Reducing the costs of decarbonising winter heating needs to be a priority

Decarbonising winter space heating in the UK will require a capital intensive heat supply chain running only for the winter months.  Policy action is needed to reduce the costs of this.

A large scale challenge …

Large scale electrification of winter heating looks to be essential if the UK’s legally binding 2050 emissions reduction target is to be met, with other approaches likely playing a lesser role (see brief notes on this at the end of this post).  However electrification of winter heating poses severe challenges.

Winter heating uses a lot of energy.  Meeting the present heat load with electricity would add about 50% to present electricity consumption in the first quarter of the year – even allowing for the efficiency of heat pumps – and proportionately more in the coldest periods.  Indeed, peak heat demand is around 300GW, equivalent to around 100 GW of electricity demand from heat pumps, which is larger than present electricity generation capacity.

With expensive electricity …

Furthermore electricity generation to meet heat demand is only required during the winter months.  Consequently, capital costs of power plants need to be recovered over less than half the year, assuming no large scale seasonal storage of either heat or electricity is available (lithium ion battery storage helps a good deal with daily system management but does not look capable of helping move the very large amounts of required energy from summer to winter).  The excess capacity on the system in summer, including solar, means that there will be relatively little chance of recovering capital costs from sales into wholesale power markets over that part of the year.  Export opportunities also look likely be limited as most of Northern Europe has similar seasonal issues.

Most low carbon power is capital intensive, so low load factor operation increases costs a lot, making winter-only low carbon electricity expensive.  Nuclear looks likely, on a rough-and-ready basis, to cost around £220/MWh for winter only operation, assuming generating plant to meet heating load runs on average for a third of the year.

The penalty for lower load factor operation is potentially much reduced if power comes from CCGT with CCS.  This is less capital intensive, so the increase in cost per MWh from running at lower load factor is much less.  However the cost is still likely to be perhaps £150/MWh for winter only operation, around three or four times present market prices.  And no gas power plant with CCS is yet being built, so a huge amount of scale-up of the technology is required.

Offshore wind is also capital intensive and relatively inflexible, but benefits from higher output in the winter months.  It is likely to be between the cost of nuclear and CCS for winter only operation, although it is unlikely to be possible to run a decarbonised heating system exclusively on offshore wind.  Generation from biomass may also have a useful role to play, but again has its limitations.

And substantial costs for the rest of the chain …

The high cost for electricity is on top of the substantial capital costs of reinforcement of the distribution grid and buying and installing the heat pump itself.  In many houses it will also be necessary to replace radiators or install underfloor heating.  This is needed to allow the heating system to operate at lower water temperatures than is usual with gas boilers, in order to retain heat pump efficiency.  Indeed in less well insulated houses heat pumps may supply only part of the load, with some top-up from natural gas still necessary.

Leading to a large total cost …

Two cases for total costs are illustrated in the chart below, which compares the cost of electric heating the cost of a new natural gas boiler for household use.  To emphasise, these are rough numbers, but likely if anything to understate the problem of high cost.  The high case is based on electricity from nuclear, the low case on electricity from natural gas with CCS.  Additional distribution costs are assumed in both cases due to the large amounts of electricity that would need to be distributed with widespread use of heat pumps.  The additional cost for an average household is around £700-1400 per year.

The additional bill for the UK’s 26 million households would amount to £18-36 billion p.a. or around 1 to 2% of GDP.  That’s just to decarbonise residential space heating.   In practice of course it’s unlikely to apply to all households, but other approaches seem likely to be similarly expensive.


Assumptions: Heat pump capital cost of £6,000-8,500 including installation, distribution grid reinforcement and upgrades to radiators/underfloor heating, likely to prove a favourable assumption in practice.  Gas boiler capital cost of £2300 including installation.  Winter low carbon power £150-220/MWh wholesale, electricity network losses 7%, additional distribution costs included in capital cost of system.  Natural gas £34/MWh GCV, gas consumption 18MWh p.a..  Boiler efficiency 85% of GCV, so heat load is 15.3 MWh, heat pump CoP = 3. Required rate of return is 10% with 15 years.  Reducing required rate of return for the consumer to 5% would still lead to a substantial premium (£550-1100 p.a.) for the electricity option. 

