Good evening and welcome to our talk about ideas for final grade control over your heat budget. My name is Amit Kucharia and I'm presenting this with this talk with my friend and colleague, Daniel Laskano. Say hi to the camera, Daniel. So in this talk, we are going to talk about some of the common use cases in thermal management in the mobile industry and take you through some of the caveats and the limitations we have in the current frameworks and proposal solutions. So let's dive in. So here's the outline of the talk. We are going to go through some terminology and then discuss the goals for the thermal framework. We're going to talk about a little bit about the Linux thermal framework, how it is used in the real world and what the current limitations are. And then finally, we're going to get to what our proposal is to improve things going forward. All right. So here is a picture of what happens currently in the Linux thermal framework. So you have in the graph two colors. The purple one is for the high key board, which is a octa core Cortex A53. This is an older board and the green line is a newer board called as the high key 960, which has four A73s and four A53s. Now, both of them have a workload that starts roughly at about the five second mark. And what you see immediately is how quickly the high key 960 jumps up to around the 80 degree mark. So this workload is basically trying to generate as much work as possible and get the platform to heat up. So what you see here is that within about 100 milliseconds, the high key 960 is reaching a temperature of 80 degrees. While on the older platform, it takes roughly about 30 seconds to reach that same temperature. And then what you see after that is the platform or the thermal framework, throttling the CPUs, throttling the frequencies to keep the temperature in check. So this is the stage for the stock and how we want to prevent these kinds of situations and hotspots. So let's get on with the terminology that we're going to need for this stock. So the first one is junction temperature limit, also known as DJ. So that is DJ is essentially a threshold, the surface of your chip or the silicon or the CPU. So beyond which your silicon can get damaged. This depends on the manufacturing process of the silicon. So we've seen boards or SOCs where this temperature can be around 1890 degrees. And I've seen Qualcomm silicon, for example, that I'm familiar with where this number is about 120 degrees. Now, the platform in question may or may not have a firmware that may trigger an emergency shutdown in the case that the temperature reaches this point. But this temperature, the junction temperature is a threshold that you cannot allow your platform to cross. Otherwise, silicon will get damaged. The next term is the skin temperature, also known as T-skin. So this is a temperature that is perceived by the user to feel too hot. So when you actually hold a mobile device or a tablet or even a network or a laptop, this is the temperature at which point you start to feel that the thing is too hot in my hands. You've probably already experienced this when you're trying to run a video or play a game for any length of time and you see the back cover of your phone or a tablet heating up. So this is the temperature that you cannot cross beyond which not only with the user feeling that the device is too hot but if you allow it to go too high, there is a risk of burns. See here, burns cause to the user. And of course, this will lead to liability issues. What you see in the picture here is a photo of two phones. The one to the left shows no hotspots. So everything is yellow, green, blue. The one to the right has this big red hotspot where the SoC and the memory and the other parts of the chip are. So the one to the left is a better designed phone in that it's able to dissipate all this heat that's generated very nicely and uniformly so that nothing feels too hot to touch. Whereas the one on the right, there's going to be this nice hot spot around the top of the phone. There are various standards which define how long and on what materials you can hold a phone without burning your skin. Obviously, the larger the form factor, the easier it is to dissipate all the heat. And so the form factor plays a very vital role in controlling skin temperature. The final term we need for this presentation is thermal design power also called TDP. So this is the budget in Watts. It's the power budget in Watts to sustain performance on a chip. Typically in a high performance use case, you want to sustain the performance at the TDP limits for as long as possible without overheating. And that is the core of what this talk is about the TDP, how to control and balance the TDP. So things like higher the ambient temperature, the lower is your dissipation budget. So if you are using the same phone when the ambient temperature is 25 degrees versus using the phone where the ambient temperature is 40 degrees, you've lost about 15 degrees of headroom and how quickly your SOC will reach the T-skin and the T-junction hotspots. So everything in the mobile world and electronics is anything that a user will touch is designed around TDP. Things are a little more flexible in the server PC world and I will come to that in a second. So what are the goals for TDP management? I'm going to divide the type of devices to do one which I call fixed TDP devices and the others which are called flexible TDP devices. So when I say fixed TDP devices, these are typically passively cool devices. They have a small form factor, there's no additional fans or anything and all they have to do is operate under the TDP budget. These are typically battery powered devices but not necessarily, but typically they are. And so you want to operate under the TDP budget without hitting the T-skin temperatures and going over and above that. So you want to sustain the performance at the TDP budget. And depending on what use cases you're running whether it's GPU intensive or CPU intensive, you want to dynamically balance your dissipation budget among the different components on the SOC. Then there is the flexible TDP devices. These are actively cool devices. So they share all of the goals of the fixed TDP devices above, but they have some flexibility in that they can have internal fans, liquid cooling or even external things like in the case of a server, they can be in a conditioned room which helps them with their TDP budgets because it cools the overall environment down. So the goal for TDP management is to operate as long as possible at a sustained performance while not regressing your use case. The use case in question can be anything whether it's playing a video or what not. So obviously most people are familiar with one of the major sources of heat. So it's typically the CPU, GPU, any connectivity or modern Wi-Fi. DSP is accelerators and lately neural processing units and the camera and memory. These are also sources of heat. So with these sources of heat in your typical device, what are the challenging use cases? So I've listed a few use cases here, mostly around the VR augmented reality, there's some gaming, the typical phone use case where I'm taking a phone call, I'm streaming a movie or simply web browsing. Each of these has multiple IP blocks on the SOC that are generating heat while they are running. And the trick is to balance all of them so you can keep within the TDP limits and control the junction temperature as well as the skin temperature on the device. So let's take the virtual reality case in some detail. So if you've ever used a headset, typically the headsets of the current generation are 1080p resolution and maybe 60 frames a second. Since these are battery powered, they have a limited dissipation budget because they can only supply a limited amount of power and the processors can only run at a certain frequency, not more than that. So the interesting thing with virtual reality is that as soon as you stop dropping frames, people start complaining about dizziness. So this entire use case is built around having extremely high resolutions and extremely high frame rates to mimic the eye in the real world. So under that limited thermal budget, if you start throttling your CPU or your GPU or your video decoder, you will start dropping frames and that may cause dizziness. So you basically regress your use case in that case. So the trick here is how do you allow the GPU and the memory, which is most often the big consumer in this particular use case, how do you allow it a bigger share of the thermal dissipation budget compared to the CPU for example. There are other use cases here like streaming movies. So in this case, your connectivity IP. So that could be your 4G connection, 5G connection or Wi-Fi. That's constantly streaming in network data. You have your video decoder that's taking this data and putting it to a display through the GPU and the CPU might be doing background tasks or participating in frame rendering in some form. Now all of these are competing for running and all of these are causing heat. So you have to balance the thermal budgets of all of these for a nice experience where you don't exceed the T-skin or T-junction. So having discussed the challenging use cases, let's take a look at what the current Linux thermal framework looks like. So here's a picture of the thermal framework and unfortunately I've lost a couple of text blocks in slight conversion, my apologies. But to the bottom right, the box, the red box is essentially your sensor. And the one above that is the thermal zone and then the light yellow box above that is essentially your governors.  