S&P Global Inc. (SPGI) Earnings Call Transcript & Summary

Hello, everyone, and welcome to today's webinar. My name is Lauren Smart, I'm the Chief Commercial Officer for S&P Global Sustainable1, and it's my pleasure to be moderating today's webinar, The Future of ESG Intelligence: Tackling Data Availability Gaps. Today is a particularly special day because it's Earth Day, and this also marks the 1-year anniversary since the launch of Sustainable1, which is S&P Global's single source of essential sustainability intelligence. So thank you very much for joining us on this special day, too. Before I introduce my [ SLE ] guests, I've got a few housekeeping items. So just bear with me for the housekeeping and then I will be intro-ing the guests and what we'll be talking about today. So we recognize that the topic of today's webinar is of great interest to you. We want this to be an interactive session and encourage you to submit questions for discussion. [Operator Instructions] And additionally, you can find any of the reports discussed today in the resource widget. The webinar is being recorded and an on-demand version will be available shortly after we conclude. If you encounter any technical issues during the program, please try refreshing your browser. And if the issues persist, please use the Q&A widget to contact us and a member of our technical team will assist you. So now we've got the housekeeping bit out of the way, it is my distinct pleasure to introduce my esteemed panel. So I am honored to be joined today by Manjit Jus, Managing Director and Global Head of ESG Research for S&P Global; and also Mona Naqvi, Global Head of ESG Capital Market Strategy for S&P Global Sustainable1 as well. So thank you very much, Mona and Manjit. Thank you for joining us today. So today, we're going to be talking about the future of ESG Intelligence, and in particular, how to address the market pain point of ESG data availability. We're going to share S&P Global's perspectives on these issues, and we also really look forward to your questions. So do make use of that Q&A widget. So as we all know, the ESG data market has been booming. There are now more users of ESG data than ever before. And that means that there are more use cases too, which is fantastic. As the financial community is seeking to integrate ESG into more different workflows and into different use cases, it creates some new needs that the ESG data sets that have previously been utilized need to expand and evolve to manage. In particular, we have a challenge around data availability. So what do I mean by that? So list -- as we -- as many of us know, the ESG data world really started within the listed equity space, and that's what the biggest drivers were for disclosure from companies. So we have the best data, the most complete data sets, typically, in large-cap listed companies. Yet, if we want to integrate ESG into every single financial decision, and we want to make sure that sustainability is central in every decision making within capital markets, we need to consider not just large-cap listed equities. We need to consider the smaller companies. We need to consider nonlisted companies. We need to consider entities that may not be companies at all. We need to be thinking about infrastructure, private equity, real estate, fixed income. We're working with the bank. We need to be thinking about all the nonlisted companies that might be within their loan books as an example. So we have a range of evolving use cases that require different types of data sets and some data gap solving to address. So to expand on that a little bit further, I am going to hand over to Mona Naqvi to share a bit more about some of these challenges that we face in the market.