There are some caveats to this.  Heat pumps make much more economic sense off the gas grid (about 10% of households) where they compete with heating oil, or with electrical resistance heating.  They also make more sense in very well insulated housing.  This will include new-build, where there is the further advantage that the capital cost of the heat pump is more readily accommodated as part of the cost of the building.  However the turnover of the UK housing stock is very slow.  As a result the contribution that new-build can make is limited, even over a few decades.

With no improvement in the service for consumers …

This additional cost does not bring a better service, and indeed some are likely to find disadvantages.  Heat pumps are noisier than gas boilers and run for more of the time, and the radiators to deal with the lower water temperatures are somewhat bulkier.  An additional cost of £700-1400 per household every year for something with no advantages and perhaps some drawbacks is likely to be politically difficult to implement.

Implying significant new policies …

There are clear lessons from these estimates for making decarbonisation of space heating more tractable.

First, it makes sense to focus initially on new residential and commercial buildings, and properties off the grid, even if this is a limited market.  Second, the benefits of additional insulation become even more compelling, again especially in new build.  Third, the benefits of improving heat pump efficiency are huge.

Fourth, reducing the capital costs of low load factor low carbon electricity is also essential.  In the absence of cost-effective seasonal storage his will in practice require low cost generation from gas with CCS, although biomass generation may also play a role.  Proving this technology at scale and achieving capital costs well below those of other low carbon generating technologies looks to be essential  if seasonal heating is to be decarbonised at acceptable cost.

Fifth, any technology for storing energy seasonally, for example as hydrogen or methane generated electrically or from fossil fuels with CCS, would be potentially transformative for decarbonising heat and much else if it could be done at very large scale with reasonable cost and energy cycle efficiency.   This is currently an underdeveloped area.

Reducing the UK’s emissions from space heating by electrification looks likely to require major technological and infrastructure developments.  All this is likely to take time, making the need to reduce costs urgent, even if large scale decarbonisation of the heating load is some way away.  This needs to be a matter of priority.

Adam Whitmore – 18th May 2015


Notes and details of calculations

Other ways to decarbonise heat

Other approaches such as the use of biomass and heat networks may also play a significant role in decarbonising winter heating, although there is not space to cover them fully in this post.  Each approach has its own challenges.  Heat networks could be fed by natural gas with CCS, either producing heat only or combined heat and power.  This requires new heat networks serving urban areas, as well as a CO2 transport network covering large parts of the country, which will be much more expensive than would be required if only large central generating plant were to have CCS.  In some other parts of Europe there are more existing heat networks, reducing costs there, although very extensive CO2 transport networks would still be required in most cases.

Use of biomass directly for space heating may also play a role, but is unlikely to predominate in the UK, for example due to the lack of storage in most UK housing, the scale of the demand, and in some cases problems with high lifecycle emissions.

Air source heat pumps look to be the most promising technology for very widespread electrical heating, although ground and water source heat pumps and resistance heating will have a role.  Reliance on resistance heating would make the problem of very large demand for expensive winter-only electricity demand much more severe.


Around 150TWh more gas is used (outside power generation) in the first quarter of the year than in the third quarter.  Replacing this much gas requires around 45TWh of electricity if heat pumps are used.  This adds about 50% to present electricity consumption of around 85TWh in the same period.

The calculation of additional electricity demand assumes that additional non-power sector gas demand in the first quarter compared with the third quarter is due to the heating load.  Totals quoted are an average of 2013 and 2014.

For peak heat demand of 300GW see, page 11.

For estimates of the coefficient of performance for heat pumps see:

Costs of electricity

I’ve assumed a 33% load factor (equivalent for running 4 months of the year, from mid-November to mid-March) for electricity to serve heat load.  This assumes that capacity can run continuously at full load during these months, which is unlikely to be the case for most capacity due to variations in demand within day and across days.  The assumption here is thus likely to be somewhat favourable.  Diurnal storage may help achieve smoother output but will add further to costs.

Full system modelling would be required to estimate the cost of low load factor electricity accurately, but would be unlikely to change the conclusions, especially for such a large change to the current system, and if anything would be likely to raise costs assumed here somewhat.

