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

Welcome, everyone, and thank you for joining us on this webinar on climate physical risk insights for the U.S. municipal bond market. Before we go in, I'd like to go over a few housekeeping items. Next slide, please. So first of all, I strongly encourage all the participants here to please ask questions. [Operator Instructions] I will do my best to see if we can kind of pepper in some questions as we go throughout the talk here, but we also aim to kind of come back to those questions till the end. So we will leave some time for Q&A. We would also love to get some additional insight from you on this webinar. So there will be a short survey after the webinar, so please take the time to fill that out. And we always incorporate that into kind of future webinars [ and kind of content and things we consider doing ]. An on-demand replay of this webinar will be available also shortly afterwards. So if you need to step out for a brief period of time, and you'll be able to access whatever you missed. And then let's hope this does not happen, but if you do run into any sort of technical issues, the best thing to do will be to refresh your browser. And then if these issues persist, please feel free to contact us via Q&A. It's, again, in the bottom of screen. And someone from our team will get back to you. So all right. So with some of those housekeeping items out of the way, next slide please, I'm happy to introduce you to our content experts and presenters for this webinar. My name is Bob MacKnight, and I'll be moderating this webinar. I'm the Managing Director and Product Lead for Climate & Nature Risk Solutions here at S&P Global and oversee our physical transition with nature risk product portfolio. I'm really excited about this as we'll be talking about the new capabilities that we've developed and analyzing the physical risk for municipalities across the United States, which is part of our broader strategy to evaluate physical climate risks globally. So joining me, I would like to introduce Therese Feng. Therese is the VP of Research & Innovation Methodology Group at Sustainable1. She has a doctorate in environmental economics and previously provided sovereign analysis and credit methodology to Morgan Stanley with Jefferies. I also have Tim Hall, the Senior Scientist at S&P Global Sustainable1. His work focuses on developing physical hazard models related to extreme weather and climate. Hall joined S&P Global with The Climate Service in 2022. And prior to that, he was staff scientist at NASA's Goddard Institute for Space Studies, where he worked on climate modeling. He also has over 60 peer-reviewed publications and was a lead author in the 2017 National Climate Assessment Report. And lastly, we have Rohan Thakkar, who is a cross-divisional emerging risks, data and product synergies lead in S&P Global's Rating division. Rohan enables climate-related capabilities, including data sets that help derive insights and objectively formulated [ trends around ] climate transition and physical risks for credit ratings [ for clients ]. So in terms of an agenda for the webinar today, I'll start by providing some context as to why physical climate risk is important with the municipal bond market, and how our solution answers key questions about how this risk will evolve in the future in the impact markets. Therese will then take over and talk through the key components of our municipal physical risk data set and the analytics approach that was used to develop it. Tim will talk through how we're applying cutting-edge science in this space and how we're evolving our R&D in this area. And Rohan will lead us into how we're incorporating this physical climate risk analysis into our ratings process and some of the exciting things that we're doing in that space. We'll have time for Q&A at the end, but please feel free to type your questions in as we go. And we'll see if we have space to answer them, as I mentioned earlier, right? Next slide. All right. So to provide a little context on this, why is this important? Well, in 2023, the U.S. experienced 28 separate weather or climate disasters, each resulted in at least $1 billion in damages. This is up from 3 in years 1980, and highlights how municipalities are increasingly needing to find -- manage financial impacts from climate change. So it's clear that extreme weather events and chronic hazards pose growing risks for state and local issuers in the $3.8 trillion U.S. municipal bond market. And one very interesting statistic to highlight from our analysis is that, over the next 25, 30 years, nearly half of U.S. states will experience severe water stress, affecting on average 40% of their residents. And just yesterday, I was reading an article that highlighted how just the city of L.A. could be facing around $12 billion in costs to put in place adaptation and mitigation investments in order to manage these growing risks. So the scale and scope of the problem is quite large. Next slide, please. And unlike companies that can move their operations to safer ground or shift where they're operating from, states, cities and towns, which fund 70% of U.S. infrastructure, are tied to their locations and can't easily diversify revenues. And given the liquidity and long duration of municipal bonds, investors are highly vulnerable to climate changes' adverse effects. Yes, there is a bit of a disconnect in the market. So -- or there could be a growing disconnect in the