These are physical sensors that give you same back temperature data across the SOC. These are connected to the Linux thermal framework through thermal zones. That's the bigger red box. And then there are governors which are basically algorithms, heuristics you use to try to cool down the device when you detect a hotspot at a given sensor. There are various algorithms like stepwise, IPA, user space, fair, bang bang for different use cases that you can choose from. The interesting bit is the cooling devices. So what the thermal framework allows you to do is configure for each thermal zone some cooling devices which effectively throttle the performance of certain IP blocks or activate external fans and whatnot. So you have again two types. As I said earlier, active cooling and passive cooling. So in the active cooling, you might have a GPIO or PWM or you just turn on a fan directly to cool down your device. Whereas in the passive case, let's take the example of a CPU and a GPU. So you might want to slow them down. The CPU you have two options today in the kernel using the CPU fret framework and the CPU ideal framework. And with the GPU, there is a depth fret framework that you can use to do something similar. The kernel also has the concept of the energy model which basically is a table containing the performance state of a device or an IP block on the SoC and its power cost. So the CPU fret and the CPU ideal subsystems can look up the energy model table to make the right decisions about what would be the right performance state for a CPU. And in the GPU case, there are now patches floating where the depth fret subsystems also starting to use the energy model. So with this look at the thermal framework, let's have a look at something in practice, some measurements that we've done. So you have two diagrams here. The one on the top essentially shows a use case where we were setting the operating point from user space for on a given platform. So let's assume that the platform has eight operating points. So we started with the bottom most operating points, let's say 500 MHz, and then kept increasing the operating point or the frequency of the chip. And you can actually follow that with the red dotted lines that you see above. So you had an operating point, let's say 500 MHz, then you bumped it up to, let's say, 700 MHz and you see a slight jump in temperature from roughly 30 degrees to slightly above 30 degrees. And so and so forth, you go around the 600,000 mark on the time scale. You see that the temperature has gone up to roughly 65 degrees. And then going further, the temperature is reaching nearly 80 degrees. Another thing to notice here is that the length of the width of the red lines is decreasing as you go forward. That's because the workload being given was dry strong, which was basically told to do a given amount of work as quickly as possible. So obviously, as you increase the frequency of the processor, it takes less and less time to do the same amount of work, except when you reach around the 600,000 mark on the time scale. And at that point, if you see the last big peak, you see that the red line is actually quite wide. The reason for that is that we had reached the 80 degree limit, which was the junction temperature for that platform in question. And we started cooling down by scaling down the frequency. So what is happening here is you're constantly throttling this frequency and then going back up and back down and back up and back down. And so you're basically, you might be going, if you look at the diagram below, you seek a corresponding change in cooling state between 2 and 3. So you're basically switching between cooling state to cooling state 3. And that can mean anything on a platform. In this case, I think it was like three different operating points. So you might be going from 1.2 GHz to 700 MHz and back and forth and back and forth. So that's the correlation between the cooling states and the temperature. And the real-world usage of all this, how the thermal framework is used. So currently it is used to throttle any available IP blocks for both junction temperature and skin temperature mitigation. Then you have user space thermal demons that rely on these thermal framework interfaces. They read their temperatures for each sensor from CISFS and then they have a way to set the cooling device state. So they can say that I want to go to cooling device state, 1, 2, 3, etc. These are opaque numbers. They don't mean anything other than they map to a certain performance point. Then more sophisticated demons might actually use the knowledge of the use cases running on the system, whether it's a gaming use case or it's a web browsing use case. So they have ways of detecting that. And they might use that to throttle specific devices. So you might say things like, I'm running a game. It's mostly GPU bound. I want to throttle the CPU. And so the thermal demon can do that from user space. It might send a hint to the GPU to drop resolution if we are maybe heating up too much or dropping or running our battery. Then there are non-linux devices, devices that are not known to Linux. They don't run Linux, but they are present as part of the SOC. So something like a modem. They might send hints to the modem through a mailbox API to tell it to try to reduce performance if possible. So these are some of the ways in which the thermal framework is currently used. And so let's go to the limitations of what we have here. So the number one limitation today is when you throttle a CPU, if this CPU is sharing a clock line with other CPUs in a cluster, you might end up throttling the entire cluster and you might lose performance as a result unnecessarily. So one recent addition to the thermal framework is the concept of idle injection, whereby you can inject idle on just that CPU without throttling the entire cluster. So that might be a good way to deal with CPU specific hotspots. The other problem limitation with the framework today is that the user space demons set opaque cooling device states. So they just say 1, 2, 3. What this 1, 2, 3 corresponds to nobody knows except the person who has actually characterized that particular device. So there's an engineer who's basically gone and tested this device at various use cases and said that in the gaming use case, I want to throttle the CPU to a cooling device state 2. That's it. And on a new device, it could be a completely new number. So it's really opaque. This doesn't help in the actual configuration and in configuring the system for different types of devices running the same SOC. So the same SOC could be running on a phone versus a tablet and it will have completely different configurations. The other limitation currently is that the user space demons and the internal governance are competing for decisions. So in a lot of cases, the user space might simply override the kernel governance and confuse them. So if you have something like a PID controller in the kernel, it might get completely confused because the user space just randomly programmed the cooling device state for a certain device and now the internal governance needs a while to catch up with that. Another limitation is that the internal governance don't actually talk to each other or share information. They're using completely different interfaces. The key observation here after all this is that the skin temperature management is essentially a balancing of the TDP. It's a balancing act wherein you have to share your TDP between different IP blocks. And this TDP is in watts, not in direct temperature units. It's in watts.  So this is the key observation that we are taking into our proposal. What we are proposing is very simple. We are saying that we should separate thermal management from power capping. What is power capping? Power capping is essentially a use case based throttling to hit certain TDP numbers. So any kind of TDP balancing is basically capping power of an IP block. And so what we are proposing is that junction temperature management should happen in the kernel and should be taken care of by the thermal framework whereas T-skin management, so the skin temperature management should needs hints from the user space and so should happen in the power cap framework. And with this, I'm going to hand over to my colleague, Daniel, to take us through the proposal of how the power cap framework can be used. So, yeah, my name is Daniel Escano. I'm working in Linaro and I'm co-maintaining with Ryuzang, the thermal framework in the Linux kernel. So, yeah, so we have a proposal and this proposal is to use the power capping framework existing today. So let me introduce the power capping very quickly. So the power capping is a CCFS interface. It's actually an empty shell giving an unified API for the user space through the CCFS control files. So we have a directory and this directory is a node for a device and this directory contains a set of files. You decide which files you want to create but they are basically divided in two categories. The first one is the power limit and the second one is the power consumption, back to a power consumption. And we also have the information about what is the maximum power consumption for this device. The power capping framework is an empty shell. So it's empty and it's up to the different controller to write the backend logic behind for power capping given the different platform we have. So today we have the Intel Rappel register which gives us the possibility to limit and read the energy consumption of the processor or the memory and set that through the MSR register. So that's the only controller we have today. By essence of this directory creation we have automatically a new hierarchy and we can set a constraint on a node and the child will inherit the constraint obviously with this approach. On the other hand we have also the user space tools. The user space tools we have a command line so that gives us an abstraction instead of having to deal with the CCFS internals and we have a library so that simplifies the connection between this power capping framework and applications. So here we have a figure that shows a typical SOC where we have DSP. So these are different sources of heating on the system. We have the SOC, DSP, GPS, Modem, GPU and Package. The idea is when we create this hierarchy the sum of the different numbers we have at the same level is equal with the parent as the same number that the sum of the children. So the W number here we have is an example just to give an example that when we sum the children of Package it's 100 so the number for Package is 100. So by doing so we can measure the power of each devices and then sum them and say okay the power consumption for Package is the sum of my children and read directly the consumption of the