In this session, what we are going to discuss is about data quality practices in Sri Lanka. So what I hope to do in this session is that I will be taking two examples, two implementations in Sri Lanka, one is from the RMNCH program and the other one is from the nutrition program. And I'm going to discuss how these elements of ensuring data quality has been applied in these two implementations. So the first scenario or the first use case that I'm going to talk about is the RMNCH program in Sri Lanka. So this in fact was one of the major DHS implementations in Sri Lanka and it took us a lot of efforts to get this implemented. The main reason being the paper-based data collection system of RMNCH has been a well-established system for more than three decades. So trying to introduce a digital system and also trying to ensure at least the minimum data quality that was already there in the paper-based system was a major challenge. So let us see how we applied these concepts of data quality while we were planning the implementation. So we might feel that ensuring data quality is a very complicated task, but I will show you how simple you can start. So for example, the first thing that we did was we concentrated about data quality while we were designing the data reforms. So one simple step that we ensured was that because we were trying to introduce this digital system to already existing paper-based system, we tried to adhere to the well-established data flows. In simple words, what we tried to do is the data entry interfaces or the data forms in the DHS to the data sets. We tried to make the user interfaces as much as similar to the existing paper-based formats. I mean, we always say that you have to optimize the data collection forms, but while ensuring that we tried to keep the flow of the data, the order in which appear, the same as how it is in data entry forms. So by doing that, we can minimize the random mirrors that data entry people might do because they just have to follow the same workflow that they are doing while entering data into paper formats. So for this instance, what we created was a custom data entry form, not a section or standard DHS to data set, but it was a custom form. So let me show you first of all, now this is the paper format that we had in the RMNCH, the main aggregate data collection form. You can see it's a very complicated forms. There are so many data items or data elements, and of course, most of these columns that you see here are the periods. So the columns, of course, do not come into this our data entry interface, but what I'm talking here is about having the same order as how it is appearing in the paper format. And we had the same workflow while we did the transition to the digital format. So here, what you're seeing is our DHS to custom data set or data entry form. And please excuse me for the language, the forms are in local language, but what I want to highlight is the same order that we have it in the paper format. We have been following in DHS to data set as well, even though we have done some slight modifications. For example, we have set up here the vertical tabs to capture this large amount of data, but the flow of information capture remains the same. And the next thing is sometimes when we are designing forms, we cannot actually replicate the paper forms into the DHS to there are some optimizations that we have to do, especially like there are things like duplicate data collection. And there are so many things that that we don't do in an optimum way in paper based collection, but which we can optimize when we are creating the data sets in DHS to. So for example, in the mental health system, this is the paper form that we are using, but when we were trying to convert that into DHS to what we actually did was we optimize the data form and at the same time, we had the opportunity of modifying the existing paper based data collection form so that it kind of added to the quality of data that is currently so that now, for example, now this is the modified paper based form that we were able to create because like when we went one step ahead, this is the DHS to base the data collection form. So to minimally confuse the users and at the point of generation of data, we tried to use the same data collection form as that we have with the DHS to. So what we did was we kind of modified the existing paper based form to be optimized paper form. This is another example of the same mental health system where we have optimized the data collection forms, right? And sometimes, of course, in this ERHMI system, there were requirements such as like when we wanted to collect this WHO MNH indicators, we did not have a paper based form to start with. So there, of course, we had the luxury of designing our paper based form also from the scratch, which was kind of easy because this is the DHS to dataset or rather the event form and that one based on that one. So when designing that one, we were able to follow the best practices. And then we came back and we designed a new paper form for the collection of data because most of the institutes that I involved with our MCH system, they do not capture the data, the electronic system at the point of generation. So usually the data entry happens on a paper based form and then that is copied into the DHS too, right? And I mean, for the expanding on this one thing that we had to make sure, like when we are having this digital transformation from paper system to the electronic system is to minimally interrupt the workflow because the thing is, I think that should be the context of most of the countries because you may have a well-established paper format and when you are trying to introduce a digital system, we should try to minimally disrupt the existing data flow. Just like the government's motive is to provide better health, not only data. So if you are just trying to enhance the data, I mean, the quality of data by introducing the digital system and at the same time, if you are kind of disrupting an existing data flow, that will have a lot of resistance during the implementation from so many stakeholders. So the other thing that we tried to do with this is that in our paper based system in Sri Lanka, we had this different approval levels. So those were inherent data quality measures that were there at free levels. So when we were introducing DHS to what we did was we did not try to capture data to the digital system just at the same time. So we preserved the paper based system and we kind of asked them to copy the paper, the data that is captured in the paper based system into the digital system and also the approval workflows that were already there. We tried to implement it DHS to so that there was one step verification before the data goes higher up along the high rock, the health high rock so that the data approval workflows was a major contribute in factor for that assuring data quality in the CRHM IS or the digital MCH system. We conducted the training programs. In fact, like what you're seeing here is one of our field level training program and the person who is in the front is the one who is leading this entire MCH system. Dr. Kaushalya, I think she's a participant in this academy also. So much grateful to her and her team for implementing all this data quality practices in her system. So here the data quality measures and how they should capture data and at that point, how to ensure data quality was an inherent component during this training programs that we conducted at field level. So what we did was we had a national team who were kind of master trainers and we were able to replicate the training programs using our trainers who are at field level. So all these training programs had a special emphasis on ensuring data quality like, for example, especially at the point of generation, point of capture of data. There were so many data quality measures, which I will be showing you in the next few slides. So they have a special emphasis that we put on data quality during this end user training program. And one other thing about ensuring data quality is to have proper user manual. So it's so peace. The thing is like the DHI is to has its own extensive user guide, which like I'm sure that that may confuse many end users if you just ask these end users to refer the DHI is to use a manual which is available online. So that of course didn't work and we realized that. So what we did was we created the end user manual, which is of course, it's a very comprehensive end user manual with the screenshots and also like explaining everything in very simple, simple manner, which could be comprehended by end users. So this end user manual was something that we handed over to the end users during our training programs, which they can keep at their workplaces and they can refer. But the thing is like this is a kind of a comprehensive user manual, which may be a bit too, bit too much when you are just trying to find something real quick when you are entering data at your desk. So for that purpose, we had another type of manual, which I will show in a while. And the other thing is there were so many SOPs that we created before we try to implement the system, because like we understood initially that when you are having this digital transformation from paper based system, which was kind of well established into the digital system, there may be a lot of resistance that that we might encounter because for the simple reason that users don't like too much of change. So we try to simplify everything so that to make sure that the end users don't see this learning curve as a very difficult one. So this is where we had different levels of SOPs. So this one that you are seeing is a checklist that all the facilities should have before entering data into the system. So here we try to explain everything in a simple way. And this we have asked them to keep by the side of the computer that they're using for data entry. But the one that we have