Is the method randomized, quasi-experimental or deemed? Can it realistically be implemented for this enrollment and construction process? Does the accounting include all relevant costs, maintenance effects and program goals? Is the studied program representative of the one being funded? Those questions expose the most consequential weaknesses without demanding that every project use the same evaluation design.
Conclusion: better evidence is a joint product
Energy-efficiency cost/benefit studies become fairer and more useful when economists and practitioners share responsibility from the first question through the final recommendation. Economists strengthen causal analysis and accounting; practitioners make the sample, assumptions and implementation story credible. The result is not a promise that every estimate will be precise. It is a decision tool that states what was measured, what was counted, where the evidence applies and which program changes follow from the findings.
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