Kieron Doick

Not long till coffee. Just gonna make sure I get the right buttons. My thanks, my personal thanks and also thanks from Forest Research from KaarbonTech will be invited to come and talk to you today. Ok. I'm gonna continue the theme of data. Hopefully, I'm gonna bring it a little bit back home to you as well. Let's set the scene. What do I mean by bringing back home to you? I think everybody in here in the room today, you either got to use as a data collector, a data user or a data manipulator. So hopefully I'm gonna pedal out some of those aspects to do that. First of all, I want you to think about the tree now, I don't know for sure, but I can probably bet my bottom dollar that the tree scape near where you live is very similar to where I live. We've got single trees. Yeah, those that are dressing perhaps people's front gardens. We got clusters of trees. Maybe I can use my pointer. I probably use my pointer. You know, we got the little clusters of trees might be on a roundabout. Might be the edge of a park might have larger clusters of trees or what we might call small woodlands. We might have lots of little individual trees, but representing a line of trees along a road or along a highway along a railway line, these different formations of trees, actually, I want you to picture in your mind and think about as we go through because actually trees in different shapes, different forms in different architecture, actually represent different ways of thinking about a tree shape and the benefits. But second, what about us or whether you're a researcher, like me, a user of data, whether you're involved in the true surveying, you're out there collecting the data, whether you're a service manager, analyzing the data and thinking about the teams, you need to send out to do the work we're all using or gathering that data. And similarly, you might be doing it for or from a safety perspective you And if you are, you'd be very interested in or thinking about of course, the quantity and the quality of the data now to dive into this a little bit deeper, we, I am going to take you through a project that we completed recently to value the nation's noble of trees. And then I'm going to talk a little bit about a project we've been engaged in to try and understand how we can improve the data, both of the collection and at the use of stage. Ok. So that's my task today. Why value the nation's non woodland trees? Well, first of all, woodlands have had a national value since about 2018. So in a poli policy scene policy context, if you want to think about the non woodland trees and the woodland trees, when you're setting your policy, actually, they need to have a bit of common ground. So non w trees as a whole were falling well behind. There are about 20% of all of our trees across the nation that are outside of woodland. So they represent a decent proportion of the trees. And so in policy terms, they, like I said, they were just falling a bit behind and we needed to bring forward the data and evaluations to support that in this project to do that. First of all, we wanted to understand what we, what have we got? What have we got in terms of total number of trees, total coverage of trees at the national and regional scale and bathroom. Sorry. Do you think we'll catch up? I just wanted to point out so national scale, we also wanted to think about some of these quality aspect. How close are the trees to people to where you live? Can you see them that sort of quality aspect? And the other quality aspect that I've just touching on at the beginning of my talk is, are they single trees? Are they lines of trees are they crosses the trees because as I say, that impacts how they provide benefit and therefore how we value them. And that's the arrangement situation there if we had it and we would have liked to have had it. We would also like to talk about the species, what species of trees were there and their condition. But for this particular project, we can consider that and already hopefully, that's making me think about, well, why not? Because we often do, but do we have that level of data that integrates and can extrapolate all the way up through to regional and national scale projects? The answer for us for this project at least know. OK. So we understand what we've got next in terms of valuing the trees, you have to think about what qualities or benefits that these trees provide for society. But one we can understand from the level of information that we get at the beginning of the project. And two, which of those can actually can we quantify and then, and can we value? And that nicely leads me to give Ken the name check and hopefully you see already the collection through back from Ken's. Talk to my talk here. We're using very similar sort of techniques and approaches. We're looking at the types of benefit that trees provide in terms of pollution removal, the amount of carbon that they store, but also the amount of carbon that they take up year on year, carbon sequestration, their flood mitigation role and potentially even the temperature and noise regulation side of things. Again, Kenton mentioned in much more detail, but many of the other benefits of trees that we recognize sometimes we can quantify, but we can't always value. And therefore this, this particular project were dropped out at this stage and grayed out next step. So we know what we've got, we know the sort of benefits they provide. these debt two columns actually are perhaps slightly less relevant to our talk and dates today. But I'll take you through just so you understand the whole project and you can understand how the numbers are generated. But for the for the benefits that are provided, which loans can we quantify and value and how do we value them? So, that's our third column. And for those, in fact, actually, this is probably the slightly the easiest part of the, of the, the process because these are the