asked them either to keep at all times next to their computers, or else to paste it on the wall where they are entering data is these ones. So these are simple, single page end user manuals or SOPs that we have created, just specifically to given tasks. So these kind of, I mean, like how this, these helps in data quality is that the thing is like, if you perceive using a system as a kind of a difficult one, then there is high chance that I mean, that's this is what we have seen that there is a high chance that they might make mistakes. So having these specific SOPs and the user manuals with the end users kind of really contributed to ensuring the perceived ease of use of this system, which of course contributed to the data quality in the end. So these kind of manuals or SOPs we had for each of these tasks. And this is exactly what we followed in our COVID implementation, which was kind of rapid implementation from the learnings that we had from our MCH system. And this is another single page user document that the MCH system is using. And then we also tried to use remote administration software, so remote desktop softwares like TeamWeaver, because like how it really contributed us in ensuring data quality is that what we saw was that when we are trying to provide support to end users, there were some issues where they cannot actually explain even over the phone. So if they actually cannot solve their problem, they tried to enter data, which might be erroneous, right? Because like if they would assume that this would be the proper way of using and which is not exactly the correct way. So here we are using TeamWeaver as a tool where we can connect from national level to the end users computer and assist them in troubleshooting. So with this, we were able to in fact observe the data quality, I mean, like how they are entering data, what are the mistakes that they're doing, as well as it helped us in troubleshooting when they're having issues. So for this one also, we had separate SOPs that we created. And the other thing that really helped us in ensuring that data entry is timely, and like data entry users are having minimal issues, is that the user support. So for the to provide the user support, we had this three tier model. So here what happens is we had this, the initial communication method, which should happen using packs or the hotline or else we are using this instant messaging platform such as Vibe. So there, of course, they can reach our initial help desk for so for simple troubleshooting, let it be public health related issues or else very technological issues like this will be attended at tier one. And then in case they cannot settle it there, they'll be directed to the tier two where we have a medical officer in public health as well as our ICT officer will be attended to these issues. And if they can't also solve them, we have the consultant community patients who are attending these technical matters related to public health issues, and the medical officer in health informatics will be who is going to attend to the very technical or technological issues. So this way it really helped us in ensuring that all the issues the end users are having are attended. And when it comes to specific data quality measures that are already there in DHIs too, we are mainly using in most of these MCH data sets, the validation rules and compulsory data elements. In addition, we are also using the data. But we are in the process of in the incorporating min max predictors, WHO data quality tools, core cards and legends in these MCH instances. But like, why, why there is a delay is that we are trying to match all these tools that are there into the existing the workflows. So that is why it is taking some some time. But this is our plan in next over the next one year's time to incorporate all this into the MCH data flow. And when it comes to review and feedback, we identify review and feedback as a major measure in ensuring data quality. So what we do is we have created dashboards which and provided our end users instructions on how they should be following and adhering this dashboard. So they are supposed to use this monitoring dashboards at the facility level, district level, provincial level and at national. So they are having clear instructions that we have provided to the end users on how to use these dashboards. And in addition, we have the national level staff who will be drilling down and and conducting desk reviews from time to time, make sure that whether there are any data quality or any major data quality issues which are happening at the level because like this is kind of like a secondary level of monitoring for all the district level staff, which we are doing at national level. And to complement this, we have regular reviews, which is happening at the phm is the field level public health midwife level, or medical officer of health is the is kind of like sub district level, our DHS is the district level and at the country level in these frequencies that we have mentioned. So how this happened is that now this what you are seeing here is a kind of like a district level review, we are all the the medical doctors who are in charge of all these sub districts will come to this place along with few public health field level staff. And we are doing a comprehensive review. So these and one key thing is that is that for all these reviews, we are using the DHS to base them see a system. So we'll be displaying the dashboards and they have to comment on any of the data quality issues that that that the expert panel from national level are asking. Now these reviews did not stop even during the COVID pandemic. What actually happened was like it was another opportunity for us to further explore what are the avenues which are available to conduct these online. So what we do did was we organized the Zoom platform that we have and through the Zoom, we were able to conduct this monthly and quarterly review at district level using this the remote technology. So none of these review activities have stopped even during the COVID 19 pandemic. So what you are seeing here is the district level staff who are joining through Zoom and the national level staff who are kind of monitoring whatever they are presenting. Right. And these data that we have gathered through our MCH system is produced as an annual report from the Ministry of Health. And we also have a MCH quarterly report for all these reports, the data is coming through the DHS to base system. And this is why, like once these reports are public, are made public, the Institute and the Ministry has to hold the responsibility. So that is why we are trying to ensure data quality in our DHS to base system because the reports are actually generated through this system only. Right. So these are some data quality measures that we have ensured in our MCH system. So with this, I'm going to move on to our second example, which is the nutrition monitoring system. Right. So this of course was was a requirement which is coming from the presidential secretariat level of Sri Lanka, where the requirement was to identify children who are having nutrition related problems at fee level, and to obtain multi sector support to address them at fee level. But then the information had to be collected and verified and transmitted all the way up to the national level and shared with the multi sector stakeholders. So the major functionality of this system was to register the children with malnutrition at fee level. And then they wanted to routine the monitor and track the nutritional parameters such as height and weight and household risk factors. So these are the routine monitoring components and then obtain household geolocation and also perform analytics and the mobile users and to synchronize the data which is collected at fee level with the central server and then share with the stakeholders. So to achieve all these requirements, we felt that it was the mobile technology that we have to use and the traditional use of laptops and computers to enter data will not really work because the data had to be captured at the individual level at fee level. So this requirement of course came around four to five years back. So at that time, the DHS to Android native mobile application was not that mature because nowadays it is having so many useful features. But those days there were some restrictions to fulfill these functionalities that were that were required by the program. So what happened was this public health midwife who's the fee level health staff health worker will collect data at fee level, which is then synchronized into the DHS to base central server and which could be accessed by their supervising officers and all the staff at district and national level using the web interface. And once this data is sent by the midwife, they are immediate supervising officer who's the medical officer of health or the public health nursing system should validate that data and the data had to be returned back I mean this validation. Once it happens only, the data could be shared with all the stakeholders who are involved in the process. So to do this, of course, what we did was to have this design this custom Android application due to the obvious reason that some of these requirements were not be able to meet by the DHS to Android application at that time. Right, so let's quickly look at few data quality measures that that were there in place in this Android mobile application while capturing data. So the first thing is like there was some there were a few simple interfaces. And now as you can see here, these are the these are few interfaces that we used and the same and simplifying interfaces itself, both identified as one major factor to enhance the compliance of the end users who are using the system is filled and stuff and also to enhance the data quality because like what we found out from our study was that when you when you have very complicated workflows, and very crowded