The amount of decarbonisation also matters.  Allowing some emissions from fossil plant running during the periods of highest heat demand, or allowing top-up from gas boilers, can reduce costs.

Hinkley C nuclear plant has a cost of £92.5 per MWh escalating with inflation.  This price is after other support in the form of loan guarantees.   Without this support the cost would be higher.  85% of the cost is capital and fixed operating costs.

Recent tenders showed prices of £114-119/MWh for offshore wind.  However there is likely to be scope for further cost reduction alongside the benefits from higher winter output to offset the costs of lower load factor operation.

Costs of early CCS are expected to be higher than the figure quoted here, but there are ambitions to reduce this to £95/MWh by 2030 for gas with post combustion CCS.  See .  However this looks likely to require substantial learning.  The capital cost of £1300/kW assumed by DECC for gas plant with CCS appears to exclude transport and storage costs and to include some early stage appraisal optimism.  I have therefore adopted a capital cost of £1950/kW ($3000/kW) including transport and storage, though reducing fuel costs to retain a total cost of £95/MWh in baseload.  Getting the total capital cost down to this level would be a substantial achievement.

In short, most of the assumptions for the cost of electricity generation to serve heat load seem to tend towards the optimistic.

Costs of residential consumers

Heat pump and gas boiler system cost calculations are approximate only and will vary greatly with circumstances.  More detailed modelling would refine them but would be unlikely to change the overall conclusions.  The costs exclude the effect of any incentive payments.  Annuitisation of capital costs for domestic consumers assumes a 10% rate of return required over 15 years, with a sensitivity to lower rates of return noted under the chart.  Domestic consumers are likely to require higher returns than this in practice, but financing schemes may be made available to reduce their cost of capital.  The change of rate of return assumption does not apply to power generation.

Average household gas consumption is from  Mean rather than median consumption is estimated.  Ofgem use a somewhat lower figure based on median consumption.  Typical gas consumption includes some hot water and often cooking use.  I’ve largely ignored these factors, which complicate the story somewhat, but again do not change the nature of the central challenge.

Climate change in UK general election manifestos

Comparing manifestos for May’s UK general elections highlights important similarities as well as differences among the parties.

All of the manifestos published by UK-wide parties make reference to climate change policy, but to greatly differing extents.  The chart below shows the number of times the various manifestos mention “climate” (in the context of climate change) and “carbon” (in the context of carbon targets or a low carbon economy).  The number of references ranges from a mere 6 by UKIP to over 100 by the Green Party.

Number of references to “climate” or “carbon” in party election manifestos

Manifesto metions

The total number of references broadly matches the extent and ambition of each party’s policies for emissions reduction.  UKIP’s references to climate change are in the context of their policy of abolishing the Climate Change Act.  In contrast, the Conservatives continue to support the Climate Change Act with its legally binding obligation of an 80% cut in emissions by 2050.  Labour go further with a specific binding target for decarbonising the power sector by 2030, and a commitment to push for a goal of net zero global emissions in the second half of this century.  The Liberal Democrats seek a net zero carbon UK economy by 2050 alongside a binding power sector target for 2030.  The Greens seek an even more ambitious binding target for the power sector in 2030 (25-50g/kWh vs. 50-100g/kWh preferred by the Liberal Democrats), along with a zero carbon economy by 2050, and a 90% reduction in emissions from 1990 levels by 2030.

It should be noted that some these targets will be difficult to achieve, and perhaps impractical.  A net zero carbon economy, for example, is likely to require substantial deployment of negative emissions technologies such as biomass with CCS or use of international offsets.  A 90% reduction in emissions from 1990 levels by 2030 requires huge and relatively rapid changes to long-lived infrastructure, and will not be made any easier by the Green Party’s commitment to phase out nuclear power within ten years.

The Liberal Democrat and Green manifestos also say much more than Labour and Conservative manifestos about the action that will be required.  The Liberal Democrats are, for example, targeting 60% of renewable electricity by 2030 enabled by additional storage and smart grid technology, all non-freight vehicles to be Ultra Low Emissions by 2040, discounts on council tax for improved building insulation, and increased research and development.  (The other parties do mention some of these issues.  For example, the Labour Party also mentions improved home insulation, and Conservatives aim for almost all zero emission vehicles by 2050 and commit to investing £500million over the next five years towards this.)

