Calculating CO2 emissions
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00:00
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Transcript: English(auto-generated)
00:08
OK, let's start for this new presentation from a Finnish team with Thana about calculating CO2 emissions.
00:22
Yeah, thanks. Hello, everybody. There are some technical issues, but I probably will start anyway. I come from a company called GISPO. It's a small GIS consultancy company in Finland. And my colleague, Maio, here is also
00:42
representing this calculating CO2 emissions for urban land use planning with phosphoryl tools. So OK, now we are there. Let's go further.
01:05
So many municipalities and also countries have ideas of becoming a CO2 neutral. And also in Finland, many of our municipalities have stated that they want to be a CO2 neutral
01:24
municipality by 2030. But the problem is that currently we don't actually know how much of CO2 emissions we are producing
01:42
in cities at the moment. It would be quite helpful to know this before you start to thinking what are the possible ways and means to reduce the emissions. So for future urban planning, we need
02:02
to access also the climate impacts and evaluate climate impacts of current urban zoning use and maintenance, but climate impacts also for land use planning schemes. And we have to take into account the possible future trends that might affect the CO2 emissions now and in the future.
02:25
So we have had this project with Tampere City and two other companies, UPCLTD and Teotokoma Finnish consultancy companies and the Environmental Center
02:43
of Finland, Institute of Finland has been involved in this project. And the aim is to use land use planning as a means to get their CO2 emissions smaller in the future.
03:01
Simple, simple task. Not really. There are lots of data available around in Finland, also open data from buildings, from roads, from heating, from whatever issues that affect the CO2 emissions.
03:24
But still, there are lots of things lacking in the data that we have to address before getting things ready. So here comes Phospho-G to the rescue. And now like, ah, music to the background, ah.
03:46
Phospho-G comes and helps us to save the planet. So now we have a beta phase tool in GitHub. And we give you a climate impact plug-in.
04:04
QGIS plug-in with really, really, really ready processed data sets in a post GIS database. And meaning really, really processed is that the guys from the other two companies
04:20
have calculated for the past six, seven months these data sets that we are using in this plug-in. I'll give you the architecture, and then Maya will explain the results of our case.
04:40
So we have a post GIS database hosted by this other company, Ubiqui-LTD. And they are serving it now as a test database where we can store all the information we're having
05:01
from the QGIS plug-in. So there are already some pre-calculated CO2 information about traffic, heating, building constructions, electricity usage. And these data sets are derived from, for example,
05:21
the city information, but also national databases. And then there are some statistical information how, for example, certain type of building is heated and what are the CO2 emissions of that kind
05:41
of type building, for example. So all this kind of information is stored in the post GIS database. And then there are also long-term predictions, these kind of models how things are
06:01
going to change in the future. And there are four different or five different models. One is like a static model. Nothing changes from the current. Some of the models are hopeful. Things get better, and we can reduce the CO2 emissions.
06:23
And some of them are more moderate models. But we need this to have different kind of scenarios in the future. So then we have the QGIS plug-in, and it's now available in GitHub.
06:42
And we add data to it, and then it fetches the needed information from the post GIS and calculates the things in the middle. So the user adds to the QGIS plug-in the urban zoning layers. So for example, a land use plan, if you
07:05
have a residential area or a park or whatever, you have to have that kind of data and put it into the QGIS plug-in. But then you need also urban zoning layers
07:21
about population, jobs, and buildings, so these basic statistics about the area. And actually, with these population, jobs, and buildings, you can calculate the current situation. And then future predictions can be calculated with the zoning elements.
07:41
And if you want to, you can also have plans for center networks or intensive public transport stations, which affect the CO2 emissions. So you put this data there, and this happens.
08:04
Cat gives, cats. And it might take a while, because the procedure and calculations are very, very complicated. So for example, for present situation, it took about a few minutes.
08:21
But when you are calculating, for example, to year 2030, it took about one hour. So there might be still some tweaking to do to make it faster. And then you get the result, 250 meters time, 250 meter grid with CO2 tons
08:46
of different main emission sources in requested years. And the bonus, they are readily visualized. And now my. Yeah, now I will show you how to actually use the tool.
09:05
Unfortunately, it's in Finnish, but I think you'll get the idea. So first, you choose the area you want to calculate emissions from. At the moment, you can only choose the city of Tampere, which is our testing area.
09:21
And you can either calculate the current emissions or the future emissions. So next, you choose which one you want to calculate. You add the required data about the population, jobs, and buildings. And if you want to calculate the future emissions,
09:42
you also add the data about zoning elements and the time period you wish to calculate. And by default, the tool calculates emissions to the year 2030. And those were the most important data sets you need to use the tool.
10:02
But if you want to be more specific, you can also add data about public transport networks and the plans of city center networks. And it's also possible to choose which scenario the tool uses.
10:21
By default, it presumes that heating regulation and electricity factors stays the same for the whole time period. And the different scenarios can relate to, for example, due to intensification of use of energy
10:42
or the role of carbon sinks, et cetera. And of course, to use the tool, you need to set up the database connection. And after this, we can execute the calculation. We have now done it beforehand.
11:03
It takes approximately half an hour to calculate to the year 2030 and maybe an hour to calculate to the year 2050. And there is the?