Thank you, Lauren, and good morning, good afternoon to all of our listeners. Thank you so much for joining. So as Lauren said, we do have a rapidly expanding ESG industry, which is a very exciting thing. But of course, there are always some growing pains when an industry is blowing up so quickly. And so we think that we've made some really interesting steps to kind of address some of these, which we're going to talk through today. On the next slide, I just want to highlight, first of all, that there is no one-size-fits-all approach when it comes to ESG. And we must remember that like any other parameter of personal preference, ESG and sustainability considerations are but one parameter of choice in an investor's capital allocation decision model, right? You have traditional considerations like liquidity requirements or risk appetite and time horizon, sustainability is but just another parameter for an individual to align their investments with their own unique mix of preferences. And like any other preference in traditional finance, we wouldn't expect all of those to be the same. Across a variety of investors, you will have a big spectrum of risk appetites and time horizons, et cetera. So sustainability is no different. Of course, there are many, many, many different facets to sustainability. But over the years of our experience at S&P, we've kind of identified these 3 core buckets that we see as being foundational to this space. And I'm sorry, still on that other slide. Thank you. We've identified these 3 core buckets. And although these are highly simplified schematics, just to kind of illustrate these cornerstones of the landscape, of course, any individual's preference or investment strategy could reliably span across all 3 and could combine them in different ways. But the point here is just to illustrate that responsible investing, ESG integration and Impact Investing are 3 distinct, in their purest forms, 3 distinct approaches to Sustainable Investing. And I think a simple way to think about this is, what is the investment objective? And subject to what is a sort of second-order constraint? Of course, with any investor, the goal is to ultimately make money. But sometimes, there are secondary considerations or even primary ones that may come before that objective, optimize in a particular set of preferences. So with responsible investing, we all know this is the approach to sort of screening out negative -- negatively screening or positively screening, in some cases, companies that may not align with your values. First and foremost, what we find, in its purest sense, that investors in this bucket are looking to align with their values subject to, of course, maximizing their return, but making sure that they can express their moral -- aligned with their moral opinions, that is of the utmost importance to them. And so as a result, the outcome or output of this for them tends to be a reputation-hitting signal, right? They are still investing, but they are also telling the market that they don't agree with this particular set of business activities, and that for them is an important outcome of this type of investing. Then we have ESG integration. And this is probably the bucket that has blown up the most in the last several years, in particular, as data has become more available. And ESG integration, I think, in its purest form is really no different from traditional investing. It is still, first and foremost, targeting the maximization of profit and returns. But it is subject to one's ability to uncover hidden risks and opportunities through the use of alternative data. So really all ESG integration is, is trying to tap into those hidden externalities, those currently unpriced assets and risks and opportunities lurking in the investment portfolio that may not be adequately addressed by traditional financial models. And so it's using alternative data, be it informed by ES&G data points. And ultimately, the goal here is to address what investors may perceive to be a systemic mispricing of assets. So the outcome would be a corrective repricing that would be beneficial to the holders of the portfolio. And then we have, last but certainly not least, Impact Investing. Again, in its purest form, the goal of Impact Investing is to advance a set of real-world outcomes or global goals. So for example, we often see investors linking their investment results to the UN Sustainable Development Goals, for example, or aligning the economy with a net zero trajectory by 2050. These are strategies that care more about the outcome in terms of the real-world impact. Although it is subject to, of course, still generating a stable financial return. So in this case, investors are very interested in understanding the real-world impacts and outcomes of their investments. And this, for those impact purists among us, who will know, this requires that information to be measurable, intentional, additional, et cetera. So where am I going with all of this? As you can see, there is a really big spectrum of use cases for Sustainable Investing. And in each of these, there are potential use cases for ESG data. It really just depends on how it gets applied. And so what we have is a situation where ESG data and ESG scores, in particular, as the sort of flagship data of this space. It simultaneously, in some cases, is trying to answer many different questions. And different data providers will have different approaches to how they answer these questions and serve these different use cases with that information. And so I just wanted to set the scene by highlighting that this discrepancy in use cases is sometimes contributing to confusion around ESG scores because how one is interpreting that data will depend very much on their vantage point, depending on which of these core 3 pillars they are primarily focused on. So on the next slide, kind of talking more a little bit about these challenges with ESG data, I think some of the primary ones to call out is that ESG scores sometimes diverge too much among data providers to be useful or at least that is the traditional market criticism that we often hear. But it's important to kind of take a step back and understand, why is that the case? We've seen already that there are different use cases out there that serve different types of investors. And of course, different data providers may be seeking to address each of those to various -- to varying degrees. And so inherently, in the methodologies that data providers are constructing, we are approaching these questions very differently. But another challenge is simply the input. The quality of inputs are not perfect and we do have inconsistent data quality, as Lauren mentioned at the beginning. And so how different data providers deal with that missing information is also going to contribute to ESG score divergence. There were notable efforts being made on a regulatory and policy scale to help address some of that inconsistent disclosure from companies, to have a more level set of inputs. But at the end of the day, ESG data and ESG scores, in particular, are a data provider's opinion on the relative mix of the most important issues, be it from a risk or an opportunity or even an impact perspective depending on their own unique set of questions and philosophies that they're trying to address with their data. So ESG scores do diverge, but that actually may be intentional. These are opinions -- expert opinions, informed by the unique mix of preferences and questions that a unique data provider is trying to answer. Another