market. Municipalities and locations with higher risk of extreme climate events pay the same rate as lower-risk communities when issuing general obligation bonds or debt for infrastructure improvements. So changing insurance markets and greater last resort to public backstops, property and business losses from acute events and rising infrastructure and emergency outlays may force pricing adjustments going forward. Next slide. And it's also important to acknowledge that there has been increasing policy and regulatory focus on physical climate risks in municipalities, whether it's the inclusion of U.S. municipals in the Bank of England's climate risk stress tests for European banks, or how the National Federation of Municipal Analysts have urged the MSRB and SEC to mandate issuer disclosure of material climate-related information, including physical risks, in bond offering statements. Next slide, please. And this is the main reason. So due to the increasing risks, the regulatory awareness, we -- one of the reasons why we decided to create this additional capability was to provide a solution that could empower people with decision-grade metrics to translate physical climate change exposures into geospatial climate, economic and demographic data informing muni market participants as to issuers that may have high climate risk exposure. To do this, we provided hazard and exposure data across 8 different decades and 4 climate scenarios for 9 physical climate risk hazards. We cover over 3,000 municipal issuers, and that cross-referenced the bond-level data with over 47,000 CUSIPs with a focus on exposures to the economically active and more populated areas within a given municipality. This data set is meant to help investment managers, rating analysts and anyone doing regulatory reporting or compliance in this area. So with that context and an overview of this data, I'd like to hand it over now to Therese to talk through some of our analytical approaches within this context. Therese, over to you.

Thanks so much, Bob. Next slide, please. So what I want to do is to focus on the data set itself and the series that are provided and give a sense of why these are valuable. Then talk about the methodology we use very briefly and then provide a sense of some of our initial findings. So Bob already indicated sort of the scope of this data set. It's really comprehensive coverage of U.S. localities across a wide range of different climate hazards, basically: Extreme heat, cold, drought, water stress, wildfire, coastal, fluvial and pluvial flooding and then tropical cyclone. So what's distinctive about this data set is that we provide several different series. One is these absolute hazard frequencies of these 9 climate extremes. And you are basically provided with numbers that are GDP-weighted so that they are metrics that are relevant to key economic areas in these localities while being representative of the region as a whole. Having absolute numbers is valuable because you're not just handed a score, but you're actually given the underlying numbers. And this is anticipating that there are likely to be future requirements from regulators to quantify these exposures. And in terms of reporting, this becomes quite significant. It's provided for 4 scenarios in 8 decades. And you really need, in any sort of analysis of exposure risk, you want to be looking across multiple scenarios. And then it's really important to understand that this is a hazard exposure number. So this is not fiscal or economic impact, and this doesn't include adaptation, climate adaptation, nor kind of the vulnerability of a given locality. But these are all very -- this absolute hazard number is very useful as a fundamental metric, and one that we can anticipate in the future adding future dimensions to, which I think Tim may address or you can ask as a question. We also provide, in the second row, exposure scores. And these are helpful on a relative sense. So these are 1 to 100 exposure scores. This is provided on 2 different scales. One is exposure relative to all values globally across all scenarios. So in other words, how does your locality compare in terms of flooding, for instance, compared to everywhere else in the world across all time periods and scenarios? And then because this is a muni kind of bond-relevant data set, we also compare to just the U.S. kind of value pool across all scenarios just for the 2020s through the 2050s time periods because this is more relevant to the issuers and also the maturities for the muni asset class. We also calculate a composite exposure score, which is integrating all these hazards together. They are not weighted in any way, but we combine them, and then we use an exponential transform to basically reduce this to, again, a 1 to 100 exposure score. But it also -- so you can compare them, but it also recognizes if a single hazard has an elevated value, and it will reflect it in that score. So these series are valuable for comparisons, and they can -- you can use this now to identify issuers with a high overall or individual hazard exposure score. You can look at concentrations of hazards within a portfolio. And even how, if you were to change portfolio weights or add or subtract specific issuers, what effect this would have on your overall portfolio. And then the final set of numbers are GDP percent and population percent exposed. And here, the way that we do this is that we use expert judgment to define conservative hazard exposure thresholds, and