The Greens have ambitions to reduce energy demand by half by 2030 and two thirds by 2050, including through a huge programme of building insulation.  They also seek very large scale investment in renewables, and plan a system of individual carbon quotas.  The fine detail of these policies is not spelt out, but one would not expect an election manifesto to set out a full and specific implementation plans.

Despite the differences, there is very welcome common ground among the parties (UKIP apart).  All support at least the targets in the Climate Change Act.  All support international action, including an ambitious international agreement to reduce emissions.  All support adaptation to climate change.  This is encouraging, in that it represents the prospect of continuing progress whoever (other than UKIP) is in power after the election.  Indeed as recently as two months ago the three main party leaders signed a joint pledge on climate change, including an agreement to work across party lines on future carbon budgets.

Delivery on all these promises will of course be the crucial test.  But in the meantime the amount of common ground between the parties continues to be encouraging.

Adam Whitmore – 17th April 2015



Note for non-UK readers:  The Conservative Party is the largest party in the current governing coalition.  The Liberal Democrats are the smaller party in the coalition, but the Secretary of State (senior minister) for Energy and Climate Change is a Liberal Democrat.  The Labour Party is the main opposition party.  The Green Party and UK Independence Party (UKIP) currently have very few Members of Parliament (1 and 2 respectively) but opinion polls show them each having significant support.

The manifestos can be found at:



Liberal Democrats:

Green Party:


For a report on the joint pledge by the three main party leaders signed in February 2015 see:

They pledge:

  • to seek a fair, strong, legally binding, global climate deal which limits temperature rises to below 2C
  • to work together, across party lines, to agree carbon budgets in accordance with the Climate Change Act
  • to accelerate the transition to a competitive, energy efficient low carbon economy and to end the use of unabated coal for power generation

Extrapolating deployment trends for solar PV

Simple extrapolation of present trends implies around 1800 GW of installed solar PV capacity by 2030, with even faster growth looking possible.  Low carbon technologies getting to scale like this, and reducing costs in the process, will help lower the political barriers to increased decarbonisation.

Following my last post looking at the large downward trend break implied by the IEA’s solar and wind projections it seems appropriate to look at what a simple extrapolation of trends for solar would imply.

The trend towards more rapid deployment of solar globally has been remarkably linear over the last few years, despite large fluctuations in individual jurisdictions.  The rate of deployment has been growing at about 6.6 GW p.a. based on historical data (6.9GW p.a. if expected totals for this year and next year are taken into account).

Global annual installations of solar PV (in GW) have grown linearly in recent years …

Trend for annual additions

Note:  Data is from BP Statistical Review of World Energy and Bloomberg.  The last two points are short term projections from Bloomberg.  Removing these and relying entirely on historic data makes little difference to the results: the gradient falls from 6.9 to 6.6  GW p.a. and the r –squared value falls to 0.97 [1].  The parameters used in the subsequent analysis are taken from the historical data only, and exclude the short term forecasts.

On this trend, cumulative capacity grows with time to the power of two.  This model fits extremely closely with actual deployment to date.  The lines are effectively on top of each other, and differences are well within the uncertainties in the data and sources of random variation in deployment in any year.

So a very simple model gives a very close fit with history …

model vs actual

Note:  Model is   Cumulative capacity = C0  + 0.5*6.55*(t-t0)2       C0= Capacity in 2006 =  7.0 GW.  t0 = 2006.5 [2]

Projecting this model out gives total installed capacity of 1814GW by 2030.  Solar PV would then account for about 8% of world generation in TWh [3], with an annual installation rate of around 150GW.  Both of these totals remain below saturation levels, so there don’t appear to be any fundamental obstacles to reaching these levels, and indeed continuing to grow.

Estimates from this simple model are very close to projections produced by Bloomberg last year, which are based on a more bottom up approach.  They are between the levels implied by Shell’s Mountains and Oceans scenarios for 2030, though somewhat closer to the lower of these [4], and above the 2020 totals.

