.jpg)
Efficiency assessment shows whether a transmission company’s costs match its operational need. It is difficult because there are only three onshore transmission owners in Great Britain, too few to form a comparator set, so the comparison has to be built from international data, from a small sample, against metrics that have to be chosen rather than read off.
Because it is the only way to show that costs are tracking operational need rather than drifting away from it. A transmission company that can evidence this has a stronger case when raising capital and when arguing its forecasts in a price control review. A regulator needs the same evidence in reverse, to test historical performance, forward-looking allowances and the claims made in support of them.
There is no universal metric for efficiency. The benchmark has to be chosen deliberately, with a clear view of how the variables relate to each other. That choice has a direct bearing on the results: in the literature reviewed for the Netherlands Authority for Consumers and Markets, model misspecification has a significant effect on efficiency estimates, and the effect is worst in small samples.
Technical efficiency, which is what economic regulators concentrate on in a price control review. It relates output, here electricity transmitted, to cost. Output efficiency, or cost-effectiveness, relates service quality to cost, and is pursued through incentive schemes and other secondary mechanisms.
Throughout this series, efficiency means technical efficiency, and the comparison is expenditure against concrete network metrics such as circuit length, substations, transformer capacity, units transmitted and peak demand. Comparing expenditure with operational scale provides the basis for assessing whether a company's costs are proportionate to its network.
Figure 1: Cost and operational metrics matrix
No, and the current Great Britain data shows why. The three onshore transmission owners expect to finish RIIO-ET2 6% below their adjusted totex allowances, and were 12% below across the first four years. Read on its own, that looks like efficiency.
Set against output delivery it looks different. Ofgem’s owni figure for forecast asset volume delivery puts all three short of the volumes anticipated when the price control was set, by 28%, 18% and 23% respectively. And the adjusted view changes it again: Scottish Hydro Electric Transmission reports a 7% underspend that, once expected end-of-period adjustments are applied, becomes an anticipated overspend of £31m.
Figure 2: Forecast totex spend against forecast asset volume delivery, RIIO-ET2
Table 1: Underspending an allowance is not the same as being efficient. Great Britain transmission owners, RIIO-ET2
The figures also raise three points about how cost performance should be interpreted.
Because it has no competitors operating under the same conditions, so the comparator set has to be assembled across borders, which is a materially harder exercise than comparing firms within one market.
A meaningful comparison also has to account for conditions the company does not control. Asset age and geography affect operating cost and are hard to measure and standardise. Nor is operating cost the only usable metric: staff numbers test whether a company’s headcount is consistent with its needs.
The binding constraint is the size of the comparator set. TCB18 drew on 17 electricity transmission system operators across 15 countries, giving 81 cost observations for 2013 to 2017, within a wider project of 46 gas and electricity operators across 16 countries. TCB21 used 16 operators across 16 countries, with a maximum of 87 cost observations for 2016 to 2020 and 76 operator-years in most descriptive tables.
The sample also brings together operators of very different sizes. In TCB21 the smallest runs 1,037 circuit kilometres and the largest 39,116, a spread of 38 to 1 inside a sample of sixteen.
Figure 3: Circuit length by operator, TCB21 electricity sample, 2020
The limited number of observations also shaped the choice of benchmarking method. TCB18 did not use stochastic frontier analysis as its main instrument because the number of observations was too small for a full-scale application, and used data envelopment analysis instead. Outliers were screened on German incentive regulation criteria, through super-efficiency and sums-of-squares tests. One operator was removed permanently as an extreme outlier and three more were identified by the model-specific tests, making four frontier outliers out of seventeen. On a sample that size the screening rule is not a technicality; it is a quarter of the sample.
Fewer than you would like, which is why the cost driver choice has to be argued from the engineering and the economics rather than discovered by fitting.
Data envelopment analysis offers no guidance at all on which variables to use, and it loses the power to discriminate between efficient and inefficient firms as the number of inputs and outputs rises, most sharply when the sample is small. The rule of thumb in the literature is that the number of inputs multiplied by the number of outputs should not exceed the sample size, and that the two added together should not exceed a third of it.
TCB18 settled on one input and three outputs: a normalised grid measure weighted by land use area, total installed transformer power, and circuit length weighted by the share of angular and steel towers. Four variables against seventeen operators clears both thresholds, and clears the project’s own convention of three times the number of inputs and outputs, with five observations to spare. There is no room in that budget for a fourth driver.
What the three drivers buy is most of the variation in cost.
Figure 4: Explanatory power of the TCB18 cost drivers, robust regressions, n = 81
The conditions that operators say they cannot control, and that the model cannot separate from the choices they make.