then we combine this with a spatial mapping of GDP and population. And this is really to start to gauge the materiality of GDP and population exposure. So we've started with first, the hazard exposure itself for this region. And you can kind of think of this as a separate dimension which is getting at kind of how much the stock of the economy or population is exposed to this hazard at a quite material level. Okay. So next slide, please. Okay. So now this is a slide which gives you a sense of the analytical approach. Here, we've basically sandwiched several different data sets. We start with the climate, basically the climate hazards which have been mapped globally. Here is an example about fluvial flood kind of mapping. Then we define a specific kind of boundary for every single locality. On top of that, then we superimpose socioeconomic data for GDP and population. And this will allow us to derive the GDP-weighted average absolute hazard and exposure scores within a locality. And then finally, we use this, the sandwiching, again to derive population and GDP exposed by looking at the proportion of cells within a given locality which have a value exceeding the threshold that we have said and basically summing them and summing their GDP or their population to get a sense of what is the percentage that is exposed above this threshold. Next slide, please. Okay. So now the following slides give you a sense of how we put the metrics and the relative exposure materiality together over time. And what we're doing -- what I'm going to just talk about is some of these initial findings as to the exposure across the U.S. The first slide here, actually, this is noted in the in Bob's comments. But here, this is a dynamic heat map which is telling you how water stress will evolve over time. Right now, the Western U.S. is particularly exposed to water stress. This is a ratio of water demand to supply. What we're seeing over time is that, increasingly, Florida, North Carolina and Virginia and Minnesota are going to start to experience increasing levels of water stress. And then by the 2050s, nearly half of the U.S. states will be experiencing severe water stress, which will be affecting, on average, about 40% of their residents. Next slide, please. Okay. This is kind of a -- this is presenting 3 different hazards of extreme heat, wildfire and drought. These are, again, dynamic heat maps. I actually don't know if they're synced. But in any event, you get a sense of how these are going to be sort of evolving over the next 8 decades. And I should just back up to say that the presentation of these results all refer to a medium-high scenario, which basically is a trajectory that we're currently -- it's our baseline which we're kind of currently tied to, but which could change in the succeeding decades as countries do or don't take action regarding their ability to control carbon emissions. So on this slide, we have extreme heat becomes an issue everywhere. And that's kind of shown by the fairly even shading across the whole country. Whereas for wildfire, what we're seeing is sort of Rocky Mountain and South Central States and California and the Southwest already have quite substantial GDP exposures to high-likelihood wildfire conditions. But then over the next 30 years, we're going to see this increase over the Great Plains and sort of the center of the country. Whereas for drought, this is now affecting about 2% of the population. And then we're going to see it expand beyond the western states eastward to the plain states in the Midwest and really constitute quite a great -- quite a severe challenge. Next slide, please. So this is a heat map of tropical cyclone frequency. And our definition is we consider category 3 and above tropical cyclones, which is quite a high level of intensity. Right now, there are 23 states that are exposed to tropical cyclones. And looking at this map, you might think, "Ah, so who cares?" This is really like -- okay, Florida is not in a good way, but -- and clearly, coasts are affected. But the issue here really is that -- so more than 2/3 of the 23 states that are currently exposed have GDP and population exposures that exceed 50%. So this really reflects the clustering of economic activity and population along the very attractive coasts. So this is a consideration that I think is really worth kind of taking into account. Next slide, please. So pluvial flooding, which is basically a rainfall-related -- sort of intense rainfall-related flooding is a super-interesting climate hazard. It's one that I think had only recently kind of hit people's radars with a very intense kind of events that we've seen in California and in the Pacific Northwest. And basically, it's centered right now in Alaska and west of the Rocky Mountains, Pacific Coast and so on. And in terms of kind of extreme values, we're seeing the ramp-up somewhat later in the forecast period, not so much the immediate decades. But I think, by the 2050s, we're expecting around 6% of U.S. counties will have material GDP exposure to pluvial flooding, and this number rises to 60% of counties by 2090. And we're seeing very striking intensification from the initial areas into the U.S. South, the Midwest and the Northeast over the next several decades. Next slide, please. So this is -- these are heat maps now of coastal and fluvial flooding. Coastal flooding is already an issue in the Gulf states, like Louisiana, Texas, Florida and also the Eastern Seaboard. But over the next 30 