Extrapolation to 2030 shows cumulative capacity reaching 1814GW by then …

 extrapolation to 2030

This is similar to forecasts by Bloomberg and between Shell’s scenarios for 2030 …

Comparison of forecasts

There are many reasons why deployment may deviate from this trend.  Many of the drivers I referred to in my previous post – falling costs, increased pressure to decarbonise, increasing availability and reducing costs of storage – may lead to faster growth.  Conversely if costs do not fall as expected capacity may be below these levels.  On balance it seems to me that the odds favour more rapid growth, perhaps closer to the Shell Oceans scenario.

Falling costs and increasing deployment of solar coincide with progress on other low carbon technologies, especially those leading to increased energy efficiency.  This sort of progress, with new technologies deployed at scale, helps lower the political barriers to policy action.  This is likely to lead to more rapid policy development to reduce emissions than has hitherto been evident.

Adam Whitmore – 25th March 2015


[1]  The regressions have not been tested for validity of the OLS regression model (e.g. whether residual are iid), so should be regarded as simply a convenient way of obtaining a gradient.

[2]  The model used may give you a bit of a flashback to school physics classes: the model is identical to constant acceleration from rest (or increase in rate of installation) = a, so speed (rate of installation) = a.t, and distance travelled (cumulative capacity) = (1/2).a.t2

[3]  1814 GW in 2030 is approximately 2540 TWh, or about 8% of global power generation, assuming this is about 10% below the IEA’s central case of 34000TWh.  The load factor assumed is fairly conservative at 16%.  Higher load factors would clearly increase the proportion of generation accounted for by solar.

[4] Shell’s scenarios show energy rather than capacity estimates.  I have converted them to capacity assuming a 16% load factor.  Their scenarios are for total solar generation, but I have not adjusted these for concentrated solar thermal power generation, which looks likely to be a small proportion of the total.

The IEA’s central projections for renewables continue to look way too low

The IEA’s projections for wind and solar capacity look much too low, continuing a history of vastly underestimating renewables growth.  Their projections are not a reliable basis for projecting the world’s future power generation mix. 

I previously looked at the IEA’s track record of underestimating the growth of renewables by a huge margin.  Since then the 2013 and 2014 World Energy Outlooks have been published, and it seems timely to ask how the credibility of their outlook has improved.   The answer appears, regrettably, to be “not much”.

The chart below shows the IEA’s long term projections for global capacity additions of wind and solar PV, taken from the current version of its central New Policies Scenario, and compares these with historical growth and short term projections.  The short term projections are likely to be quite accurate, especially for wind, as projects due on this year or next are usually already in progress.

Annual net global installations of wind and solar:  comparison of IEA long term projections (New Policies Scenario) with historical data (to 2014) and short term projections (2015-6)

installation rates corrected

Notes:  Historical data is from BP, the Global Wind Energy Council and Bloomberg.  Short term projections are from Bloomberg, as of February 2015.  Long term projections are from the IEA World Energy Outlook, 2014, New Policies Scenario.  IEA projections are for 2012-2020 and for each 5 years thereafter, and are shown at the mid-point of each interval. (The original post contained a minor plotting error for some of the historic data, now corrected.  The change reinforces the story as it correctly emphasises further the rapid growth of solar.)

The historic and short term forecast data shows a clear and strong upward trend in the rate of capacity installation for both technologies, although for wind this has somewhat moderated in recent years, and there has been considerable year to year policy-driven volatility.

The IEA’s projections show a sharp reversal of this trend, with net installation rates falling to well below current levels, and staying there or falling further for the next two and a half decades.  The average annual installation rate projected by the IEA over the period 2020-2040 is nearly 30% below last year’s outturn for wind, and nearly 40% below what’s likely to be put in this year.  For solar PV the decrease is even greater, with projected installation rates 40% below last year’s outturn, and nearly 50% below what’s likely this year.  This implies a substantial contraction in the wind and solar PV industries from their present size, rather than continuing growth or stabilisation.  The IEA projects correspondingly small proportions of the world’s electricity generation coming from wind and solar PV.  Even a quarter century from now their projections show wind accounting for only 8.3% of generation (in TWh) and solar PV a mere 3.2%.

It may well be that renewables installation rates begin to grow more slowly and even eventually plateau as markets mature.  But a sudden fall by around a third or a half of current levels sustained into the long term seems to run against the main prevailing drivers.

