New Sustainable Environment Research Centre Blog

We have moved blogs to http://sercusw.wordpress.com The new blog will be focused on sustainability issues as before but will also highlight work being carried out at the Sustainable Environment Research Centre at the University of South Wales. It will also be updated regularly…promise!

See you over there!

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Wind Turbine Syndrome?

I started writing this post last year but didn’t get around to finishing it. Then last week the topic popped up in the news again with the release of a study Spatio-temporal differences in the history of health and noise complaints about Australian wind farms: evidence for the psychogenic , “communicated disease” hypothesis. The original report that got me interested was this one by the lead author of the study (now with a follow up). The key take home being that the nocebo effect is likely to play an important role in the cause of health complaints such as the so called wind turbine syndrome.

Wind turbine syndrome as a term has been popularised by Dr Nina Pierpont. It is a loose grouping of symptoms that are claimed to be caused by infrasound and low frequency noise produced by wind turbines. Wind Turbine Syndrome however hasn’t really made any traction in peer reviewed literature to date. A 2011 review of health effects and wind turbines suggested that health effects reported were due to annoyance related to a change in the environment and not turbine specific variables such as noise or infrasound.

Last weeks study, uses statistical analysis of data from Australian wind farms to investigate the extent of health issues associated with wind turbines, spatially and temporally. They find that only 1 in 272 of people living within 5 km of wind turbines report symptoms. They also found that the bulk of complaints have occurred since 2009 when anti-wind farm groups started to add concerns about the health effects of wind farms to their other issues. Additionally, the majority of those complaints came from 5 wind farms that hat been targeted by anti-wind farm groups. Some wind farms have never been the subject of complaints of noise despite having been operating longer and being of comparable size to those where complaints have occurred.

Another new study reports an experiment to determine whether the publicisation of symptoms associated with wind turbines could create symptom expectations in people. In their experiment they subjected participants to real and faked infrasound to see if they developed symptoms. In order to test the “communicable disease” hypothesis, they primed some of the participants with online information about the health problems caused by wind turbines. Those participants who were primed with the information reported symptoms consistent with their priming, for both the real infrasound and faked infrasound. This suggests that psychological expectations could explain the link between health complaints related to wind turbines and exposure to those wind turbines.

The prognosis doesn’t look good for wind turbine syndrome.

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The answer is blowin’ in the wind

Recently I discovered a story about wind farms that suggested that there was a “…possibility that hundreds of derelict wind turbines, coated with indestructible toxic chemicals, will be left rotting on Welsh mountains for hundreds of years.” The basis of this was a report from the Renewable Energy Foundation (REF) that claimed that the “normalised” load factor of wind turbines declines dramatically over time.

[The load factor of a wind turbine is defined as the amount of energy produced in a given time by the turbine, divided by the installed capacity of the turbine multiplied by the amount of time it was generating energy for.]

If the load factor of wind turbines is decreasing as they age, the implication is that wind turbines are generating less energy as they age, and therefore they are a bad investment. We shouldn’t be wasting our money on them, and so on and so forth…In fact they state in their report that “The results of the statistical analysis demonstrate an unambiguous and statistically significant decline in the operating performance of wind farms as they grow older.”

Helpfully, the report includes a reasonable description of its methodology, and the data they used is available to download from their site. This is where it gets weird(er). The report includes a box plot (Fig. 8) of load factor by age for UK onshore wind farms and there is… no clear trend in the median value of load factor by age.  If you plot wind farm output against time for any of the wind farms they have in the data set it is easy to see why.

The output of the wind turbines doesn’t really change over time. It goes up, it goes down, but over time it doesn’t really change too much at all. For Example, Figure 1 shows the energy produced between April 2002 and February 2012 by the Four Burrows wind farm. Four Burrows is a 4.5 MW wind farm which came on line in 1995. The farm is made up of 15 x 300 kW Siemens wind turbines and is situated near Truro. It is fairly typical of ’90s wind farms in the UK. You don’t have to take my word for it, you can download the data and see for yourself with any of the wind farms in the database.