challenge, and I guess this really does build upon the first, is that ESG data, at least in its headline score form, is relative, not absolute. By design, ESG data is assessing the risks and opportunities that companies face within their industries. And this is important because, of course, when you link ESG to the real economy and you're talking about big precedented macroeconomic shifts unfolding, these are going to invariably affect different industries differently. And there is a difference in the availability of technologies and efficiencies across the different industries we see. So it's important to acknowledge those differences. And a nice counter example to help illustrate this is if we simply wanted to target absolute ESG score performance, meaning the absolute best, irrespective of the industry, we might end up with a portfolio that's not very well diversified. An example of this could be media companies, for example. They're typically less carbon intensive than an energy company. So if you're going purely based on who has the lowest carbon emissions, you'd end up solely investing in media companies as an example. And that may be good in terms of measuring the impact of an individual investor strategy, you might be able to say you've reduced carbon emissions relative to the benchmark or the market as it is, but have you done very much to actually help change the behavior of those companies? So sometimes that relative aspect of ESG is important because it encourages companies within their industries to be assessed on the basis of available technologies and the business function and activities that they are actually pursuing. And it encourages and incentivizes companies to do better within the world they operate within. And if we solely took an absolute approach, then we may not actually help benefit companies and influence their behavior positively, whereas if we take a relative approach within industries, by design, we can help companies improve relative to their industry peers so that when we end up with a diversified investment portfolio that is truly reflective of the market as it is, we can actually help drive better performance standards. And last but not least, another concern sort of related to all of this is that ESG scores, at least in their headline form, are muted in the aggregate or at least the underlying signals are. And that, I think, is a really important thing to highlight. And it gets to the crux of the issue, which is that there is no single silver bullet that can single-handedly answer every question. And so naturally, an ESG score is the summation of many different questions based on the individual data provider's philosophy. And it is a useful entryway to understand at a high level what is the data provider's overall expert judgment on this. But of course, if you want to better understand the underlying signals, lots of data providers, including S&P Global, we make those underlying data available. We actually provide up to 1,000 data points per company where available, so that you can back test and stress test the underlying performance signals associated with one individual data point. But we should also remember that if you're an investor who has multiple sustainability objectives and you care, let's say, about climate justice, not just having a climate-neutral investment portfolio, but you actually also care about the social implications of your investment choices, then you ultimately end up with an optimization problem. And an optimization problem is, in an investment sense, sometimes difficult to explain because you're trying to aggregate numerous metrics all at the same time. And so we see in investment strategies, you do have many optimization models that are really good at perfecting the relative trade-offs between these different preferences, but at the expense of making it harder to explain. Whereas a headline ESG score, which may, on the back end, incorporate all of these different questions, at least makes it easier to explain headline changes in a portfolio. For example, you can have a rules-based approach, where you're screening out the bottom 25% of companies based on their ESG score ranking. And therefore, you can explain decisions even if you're baking in multiple assumptions. So either way, there are trade-offs with whether you choose the individual data point or whether you go through the headline score in terms of their explanatory power for the changes driven by our strategy. And that's a really important thing to remember. And on the next slide, just kind of to close out my comments here for today, I think underpinning those 3 bigger picture challenges are 3 very pointed and specific sources of potential confusion in the marketplace that have the potential to perpetuate one another in a bit of a cycle. The first is that there are black box methodologies, or at least differences and approaches, from data providers when they create their ESG scores. And it isn't always perfectly clear what they are doing. So Lauren has already talked about some of the inconsistencies in data availability. I've already talked about differences in use cases for ESG data. When you reconcile those 2 things, you end up with missing information and needing to triangulate it in some way. Which use case are you ultimately serving? What data is missing? And how do you fill those blanks? And so different data providers have their own proprietary approaches to how they do that, which contributes to some of the black box methodologies because every data provider has their own secret sauce. If there was perfect transparency in the market from companies and there was perfect clarity on what ESG scores were measuring and that was universally defined for all providers, we may not have this as big of a problem, but it is, at the moment, a challenge that the industry must overcome. And that is largely driven by the fact that there is, as we've talked about already, inconsistent data quality coming from disclosures from companies being patchy. And as we've talked about already, last but not least, there's a co-mingling of use cases. And these 3 challenges or sources of market confusion somewhat perpetuate one another. If you think about it, having a black box methodology means that companies don't know how they're being ranked or scored and so they're disclosing different metrics. It also means that companies -- sorry, also means that investors may be using that information differently because if you don't know how to interpret it, you will apply it in different ways. And that inconsistent data quality on that second row is also contributing to this challenge because if you've got patchy, missing information, obviously, data providers will have to adopt different approaches to filling those blanks which contributes to those black box methodologies. Similarly, if you've got inconsistent data quality, that also lends itself to different interpretations, again, contributing to that co-mingling of use cases. So I mean, I won't go through everything. But as you can see, these have a reinforcing effect on one another. And this is why I think there is potential for a lot of confusion in the marketplace around ESG scores. There really requires transparency, clarity and quality to really overcome these challenges and help cut through and make sure that our clients know exactly what data it's trying to measure, which questions it's answering, and our process and the robustness of our methodology for addressing those questions. So with that, Lauren, I'll hand over back to you.