published values. The, what's the value of a ton of carbon or government? How does that, what's the value for a kilogram of air pollution removed from the air that we bring again? Is the government value? What's the value for a meter cube of rain that doesn't go into our draining system? Well, the water company present a value for how much it costs them to clean up this dried water. So we can use it to publish values and quite nice. So simply column two times, column three, gives us our monetary account as we call it or our our valuation, our total valuation that pretty much sets the scene for the, the project in terms of the data that I wanted to get across. But we've before we talk about how we can actually improve that data and make more of it. Let's look at the valuations that were arising within the project. We actually considered eight different tools and methodologies upfront to see if they were applicable to this type of approach to valuing the Nation Nations Trees. Of the eight, only two tools were carried forward all the way through to the end of the project. Can talk to you about Ire Eco primarily but also Ire design. Another one of the tools from the Ire Suite that you might be familiar with is I tree canopy and Ire canopy was one of the tools that we were able to use in this project. The other valuation approach that we adopted was what's called the woodlands natural capital account. But where we then undertake an economic approach called a value transfer. So you assume that the non woodland trees provide the benefits of the woodland trees and you can transfer the benefits that they provide the valuations they provide across to the non woodland trees. Now, there is some clever thinking that you do in within that so that you don't apply all values across all of the time. Ok. So those are the two approaches that we were able to take and what sort of values were coming up. Well, we were able to do the valuation for each of the countries individually. And then of course, add them up for Great Britain and at UK level, you'll notice that these prices are quoted in 2020 prices because that is those were the valuation units that we were able to apply in that country area. So let's have a look at a couple of examples. So let's take air pollution removal, for example. So for England, the total valuation was estimated at 921 million pounds per year from all of our non wood trees and you can sum up all the way up to the UK. So 1.4 billion pounds worth of service per annum from those treats. How does that compare to the other methodology that we adopted? Quite a lot lower 199 million for the for, for England. But in the same order of magnitude and that's kind of reassuring. And of course, if you sum that up to the UK level 272 versus 1.4 million. Now, the nice thing about this approach is that the two methods show you actually kind of the level of confidence you can have in the data because actually if they're in about the same ballpark, you think actually they're kind of working to the same sort of level of consistency. And therefore you have some sort of confidence in using the data we know from the data we use in terms of how much have we got, what total hec these trees represent. So you can actually reduce these figures down actually to per hectare figures and then you could say, well, actually one hectare of non women trees provides in this case for England about 2000 pounds of service per year. Now, thinking back to our previous speaker to James, if you think about whether or not you wanted to try and build a an example to your seniors to say, I want to spend some time developing a business case to investigate the value of each more deeply and more more relevantly to our local county that actually these first estimate figures are ideal for that 2000 pounds per hectare is a really good average or first estimate figure for you to use and then say to the to your seniors. But actually, that's a, it's a very much an average figure, a radical figure. But if it's in this ballpark, we need to be investing more to find out the data, to do the studies, to get the local state. And that's really or partly why we did this work or one of the reasons why? OK. So as we move into next to say about, well, actually, sorry, I forgot the point. Um that project, if it's not being clear, you already stresses because I feel stress is that it was very heavy. You know, you need data at lots and lots of different levels. We need you to understand not only where the trees were but what composition they were in. And we also needed to know a level of confidence in that data. Who collected it? How did they collect it? When did they collect it? How robust is it, is the data collected in that area applicable to the data collected in that area? How consistent is it across the whole country? Then you sort of question were really important when we were doing the evaluation because if the data didn't have that quality or that consistency, we wouldn't want to, to scale it up to regional or national numbers, you know, it just wouldn't be a usable approach or give you much confidence in the data arising. So with that in mind, I take you on to the second part of my talk now and a parallel project that I want to talk about and it is all about understanding data quality and data standards. And in in order to do that, let me first introduce you to a word which you may or may not know. But interoperability definition in the dictionary is the ability of, of computer systems or software to use or interact with data to you. And me, it simply means collect once use many times. And I think again, that just brings us back a little bit to our understanding of why this is relevant to us. If you're working in the data collection field, how you collect that data, the quality that you collect it to and the units that you use to store that data actually then can have an impact to trigger that effect to the data