years, we're looking to see very large increases in terms of absolute hazards for coastal flooding in the Northeast. And so knowing this in advance, it's helpful to sort of kind of think that resilience planning should take into account this rapid escalation. And some of the key adaptation measures will include approaches such as managed retreats, nature-based solutions, updating current infrastructure and so on. As for fluvial flooding. Right now, this is kind of interesting. A far higher proportion of state GDP is currently exposed to fluvial flooding rather than coastal flooding, and New England is particularly at risk. So this is another -- in this particular graph, notice that there are blank spots which are basically areas that don't have any fluvial flood risk. So great. Next slide, please. Okay. So this is my last slide. And recall that I spoke about the calculation of composite exposures. So this is where we've taken all the climate hazards that a given locality faces and put them all together in a single score. And I think it's clear that the U.S. West and Florida and the Gulf Coast face some challenges here. And one of the takeaways from this particular slide is the sense of local exposure to compound acute events, and compound acute events is just what they sound like. Acute, we focus on acute because the majority of events on NOAA's billion-dollar weather disasters, which Bob alluded to earlier, are acute events, basically flooding and storms and wildfire and so on. And these are the things like the Maui wildfire; the camp fire at Paradise, California; Hurricane Sandy and so on. And very notably, acute events have been growing in intensity and scale. And this data allows us to find that about 17% of U.S. counties are projected to have exposures -- compound exposures to 2 or more acute climate hazards, both fluvial or coastal flooding, tropical cyclone and wildfire, during the 2020s. And then this figure rises to 20% by the 2050s. So these are just acute events. And as I mentioned, these are rapidly growing in both intensity and scale. But I would say that also these warrant attention because of the potential for these to interact and really have nonlinear or potentially exponential types of impacts and interactions with other factors that contribute -- that can contribute to this magnification of impacts. So this is something that I think there's a lot of research ongoing to understand the interactions, but it's something to pay particular attention to. And then if you look at all hazards of chronic and acute, it's really the case that, over the next 30 years, it's localities in California and the Southwestern U.S., the Rocky Mountains and the Gulf Coast that will really face the highest exposures to multiple climate hazards. I think I'm going to conclude here and turn it over to our next speaker, Tim.

Thank you, Therese. Really interesting stuff. I'm Tim Hall, and let's see if we can get to the physical hazards. We're on it now. Okay, thanks. I'm just going to flesh out a little bit of the input -- some of the input data to Therese's analysis that she talked about. And if we could go to the next slide, there we go, I put together a little sort of flowchart, if you will, for some of the climate hazards that we currently model and also what we're currently working on. I sit in the so-called science team, which is part of the Climate Center of Excellence in Sustainable1 at S&P. And we are a small group of climate scientists charged with developing, building the physical hazard models that underlie some of the work you're seeing. And I wanted to start off by saying that, and I think Therese mentioned this, that we -- in almost all cases, we project our climate hazards out to the end of the century, decade by decade, to the 2090s. And we do it under 4 climate scenarios. These span the range from a rapid transition away from a carbon economy to a so-called business as usual. And these are scenarios are defined by IPCC. They are the shared socioeconomic pathway scenarios, the SSP scenarios. And if you're familiar with the lingo there, it's -- we do 1-2.6, 2-4.5, 3-7.0, which is the kind of mid-high scenario that Therese was talking about, and a higher scenario called 5-8.5. And these -- this is like -- it's crucial to do these 4 scenarios because no one knows for sure how technology and society will transfer away from a carbon-based economy and how rapidly that will occur. So you have to bracket the range of possibilities. And on inputs for these hazards, which I'll talk about in a second, under a wide range of data inputs, one of the biggest and most important for our projections into the future are large suite of global climate models that come from a large number of academic institutions, government labs around the world. We use about 30-plus climate model inputs. This is the most recent generation of global climate models known as CMIP6. And it's fed the most recent IPCC state of the climate report. We model a suite currently of 7 or 8 or 9 hazards, depending on how you count them. I've got that here listed in yellow. There -- many of which Therese has already mentioned: Coastal flood, fluvial flooding, pluvial flood, tropical cyclone, drought, wildfire, a couple of temperature metrics. And water stress is also in there. And part of our job is constantly maintaining those and update those. But since we're sort of in an R&D group here in the science team, most of our time, we spend working on developing new hazards or enhancing