22 July 2017

The slow road to getting open data from the Government's Clean Water 2017 water quality monitoring sites

Who remembers the National Government's consultation over it's proposed Clean Water package 2017?

Who remembers the headline announcement of the proposal? - that there would be a 'target', that 90% of rivers and lakes would be swimmable by 2040?

The environmental NGOs were very critical of the target (and the proposal as a whole).

The Green Party said the new swimmable standard was just shifting the goalposts.

Forest & Bird's Kevin Hague described the proposal as a reduced swimmability standard.

Marnie Prickett of the Choose Clean Water group described the proposal as "fraud" as it intended to change the definition of swimmable to meet a lower standard.

The environmental NGO's argument was that the new proposed 'risk' standard for swimming (expressed in E Coli as an indicator of faecal matter and pathogens) allowed a one in a twenty probability of getting sick when the old standard was a much more precautionary one in a hundred probability of getting sick.

Dr Siouxsie Wiles and Dr Jonathan Marshall explained that the change in risk wasn't quite as simple as that. As did University of Auckland Professor of Biostatistics Thomas Lumley.

However, I thought there was something wrong with that 90 percent number. I seemed to recall Green MP Eugenie Sage saying in 2014 that more than 60 percent of the monitored river swimming sites were unfit for swimming.

The Clean Water package 2017 included this barchart which shows that the 90% 'swimmable' target (and five new swimming quality categories from 'excellent' to 'poor') are actually expressed in a different variable: length of river measured in kilometres (not in number of monitoring sites).

It also shows, in the left-most bar, that the use of the use of the 'length of river' variable in place of numbers of river monitoring sites, results in a very different result.

On the basis of recent data, 72 percent of kilometres of rivers currently meet the 'swimmable' standard (the sum of the 'Fair', 'Good' and 'Excellent' quality categories. Expressing the results in kilometres of river lengths and not in numbers of sampling sites immediately enables a more positive spin to be put on the results.

The underlying data must be water quality sampling results from NIWA's National Rivers Water Quality Network (NRWQN) and sites operated by regional councils.

So, way back on 15 March 2017, I asked for the underlying sampling data from the water quality monitoring sites.

I felt I had expressed my official information request sufficiently clearly to get a reply in a reasonable time.

On your website on the page "Clean Water package 2017" there is a bar chart explaining the target of 90% of rivers and lakes swimmable by 2040 included in the report "Clean Water, ME 1293". The bar chart is also on page 11 of report "Clean Water, ME 1293". The bar chart shows kilometres (which I assume are lengths of segments of rivers) in each of the five 'quality' categories (Poor, Intermittent, etc) with a time variable which has three bars; "Current", "2030" and "2040".

Will you please provide me with the underlying data; which I assume must be water quality monitoring site results (and future predictions for 2030 and 2040) analysed by the five quality categories and the three time categories "Current", "2030" and "2040". Will you also please include the name or number of each monitoring site, its region and for the "Current" selection, the sampling period for the actual E Coli counts. Please provide this data either in comma separated values or Excel 2007 format via the FYI website.

However, I had to lodge a complaint with the Office of the Ombudsmen to eventually obtain the data. That only happened after the investigator from the Office of the Ombudsmen brokered a deal with the Ministry for the Environment. He rang me and said that the Ministry didn't want to give me the data in either .csv or .xls format as I'd requested as the data was in a special binary format; .rdata, specific to a certain statistical programming language named after the letter 'R'.

In other words, it appeared to me the Ministry were claiming that a 'technical' problem in providing me the data I had requested, and not a problem of intent to frustrate the information request.

Sure, it's fair enough to take the Ministry at their word that they didn't intend to delay and frustrate my request. However, whatever the intention, it was still a delay from my point of view as the requester.

I told the investigator I would be happy to get the data in .rdata format. I also expressed the view that it would have only been a very short line of 'R' script to convert the .rdata formatted file into .csv format. And that it was a weak reason for the delay and for not providing me the data in .csv format. I observed that the Ministry's response was pretty unsatisfactory from an open data perspective. The investigator said he couldn't comment on open data issues, as we were in an official information space.

I was finally emailed the data in .rdata format by the Manager, Executive Relations, on 5 July 2017.

I used this R script;

to write the .rdata file to a .csv file.