interfaces, it contributed adversely to the data quality because the end users can make a lot of mistakes, because they just become confused due to the complexity of the interfaces. So using simple interfaces one was one major practice that we followed. And secondly, because they were collecting this height and weight values of the children with malnutrition, what we did was we provided them a visual verification of the data as they entered. So if you can focus in on this height and weight values that are entered here, you can see that color of this one is greenish. So that and this one is kind of like a yellowish amber color. So what happens is now when someone starts typing height, what will happen is like, there will be some age verification based on the so we have the date of birth of the child and today date and with this, we'll be able to know which which is the age and there'll be a kind of a verification with the comparison of this WHO. There are standard ranges, the reference ranges for different standard deviation values for height and weight for that particular age. So if the the height of weight value is okay, it will be the kind of a greenish color. If it is slightly bad, it will be amber and if it is really bad, it will be red color. So that way, there'll be a visual verification that happens at the point of data entry, so that it will give an idea about if there is a major mistake done by the end user at that point itself. And then of course, to further assist with data quality as well as to support the interventions, what we did was we provided some longitudinal charting. So these are kind of like analysts analytic outputs that we integrated into the system, which contributes to enhancing data quality. So here, what happened is like, in the mobile app itself, the end user after entering the height and weight values can verify by observing this height and weight charts and see whether there is a I mean, kind of a significant abnormality in any of these values that have been entered. So there'll be a kind of this kind of like a dual verification of the data that was entered by the end user. And in addition, they have a simple reports and simple analytics which are available to the end user in the mobile application itself. So it kind of provided them a very brief overview about say like here, what you can see is like how many children of each of these nutrition problem where they are under her care. So that kind of analytic outputs are available for the end user. Right. So once this data was captured at the field level, then next there was some some validations that happened at facility level. So for this one also, there was a custom web app which was designed mainly to cater the requirement of data approval of tracker data, because this is tracker data, right. So this, even though it's collected by a custom Android application, the data is sent to the DHS to system. So that means all this has to be in compliance with the DHS to data model. So this tracker data that is entered here will be verified by their supervising officer. So the supervising public health system has to go through this data, and they will be the ones who will be marking the mess completed after ensuring that they are adhering to the, I mean, at least like these these are plausible values. That's what they can maximum, I mean, at maximum level they can do. So once this is done, there will be a data approval, which happens at the medical officer of health, the moist level. And once the data approved only, the data will be visualized and shared with other stakeholders at provincial and national level. And in addition, there were several data quality measures that happened at various other levels, which kind of existed in this entire process, because like the data had to be shared with the multi sector stakeholders. This was a crucial step for validation of data that is captured at the field level. Right, so I think I have consumed almost 25 minutes so slightly over, so I will stop it at there. And if there are any questions, I'm willing to take it. Thank you so much. Great. No, thank you so much for that was an outstanding presentation. I really appreciate how you touched on so many various elements of data quality that people don't always think about. We're always so you're exactly right. We're always so concerned about checking the data once it gets into the system and the different tools that we have in DHS to, but you're you hit the nail on the head, so to speak, when you talked about like form design, standard operating procedures, training processes, you know, all of these things are absolutely critical to data quality. And I'm really we tried to make that point yesterday. And I'm really glad you were able to drive it home today. We did have one question from the community. And again, everyone, we are taking your questions on the slack channel, just like we did yesterday. So please, if you have a question, post that to the slack channel. There was one question. They are asking if you faced any challenges or you had to change anything during the implementation of these various reporting forms and data quality checks. So once you started actually implementing, how did you were there any changes that you had to make in the process? Well, okay, so as I mentioned, one crucial fact was that we had to minimally disrupt the existing workflow. So one major thing, I mean, like, this was not a very kind of abrupt implementation. So there was, I mean, especially my other colleague from Israel, was the one who was greatly involved with this MCH system. So there was like, at least more than one year of planning in in assessing all the workflows and paper forms to ensure that I was and there was, I mean, the other thing that I did not mention here is the testing and piloting that goes into the entire process before you actually implement the system because like, most of these data quality measures should be captured at that point, if the system is not capturing data, I mean, like, there are errors in data quality that you observe during this piloting process, those have, I mean, you have to come back and reverse whatever the workflows or the data sets designs that you are following. So there was a lot of planning. And that's how I mean, after doing that only, we went for the first round of implementation. So there were a lot of issues that we found at that phase that were corrected. And then what happened was, during the initial round of change training that we did all around the country, there were some feedback that were coming related to different issues with the data sets and accessing the system workflows, things like that. So during that time, we accommodated a lot of changes that came from the from the end users. But after the first round of implementations, we kind of like what we did was we received feedback from the end users, but we did not go on changing the formats, the data formats from time to time. But what we informed them was that if you need us to accommodate something in this, the designs of the forms, so this will only be done, maybe at the start of next year. So we kind of like, if they are not really critical, we kind of collected all of all these things. And we discuss internally within our team. And maybe at the start of next year only, we will accommodate them into the DHS2 based forms. But if they have a major changes that required, maybe collect, I mean, like to decide whether this data item has to be collected and all, that of course had to go to the public health specialist, and there has to be a decision that is that is required, which is of course, totally out of the scope of the DHS2 implementations. So yes, the other challenges, but we were careful not to entertain and include all the changes made by the end users, but we reviewed and incorporated them on time-to-time basis. I think that's quite clear. Thank you for that answer. A couple of more questions. I know you're a busy man, but if we can just keep you around for a few more minutes. Question from Lisa Grout. In the application that where you're tracking individual children, do you do the validation child by child? Yeah, good question. Yes, the thing is, it's a very difficult thing. I mean, like this is one reason like why we try to incorporate all these, the color verification and things like that at the point of data capture, because otherwise it's going to be a very hectic task for the supervising officer to go through all the records. So what we have, I mean, like, we have made provisions in the system to verify child by child, but when we are implementing what we have asked them is like, because the thing is, these are kind of like when you try to have these systems implemented in a couple of districts or provinces, the infrastructure could be even technology, mobile, I mean, like the network, as well as human resources, they tend to greatly vary from district to district or even from health facility to health facility. So what we have instructed them with, like, you always need to like, this is, I mean, the system has a potential, but like, it really depends on the capacity that you have in your health facility. Like, if ensure, I mean, like ensuring data quality. So there is always a kind of a fine balance between data quality and actually capturing data, because like, if you have minimal capacity and you try it and your, your priority number one is ensuring data quality, then no data will come out from that capacity. So it's kind of like we have given them the choice. So this is what the system has. This is the best practice, but it's totally up to you to decide on what you should do. But we have given them some hints as to like how to capture if there are any major data quality issues. Like, like, I mean, we have asked them to observe the data entry practices of the end users. And if they identify from the preliminary study that there are a couple of end users who really do these mistakes over and over