25 March 2018

Charting New Zealand Greenhouse Gas Emissions by Sector 1990 to 2015

I have created a revised chart of New Zealand's greenhouse gas emissions analysed by economic sector for the years from 1990 to 2015. Something I have done before. Before that I made a chart of just the gross and net emissions.

This first image is an uploaded .png file at actual size (560 pixels wide) which is the width of the text container in the blog's template.

For a comparison, this second image uses Flickr's embed code to show a larger file (1280 pixels wide) I uploaded to Flickr. It's not a very large file; 128 kilobytes. That seems minute, when I am uploading 4MB or larger photographs to Flickr. If I wanted a larger file, I could output the chart from R as a .tiff format file.

NZ-Net-ghg-sector-2015-1280box

The first smaller image uploaded to Blogger seems slightly clearer.

The data source is of course;

"New Zealand's Greenhouse Gas Inventory 1990–2015", Publication date: May 2017, Publication reference number: ME 1309, Full report - New Zealand’s Greenhouse Gas Inventory 1990-2015, and supporting tables and files. CRF summary data [Excel file, 45.6 KB]

The key difference from previous charts is that I have omitted gross emissions or as the Ministry for the Environment calls them "Gross emissions without Land Use, Land Use Change and Forestry (LULUCF)". Gross emissions frequently if not mostly seem to be the focus of analysis of trends and achievement of targets. Land Use, Land Use Change and Forestry emissions frequently get omitted.

I started with the sector emissions, then I added net emissions. Net emissions are the sum of the sectoral emissions obviously. Net emissions (with a qualification I may address in another post) are what end up in the atmosphere. So from a science-informed viewpoint, analysis of trends should be based on net emissions.

What struck me is that this format highlights a different interpretation of trends. Look how much of NZ's 1990 emissions were 'counter-balanced' by Land Use, Land Use Change and Forestry. In 1990, the land use and forestry sector sequestration (removal) of greenhouse gases was equivalent to the sum of the other sectors excluding agriculture. The 1990 net emissions were the same as the emissions from the agriculture sector. In other words, if you excluded agriculture emissions, NZ's emissions in 1990 would have been 'net zero'.

Since 1990, the land use and forestry sector sequestration has declined by 21%. In 1991, the land use and forestry sector sequestration 'counterbalanced' 100% of non-agriculture emissions. In 2015, the land use and forestry sector sequestration only counterbalanced only 57% of non-agriculture emissions. As long as land use and forestry sequestration is measured consistently over time, this trend can only get worse. A lot of commercial forest planting happened in the 1990s. These forests will soon be due for harvesting. That's why I want to scream each time I hear some pundit say forestry will be a 'get out of jail card' for growing emissions in other sectors, notably agriculture.

Here is the R script (with a couple of Linux Xterminal commands) for obtaining and preparing the data and for creating the chart.

22 March 2018

Kevin Anderson Are universities making the world worse? Education and research in an age of climate change

Centre for Environment and Development Studies in Uppsala, CEMUS, From Almedalen. Published on 10 August 2017.

Kevin Anderson, Zennström Visiting Professor in Climate Change Leadership, Uppsala University.

Josefin Wangel Weithz, Associate Professor in Sustainable Urban Development, KTH.

Johanna van Schaik Dernfalk, Unit manager, Environmental and Agricultural Sciences, Formas.

What is the role of universities in response to the great environmental and social challenges of our times? In an age of escalating climate change, ecological unravelling and societal instability and uncertainty, what is higher education and research really for? And for whom?

01 March 2018

Kevin Anderson Living within our carbon budget: the role of politics, technology and personal action

There are a lot of Kevin Anderson talks I am yet to catch up on. This isn't strictly speaking a 'talk'. It has Kevin Anderson talking but with an animation. It is 5 minutes 8 seconds long.

Carbon budgets tell us how much carbon dioxide we can put into the atmosphere while still limiting temperature rise to relatively safe levels. Professor Kevin Anderson explains how technology, politics and personal action each play their part in helping us live within our carbon budget.

Published on 2 May 2017 by the Centre for Environment and Development Studies (CEMUS) in Uppsala, Sweden.

10 February 2018

Kevin Anderson Delivering on the Paris 1.5°C and 2°C goals

Lantbruk 1.5°C - Mat och klimatansvar - a talk by Kevin Anderson. 28 minutes 8 seconds long

Kevin Anderson, Zennström Visiting Professor in Climate Change Leadership, Uppsala universitet, Deputy Director of the UK Tyndall Centre, and Professor of Energy and Climate Change at the University of Manchester.

Greenhouse gases are global by nature but, the amount of greenhouse gases that are emitted, and the consequences of their warming effects, differ between countries. For some, climate change is already today a matter of life and death. The fact that the world’s leaders could come to an agreement in Paris gives some hope, but what do the goals of 1,5 or 2°C actually mean in practice?

19 September 2017

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.