The .rdata file is WQdailymeansEcoli.rdata at Google Drive.

The .csv format file is WQdailymeansEcoli.csv at Google Drive.

Now I just need to find the time to analyse the sampling sites data.

16 July 2017

Tramping the Nina and Doubtful Rivers Lake Sumner Forest Park

I have just been tramping! The first time for a while and only the second time since a knee cartilage operation in 2016.

The photograph to the left is of Upper Nina Biv.

With three old friends I spent four nights and three days in the Lake Sumner Forest Park.

From 8 July to 11 July 2017 we tramped up the Nina River, to Nina Hut. We day-tripped to Upper Nina Bivvy and then crossed Devilskin Saddle (after visiting Devils Den Bivvy) to the Doubtful River and we stayed our last night at Doubtless Hut.

Finally we walked out to State Highway 7 and drove to my friends' relatively new 'bach' at St Arnaud just before snowfalls closed Lewis Pass.

I have popped my ten better photos to an album on Flickr. And the photo below is the result of some 'Flickr 'embed' script.

Nina and Doubtless Rivers July 2017

What did I learn and bring back from the tramp?

I could say that oh: I took too much food (I had some left at the end). I needn't have taken a pair of fleece pants (yes it was cold, we all tramped in 'polypro' tights but a spare dry pair of tights would have been lighter). And never omit your sleeping mat! (It should always be in your pack even on a 'huts' trip and even more so in winter!)

But that's just a checklist for next time.

When the trip was being planned I said I'd only tramped in the area once or twice. Actually I'd temporarily forgotten I had done a lot of tramps between Lake Sumner and Lewis Pass. The trip evoked a lot of memories and a lot of yarns ("There was another trip I may not have mentioned, where we bush-bashed up to Brass Monkey Biv..") which my long suffering tramp companions politely heard out.

Even little-suspecting fellow trampers we met heard a few yarns! We meet two wonderful women teachers from Christchurch who were outdoor instructors. It of course turned out we knew friends in common and that launched more tall tales from our respective archives.

The big wake-up for me was that although I do look like a bald and white-haired 55 year-old with a lined-face, I got that way by doing a lot of tramps and other adventurous things!

And also that my old tramping friends are still my bestest-ever friends.

17 June 2017

New Zealand greenhouse gases by sector from the inventory

I have made another chart from the New Zealand's Greenhouse Gas Inventory 1990–2015 released the other week by the Ministry for the Environment.

It shows the greenhouse gas emissions by sectors of the economy. It includes 'negative' emissions, more properly called carbon removals, or carbon sequestration or simply carbon sinks. This is the sum of all the carbon dioxide taken out of the atmosphere by the sector of the economy called Land use, Land use change and Forestry.

Here is the chart.

This time I took a more traditional R approach to getting the the data into R from the Excel file CRF summary data.xlsx.

First, I used opened an X terminal window, and used the Linux wget command to download the spreadsheet "2017 CRF Summary data.xlsx" to a folder called "/nzghg2015".

I then used ssconvert (which is part of Gnumeric) to split the Excel (.xlsx) spreadsheet into comma-separated values files.

The Excel spreadsheet had 3 work sheets, 2 with data, and 1 that was empty. So there's now a .csv file for each sheet, even the empty sheet. And we read in the .csv file for emissions by sector.

The final step is to make the chart.

06 June 2017

The latest inventory of New Zealand's greenhouse gases

On the Friday before last Friday, the 26 of May 2017, Minister for Climate Change Issues, Paula Bennett and the Ministry for the Environment released the latest inventory of New Zealand's greenhouse gases.

Minister Bennett and the Ministry have as their headline Greenhouse gas emissions decline.

I thought would I whip up a quick chart from the new data with R.

I pretty much doubted that there was any discernible decline in New Zealand's greenhouse gas emissions to justify Bennett's statement. We should always look at the data. Here is the chart of emissions from 1990 to 2015.

Although gross emissions (emissions excluding the carbon removals from Land Use Land Use Change and Forestry (LULUCF)) show a plateauing since the mid 2000s, with the actual gross emissions for the last few years sitting just below the linear trend line.

Gross 2015 emissions are still 24% greater than gross 1990 emissions.