again, then you can either focus on them when you are validating the data or as to provide them kind of refresher training and guidance to ensure that they are not doing those mistakes. So there are I mean, like the simple asks is like, we have not kind of strictly enforced these rules and we have asked them to, I mean, we have make it so flexible so that the end users can, the health facilities can decide which is the best option for them. Right. Okay. Thank you very much for that. The questions are coming in quite quickly now. It seems like people are really intrigued by your presentation. Just two more questions for you before we need to take a break in and move on. We have one question. I'll just go ahead and ask both of them now. We have one question is, how long did it take to implement these various data quality checks? And then the other question is, what happens if suspicious data is found in the review process? How do you go back and make the change and how to keep track of the changes that you've made? Yeah. So I will take the MCH instance for this example. I mean, the two questions are, so like the MCH instance was initially set up in 2017 and now we have passed three years of implementations. So data quality, ensuring data quality was not the number one priority during 2017. So it was in 2017, it was mainly about addressing user issues in accessing the system, entering data and to ensure the completeness. Even those are data quality measures, but like we didn't go into this finer data related issues in the first year. But then from year 2018, 2019, these years we focus mostly on the data quality. I mean, like things, especially we put a lot of emphasis on during the reviews because these reviews used to be happening based on the paper data that was I mean, previously before 2017. So then there was some transformation required from 2018 onwards to use the system for this review meeting. So it took some time into that. So it was like, so what I would like to say is like, if you see our plans for future, so we have more plans like compared to what we have done so far. So it's a work in progress. So we have achieved some level as of now, but we have not to do. So we can't say like we have ensured data quality in these last three years. We have done, we have taken some major steps, but there's not more to do. So it's really about like what you try to achieve. And sorry, what was the second question? What happens if you do find suspicious data in the review? How is it changed and how do you keep track of the changes? Yeah, the thing is like now that of course, like what happens is like they try to understand what is the issue with this data during this review. So for example, now I mentioned about these desk reviews that our staff is conducting at national level. So during the desk reviews, if they identify some some data quality issues before the reviews, they will contact the health facility and advise them. And if for some reason, if the same mistake is happening over and over again, they really try to talk to the health manager or the public health doctor and identify why it is happening and try to like if they identify the issue that this may be happening recurrently. So then there may be some training gaps, which if we identify, we even like what we try to do is if we feel that in a particular district that there is the same mistake that is happening over and over again, we try to organize a refresher data analysis training where we also talk about data quality during that process. And then the other thing is sometimes like, I mean, to ensure the timeliness, we lock the data after a particular given period of time. So then we have to put what we do is we will put lock exceptions and after accepting that it's a mistake and when they have like the end user, I mean, at the facility level, they accept that it's a mistake which has been communicated to national level, then only we will put lock exceptions and unlock the data for it to be correct. So those kind of measures are what we do when we find out there are some data quality issues. Okay, that's that's very clear. And again, thank you so much Pamud for the wonderful presentation. There were many quite a few questions that I don't think we have time to get to we


At this point I'd like to introduce our next speaker Chris Field who's the Director of the Stanford Woods Institute for the Environment and the Melvin and Joan Lane Professor for Interdisciplinary Environmental Studies at Stanford. The title of his talk is Natural Climate Solutions, Important but Not Dominant, Pieces of the Puzzle. Thanks everyone for joining this morning. It's a pleasure to talk about one of the most consequential topics in our portfolio of approaches for dealing with climate change. The essence, the take-home message that I want to leave people with is in my title. Natural climate solutions can be a critically important piece of the puzzle. It's very unlikely they'll be a dominant component. Sally's already laid the foundations for many elements of that and I'll fill in some of the details. I'm going to start with the idea that we have a carbon budget and that the total amount of carbon that can be emitted from the beginning of the industrial revolution until the last time is emitted is really set and that the only way we can back down the warming scale is to remove carbon from the atmosphere. One important aspect of this that's not widely understood is that we know over time that the carbon that's emitted to the atmosphere is partitioned gradually between the atmosphere and the oceans. Arrhenius knew this in the late 19th century but a striking feature of the way the relationship between carbon dioxide and temperature plays out is that even as CO2 is partitioned increasingly from the atmosphere into the oceans we don't see a parallel decrease in the amount of warming. You can see that in the right hand result from Matthews and Caldera here where even with a very large pulse of CO2 into the atmosphere at the at the start of this model experiment and that both gradually partitions into the ocean over time you see over a 500 year period temperature staying essentially the same and it's because the same process that's removing CO2 from the atmosphere the gradual overturning the circulation of the ocean that's taking up CO2 is also limiting the ability of the oceans to remove heat so as we move through time a larger fraction of the CO2 was in the oceans but a smaller fraction of the heat is moving into the oceans and the consequence of that is that you have this effect of CO2 that leads to essentially permanent warming and the consequences this carbon budget. Let me just run through the details of that carbon budget one more time this one's for 1.5 c target but the fundamentals are the same no matter what budget level we talk about the overall budget but forever budget for a 66 probability of staying under 1.5 c is a little less than 2800 billion tons. Through 2019 we emitted a little over 2300 billion tons and that leaves about 485 forever if the ideas were going to have a 66 and a half percent chance of staying under 1.5 c. As Sally said the total emissions from fossil fuel combustion and land use in 2019 were around 43 billion tons everybody's good at math you can divide 43 into 485 come up that if we were to stick at our current emissions rate we would use up the remaining budget in in about 11 years and if we are going to solve the climate challenge staying anywhere close to 1.5 c by limiting emissions only we have to dramatically find the accelerator pedal on degarbonization and the question for today's session is to what extent can carbon dioxide removals provide a complementary series of strategies that allow us a little more time and if you look at the IPCC emissions scenarios what you can see is that there's of course a wide range of possibilities for staying within this budget and the overall emissions trajectory for the 21st century is going to be some mix of emissions from fossil fuel some mix of emissions and uptake from agriculture forestry and other land use the brown slices in the young figures across the bottom and potentially negative emissions from biomass energy from carbon capture and storage and and and what these scenarios illustrate is that there is of course a wide range of fossil fuel emissions that are consistent with staying under 1.5 but a higher longer lasting level of fossil emissions needs to be compensated by a larger longer lasting combination of natural sinks agriculture forestry and other land use and biomass energy with carbon capture and sequestration so that when you get out to the to the extremely high levels of persistence of fossil fuel combustion that are shown in in p4 here there need really massive amounts of of negative emissions here shown as coming from BEX but the negative emissions to offset the lasting fossil emissions a lot of the idea for exploring negative emissions has come from what tally describes it's a difficult to decarbonize parts of the economy and it is really important that we focus on those but it's also important that we not use the distant prospect of negative emissions as an excuse to avoid decarbonization in the near term and when we talk about the economics of decarbonization we need to be really careful to recognize that there's also an important moral hazard where we're simply putting off the action based on the idea the untested idea I should emphasize that we can effectively remove CO2 at a reasonable cost sometime in the future a lot of the motivation for thinking about natural climate solutions comes from the fact that fluxes in the natural carbon cycle are are really really large if you look in this figure at the emissions from fossil fuel you know they're on the order of eight to 10 billion tons of CO2 per year so since this on land is is more than 100 the movement of of CO2 into the water by solution is is also a very large number something like 80 so these