So we're not gonna post on your videos. It's good to see you all get a good release.  And then you kind of like to send it to our website. So I'm gonna send it to you all.  So what's the best way to get ready for that? That's how you post it. So I'm gonna send it to you all. So I'm gonna send it to you all. So you're gonna send it to me. So I'm gonna send it to you all. So now I'm gonna send it to you all. So what we're gonna do is So what I'm gonna do is So what I'm gonna do is What I'm gonna do is Good morning, everyone, and welcome to Carnegie. My name is Bill Burns, and I'm the new president of the Carnegie Endowment following in the footsteps of my superb predecessor, Jessica Matthews, who I think is also here today. Nothing reinforces my sense of good fortune more than efforts like the Oil Climate Index, serious substantive scholarship that not only helps us to understand a global challenge, but also to do something about it. There is no question that among the overarching challenges facing the United States and the world, climate change is at the very top. All of you know, as well as I do, that we are simply on an unsustainable trajectory. The facts are as clear as they are compelling. Well, but one of the 10 hottest years in history have occurred in the 21st century. In the past three decades alone, Arctic sea ice has lost half its area and three quarters of its volume. Sea level has risen by about three to five inches since 1950, presenting a real and present danger to half the world's population that lives on or near a coastline. These dramatic trends have led the Department of Defense to conclude that climate change poses immediate risk to U.S. national security. In addition to the risks it poses to our coastline cities and environment, the report argues that climate change is a threat multiplier that makes nearly all other global challenges from poverty to pandemics more severe and more intractable. The energy revolution over the past few years and the understandable enthusiasm it has generated in the industry and among foreign policy strategists must not distract us from this challenge. In fact, it gives us, for the first time in a long while, the opportunity to make strategic choices. These choices will not only enhance our energy security at home and strategic leverage abroad. They will also make a consequential impact on the pace and scale of global climate change. One of those crucial choices is how we deal with the hydrocarbon deposits that once seemed out of reach. The hard truth is that we can no longer afford to treat oil as a homogenous commodity. There is no such thing as standard oil and no such thing as a standard amount of greenhouse gas emissions. In fact, as the index makes clear, the climate change impact of different unconventional oils varies significantly. Of the 30 unconventional oils analyzed in the index, there is an 80 percent difference in total greenhouse gas emissions per barrel between the lowest greenhouse gas emitting oil and the highest. This is a considerable spread, one that is not only expected to grow as new and unconventional oils are identified. If we're serious about combating global climate change, we have to factor the different climate footprints of different oils into decisions that drive investment, development operations, and governance of the oil supply chain. These decisions are real, they are imminent, and they will have lasting consequences. But these decisions are not ours alone to make. These are decisions that are facing countries around the world, from Canada to China and Nigeria to Norway. This is precisely why we're releasing a global index, a shared, standardized, verifiable, and open-source tool that captures the climate impacts of new oil resources and gives policymakers, companies, and consumers around the world the information they need to make sensible and sustainable energy choices. Today's event is the culmination of an extraordinary amount of hard work by many people in this room. But I know it is just the beginning of an important and timely conversation about the oil frontier and global climate change. I can't think of a more urgent undertaking, and I can't think of a better person to get this conversation going than my exceptional colleague and the Director of Carnegie's Energy and Climate Program, Debbie Gordon. So welcome again, and thank you very much. Thank you, Bill. Welcome. It really has been about two years since we first conceived of this project, and it's a pleasure to be up here and to release it today. First a bit of background as to why Carnegie and our partners, Stanford University and the University of Calgary even thought to do this project and to work on an oil climate index. I want to take you back. It's a little bit back beyond a lookout in the audience, and many of you weren't even born yet, so it's really back quite a ways.  Gas shortages, rationing, printing of ration stamps, oil price spikes in the 1970s left a lasting impression that oil would someday run out. In 2005, oh, there's the lines in 70, and in 2005 it happened again, this time for very different reasons, Hurricane Katrina in this case, left the public again reminding us that oil's prone to disruption, it's a scarce resource, and it's going to run out some day soon. That message really stuck in a lot of people's minds. We were moving beyond petroleum because of that. We were thinking about alternatives. The world wasn't going to be about more oil. The world was going to be about beyond oil, and what we were going to do to technologically transform an entire sector, the oil sector, and the transportation sector, but things aren't really playing out exactly as we expected. Alternative fuels, oil alternatives, mounting global demands over the last decade have brought the price up and have brought a whole array of oils to the forefront that no one was really ever thinking about, and it was really the high prices that did this and the underlying demand. So it's true that conventional oils are dwindling, and it's true that we're not going to have conventional oil forever, and it's getting much more difficult to get there, but there's a growing array of unconventional oils that are coming to the forefront. In fact, I shouldn't even call them oils because they're really unconventional hydrocarbon. They're hydrogen and carbon that you can make into petroleum products. They're accessible, albeit at a much higher price, but we have fracking, shale, which makes condensates, lighter oils in the U.S., sometimes not so light, oils in China. You have condensates, you have natural gas liquids, very different from oil, but it can be made into petroleum product. We have heavy, or rather, this one is the digging and melting tarry oil sands and extra heavy oils in Venezuela that come from very different parts of the earth. We have steaming, heavy, watery, depleted oils in California and Indonesia. We have transforming, I hope they're in different orders here. Oh no, we have ultra deep oils in terms of Russia. We have one oil in our index on Russia, the Gulf of Mexico and elsewhere, and we're going into ultra, ultra deep oils. We have transforming Kerrigan in Estonia and China in the U.S. Rocky Mountains and also in Israel, transforming an immature oil into conventional oil over time, totally different path than what we've ever known before. And we have converting gas to liquids in Qatar and in South Africa and yet other resources that we don't even know about yet. In other words, at the right price, there really is no limit to oil. It's just going to keep changing. So $100 a barrel price is spurred new oil development. We've seen this over the last five years, but even as oil prices drop, we're still seeing tremendous competition between oil. Oil is competing against oil. Oil's not competing against alternative fuels in any large measure. ExxonMobil projecting out, this is their most recent outlook to 2040 that just was released. ExxonMobil, BP puts out the same thing, so they agree. They predict that oil will be the number one global energy source by 2040. And most growth, interestingly, will happen in freight and will happen in petrochemicals, not in gasoline. So even the demand side of oil is changing. In other words, what are we going to make out of this hydrocarbon? We're not going to make the same thing over time. It's new oil and new products that will be demanded over time. Oil consumption is not slated to dampen, despite the fact, as Bill mentioned, climate change is becoming an increasing concern and problem. The year 2014, as Bill mentioned, was ranked as the world's warmest since 1880. Fossil fuels, oil, certainly along with coal and gas, are the major culprits. As the change in climate results in higher costs, the environmental limitations of production of oil and consumption will spur public policy. If oil consumption is increasing and oil is evolving into these new forms, this begs the question, do you know your oil? And that was the genesis of this project. And if climate change is a major global challenge, as many of us believe it will be. And by the way, we had John Holdren speak to us last night, the President's Climate Science Policy Advisor, and on our website we'll have his PowerPoint linked as well. So you'll get to see what he's thinking and what the White House is thinking on climate change. But if climate change is the major global challenge that we are thinking that it is, it then begs the second question. How do oils and future prospects for these hydrocarbons stack up against each other? Because we have a lot of choice here, and we really need to compare them so that we can make the wise choices here. So to address these questions we use, this is Stanford, Calgary, and Carnegie. We use the best open source data that we could find to develop an oil climate index. As Bill said, we have 30 oils, only 30 oils, I would add, of the hundreds if not thousands of different sources of hydrocarbon on earth that we were able to get consistent information on enough to model in the oil index. We were willing to embrace many more than 30, but it was really 30 oils that we were able to wrap our arms around, which tells you what phase two will be for this project. So there were three different overarching findings that I want to highlight that will come out today. First is the oil climate index accounts for the entire barrel of oil. The analysis done to date in this realm has really looked at the output of oil all being, say, a barrel or a megajoule of gasoline. We didn't want to hide anything. Oil makes things today, and as I said in the future, less gasoline. It makes a slate of products. The carbon that goes in goes into a whole array of products, and we wanted to capture all of it to give a real footprint for each oil. So we looked at the entire barrel of oil. Also, groundbreaking, the oil climate index employs a brand new tool here to for has not been there, which is a refinery, an open source refinery model. That we can model oils through the refinery not only to tell us the greenhouse gas emissions associated with refining different oils, which are very different. And Jewel will talk about that in the next panel. But also the different slate of products that come from these oils, as I just said. Because every oil makes a different slate of products, so therefore downstream has very different impacts. And then the OCI expands upon the upstream model OpG that Adam will talk about on the next panel, the cutting edge model that the state of California and the European Union used to model the extraction, the production, the upstream emissions. So there are some cutting edge tools that come out of this. The oil climate index itself is a cutting edge tool, and our real goal here is transparency. So before we launch into the first panel, let me just briefly summarize our key findings. From the 30 test oils that we modeled, as Bill mentioned, from phase one of the oil climate index, we found an 80%, 80% difference, almost a factor of two between the lowest greenhouse gas emitting oil and the highest. You can't treat oils as if they're all the same. You can get tremendous greenhouse gas emission reductions just from choosing oils smartly or bringing down those emissions from the highest oil. So there's an opportunity here. Secondly, we also modeled oils not just per barrel of oil. We wanted to know, would these emission ranges change if we, if we modeled them per megajoule of products out? Because these oils make different products and per dollar value of products out. How much is recouped in the market from these products? Because that drives this market and the emission ranges were still large. So no matter what basis you put this on, there's a very large range of emissions. We had no way to assess whether we captured what the total range will ultimately be. We believe this 80% difference is actually broader as we go into more extreme oils. We just don't have data today to look at Arctic oils going into permafrost, ultra, ultra deep. Some of these oils are just not known. There were four categories of oils that were especially challenging, will be especially challenging in terms of their climate impacts that we identify in the report. Gassy oils, oils that have a lot of natural gas associated with them. And when that gas is flared or burned and wasted instead of captured and sold become very greenhouse gas intensive. We had a second category was extra heavy oils. They're high in carbon. They're loaded into the earth with a lot of carbon. So you have to manage that carbon very carefully. If you just turn it all into product and combust it, they end up being high carbon oils. We have watery oils from depleted oil fields. When an oil field depletes, there's less oil there over time. You end up with more water, but what you do for some of these oils is you lift a lot of water to just get a barrel of oil. Some of the California fields, you have to lift 50 barrels of water to get one barrel of oil. And you can imagine carrying 50 barrels of oil on your back. I mean, this is a lot of mass. So they become very greenhouse gas intensive. And then the last category that we only could really dip our toe into is oils in extreme environments. We have russian chivo in our sample set from the Sacklin field and it's a 30,000 foot deep well. Basically drilling to the center of the earth. So there are definitely extreme oils out there. And while we've covered every geography, what's really interesting to note is, and you will pick this up right away if you look at the report, we don't have any US light-tight oils in our sample. And it's a huge omission. And the reason why we don't have the Balkan and we don't have Eagleford and we don't have the Permian is there's not good data out there on them. The data severely lags when it comes to the oil sector. So that is, was actually I think the most surprising finding for us in this report and the way forward is oil data transparency. In order to really be able to evaluate oils, you need to know what they are. You need to know the underlying information. And not having the US light-tight oils is a very good example of how little transparency there is. And there were a lot of reasons for this. In some cases, there is data, it's not standardized. So you can't use it to compare oils. You can't use data that is measured all different ways. In some cases, it's not up to date. In some cases, it's not verified. In some cases, you need express permission to use data