users and secondary data users. OK. Your policy creators might want to use the data to understand how they write the next tree strategy. But actually, can you transfer that data to me as a researcher and I still make good use of it. Recognizing that this Yeah, there we go. Recognizing that this is a challenge to the sector with partners. We undertook a project called the Individual Tree Data Standard project. Now, it was under a larger umbrella called the Community Tree project and it was funded by innovate UK. So we found a G A spatial commission. So my thanks to them, we worked with our partners at Tree Works, environmental partnership and with open university to conduct this project. And we reached out actually many of you or some of you might have been engaged because what we did reach out, we wanted to recognize that you were the ones collecting the data in the first place. So how are you collecting the data? What process were you going through? What thinking were you conducting? And how often were you collecting the data? And also what units were you using? What we didn't want to do was create a completely new approach, a completely new system just to be rejected by you all because, well, actually it's completely new and we've already got system that works. What we wanted to do was try to integrate with the approaches that are already out there, understand what was already out there and try and offer some tweets some refinements so that more of us can collect data that can be used in a consistent way and be used more often. So we went through a process of questionnaires workshops and drafting a dra creating a first draft and sharing that out for consultation and then tweaking it. We're just going to take you through a couple of points here to show you what we came up with. So the individual tree date has started as data variables variables by name. It also has a prioritization unit. Now, some of these data variables fall within the required data. We ask you to always collect things, but actually, we're not asking a lot, just five different variables fall under required. The next prioritization is recommended. We recognize from the information you gave us that most of you would collect the these in this information. Anyway, if you all did it, then it would make that data sharing that much more useful. I like to kind of think of the data standard as a bit like a train. You've got the engine at the front. If this was a steam engine, you'd then need a coal carriage just behind it to keep driving it along. But as for the rest of the data, the number of carriages you have on that train. Well, that's optional, depends on your local needs. But within the optional data, we have data groups for tree basics, we have data groups that can be categorized around crown information or around tree health information. So recognizing that people were collecting this data anyway, recognizing that they fall into natural groups, we could then put it all together and add some more clarity about how that data should be collected in terms of the units that used or the format it was held in and and sometimes it was about recognizing that you, you had a list, a prescribed list of information that you were going to use such as for age class. Um and trying to offer a standardized list so that between this local authority and that local authority, you will work into the same standardized list. And therefore cross pollination of that data at the national level would be so much easier because we're not trying to quickly align what they mean and what they mean and with what they mean, how does the data standard line up with what others are doing? Well, I've got two comparisons for you here. First of all to Ire Eco, the project tool that I can talk about quite a lot. So Ire Eco has quite a large data requirement actually, when you're out in the field getting the data. But unsurprisingly, all of the required data is automatically asked for by Ire Eco. So that's great. They immediately fit into the data standard. It also I code requests all of these tree basics information. So a lot of the recommended data information and all of these pieces of information. So again, one so some, some consistency here, some, some read across the data center, but that's where it stops. It doesn't require information, for example, on the organization or the surveyor, I mean, it can affect the surveyor information but it's not a requirement. And will it always and again comment, there is space for comment but it's not always replied. But there's a really nice consistency in in the it eco approach to the data standard. And what about carbon tech? Well, actually, they, they, they did this analysis for me and let me know I'm ready for the talk. Actually, their software lines up extremely nicely to the data standard. All of the required data is there, all of the recommended data sets is are are listed there and actually a fair few of the optional data as well. So if we see more and more people adopt the data standards, we'll generally see from what we've been taught or being told about the way people are collecting data and the software they're using to store the data that there's only little tweets that are required actually to, to improve the data and the type of data that's being stored. And therefore, we can actually ease the research and actually even the dissemination of the data. And hopefully, I've made it, you know, quite straightforward and quite clear to you as well actually, because we are all engaging with the data at different levels through our different roles and tasks that we can all get involved in this, either in the collection of the data or the transferability of the data and either gained from that or actually help others gain from that. So I hope that's been helpful for you. So in summary, let's not underestimate the value of good interoperable data. Thank you very much. Thank you. Thank you.