The imperative to reduce carbon emissions from power generation is ever greater.  This looks likely to continue to be a strong driver for renewables growth through direct mandates for renewables and (especially in the long term) through incentives from carbon pricing.  Renewables are also highly compatible with other policy objectives such as security of energy supply.

Renewables are much more cost competitive than they were, both with other low carbon generation and with conventional fossil fuels, especially if fossil generation includes the cost of its emissions.  Costs for wind and especially solar are expected to continue to fall.

Some argue that the total subsidy needed by solar and wind will limit their growth.  However as costs fall any remaining subsidies required will continue to fall even faster in percentage terms (so for example a 20% decrease in costs may lead to a 50% decrease in required subsidy).  This is likely to limit the total additional costs of renewables even as volumes grow, and especially in the 2020s and 2030s as the proportion of projects requiring no subsidy grows ever greater.

There is also scope to increase the installed base of renewables globally to well above the levels projected by the IEA without causing significant problems for grid integration.  In any case such obstacles are likely to reduce over time with improved grid management, greater interconnection, and falling costs of batteries.

Given these drivers the IEA’s projections appear to be close to or below the bottom end of the credible range for rates of deployment, especially for solar, rather than the central case they are intended to represent.  They do not form a reliable basis for assessing the future of the world’s power generation mix.

Adam Whitmore – 27th February 2015

Randomised trials of energy efficiency policy

Greater use of randomised trials could help the uptake of energy efficiency by identifying which policy interventions work best.

More efficient use of energy is high on almost everyone’s list of good ways to reduce CO2 emissions.  It can lead to large scale emissions reductions, is often cost-effective, and tends to be highly compatible with other policy goals such as energy security.

Efficiency standards for buildings, vehicles and appliances have played a critical role in improving energy efficiency, and will continue to do so.  But standards are not the whole story.  Rates of uptake of more efficient technology and processes and other changes in consumers’ behaviour can matter greatly.

However it is often impossible to know in advance how innovative policy interventions will affect behaviour.   Consumers’ responses to novelty are unpredictable, and judging likely response is further complicated because consumers’ circumstances are often complex and varied.  Even afterwards it may be difficult to judge whether an intervention has been effective because it’s impossible to say what would have happened otherwise.

Fortunately there are models from elsewhere that can help address these issues.  A well proven means of judging the effectiveness of interventions is the use of randomised trials, in which one group is subject to an intervention and a similar control group is not.  These trials look to avoid biases such as self-selection, for example where those most interested in something may participate disproportionately.

Double blind randomised control trials for new drugs form a benchmark for such tests.  This approach requires two groups to be chosen differing only in whether they have a new drug or a placebo, with neither the patient nor the physician being aware who is getting which.  Provided that all such studies of each new drug are published – a controversial area – there are two comparable groups, and valid statistical inferences can be drawn about whether the drug has been effective.

The double blind element of medical treatments is not always easy to reproduce in other fields, but the use of controlled trials is common in other areas.  Technology companies often roll out two different versions of software online to subsets of users to see which gets the best response, as measured for example by click-through rates.    This approach allows decisions to be data driven rather than based on judgement or experience.  Tests on users may be ethically controversial, as Facebook found with experiments to its news feeds.  And outcomes are not always desirable from the consumer’s point of view, for example when an option to turn down an offer is less visible on screen, even if few people want the offer.  But effectiveness will likely have been demonstrated, at least for major websites.

Development organisations have used similar approaches in looking at uptake, for example testing different ways of increasing uptake of immunisation programmes [1].

Data driven decision making of this sort is often contrasted with traditional decision making based on the judgement of someone senior, which is sometimes referred to as HIPPO based decision making (Highest Paid Person’s Opinion).  It also goes beyond a vague requirement for evidence based policy making, in that it requires a certain type of evidence to be gathered.  This reduces the often-noted risk that evidence based policy-making turns into policy based evidence-making.

Controlled trials are now beginning to be used to test interventions designed to increase energy efficiency.  In a trial in Norway [2] the labelling of appliances was changed to make it more meaningful to consumers.  Labels in some stores showed lifetime electricity running costs and improved staff training while the control groups had labelling showing only annual kWh and no training.  For fridge-freezers no significant effect was found. For tumble dryers the combined label and training reduced average energy use of tumble dryers sold by 4.9% while training alone led to a 3.4% reduction. The effect was strongest initially, but declined over time.