that's in the public realm. So the fine print is, here's the information, but you can't use it unless you get permission. And then in some cases, it's held privately. And either it's very expensive or as we found, not for sale at all. So oil data is going to be a huge issue going forward. We're looking forward to an illuminating day as we explore the oil climate index together. Here's the agenda for the day. The first panel will be our partners, and we'll actually talk to you a little bit about hopefully not geek out too much, but we'll talk to you about how the oil climate index was constructed. And we'll discuss opportunities and challenges for reducing greenhouse gas emissions in the oil supply chain. Then we'll take a quick coffee break. And then we're gonna have a really fun preview of an animated web tool that Development Seed is working with us on, which will be released next month. And my colleague Eugene Tan and I will walk you through just quickly, an animated presentation of what this web tool will do, which will allow everyone to actually get in and see these data and move them around and play with them. And then we will have a second panel, which I'm very excited about, where we can all engage in a conversation on applications of the oil climate index. There are applications for policy making, there are applications for the investment community, there are applications for businesses, for NGOs. There really, there are applications for countries. You know, what are countries gonna do? I was talking to Germany last night about what are countries gonna do with this information. So, and then we will conclude with whomever remains, we'll conclude with a conversation. We have the last hour set up to really answer your questions with the developers to have a conversation about the oil climate index in terms of any, we don't wanna do too much technical during the day, but we'll end with everything technical. Anything you wanna ask, as deep as you wanna drill. So with that, let's move to our first panel. I'll invite up the first panel and we will launch right in. Thank you very much. Okay, so the way this panel will go, I will moderate. Adam, Jewel, and Debbie will each speak for ten minutes talking about the upstream, the midstream, and then the downstream emissions. And then after that, we'll have, I think, about 45 minutes for questions. And our goal is to have a conversation to learn from you and for us to explain what we've done in the most transparent way so that we can find out what other data is out there and what other wisdom that we haven't yet tapped is out there.  Sure, thanks, John. I hope you all can hear me. Seems like my mic is on. So I'll talk about our upstream modeling effort. That is the modeling of the emissions associated with extracting crude oil. Just a note of gratitude to my students and advisees and post-doctoral scholars who've helped me with this model. I certainly couldn't have built it all on my own, in particular. I've worked with Karash Vafi, who's my post-doctoral scholar, and Yuchi Sun, who's a graduate student who helped me with this. As Debbie mentioned, there's a wide variety of crudes being developed currently. Resource quality, location, depth, nature of the reservoir, nature of the hydrocarbon, all these things vary. And they vary more than ever before. The idea that oil is a sort of a consistent commodity across fields or across regions of the world is less and less true over time. Technological improvement has allowed access to resources that we've known about for a long time, but really weren't able to access. So the Canadian oil sands were discovered basically in the centuries ago, but were not commercialized until the 1960s, right? And so the combination of technology, higher prices, has enabled us to extract resources that were previously known to exist, but weren't able to be extracted in an economic sense. This trend is likely to continue. The oil prices is down for now. It remains to be seen where it goes in the future, but this trend is likely to continue. Conventional oil resources or traditional oil resources, as we think about them, are increasingly limited. They're increasingly limited in a geologic sense. They're increasingly limited in a political sense. For a variety of reasons, investment in unconventional resources in North America is a really good idea from the perspective of international oil companies. They're accessible, the regimes are transparent, and investment can proceed to pace, this sort of thing. There's a variety of geopolitical considerations that are pushing us towards these as well. Unfortunately, most accounting and assessment methods that currently exist for assessing the greenhouse gas intensity of oils don't really differentiate between these types of oil resources. So for example, most assessments at the level of, let's say, an IPCC-like global assessment of emissions don't tend to separate out oil resources into their particular characteristics and the carbon intensities thereof. To a first approximation, maybe this was acceptable in the past, but it's increasingly less acceptable as we move into things like oil sands, tide oil, et cetera. I'll be talking today about the model that we've used to model upstream crude oil. This is called the OPG model. It's the Oil Production Greenhouse Gas Emissions Estimator. This model has been developed with support from Carnegie Endowment and the California Air Resources Board over the last three or four years. Just a few things to mention about OPG. One, we use the word estimator on purpose. So this is basically a tool that allows you to estimate what the emissions are going to be from a crude oil given a certain amount of information about the crude oil. The goal of the tool is not to calculate the exact emissions from any particular crude, but instead to be able to work with limited data sets, which we'll talk more about are often a challenge, to estimate what the crude oil emissions are likely to be for a particular operation. Some goals associated with developing the OPG model. First, we wanted to build a rigorous engineering-based model, so we wanted to avoid a simple sort of factor-based analysis where you don't account for engineering details, that is we wanted to account for the actual characteristics of resources and petroleum engineering technologies that are required to extract them. We wanted to use disaggregated data for specificity and quality. If you don't basically capture the detail inherent in these resources, let's say the flaring rate or another characteristics of an oil, you sort of lump everything together and you can't really disaggregate what's good and what's bad without really breaking out these underlying factors in some detail. So we wanted to do that more carefully. We wanted to use public data where possible. We wanted to document sources for every equation in the model. You may argue with how we computed things and you're welcome to always send me an email, but at least you can see what we did, right? And so this move towards transparency is very important to us. Unless we wanted to maintain the model, it's free to access, use and modify by any party and this will remain the case indefinitely. It's available on the Stanford University web servers and anyone can use it for any purpose basically, free access. The up-to-you model structure very briefly, there is a main, let's see if this works here, there is a main user input some result sheet that's a user accessible front into the model where you can put in basic data about an oil field which then basically is populated back to a variety of quite detailed computation sheets that compute emissions, for example, from drilling or from extraction and that rely on a number of underlying characteristics. You can interact with the model in a variety of ways and at a variety of levels. We do have a significant amount of tutorial information available in the documentation to the model. Opti has been used to analyze hundreds of crude oils in the past, California Air Resources Board uses it to model its baseline for the California Low Carbon Fuel Standard and they've modeled about 300 oil fields. Some consultants have done some work for the European Union where they've modeled hundreds of oil fields or crude blends and so this has been used for very large numbers of oil fields. Here are some of the basic results for the upstream analysis and the OCI. Now, I mentioned in the previous slide that this tool has been used to analyze hundreds of oil fields. The way the OCI differs is that we really spent a long time, in fact about a year collecting data on these 30 oil fields and some others as well. And really dug in deep and tried to do a very detailed data analysis. So we cited, now we're up to about 200 different technical sources to get information for the oil fields in the OCI. I hear the 30 fields, this is probably illegible here, the names of the particular fields there in your report. The particular names are not of interest for this first presentation, although we can get into them in Q&A if you're interested. An important point is that we have greater than 10x variation in upstream emissions by crude. The China Bozhong field and Nigeria Obagi field come in at more than 200 kilograms per barrel. Those are two fields with high flaring rates. In contrast, the Norway EcoFisk field, which is the North Sea, Norwegian sector, very tightly regulated, high quality oil field. Flaring is basically outlawed. Comes in at somewhere around 20 kilograms. So again, this greater than 10x variation. Some obvious, what's up in here? Okay, some obvious drivers emerged as we started to dig into these 30 fields. One is flaring. And so here I've tagged with a little red star the fields where flaring is an issue. And so I mean, we've got China Bozhong, Nigeria Obagi. These are fields where the flaring rate is high. There's a large amount of associated gas which isn't captured and used, but as I said, burned off for mostly economic reasons. It's not considered economic to extract and utilize the gas and so it's burned. Next, we have some fields with thermal recovery. As Debbie mentioned, there are these heavy viscous oils. So extra heavy oils and bitumen that basically have the consistency of peanut butter. They won't flow under normal reservoir conditions. Heat carrying substance typically steam is injected into a reservoir to heat the oil and allow it to flow. And these are fields such as Indonesia Dury or California Midway Sunset. Some crews are upgraded. So for example, when you produce this very heavy crude, often it needs to be processed or upgraded before it can be sent down a pipeline. And so here we have fields like Venezuela Hamaca. It actually comes out of the ground pretty easily for a variety of reservoir and crude characteristics. But once it gets to the ground it's a very thick and viscous crude and so it's upgraded before it's sent to market. And this is quite energy intensive. You can think of it as a refining like process. And then last, there's some fields that are depleted or gas rich. So for example, the Alaska North slope field, very high gas oil ratio, no way to get that gas to market and so that gas is processed, recompressed and re-injected. And this can actually be quite energy intensive. Also fields that are depleted and have a high water oil ratio will tend to have high emissions. So basically what you see here is that all these crews on the higher end of our slate have one of these characteristics that Debbie talked about. They really can drive high emissions. Data availability is the most challenging factor. If I had to bring up sort of one thing that's the issue with doing this sort of analysis, it's data transparency. Transparency is generally lacking in global oil operations. This is pretty widely known, but it's sort of surprising when you dig in how little information is available about some of these fields. Some regions this is not the case. Really good data availability. To be commended on their longstanding regulatory efforts to collect data. California, Alberta, North Dakota. So some regions there is quite good data available. Some regions essentially have very little information available. This is the case I would say in the majority of global oil producing regions. South America, China, Russia, it's very, very hard to get data on these regions of the Middle East. Global technical literature is a source. We cite it as I said about 200 different technical sources largely from petroleum engineering literature. Society of Petroleum Engineers. So there is information out there. It's just not collected in a consistent way. It's not a substitute for consistent and coherent government reporting. And it tends to be sparsely available and available only in certain circumstances. So that's a challenge. One of the interesting things that I'm gonna be looking at in the future is what technological sources, technologies exist to circumvent data challenges. And so a good example here is remote sensing. Here's some work from the National Oceanic and Atmospheric Administration of Offshore Nigeria and they've basically sensed flares using satellite information. So I think there's a lot of opportunity to use technology to basically circumvent some of these lack of transparency issues that otherwise may be very challenging. Maybe very challenging to deal with. And we'll talk more about this in the coming session. And I think that's all I have here. Oh yeah, so just a few conclusions. It is possible to explain the differences in the carbon intensity or different crew off-sources. Global crews do vary significantly in their upstream emissions. We see at least a 10x variation. As Debbie said, that's probably, the real variation from high to low is probably wider than that if we sampled wider. Data are difficult to obtain especially globally and more work is needed in both regulatory processes that enable consistent and coherent reporting of data and also technological approaches. If we're not gonna be able to compel some producers to produce transparent high quality data, can we use technology to get more information? Okay, thank you. Thank you, Adam. Now we've analyzed what it will take to pull oil out of the ground and the next phase we'll talk about, Jule will talk about refining that oil and what are the drivers for emissions related to refining. So, Jule. Great, thanks, John. Okay, so I'm gonna talk about the midstream processing of crews using our model pre-limb, which is the petroleum refinery and lifecycle inventory model. And I'll just start by giving a bit of background on how I got started in working in this area. I actually do energy systems analysis and lifecycle assessment of large scale energy infrastructure as my main area of research at the University of Calgary. And I've spent the last seven years or so with a project the majority of my work has focused on lifecycle assessment of oil sense technologies. So looking at the Alberta resource, how it's being developed using existing technology and what role emerging technology might play in terms of the economics as well as the environmental impacts associated with extracting and processing this resource. As part of that, we've focused a lot of our efforts on the actual extraction phase and the upgrading phase that can potentially be used in the oil sands. And as we were looking at these operations, we were seeing that there was a diversity and quality of product that could be shipped to