our current hazards in various ways, or longer-term projects that span R&D over the next couple of years. For example, the new hazards down below that we're working on, and they're in various states of preparation are landslide, changing wind patterns, winter storms, land subsidence. But the enhancements, equally important, are enhancements to our current hazards. And for one, is that we're always working to increase resolution and granularity of the physical representation of the hazard. Another one that's near and dear to my heart, and I'll talk about more in a couple of minutes, is the probabilistic analysis of hazards. And that's an analysis that recognizes that many of the hazards, especially the acute ones, are fundamentally stochastic in nature. And in addition to that stochasticity, there is a high amount of uncertainty in how they'll evolve in the future. So it behooves us to represent those 2 sources of uncertainty in a probabilistic manner. I'll show examples of that. Some of our hazards are change metrics. They represent the change from a baseline state, a historical baseline state. But they don't always provide the absolute value in the baseline, so we're often working to enhance that by supplementing those hazards with the baseline value so we have change and absolute hazard metrics. We are doing various types of extreme value analysis on our hazards to look at high-impact remote events, events that can happen once in 200 years, once in 500 years on average that are highly unlikely each year but could have catastrophic impacts. And of course, we're always updating our hazards with the most recent climate model data and historical data inputs for validation purposes. In terms of broader impacts, one of the things that's quite interesting and potential scary are -- goes under the guise of climate tipping points. I mentioned the 4 scenarios that span a range of technological evolution and transition from a carbon-based economy. But it is possible, not likely, but possible, that the nonlinear aspects of the climate system could hit like physical transition points, where there's a sudden and catastrophic change into some other state that could lead to impacts, climate change impacts that are far greater than any of the scenarios would provide. An example is like a rapid and catastrophic collapse of, say, the Antarctic ice sheets, which should lead to sudden rapid sea level rise and increased coastal flooding. So these aren't likely, but because the impacts are so large, we're making an effort to include such scenarios as storylines in our analysis. That's a long-term project. Correlations across hazards is an interesting and difficult topic we're working on. And also, we're making a push -- so far, we've talked about decadal projections, long-term climate change impacts. But actually, on a shorter time scale, the next season, the next year, the next couple of years, natural fluctuations are the dominant mode of variability. So we're also making a push in seasonal forecasting to supplement our decadal change projections. Next slide. So I thought I can't talk about all that, obviously, but I thought I'd just give you a little bit of a flavor with 2 examples of how we're going about treating uncertainty probabilistically. And the first example is for coastal flood. And to appreciate the importance of this, you have to realize that a coastal flood event is, in a sense, an extreme weather event. And as an extreme weather event, it's not predictable as a specific event more than a couple of weeks, a typical weather forecasting time scales. And so in any given year, how extreme an event occurs, or how if it's -- if you're lucky, you don't have an extreme event, has a certain amount of fundamental randomness to it. So I might call pseudo-random fluctuations, if you will. And we summarized those probabilistically by talking about return periods. So we might say the 100-year return period would be the -- what magnitude could be exceeded with a 1% probability each year? Or the 200-year return period, what could be exceeded with 0.5% probability each year? So that's one way to talk about the underlying stochastic nature of the hazards probabilistically. And here, I've got an example. This is just in Singapore and Eastern Singapore, that satellite image on the upper right, it's a shipping facility. And that red marker there indicates a point on that map. It's a 90-meter by 90-meter pixel. That's our resolution for coastal flood in the global domain. We actually have 30-meter resolution in the U.S. And then below that are the flood depths at that 90-meter location, flood depth above ground as a function of decade going forward from the current day out to the end of the century in meters, the depth in meters on the Y-axis. And each curve is a different return period. So it says like the top curve, the highest curve, is what could happen with an average return period of 1,000 years or 0.1% probability per year. You can see it moving up. In the current day, it's about 1/3 of a meter up to higher than a meter. This site, by the way, is elevated at about 1.5 meters elevation. So flood depth is highly sensitive to elevation. And that's why we need to have high resolution because topography has a lot of resolution, a lot of variability spatially. So that's one source of uncertainty in the coastal flood. That's a fundamental uncertainty in just the fact that flooding is a stochastic process. But another source of uncertainty, of course, is sea