Fantastic. Thank you, Mona. So transparency, clarity and quality. I think those are absolutely key for us as we think about the future of ESG intelligence. I'm going to hand over to Manjit now to talk a little bit more and -- about how we are incorporating those aspects into our approaches to filling data gap. So Manjit, over to you.

Thank you, Lauren. So maybe before I dive into the details, information gaps come in a wide range of shapes and sizes in the ESG space, so that could be missing company disclosures in certain parts of the world, depending on the size of a company, depending on whether it's listed or not. There can be differences in expectations of what should be disclosed, so different stakeholders will have expectations of what should be disclosed in the public domain. And already there, you will have data gaps because people have different expectations of what should be reported. There are also data gaps if you are doing controversy screening and you're using news. There are parts of the world where there simply isn't a reliable set of media or news to be able to screen for certain risks. And also, there may be data that simply is just so new and is so under -- yet to be defined that this data is something we would like to have, but simply can't get our hands on yet in the quantities that we ideally would. So I think data gaps come in a lot of different shapes and sizes. And today, I wanted to walk through how we are addressing those and thinking about those, specifically in context of our ESG scores as just one example of how data gaps can be filled and how we're thinking about making sure that while we're doing this gap filling, we are considering a number of factors, transparency being one of them. So on the next slide, I've listed the challenge, at least from an ESG scores perspective. So I think one of the challenges we, as an industry, face is that we are constantly being asked to cover more and more companies. This was a few years ago, maybe less of an issue when we were only covering a few thousand companies. But now obviously, the need for ESG data has expanded so greatly across companies in all different markets of the world, across different asset classes. And therefore, as we continuously increase the number of companies we're researching, you do get to a point where the amount of reported company disclosed information cuts off quite significantly. And that cut off, even though the amount of information that is being reported has been improving significantly over time, that really, really steep cutoff tends to happen around 4,000 or 5,000 companies. And I think people forget that sometimes that when you're researching company number 8,000, there really isn't a lot of information available in the public domain or there may be very basic ESG disclosure. And as already alluded to before, regulations and I think increasing demand for this information from the investment community will help drive this. But realistically, I think this will take another couple of years. So that leaves actually the vast majority of most data providers' universe struggling with this information gap because there is little to no information available on kind of the basic ESG factors that most providers would look at. The differences are pronounced differently depending on the size of the company, the industry sometimes even that a company can be in and the region. So we definitely see there are kind of elevated levels of maturity on ESG reporting in some industries and some industries have managed to kind of get by with companies not really reporting much at all. So that's a factor, that it's not evenly -- the data gaps aren't even evenly distributed across the universe of companies that you would cover. And I think, again, as Lauren mentioned before, the more we get into small companies, companies in large corporate supply chains, privately held companies, the information on ESG, but even financial information generally drops off quite significantly. So that is a huge new frontier to tackle because we are seeing increased interest and also having transparency on ESG in these companies as well. We -- I think there is a lot of modeling used today. I mean, at S&P, we've been using models for over 20 years to fill gaps in environmental data that's being reported. Companies themselves often don't report Scope 3 emissions or don't have the full view on all the emissions that occur in their value chain. So in the absence of that, even companies come to us for our modeled value to kind of somehow orient themselves on what the actual value would be or to cross-check on whether the actual value they're collecting makes sense. So I think modeling is well understood as a very kind of scientific approach to trying -- to try to fill information gaps, and it works very well with quantitative information, but it becomes increasingly complex when you're trying to model more qualitative information. And I think often as users of this information become more sophisticated and aren't just using the outputs but are more interested in how these modeling approaches are actually working, but there may be a current lack of transparency on modeled approaches. So thinking ahead, if you're going to apply modeling as a way to fill gaps, then that needs to be done in a very transparent way. So what are some opportunities? It's not just about modeling. And I think we, at least at S&P, are very much focused on corporate engagement. We also believe that more transparency, more reporting benefits the entire market. The research framework that we've run for over 20 years is very much focused around corporate engagement and kind of promoting companies to think about upcoming ESG topics, to start disclosing on areas that they may not feel 100% comfortable disclosing on yet, but it's a process. And we've seen the readiness of companies to disclose information to -- not just us, but to their stakeholders has increased dramatically in the last few years, and we'll continue to do so. So there are other ways, and I think having more simplified metrics as a starting point for many companies would be a positive thing. A lot of the ESG reporting frameworks that exist today can be quite daunting. And so I know there are efforts underway to, for example, set a baseline, set up ESG metrics, basic things around diversity or environmental metrics such as Scope 1 and Scope 2 emissions, that companies can kind of ease into this idea of having to report information. And already then, you have a pretty good set of basic information across a very large number of companies. Engagement. I think we are seeing a lot of uptick in terms of private equity investors, for example, starting to want to engage with their portfolio companies that are private, that may be very small on basic ESG topics because they themselves feel the need to report on the ESG profile of the companies that they're invested in. And so also, I think dialogue education with the private market and smaller companies using simplified metrics, slightly modified approach is a good starting point to make sure that companies warm up to this idea that ESG disclosure certainly won't go away and that there are ways to do it, and which then, for us, benefits us by filling gaps. The good news is that I think gap filling generally is very well understood in the ESG space. So most clients we speak to, they are using some kind of gap filling when they are looking at ESG information, whether that's provided to them by the providers of their ESG information or whether they're applying their own techniques in-house. So I think modeling as a way, for example, to fill gaps is not something that is new. It's not something that the users of ESG information shy away from. It's just maybe to what degree that they actually understand what is being modeled, how is something being modeled. So really, for us, I think the opportunity that we see is that if you're going to use modeling in gap filling, then be transparent about it. Alternative data sets can be a great way to fill these gaps. So maybe it isn't all about company reported data, but we're also seeing that there are many different ways that you can use information that, for example, S&P already has on companies to start forming an opinion about, for example, their exposure to certain ESG risks or the possible negative or positive impacts that they would be having on the world through the products and the services they sell. And these are things that companies sometimes themselves aren't