Package. So we have the power capping framework today and we have on the other side we have the energy model which was introduced for the EIS which is the energy aware scheduler and this EIS is asked to deal with the big and the little and it's power information, energy information to take the best decision to put tasks around. We have the big is almost eight times consuming more power than the little so the little are very energy efficient and the big are very powerful, have a very high compute capacity and so we have to take the right decision otherwise we cannot save any power in this case. So we really need here information about the power consumption in order to take the right decision. So that's what provides today the energy model which is basically a table giving the performance state we can call that OPP and on the other side we have on the table we have the power consumed for this performance state and with this information we have enough information to know at least what is consuming the device because we can know the performance state and thanks to the performance states we can know the power consumed. As we have this performance state and the power we can do a model of constraints with the power copy or key and show not necessarily the connection between the devices in terms of hardware we don't want to represent the floor plan but what we want to do is to show the constraint between these devices in order to balance the power budget across the different nodes. So as we saw before in the figure we can assume the number of 8 children and we get for the parent the sum of the children. So the parent max power is the sum of the max power of the children and the power limit of the parent is the sum of the power limit of the children. In this hierarchy the leaves of these three are the devices themselves being able to give their power consumption through the energy model. So of course the devices are not consuming the same amount of power and we need a way to characterize that and for that we use the weighted node in this tree for each node of this tree which is the component of the power cap directory we can show a weight for each of them and that characterizes the amount of power they are consuming when they are running and that is based on the maximum power so it's just a percent yield so we have the sum of at each level which is equal to 1024 the 1024 is a common practice in the kernel for optimization purpose when dividing and it's a way to show a percentage of usage. So here in the example of the package we can see that the big is consuming much more power than the little and for this reason the weight is higher so that means if we set a power budget at the top level the big node will get the highest power limit power budget from the parent. So now let's show an example on how all these can work together so we do a redistribution of the power limit so we restrict the example on something simple and already existing today so we have the package, the big, little and the memory and then we decide to set the power limit on this package of 2800 mW so that leads us to this table so we saw on the left column we have little, big and memory on the column in the middle we have the weight of different nodes the little, the big and the memory the weight we had just before in the figure and then we use this weight to distribute the memory limit the power limit we put on the parent and that leads us to these numbers in the static limit column so the next information we have here is the dynamic limit so at the beginning we stick to the static limit the constraint we have here is the dynamic limit the total in the dynamic limit is always equal to the total of the static limit so it's 2800 mW here always then we have the column with the different devices using power and then we have the free power column and we can see here that the big the big device which is the big cluster has a very high power limit but it is actually consuming a very little number of power in this case so it remains 1450 mW for this device and it's a waste of power we should be able to give this power to all the children in this case so giving them the opportunity to work to work in a higher performance state so for that we take the free usage of the free power we have for the big and we apply the remaining weight we have here we have 128 and 256 which is roughly 33% and 66% and so we give 33% of the free power to the little and 66% to the free power to the memory here we have the dynamic limit which change but the total is always the same as the static limit which is what we want the user the used power is always the same it does not change and the free power is the same of course because we don't have any power usage change here so now the little actually it has a small room for power and one small performance so it is increasing its power and it happens that it reaches the point where it has an OPP of 766 milliwatts so it remains some power which are unused but it's okay it's fine because we don't need more power and the user power is under the limit which is what we want and the power is equally distributed among the different children and so that's fine now the big wants more power and because it wants more power it reaches the point where it is consuming 1450 milliwatts and we break here the power limit so we are above this limit so we have to do something now for that so first step is we reset the dynamic limit and now the big is okay it's in the limit but the little is above this limit and we see that we still have 300 milliwatts to be distributed so we apply the same now so 33% to the 300 which is 100 and 66% to the 300 which is 200 and we are still violating the power limit but we still have 200 which is not needed for the memory so we can take this 200 and give them to the little but we are still above the limit so there is no free more power and we have one which is above its own limit so we don't have the choice we need to cap it we cap it then that reduce the user power and now the maximum, the user power is equal to the dynamic limit which is equal to the static limit of the entire package so we saw that by doing that we are able to put constraint on the node and let the children to balance their power and give their power and taking them back but also we might want to create some bandwidth to the children so by setting for example a constraint on the parent and setting a constraint on a child only one child then we can't force this power limit so the distribution will be against the ones which did not set their power limit so we dedicate a bandwidth to the children also we saw that we can create for the same node different constraints several constraints for the same node and that is unlimited we can put any constraint we want and each constraint we can define a semantic for it so we have constraint 0 which is what we just described right before setting the power budget but now we can say I set this power budget but I will let this power budget being break but during an amount of time so that allows for example the patch rendering without capping the power we let the page rendering we absorb the peak of load without capping everything so that gives us some room it's like the turbo mode of the SEPI track also we have the power limit timeout so if we have a lot of constraints we can say ok we set this constraint and this constraint will automatically be removed after a while and that might help the user space to manage the constraint so today the status is we have the energy model the energy model is relatively new it was introduced with the EIS which is I believe is coming from the Canon number 3.4 not sure but it's recent and it is still evolving there is currently a work of Lucas Luba which is trying to generalizing the energy model to the strip device which is a very good thing so that means we give the opportunity to every device on the system to define their energy model if they want to what would be needed is the power QS because we might have different devices belonging to the same voltage domain and we might want to set the power for this domain without impacting the other devices and we want to do that like the Freq QS we have today in the PMQS framework obviously if you want to have something unified in the power cap with the energy model something consistent we should get and set power callbacks inside the energy model structure in order to abstract this operation and let the power cap energy model being as much as as much as generic as possible also the GPU support is relatively new and we need to improve the metric with the load and make sure that they are consistent across because let's imagine we have the highest OPP on the GPU it's consuming a lot of power but actually the dynamic power is 80% because the load is 80% if we don't take care of this 80% and we consider that we are consuming 100% of this OPP it's 20% difference and 20% on the GPU it's a big amount of power and we need that to balance the power across the other children concerning the work we are doing with the power capping using the energy model we are working in the prototype and right now we are at the level of automatic power rebalancing which is a bit complex it's working but we are still we have still issue with that and the next step when that when that is fixed is to do power versus performance measurements and and of course we have to compare that with the existing solution we have with the thermal demons in the future we need of course to have more devices supporting the energy model and that is challenging because we have to include non-linux devices on the SOC so we have for example the modem the discussion is through mailboxes and also we need a way to describe these constraints in the DT so every SOC vendor is able to define this constraint via the DT and use their logic for that so as a conclusion the thermal framework is a bit abused by the different tools to manage the TDP on the entire SOC and for this reason we think that we should restrict the thermal framework to its primary goal which is protect the silicon and prevent hotspots on the other part we saw that we have more and more complex SOC having a lot of source of eating and this