TCB18 invited operators to claim special conditions not captured by the model. Eight did, submitting twenty-five claims; sixteen were rejected and nine were investigated, and none of the nine became a separate cost driver, their effects being absorbed by correlation with variables already in the model. These were genuine cost pressures, but the model did not treat them as independent drivers. Their effects were already reflected in other variables.
The difficulty was even greater when it came to service quality. CEER ran two surveys to find a quality metric that was universally relevant, measurable and verifiable. Responses came from two operators the first time and remained low and diverse the second time, and no quality parameter entered the model. The same pattern appears in the wider literature, where most studies disregard operating environment differences altogether.
So the frontier is drawn on costs and physical assets, corrected for land use, topography and soil through the normalised grid, and silent on everything else. This is an important limitation when using the benchmark to determine how much a company should be allowed to spend.
Two years at best, four and a half at worst, and five by the time anyone uses it.
TCB18 reported on 17 July 2019 on data ending in 2017, a two-year gap. TCB21 carries a release date of 31 July 2024 and a final report dated 21 January 2025, on data ending in 2020: four and a half years. A regulator consulting the most recent pan-European benchmark during a 2025 price control review was working from evidence five years old.
Figure 5: The gap between the data and the verdict
Table 2: The two most recent pan-European electricity transmission benchmarks compared
The time lag reflects the stages involved in producing a comparable benchmark. Data has to be collected from operators under common definitions, validated nationally, cross-validated centrally, then modelled. TCB18 ran from December 2017 to June 2019 on data already two years old when it started.
It is also about to be formalised, at least in gas. Article 19 of Regulation (EU) 2024/1789 requires ACER to publish a cost-efficiency comparison of gas transmission system operators, the first by 5 August 2027 and then every four years, with national regulators obliged to take the results into account when setting allowed revenues. Electricity transmission has no equivalent standing requirement: TCB18 and TCB21 were CEER projects, run when the regulators involved chose to run them. A four-year statutory cycle fixes the cadence; it does not shorten the lag between the data and the verdict.
Yes, because it compares a company with peers rather than with nothing, and because the results are not trivial.
TCB18 reported mean cost efficiency of 89.8% for 2017, with four best-practice peers and four screened-out outliers. The distribution matters as much as the mean: the median operator scored 0.991 while the lower quartile sat at 0.795, so the average conceals a small group a long way from the frontier. On the model’s own arithmetic, operators could on average save 10.2% of benchmarked comparable totex, which the executive summary rounds to about ten per cent of total comparable expenditure.
The results still need to be interpreted in light of several limitations. Differences in regulatory practice, in mandates and statutory obligations, and in the variables that resist measurement all reduce comparability. Accounting for them as far as possible is what makes an assessment as accurate as it can be. An international benchmark gives a defensible range and a set of peers, with both the TCB18 and TCB21 teams emphasising the need to interpret the results in that context.
I was delighted that MCC's work was completed on time, and within budget, helping us deliver important changes and improvements, to the benefit of our stakeholders. MCC's report is published on the CCC website.
I am delighted to recommend MCC Economics. Specifically, I worked closely with PJ, who helped us with our Nuclear and CCUS projects. PJ helped us develop new policies and answer questions from our stakeholders. His support helped us deliver important changes and improvements, to the benefit of our stakeholders.
MCC Economics has helped us better understand the most important issues for our stakeholders, including: charges, shareholder returns, debt payments and inflation impacts.
I am delighted to recommend PJ and his team at MCC Economics. We've been working together on National Policy Statements to help meet net zero targets for 2030 and 2050. We initially appointed MCC Economics to support us on offshore wind consultation analysis and have recently reappointed MCC Economics to undertake a larger consultation analysis role across all sectors, including hydrogen, CCUS and networks. I can confirm that PJ and his team have shown excellent spreadsheet skills, alongside very good project management, planning and analysis skills, helping us deliver important changes, and continuous improvements, to the benefit of our stakeholders.
I am delighted to recommend PJ and his team from MCC Economics. They helped us with our price controls for Heathrow airport and for NATS (En Route) plc (the air traffic services provider). Specifically, the MCC team helped us deliver important changes and improvements to our financial models and supporting policy documents, to the benefit of our stakeholders.
I am delighted to confirm that I worked with PJ on a retail project in 2015. The project helped stakeholders understand electricity costs and charges. Specifically, the project helped us explain to stakeholders, internally and externally, why electricity charges differed across the regions (GB, NI & Ireland). PJ was a key member on the project team, which helped deliver changes and improvements in the understanding of energy retail.
.png)
Evidence-based analysis for transparent, defensible, and effective decision-making.
© All rights reserved – MCC Economics 2026.