Figure 1: Four Burrows wind farm output.

The question is then, how do they come to the conclusion that load factors are decreasing over time? Well apparently “…the distributions mask crucial differences between the performance of wind farms as they age because they do not control for the differences in location and wind availability over time.”

This doesn’t actually make a lot of sense. If we remember the definition of load factor, we can see that all it does is tell us what proportion of the installed capacity of the wind farm is generated over a given time period. If we consider that the energy produced by a wind turbine is dependent on the wind speed at a particular location and time, then load factor would appear to account for differences in wind availability over time.

Regardless, they go on and produce a fixed effect model to take into account the “previously unaccounted for” locational and wind speed effects and produce their answer. Now, I am not a statistician, but my suspicion is that this model is not suitable here, or as above, its not really even necessary to use it for the purpose the report puts it to. Luckily, my thoughts on it are not important, because it doesn’t actually make any meaningful difference to the wind industry if this method of analysis is correct or not.

What matters is the amount of energy actually produced by the wind farms. Even if you agree that the fixed effect model is a reasonable way to determine a “normalised” load factor for wind farms, and that this “normalised” load factor does decline over time, it doesn’t matter. The same data  shows that the amount of energy produced by those wind farms is not dramatically declining over time, and so the performance of the wind farms is also not dramatically declining over time, contrary to what the report would like you to believe.

So why would you go to all that trouble to produce a “normalised” load factor metric? The answer is blowin’ in the wind…

 

 

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Only the juciest cherrys picked: guaranteed!

There has been a bit of a flap amongst certain types of blogs over the past weekend. This has been mostly centred on an article and update that appeared in the Mail on Sunday, claiming as certain types are wont to do, that global warming has stopped! or paused! or what ever the ‘term du jour’ is. I don’t want to pile on but this is basically the same sort of thing that the GWPF have been doing with their “21st Centuary Global Mean Temperature” figure as addressed here previously.

Tamino is on the case again with this one, as are Skeptical Science.

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Like as the waves make towards the pebbled shore

When I saw this latest story about wave power, I was surprised that it was news. But apparently news it is, and actually it is worth passing on.

The Carbon Trust have produced a report that sets out, apparently for the first time, the UK’s wave power resource and have found that the majority of that resource is in the south west of England and off the west coast of Scotland. They estimate that about 11% of the UK’s current power needs could be met by large scale wave farms.

Like I said, I was surprised this was news because the Carbon Trust has published similar information to this previously. This latest report however goes further than previously and so the reason for this post.

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Zero carbon, or not zero carbon? That is the question.

A student made me aware of an interesting recent  judgement by the Advertising Standards Agency (ASA). The complainant had challenged that “Solartwin.com zero carbon solar power & water heating” was misleading and could not be substantiated. The issue arose around the definition of “zero carbon” and whether  that description could be applied to the technology for sale.

The interesting part to me was that  the ASA upheld the complaint and I was keen to see their justification. This is where it gets “fun.” The assessment published on their website (linked above) does not actually seem to support their decision very well.

They state:

‘…we noted that within the Defra research paper, dated February 2011 and entitled “Consumer understanding of green terms”, only 46% of respondents stated they were very familiar or familiar with the term zero carbon. We noted that the report commented that “If unsure, participants often interpreted terms in their most literal sense (e.g. ‘zero carbon’ was often translated in the group discussions as ’emits no carbon’).”‘

If unsure, “zero carbon” was taken to mean “emits no carbon.” This is actually how Solar Twin defined zero-carbon in their submitted material to the ASA, and the ASA noted this.

They continued:

‘We also noted the ad was for a solar panel supplier and considered that the availability of a product which produced zero carbon during its full life cycle was likely to be a factor that would effect a consumer’s transactional decision. Therefore in the context of the ad, we considered that consumers were likely to interpret the zero carbon claim to mean that no carbon was produced in the full life cycle of the advertised products.’