is a problem to manage that because they are getting more and more complex and the thermal framework does not fit very well in this and we want to preserve the temper for the skin that means there is a hole here there is a missing feature and we do believe the power car framework with the energy model is a good solution because we can model the constraints but we do not pretend to know how the SOC is working and we can delegate that to the user space where the SOC vendors are the one which have the best knowledge of all work their SOC also the system is what is centric and because we can have more constraints we have an extensible solution so poor capping with the energy model we believe it is a poor solution and that can make the life easier for the SOC vendors thank you we welcome any questions if there are any at this point we are slightly over time but we still have 4 minutes of questions question do you expect the user space implementation to be open source so if you are talking about a thermal demon that would use this we don't see why not so you could still have an open source implementation which would provide user space to the power cat framework having said that there is nothing preventing somebody from a closed source solution as well I think we don't have any more questions so if you want to reach us offline I ask and ask any question we are available for that can reach us by email also so thank you very much for attending this session and I hope this proposal raise some interest from you thank you Amit do you want to say something we are available we are available on Slack and you also have our email addresses and the PDFs are available on the shed.com website so feel free to contact us thank you


Hello everyone, I am Ati Bhi, a software engineering intern with Morgan Stanley. Previously, I interned with Grafana Labs where I contributed to Prometheus. Today, I am going to talk about new matchers in Alert Manager. In this slide, you can see various examples of the new style of matchers. Example 1 shows two equality matchers combined in a long-form YAML list. The same in a short-form YAML list is shown in example 2. In example 3, you can see that we can put the matchers in a whole string as well. Example 4 shows you rejects matchers. In example 4, if environment matches anything that starts with production and when BAS is not equal to anything that ends with QUX, then the matchers are valid. In example 5, we can use YAML block quoting and if we do that, we only need to worry about the open metrics escaping inside the block. Matchers are used in various places. They are used in the Alert Manager config for routing and inhibition. They are also used for silences. Matchers existed in Alert Manager but I implemented a new style of matchers that are an improvement. The old style of matchers did not have support for negative matching. Negative matchers are helpful in cases such as when you want to send a notification or alert to all Slack channels except one. It is also helpful when your alert is firing in one cluster and you need to send notifications only at that time. Earlier there used to be many different config options for rejects and normal strings. They have all been merged in the new matchers and simplify the config options. Matchers for silences. This was worked on by Worm and Vladimirov. Silences are a way to simply mute alerts. It is configured based on matchers. Incoming alerts are checked whether they match the equality or rejects expression matchers and if they do then the notifications will be configured accordingly in the web interface. This slide shows a comparison of the old and new matchers for silences. As you can see, the old style of matchers did not support negative matching and rejects required an additional check mark. The new style of matchers changes it to the same UI that Alert Manager has for alert filtering on the landing page. This makes the UI consistent, easy to use and compact. Matchers for routing. A route block defines a node in a routing tree and its children. Its optional configuration parameters are inherited from its parent node if not set. Every alert enters the routing tree at the configured top level route and then traverses the child nodes. If an alert does not match any children of a node the alert is handled based on the configuration parameters of the current node. In this slide you can see that the new matchers add support for negative matching and combine the equality and rejects matchers. Matchers for inhibition. Inhibition is a concept of suppressing notifications for certain alerts if certain other alerts are already firing. As you can see in this slide again, there are two types of matchers for inhibitions. Target matchers are matchers that have to be fulfilled in the alerts to be muted and source matchers are matchers for which one or more alerts have to exist for the inhibition to take effect. The new style of matchers combines the equality and rejects matchers for target as well as source and add support for negative matching. In this slide you can see the usage of old matchers. For example, if we want negative matching for a black hole receiver we would need a config file similar to the one shown here. If an alert is labeled team equals database we need to send it to the database pager but if it is also labeled environment equals dev we should not send it. The config file is greatly simplified with the help of new matchers as you can see in this slide. For more information please refer to the alert manager documentation. Thank you.


Welcome to the session. Let me also remind everyone about some of the housekeeping rules that we will be observing during this session. I see more people joining in. Hi Anne, hi Lucy, hi Demi Lola. Can we move to the Zoom slides please? Yeah, please note that this session is being recorded and parts of it will be available on YouTube channel of IID. And also I request all the participants to please do not share the link outside this meeting group. And please close all non-essential applications on your devices such as Skype. Please mute your microphone if you're not speaking and you can also turn off your video if in case you face any kind of technical problem and please feel free to respond through the chat box and put in your comments and questions so that we can also make it more participatory. So welcome, welcome everyone again. And now let's just kick off the discussion and try to understand why we have, we are having this session and what is that we are trying to achieve through this session. So from our experience we have seen that communities have years of experience in dealing with multiple crises and they have the experience of also testing out different solutions which they learn and share with their networks and they build and they teach and learn from each other different strategies and solutions which are also transferred and scaled. But despite the community knowledge, these, the communities are not recognized as knowledge holders or experts. So information collected by communities are also representative of their own priorities and needs. And in our work we have seen how communities knowledge is being used by the government to help in adaptation planning and decision making. When we talk about innovation, we mostly understand it in terms of technological innovations but innovation can also be process led and it can also mean putting in place some of the relationships that help to shift the dynamic between different kinds of actors. And as vital commission we also believe that the empirical knowledge coming from communities must be valued and communities must be seen as practitioners who who always test solutions and bring their strategies to action, which can also be useful for informing climate action and has implications for climate adaptation. So today in this session we want to hear from the community leaders and NGO practitioners who work with communities about the different kind of research they do, which leads to innovation and also discuss what we can learn from some of these examples to promote institutional recognition of grassroots communities in urban areas and also how we can help them to deepen, disseminate and link communities knowledge with that of professional knowledge of the practitioners, scientists and people working in the universities. We have with us very, we have with us for very experienced and dedicated grassroots leaders and community practitioners who will share their experience and strategies of how they have done community research in their, in their work and how they are also using this process to build partnership with the government. And this will help us in understanding a little bit about why a community led data process is indeed innovative and transformative. So, I would like to invite our first speaker, Veronica Katulushi, who is from Zambia, homeless and people's poor people's federation in Zambia. Veronica is the grassroots leader and national facilitator she is associated with viral commission is and also she's a member of the SDI network. Unfortunately, Veronica is not able to join us directly today because of some issues but we have a video that we want to show you where she will be talking about the social tenure domain model, which is a propo land information tool that she has used in the community to map the land boundaries and this is a tool that has helped the different communities to come together, they found the information through the neighbors and then the information is kind of presented to the government and government accept this information. So, I request Ariana to please play the video for Veronica. Sorry, I'm not able to hear the audio. The social tenure domain model is a tool that is used to map and link people's relationships to their land. And the communities, especially the informal settlements, we experience a lot of challenges like flooding, waste, droughts, etc. When it comes to flooding, we experience a lot of diseases because of the water, the stagnant water, or malaria, the waste that is accumulated. Now, it is a tool, the social tenure domain model, that we capture all these challenges. When you go out in the communities with questionnaires, those questionnaires will guide us on what will come out as priorities within a speculated community. We are going to ask the beneficiaries the questions and the challenges that they are facing. Whether with the council or the local authorities, we work in conjunction so that they also are able to touch what is on the ground, the challenges that the communities are facing. When it comes to land, we use the GPS machine which will capture the boundaries of the beneficiaries. And when we do that, of course, we call the neighbours have to be part of the people that are monitoring or that are doing the mapping. So that they agree to say, yes, this is a bona fide member of the owner of this land. So from there, the data that is collected will go to the local authorities. When it goes to the authorities, the local authorities will put in place. Because this information has come from the people within the communities, from the grassroot people and it carries waste. Because the data that is captured is for the people and it will benefit the people. A policy is in place and because of the findings, the challenges that are found there, it will help the government to put in place. For instance, now, because of the land ruggles, the land issues that were involved in the past. The government now has put in place to say that 50% of the land has to be given to the women. 