necessarily broadly reporting, but we may have information on. And that, in combination then with company-reported data, certainly helps to fill a lot of gaps while not having to wait for corporate disclosures to evolve. And then I think the trick is how do you balance these 2 things, right? How do you make sure that you are gap filling and that you are trying to fill disclosure gaps in your data sets, while at the same time, trying to promote more disclosure? Because often we get asked the question, "Well, if you're gap filling, then what's the incentive for a company to actually disclose that information?" And because we do feel that more information should be reported more broadly, that is something that we also consider. So on the next slide, I have showed an example. This is a screen shot from our CapIQ Pro platform, and this, I think, illustrates how we're thinking about transparency, not just in applying estimations to our scores, but also in how we actually build our scores from kind of the different buckets. So here is a company score disaggregated into 3 buckets. There are parts of our ESG scores that require information to be available in the public domain. So that is kind of that baseline measure of transparency that we expect all companies to report on. These, again, are basic metrics around corporate governance, basic metrics around environmental data, like water consumption, waste production, greenhouse gas emissions. Then we know there's another kind of set of questions which are maybe slightly more advanced or go into a level of detail that maybe not all companies are reporting on today. Some companies may be reporting on it to us. Some companies may have this data internally, it's just not in their sustainability report yet. And those are maybe things that are also slightly more forward-looking in nature. And together, those kind of -- those 2 buckets create our classic ESG scores as they've been constructed for the last 20 years. Now we know that for some companies, there are limitations to what they can report that is -- sometimes there are constraints in terms of the size of the team that's responsible for ESG reporting in the company, the amount of resources they can mobilize to produce their sustainability report or to respond to our survey each year. And so we also believe that to fill some of those gaps, we can also take a more light-touch approach to imputation or adding imputation models to estimate some of these gaps, without just saying we're going to estimate all the gaps that we have in our scores because we do believe there are things that the company should be reporting on. And if you're not reporting on it, then we're going to give you a 0 one way or the other, which will impact your score. So what we've done is we've applied estimation to a selection of questions that we have in our research framework, being quite selective about where we're applying it. So we're not just supplying estimations across the board. We have something called our disclosure analysis, which is essentially an assessment of how transparent a company is on those very foundational ESG metrics that we believe all companies should be reporting on. This is very much in line, I think, with what reporting standards are discussing today, kind of trying to coalesce around maybe not all the metrics that exist in all the standards. But what are the core set of ESG metrics that are really important, the things related to climate change, the things related to diversity that most people care about. So we have this baseline for transparency, and that's really an important part of this because it kind of sets guardrails for how much estimation you want to apply. A question we often get is, "Well, how would you make sure that you're not over kind of estimating something? Or that you're giving a company that hasn't disclosed information that they would get too much of a benefit by not having reported on something versus a company that has reported on something?" And I think that is where our disclosure analysis kicks in. And I think this is quite unique to us is that we want to make sure that the amount of estimation that we can apply is really being driven and controlled for by how much information the company is reporting. For example, if a company doesn't report basic greenhouse gas emission data, should we really expect them to have a very robust climate strategy? And should we really be giving them a huge benefit of the doubt on some of these climate change questions when they don't even disclose basic environmental data? So that is something that we spend a lot of time thinking about. But I think the most important thing that I really want to illustrate here is that we are trying to provide this in a way that you can always disaggregate the different parts. So you can say, I'm looking at a score that has estimations in it. And that might be relevant, and I'll get to that on the next slide, for certain use cases and certain applications of ESG scores. It actually might be necessary. But at the same time, if you're an analyst and you want to understand, well, I'm really interested in how transparent a company was on a specific topic and why haven't they reported these metrics, you can dive into the details and kind of remove that estimation, look at the score without that. And we believe that this is pretty important because I think having a single score that has some reported data and some estimated data all baked into kind of the same thing without being able to tell what is what is something that will become increasingly confusing to the people that are trying to look under the hood of these scores and understand how they're being constructed. One of the other things that we are planning to provide are confidence levels. So to each score, assign how confident are we in the model. And that is really based on how much information were we able to find. And in some cases, that may be quite a significant amount of information. And in other cases, for some companies, there may be a -- there may only be 2 data points that they report in the public domain. And based on that, you would want to make sure that the person using the score really understands how that model was applied and the confidence level that results from that. So if we go to the next slide, I think really in the ESG scores context, what this is trying to solve for is creating a distribution of scores across a growing universe of companies that feels a bit more normalized. And if you look at ESG reporting, it's not a perfect distribution of companies, right? And this is what I was saying before, you have a very small set of companies compared to the tens of thousands of companies potentially or hundreds of thousands of companies that you could assess in the world that actually report on ESG information. And this is likely to stay the same, is to stay relatively consistent in the near term across such a large universe of companies. So if you have a score that's based purely on what a company is disclosing, what we found as we added more companies is that you have a lot of companies and their scores clustered around the lower end of the spectrum. Companies that are getting very little points because they're not disclosing anything. This, again, if you're looking for transparency and understanding how a company is reporting, that is maybe a very useful score to have and that is very useful information to have. But if you are trying to do kind of a broad portfolio construction and you need to be able to pick from a set of companies -- of scores that are very comparable across a very large number of companies, then you need to have a slightly better distribution. And so by essentially applying estimations to fill gaps, you are creating a distribution that is slightly more normalized. It's not a perfectly normal distribution because we also don't believe ESG is perfectly normally distributed. You will have companies that have been doing ESG for far longer than companies that are just starting today. You do have companies that are much more advanced than others. But this at least allows a set of use cases to be able to use these scores more effectively, while again giving you that transparency to go and look under the hood and see, okay, well, how transparent is the company really? And how is this estimation being applied? So I'll pause there, Lauren, and then hand it back to you for, I guess, a discussion, and I see there's lots of questions coming in, so we certainly also want to get to those.

