MPAC and Wolfe Island, again.

INTRO

Several months ago Stewart Fast, a new professor at Queens University in Kingston, Ontario, undertook a study of why southern Ontario was such a hotbed of anti wind energy sentiments.  His conclusions were interesting, and I’ll be having more to say about them in a future posting.  As part of his study he looked at property values and in particular he looked at MPAC (the Ontario real estate assessors), Wolfe Island and the property assessment reductions thereon.

As it happens, I had also looked at MPAC and Wolfe Island and posted on it about 18 months ago.   It seems that Fast and I used the same FOIA-obtained spreadsheet.  My main conclusion was that there seemed to be a large number of large reductions on Wolfe Island, but there wasn’t enough of a pattern to convincingly tie the reductions to the 86 wind turbines on Wolfe’s west end.

I’ve also posted on MPAC and property assessments in a 4-part series.  My main conclusion, contained in part 1′s section, was that MPAC seemed to be hiding the reductions by lowering the values in neighborhoods that just coincidentally happened to be around wind turbines, but not formally incorporating distance to a wind turbine into their regressions.

What Dr. Fast’s work added to mine was that (1) he was able to group MPAC’s reductions on Wolfe Island by their distance to the nearest wind turbine, and (2) he reminded me of how to usechi-square to test the differences between the bands for statistical significance.  The quick summary is that MPAC has been providing reductions to properties close to wind turbines significantly more often that those further away.  And I’m not using the word “significantly” in some fuzzy qualitative manner – I mean “significantly” in the hard statistical quantitative manner.  In other words, the odds of the getting a wind-turbine-centered pattern just randomly are vanishingly small.  Wolfe Island provides a good hard-to-refute example of how MPAC is finessing the numbers to deny the obvious.

THE DATA

The raw data (i.e. the spreadsheet) is quite detailed, so to save space here’s the summary of it.  There are 4 major areas in the municipality of Frontenac Islands:  Wolfe West (where the turbines are), Wolfe East, Howe Island and Simcoe Island.  The number of properties and total reductions are in the following table.

fi-data-base

As both Fast and I have written, these numbers aren’t really indicative of anything having to do with wind turbine proximity. About the only thing that stands out is that Simcoe Island had a far higher rate than the other areas, which was at least partially due to reasons other than wind turbines.

The next step was what Fast added: he was able to use GIS software to group the reductions into buffers based on the distance to the nearest wind turbine.  He had 5 buffers: < 1km, 1 – 2 km, 2 – 5 km, 5 – 10 km and > 10 km.  He used chi-square to see if there were significant differences between the buffers and found that the 1 – 2 km and 2 – 5 km buffers were significantly more likely to have reductions than the other buffers.  As he said, this may be suggestive but is not quite conclusive.

Dr. Fast graciously provided me the data that went into his buffers and I re-ran his chi-square calculations to make sure I could replicate his results.  Initially I thought the non-significance of the < 1 km buffer (you’d expect the buffer closest to the turbines to show the most significant effect) was due to the income-producing nature of any land close to a wind turbine, plus the setback that rendered about 25% of that buffer unoccupied.  While those could be important, I also noticed that by chance there were a lot of reductions just outside of the 1 km border.  As an example I show the following picture of the reductions around Wolfe’s main city, Marysville:

wi-reductions-wegmap-st-marys

The 1 km buffer ends about where Highway 95 T’s: to the left is inside that buffer while to the right is in the 1 – 2 km buffer.  Since all the buffer borders are fairly arbitrary anyway, I decided to proceed with 4 buffers, with my results below.

fi-data-buffers

As the buffers get closer to the wind turbines you can see that the ratios of reductions to properties generally gets higher.  The chi-square is a test to see if these ratios could simply be due to chance.  The “Chi-2 p” column provides the p (probability) that this buffer varies from the total Frontenac Islands ratio by random chance.  The two buffers closest to the wind turbines have less than a 1% chance of having values that high by chance, while the buffer farthest from the wind turbines has a much less than 1% chance of having a ratio that low by chance.  Note that BOTH close-in and far-away reductions are significantly different from the mean, and in directions that are BOTH consistent with the hypothesis that wind turbines are associated with property assessment reductions.

DISCUSSION

In my earlier posting on the MPAC 2012 study I predicted that they would lower assessments close to wind turbines while never explicitly recognizing wind turbines as the cause.  I offered up Wolfe Island as an example of how this might proceed.  Thanks to Dr. Fast and a fair amount of serendipity we now have a solid indication that MPAC is proceeding as predicted.  While some aberrations in assessments and reductions would be expected (some chi-squares show significance where none really exists), the pattern shown above is just too consistent to be cast aside as coincidental or anecdotal.  We proposed a hypothesis that there would be more reductions closer to the wind turbines and the data clearly support that hypothesis.

Dr. Fast does have a point that this is indirect evidence of lower values.  After all, we don’t have the municipality’s “bufferized” assessed values, not to mention bufferized sales data.  In the 2012 study, MPAC did provide bufferized assessments for the entire province and they show a 25% decrease within 5 km of the turbines, a result that somehow got lost in their summary.  As for actual sales, there have been so few, especially on Wolfe’s west end, that any sort of statistically-valid testing would be difficult.  In the meantime, reductions will have to serve.  That MPAC seems to be going out of its way to hide this trend indicates that MPAC is being used to implement a political agenda – a problem greater than just wind turbine assessments.

Overall, some 22% of the properties in the Frontenac Islands were granted reductions by MPAC.  I’d love to know if that is typical – it would be interesting to study the assessments and reductions in the municipalities with and without wind turbine projects.  Unfortunately, as even Dr. Fast commented, MPAC has made getting their data just about impossible.

In his report Fast says that the evidence of reductions due to wind turbine proximity is “suggestive but not conclusive”.  Given the numbers above you are of course free to come to your own conclusion, but to me they are more than “suggestive”.  Perhaps there are confounding effects from the 2008 melt-down and following economic malaise, but I’d think they would affect the area as a whole.  If it isn’t the wind turbines that produce this rather clear pattern, then what is?

Wind Farm Realities, Oct 28 2014

One thought on “MPAC and Wolfe Island, again.”

  1. Here is a quick overview of carbon emissions, climate, extinction of species and the stupidity of wind turbines ……….

    After decades of wind energy growth Europe has installed the equivalent of around 117,000 1MW turbines (about twice the US). The EU consumes about 14 million barrels (25% less than the US) of crude oil a day. If these turbines are running at about 20% of capacity (which is unlikely), the net energy from these turbines equals no more than 1-2% of the energy Europe receives from crude oil.

    But there is something even more important, the amount of energy created from wind energy has not even covered the yearly increases in consumption of crude oil in Europe or America.

    Want to replace coal with wind turbines? Based just upon 2013 consumption, America needs to build about 3 million 1MW turbines and have them running at 20% installed capacity. To replace the crude oil America uses we need another 10 million 1 MW turbines. But we have only 61,000 and would need to build over 200 times the current number of turbines.

    Then since wind turbines contribute so little energy in the big picture and so many of them would be needed, how is Europe or America going to fit millions upon millions of turbines into their shrinking open spaces? Where is Europe or America even going to find enough good wind? It can not be done but if you listen to the paid wind industry shills and corrupt politicians these turbines will save mankind.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s