20% has to be given to the youth and other vulnerable people. Which means the STDM or the social tenure domain has been accepted by the stakeholders and by the government. When it comes to droughts, people are made aware, they are sensitized. They have to be in the readiness of the droughts that are coming. They are aware of which crops to plant in their fields. What is it that we are going to do when there is a flood? So they are in readiness of all those challenges. This helps to sustain even the livelihoods of the people. Through the infrastructure, they are able now to build even stronger structures in readiness because the climate has changed. And now, how do they adapt to that? When we are talking of sack gardens, the nutritional status of the household, we are encouraging the women to plant sack gardens, even backyard gardens, so that they are able to conquer the poverty levels within our homes. They are able to plant vegetables and sell as income generating activities. So instead of doing the poverty levels, when we come to the West, which is accumulating anyhow, the separation, we separate the West and that can be solved. Because if we just dump anyhow, we are vulnerable to diseases like diarrhea, cholera, typhoid, in all those diseases, even malaria because of the stagnant water that is accumulated. So together, the STDM has proved a tool that can be used in the communities. Thank you. That was Veronica from Zambia and she is sharing about the social tenured women model, which helped them to map land boundaries of the informal settlement communities living in Lusaka city in Zambia and how this whole process has using both community mapping and GPS technology, they are able to identify not only the land in which they are living, mapping the boundaries, but also the many challenges that they face. And this has then been accepted by the government which held the communities to secure land tenure and that then enables them to look at various options to secure their food, nutrition, livelihoods and other things. Thank you and thank you Sheila and Surangina also for adding in your comments regarding the context, giving a context to our listeners. We have with us today our next speaker, Christine Mutuku from SDI Kenya. Hi, Christine. Christine is a community organizer and a federation leader in Mungano, Vavana, VGG. And she has been extensively involved in city led projects that city led projects and working with the government, which has been which which also includes the Mukuru special planning area which will be talking about today. Welcome Christine and please tell us what kind of information do you collect from the communities. What is really unique about this process and how does it help you to build your relationship with the government. Over to you Christine. Thank you. Hi everyone. My name is Christine Muelu. I come from Kenya in a former settlement known as Mukuru. And Mukuru is in former settlement whereby we have faced many challenges through climate change. We are faced flooding. Then after the flooding, this is whereby outbreak of diseases have really risen. People are really suffering that we are really suffering out of the outbreak of diseases. EG, for example, malaria, we have typhoid, corrella, and out of that, we came and we sit down beside we have to plan for ourselves. Then because Mukuru is a big slam. We decided to collect data for ourselves, we community and the technical people assisted us to collect the information. We are by we use the profile informs to write the information that we are collecting from the from the informal settlement. And we use the also FGD focus group discussion to to inform the people why we are collecting the information and also the community to talk for themselves. The kind of strong that we are going through and the information that we are collecting was counting the structures with which are located in our slums and also taking the total number of people. All the tracking the number of community families in that area. And also we were using GPS to locate all the facilities which we needed to know they were our main schools we have in hospitals. Those are examples there are things and also sanitation toilets. We want to know how many they are because of the great number of the community. When we when we came to to know why we are collecting the information we wanted to know who are the people living in Mukuru. And I will say profit from this data and how will we be able to communicate with the government. So we were able to communicate with the government through this data that we collected and we made sure the data was correct for the government to know that. The community is fed up with this kind of outbreaks and we needed a change in Mukuru because Mukuru is a private land and we used to face a lot of eviction things that sometimes our houses were touched. So we were so we should be start we were standing firm enough to give our report to the government and out of the reports that we collected. The county government came in they worked around Mukuru and exactly they found that the data that we collected it is true. These people are suffering because they are not there is no plan. We could only build the structure wherever you you find us. So after the county government coming in that is where they started locating where the roads will go through the communities where we are proposing. But when the government came in they said the proposal of the community was not enough because we needed FIACO inside the community in case of the outbreak of fire in the case of the outbreak of diseases. So they are right now as I talk Mukuru was declared to be a special planning out of the effort of the community and out of this challenge of climate change whereby we as the community or me as a community leading community leader I was ready to see a change in our community challenges we are there but where we are we can say we have seen a change in Mukuru and not even enough change because we are still persisting for the things which we need to be put in Mukuru. Whereby they have set spaces for the hospitals to be built and in that process we say like community we are happy because the hospitals will be located in Mukuru and people will not be suffering while looking for far and distance hospitals to go. We thank our government because of the struggle of our community that we have done. Thank you. Thank you Christine that was indeed very powerful and it's it's such a great example of how the information collected and organized by communities is helping the government to sort of create opportunities to work in partnership with the communities in in planning the area where they are living and in urban settlements we know that communities are in constantly facing eviction and demolition threats and and so so when when when when collecting information about their different structures and the number of people living in this informal settlements are being taken to the government that sort of provides real data data from real data and then this is this is the way how communities are establishing the relationship with the government and also ensuring that they are included in planning of that area. So, thank you so much. Christine for that example. Now let me bring in Sonia Rodrigo from Philippines. Sonia is a national community leader from the Philippines and has been involved in slum profiling and mapping efforts, some of which has led to inclusive upgrading of slums in partnership with the government. Thanks Sonia for joining the session today. Could you also tell us how what approaches or tools you use while collecting information from the communities and what sort of results, some of the results that you have achieved through this. Good afternoon everyone. I'm Sonia from the Honest People's Federation Philippines, and I would like to share some of the experience of the Federation regarding how communities were transformed because of the data that we have gathered. So, if you see the context in the Philippines, you are prone to typhoons, landslide and flooding, and especially in those areas, we call the danger areas or high risk areas. At the same time, we also face threat of eviction and demolition and some man-made hazards like fire, health hazards due to poor sanitation, no access to potable water, proper toilet, and limited access to basic services. So, in this pressing issue that we are facing, the Federation decided to conduct a community-led household survey and also mapping process. Because when you check with the national government, the national government generated data, you notice that there is a big mismatch in the youth's gap regarding the information about the communities. There is no updated data that represents the current numbers and situations of the communities. So that's why this is one of the reasons