a refinery from this resource alone. So when you extract the bitumen, which Adam had said is sort of the consistency of peanut butter, you have two options in order to actually ship that through a pipeline. You can either dilute it using a lighter product like naphtha or natural gas condensate, or you can have a mini refinery that would actually upgrade that product to a synthetic crude oil and ship that as a lighter product through the pipeline. And with these two options, once those two products end up at a refinery, the implications in terms of how much energy and the resulting greenhouse gas emissions associated with processing those two crudes are drastically different. And so we looked to other models, other life cycle models to help us differentiate between the emissions that would result if you had a diluted bitumen versus a synthetic crude oil and found that the existing life cycle-based models were not sufficiently capturing that detail. In addition, the fact that refineries are very unique. So the type of refinery that you send these products to will also have the implications associated with what it will take to actually process those fuels to transportation fuels. And so for those reasons, we actually endeavored to build our own model that would allow us to tease out both of those two aspects. So looking at the differences in energy and greenhouse gas emissions associated with processing different qualities of crude and in addition to that, looking at the effects of different types of refinery configurations. So those were the objectives in actually setting up this model. Again, it's a life cycle-based model. So we're using life cycle approaches. We're looking at indirect effects like the intensity of the electricity that's used and things like that. And, but what we wanted to do is make sure that it was something that was accessible and usable. And so we've developed this in an Excel-based platform. So just a little bit about the level of detail in the modeling. What we're trying to do is capture as many possible refinery configurations that we can and represent that at a process unit level and still remain generalizable so that you can look at a range of refinery configurations and you're not modeling any one specific refinery, but you're looking at the range of possible configurations that are possible. So as I said, we do this at a process unit level. I won't go through the details of this, but again, it is in your report that you can look at. The main thing is that we have sort of, at least three main categories of refinery configurations. So we have what we call the hydroskimming refinery which sort of takes in these blue process units. So it's a very simple refinery that does basic processing of lighter crudes. We have the possibility of looking at medium conversion refineries. And then we also have the capability of doing deep conversion refineries for heavier products that would be processed within the refinery. And so the user of the tool can go through and actually modify the refinery configuration that you would have going through that. We also have a crude assay inventory associated with this. So we've collected publicly available crude assays where it provides details that go beyond just the API, the density and the sulfur content of the crude itself and actually looks at the characteristics of each of the fractions within that crude. So we take that as input into our model and use that to actually dictate the flows through each of the process units, the prediction of the final refinery products, as well as the estimates of the energy and greenhouse gas emissions associated with processing that particular crude. So these are the same 30 crudes that Adam presented in terms of characterizing the upstream emissions. These are the emissions associated with processing these through just the refinery component. So you can see that we've got quite a divergence as well, comparable to the divergence and the variability that you see in the upstream emissions. And the different colors that are here are showing the different crews that go through different refinery configurations. So as a default in our model, we take the API and the sulfur content of the whole crude and use that to dictate the most likely refinery configuration that that crude would go through. So if it's a light, sweet crude, it'll go through a hydro skimming refinery. If it's a sour, heavy crude, then it'll go through a deep conversion refinery. That's just the starting point. You can actually look at processing these crudes through a range of different refinery configurations, but this is just the base case that we have that's represented. And you can see here that we have quite a bit of variability. We also have a range of crudes within our dataset that actually span that range of possible types of configurations that you might end up in and the types of crudes that would end up in each of these types of refineries. You'll also see if you actually try to compare the ranking that you get for the upstream, it's very different than the ranking you get when you look at the midstream emissions. So if you have high emissions at extraction, that doesn't necessarily correspond to a higher or a lower set of emissions at the actual refining stage. To actually explain the source of the variability, this is the same set of crudes in the same rank, but what it's doing now is it's actually breaking those emissions down into where are those emissions coming from? And so the black is the electricity emissions from the electricity that's consumed to process each crude. The green is the heat that's consumed. The blue is steam. And then the gray is the hydrogen that's produced to actually process these crudes to produce things like transportation fuels. And so you can see that the biggest differentiator between the emissions of different crudes is the amount of hydrogen that's consumed to process these crudes. So how much hydrogen you need to add in order to convert your crude oil into a lighter product like gasoline and diesel. So that's hands down the differentiator between the emissions and you can see that you get an order of magnitude or more difference between the light crudes versus the heavy crudes or the high hydrogen content crudes versus the low content. One thing that we looked at and became quite apparent is there's a lot of data about crudes internationally that are then characterized by their whole crude API and sulfur content. And what we found very quickly in doing this modeling is that the whole crude API and sulfur is not a good indicator of how many emissions you're going to incur in order to produce products from that crude. And so what we've done here is we've left the crudes in the same rank that they were presented in the previous figure. But what we've done is actually provided the API of the whole crude for each of these crudes. And so you can see that the rank that you get based on greenhouse gas emissions is not in any way the same rank that you would get if you actually rank these crudes by API. And if we do this for sulfur content as well as hydrogen content of the initial crude, we don't get any sort of correlation between the API, sulfur or hydrogen content of the whole crude. And so this leads us to believe that in order to characterize energy and greenhouse gas emissions associated with processing these crudes in a refinery, we need to go into a deeper level of characterization of these crudes. And so that's where we need detailed assays to represent the characteristics of each of the fractions of the crude and getting the distillation curve associated with that to really understand what the characteristics of those crudes are, to then predict how much hydrogen is required, how much energy inputs are required and things like that. So additional insights that we've found in terms of now that we've identified where the largest sources of the emissions are, we can look at how do we reduce emissions in this sector. And so because hydrogen is driving the differentiation between the different crudes and the amount of greenhouse gas emissions that you result, focusing on either reducing the hydrogen itself, the amount of hydrogen that's consumed to produce those products, or increasing the hydrogen production efficiency or lowering the greenhouse gas emission intensity of that hydrogen production, any one of those is going to tackle that big driver of the greenhouse gas emissions as well as things like carbon capture and storage from the most concentrated highest volume sources of CO2 within the refinery are also options. And we have been doing assessments on sort of the cost effectiveness of different measures to actually reduce these emissions. And these results that we're presenting here are really helpful in informing the types of technologies and the assessments that we would need to do to look for cost effective solutions. The additional insights then that we've got from this is that we're always trying to strike the balance between taking the systems level approach where we can look generally across the sector in characterizing emissions with also recognizing the fact that refineries are very unique, they're dynamic and they're complex. And so this balance is a very difficult balance to strike. We've spent probably twice as much time trying to evaluate and validate our model than we did actually building the model itself. So we've gone through a range of different activities to try and actually test our model and our results against other estimates and other types of calculations that you could do to estimate these things, but we're not done. So we're looking for opportunities to help continually try to validate and improve the model. And I can talk about some of the efforts that we've had towards that end. But the other biggest challenge that we see as we go through again is the data issue. So this is gonna be a common theme throughout the day today. Is that data is not readily available for the type of analysis we're looking for. We need two main sources of data. One is the crude assay information, the detailed characterization of the crudes themselves. And while a lot of that information is posted on individual company websites and things like that, it's not necessarily in a consistent format. It's also not necessarily in a format that would provide the details that are required to estimate energy and greenhouse gas emissions. The information that we need can sometimes differ from what's used to market a crude, for example, which is why primarily this data is posted on company websites and things like that. The other piece of information that we need are energy requirements from each of the different process units and real data of operating facilities. So through the life cycle assessment of oil sands technologies project, we've worked with individual companies and actually looked at confidential refinery data to help validate the assumptions and the representation of the units within our models, but there's not necessarily very much in the public realm that would allow us to do that. So that would be another area where the data for this type of modeling would be required. The last thing is just we're continuing to develop this model. So it's an ongoing basis. And one of the aspects we focused on so far has been the input crude. So actually looking at the characteristics of the crude and using a float case to determine the energy and greenhouse gas emissions associated with this. What we'd like to do now is also look at a fixed case where we can actually dictate the amount of gasoline or diesel requirements that you have coming out of a refinery and look at the implications of different types of products that you might be requiring. So as you shift in the types of products that are being demanded from refineries, what are the implications associated with that? So that's a current effort. Looking at more detailed assessments of refinery products. So our focus to date has been on transportation fuels. That's really been our interest in creating this model. But what we'd like to do is look at other products that are coming out of the refinery and develop the capacity to look at those in more detail. And then lastly, just doing some additional statistical analysis to really make sure that we're capturing the range of possible refinery configurations and different types of crews going through the refinery to better characterize both the variability and the uncertainty associated with doing this type of modeling. So that's the high level overview. I'll leave it at that. And hopefully we'll get some discussion and some questions later on. Thank you. Okay, thank you, Juul. So Debbie's now gonna talk about combustion emissions and transportation emissions associated with getting the oil from the refinery to the people who are going to use the products. Thank you, John. So together with, well, first let me say that it's really because of Juul's model, Prelim, that we get the slate of petroleum products, the output from that model. So we know how many barrels of product of all of these different products, gasoline, diesel, jet fuel, fuel oil, bunker fuel, all of these products that come out of the refinery. We get that out of Prelim. So having an open source model for the midstream, not only in terms of giving us emissions from refining, which is an important part of this equation, but in terms of allowing us finally to differentiate a downstream variability by oils by the products, unique product slates that these oils have capacity to produce. So using the output from Prelim, we're now able, and what we've done is develop a module that is able to look at the emissions from that full slate of products that come from a given oil. So Prelim gives us that capacity to do it. So together with our current Carnegie Junior Fellow, Eugene Tan, and also John Kumi and myself, we developed OPEM, which is an oil product emissions module.  