That's fantastic. Thank you very much, Manjit. Whilst we are waiting for some additional questions, we have lots already. But remember, there is that Q&A widget that you can go to. We do also have a polling question, which we can just flip to now. This is just to help us in terms of understanding where interest areas lie. So if we go to the polling question. Our polling question is not coming up. Never mind. Let's go to the questions and we'll address the polling question in due course. So we have had lots of questions, but there's one which I think I'm going to throw over to Mona actually to start with. So we've got a question here saying, why is it that there are so many complaints about the divergence of ESG scores? Why do we think that people struggle with providers having different expert opinions in this domain, but no one complains about economists having different forecasts or equity analysts having different assessments of a company's potential? Mona, over to you.

I love this question. It's framed it in exactly the way that I would probably go about answering it, which is it is odd because it is true if you look to financial analysts' and other domains' or economists' opinions and sell-side research are -- some of the value comes from the fact that there is a difference of opinion across different providers. And I think part of this stems from -- it goes back to this idea of ESG being either relative or absolute. I think for many people, ESG, they feel it is -- the way it feels it should be is that it's like some sort of absolute objective truth, is something more sustainable or not, but also we must recognize that sustainability is a very complex topic. It is dealing with time horizons that are forward-looking in many cases, of which there are, of course, embedded uncertainties, and we don't have crystal balls. So like any other sort of investment research that is forward-looking, it's going to have a lot of assumptions and opinions baked into that, hopefully informed by expertise. But you will never have that perfect vision into the future to know exactly how things will turn out. What we can hope for is an opinion on the resilience, readiness and preparedness of companies to face these upcoming risks and opportunities. And so of course, that's going to have some texture and difference in folks' opinions. Another reason why I think that there is this common misconception. Again, it comes back to that co-mingling of use cases. I don't know if there is broad understanding or education around the fact that Sustainable Investing can take many different forms and that this data is intended to support not just one approach to ESG investing. We often hear complaints that there are so many different funds out there that have ESG in the title that do different things. And I've said in the past that one acronym cannot explain everything. As with any traditional investment, it's always advisable for individuals to read the prospectus, to understand the underlying investment objective and to truly take on that due diligence of understanding what it is trying to achieve before making any assumptions just because it has ESG in the title. But it doesn't mean it's not appropriate to have ESG in the title. ESG, as we've discovered today, can mean many different things. It really just depends on how that information gets applied.

Perfect. Thank you, Mona. A question for you, Manjit. This comes to the point around transparency and quality. We've had a couple of questions along the same thing. How do we prevent greenwashing as there are more eyes on the ESG data that's arguably more incentive for greenwashing. How do we manage that? And have we got processes that can help to identify it?