why we have to lead the community-led household survey. And also for our national and global advocacy, as we are part of the SDI, and for community information and monitoring itself. So when you conducted the data through our household survey, we were able to gather information around the tenure status of the community, the ownership of the land, the land area, the boundary maps, and also we have conducted settlement profiles. We also have information around number of households, the houses and type of housing structure and materials used, what are the basic infrastructure and services available and lacking in the community. And at the same time, resources available in the community that can mobilize and current plans and opportunities, as well as strategies that can be supported. Because in communities itself, there are a lot of people's plans that has to be supported and ignored by sometimes by the government. We also have information about some locally initiated adaptation strategies that can be scaled up or replicated. So using this data, we engage the city government around the issues of slum upgrading because some of the issues around this danger areas is not about resettlement, but some of them only needs slum upgrading. And we also engage government around the issue of housing and also the future projects and targeting the this high risk communities. We also use this data information to mobilize and organize communities to set a wide networks and to think of the collective solutions rather than individual solutions. The data also serve as a tool in increasing local development and shelter plans to improve the conditions of specific disaster prone communities and those facing eviction and demolition threat. We also use the data to explore alternative building materials because you see in the Philippines the cost of building materials is very expensive and materials that are renewable, affordable and disaster resilience as we used to bamboo technology and bricks. We're able to secure land for some families as a result of our data and our advocacy with the government and we always say that we prioritize those families living in danger areas. We, because of our data, we have obtained support from the government for the use of the alternative building materials and accreditation for these materials to be used in our housing project. We have institutionalized partnership and influence policies, local government related to issues of land, community upgrading and disaster intervention as we became a member of the government local special bodies. We have the local development council, this is the council where Federation seats and this is where development plans and budgets are being presented and approved and we are part of that. We're also part of the local housing board where the policies around housing is being discussed. We have become part of the local shelter plan and the most important thing is that we become part of the local disaster risk reduction management council within the city where disaster intervention mitigation and plans are being discussed together with some stakeholders and academic and the private sectors. So, our data made us to become one of the major stakeholders and was involved in the decision making process of the city when it comes to urban development and all issues related to the meetings and help government to make informed decisions. Thank you very much. Yeah, that was a very fantastic example of community generated information. And you have you spoke about having a combination of different tools like mapping each households and what kind of structures are there what are they different with the different issues that communities are facing and the some of this information were not available with the government but your but the data community collected has helped the government in relocating this communities and also locating land. And then you also spoke about housing upgrading housing and using alternative technologies, which is also an innovation and I think this I think this held the communities in Philippines to be part of the local decision making and this sort of also gave a basis for the city level in the for the government. So thank you thank you for your for sharing your experiences again. Now I would like to bring in Matthew from practical action. Matthew works with the practical action as a area coordinator in the Lake Victoria region, and he has a lot of experiences in organizing and working with communities. Welcome Matthew and please tell us how do you do research in your community and what is what tools you use and what impact has has this created over to you Matthew. Thank you very much. Hello to everyone. My name is Matthew O'Kello as has been said, I work with practical action and it's national development agency. I am based in Kisumu and this morning I will be sharing with you experiences from participatory planning information for dialogue and innovation from Kisumu city in Kenya. For those of you who already know Kisumu is the third largest city in Kenya with a population of about 500,000 people. And up to about 60% of this population live in low income settlements or slums if you like. Low income settlements or slums are characterized by poor housing conditions as you can see on your slide. Low levels of services ranging from limited access to safe water and basic sanitation facilities. The effects of climate change are already evident. And we have seen over the last few years, greater extremes, some months much wetter, and thus drier, seasonal flooding is a growing concern. For many years, decision making was a top down. Local budgets for priority projects have been available, but largely poorly spent to address priority issues affecting the poor. Now, as a result of this, practical action has been engaging with local community groups organized into neighborhood associations. And from the year 2008, we have been working with local organizations, partner national governmental organizations to be able to support participatory planning processes, including mapping or context analysis, ranking risks and problems, identifying key challenges that affect local level development and identifying the local people to prioritize some of these solutions. We have been able to identify actors and build relationships with local people, as well as external actors, so that we could be able to address some of the challenges that affected the local people. And some of the outcomes of these tools that we did use included what we have referred to as strategic local level plans, or if you like, local work development plans, which have been able to feed into the county integrated development plans or annual development plans. One of the things that we have seen is that these approaches have been able to build consensus and create space, especially for women and poor tenants, and have been able to engage, been able to increase the engagement with local government, such as in the preparation of annual development plans, practicing together and bringing their voices to bear on key decision making plans and budgets. And I want to confirm that we have seen better results through this approach. The neighborhood associations have been able to mobilize local actions, including people who live in some of these areas. And we have seen this especially in the year 2020, when we did experience some of the worst floods. And this was also the year that COVID hit hard. And the neighborhood planning associations were not only the first people on the ground to respond, but they were more in terms of identifying the greatest areas that needed to be, for example, opened up. They were able to identify areas that have come to water logging, and were able to support the most vulnerable families to move to higher ground, even as they sought help from local government, such as departments of water, departments of health, and other external resource organizations to come to the aid of people who needed the support most. Now, we have realized that for many years, planning was such a professionalized and a top-down tool that did not respond more specifically to the needs of the people who needed it most. And therefore, we have seen that with these participatory planning processes, we have been able to show better outcomes with community leadership, grassroots structures, and people have been able to identify and prioritize local actions that respond better to their situation. We think that in the same manner, climate adaptation suffers more or less the same fate. It is highly professionalized and decision-making again is top-down. We are persuaded that with community leadership for dialogue and innovation, there can be better outcomes just like we have seen with participatory planning processes. That means greater community control over decisions and monitoring of decisions and plans, such as spending of funds for adaptation in urban areas. This might require ongoing light-strike support to neighborhood planning associations to ensure representation and ability to respond better to some of the challenges that people are struggling with. Thank you so very much. This is my presentation coming from Kisumu City in Kenya. Thank you, Matthew, for the presentation. I also see Suranjana's comment on, she's saying that over-professionalizing of planning often leads to inability of the plan to respond to the needs of those who really need development. I think it is very well demonstrated from your presentation. You have mentioned about some of the participatory tools that communities are using to not only collect information but also engaging with the wider community stakeholders like the government and informing them about their priorities. This has been able to create a space for many poor women and marginalized tenants living in the urban settlements in Kisumu, Kenya. I think the process has also generated increased engagement with the government and has supported in developing county development plans. So thank you, Matthew, for the presentation and also thank you to all the speakers who have joined us today and thank you for sharing in your experiences of the great work that you are doing. We really saw a diverse