But largely what it does, like I said, is it takes the outputs from Prelim and assigns the emission factors that are in the literature. It assigns emission factors to both the transport of these products around the world by a variety of different bunker marine vessels, by barges, by pipeline, you move product all around the world, and then it assigns the emission factors to the slate of products, and we get the combustion emissions. Not all, but most of what comes out of a barrel of oil is combusted. As Joel mentioned in the second phase, the refinery fuel gases that make petrochemical feedstock, which interestingly is the part of the oil industry that Exxon and BP Project will grow. And what's interesting about that is that's a high value product that doesn't produce greenhouse gas emissions as long as you don't have fugitive emissions of methane, but you don't burn it to get value out of petrochemicals who make it into high carbon fiber and heart valves and nail polish and all of these other things that we use. So the growing part of what we're gonna add in phase two of petrochemicals is a really important part of this discussion. But at this point, all of that is assumed to be used in the refinery process, so we haven't split out petrochemicals as their own product. So the assumptions in our model were that the, I should say, if it's not complex enough in terms of getting oil data, as both Adam and Joel have mentioned, the transport of crude and the transport of product is even more complex. There's absolutely no website on earth that a public, a member of the public, or a policymaker, or anyone who cares, can go to to say, I wanna trace this oil and know where it travels, both from the crude itself to wherever refinery it goes in whatever part of the world, and then from the products back around the world to wherever it's demanded. Not only those numbers not transparent, they change a lot. It's a very dynamic market. And it's also a changing market. It used to be the case that a lot of crude was refined close to where it was consumed, which is why America has the biggest refining infrastructure at the moment, the biggest refining complex. That's changed. What's happening now is products are really mobile. Things move around a lot. And in this price environment now, what we have is some products are going into storage because there's not a demand for them right now, and then they move twice. So they're going from the refinery to storage somewhere halfway around the world, and then they're going back to demand centers maybe months later. So we're even seeing kind of transport times two from some of these oils. That said, we don't have a great handle on the transport emissions. We did a very basic case of transport because there's such a lack of information on how crude and product moves. What we assumed is all crude in the world goes to Houston. It's all in Houston. Just imagine it's there. And justified by the fact that Louisiana and Texas have the largest refining capacity right now in the world. So all crude goes to Houston. That crude transport is captured upstream. So Adam's model has transport of produced crude to the refinery gate. So that's in Adam's model. And then what we have is from Houston, where does it go? Everywhere. So what we've assumed is the US is still the largest consumer China's catching up or has caught up. But basically that product as a very lower bound of emissions, that product moves to New York, to the harbor in New York and then couldn't go anywhere. But instead of going anywhere, it's just moving to like the Boston DC metro area. So it's a very small look at how products might travel around the world. Given those very limited movements, transport is about a 2% of global greenhouse gas emissions. So transport is pretty low. If you assume a very small limited movement, what we're curious to do in phase two, and the model that we'll go through later has a lot of user definitions for this. So we can play with it. Does it go by pipeline? How far does it go by bunker, by marine vessel? So you can play with it. We will see if that doubles or even a little bit more, but the reality is the transport of product is pretty small when it comes to the total greenhouse gas footprint of any given oil. That itself is a really interesting finding because it means that from a climate regime, which means from an economic regime, it's moving because it's cheap to move it. We use very bottom of the barrel low quality like bunker fuel products. It's pretty easy to move these oils around the world and back. It's not unusual to have a crude start in Russia, come to the, or maybe Russia's a bad example, start in Saudi Arabia, come to the Aramco refinery in Houston to be refined and then have the product go back out to China. That's the world that we live in now. It's a very mobile world. So there are greenhouse gas implications of that, but there are probably more geopolitically strategic and other environmental concerns about that. So that's downstream. The bulk of the product, the bulk of the emissions downstream obviously are on combustion. That is, everything here is moving toward product. So the bulk of the emissions are on how much of this oil, how much of the bottom of the barrel is combusted? And is there, the ultimate question then is, is there value to be found in oil someday in the future that's high value like petrochemical feedstock that's not combusted, therefore not greenhouse gas emitting, but has high value? Because that is one major opportunity for the industry to have value over volume, that you really start to put a smaller volume of product there, use less and just burn it and use more in high value places that doesn't lead to climate change. So I have here just quickly motivations that I mentioned was these before. We wanted to include the full barrel of oil, everything. We wanted to use petroleum product output from prelim calculations, which I mentioned, and we wanted to show that the product slate is very, it's variable. Here, you know, up until now, it was assumed a gallon of gasoline or a gallon of diesel came out of every barrel of oil, which meant there was one number for combustion. It turns out that the combustion emissions in our model, I think this is the next slide. No, oh, let me just go through this. This is the, these are the total, these are the downstream emissions going by types of oil by category. So these are the downstream emissions only from transport and combustion of products that come from those oils. There is a 50% variation, but as you can see, largely it's the extra heavy oils that have the high carbon in them, that going to market, and then there's a smaller variation between all of the other oils, but still about 100 kilograms of CO2 per barrel. So there's still a significant amount. As you saw, Jules, 100, Adams, 250 kilogram high ends, there's still about 100 kilograms of carbon per barrel of oil between oils that other than the extra heavies that have varying products and you still have potential to get carbon out of those varying products that differ by these oils. So there is no one number for combustion, which I guess is my bottom line here. There's a variation like there is an upstream that you saw with Adams Opti-Model, like you saw with prelim variation emissions between oils, there's also a varying emission profile between oils when it comes to the full slate of products that any given oil puts out and what the combustion of that oil results in. And it's that variation that brings up questions about demand and use and how do we go here to reduce greenhouse gas emissions. So this is my final slide. It just shows you together, blue is downstream emissions, which I'm talking about, gray are Jules emissions that she was talking about for refining and green are Adams emissions in terms of Opti's upstream emissions. So you can see the full gathering of all three parts of this model, upstream, midstream, downstream, from any of the given 30 test oils that we went through. And what's interesting here to note, and you don't see, you'd have to run through the calculation, so I'll tell you the numbers, upstream emissions vary from five to 33% of total emissions. So the range for any given oil in terms of its total emissions, the share of its total emissions that are captured in that extraction process of pulling the oil out of the ground and producing it, range from five to 33, 5% to a third of emissions come from upstream. The midstream emissions range from 3% of total emissions for any given oil to 15%. So again, a lot of variety here. And then the kicker that was really surprising to me actually was the downstream emissions, which again, up until now have been dealt with one number to stand in to say, oil produces downstream this many emissions come out of a barrel of oil, 60 to 90% emissions, 60 to 90% of the total emissions from any given oil are captured in the downstream. So again, huge range of emissions given the oil that you're going to take out of the ground and then ultimately get into product to burn. And what that tells you is it would be really smart to know ultimate demand for the oil. If you're gonna make a lot of petrochemicals in the future and not a lot of gasoline, you're gonna go for a different kind of oil if you're going to wanna reduce greenhouse gas emissions. So if greenhouse gas emission reduction is a goal, which I will add is not a goal in this enterprise right now. If greenhouse gas emissions are added to this kind of, this make it a three-pronged approach, we have the economics of oil drive a lot of oil decision-making. The geopolitics of oil clearly drive a lot of decision-making. You can't argue with either of those. They just do drive what's going on in the market. The third rung is climate change. And if you add that prong of climate change and you have a sense of where the world is going to demand product, which is the downstream, you then have a different strategy for what types of oils preferentially from a lower greenhouse gas point of view make certain products. So you can get really smart in terms of where this market is going and how to get there with the smallest greenhouse gas emission footprint, which is really what this project is all about. A lot of choice. There's a lot of choice in the future. There are many opportunities to reduce emissions both in processing, in extraction, and then ultimately in planning and matching supply and demand, not just from an economic point of view, which is what we do typically, but from an externality point of view, from an environmental point of view, matching supply and demand will really matter here. So I think with that, we'll hand it back over. We have plenty of time for discussion. So we went quickly. So I'm gonna take the moderator's prerogative and ask a couple of questions to start. But when we go to the audience, is there a microphone that will be passed around? Okay, there's a few microphones in the back. So the ground rules, please state your name and your institution and ask a brief question with respect to everyone's time. So no soliloquies, please. So let me ask a couple of questions first to kick us off. We talked a lot about the need for data. And so what I wanted to ask each of you for each of the components that you've presented, what are the one or two key areas where you would like to see better data? And then what institutions would you ask that question of? Like who might be in a position to give you that better data? Adam? So as I mentioned earlier, I personally think one of the big opportunities in the data front on the upstream, as we saw really at that high end of the upstream emissions are the flaring crutes. And so where I think the biggest opportunity probably is additional work on the remote sensing and verification of flaring emissions around the world. There is a satellite that was recently launched in 2012 and updated improved satellite launched by and operated by NOAA, National Oceanic and Atmospheric Administration. We're starting on a, I've got a master's student working on verification of the accuracy of the results of the satellite, in particular for looking at reason with small and medium sized flares in the Bakken. Not to put too fine of a point on it and the results haven't been peer reviewed yet, but we're not seeing overly, we're seeing some issues with the accuracy of how good the satellite is basically. I've seen some of these flares, which actually raises issues for how good is it? If we know, for example, we're using a suite of thousands of wells in the Bakken that are flaring every month, right? And we can use this to ground truth, basically with the satellite seeds. It's not seeing what we're seeing so far is that it's not seeing a lot of these flares, it's not seeing them consistently. And it's actually underestimating them when they see that, when it sees them. Again, these are very preliminary results, but it's raised lots of questions for what's happening in Russia and other parts of the world where we don't have this ground truth data to check. So are we underestimating flaring emissions globally because the satellite only has a certain sensitivity? So I think there's a lot of potential collaboration there with federal agencies that are working in the remote sensing earth sciences area to try and get a better sense of what I think is probably the most important global driver as this flaring issue. With respect to the other big issue is probably the thermal recovery and the use of energy for generating steam. That tends to be somewhat more available because it happens in a lot of regions that do have good data reporting, Alberta, California, but there could be more global data availability on that. There is an increasing move in the Middle East, for example, towards heavy oil recovery, and the information aren't presented from those countries. And so as heavy oil production spreads, this relative availability may become less advantageous in terms of the thermal recovery side of things. Great, so as I mentioned, there's sort of two main areas of data collection that we would like to see. One is on the crude assay side and in that we do rely most heavily on company data that's presented on individual company websites and the issue is just more about consistency in terms of the actual fractions, the temperature ranges, those kinds of things, but also in terms of the comprehensiveness of the assay. So we're still sort of trying to understand why do some fields get reported on all company websites versus others where we can't find any data publicly available for things like the Bakken or Eglford. And so the consistency of reporting that kind of thing. On the other side is the refinery energy data and for both of these types of data, that information resides with the individual companies that are operating these facilities. So the nature of the data that we would need is very different from Adams. We can't use satellites, there's no technology fix that would allow us to collect this data. It's more about how do we engage with industry in a way that they can provide helpful information without actually divulging anything that's sensitive to their operations. And I think that that's a difficult balance to strike. But again, it would need to be at an international level as well because we need that information coming from different operations around the world. We could start in North America and so we could look to agencies like the EIA, for example, but I think it ultimately would have to be an international effort and I think it would be a challenge to do that. So I'm sort of recognizing the fact that that's gonna be tough to do because it is residing in individual companies and a lot of that data is close enough to really sensitive information about how these refineries are operating that it will be a challenge. Well, I will definitely underscore Jewel's assay request. We even met with the Department of Energy about a year ago now when we were embarking on this or in the midst of the project thinking we can get a lot more data from DOE. And it turns out that the assays just aren't consistent, aren't really collected properly and that since that gives us the product slate which is all about combustion, it's really important and I'd say that that assay data is the single most important thing to the downstream part of the oil climate index. But I would also put in there emission factors and I have to thank and call out Michael Ang doing the Greek model because there is transparency out there in terms of emission factors which is fantastic, EPA is one of the main agencies but there are many that actually publish these and update them. But I would also say that as product quality changes, diesel's a good example. Some of the world uses a high sulfur diesel or a bunker fuel. Some of the world uses a much lower sulfur. These products change over time. So keeping emission factors current, global warming potentials is another question. Those change over time with IPCC. How