For net emissions (emissions including the carbon removals from Land Use Land Use Change and Forestry) the data points for the years since 2012 sit exactly on the linear trend line. Net 2015 emissions are still 64% greater than net 1990 emissions.

There was of course more data wrangling and cleaning than I remembered from when I last made a chart of emissions!

The Ministry for the Environment's webpage for the Greenhouse Gas Inventory 2015 includes a link to a summary Excel spreadsheet. The Excel file includes two work-sheets.

One method of data-cleaning would be to save the two work sheets as two comma-separated values files after removing any formatting. I also like to reformat column headings by either adding double-speech marks or by concatenating the text into one text string with no spaces or by having a one-word header, say 'Gross' or 'Net'.

Of course, that's not what I did in the first instance!

Instead, I copied columns of data from the summary Excel sheet and pasted them into Convert Town's column to comma-separated list online tool. I then pasted the comma-separated lists into my R script file for the very simple step of assigning them into numeric vectors in R. Which looks like this.

Then the script for the chart is:

The result is that the two pieces of R script meet a standard of reproducible research, they contain all the data and code necessary to replicate the chart. Same data + Same script = Same results.

I also uploaded the chart to Wikimedia Commons and included the R script. Wikimedia Commons facilitates the use of R script by providing templates for syntax highlighting. So with the script included, the Wikimedia page for the chart is also reproducible. Here is the Wikimedia Commons version of the same chart.

NZ-ghg-2015

For comparison, here is my equivalent chart of greenhouse gas emissions for 1990 to 2010. Gross emissions up 20% and net emissions up 59%.

What can I say to sum up - other than Plus ça change, plus c'est la même chose.

25 February 2017

Graph of atmospheric carbon dioxide concentrations from another cool data package

I feature another cool self-updating data package, this time of concentrations of atmospheric carbon dioxide recorded from the well-known Mauna Loa Observatory, in Hawaii. Graphs of this data are perhaps the most iconic images of anthropogenic climate change.

This post features the atmospheric carbon dioxide data package. Again, it is one of the Open Knowledge International (OKFN) Frictionless Data core data packages, that is to say it is one of the

"Important, commonly-used datasets in high quality, easy-to-use & open form".

The data is known as the Keeling Curve after the American chemist and oceanographer Charles Keeling. It is an iconic image for anthropogenic climate change.

Like the global temperature data package, the atmospheric carbon dioxide data package is open and tidy and self-updating and resides in an underlying Github data package .

Similarly, the data package can be downloaded as a zip file and unzipped into a folder. That will include the data files in .csv format, an open data licence, a read-me file, a json file and a Bash script that updates the data from source.

I can run the Bash script file on my laptop in an X-terminal window and it goes off and gets the latest data and formats it into 'tidy' csv format files.

Here is a screenshot of the script file updating and formatting the data.

Here is my chart.

Here is the R code for the chart.

13 January 2017

2016 the warmest year on record via a cool self-updating data package of global temperature

Radio New Zealand reports that 2016 was the new record warmest year in the instrumental record, so I will pitch in too. But with an extra touch of open data and reproducible research.

It's been a while since I uploaded a chart of global temperature data. Not since I made this graph in 2011 and then before that was this graph from 2010. So it's about time for some graphs. Especially since 2016 was the world's warmest year as well as New Zealand's warmest year.

When I made those charts, I had to do some 'data cleaning' to convert the raw data to tidy data (Wickham, H. 2014 Sept 12. Tidy Data. Journal of Statistical Software. [Online] 59:10), where each variable is a column, each observation is a row, and each type of observational unit is a table. And to convert that table from text format to comma separated values format.

I would have used a spreadsheet program to manually edit and 'tidy' the data files so I could easily use them with the R language. As Roger Peng says, if there is one rule of reproducible research it is "Don't do things by hand! Editing data manually with a spreadsheet is not reproducible".

There is no 'audit trail' left of how I manipulated the data and created the chart. So after a few years even I can't remember the steps I made back then to clean the data! That then can be a disincentive to update and improve the charts.

However, I have found a couple of cool open and 'tidy' data packages of global temperatures that solve the reproducibility problem. The non-profit Open Knowledge International provides these packages as as part of their core data sets.