11:22
Yeah. And when the calculation is ready, you get two layers to good GIS. And they saw the biggest CO2 sources per grid and the total CO2 emissions. But since you get the data to your good GIS, you can visualize it the way you like.
11:43
And this visualization here, there is the two layers. And it's improved with the time manager plug-in thanks to Sanma. Yeah, Nanitek Tracer. There is another image that shows how the different scenarios have effects on emissions.
12:06
You could use this tool with different data sets and different zoning plans, different scenarios, and easily see how different choices have effects on emissions.
12:21
And this supports the urban planning and also the decision making. We've noticed that good GIS is a great tool for testing these kind of data sets based tools because it's very simple to create a prototype
12:41
you can develop on later. And for example, in this case, there is a heavy calculation process on the background. So it's very important to create a prototype first. It's possible to reuse around 70% of calculations
13:02
in some other areas in Finland and some parts also in the rest of Europe. And what I think is great about this tool is that it's, of course, an open source solution, which is rarely the case in emission calculation. But it's also a good example how
13:22
to mix spatial calculation with emission calculation. And then a few words about what could be done next. Of course, there are many targets for development, such as simply improve the parameters.
13:43
And one obvious discussion topic is how the user should be able to control those parameters. Could it be better for a user to have a wide access to change them or something like that? And then, of course, after testing this tool with good GIS,
14:05
it's now easy to make a web-based solution. There could be a dashboard with many patterns to control. And you could easily see how different choices have effects. That solution would, of course, require
14:23
that the calculations are done beforehand and only sometimes updated. And then my favorite, 3D. It could be useful to have, for example, such development that heating regulation and electricity calculations
14:46
are done knowing the cubic volumes of buildings. And of course, we could get 3D visualizations. That would be awesome. And here you can see our partners in crime.
15:04
And Sanne will do. So huge thanks for the whole team. You'll be good, the other Tagoma, Tambora City, and of course, our colleagues. And if you have any questions, we're happy to help you.
15:21
You're not late. Thank you very much. Any questions? Yes. Thank you very much. Yeah, actually, the question about data, yeah, you said a couple of words. But the question is, I'm wondering, how many countries do have such data that
15:43
could be used in this model? And do you have such research? And if no, are there some initiatives to collect such data that could be actually implemented and used in the model? Thanks. I think that you probably know better than me
16:02
from your own country what's the situation. But the most crucial part of information is the population information, building data. We have used two different data sets just to test what's the difference. Now we are using the 250 meter grid, which
16:23
is this zoning element that the Environmental Institute is providing for us. And the building information is inside that grid data and also population and jobs. But the problem with that data is that it's anonymized.
16:44
So it drops some buildings off, for example, huge shopping malls, because there's only one building in that square. So it drops that information off. And we just noticed that where is the huge shopping
17:00
mall and the huge emissions. And then we noticed that the data is anonymized also for buildings. And also for people, it drops information. If there's, was it 10 person in one, so it doesn't show the accurate number.
17:21
So if you have this kind of information in whatever format, but population, jobs, and buildings, so it can be also point data or something else. Then you can use that also. But if you want to calculate the future,
17:43
then you need the land use plan. That's the crucial thing, because it relates to that. Yes. We have two minutes left. Sorry. Thank you very much for your eye-opening presentation.
18:01
It's very complex to ascertain the carbon emission as such. My question is, of course, I have a lot of questions, how you put the data and how the model work. But my question is, of course, in this city, if you have a woody vegetation that offset the carbon emission, have you taken into account?
18:21
I mean, if you have trees, if you have parks which is full of forest, so that actually offset the carbon emission. So the question is whether you have taken into account. We had a data set from that Dunbar region about the carbon sinks.
18:41
They had been calculated from some, was it Korean land use data sets and some other parameters. And they tested it, and then they noticed that the data was not accurate. So we are lacking that kind of information. But of course, you could take the green areas into account.
19:03
But in Finland, at least, there's a huge debate how much do they actually affect and how much they swallow the carbon. So we don't actually know yet what are the parameters which we can use.
19:21
And of course, it depends from the area where you are in northern parts. It's a different kind than in some Amazonian area with huge carbon sinks. One last.
19:42
Is the tool already available for public? Can we already search for it and try it out? If you're not finished. And it's a test now at the moment only in Dunbar region. And you need to have the test user account to the database.
20:05
But we can arrange testing for sure if you want to test it. And we can, of course, show it here during the conference because we have the straight access. OK, if you have some more questions, we can stay here.
20:24
Because there's no more conferences afterwards. Sorry, one more question. How do you think could you implement, for example, open street map data that could be transformed, I would say, and fly to population data,
20:40
somehow from buildings and the heights of the building to jobs from the land use data, for example, to be used in the model? So just from the open street map, do you think would it work out? Yes, the building data. But you need to have also the type of building.
21:03
So the open street map data should be more accurate. So maybe we could have a community creating better data then. So at the moment in Finland, we have from the most building data from the National Land Survey or the, well,
21:23
the government data is quite good. So in areas that the government data isn't that good, of course, you could just use open street map building information. But difficult question.
21:49
But maybe. I don't really know how it would be possible. But I think statistical institutes in quite many countries are collecting, in some areas
22:04
at least, hopefully, the job information. The lunch break time. I think you're all starting. Thank you very much, Sana. Thank you very much.
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