do we treat the methane emissions, the associated nitrous oxide emissions? That matters to the bottom line of the greenhouse gas impacts from these products. So keeping emission factors up to date and keeping, having more of an eye toward the fact that these things aren't expected to be fixed in time because they change as the actual product changes itself will hopefully have regulators continually re-looking at emission factors as opposed to lock that one in. We know exactly what gasoline gives us. How many grams of CO2 equivalent per barrel when it, in fact, as gasoline or diesel change, those emission factors change. So I would like to see that remain very dynamic. And the last one, although I think it's probably, like I said, the least important part of the oil climate index is the transport of oil, which is, or product, which is a bit counterintuitive to people. But I do think for many different reasons, having more transparency and data on product movement, oil and product movement is going to be an increasingly important thing for the world. And I don't know if that means traders. I don't know if that means governments in concert with traders. But the reality is if you go, I know BP has its oil movement. So it has a map of the world and it shows crude oil movements in total numbers for the year. But it does, and it goes from location, has arrows all around the map. You can find it on their website. But it never says anything about product. It's just assumed that wherever the crude flows, the product flows, which we know isn't true. So having much more transparency of product movement I think is going to be a necessary thing. We've had ruptures of rail lines. We've had rupture, I mean ruptures of rail cars, but even more importantly ruptures of pipelines. And I had a reporter ask me yesterday, she said a couple of years ago, the rupture in Arkansas. I tried to do a story on this and I was trying to figure out what oil was in that pipeline. She wasn't doing it from a climate perspective, but she was doing it from writing the story of what's in the pipeline. After investigating it for weeks, she couldn't get any information on what oils, plural. It wasn't one oil, it was co-mingled. There were a lot of different oils in the pipeline. And so this becomes a very community-based issue as well. What's going through my backyard? What's actually in that rail car? What's actually in that pipeline? It matters and it will matter. So I think the transport and transparency on transport, it's going to matter both for our work, but I think for much greater purposes. Yeah, so one more point on the assays in particular. There is a safety component as Debbie just mentioned and understanding the characteristics of oils that are going through pipelines or on rail lines is important not just from an energy and environmental emissions perspective, but also from safety perspective. And so that's one potential driver for increased transparency is to focus on, we need to know what's in this oil because if it's a more volatile oil, the likelihood of a catastrophic accident is higher. And so just understanding that from a safety perspective may also give us better data to do the energy and environmental impacts. So let me ask one other question. And Adam, we've talked about the uncertainties associated with upstream emissions in different fields where you'll have a single field with multiple companies in the field and they will give you very different numbers on the emissions associated with that particular oil. And so what I'd like each of you to talk about is where you think and what are the one or two greatest sources of uncertainty in the components of the total emissions that we've been, that each of you have looked at? Yes, this uncertainty is a very important question.  So we've actually published three papers on uncertainty now. The first one was the verification. So we compared our model to other models that exist. And then we had two papers where we looked at uncertainty either at a field level or at a basket level. And I'll talk about what that means. At a field level, what we did is we said if we want to predict the emissions from a particular field, let's say California midway sunset, which is the fifth from the left there. If we want to predict emissions at this field, what actually matters? What do we actually need to know? So what we did is we took eight key parameters and we either selectively drew them from an uncertainty distribution or learned them. That is, we fixed them to a fixed value. And we saw how the basically the uncertainty shrunk as you fixed different parameters or learned them essentially. What we, and this enabled us to basically determine what factors are most important in reducing the uncertainty envelope around a particular crude, which was very interesting. And there we found perhaps not surprisingly things like API gravity, steam injection rate, et cetera, were really important drivers of uncertainty. We also looked at the uncertainty in a basket of crudes. So this is very important for regulatory efforts like in the European Union or in California. And as it turns out, the uncertainty in a group of crudes, and we looked at the uncertainty in a basket of 30 crudes, which happened to be these 30 crudes, is much less than the uncertainty in any particular crude, which is actually good from a regulatory perspective. There's sort of an evening out of or a canceling of some of the uncertainties associated with aggregating crude oils up into sort of a regional or nationwide average crude basket, which is nice to see. In terms of what I'd like to see to better characterize the uncertainty on a field-by-field basis, there's a couple of things that come to mind. One is that a lot of data are collected, but they tend to be pretty messy if you dig into this. I have a PhD student. I'm worried I'm gonna lose the guy. He's very smart guy. He spent literally 12 months of his life digging into the data sets from North Dakota, which North Dakota collects a lot of data. This is a study for Bakken funded by Michael Wang at Argonne National Lab. It's just forthcoming, really detailed analysis of the Bakken. What we found is that when you looked at, for example, how much water was injected in fracturing, and you looked at the North Dakota data set versus the data sets reported to organizations such as FracFocus that collect this data on an independent basis, huge variation between what a single well would report to these two different databases. Ostensibly the same well, the same fracturing job, really different information. And this is not the only instances where we've seen where data sets that should align when you dig into them and really try and do it carefully, they don't align as well as you'd like. And so one effort on the uncertainty front would be to improve the accuracy of data collection and reporting in the verification of data. We found a lot of errors where the numbers were almost the same except the person entered an extra digit into one of the databases. And so you grew the number by a factor of 10, stuff like this, right? And this really ends up mattering. And so it sounds mundane, but just improving the quality of the governmental data sets that are recorded already. And then obviously, as we mentioned earlier and as John just alluded to, where reporting is uneven, I mean, this swamps all other sources of uncertainty. So if you can get characteristics for a particular field or a particular part of one field, but not another field, you end up using a lot of default values. And so, and that drives a lot of the uncertainty. So moving towards a more consistent data collection would help on the uncertainty front and then moving towards just better quality of data verification and vetting of data that's reported to various government databases would help as well. Great. So on the refinery side, one of the things I like to think about when I look at these types of aspects from our model is the differentiation between uncertainty versus variability. And I would say that hands down, the variability is much, much higher than the uncertainty associated with the calculations that we do. In terms of uncertainty, we have uncertainty that are sort of typical sources of uncertainty that you have in life cycle modeling where you're taking large amounts of data from disparate sources pulling them together and doing calculations on that data. So we definitely have that sort of level of uncertainty that you would typically see in a life cycle model. In addition, in terms of the assay information, because it's reported in different formats, oftentimes what we have to do is actually transform that data. So for an example, we need nine cuts to separate the crude into nine different fractions. Often that data is reported, say, with four fractions. So we need to use transformation methods to sort of separate out those four fractions into the nine that we need. And obviously in doing that sort of transformation, we're gonna be introducing uncertainty into the process. So that's another one. Another one is what we're finding is across the different companies, the error associated with the data that they're presenting is different. So some companies, and it's not clear to us whether it's the method by which they're actually characterizing the crude or whether it's just the way that they're presenting the information, we get inconsistencies in the data such that different companies assays will introduce a different level of error into the model. Those typically tend to be less than 5% in terms of the overall errors on the estimates of emissions. But that number is different for each company. So that's another example of the type of uncertainty that we might see. In terms of the variability we see, we can see quite a bit of variability for crudes for example over time. So seasonally in terms of different market forces are going to drive different blends of crudes and things like that. So when you take an assay from a pipeline in September that assay might look very different than an assay from that same pipeline in say April. And so we get variability about those kinds of things. We also get variability where we try to match up a field that Adam has characterized with an assay that comes from that same field. There is often a disconnect between those two because we might be sampling a crude from one aspect or one area of that field and Adam might be representing the production emissions from another part of that field. And for some of these fields, they're very, very large. And so you can see very different estimates in terms of the quality of those crudes. The other part is the uniqueness of the refineries. So that's another aspect of this is that we have sort of 11 configurations that we can represent that doesn't represent a full suite of all operating refineries. And so different combinations of process units operating with different capacities and things like that can also influence the results. But again, I would say that that's variability in different ways that you could operate the refinery as opposed to uncertainty in our calculations. So in addition to the transport which I've just mentioned before, I would say the two things that really come up for uncertain downstream emissions are the float versus the fixed case that Joel talked about. Again, a float case says we're gonna run this oil through a refinery and do what the refinery can do best with that oil. It's kind of saying this is an engineering optimization around that oil. And the fixed case, which is much more what likely scenario, especially in a high demand environment, in a low demand environment, it changes a bit, but in a high demand environment, the market wants certain products. So it's more fixed. It wants this much gasoline. It needs that much diesel. And so the uncertainty now with the downstream resides in the fact that we've kind of let these oils go where they optimally will go, but it will be curious in phase two to see how these downstream emissions might shift. And I don't expect them to shift a tremendous amount, but how they might shift in terms of the fixed case. When we're saying we want this much petrochemical feedstock, this is what the world demands. We want this much gasoline. This is what the world demands. And we'll see how that changes downstream emissions a little bit when you've fixed the demand of these products. The second one that I'd say is probably one of the most uncertain parts of the downstream, and we actually have it. You'll see it later on the web tool is the use of petroleum coke. So petroleum coke is the bottom of the barrel when it comes to product. It's the carbon that's left over when you've done the best refining job you could possibly do by wringing out any bit of liquid that is in this to make a product, you end up with a solid process, a solid product of almost pure carbon and high impurities that is higher in CO2 than coal. And it comes mostly out of the heavier oils. It comes out of every oil a little bit. The more carbon that goes in with an oil, the more coke you get out. And so the big uncertainty in our model is is that coke burned? Or isn't that coke burned? And sometimes it's stockpiled and waits to find a market because it's a low value product and it competes against the price of coal. And sometimes as we've found in Houston, it's shipped to Asia and Asia doesn't even account for it. They don't even know that it's coming in from the oil barrel. And so we're doing some work with, from this point on with the Carnegie Xinghua Center, our partners in Beijing because China's been a huge recipient of pet coke from the US. In other words, we are shipping carbon to China and no one's talking about it as we're cutting our own climate emissions, talking about reducing greenhouse gas emissions. And the reason is probably less that it was like a devious thing to do. It's much more so that it's economically valuable to burn something cheap. It's very degraded and it's not really accounted for as oil by the time it leaves as pet coke. It goes into the industrial sector. It doesn't go into the transport sector. So the fate of pet coke going into utilities around the country, around the world rather, is a really big question for the heavy oil emissions. If you don't burn the pet coke and you think of a really creative thing to do that's noncombustible with pet coke, I mean, I think sometimes high carbon fiber if you can get all those impurities out, but do something really benign with the bottom of the barrel, you can actually reduce the downstream emissions from the extra heavy oils quite a bit. Okay, so I'd like to turn it over to the audience for questions. We have a few mics floating around. So this young lady is first. So please identify yourself, your institution, and then a very short question, please. Hi, my name is Mariah Dieters. I'm with the Energy and National Security Program over at CSIS. I first of all just want to applaud all of you for a really fantastic job. This is a very enormous undertaking with a lot of uncertainty. And I know myself trying to find data on oil anywhere outside the US is nearly impossible. So I really commend you on what you've done and the report that you've produced. My question is actually for Ms. Gordon on the downstream emissions. And I was wondering how much the new uncertainty with regard to the changing regulatory framework in the marine transportation sector has affected your findings as the