One package is the Global Temperature Time Series. From it's web page you can download two temperature data series at monthly or annual intervals in 'tidy' csv format. It's almost up to date with October 2016 the most recent data point. So that's a pretty good head start for my R charts.

But it is better than that. The data is held in a Github repository. From there the data package can be downloaded as a zip file. After unzipping, this includes the csv data files, an open data licence, a read-me file, a .json file and a cool Python script that updates the data from source! I can run the script file on my laptop and it goes off by itself and gets the latest data to November 2016 and formats it into 'tidy' csv format files. This just seems like magic at first! Very cool! No manual data cleaning! Very reproducible!

Here is a screen shot of the Python script running in a an X-terminal window on my Debian Jessie MX-16 operating system on my Dell Inspiron 6000 laptop.

The file "monthly.csv" includes two data series; the NOAA National Climatic Data Center (NCDC), global component of Climate at a Glance (GCAG) and the perhaps more well-known NASA Goddard Institute for Space Studies (GISS) Surface Temperature Analysis, Global Land-Ocean Temperature Index.

I just want to use the NASA GISTEMP data, so there is some R code to separate it out into its own dataframe. The annual data stops at 2015, so I am going to make a new annual data vector with 2016 as the mean of the eleven months to November 2016. And 2016 is surprise surprise the warmest year.

Here is a simple line chart of the annual means.

Here is a another line chart of the annual means with an additional data series, an eleven-year lowess-smoothed data series.

Here is the R code for the two graphs.

25 November 2016

NZ Aluminium Smelters Ltd and their free allocations of NZETS units - carbon price or carbon insurance policy?

This post is sort of a 'review article' post synthesizing all my previous posts about New Zealand Aluminium Smelters Limited and how their overly generous free allocation of emission units under the emissions trading scheme shields them from a carbon price. NB as of 17/02/17 this post is in it's final form.

In each year that New Zealand has had an emissions trading scheme, the trans-national company New Zealand Aluminium Smelters Limited was given a very generous 'free allocation' of emission units. First, back in 2010, and in the years following and, bringing us up to date, in 2015.

I have written several blog posts about these free allocations. In the very beginning, back on 7 October 2011, I wrote 150% Pure Subsidy which was also posted at Hot Topic as 120% Pure Subsidy.

In that post I argued that New Zealand Aluminium Smelters Limited, the operator of the Tiwai Point aluminium smelter, was being 'over-allocated' emission units under the New Zealand Emissions Trading Scheme (the "ETS"). That the company was being given more free emission units than the emission units it was required to surrender for it's emissions. And therefore the company was not 'facing a carbon price' under the emissions trading scheme. It was being shielded from the carbon price. In other words, the allocation of free emissions units acted as an 'insurance policy' against ever facing a carbon price.

The company was given an industrial allocation of 210,421 units for the six months from 1 July to 31 December 2010. I estimated that the smelter company was required to surrender between 143,000 and 172,000 emissions units for the six months to 31 December 2010. Therefore the estimated degree of over-allocation of units was between 120% and 147%.

The over allocation is obvious, I thought, when we compare the emissions factor (as used in our greenhouse gas inventories) of producing a tonne of aluminium, with the allocation 'baseline', the number of emission units allocated per tonne of aluminium produced.

In the CRF tables/spreadsheets (20MB zip file) released with New Zealand's Greenhouse Gas Inventory 1990–2014, the 2010 emissions factor for producing a tonne of aluminium is 1.67 tonnes of carbon dioxide with an additional 0.14 tonnes of carbon dioxide equivalent for perfluorocarbon (PFC).

In October 2011, the Climate Change (Eligible Industrial Activities) Regulations 2010 specified that New Zealand Aluminium Smelters Limited was allocated 2.556 emission units per tonne of aluminium produced in 2010.

That allocation 'baseline', 2.556 units per tonne of production, exceeded the 'inventory' emissions factor in carbon dioxide equivalent (1.67 + 0.14 = 1.81) by a factor of 1.4. As indicated in this bar chart, which you could say represents a mental model of how the free allocation works.

Then, on 20 October 2011, I wrote 120% Pure Subsidy: Part 2 which was also cross-posted at Hot Topic.

In that post, I was given feedback that the free allocation of units to emitting industries included extra units for "ETS electricity pass-through costs".