natural fluxes that are at least 10 times the size of the endogenic fluxes has led to the question of well can't we tweak these in some way so that the balance between uptake and release on land or between uptake and release on the oceans is shifted in the direction of increasing storage and that's the whole idea of natural climate solutions which involves a wide range of technologies that go all the way from reducing deforestation and forest degradation to modifying the way we manage soils to be able to increase the amount of carbon that that agricultural and grazing soils contain when we look across this entire suite of candidate natural climate solutions it's important to recognize that they fall into two important categories some natural climate solutions are about slowing emissions and it's still the case that a significant fraction of annual CO2 emissions come from land use change especially deforestation in the tropics but reducing forest degradation improving nutrient management and croplands to decrease emissions of methane and nitrous oxide and protecting coastal peatlands are all about slowing emissions that's something we need to do to get to zero emissions independent of anything we do that creates natural sinks but then there's another set of natural climate solutions that are really about increasing sinks generating negative emissions and that includes techniques like regrowing forests where they're currently not forests have forestation putting forests where there had not historically been forests or various techniques that that might be used to increase the carbon stock in agricultural and grazing soils when we look at these techniques it's important to recognize that land use change is still an important source of CO2 emissions and it's this plot from the global carbon project shows land use change still constitutes something like 10 to 15 percent of total annual emissions and when we talk about decreasing those emissions we're still on the on the emission side of the budget not on the negative emission side so we contrast these two kinds of components natural climate solutions remember that the decreasing emissions part is bending us away from business as usual and the increasing sinks part is pushing us into the negative emissions very different implications of the two and we can think about them as constituting a portfolio but the implications of the carbon cycle are really quite different an important backdrop to the potential for natural climate solutions is that it's already the case that we're seeing large amounts of carbon uptake in the oceans and terrestrial ecosystems on an annual basis if you look across the the global map of of forests what you can see is that there are large amounts of deforestation in each of the tropical continents there are large amounts of of regrowth on each of the tropical continents and their their substantial forestry sinks on each of the tropical continents and if you add that up globally what you can see is that for every ton of carbon dioxide that's emitted to the atmosphere something like 30 percent is stored in these background sinks on land and an additional 20 to 25 percent is is stored in in the oceans these these natural sinks are an important backdrop to the prospects for natural climate solutions and most of our calculations about the potential for natural climate solutions are in the context of the expectation that these background sinks continue to occur and one of the real challenges as I'll show in a minute is the prospect of double counting of establishing programs to generate sinks that are in fact already being accomplished either on land or in ocean by these unmanaged processes so what is the the prospect for natural climate solutions on land and that's what I'd like to spend the the last few minutes on here there have been a number of estimates one of the most comprehensive ones was published in 2017 by Bronson Griscombe and colleagues and they identified a portfolio of 20 natural pathways with some potential to either decrease emissions or to increase carbon storage and they categorized these by the number that were potentially available at a carbon cost of less than $10 a ton less than $100 a ton and a total potential and what you can see from from their analysis is that the large majority of the potential fluxes are in forests especially in reforestation and the the idea that we could grow a lot more for us we have now has been a really important part of the dialogue over the last several years it was sort of brought into sharpest focused by a paper that was published in science in 2019 by bastin and and and colleagues and they used a clever but not very realistic AI technique to say well where does the earth have climate conditions that could potentially support for us and if you say well what would the difference in the biosphere be and the difference in carbon stocks if there were a forest everywhere that has a climate that's consistent with that and they ended up with a very large number that that if you could wave a magic wand and have forests everywhere that could support it it would add something like 752 tons of CO2 to the forest estate the magnitude of this number and the enthusiasm surrounding it was really stimulate a lot of our conversation about what we could do if if by waving a magic wand we could get a trillion more trees and those trees lived maturity and they they established stable permanent forests it could make a huge difference but that's a very very optimistic and a very non-mechanism-based way to think about the problem other groups that have taken a much more mechanistic approach including this report from the national academies on negative emissions that um Steve college chair Sally Benson was a member of the committee come up with a conclusion that if you really look across all of the mechanisms that are likely to be available on land the notions tend to end up with estimates on the order of five to 60 tons of CO2 per year little more than 10 percent of current total fossil emissions the debate over these issues led Chris Anderson who's going to moderate the discussion later today and a number of colleagues to argue that we really need to focus on this question of what the overall potential of natural climate solutions is and be realistic about it as we look to the future and one of the things that that's stimulated many people to do is try and figure out where we are in terms of actually being able to deploy natural climate solutions and I'm going to talk about a synthesis that that that I've been participating in recently was led by Connor Nolan and if you look at all the estimates of the potential for carbon storage in the terrestrial biosphere you end up with a huge range of possibilities all the way from substantially more than a thousand billion tons of CO2 to less than a hundred billion tons of CO2 as the maximum capacity the orange bar across this chart is the interquartile range for the expectation the requirement for negative emissions in the IPCC scenarios for the 1.5 C report one of the things that we realized in looking at all of these estimates is that when we think about natural climate solutions there are really two quite different conceptual models that drive people's thinking and you might understand those conceptual models as understanding the carbon storage capacity of the terrestrial biosphere as either operating like a silo we cut down a bunch of forests in the past and we can refill the biomass in this forest but but once it's refilled we're we're at capacity or you can think about the terrestrial biosphere as operating much more like a haystack and the the more carbon you dump on it the the bigger the pile of hay gets to be if you think about the carbon storage in the terrestrial biosphere is as fundamentally driven by things like the increasing CO2 concentration in the atmosphere by general phosphorus fertilization or intensive human management of things like forest plantations you tend to drift more toward this haystack model and the conceptualizations that are more tied in the idea of restoring us to the pre altered biosphere give you estimates that are on the order of a hundred billion tons of carbon potential storage and the ones that are more haystack oriented produce numbers that are in the thousand billion tons or greater and one of the things that's that's really interesting is if you look at estimates of carbon loss from all the human activities today you end up with with the indication that human activities have released something like 700 billion tons of CO2 from ecosystem to the atmosphere and the estimates of the potential gain from natural climate solutions have a mean of 300 to 400 billion tons but if you ask how much of a land sink has there been adding up all of the land sink that's occurred since about 1960 that number adds up to almost 500 billion so if we really are operating in a silo world where the total capacity is something like 750 billion tons and we have already restored 500 billion we don't have very much left if on the other hand we're in a haystack world with potentially unlimited storage that that may not be relevant when we looked at all the information and tried to figure out what the actual maximum capacity might be it's hard to imagine that if you look at the combination of biochemical constraints from nutrient and water availability and climate feedbacks if you look at the economic constraints about land tenure and the cost of intervention and if you look at political cultural social and economic constraints it's hard to imagine that during this century we could deploy natural climate solutions that are outside the range of something like one to 200 billion tons of CO2 total that's enough to really make a dent in the solutions to the climate challenge but it's not enough to abandon decarbonization it's not enough to abandon other approaches to CO2 removal and as is the case with almost every technology intervention