market and the industry is kind of responding to the new regulatory frameworks and the installation of ECAs and the demand profile is changing accordingly. How much of, not only the fuels that are being transported, but the fuels that are being used for transportation and that changing demand profile affected what you found. Yeah, thank you. Thank you, that's a great question because of marine transport of petroleum products. I mean, when you're talking about moving transcontinentally, especially ocean bearing, you're stuck with marine vessels. So that is the way that the product moves. Over lands, you have pipelines, but ocean bearing, you're largely in these marine bunkers. And bunkers are really difficult because they're international. So when you have things that happen in international space, it's much harder to track them and verify them. And so I applaud movement in this area. It's been a long time coming to get a better handle on marine bunkers and the tankers that move in the oceans. But this goes into what I was saying earlier that it will be increasingly important to update emission factors and have a much better sense. We actually use lower sulfur bunker fuel now here, but a lot of the world doesn't. So what we don't use sells at a premium elsewhere. You know, it isn't as if it doesn't necessarily get produced, it's that high sulfur bunker fuel will find its way to Asia and will be there. So we have to be, I think, much more uniform in terms of trying to, if you're looking at opportunities, in terms of reducing the emissions from the transport, especially at bunkers, of this bottom of the barrel fuel source, bunker fuel, I think that it's going to take some sort of global international regime to deal with an international area. But it is, I do think that dealing with both aircraft and also with marine vessels has been a long time coming. And I think it will only help illuminate what we've been doing and making the model smarter. Over here on the right. Thank you, Daniel Hoopman, Johns Hopkins. Also thank you very much for this kind of work and making this data publicly available. I think it will be very helpful for many researchers. Mike, you mentioned that you want to extend it from 30 types of oils to about 50. Did you think about also including a couple of different biofuels in there? Because it would also make it for a very interesting comparison on the production side, so different feedstocks for biofuels. You have blending at the refinery, at the midstream level, and then biofuels are also transported. So you have different types of emissions. So I think it would be very well complementary to the kind of work that you have been doing. Thank you. Do you have anything, Adam? I can tackle that. So you're sitting directly behind the world's guru in comparing oil and biofuels. So that's Michael Wang who runs the Greek model at Argonne National Lab, at least the US guru. These models are pretty detailed, so they're very focused on oil and gas operations. We've got gas separation and compressors, re-injecting, hydrogen sulfide, and things like this. It's very, at least my model, and I think Joel would probably say the same, it's very oil-focused, and so it doesn't lend itself really well to integrating biofuels directly into our modeling effort. I think how that could work best would be for us to collaborate with folks like Greete or the Canadian Equivalentist Model called GH Genius to basically work our modeling methods into the way they treat oil, and then they already treat biofuels in really incredible detail. I mean, lots of information on biofuels in those models, and so that would, I think that'd be an easier way to do the biofuel comparison than certainly trying to change my model. I can't imagine the effort involved in trying to put biofuels into our model, but it might be easier on the refinery because it's a blending issue, but yeah. Yeah, so there are a number of possibilities for additional analyses that will feed out of this, so we have all this data for these oils that Adam and Joel and Debbie have analyzed, but this is one of several areas where we're looking to extend and do additional analysis as an add-on to what we've been working on. Yeah, I'll just add the need to do something like that, so I think that we've kept these, from a research community anyway, we've kept these things very separate, so you're either a fossil LCA person or you're a biofuels LCA person, and I think that while it would take work and effort, I think that at least sort of looking at different models that have modeled these things in detail would be really important to do, and I think we need to do more of that. And I can just add, Adam might be able to elaborate slightly, but it was interesting in a meeting with the California Air Resources Board about this a few weeks ago, it became clear to us that there is a lot more regulation around a gallon of biofuel, so in other words, California has to really go through a lot of hoops to show and prove and offset its emissions for that gallon of biofuel, which ironically is like the breakthrough here, you know, the alternative, whereas the North Dakota Baughan that's being railed into California now, often from very high flared facilities with high greenhouse gas emissions, is kind of hidden in an average. It's just not really, so there's not a level playing field in terms of these, and so it will take probably more integration over time or at least other researchers building out around this to compare where biofuels or other alternatives fit. Okay, so let's move to the other side of the room. Nancy, I think you had your hand up earlier. Nancy Skinner, UC Davis Energy Institute. Given that there's 30 oils characterized in the model, if you have a state like California, which is already setting policy around trying to meet certain GHG goals and quantifying that, what policy type of direction would you give any entity but a state in particular who's trying to utilize something like this given that it's only partial? I've been working with the state of California for some time. The person who is doing this for the state of California, his name is Jim Duffy, incredible guy, putting a ton of effort into this and really helped with our model development effort. CARB, yeah, he's there, resources board. The challenge with using this in regulation is as follows, so this is 30 crudes done at a significant level of detail, more so than most of the other studies that have been done, linking the downstream refining with the upstream. So that's really the novelty here. Jim has run 300 or so fields for the state of California. California crudes, through OPG. Through OPG, yeah, through OPG, not through prelim. California crudes are about 40% of California consumption and we've got really good data. Got good data in North Dakota, Alaska's been very cooperative, et cetera. There are stories and so we think maybe two thirds of California crude is very well characterized, 60%. The remaining comes internationally from places where reporting is very poor and so I think the issue with using this tool in regulation is that there's no requirement when a crude comes into California, a crude tanker comes into California, little information is required to be reported about it. So you can imagine a regime where essentially a post-it note is literally, the motivation when we developed this tool was that we wanted information that could be written on sort of a three by five card or one piece of paper that is 20 pieces of information, five pieces of information, 50 pieces of information at most, numbers that you could write down and record for every crude that would give you, once you plug them into a model, give you a good estimate. So right now there's a lot of tankers that come into California or Houston or wherever where little to no information is provided about these crudes and so I think from a regulatory perspective, a requirement that if crude, and may I be so bold as an academic to just suggest that this happened, I don't know how this would happen, but a requirement that if you're gonna bring, a couple hundred thousand barrels of crude and in on a tanker, that's a lot of money, please a fix a post-it note to it that says, how much water was there, were you steaming, how much did you steam, did you flare, right? Just collect this information so that at least the information is there and then folks at DOE or whoever else could use our model or another model and the transparency can basically the whole, you're lifting the whole level of sort of data transparency up and then that enables all sorts of whether it's the little carbon fuel standard or another regulatory effort, you're just basically increasing the level of expectation for you bringing crude, please tell us these 10 things that are important. Yeah, sort of, yeah, some sort of disclosure requirement that really enable, and I'm not saying our tool is the way it would be implemented, but it would enable any sort of effort, yeah. I'll just add quickly on the refinery side, regardless of how much data we're able to collect and things like that, we will never be able to report an estimate of a specific refinery and a specific crude to four significant figures. That's not the ultimate goal of this tool, so recognizing that in terms of informing policy, it's important to know the objective of the tool is to look at the relative differences of different crudes and the range of possible performance that you might get out of a crude depending on the range of configurations that you might put it through. So I think that's really important to emphasize in a policy context that it's not going to be tracking things to four significant figures, it's going to be the relative differences between, yeah. I would second that for Opti. We remember the estimator part, we can't do this to, we're not measuring the speed of light here, so. Okay, Jessica, you had a question in the back. Yeah, Jessica Matthews at Carnegie. Yeah, Debbie, have you thought about, it would seem as though an agreement, an international agreement to develop standards for consistent and comprehensive universal data recording would be a natural and relatively easy outcome of Paris. And maybe set up a IEA, IPCC working group for something that would develop the standard. Have you thought about that? Is there time to push that in advance of Paris? That's an excellent question. What I have been, and I have been thinking about that and I've been thinking about the loss of the Office of Technology Assessment. I think that there are institutions that we used to have in the US that could have accelerated this at light speed and having some sort of standardization practice set up to the point where ultimately, where we started with this was, could these oils be tagged? I mean, the post that Adam is talking about, if it was standardized and disclosed and consistently reported, and there's no favoritism here.  Your underlying inputs to that oil, these oils that are co-mingled in pipelines and rail cars and marine bunkers, they could actually be biotagged some way so that ultimately, even if it's for security purpose or even if it's for piracy or whatever it is, there are reasons to actually think of a standardized way to and impose a standardized way that this reporting comes out and these oils are marked as such. I think it's gonna help a tremendous amount in terms of climate management, but other things as well. That's already happening for folks that we're gonna try to track the oil being sold by ISIS using chemical fingerprinting. So this is not, yeah, this can be done. So, but my question really had to do with preparations for Paris. Oh, for Paris. For Paris. I mean, this is the opportunity to get an international agreement. And an agreement to put together a group to develop those standards as an international agreed goal would be something that would be a, it's a likely achievable outcome. And a, you know, not once in a lifetime, but a very rare opportunity to pull together, I mean, to do this in a global setting. Yeah, and I think you are exactly right because when I think about the U.S. driving a lot of these new oils and having the capacity to do this, together with the agreement with China, who's the world's largest importer of oil and cares about climate and has the capacity and the desire possibly to see what oils they might wanna choose to import over time or develop themselves, this might be a perfect opportunity to do it. I don't know, we absolutely can carry it there and I'm already planning to be there, but you know, we will have to get hopefully meaningful participation or not being pulled back by the industry. I would love to see the industry, you know, embrace transparency here. Yeah, so I think that the argument is that there's enough variation in these oils that you need to do this sort of tagging and there's other reasons for creating the post-it note, namely safety being the primary one. So there's a lot of good arguments for more transparency in the chemistry and where the oils come from and I think you can make a good case for that. The question of how that gets to the people who are doing the negotiations is for someone other than me to answer it. Here, there's a mic behind you. Ken Dillon, Siancia Press. You mentioned that petrochemicals are slated to become an ever more important percentage of the entire market. I wonder if you could give us some notions about what the current status is and what it will be, say, in 15 or 20 years and what kind of efforts might be made to extend your model or to get more specific about aspects of that petrochemicals market. For instance, what kind of oils might be most adaptable to the new types of products being put out in the petrochemicals market and the whole question of whether, okay, maybe there's not gonna be so much emission of greenhouse gases, but maybe some of the products that are coming out of there are plastics and so on might be even more damaging from an environmental point of view. Well, certainly the petrochemical questions are far beyond climate change. There are water issues, there are disposal issues. I mean, there are many issues of petrochemicals, but looking at it from a climate perspective in terms of a noncombustible use of hydrocarbon, the petrochemicals generally come from the light ends. So it's the lightest hydrocarbons, the top of the barrel, so to speak, that makes petrochemical feedstocks, so the lighter oils make the most petrochemicals. So there's that affinity between lighter oils, the oils that just have higher API gravities will make more petrochemicals. I didn't study, I just pulled it up for this so I can easily send you to the new outlooks that the oil industry is looking toward in terms of the outlook to 2040 on petrochemical, new petrochemical demand moving ahead, but I think I'm trying to remember what it was. I wanna say it was a doubling the petrochemical use in that part of the barrel was something like a doubling. There was a very big movement toward petrochemical and commercial transport. And largely because the US and Europe and China have very successful fuel economy standards.  