range of approaches that grassroots communities in urban settlements are using to collect information and also how it is helping the government in developing climate adaptation planning and decision making. So now then, how do we really strengthen and support community knowledge and research and the efforts that they're putting in. We just want to touch upon a little bit on the innovation, on the kind of innovation also coming out. It's which I earlier mentioned about the process that is innovative in the way communities are able to link to the not only the government but multiple stakeholders and where the government is recognizing communities and partnering with them. And also we saw some technical innovation, as in the case of Sonia who mentioned about alternative housing or upgrading the infrastructure that is more suitable for climate change and can and is and is and it's a that stands strong during climate risk like floods and droughts. So, I think now we can move to the next segment of our session and I would like to invite my co facilitator Lucy Stevens from the practical action. She is the head of practical action cities fit for people program, and she will guide us through the next process. Thank you. And over to you Lucy. Thank you very much and Russia. So now we have an opportunity for us all to share our experiences to discuss and engage with each other. Because we're going to have a good amount of time for some breakout groups stimulated by by the presentations that we've had so far so it's my job now to tell you how that's going to work and some of the questions that we're suggesting about that you will discuss. We are going to divide into groups and each of those groups will have a facilitator and a note taker who will be able to share some of your points back afterwards. In your group will be quite a small group but if you can agree somebody who would be willing to report back. When it comes to reporting back, you'll need to just pick two or three key points, because we will only have a few a few minutes to report back from from your group really it will be a chance for you to learn and discuss within the group. We will have about 25 to 30 minutes in the in the discussion groups. I think if we make 25 minutes that will allow us enough time to hear from the groups afterwards. Our Zoom coordinator, Sohail is going to allocate people into a room so I'm sure that will all work very smoothly so you will find yourself in a group automatically fingers crossed that works I'm sure if we need to do anything Sohail will let us know and then you will be returned automatically after the 25 minutes back to the plenary. Okay so what are we going to be discussing. You'll have a facilitator in your group but there will be a couple of breakout group questions. Which help to link us to this theme of the conference and the themes that have been discussed. The first one is, we've heard about lots of different community driven data collection tools and approaches. So how can those help in adaptation to climate change. We've seen a few examples of that so it'll be good to discuss more and hear from you in your in your contributions. The second question is goes a bit beyond what we've heard a little bit beyond what we've heard today but how can the government institutional partners researchers donors and so on strengthen and support community led research for urban adaptation. So that this kind of urban adaptation can be a better part of national adaptation plans and so on. So yeah, how can how can we make that to support work. Also, as part of the breakout group, you can have an opportunity to ask people questions that might have come up for you during the presentation so do take the opportunity to do that don't feel you have to only stick to these breakout group questions. You may find that you've got questions for the presenters so please feel free to, to go ahead and ask them. Okay, so I think we're ready to split off into our breakout groups. So I think that should, that should be able to happen. Let us know. Yep. rooms are ready. I'll be basically putting everyone in them. Thank you very much. You have been invited please join in. So when you get a little notice on your screen you have to click to join. Have a good discussion and we'll see you all at the other, when we come back to a plenary. Wonderful. Welcome back everybody and I hope that you had a very fruitful discussion in your, in your breakout group. I'm just going to make sure I can see who has been leading each of those, each of those breakout groups. So that I can invite people to provide us with some, some feedback we certainly had a very fruitful discussion in our group. But you don't, I will be providing a bit of feedback from our group but so that we break things up a bit. May I go to the group that was facilitated by Ariana and Zillire to provide some feedback. Feel free to share your notes or just to give us two or three points we will just have to limit you to two or three minutes to share back some of what I'm sure was a very rich discussion. Yeah, so I was taking some notes during that group. There was a lot of discussion but I can only mention a few. On the first question, what was mentioned is that it provided good insight in the local level, the challenges that they face and the experiences and also provide some room for what would be the best solutions. It also ensures that it empowers leaderships to the communities and that it provided a platform for dialogue. Some other aspects that were mentioned were that the differences in communities that it's important to also pay attention within that for that, because communities are not an homogeneous groups so that we should pay attention to that. And that we must ensure that the plans that are developed based on the data that these are up to date flexible and include the newest strategies and methodologies so that there is flexibility possible within the plans for improvement. For the second question, it was mentioned that it could help. Let me get back to what the second question was sorry. So how could the government and institutional partners strengthen and support community led research. So what was mentioned that local innovation platforms which includes government researchers and communities could be helpful in providing new evaluation options and to engage different kind of stakeholders. That it's important to also ensure that the data is available and that it could be disseminated. And that there should be a link between the data and the planning so that we engage in bottom up planning. And that we should be careful on how the communities are using the data and options. What could also be done is that the institutional donors could provide funding that they the researchers could help train facilitators in certain methodologies and provide insight in the newest methodologies. So I hope that my group will add to me in the chat box. Wonderful. Thank you very much. Yes, please do add any other points so that we can capture them in the in the chat box. I'm, if, if, if there are further points that you that you that really struck you as part of the discussion. Let me pass on to Christine and and Wesh's group, please. I'll just share my screen so that Christine can present. We can we can see that fine if you. Okay, okay. I mean it's not full screen but it's perfectly fine so yes please go ahead. So there you are. Lovely. Christine. Okay. Hello, can you hear me. Yes. Yes, let's go ahead. Oh, okay. I'm using just a small phone so I cannot read the, the, the notes please just present just read the notes please. Okay, yeah, thank you. Thank you Christine. So mainly, I mean, we in the, in the first question on how community driven data tools and approaches helping adaptation to climate change. We discussed a little about how the community community data collection process really starts. How, how the community members and leaders they convince and communicate the communities on about the real problems and help them to prioritize what is affecting them. And the whole process of data collection then sort of empowers them to talk to the government and helps them in negotiating and bargaining resources, because they are coming from a point of strength and they talk to the government basis this data, and also, this is the whole process is both empowering and building the capacities of government and since it is a community driven process it is helpful for generating plans that that that is actually used and helpful for the communities. So for the second question on how government and institutional partners can really spend and support community led research for adaptation. There were mainly two points that came up which is again related to building communities technical capacities to collect and present the information and and also putting untied resources at the hands of communities through which they can undertake the research and innovation we mainly discussed about the resources in the in the context of communities, you know, that doing communities time and efforts for collecting the information and who really compensates them for for taking up this initiative. So, yeah, these are our points from our groups if anyone wants to add from the group please do add. Thank you. Thank you. Thank you and washer and Christine. Our next group is Chris and Teresa. So, the, the main points that we have discussed in our group is just three for the first question. So the first one is that institutions are driven by their policy. When it comes to data gathering or research, which is not answering the need of the people. So, community driven data is more directed to fit for purpose planning so it can be so that the plan would be more relevant to the need of the community. And the second community data help help us know the key blockage points and the facilitating factors in the in the in addressing climate change. Like, for example, the, the expose the real issues, the real risk, then the gravity of the, the situation, the details of what action needs to be done in the community. And then the third one is that adaptation tools are key to identify which systemic gaps and leverage points for improved tracking of adaptation