A similar change of labelling was undertaken in the UK in a joint study by John Lewis department stores working in collaboration with the Department of Energy and Climate Change (DECC) [3].  A statistically significant but small effect (0.7% increase in efficiency of appliances sold) was observed.  Another, earlier, study on interventions for households with difficulties affording enough energy found no reduction in bills, but an increase in comfort [4].

It is encouraging to find such approaches beginning to be adopted.  However they appear to remain very much the exception not the norm.  There are many other areas where such trials could make a large contribution.  Smart metering in particular could benefit from this.  There are many options for both design and use of smart meters.  It is far from clear which will work best.  Trials are needed to find out.  Although there have been a few such studies [5] many more are needed.

Such trials are not as cheap or easy as making a judgement about what will work and hoping for the best.  And they represent a high hurdle for interventions to clear.  But they are more robust as a result, and should lead to more effective (and cost-effective) outcomes.  Controlled trials need to become more widespread if energy efficiency is to make a full contribution to reducing emissions.

Adam Whitmore – 10th February 2015


[1]  The use of controlled trials to look at poverty alleviation and development is described (among other topics) by Abhijit Vinayak Banerjee and Esther Duflo in their book Poor Economics and more concisely and relevantly by Duflo in the accompanying TED talk.

[2] Kallbekken et al. “Bridging the Energy Efficiency Gap: A Field Experiment on Lifetime Energy Costs and Household Appliances” Journal of Consumer Policy, 2013



[5] See for example:

Clearing the air on wind power output

Loss of output from wind turbines as they age is roughly in line with that from other technologies.  Looking at an earlier claim that deterioration is much more rapid than this provides useful lessons for spotting erroneous results that could distort policy debates. 

Large wind turbines are a relatively new power generation technology, and not much has been published on how their output changes over time.  This means that new studies can attract a good deal of attention.  For example, a report by Professor Gordon Hughes for the Renewable Energy Foundation (REF) – an organisation highly sceptical of renewables, despite what its name might imply – made the extraordinary claim that load factors for wind fall by over half in their first 15 years of operation [1], and further suggested that wind capacity would rapidly become uneconomic, so that “few wind farms will operate for more than 12-15 years”.  Such a severe decline in load factor and correspondingly short life would make wind power much more expensive than is commonly assumed.

Two researchers, Dr Iain Staffell and Professor Richard Green, at Imperial College London have since taken a closer look at this claim, and found that it simply does not hold up to scrutiny [2].  Looking at what they found provides wider lessons about how to assess extraordinary claims that might affect policy.

Staffell and Green’s first step was to carry out some comparative sense checks.  They looked at gas turbines, which are similar to wind turbines in that they are also large chunks of rotating metal subject to considerable wear and tear.  Gas turbines typically lose output at a rate of around 0.3-0.6% p.a. with careful maintenance and component replacement, or 0.75% to 2.25% without.  This is very much less than the rate suggested for wind turbines in the REF study, even if wind turbines are not well maintained, for example due to their remote locations.

The next step was to look at the lifetime of existing wind turbines.  The UK has 45 wind farms over 15 years old.  35 of these (nearly 80% of the total) are still operating, and of the remaining 10 only one (2% of the total) has been closed completely.  The other nine have been repowered with larger and more modern turbines to increase output.  Five of these wind farms had been repowered when 17-20 years old, past the operating life predicted by the REF study. These statistics simply disprove the prediction in the REF study that most wind farms would be retired after 15 years.

With these results already falsifying the contention of short lifetimes, Staffell and Green looked directly at falls in output, tracking the performance of each installation over time.  Modelling changes in output as turbines age requires correction for variations in the weather.  Fortunately very detailed data on wind speed and direction is available – the study used 500 million data points from NASA.  Taking this into account showed that turbine load factors do indeed fall with age, but by 1.6% p.a. (0.4 percentage points), within the range for conventional technologies and much less than the 5-13% p.a. found by the REF study.