It means first and foremost, there's gonna be a thought of how to use oil differently. So that's part of the shift that's going on here. So it's like the proverbial balloon, you push your hand in one place and you have fuel economy standards, which are fantastic, but you still have to manage oil supply. And that maybe goes to just answering a different question, but fuel economy standards are necessary in this regard for reducing climate impacts from oil, but they're not sufficient because we'll find them coming out other places. And hopefully we'll direct it to less greenhouse gas uses. And one following point, I think the obvious thing here also with petrochemicals is to look for win-win. I would prefer to see the oil go into some sort of advanced super strong composite that can make our cars much lighter. This is the Amre Levin's Ultra Efficiency. Let's use that oil to make super advanced carbon fiber cars or something like this, right? That are safer, much more fuel efficient, much more advanced, and then we use less oil and then that oil ends up in a material rather than being burned. And so I think looking for ways to kind of get some synergy is the way to handle the petrochemical side. Okay, so I think, let's just take one more quick question and then we can move to the break and we will have another panel at 11, is that correct Debbie? Yes, there'll be a break, a break and then we start again at 11, oh no, 11.15, I'm sorry, the breaks now will be in a minute and then we'll come back at 11.15 and we'll do the web tool, which is gonna be fun. Okay, so one more question. Michael? Thank you very much, I'm Michael Wong from Argyle National Laboratory. First comment on the biofuel bladding issue. I think the scope of this project to look at a barrel of hard carbon, not as a bladding effect of other fuels. So stay with your scope to look at this hard carbon barrel. Of course, when you have bladding, you may have some changes in refiners. Like if you have E-turn, you're going to have RVP or whatever and you're going to have some complications in refiners but it's not going to overwhelm what you're doing. And for Adam, I have a question. This is the more comment for you to consider in phase two. That is, I did not see offshore oil recovery from your 30 year crude types. And I have not seen specifically CO2 enhanced recovery. And if you add CO2 in, what's the liquid rate of CO2 and you need to build that into your carbon calculation. So it could have some different implications. For Joe at downstream Deborah, the key problem is the products slide together with the crude asset. So these two, you'll work together to generate the midstream and downstream carbon density. The petrochemical area, my comment is, don't be too optimistic that we're going to move more to petrochemical from crude barrels because we have nitric gas. And the nitric gas kind of gets into petrochemicals, especially nitric gas liquid. As you see, your large petrochemical plants now are building in North America because cheap nitric gas. So my bet is we're going to still produce significant amount of liquid fuels from crude. And for the more in terms of the product slide, there is a difference between Europe and North America refiters. And if we're not careful, people can get into the gaming area. That is, if you look at European refiters, they produce two units of diesel, one unit of gasoline. We produce about 1.5 unit of gasoline at the one unit of diesel. So the diesel gasoline shield is different. And for the more, the heavier part is very different. We produce maybe about 5% of residual oil. They produce close to 20%. So when you add that, you know, suddenly the European refiters looks great because they just, they're not as complicated as US refiters. So that's where maybe in phase two, you can look at the general product slide at the built, that's in, so people would not get into gaming. At the final, on the next question about policy, I'll be quick on policy implications. So look at this chart on the screen. What does this chart tell us what we should do? But clean production for upstream, that's very clear from Adam's result. Then when you look at the crossbow of some types, what are we going to do? Are we going to insist as a clean production at the bottom of your better accounting for different crude carbon footprint, as they think about some kind of your policy for different crude at the different companies to have some carbon trade. So that's where I think you can think further to come up some policy recommendations to use this chart. Okay, so we'll, I think we'll leave it at that unless anyone has a, because we're going to get into more detail on these modeling questions and the applications of the models in the later session. So Debbie, are we back at 11 or 11-15? 11-15. So we have a break, we have coffee outside and we'll be back and we'll set up and we'll have the animated tool when you come back. And if you have any questions that were not answered in this session, please come up and talk to us during the break. Thank you. Another such demand from foreign nations to foreign electricity, like that's a source that's really growing. It's like it's essentially a huge more energy standpoint as opposed to interplace it somewhere electricity generation, local waste and so on. And so any major, any major block of electricity is going to be offset by some other things that we can all accept. So there will be for every bit, I mean, it's gas versus coal versus new coal. It's just nuclear forces that go up in here. So the two options, there's a lot of combustions, it's just hydro or it does, you know, we can think of another way to deal with it to make it responsive to you. And that's what it's like. And it does, you'll see in the world that it shows a very big difference in the amount of electricity that's going to be generated. It's just a lot of electricity. Yeah, I mean the function is there, but you could have it on the, yeah they've got a way to do it somewhere else. Yeah like that too. Yeah, that's right. There's a array of things there. So actually what this looks like right now is whatXT is the now. It looks like a lot ofytes that are going out and running out of electricity. There's a number of other types of heat that I think are likely gonna build otherblock caps like diabetes and that can stubbornly curve over charges state types of things. And it's lots of things that there's going to see So you can get financial support and take advantage of the amount of money that's available to you as a client. There is no such thing as a client. So how are you staying at the bottom? Why is this so important? Any other questions? Do you have a fixed name yet? Yeah, I do. Fixed name? Yeah. Do you have a fixed name yet? Yeah, I do. Hello. No, I'm not. No. I can't hear you. Sorry, I'm not. A fixed name? Yes. Any other questions? I'm not. Any other questions? I'm not. I can't hear you. No. I can't hear you. So it's not going to change anything, it's just going to shuffle the chair down. 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There are estimates of midstream emissions. We saw in particular that hydrogen is one of the big hypothesized contributors to refining GHGs. I think it's nice that we have the EPA reporting all major facilities. So you can take a look at someone's subpart C or subpart X or subpart Y and also break down those numbers accordingly. I think it's a great resource. But is there anything on these particular websites that says that these are estimates, that these are not measured numbers from industry? I think there's certainly time for our developers to put that in. But I think it's exceptionally important if people are going to be basing policies around computational numerical estimates and fortunately with some good underlying engineering calculations. I think that's exceptionally important for people to know to make informed decisions. Yeah, it's a very good point. And we certainly can add that. And as Jules had mentioned earlier, and I think it's true and certainly Adam's model is the estimator, that what we viewed and the last page shows a lot, it's less important in terms of an absolute number. But we know this in general in policymaking circles. It's less important than the absolute. But when you're trying to change policy, it's a lot about relative. It's a lot about on the margin and the relative difference between oils. So what we're trying to capture here is also this relative difference in how oils sort and reorder themselves from different perspectives. But a disclaimer makes a lot of sense because of course this is all based like it shows in the report on the calculations that we've done from these models. So we take full responsibility for this being our product. But we also invite much better information. I know you've been great in terms of providing.  Exxon's been great. I know with the prelim model and with Jules, we've gotten varying help from oil companies and it's been really great to have validation and help to improve data as we've been talking about. And also fine tune how all of this is thought of because greenhouse gases are only more and more important over time, but it's a new regime. I mean, the oil industry started hundreds of years or at least 100 years ago and greenhouse gas emissions weren't part of the conversation. So it is like kind of a... Yes. So when this is, we will have a web page now with this event will be on there. The report is on there. So on the Carnegie web page, as soon as this is released and we'll have another reach out now to all of you will be a URL, which will be available. It's a pretty simple one, but I don't even remember it and it's not launched yet. So I wouldn't want to give it to you people. We're told people that go to a URL that's blank or under construction never come back. Like you have one shot with the URL with most people. So we'll launch it officially and hand it out to everyone, but Carnegie's web page will be the place to find it. So now we have, we ventured into this a little bit, but this is exciting. I think we ventured into the policy space. Now we have our second panel to talk about applications of the oil climate index from all of your varying perspectives. You know, you all come from very different backgrounds and have very different concerns and uses. So we have a policy conversation followed by lunch. And then all of the questions we weren't able to answer before when several of you came up. The last hour is really devoted to those that want to stay around to really dive deeper into individual or group conversations with us. And that will get very technical if you wanted to. But for now we'll talk policy. Alright, thanks Debbie. I'm Rob Barnett from Bloomberg Intelligence here to join this panel here today to help moderate some of the discussion on how you kind of take some of what we've heard about and apply it to policy making, investing, you know, bringing into some situations that, you know, companies and various stakeholders face. So Debbie and David are both from the Carnegie Endowment and we've also got John again from Stanford. So, you know, I think let's just kick off, let's jump into the hornet's nest. I mean, we've seen this really interesting set of data. One of the most high profile energy issues going on in town for some time has been the Keystone XL pipeline. I don't know if any of you would want to tread there, but can this help frame that discussion? Can it help inform how the president should think about it? How the Congress should think about it? Maybe you don't want to tread on whether it tells you whether you should or shouldn't do the project, but how can it at least help inform the debate? And really that can go to any of you. It's an excellent question and I definitely respect, Rob, your intention to bring this to the issues that are in the news and the headlines and to try and kind of get some comment on what's perhaps one of the most political energy issues that we face today. But I would actually push back a little bit and make the argument that what the oil climate index shows is the potential pitfalls or dangers or at the very least lost opportunities of making environmental policy or climate policy on an ad hoc basis via, let's say, decisions on cross-border infrastructure like the Keystone XL pipeline that come across the president's desk from time to time. And I think so it's worth pointing out that the Keystone XL issue whether explicitly or implicitly is largely being debated and deliberated over right now for a whole litany of different factors and considerations when you determine national interest, but the one that's most salient, the one that's garnered the most attention is essentially the oil that will flow through it. There's a small volume actually of light sweet crude from the Bakken that would come through it, but for the most part, the XL extension is allowing more Canadian oil sands to come to the U.S. Gulf Coast refining hub. And it's worth noting that there's been so much scrutiny on the Canadian oil sands on this extremely heavy, bituminous oil from Canada, but the same level of scrutiny or at least the same level of application of some sort of litmus test is not placed on other similar oils which have in the past or are presently or could be in the future arriving in this very key global refining hub in the Gulf Coast. So that includes similar oils in Venezuela in the Orinoco Basin. That includes heavy sour oils coming out of Mexico. You know, Mayan crude is the benchmark there. It includes some heavy Colombian oils and a variety of others. And so I think what the Oil Climate Index points out is that it's incumbent upon the administration. It's incumbent upon Congress and it's incumbent upon other stakeholders in this space, including an industry, to think proactively and to think constructively about a comprehensive framework for integrating climate considerations into the decision-making of actors within these key refining regions like the Gulf Coast. And the other thing I'd point out too is that this is really an opportunity and I think this story's been lost, but it's perhaps coming back. I sense it coming back in some of the discussions I have with counterparts in Quebec or folks even in other areas of Canada or Mexico, is that there's with shale oil production in the US, with the oil sands production in Canada and with now the revisions to the hydrocarbon law in Mexico and essentially the opening of a new chapter in Mexico's oil history, there's renewed interest in the North America as an energy platform. That's often talked about as kind of an energy production platform, but I would argue that there's equal thought needs to be given to the regulation of that energy production and of coherence in approaches. I think Alberta and companies operating within Alberta, even the oil sands companies oftentimes, have a shadow carbon price. The province of Alberta has a carbon price that the shadow price that companies use is even higher than that price. But in many times it's been, the ball is in the Canadian Federal Government's court. It was not well received by many when Prime Minister Harper applauded the decision by Australia to scrap its carbon tax and that was seen as a confusing signal in terms of what direction Canada was looking to go to managing its own domestic emissions and there's perhaps an argument to be made for a North American approach on streamlining regulations and approaches to managing carbon, particularly in this low oil price environment as we talk about the importance of North American production years ahead. So let me just add one bit of context. Our focus is narrowly on the total emissions associated with any particular oil and I think it's a mistake to focus just on one set of oils thinking they're always high