Yes. Absolutely. And I think we can probably never completely eliminate greenwashing because that's kind of up to an individual company to decide what they're going to put in their report and how they decide to portray it. However, I will say because this is so looked at today by so many different stakeholders and by investors, by NGOs, by employees, I think there is so much pressure on companies to really be careful around what they put in their reports and what type of claims they make. And so I think, from a reporting point of view, I think it's improving just because there are so many eyes on this. From our perspective, I mean, we are very careful about what type of information we actually accept as part of our assessment process. So we actually end up rejecting a lot of information that we feel isn't accurate. Not necessarily that it's greenwashing, but information that might be off, numbers that might be off and that is ending up to become a part of kind of a feedback loop, a dialogue with companies to flag things that we think look like there might be mistakes. I think real world, looking at real-world outcomes, though, is probably the best way to prevent greenwashing, right? So I mean it's often said that companies with the best human rights policies are the ones that are the biggest human rights violators. So why do they have the best policies? Because they've been kind of forced into having the most robust comprehensive policy because of their poor track record. So just looking at the policy alone, obviously, would potentially give you one view and then looking at the real-world outcomes, and I think that's where alternative data set comes in. So if you're looking at climate policies, that's one thing and -- or net zero commitments, that's another thing, and it's great that companies have these. But if you're not looking at like the actual data around trajectories and whether it's actually realistic that these companies or these industries can even meet these targets that are being set, I think that can help inform your overall assessment of a company and kind of give you indications of, well, there might be some misalignment, for example, with what a company is saying in their reports versus what actually happened.

That's very helpful. Thanks, Manjit. Quick one for you, Mona. We've got a question here about, do we think that is likely -- we're likely to see governments and regulators requiring an increased level of disclosure or baseline of transparency on ESG factors? Do we think that will be useful? Or do we think that the solution to data availability is best held from within or dealt with by corporates and private enterprise about public policy requirements? What are your thoughts on that question?

It's a great question, and it's very topical. Certainly, this is on the agenda for many policymakers today. And there have been some pretty exciting announcements and collaborations across -- from many different groups leading in part to the formation of the ISSB, which we're very excited about at S&P Global, and we've contributed to many of these forums and working groups to help standardize corporate disclosure across the board. I do think that standardization is going to be really important as we go forward. Not necessarily because that's going to contribute to standardization of ESG scores, mind you. I just think that the inputs going into those scores and the research conducted by experts such as ourselves could benefit from greater standardization. We actually find at the moment that around 20% to 30% of the data we collect as is from companies in the public domain, we need to either reject or correct or modify in some way to make it up to our standard, up to scratch. So clearly, there are some challenges with the way companies are disclosing at the moment. This may not necessarily be intentional. I think many companies have the right interest in mind, but there are discrepancies in methodologies. There are differences and nuances of what percentage of assets you cover. So having more clarity on those specifics to help companies make sure they report the right thing would be really useful, and that's something that we at S&P have long advocated for. But we should also be cognizant of the fact that consistent disclosure will not equal consistent opinions. And opinions will always be based on the expert judgment and philosophy and unique perspectives of a data provider that will look to these different inputs and still come out with different outputs.

Great. Thank you, Mona. Manjit, turning back to you. We have got several questions about imputations. I'm going to fire a few of them at you all at once and you can have a go at answering some of these. So can you provide 2 to 3 examples of some of the most popular alternative data sets to fill gaps? Do we fill gaps based on materiality or do we prioritize based on materiality? Are there any data points that we don't impute information for? And I will -- just those 3, actually, I think that will cover most of the questions we have around imputation.

So I'll start with the third one, so in terms of what we are imputing and what we are not. So as I mentioned before, a lot of -- roughly 1/3 or actually more than 1/3 of the research framework that we use is actually looking for information to be in the public domain. And these are things like corporate governance data that you would generally expect to find in a company's financial reporting. Increasingly, things like greenhouse gas emissions data, which should be, I think, kind of standard in terms of reporting, basic metrics around diversity. So there are kind of a lot of anchor points or, for example, a human rights policy. That is something that should be in the public domain for all stakeholders to see. So these are questions that we certainly wouldn't try to estimate because either the information exists in the public domain and you can find it or it doesn't. And so giving companies kind of a free pass is not something that we would want to do. The question around the 3 kind of main data sets that you would use. I mean, in this case, for these scores specifically, we're actually looking at how well a company has reported on specific areas to kind of predict or estimate what a likely score would be on other areas. So we spend a lot of time looking at the correlation of different parts of our research framework to kind of build the model that does this. On other approaches to ESG that we use, often you can start by using more country-specific data, so more kind of macroeconomic data or industry level data. So if you know, for example, where a company is operating and in which country it has its biggest revenue streams from or where it has its operations, you can start forming an opinion about potentially what type of ESG risks it might be exposed to. So certainly, kind of company, company-level data is often used, for example, in the case of our ESG scores, country-level information, industry information. These are things that we are using kind of foundationally across a lot of the modeling that we do within S&P on ESG. And can you remind me of the third question, please?