we look at in in in the climate challenge we really need to look at a very broad portfolio and we need to think about ways that we can utilize natural climate solutions in a way that takes advantage of their co-benefits but looks hard at the implications for equity looks hard at the implications for ecosystem services and really opens the door to thinking very very hard about making natural climate solutions a part of a strategy to build a better world and not just to solve the climate problem that's where I want to stop and I would love to take any questions thank you Chris that was that's very informative as always very interesting so we do have a couple of questions from the audience can we start with Andrew Robertson would you like to ask a question yeah no problem everybody can hear me yeah so my question was really putting together your whole process we've got this sort of limited time frame where we need to start drastically reducing emissions reductions if we're going to still be emitting that 40 gigatons a year you know the 11 years you said at the beginning how realistic do you feel it is that we can ramp up those different nbs or ncs opportunities because obviously it takes time for plants to grow or people to change attitudes or implement new practices so if we need that if we need to fill it in the next 10 years or so a lot of those studies which are look at the ncs potential say that we can do this by 2030 or something how realistic do you actually think that that is well one of the things that's really clear is that we're not going to accomplish big changes in either emissions or in uptake in nature and unless we make a real commitment to it and we've spent an awful lot of time talking about decarbonizing energy in industry and a lot of time talking about deployment of natural solutions but we spent very little time investing in either investing either in the learning that's necessary to deploy at scale or in the deployment at scale I'm not optimistic that we'll see a dramatic set of changes in the next year things could change dramatically in only a few years but one of the most important enablers for that is the recognition that this time pressure was really extreme and when I think about the great potential of natural climate solutions to contribute it's really reassuring but when I think about the potential for discussion of natural climate solutions to be used as an excuse for delaying action and all the other things we need to do it's really concerning yeah I at that point I just said it's all a follow-up I guess looking at I was really interested and really loved that chart of all your the different potentials going from that thousand new terms to the very small all those different studies is that in that study do you standardize for the number of years so like each one is timed up by 30 years or something or is that yeah I just remember looking at it it's a great question the estimates that have been done have been done in two fundamentally different ways some calculate the size of the remaining storage potential and and that's the kind of a time independent remember how much carbon could the terrestrial ecosystem biosphere contain some are based on a rate of what's the maximum storage per year and and some have a number of years that you could apply that and in some cases we had to make an estimate of them the number of years and so in the in the griscombe paper that I showed the results from they they used a rate-based analysis that applied for 54 years and and when there was no indication of how long we're just we used that as an estimate but it's a big unknown for all these studies lovely okay thank you we have quite a few questions rolling in I'd like to go to Navin next Navin would you like to ask your question unmute yourself when you're able to hi thanks Chris for a great presentation so my question was you know whenever we talk about natural climate solutions most often we consider the earth as a homogeneous system right so there is so much of carbon to be taken out and you know so these are the solutions and we look at it with earth as a homogeneous system but given the you know if you look at the net primary productivity or carbon dioxide the spatial distribution across the earth will lend to different portfolio solutions being effective in different ways as a part as a as a as a function of the location the time right so your thoughts on on your studies on what you have observed from that point of view yeah thank you well yeah it is really important that the productive potential across the earth surface varies dramatically from the tropics and poles it's also important to keep in mind that at least historically the relationship between existing carbon stocks and net primary production isn't anything like linear and some of the largest carbon stocks are in high latitude ecosystems especially where carbon is frozen in soils and the net primary production is really low what we're really looking at in terms of the ability to generate additional carbon sinks is the balance between the uptake and the release and the release can either be from decomposition and respiration or it can be from disturbances and the disturbances can be anything from fire insect outbreaks to harvesting or clearing for agriculture and it is indisputably the case that the opportunities for storage very additional storage vary dramatically from place to place and they depend not only on this sort of natural cycle of of input and output and disturbance but also on any additional resources that might be applied and those additional resources could include things like pseudo fertilization from the atmosphere or nitrogen deposition from pollution or the addition of irrigation water in general my expectation is that we're unlikely to see very many natural climate solutions that are lucrative enough that we see massive inputs in terms of for example irrigation or fertilizer applications okay thank you we still have time for at least one more question lauri weber would you like to unmute yourself when you're able to and ask your question chris thank you so much for this and one thing that i find puzzling regularly is that there's often this juxtaposition of an either or solution it's either one or the other and i'm glad that you focus on the portfolio approach where we need it all and we're going to do it all so i have two questions for you you know our natural technologies are proven and they're deployable now and they're a least cost solution so i'm wondering if this doesn't tend to argue that we should be immediately deploying deploying this now some of us have been arguing for 25 years to immediately deploy it but that gives us some time to focus on the other technologies that as you point out are rather distant and untested in the geologic and bex arena and secondly both you and sally have focused on the cultural challenges of transforming the land use sector and i'm wondering if you see any lessons from how we've approached the cultural challenges in transportation and in fostering renewable energy that could be applied in the natural solutions sector great questions thank you i think we should be doing the best job we can for stewarding natural ecosystems always and in many cases there's a wonderful alignment between the kind of interventions that we want to make to do good stewardship and the kind of interventions that increase carbon stocks and that it comes into sharpest focus with any deforestation primarily in the tropics and the potential to decrease emissions by more than 10 percent and there are lots of reasons to think that in terms of the technology that that could happen really soon but it also has to happen in the context of of real people making real decisions and the reversal of the trajectory and in brazil after bolson are all became president is a is a good example of the kinds of difficulties we face but i totally agree that we should be deploying the natural climate solutions now when they work but i want to argue against the idea that that means that we have more time to work out solutions to the decarbonization that the time pressure for the whole climate challenge is really intense and we want to be sure that that we don't lose track of the intensity of the time challenge by the fact that there are compelling natural climate solutions that are available now they're not enough to to allow us to take the foot off the accelerator pedal for the solutions across the entire suite