carbon because there are other kinds of oils that are very different from that which are in Alberta that have very high carbon emissions as well when you look at the total life cycle. So I think our work is intended to be a caution to people who want to make simplistic generalizations about oil. There's so much complexity in the total emissions and you just can't assume because oil comes from a certain place that it's going to have total emissions that are more than anything else and you just need to be very careful and look at the whole life cycle before you make decisions about the emissions of those oils. So I think that the nuance and the complexity that you're describing is often not appreciated here in town. I think folks look for... Shocked. I'm shocked to hear it. Rules of thumb, right? You know, I would argue that... You're right. I mean, Canadian oil fans, they're not the only high emitting crude out there. How does a policymaker wrestle with that complexity? Can they use this tool? Can they use other things like it? How do you translate this information into boots on the ground, putting out policies that try to make the system more efficient or less carbon emitting? I would just add, and whether it's the Canadian oil sands or many others, it's interesting to see who becomes the recipient of carbon and passes it down the chain. So policy... there are many different policy makers that have a hand on this. So say the Canadian oil is an example, who's refining it? Is it getting upgraded in Canada? Well, then it's their carbon. Is it being exported here and we're upgrading it? It's our carbon. Are we exporting the pet coke? Whose carbon is that? Is it the carbon that's consumed by a country? So there are policymaking opportunities and challenges at every step in the way and in the process here and the economics barring a carbon tax. The economics drive this in the wrong direction. There's so much value in high carbon oils because you can turn it into all of these products and do so much with it at a high price environment that you end up kind of, you know, forcing it on people without any policymaking regime. But I'd say the overarching one that the oil climate index does inform and sometimes I'm thankful that we didn't have a cap and trade many years ago. We didn't have any differentiation on things like oil. So a cap and trade would have been basically one greenhouse gas metric for oil when now we know there's a lot of competition within oils. There's a lot of marginal improvement within this whole regime. And so a carbon tax at this point to actually price that externality with this kind of information is much more meaningful for change. Yeah, just to kind of play off of what Debbie said and to talk a little bit about the potential applications of this on the policy side of things. The way I like to look at it is to kind of see that there's traditionally, you know, policy or means of dealing with climate change is often bifurcated. So you have command and control regulations, top-down regulations or standards. And looking out at the horizon you have the oil and gas methane regulations that will continue to be developed and will be promulgated by the administration before its term is over. You also have, you know, perhaps down the road if a conversation starts on this, you have the possibility of thinking about standards for refineries. We talked about some of the opportunities but also some of the challenges of that at the end of the last panel. Particularly the idea that you would want to, in dealing with any refinery, you would want to take into consideration and weight the complexity of that refinery so that you're not putting at a competitive disadvantage refineries that have more process units versus those that are relatively more simplistic and have fewer process units. The other side of that equation are market-based mechanisms, of course. Like Debbie just talked about, carbon price, carbon tax, cap and trade. Either setting a price or setting a quota and allowing the market to discover that price. One of the major obstacles to this when we talk about oil or when we talk about the transport sector in general, we saw this with Waxman Markey, with the cap and trade bill that failed back in 2010, is that the carbon price needed to incentivize change and investment in alternatives and massive reductions in emissions in the transport sector is likely far higher than that which would be needed to achieve the same thing in the power sector. There's often times the challenge of marrying those two required prices and how you approach it. This gets us to kind of the thesis, the antithesis, and then, of course, the synthesis and the Hegelian sense of what is the hybrid approach? The hybrid approach that's been chosen so far by a number of governments, including in California, as well as in the European Union, are emissions intensity standards or what are also sometimes called low-carbon fuel standards. These basically create a marketplace within the transport fuel sector where lower-carbon oils are competing against higher-carbon oils, where alternative fuels such as biofuels or electric fuel for vehicles is competing against conventionals, but they run into a number of challenges, including challenges based on the premise that they could have difficulty at the WTO if they were raised in a trade dispute, as well as challenges and probably the first and foremost challenge that we've discussed here today, the availability of data. And what the EU has done, which is significant, is they're not yet going so far with their fuel-quality directive as they had initially planned to. They've had to walk it back because of these challenges, but they are requiring that companies report on the origin and some of the key specifications of the crude that's brought into the European Union. But both California and the EU need to think a little bit harder about the particular data that they're collecting and how that could be expanded and then the platform that gives them to build policy into the future. So I'm going to make a bold prediction that carbon taxes are sort of off the table for the federal policy here in the US for some time. And so when you think about the federal policy landscape, certainly a lot of interesting things going on, the state level, a lot of interesting things going on outside of the US, but the federal game in town is EPA. Now you mentioned methane standards. When you look at the data and you look at the portfolio of things that EPA could be doing under the Clean Air Act, is methane the right place to start? That seems to be where the administration is. Does the data bear it out?  I would say I definitely think methane is a good place to start. I think it's like a lot of things that we have to tackle as necessary but not sufficient. We don't have a great handle from especially gas operations, but certainly oil operations as well about not just flaring but fugitive emissions. And I think that that's a very important place. But I do think, I was thinking about what handle do we actually have? Again, going back to our overriding theme of better transparency, more data in this entire enterprise as we're thinking ahead to manage it better. And I was thinking about environmental impact statements. So that's right in EPA's wheelhouse. If EISs were actually part of the National Environmental Policy Act, the part of NEPA, if they were in their requirements and government specified that when you go and drill, you have to actually provide this data in a standard way back into the system. So again, it's like a reporting regime that's set up before infrastructure is designed and planned because once infrastructure is in place, this is a very durable industry which is a good thing for the consuming public but it also means things last for a generation or longer. So once you build something in the oil and gas industry, you're with it for 50 or more years and it wouldn't be fair to change that rule because they're expensive. These are capital intensive regimes. So you have to somehow get into EPA, not just the downstream regulation of changing things and pushing modifications up which is always very difficult to do in Washington or any state for that matter in a policy circle, but building in data collection and information before you've made the decision to actually go into that field. We're going to need this even more so if it's coal to liquids or if it's carrage in, we're talking about brave new worlds in the next 50 years of going into oils that I was showing you and we need a lot more information before those are all permitted and go through. This is all part of the permitting process. I think how do you, because information isn't just for policy makers, it's for investors, it's for folks who work in companies too. How do you take the data and apply it to best practices for companies? Everybody wants to beat up on the oil and gas industry. It's a common theme, I feel like, but did you learn anything? Can you go to companies? Even if there's not a policy regime in place and say, hey, this is how you can reduce emissions and save money at the same time, did any of that kind of stuff come out of this work? I think it was covered already and we'll certainly cover it more in the last, the very last panel, but there are a lot of operating opportunities, I'll call them, that we found in OpG and Prelim in terms of better ways to generate your heat, better ways to generate your hydrogen, maybe renewables, more in concert with the oil and gas industry that we've ever seen before. So you're generating steam with renewables. So there are definitely opportunities which is interesting, they might not be the first place you would go economically on your site in terms of operations, but if you knew greenhouse gas control was a priority, even you were compelled to do so, you would pick and choose to do certain things that would actually manage these oils much better and reduce their greenhouse gas emissions.  So those are the kinds of opportunities that are there I think for the industry. The only other thing I can add that I've been thinking about is what kind of tool is this for investors? So investors are the powerhouse for how oil comes out of the ground. And if you don't have a bank or someone lending you money, and I'm not talking about the majors that tend to finance themselves, but if you're an independent producer, you're getting money from someone, and that's a big problem right now in the low price environment. That money's drying up. But investors, and there are many social investors out there, they don't understand their portfolios. So this is the kind of information that if you could drill down into your portfolio and say, oh, I have really high greenhouse gas oils, maybe you don't want to totally divest from oil. But the first thing you can do before you totally divest is start to invest wisely into oils at least that are at the top as opposed to the oils that are at the bottom of the list. So there's an opportunity there as well. So let me bring in an analogy from the information technology sector. Some companies that are customer facing and that have very high electricity use, namely data center operators, Apple, Microsoft, Google, Facebook, they've made a business decision to move towards greater use of renewables. And Apple, for example, announced in the last year that they were moving to 100% renewables for their data center electricity use, and eventually in the next year or so they'll have all of their facilities on renewables. And they did this because it made business sense. There's a business risk associated with carbon emissions, and they wanted to move the conversation past that and simply say, you know, if you're concerned about climate issues, we understand that's not an issue for us because we've dealt with the problem. So they've moved past the problem. And I think having the kind of information that we have generated here, hopefully in greater numbers and greater comprehensiveness, will allow investors and other actors in the industry to take proactive steps and say, look, you know, our greenhouse gases from our fields are much better than those of our competitors. And so that's an example of where you could start to see differentiation among private players who want to gain some sort of advantage from this. But they couldn't do it without having the kind of data that we've been generating. Yeah, I fully agree with that. And, you know, a point I wanted to make is just the opportunity that this presents to mature and to make a little bit more nuanced the kind of divestment narrative and the divestment campaign around fossil fuels and speaking in particular about oil. Just in the sense that I think you've seen this evolution from initially it was a moral case that was being made. We have to divest from oil because there are these leading studies which have come out in the past few years that only burn X many tons of carbon or carbon dioxide and put that into the atmosphere. And then you saw counter arguments say yes, that may be true, but for every investor that divests even significant ones like Stanford or like the Rockefeller Brothers Fund for every investor that divests from fossil fuels if that fossil fuel asset is impinged or the price is depressed even for a temporary period of time and eventually return to intrinsic value as other perhaps less climate motivated or less socially motivated players move in and swap up that asset. And who could we think of naturally there would be a number of national oil companies that might be not under the same sort of scrutiny or pressures on climate change that international oil companies find themselves in the West or in much of the developed world. And then you saw the argument that this is smart business sense regardless that divestment should take place because eventually these assets will be stranded through either a prohibitively high carbon price or through some other sort of draconian regulation that will diminish the assets that these companies hold on their books. But unfortunately what you haven't seen is you haven't seen you've seen some kind of leading movements of divestment but you haven't seen this sort of leaning forward stakeholder engagement and people using ownership in a proactive way to ask the right questions of oil companies like John just highlighted. So I'll just throw out there. I looked at as of the fourth quarter of 2014 a database called the shareholder database for research and education on various different shareholder resolutions that had the word climate change in them, shareholder proposals that were raised for public companies. There were 64 that I found that included the word climate change. Of these half 32 were successful. Of those 24 of them required disclosure of climate related risks. Of those 24, only 14 of these involved in a fossil fuel firm or a financial institution that was involved in fossil fuel lending. Of those seven required some sort of additional or enhanced analysis or data disclosure. Of those seven, only one called for an actual change in business practice. That was actually quite a ways ago that was enhanced investment management back in 2007 asking a Canadian oil sands operator, Suncor to essentially offset its additional greenhouse gas emissions. After a series of negotiations with Suncor that investment management company dropped its request. So the point is that this tool can be part of an intersubjective process wh