Here I think the key phrases  are “no carbon produced” and  “full life cycle.” Solar Twin provided evidence and the ASA noted it, that in fact although the products do contain embedded carbon from their manufacture, over a “full life cycle,” they effectively pay back the embedded carbon within their lifetime because they offset traditional fossil fuel derived energy (Actually, it could be argued that as they pay back the embedded carbon within their lifetime that the products are better than “zero carbon.”) One possible interpretation of this is that over the full life cycle of the products, no carbon is produced.

They conclude:

‘Since we understood that carbon was produced in the manufacturing process, we concluded that the zero carbon claim had not been substantiated and that the ad was likely to mislead consumers to their detriment.’

If I was marking this as a piece of students work I would have to say that their conclusion does not follow from their discussion! I can understand their concern, and it is important that consumers are not mislead about the possible benefits of domestic solar power, but I am not that impressed by the ASA grasp of the issues here.

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Out of Time?

The International Energy Agency put out their World Energy Outlook (WEO) this week. I don’t seem to remember the WEO getting a huge amount of press usually, but this time it comes with a warning that guarantees headlines. “The world is locking itself into an unsustainable energy future which would have far-reaching consequences…” reads the tag line for the press release accompanying this years WEO.

The basis of the statement is that by commissioning new fossil fueled generation capacity and infrastructure now, we lock ourselves into using that technology for the foreseeable future and into emitting the associated greenhouse gases.

“The WEO presents a 450 Scenario, which traces an energy path consistent with meeting the globally agreed goal of limiting the temperature rise to 2°C. Four-fifths of the total energy-related CO2 emissions permitted to 2035 in the 450 Scenario are already locked-in by existing capital stock, including power stations, buildings and factories. Without further action by 2017, the energy-related infrastructure then in place would generate all the CO2 emissions allowed in the 450 Scenario up to 2035.”

The kicker is the IEA estimate that for every $1 not spent now on clean energy, it will take $4.30 in 2020 to have the same effect as that $1 spent today would.  The problem is that CO2 is a long lived gas in the atmosphere. About half of what we emit today hangs around in the atmosphere for centuries. Therefore delaying action to reduce CO2 emissions now places a heavy burden on our future.

This is basically the same message as a Real Climate post of last year. They show that it is not credible to delay action on reducing CO2 emissions in favour of easier short term fixes, because in the long run we will be worse off. At issue is that regardless of how many rounds of talks, how many CoPs or summits we go through, no one is able to agree on a course of action.

The perception seems to be that it is a problem we can address in the future, the reality seems to be that it is a problem we should have already addressed by now.

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Yet another round of cuts

The Government just can’t seem to stop announcing cuts! This week the Government has proposed a plan to cut GHG emissions by 50% from 1990 levels by 2025 en route to 80% cuts by 2050.

Under the Climate Change Act 2008 the government is committed to an 80% cut in GHG emissions by 2050. To realise these cuts the Government has to meet a series of interim targets. These targets cover 5 year periods and have to be set 3 periods ahead. We are reaching the end of the first period (2008-2012) and so they have now announced the proposed carbon budget for the 4th period (2023 – 2027).

The budgets for the 4 periods can be seen in Table 1. These budgets represent the sum of the amounts of GHG allowed to be emitted in each of the 5 years they span.

Table 1: UK interim carbon budgets.

Budget 1 

(2008 – 2012)

Budget 2 

(2013 – 2017)

Budget 3 

(2018 – 2022)

Budget 4 

(2023 – 2027)

MtCO2eq 3018 2782 2544 1950
% reduction from 1990 22.8 28.8 34.9 50.1

This leads to the question: Are we actually going to meet any of these targets? It is all well and good proposing strict targets if we don’t actually meet any of them. In 1997 the Government pledged to reduce CO2 emissions by 20% from 1990 levels by 2010. This hasn’t come to pass. Preliminary figures from DECC show that only a reduction of 16.5% was achieved.