technologies. This is more of the monitoring side of the plants and the strategies that were implemented on the ground. For the second question, we did not really have the time to, to answer, but it, it, we agree to the points that was made by the first group, really empowering the community to strengthen them to, you know, their strength and their capacities to really develop this, their skills more and then so that they can, you know, they can really use this as a leverage for the, for the government to, you know, really listen to them and accept this data that this is the the true data that is provided by the community. And this is the actual situation of the area of the of the community. So that's it. If my group can still add these two. So thank you. Thank you so much, Teresa. I was in the, I know we've got one other group. I'll just give a short bit of feedback from the group that that I was participating in. One of the, one of the key points we made is that we know that these that actually there's a climate funds and development funds that are quite similar. Now actually, all development to be good development needs to be conscious of climate issues. So, so we don't need to see a big distinction between these two things and the kinds of issues that are being raised by these sorts of community planning. These days are naturally identifying some of the issues that are really key to adaptation. Because these are things that are on the ground because they are grounded in reality, they are, they are geared towards helping with with issues of adaptation. And that and that and they are crucial because the city doesn't doesn't have an idea of the sorts of vulnerabilities, the details of who is affected and how on the ground. They often miss out on some very key insights in the absence of this kind of disaggregated community level data in relation to the second question. There's that where there was a sense of frustration that actually we have that these kinds of processes have to be justified and reinvented and rediscovered every time one by one at the city level. You know, we need to find a way to leapfrog so that these become the norm. We also talked about how they can be institutionalized as part of the ongoing practices within at the city level and how that can be supported. Actually, we found that once can once city managers see the value in it, it is easier for it to become institutionalized and that value becomes apparent quite quite quickly. And also there's a challenge of lots of turnover of of people in leadership roles at the at the city level. So, so, so there needs to be some kind of practicing over time. And a sense that we just need to get over the hurdle where communities are treated as if they won't understand these climate issues but in fact they understand the realities of these situations all too well. We passed to the last group, which was led by Sir Anjana and Ruby. Would you like to provide any more, any of your feedback from your group. Hi everyone. We had a really interesting discussion in our group, or rather, should I say we began to touch on some very interesting points on the first question around. On tools, I think we had more of a discussion on that re emphasize the power of community led data so people talked about, you know, community led data reflecting the true realities and the priorities of communities. They talked about the ability to negotiate from a position of strength they talked about the up to date data and knowledge that communities bring in their research often researchers are studying material which comes from a fairly long time ago so this is often a time up to date data. And we also had someone talk about the need to better analyze human animal conflicts and their impacts. Regarding how other institutions can help communities to strengthen their work and to leverage their research in ways that strengthen communities we actually had some terrific insights there. The first is someone talked about the need to actually document and communicate more the kinds of knowledge and innovation that communities already have in place for example their own indigenous early warning systems. The second was the idea that communities need sometimes universities to endorse and legitimize their data and knowledge and I think sometimes that can be a bit of a double edged sword but it's an important thing to think about. And community leader from Zimbabwe talked about how the local government was far more willing to accept their the community data when once they had started partnering with a community and the other interesting piece that came out was we had a consultant in our group who said she was very willing to work with local communities who were engaged in urban planning processes to and use their data, you know, in places where she she was working. And that brought up the issue of do communities have rules and principles about who can access and how they can use their data and Sonya from the homeless people's federation also talked about the rules they've set up in in the Philippines and where in which the local government actually has to ensure that the communities are made aware of who's using their data and how they're using it. So I thought that that we started off a really exciting conversation which is to be continued. Thank you. Thank you. Was there any other group or have we now completed feedback from all of the groups. Is there anyone else who needs to provide feedback I think I've got the full list but I just wanted to double check that there isn't anyone else expecting to provide feedback. Lucy, can I can I share something. Yes. Okay, the first one as I mentioned in the chat looks like you know gender disagree this aggregation, or, you know, the different needs of the communities how communities are actually reside in a particular area, and what are their needs, and how they are affected by the impact of climate change and how they are exposed by the urban flooding. So that need need to be documented and the knowledge of exposure actually you know there with communities and that is not actually well documented by the government if the government could validate those information from the community or from the community leaders that could in the bottom up planning process, and that is, you know, the demand driven planning process, and as many people actually said that could contribute in support, this is making system of the government. There is one. The second one is risk, risk sensitive land use planning is a crucial element that the government should you know recognize in any settlement. If the data, the community data can be linked with those government lead risk sensitive land use plan at the, you know, urban settlement or for the urban leaders, that would help to validate the crucial or pertinent need of the communities. So these two area particularly linked with bottom up planning and the validation of the data and recognize the need of local community, I stop. Okay, thank you, Darren. Sir Anjana, did you have your hand up for any last point or are you okay? I just put it in the chat. I thought that she talked about using the social tenure domain model to negotiate for communities that they should be, they should have their drainage upgraded rather than being relocated and that was something that they use their mapping to do. Wonderful. Well, thank you very much to everybody for you to all of you for your participation for your, your time and attention today. It's been for me it's been a really fascinating discussion. I think at the beginning we were we were setting ourselves the challenge of seeing how that's kind of how innovation can be stimulated in terms of climate adaptation through collecting lots of different tools that we've heard about for community driven data and how that is crucial to this kind of innovation and adaptation at the local level. We've heard about a range of different tools that people have been using. We've heard about lots of mapping, digital tools, a blending of kind of on the ground things with with with digital information. We've, we've also heard about how organized communities, why that's really going to be important in terms of the future of community based adaptation out the future is urban. Many of our cities in the development, many of our cities are informal with large percentages of the population facing some massive kind of climate risks and increasing risks. We need to harness the innovation that comes from real time community driven data and resources but not only that that data needs to be owned by and held by communities so that it can be used in in the very best way to stimulate that adaptation. There can be challenges in taking that information in taking that process up and scaling it up but we need to get over the point that we don't need to prove this time and again at the city level. This should become part of the mainstream way in which larger scale funds can be disseminated and trickled down to the to the community levels so they can help to support the kinds of actions that the communities know will have the biggest impact in helping them to adapt. We need to make sure that we don't over professionalize an issue which communities know at at their own at their own level. So I think it leaves me just to pass back very briefly and we're sure if you would like to say any any final words as as we close and thank people everybody for their participation in particular the everybody who's shared their stories today and been part of making this session work which it's been it's been great to hear your voices from all over the world. Thank you Lucy. And I think it was a very enriching discussion both in the plenary we heard from our different NGO and your practitioners and also the grassroots leader bringing in some of the interesting insights of their work. And also we we are hearing some of the very useful conversations from the breakout groups I know the time was very short but I hope we continue having communication and interaction and how better to strengthen community research process. So thank you everyone and thank you for joining us for this session today.


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