As the report "Development of industrial allocation regulations under the New Zealand emissions trading scheme: Consultation document, (MfE December 2009, ME 984) stated;

"A number of energy-intensive firms will face higher costs of production because of the electricity used in their production"
because, Q.E.D.
"The NZ ETS will increase the costs of generating electricity from fossil fuels and geothermal sources".

This was also explicit in the original Labour Government report "The Framework for a New Zealand Emissions Trading Scheme" of 2007.

It stated in the fourth bullet point to subsection '5.3.1 In-principle decision on levels of assistance through free allocation' (with my underlining), that;

indirect emissions associated with the consumption of electricity, as well as direct emissions from ... industrial processes will be included in the concept of emissions from industrial producers ... The basis for allocation for electricity consumption will be one that compensates firms for the cost impact”.

However, the total free allocation for both direct emissions and the 'ETS electricity pass-through costs' "would operate within a total envelope of assistance to industry defined as 90 per cent of 2005 emission levels", (subsection 6.5.2.1 Free allocation Level of total assistance to industry).

This allocation 'envelope' (almost a 'cap') of 90 percent of 2005 emissions was dropped in the 2010 Cabinet Paper "EGI Min (10) 14/9".

For highly emissions-intensive trade-exposed emitters, the allocations would be based on actual production (i.e. an 'intensity' basis where allocation would increase if production increased) for the industry (Paragraph 14). The "90 percent" (of historic emissions) became a "90% level of assistance" (Paragraph 20) which then became an input to the formula for calculating the allocation number; 'Allocation (in units) = Level of Assistance × Quantity of Production × Allocative Baseline' (Paragraph 32).

The 2010 Cabinet Paper "EGI Min (10) 14/9" established a proxy for the 'ETS electricity pass-through costs', the electricity allocation factor (to calculate ‘emissions’ per megawatt hour of electricity used, paragraph 8) as stated in paragraph 37:

An electricity allocation factor of 0.52 tCO2-e/MWh has been used to calculate proposed allocative baselines. This was the factor proposed in 2008 by the Stationary Energy and Industrial Process Technical Advisory Group (SEIP TAG) to offset the expected increase in electricity price as a result of the introduction of the NZ ETS. This factor was intended to reflect increases in electricity price to the end of 2012 and will need to be periodically updated.

So the counter argument is that New Zealand Aluminium Smelters Limited faces a carbon price through increased electricity costs rather than through the number of emission units surrendered for it's direct emissions.

We may say the allocation baseline has two parts; a direct emissions baseline and and an electricity/(energy) baseline. The free allocation of additional units for the ETS electricity costs lessens the impact of that carbon price (without removing it entirely). This bar chart, where the allocation baseline is less than the sum of the various emissions costs, is the mental model for this narrative for the free allocation.

However, the bar chart isn't the last word. I just made up the numbers to show the idea.

Free allocation to the smelter includes ETS electricity costs. What could possibly go wrong?

Back in the mid-2000s, when the ETS was being developed, what else did we know about the New Zealand Aluminium Smelters Limited electricity contract with Meridian Energy?

We knew it was secret, controversial and far too cheap.

Brian Fallow in 2004 estimated the electricity price to be just over 5 cents a kilowatt hour. Another 2008 cost estimate was $52-$54 a MWh (5.2c - 5.4c a kilowatt hour. The Campaign Against Foreign Control of Aotearoa (CAFCA) thought the cost in 2007 was 4.7 cents a kilowatt hour.

Brian Fallow also points out the pre-2013 contract exposed perhaps 10 per cent of the supply to the floating wholesale electricity price and that New Zealand Aluminium Smelters were very sensitive about the variability in wholesale prices when the hydro lakes had low storage levels.

The design of the generous free allocation regime moved the 'discounted' (but apparently still real) ETS 'carbon' price away from the direct emissions and to the ETS electricity pass through costs of an aggressive transnational company with the largest volume, cheapest and most secretive electricity contract in New Zealand. It would be harder to think of a policy more likely to result in regulatory capture and rent-seeking.

Allocations including indirect energy costs may make emitters net sellers of units

There is one other important implication of upstream (ETS-related) energy costs being included in the 'allocation baseline'. The total allocation may well be greater than 100% of their direct emissions. But that doesn't matter if the emitter still faces some reduced electricity ETS cost pass-through.