Hey everybody, Forest here ripping 21-22 Prison Basketball hobby 12 box full case number one. This is a tiered random style break. You can always find the results of any of the randoms at LeightonSportsCards.com. Thanks everybody for joining and good luck. There's also a link in chat. Yeah, one card doesn't make a product though to be fair. I think there was another one of one for like a Jalen Green I think is what Lane was saying. That's crazy man. All right, here we go. All right Rockets Rookie Jalen Green Thunder Silver Ty Jerome Warriors Hyper Clay Thompson Anton Robinson Earl for the Thunder Ruby Wave Raptors Gary Trent Jr. Hornets Blue Kelly Ubrey to $199. Yeah Eve I know I know what you mean. Got Silver for the Suns DeAndre Aiton Red Ice well not the Red Ice but the 75th anniversary Rockets Josh Christopher Rookie their number to 75 T-Max 75 years. It's happened a lot recently, right? It feels like it's happened like almost every product. Sir Butler Ruby Wave Wizards Denny Avdea Hyper Rookie Wizards Corey Kisper and no color blast in this. Scotty Barnes Rookie for the Raptors. Or I'm pretty sure there's not. Kaminga Rookie for the Warriors Silver Rookie Sixers Jaden Springer Silver Lamello Prismatic Cameron Thomas Nets Rookie Hyper Fearless Trey Young Hawks Magic starting off nice here Rookie Otto Frans Wagner Nice Frans for you Orlando. I don't think Prism has really ever had. Prism Massimo has really ever had a lot of short prints or like short printed inserts. Didn't last year. Clippers Rookie Keon Johnson Jaden Springer Silver for the Pistons Isaiah Stewart Hyper Nets Bruce Brown Sanci Aldama Pulsar Celtics Peyton Prichard Jazz Orange Hassan Whiteside to 49.  Trey Murphy the third. Silver for the Mavs Tim Hardaway Jr. Kings Red here in Fox 299. Steph Curry Prismatic Insert. Thanks Eve. There's not a super short print insert if that's what you're asking. Rockets Rookie Alfred Shengyun Jalen Johnson Hawks Ruby Wave Keon Johnson Clippers Rookie Celtics Hyper Josh Richardson Pools Rookie Iota Sunmu Got Jason Preston Clippers Silver Lakers Magic Johnson Silver Fearless Ben Wallace Pistons Pacers Got a TJ Warren Silver Otto here. I'm pretty sure that's how Prism Basketball has just always been. LaMelo Ball David Johnson Raptors Rookie Purple Ice Dwayne Bacon for the Knicks to 125. Football products are so loaded with like and I mean they they print a lot more of like a lot of the football stuff like downtowns and kabooms used to be like really rare and now they're becoming more common. They just haven't really added on the basketball. They have them. They have those cars. They're just they're tougher to hit. They're rarer. More rarer rarer. Isaiah Todd. Alfa Ruka Minu Spurs Silver Hyper Rookie Clippers Brandon Boston Jr. Pistons Rookie Bay's Cade Cunningham Ruby Wave Kings Rashawn Holmes Purple Rockets Rookie Jalen Green Boom 33 of 99 HP with the Rockets. Not so far on this break Johnny. Just in the second box though. Just a few packs into it too. Franz Wagner Rookie Magic Celtic Silver Dennis Schroeder Red for the Thunderstrike Hildes Alexander to 299. Jimmy Butler 75 years Zaire Williams Bay's Rookie Grizzlies Ruby Wave Washington Davis Bertans Hyper Blazers Anthony Simons McBride Rookie for the Knicks Zagrowski Silver Rookie Grizzlies Santi Aldama Silver Lebron Mindset Insert Lakers for Blue Ice for the Natsukairi Irving to 125. Magic! Jalen Suggs Rookie Auto now. Got a Wagner and a Suggs in the first two boxes. Matt T with the Magic. Geez. Drift Cooper Silver for the Magic Markel Foltz Hyper for the Knicks Emmanuel Quickly. Did you say Suggsational Randy? Warriors Rookie Moses Moody Pulsar Siddiq Bay Pistons Patrick Williams Bulls Red numbered to 299 and Edwards Book Knight Rookie Usman Garuba Rockets Rookie Silver for the Suns J Crowder Blue Ice Bucks sorry Pelicans Devontae Graham to 125 Dayrun Sharp Nets Rookie Joshua Primo Spurs Ruby Wave Alonzo Mourning Hornets Hyper Washington Denny of Dia I didn't say it was bad you know I just I'm a little stunned right now Rookie Silver for the Clippers Keon Johnson Wide Screen Silver Step Curry Warriors Hornet Silverado PJ Washington Jr. Davion Mitchell Kings Orange Wave Mavericks Maxie Claibor to 60 Kai Jones JT Thor Silver Jon Marant Grizzlies Hyper KGT Wolves Magic Rookie Base Jalen Suggs Pulsar Paul Pierce Celtics Kings Red Marvin Bagley 299 on the Red Nets Rookie Kessler Edwards Thunder Rookie Josh Giddy we got a nice card coming come on Silver Jay Chantate Rockets Black and Gold Jazz Rudy Gaye nice one Jazz three up five there you go Utah Herbert Jones Pelicans Toscano Anderson Warriors Ruby Wave Hornets Mason Plumlee Kings Hyper Buddy Heald Josh Christopher Rockets Rookie Cade Cunningham Silver Rookie Pistons Awesome third box of the case here there you go Pistons get that one Mag for you Silver Fielder Zion fall by Rockets Base Rookie Otto Josh Christopher Bones Highland Rookie Nuggets Chris Duarte Pacers Thunder Isaiah Robbie Purple Ice to 149 yeah 149 Thunder Rookie Aaron Wiggins Luca Garza Ruby Wave Lakers Carmelo Anthony Hyper Rookie Wizards Isaiah Todd Silver Celtics Paul Pierce Magic Red Robin Lopez 299 Jaden Springer Charles Bassey Hyper Prismatic Giannis Antedicumpo Bucks Bulls Auto Alex Caruso not numbered there's a Kispert Rookie Base for the Wizards Silver Magic Cole Anthony Cavs Rookie Evan Mobley Trey Mann Thunder Brandon Boston Jr. Clippers Mavs Ruby Wave Boban Marjanovic Blue Ice Nix Dwayne Bacon to 125 Spurs Hyper Doug McDermott Nets Silver Paul Millsap Purple Montrez Herald Wizards Number 299 T-Max 75 years no Randy I'll probably catch highlights I kind of saw a little bit of what happened what it what happened in that game because uh uh there was some Tom Fulory afoot there's some shenanigans for sure we had Yankees Red Sox on and about to like the fourth thing I couldn't take it anymore Kaminga Rookie Butler Rookie Ruby Wave Blazers Yusuf Nurkitsch Purple for the Hornets Terry Rozier to 99 Scotty Barnes Raptors Silver Rookie Wizards Corey Kispert Red for the Blazers Norman Powell 299 Westbrook Laker 75 years Nets Cameron Thomas Ruby Wave Sons DeAndre Aten Rockets Hyper Jay Sean Tate Greg Brown III Blazers Rookie Shreve Cooper Hawks Hawks Silver Clint Capella Fireworks Silver Jazz Donovan Mitchell Isaiah Livers Dallin O'Banson Orange Wave Raptors Vince Carter to 60 Nice Vince Sanity oh boy yeah any bulk call is uh it's pretty tricky especially when it brings in a freaking run dude Silver for the Jazz Hassan Whiteside Warriors Hyper Andrew Wiggins Raptors Rookie David Johnson Bulls Rookie Ayodo Sunmu Rookie Magic Silver Frans Wagner man the magic are doing well that mag for you magic blue for the magic Robin Lopez to 199 Ruby Wave Raptors Shredvan Bleat Hyper for the Mavs Wang Jiji Robinson Earl Rookie Jalen Green Rookie Bass for the Rockets Buck Silver Chris Middleton Silver Whitescreen Jazz Donovan Mitchell Lakers a Meadow World Peace Auto Yo Lakers Trey Murphy III Jazz Rookie or not Jada Pelicans Rookie 75th Anniversary Hornets Terry Roseira to 75 Clippers Rookie Otto Brandon Boston Jr not numbered Jalen Johnson Hawks Rookie Nuggets Silver Jamal Murray Nuggets Hyper Jamal Murray Jason Preston Clippers Alperin Shengyun Rockets Hulsar Rookie Rockets Jalen Green there you go Rockets Red Rookie Spurs Joe Weisskamp to 299 we'll mag that Jalen Green no idea Bradley I have no idea I don't follow that man's life not that you do just saying Scotty Lewis Ruby Wave Knicks RJ Barrett Red for the Cavs Kevin Love to 299 Jaden Springer Rookie for the Sixers Charles Bassie Silver for the Bulls Zach Levine Blue for the Nets Bruce Brown to 199 Silver for the Mavs look at Donchance 75 years mellow ball I gotta figure out my life how can I how can I pay that much attention to somebody else man Keon Johnson I don't have the time or energy to be nosy Ruby Wave Pistons Jeremy Grant Blazers Hyper Damian Lillard Trey Murphy III Pelicans Rookie Silver Rookie Cavaliers Evan Mobley boom Areas for the Cavs for a bunch of Mobleys tonight we'll get that mag pretty solid uh first half of this break here Silver Fearless Jamerant Grizzlies Luca Garza Orange Celtics Jalen Brown to 49 Nice Jalen Brown James Booknight Hornets Rookie Aaron Wiggins Thunder Crawl Anthony Towns T-Wolves Silver Kings Rookie Hyper Namias Keita Kessler Edwards Nets Rookie Sun Silver Miles Bridges Blue for the Pelicans Kira Lewis Jr to 199 Toscano Anderson Warriors Bones Highland Rookie for the Nuggets Ruby Wave Rookie Grizzlies Santiago Dama Hyper for the Pelicans Jonas Valenchunas a mellow insert Quentin Grimes Rookie Nicks Silver Rookie Grizzlies Zaire Williams Silver Prismatic Kauai Leonard Clippers Santiago Dama Mojo Rookie Otto to 25 you go Memphis 2 of 25 on the Aldama oh he is Bradley that's interesting Don McKellishealy Todd Edwards Purple Prism of Sun Whiteside Jazz to 99 Pacers Rookie Bassado Chris Duarte Yeah, thank you for informing me I was honestly like I couldn't think about anything else Trey Mann Brandon Boston Jr. Corey Kispert Rookies Jazz Silver Joe Ingalls Hyper Blazers Youssef Nurkitsch Cavs Rookie Evan Mobley Pulsar Cavs Jared Allen Red Rookie Thunder Aaron Wiggins to 299 Yeah banks but it's it's a full for tonight it'll rip tomorrow night if you order right now and it'll be after group break so probably start around midnight eastern tomorrow night Mama Kellishealy Purple Ice Hawks Bogdan Bogdanovich to 125 Zaire Williams Grizzlies Rookie Joe Weisskamp Frans Wagner Rookie Magic Celtic Silver Robert Williams III Celtics Hyper Peyton Critchard Zacharowski Ruby Wave Pacers Justin Holliday Heat Purple Bam Adebayo to 99 Miles McBride T-Wolves Silver Malik Beasley Red Kyle Anderson Grizzlies to 299 Anthony Davis Lakers 75 years JT Thor Ruby Waves Celtics Dennis Schroeder Hyper Bowls Tony Cuccoach Magic Rookie Jalen Suggs Kai Jones Rookie Hornets Anthony Edwards T-Wolves there Silver Brandon Bosson Jr. Clippers Rookie Silver Fireworks Steph Curry Warriors Garuba Rookie Rockets Moses Moody Warriors Blue Ice Bulls Rookie Iodosunmu Nice one Bulls 39 of 125 We'll get that mag for your Chicago Nick's Rookie Silver Auto Quentin Grimes Primo Rookie for the Spurs Book Knight Hornets King's Silver Rashawn Holmes Luca Garza Pistons Rookie Hyper Dearon Sharp Nets Rookie Pulsar NS Cancer Celtics Red Xavier Toman Grizzlies to 299 Isaiah Jackson Pacers Spurs Silver Doug McDermott Nets Blue draws in Petrovich to 199 Davion Mitchell Kings Ron Jordan Poole Ruby Wave Warriors Blazers Hyper Larry Nance Cade Cunningham Base Rookie Pistons Isaiah Todd Spurs Silver Rookie Joe Weisskamp Mindset Silver Yanis Nets Rookie Base Auto Dearon Sharp All right the second half here six down six to go Nice Prismatic Hyper Lebron James Lakers Raptors Scotty Barnes Rookie Base Auto Boom Heck yeah there you go Toronto get that mag for you Bronze Wagner Magic Rookie Base Heat Silver Jimmy Butler Hyper for the Rockets Kenny Martin Jr. So Steph on the Team Lebron variation Zacharowski, Zaire Williams, Bulls Pulsar, DeMar DeRosin, Blue Ice Cavs, Darius Garland to 125 Miles McBride, Jason Kidd, Nick Silver, Evan Fournier, Purple Cavs, City Osman to 99 Ruby Wave Wizards Rui Hachimora, Pacers Hyper, Miles Turner Magic Rookie Jalen Suggs Kai Jones Hornets Silver for the Bulls Patrick Williams Silver Wide Screen Donovan Mitchell Jazz Spurs Zach Collins Base Auto Josh Giddy Rookie Base South Camille Blue Ice Suns J Crowder to 125 Primo Rookie Base Spurs Rookie Silver Pacers Isaiah Jackson Kings Hyper Marvin Bagley Nets Rookie Day Run Sharp Ruby Wave Cavs