The results are illustrated in the chart below, which shows the annual change in weather corrected load factor (the absolute rate of decline) for each onshore farm in the UK against the year the farm was built.  There is an apparent tendency for newer turbines to lose capacity somewhat less rapidly, but it is unclear whether there is because newer technology is more durable, or because turbines are more carefully maintained during their early years.  The few that had increased output substantially (at the top right) were likely moving out of an early commissioning phase.

 Wind Decline Figure

Source: I. Staffell, R. Green / Renewable Energy 66 (2014) 775‒786

Together these findings show that the results reported by Renewables Energy Foundation are simply incorrect, and that a material but manageable rate of output decline is to be expected from wind plants as they age, as for other technologies.  In a sense this is a boring result, in that the conclusion is well supported by evidence and makes good sense with few real surprises.  But it is nevertheless an important result, because it implies that wind power can continue to make a growing contribution to decarbonising the power sector.

What lessons can be drawn from the comparison of these studies?

The first is that often a few simple sense checks – such as what has happened with comparable technologies and whether 15 year old wind farms were actually being retired – can help identify claims that are unlikely to be true.  Such simple checks were not reported in the REF study, raising immediate suspicions about the result.

Second, a study that relies purely on statistical techniques, as the REF study did, rather than using physical data – wind speeds in the case of Staffell and Green’s work – is doubly suspect, because it will tend to say little about what is driving results.

Third, it’s important to take account of implied information from the private sector.  Experienced investors continue to put their money into wind farms.  While private investors can and do make mistakes it is unlikely that the numerous investors in wind farms the world over have all either ignored or missed something as simple as rapid output degradation over time.  The fact that very large investments in wind turbines continue to be made suggests there is a good deal of unpublished analysis that contradicts the REF contention.

Fourth, the results of a single study should always be regarded with caution.

Fifth, it is appropriate to be sceptical if results are likely to be congenial to those publishing them, as they were in the case of the REF study.  There may be deliberate misrepresentation, but this is not necessarily so.  Studies have shown that people are more prone to misinterpret data when doing so leads to conclusions that support their world view [2].  Independent academics such as Staffell and Green – who work at Imperial College Business School and are not any kind of lobbyists for wind power (or any other kind of power) – can act as a useful counterbalance to this tendency.

Rigorous, genuinely independent public domain work can play a valuable role in keeping climate change policy debates well-founded.

Adam Whitmore – 12th January 2015


[1]  The REF study is Hughes G. The performance of wind farms in the United Kingdom and Denmark. London: Renewable Energy Foundation; 2012. URL:

[2] I. Staffell, R. Green / Renewable Energy 66 (2014) 775‒786.

[3] See Kahan and Peters

Before Father Christmas becomes a climate refugee …

As this is my last post before Christmas I thought I would look forward to some good cheer and also perhaps some seasonal gifts.  Here’s my request to Father Christmas at the North Pole (or according to your preferred tradition Papa Noel, St Nicolas, Santa Claus, or another bringer of good cheer at this time of year).

“Dear Father Christmas,

can we please have for Christmas something that makes global carbon dioxide emissions rise no more than 0.5 % p.a. until they reach that peak, leads them to peak in 2025, and then fall at 3.5% p.a. forever, so that global temperatures this century increases by no more than 2.0 degrees centigrade due to extra carbon dioxide in the atmosphere.

Thank you”

You can fill in your own numbers for your own particular wish in this spreadsheet

Adam Whitmore’s summary of the Allen and Stocker model

Sometimes it seems like it will need a miracle from Father Christmas to get to the sorts of numbers you have probably entered, certainly if they are like mine.  But if the world can at least make good progress towards these numbers next year, it will make the best Christmas present the planet could have.

Here’s hoping it works out that way, and in the meantime enjoy the holiday season.

Adam Whitmore – 18th December 2014


If you want to know more about the basis of this calculation see my earlier post here.  The parameters define cumulative CO2 emissions given current emissions (area under the curve), and this converts to linearly to temperature. I’ve assumed a transient climate response to emissions (TCRE) of 2 degrees, a variable which is subject to considerable uncertainty.  The calculation is for CO2 warming only, and there may be another perhaps 0.4 degrees due to other greenhouse gasses, so you might want to be more ambitious about what you wish from CO2 than I have been here, even though the numbers already look rather ambitious.