The big 'emission intensive' and 'trade exposed' emitters will always be net sellers of emission units. It very hard to see how a net seller of emission units is, as Nick Smith liked to say, "facing a carbon price".

As an example, there wasn't much doubt that New Zealand Steel's direct allocation of units exceeded their emissions liability.

As Jan Wright observed in her submission on the electricity allocation factor:

"The pertinent question, then, is how much electricity prices will increase as a result of carbon pricing. But electricity price increases are very hard to predict, due to the complexities of the New Zealand electricity market and the need to cater for rising electricity demand. Despite the difficulty, it is imperative the number of credits given to industry to offset electricity price increases should be accurately - and transparently - determined."

The critical questions are therefore "What are the extra costs to the smelter of thermally generated electricity caused specifically by the emissions trading scheme? How are these extra costs measured? Are the costs and method of measurement transparently disclosed?"

It's not classic cap and trade its a double-dip

Let's just be very clear that this idea of the allocation base including upstream ETS energy costs is conceptually a departure from the classic 'cap and trade' model of emissions trading. In strict cap and trade, with a real cap on emissions, and with 'grand-parented' free allocation of the 'capped' units to emitters, the energy sector would be allocated a share of the cap to reflect their direct emissions from energy generation. That allocation, being a part of the finite cap, could not go to both the energy companies with thermal fossil-fuel generation and to the 'downstream' industrial emitters.

In other words, the allocation of extra units to industries because of additional 'up-stream' carbon-intensive energy costs caused by the emissions trading scheme, is the allocation that would have gone to the energy companies in the classic model. That would not be possible in true 'all-sectors' emissions trading scheme with a real cap. It's only possible in our emissions trading scheme because it only applies to parts of the economy and as it is uncapped.

But lets get back to the issue of the 'ETS electricity pass-through costs'. At the time of 120% Pure Subsidy: Part 2 I argued that it was a nonsense for the free allocation of units to a smelter to include a compensation factor for upstream carbon-intensive electricity costs, when that smelter owed it's existence to a dedicated source of hydroelectric generation from Lake Manapōuri. Also the generator the smelter contracts it's electricity from is the 100% renewable Meridian Energy.

The counter argument is that that the contract (or contracts) with Meridian prices some proportion of the electricity supplied at the whatever the wholesale price is at a point in time. And as explained by Brian Fallow, the wholesale price may include an ETS component when coal generation is setting the marginal price.

Then, on 2 November 2011, I wrote Nick Smith fails the smelter spin test, also cross-posted at Hot Topic.

In that post, I argued that the then Minister for Climate Change Issues Nick Smith was incorrect in saying that New Zealand Aluminium Smelters faced a carbon price and that European aluminium smelters did not. Even though the European smelters were not (at that time) participants in the European emissions trading system, the (upstream) electricity sector was and therefore there was a carbon price passed 'downstream' to the smelters from the more carbon-intensive European electricity generators.

On 23 April 2012, I reported that New Zealand Aluminium Smelters Limited had won the 2011 Roger Award for being the worst transnational company operating in New Zealand.

On 9 September 2012, I wrote Power to the smelter? New Zealand Aluminium Smelters Limited wants to pay less for electricity for the Tiwai Point aluminium smelter. That post noted that New Zealand Aluminium Smelters Limited was renegotiating the electricity supply contract with Meridian Energy.

I concluded that New Zealand Aluminium Smelter Limited had breathtaking audacity in threatening to close the Tiwai Point Smelter if they didn't get lower electricity costs, when they already enjoyed the lowest electricity cost of any sector in New Zealand. In 2011 New Zealand Aluminium Smelter Limited paid the very lowest average rate for electricity in New Zealand; 5.03 cents per kilowatt-hour! Residential users paid 22.6 cents per kilowatt-hour, or four times as much.

On 11 September 2012, I riffed off a gangster meme and wrote the evocatively-titled Rio Tinto Alcan New Zealand Ltd plays godfather: nice aluminium smelter you got, be a shame if something happened to it, also at Hot Topic.

I noted that New Zealand Aluminium Smelter Limited was again threatening to close the smelter and in effect saying "Shame if something happens to" the smelter workforce, the Southland economy, the New Zealand electricity market, Meridian Energy and the conservation program for the critically endangered kakapo.