Cambridge Econometrics suggest that we are going to narrowly miss the first 3 carbon budgets based on their projections, whilst the Government projected last year that we would meet them.

To meet the first 3 budgets requires that GHG emissions drop about 1.2% of 1990 levels per year. This is not too far off the slope of UK GHG emissions from 1990 – 2007 as estimated by a linear fit, so on the face of things, it doesn’t look too far out of the realms of possibility that we might meet at least the first budget. Indeed Cambridge Econometrics think it will only be narrowly missed, but that for subsequent targets the gap between performance and target will widen. Currently, GHG emission reductions seem to be on target (Figure 1).

UK GHG Emissions

Figure 1: Annual UK GHG Emissions 1990 – 2010

One issue could be to what extent it is possible to decouple economic growth from energy use. From the preliminary figures for 2010 it doesn’t look as if we are there yet, emissions rose again as the economy started to recover. Although, not above the point they might have been at anyway. Another issue is the sustainability of the historic rate of decrease. Part of the existing decrease is due to the migration from coal fired power stations to combined cycle gas turbines. The relative costs of coal and gas are an issue here, as CO2 emissions in the UK currently broadly follow the amount of coal utilised for energy generation. This means that as the market fluctuates so too do emissions. Another part is likely due to the reduction in manufacturing capacity in the UK and our reliance on imports from overseas. In order to maintain and eventually increase the rate of decarbonisation (required to meet the 4th carbon budget) it will be necessary to invest in large scale development of renewable energy technology as well as energy efficiency measures. This would reduce the influence of coal prices on emissions by reducing the proportion of energy generated in that way. A knock on effect could be economic growth in the renewables sector and the UK as a whole as companies such as Vestas invest in the UK.

The policy designed to meet the 4th carbon budget will be announced in October.

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World Water Day

I have an article published today for World Water Day on the University Website that I thought I would leave a link to from here, it doesn’t go into too much detail about anything but has some links to places you can find out more. This one is probably a good place to start.

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What a difference a year makes.

This post is a belated update to the lies, damn lies, and statistics post from the end of last year. The below figure has been updated with the temperature anomaly for 2010, and we can see that it sits just where we expect it to be if global warming was continuing on as before. So, no surprise there then.

Global Yearly Temperature Anomolies 1975-2010 from HADCRUT3v

All of the commonly used data sets tell the same story. However, in order to clearly demonstrate that we need to use a “trick!” The three most commonly used surface temperature data sets are those developed by the Climatic Research Unit at UEA, GISS TEMP from NASA and the NCDC from the US Department of Commerce. In fact these are the data sets that the World Meteorological Organization uses to calculate the global average temperature (2010 is in a three way tie for the warmest year on record). As before I will plot annual means from 1975 to 2010, this time for each of the above data sets.

Global Annual Temperature Anomaly

We can see that the GISS and NCDC data sets agree pretty well with each other, but the HADCRUT data set is consistently lower than the others. This is due to the way the temperature anomalies are calculated for the different data sets. Each of the above groups calculates the temperature anomaly from a different base line. For HADCRUT the base line is from the average 1961-1990 values, for GISS 1951-1980 and for NCDC 1901 – 2000. These different baselines create the offsets seen in the above figure. Once we set all the baselines to the same value we can better compare the different data sets. (I have chosen to set all of the baselines to 1961-1990 as this is the baseline used in the previous post.)

Global Annual Temperature Anomaly same baseline

We can now see that the three different data sets agree much more readily with each other. We have to note that altering the baseline does not change the temperature trends. In fact anomalies are used precisely so that it is possible to easily look at trends in the data. This is because they represent changes in temperature and not absolute temperatures. We could imagine a scenario where we wanted to look at the temperature history of a couple of different sites, one with a higher average temperature than the other. In order to make meaningful statements about the relative temperature changes of each site in comparison to each other we would need to normalise the temperatures in some way. This is what baseline averaging to produce anomalies does and it enables us therefore to make comparisons between different locations and estimate trends accurately.

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