The third question, maybe you covered it. And there's additional couple of questions actually that are just coming in, Manjit, which is around, do the imputations cover or conceal trends? Why is the distribution normal when we incorporate the imputation?

Okay. So does it conceal trends? Not necessarily because the -- at least our approach to imputation will develop as more disclosure is provided. So basically a company that discloses very little in the public domain will benefit less from imputation or from gap filling than a company that is improving its reporting year-on-year and providing more information. So we wouldn't expect the scores to be static. A lot of the trend-type questions, so whether it's looking at health and safety indicators or whether we're looking at trends in greenhouse gas emissions, that is information that we're not necessarily always going to model. Or if we do model, we're modeling it based on expected emissions according to a company's growth in revenues or production. So the numbers are moving with how a company is changing year-on-year. So it may not always be able to capture trends 100%. But certainly, it would -- it will not be kind of a static -- it won't be static year-on-year because the nature of a company is changing as well as the ESG data that they are reporting. Also I think there's an incentive to kind of disclose more. So if -- I think one of the key points for us is that we also want to make sure that an investor using the information can very clearly see if something is imputed or not. And I think often what our clients tell us is, "Well, it's great that you have this overarching ESG score, but what I really care about is the underlying data." And if we've modeled an ESG score, they would go to that ESG score, look at it and then realize, "Well, all the underlying information is missing." And that, I think, is also kind of another engagement mechanism for then an investor to go speak to a company and say, like, "Why aren't you reporting this information?" And then that hopefully gives us an incentive to disclose. Why does the distribution improve? Because we are basically removing a lot of the zeros and a lot of the very kind of low scores at the tail end that you would normally find when you look at a universe of 10,000-plus companies. So -- because for most companies in that universe, you'll find [ sub ] information and so that [ sub ] information will lead to a little bit of a kind of an uplift in terms of the scores. And also companies that are reporting a lot of the basic ESG information historically in our approach were penalized for not disclosing on some of the more complicated areas of ESG. But unfairly potentially because this is information that often is not yet widely reported in the public domain. So now they, with this application of estimations, benefit much more and that boost kind of shifts everything a bit more to the right. So again, it's not a perfect normal distribution, but it's moving away from that really heavy tail of lots of 0s and 5s that you would find based just on public disclosure.

Wonderful. Thank you, Manjit. That's really helped to answer lots of our questions. I think we've only got time for one more question. So Mona, I'm going to come to you. Would you consider ESG scores as supportive for traditional risk management? Or does risk management need its own ESG approach? And it probably comes back to the point you made at the beginning about different use cases and different approaches.

Does risk management require its own ESG approach? That's a very interesting question. I think -- I mean, it depends what aspect of risk management one is thinking about. If you're thinking about it from a corporate lens that perhaps there are different approaches that ESG could support, but I think from a risk modeling in a financial sense that -- what I think ESG can help benefit is actually support existing risk models by augmenting with the correct holistic information. Because, I mean, that's really the core ESG integration, what it's about. The theory goes that current traditional financial models for risk and valuation are based on narrow definitions of performance and narrow data availability typically rooted in financial statements and balance sheets and cash flows. But that this is quite short term in its time horizon and is missing many of the hidden externalities and difficult-to-quantify assets like intangible assets that are an increasingly a more important driver value for companies today. And so ESG data, be it a score or the underlying information that supports it, can help augment and round out existing financial analysis around modeling risk to make it complete. And that is the belief of investors who adopt ESG for ESG integration purposes. It's very consistent with traditional risk management. It's simply looking at all available information and making a more informed investment decision based on all the available information. So I think they are very much consistent. I don't think a different approach is needed. What's perhaps needed is a different approach to traditional risk modeling to begin with that is more holistic, that incorporates ESG. The fact that we even call it something stand-alone as ESG is perhaps a misnomer. It's really just a more due diligent approach to modeling risk in the first place.

Fantastic. Very impressive to get that answered in 2 minutes as well, Mona. The -- there have been so many additional questions here that I could -- we could continue this conversation for another half an hour. But sadly, our time is up. So I just want to say that we will be circulating after the presentation, the presentation materials, and we'll try and get back to you on other questions that we weren't able to answer. There's going to be a recording circulated. Please also when we turn off in a moment, there's going to be a survey. Your feedback is very much appreciated. So do let us know how you found this. And that just leads me to say, thank you, Manjit. Thank you, Mona. Happy birthday, Sustainable1. Happy Earth Day, everybody, and I hope you have a good rest of day and weekends. Thanks very much, everybody. Bye.