Kevin Love Red Tobias Harris Sixers to 299 Lebron Prismatic Isaiah Jackson Pacers Rookie Nugget Silver Marcus Howard Purple Ice for the Blazers C. G. McCollum Out of 149 Mavs Luca Donch at 75 years of the NBA Davion Mitchell Ruby Wave Pelican Zion Williamson Hawks Hyper DeLon Wright Kate Cunningham Base Rookie for the Pistons Isaiah Todd Jazz Silver Donovan Mitchell Silver Wiseman Prismatic for the Warriors Rookie's IO Dosunmu Jason Preston Alperin Shengyun Purple Ice Jazz is on white side to 149 Raptors Rookie David Johnson Lakers Silver Russell Westbrook There's a big ding on that card. Yeah, I'm going to send an empty Boxing Rapper for the Lakers spots. Ding on the bottom too. So if you wish to get that replace Panini will ask about Boxing Rapper most likely for a ticket or a support ticket. Hyper for the Nets Rookie Day Run Sharp Asi Pulsar Celtics Dennis Schroeder Pelicans Red Devante Graham Red is to 299 Clippers Rookie Keon Johnson Jane Springer Sixers Silver T-Mac for the Magic Blue for the Mavs Reggie Bullock Out of 199 Tramer for the Third Pelicans Rookie Ruby Wave Jordan Poole Warriors Renforbs Hyper for the Spurs Jalen Johnson Hawks Silver for the Hawks Bogdan Bogdanovich Silver Hawks again Trey Young Mindset Insert Redemption Shaquille O'Neal Shaq Signatures I would imagine this is Lakers. It's possible that it's not. Card number 12 Lakers on the Shaq. Congrats on that one. You guys have been watching Summer League, Garza, Nimeous Keita, Purple for the Hornets, PJ Washington Jr. Out of 99. Bowls, Elton Brand Bowls Auto. I do not remember that. Aaron Wiggins Warriors Rookie Son Silver Jalen Smith Warriors Hyper Andrew Wiggins Rockets Rookie Jalen Green Ruby Wave Nets Patty Mills Nets Red Kyrie Irving Out of 299 Silver Rookie Pacers Isaiah Jackson Orange Wave Raptors Gorin Drogic Out of 60 75 years David Robinson Yannis Bass Isaiah Livers Banton Ruby Wave Ricky Rubio Cavs Hyper Dylan Brooks Grizzlies Jazz Rookie Jared Butler Silver for the Pistons Sabin Lee Silver D-Book Firework Sons Yeah, we've seen a few Camille. The insert's not super rare. Edwards Ann Lamello Giddy Rookie for the Thunder Suggs Rookie Magic Purple Ice Kyle Lowry Heat Out of 125 Kenny Hardaway Kessler Edwards Nets Sons Silver Macal Bridges Warriors Hyper James Wiseman Isaiah Williams Grizzlies Rookie WiseCamp for the Spurs Pulsar Thunder Darius Basely Justin Holliday Pacers Red 299 Zacharowski Silver Rookie Sondra Mamakalashvili for the Bucks Magic Johnson Blue, this one is, let's say that is a little too miscut to 199. They get an empty box wrapper. I think we already have one for the Lakers anyway. It's just completely miscut to the point where it's like interfering with like the car design. Blue is to 199. Miles McBride Nicks Ruby Wave Clippers Key on Johnson Rookie to the, so not number on Ruby Wave Hyper Damie Lillard Blazers Jones and Thor Silver for the Grizzlies Desmond Bain Silver Fearless Lakers Anthony Davis another Lakers dinged card here. What is going on? How does he do anything like this all night or even all day today? Redemption Zion Williamson Flashback Signatures Boom Pelicans Yeah, Zion's coming back y'all. There you go Pelicans Cade Cunningham Rookie Base for the Pistons Isaiah Todd Wizards Cade Cunningham Orange Rookie Big Card Pistons Boom What a box 47 of 49 Orange Cade Tony W with Detroit at his sweet Rockets Silver Auto Kenny the Jet Smith Franz Wagner Magic Rookie Sandro Rookie for the Bucks Sun Silver Dario Sarge Hyper for the Hawks Dominique Wilkins Mellow Base Herbert Jones Rookie for the Pelicans Toscano Anderson Warriors Ruby Wave Magic Cole Anthony Desmond Bain Grizzly 75th Anniversary to 75 Nice one Grizz Chris Duarte Pacers Rookie Josh Christopher Rockets Silver for the Heat Tyler Hero Red for the Pelicans Kira Lewis Jr. Out of $2.99 Jimmy Butler 75 years Edward's there Nuggets Rookie Bones Highlands Ruby Waves Sons Jalen Smith Rockets Hyper Rookie Uisman Garuba Wizards Rookie Corey Kispert Rhymes Silver for the Nuggets is Michael Porter Jr. Silver Prismatic Warriors James Wiseman All right three more guys Aaron Wiggins Thunder Rookie Ruby Wave Hornets Miles Bridges Purple Ice Rockets Daniel Tice to 149 Charles Bassie Scotty Lewis Silver Rookie Bulls Iodosunmu That's a nice one there Blue Ice Mavs Reggie Bullock to 125 Pistons Isaiah Thomas 75 years Lamello Bass Oh Serena Ruby Wave Sons Jay Crowder Hyper for the Bulls Kobe White Keon Johnson Clippers Rookie Anthony Edwards Got a Silver for the Knicks will be Toppin Silver Prismatic Warriors James Wiseman Trey Man Thunder Heat Red Duncan Robinson to 2.99 Pistons Luca Garza Nymie S. Kata Silver for the Sixers Seth Curry Hyper Rookie Thunder Aaron Wiggins Josh Christopher Rockets Rookie Herbert Jones Pelicans Rookie Hulsar Pistons Hamadou Diallo Kings Blue Marvin Bagley to 199 Duarte for the Pacers on the Rookie Silver Cavs Rookie Rubio Orange Bismac Viambo Hornets Orange is to 49 Quentin Grimes Knicks Bones Highland Nuggets Rookie Ruby Wave Rookie Pelicans Trey Murphy the third Pacers Hyper TJ Warren Mamello insert Clippers Rookie Brandon Boston Jr Wizards Corey Kispert Celtic Silver Bill Russell Silver Fearless Lakers Anthony Edwards Pacers Bass Auto Miles Turner now Josh Giddy Thunder Rookie Bass Fearless Hyper Damian Lillard Blazers Pacers two hits in the box now Rookie Otto Isaiah Jackson Tuscano Anderson Rookie for the Warriors Kessler Edwards Nets Rookie Gary Tran Jr Raptors Silver Ben Simmons Hyper for the Sixers Edwards Fireworks Baltimore Pelicans Rookie Herbert Jones JT Thor Hornets Wizards Rad Bradley Bealt to 2.99 Raptors Silver Gorin Draugich Wizards Hyper Davis Bertans Pistons Rookie Cade Cunningham Ruby Wave Grizzlies Kyle Anderson Purple Ice Bulls Nicola Busevich Number to 149 Nets Rookie Day Run Sharp Spurs Joshua Primo Celtics Silver and As Cancer Blue Ice Bucks Drew Holiday That's out of 125 David Robinson 75th 75 years David Robinson Pacers Isaiah Jackson Ruby Wave Celtics Marcus Smart Hyper Heat Dwayne Wade BVM Mitchell Kings Rookie Bass Heat Silver Dwayne Wade Silver Mindset LeBron James Lakers Insert Zacharowski Mindset Hyper Hawks Trey Young Wizards Bass Auto Ruby Hatchimora Wizards Silver Daniel Gafford Cavaliers Hyper Rookie Evan Mobley Congrats Cavs That one mag for ya. Sugs Rookie for the Magic Spurs Pulsar Jarrick Whites Pacers Blue Justin Holliday It's out of 199. Yeah, I know he's not uh it used to be different I thought he definitely made it worse but it used to be a lot different from our from our remember Franz Wagner Magic Rookie Josh Giddy Thunder Son's Silver Dario Sarich Blue Ice, Celtics Robert Williams III Blue Ice is to 125. Hornets Ruby Wave Alonzo Morning Hornets Hyper Miles Bridges Grizzlies Rookie Zyre Williams Joe Weisskamp Chris Duarte Silver for the Sonics Gary Payton goes to the Thunder Widescreen Silver Steph Curry Warriors Kai Jones Bass Rookie Auto Hornets And last box let's see some crazy stuff here guys. Good luck Raptors Rookie Scotty Barnes Warriors Rookie Jonathan Kaminga Ruby Wave Sixers Tobias Harris Wizards Red Ruby Hatchimora Out of 299 Magic Silver Mo Bamba Mojo Nets Paul Millsap 25 Brooklyn 75 years Carmelone Jazz Hawks Rookie Sharif Cooper Cameron Thomas Nets Pelicans Ruby Wave Zion Williamson Hawks Hyper DeAndre Hunter Greg Brown III Blazers Silver Rookie Trayman Thunder Silver Prismatic Kyrie Irving Nets 75th Anniversary Heade Bamba to Biota 75 Samanovic Jared Butler Jazz Rookie Clippers Silver Eddie Jackson Reggie Jackson Eddie Jackson for the Bears Hyper Chris Boucher Rafters Bulls Rookie Iota Sunmu Jason Preston Rookie Clippers Pulsar Pacers Germain O'Neill Purple Ice Rookie Pistons Luca Garza 150 David Johnson Raptors Rookie Bulls Silver Alex Caruso Orange Waves Spurs Thaddeus Young Jaylin Green Rockets Rookie Bass Ruby Wave Sons Daphon Marbury Warriors Hyper Wilt Chamberlain Anton Jeremiah Robinson Earl Kings Silver Jason Williams Silver Fireworks Nets James Harden Thunder Rookie Otto Jeremiah Robinson Earl Jaylin Johnson for the Hawks Yanis Fireworks Hyper Devin Booker Sons Tray Murphy III Mojo Rookie Otto Pelicans 10 of 25 Nice Hit Pelicans Austin Arrives Shengun Rookie Cavs Silver Kevin Love Wizards Hyper Thomas Bryant We'll do it You set guys to do a recap if I'm prism dust on my DNA dude It's true it's everywhere bearing me in six feet of prism dust Okay, I can't live my arm all right guys got autos Tray Murphy the third mojo to 25 Jeremiah Robinson Earl Kai Jones Ruby Hachimora Isaiah Jackson Miles Turner Kenny the Jet Smith Elton Brand as a bull Shack Redemption and went to the Lakers Zach Collins Spurs Dayrun Sharp Quentin Grimes Santiago Dama Mojo to 25 Chris Duarte Brandon Boston Jr. Metta World Peace Alex Caruso Josh Christopher PJ Washington Jr. Jaylin Suggs TJ Warren and Trons Wagner It's all the mags here. He had a hyper Evan Mobley for the Cavs Silver Cade Cunningham Pistons Rookie Silver Frans Wagner Magic Pulsar Jaylin Green Rockets Silver Evan Mobley for the Cavs Blue Ice Bowls Iowa to Sunmue Orange Cade Cunningham Pistons Card there Scotty Barnes Rookie Otto for the Raptors Had a Pelican Zion flashback signatures redemption Black Gold Rudy Gay Jazz to five and Purple Rookie Jaylin Green Rockets You teams really well man. Thanks again everybody personal starting just a few minutes here


Looking for a quick, no-soldering project for Halloween? Have no fear, this project gets you from zero to a flapping vampire bat in no time. You can build this animatronic bat with a circuit playground express and two servo motors. The servos have alligator clips that can clip to the circuit playground's pads. The bat's wings attach to the servo motors. Circuit Python code running on the circuit playground moves the servo motors in a flapping motion. The code also utilizes the LED animation library to animate the circuit playground's NeoPixels. To get flapping, power up the bat with either USB power or a JST battery pack. To see how you can build your own vampire bat just in time for Halloween, check out the Learn Guide at learn.adafruit.com.