For a couple of years, I didn't really think about smelter until I looked at the Official Information Act releases by the NZ Treasury about the New Zealand Government's payment of $30 million to New Zealand Aluminium Smelters Limited in 2013.

Amongst the dozens of documents was an email between officials with a familiar title which made me laugh; Email to Officials: Rio Tinto Alcan NZ Plays Godfather: Nice Aluminium Smelter you got, be a shame if something happened to it.

In this email, one official noted to another that Meridian Chief Executive Mark Binns had emailed them asking if the electricity costs mentioned in my Hot Topic blog post were correct and that yes the numbers were correct!

Another couple of years went by. As they tend to. Then, on 9 April 2016 of this year, I wrote Opening up the data on emissions units in the NZ emissions trading scheme. In that post I noted with some surprise that the updated data on free emissions unit allocations showed that New Zealand Aluminium Smelter's 2013 allocation had increased by a factor of five from the 2012 allocation. And of course I made a bar chart.

So what happened in 2013? The free allocation increased from 301,244 units in 2012 to 1,524,172 units.

What happened was that the 2013 allocative baseline for aluminium production changed from 2.062 units per tonne to 10.441 units per tonne. As you can see from this bar chart.

Wrapping it all up

In hindsight, it's obvious from the June 2010 Cabinet paper Industrial Allocation under the New Zealand Emissions Trading Scheme: Group One Activities, Ref no: EGI Min (10) 14/9 that although there was a generic 'electricity allocation factor' of of 0.52 tCO2-e/MWh, that would not apply to New Zealand Aluminium Smelters Limited.

They would instead have a 'bespoke' arrangement for the electricity component of the allocation baseline.

This apparently involves an annual "reading" of the highly confidential ultra-cheap electricity supply contract with Meridian. There are a number of potentially ambiguous statements about how this is done.

Paragraph 38 states;

"Specific electricity supply arrangements mean it is appropriate to prescribe specific allocative baselines for aluminium smelting. The Act contains the ability to adjust allocative baselines where particular electricity supply arrangements affect the electricity price increase a particular firm faces. The rationale for this power is to prevent large over-allocations where electricity related contracts prevent a full pass-through of electricity costs."

Paragraphs 40 is in first-person and active tense (think of Nick Smith speaking confidently) and it states (with my underlining)

"I have since used my powers under section 161D of the Act to request electricity contracts and related information from NZAS. [Deleted] In particular the analysis suggests:
  1. An average pass-through of electricity costs to NZAS during the transition phase (until 2013) of [Deleted] compared with the pass through of 0.52 tCO2-e/MWh that would otherwise be assumed.
  2. Using the default pass-through of 0.52 tCO2-e/MWh would result in an average over-allocation to NZAS of [Deleted] during the transition phase.
  3. The actual pass-through to NZAS during the 2010 to 2012 period is likely to be significantly higher or lower than the average value above".

So it's not just a matter of reading the contract. There is also "related information" from New Zealand Aluminium Smelters Limited. There is also an "analysis". This "analysis" suggests that actual annual pass-through electricity costs vary from year to year and may be more or less than than the electricity allocation baseline. However, in spite of this variability, the average pass-through electricity costs for the years 2010 to 2012 is known (but has been deleted to keep it confidential) and is less than 0.52 tCO2-e/MWh.

Paragraph 9 of the Executive Summary states a fairly firm conclusion;

"Information obtained from New Zealand Aluminium Smelters Limited (NZAS) enables electricity pass-through costs that NZAS faces for 2010 to be determined with reasonable certainty at this point."

Paragraph 41 states; "to reflect the actual electricity costs to NZAS, the allocative baseline for NZAS would need to be amended at the beginning of 2011, 2012 and 2013 to ensure that final allocations more accurately reflect the pass-through of electricity costs to NZAS".

So, in conclusion, the Ministry for the Environment has set up a regulatory process where New Zealand Aluminium Smelters Limited is enabled and encouraged to annually provide the Ministry with "related information" and "analysis" of the electricity contract - in order to set the allocation baseline and therefore the number of free units they will be allocated. And this information analysis is not disclosed. It's hard not to conclude that this bespoke process allows New Zealand Aluminium Smelters to annually nominate it's preferred free allocation of emission units.