level rise. On top of that stochastic process, the flood frequencies are increasing because sea level is increasing due to a variety of climate change physical processes. The warming oceans, warmer oceans takes up more volume, the oceans are expanding. The ice sheets are melting, they're contributing to the ocean level. And these things have a highly local nature, too. They're not just a global, uniform rise. They vary a lot with the region. But it's -- of course, it's quite uncertain. Our understanding of these processes are uncertain. So we have to describe that sea level rise itself with probability distributions. So the figure on the lower right is the distribution of sea level rise. In this case, it's for scenario 5-8.5, the rapid scenario. And that's meters of sea level rise locally in Singapore on the X-axis, and these are probability distributions. The different colors are different decades. We have it decadally. I didn't pop them all because the pocket's too busy. But you can see, by 2100, the dark red curve is a wide range of possibilities. And it might be centered at about 0.8 meters of sea level rise, but it could be up as high as 1.5, even a bit higher than that if you look out at high percentiles. So we take those uncertainties, and they get mapped on to the return period uncertainties for a combined analysis that includes both the stochastic nature of the hazard itself plus the large uncertainty in how that hazard will change. The next slide. And here's just an example for tropical cyclones. It's kind of a similar idea. The tropical cyclone model is -- unfortunately, global climate models don't simulate tropical cyclones very well. So we have a pretty sophisticated statistical model that takes some input from the global climate models. And it trains it on historical tropical cyclone data, and we use that to make our projections. And we are -- we've currently shifted to a metric. We're shifting to a metric where we actually have wind speed, surface wind speed from the tropical cyclones. And again, we're looking at different locations. We're looking at it in terms of the return period of wind speed being exceeded with different return periods on the 10-year, the 50, the 100, the 500, the 1000. We're using 9 different return periods. Yes. And so that's one source of uncertainty. Just like in coastal flood, what's the return period? It's -- tropical cyclones are fundamentally an extreme weather event, so there's an underlying stochastic nature to them. But in addition, how do tropical cyclones change in climate is highly uncertain. So that uncertainty comes from using a wide range of climate models and other sources of uncertainty. And we can -- on top of the -- for each return period, we can provide a confidence interval that it won't exceed such and such, like the median, the 75th percentile, the 95th percentile, the 99th percentile. And it really matters for tropical cyclones. This is just one example down here in the lower left at Hong Kong. And you can see that the red is the 500-year return period. So the 0.2% probability per year of the wind speed being exceeded. And this top dash line is a category 5, a catastrophic hurricane. So you can see that in the current day, back here on the left of the plot, the different confidence intervals are all below Cat 5. But by mid-century, there's a 1% chance that the 500-year return period will actually exceed Cat 5. So depending on your risk appetite, you would look at different confidence intervals. Anyway, I hope that's given you a bit of a flavor of just one aspect of the current work that's going on in the science team. And happy to entertain questions in the chat. Thanks.

Tim, that's great. Thank you. It's fascinating to see the underlying science behind all this analysis. And Therese, I want to come back and thank you for -- it's very interesting to see how some of the hazards will evolve over time and which municipalities are exposed or not and how that changes over time. So I'd like to kind of now hand it over to our last speaker, Rohan Thakkar, who will talk through how this analysis is actually being incorporated into ratings, which I think is very interesting as well. So Rohan, over to you.

Thanks, Bob. A lot of exciting stuff from Therese and Tim. Great. Hello, guys. I'll quickly walk through on how we think about incorporating ESG into credit ratings. Environmental, social and governance factors typically incorporate an entity's effect on and impact from the natural and social environment and the quality of its governance. From a credit ratings point of view, we are interested in those ESG factors that can materially influence the creditworthiness of an entity and also where we have sufficient visibility and certainty to include in our ratings analysis. And that intersection is where -- is what we call as ESG credit factors, which combines between ESG factors and credit factors. Next slide, please. Now the way we typically look at any risk is if the pressure of a specific risk grows faster than its mitigant, that's where we come in and look at it if it involves a rating action. And Sustainable1's data is going to provide us with the visibility on the risk exposure in this case for physical-related risk. Next slide, please. We've been incorporating climate-related risk qualitatively for some time. As you can see here, across different sectors in various years, we've been doing that for some time. The orange bars reflects on the USPS sector. And 2021 is where you see maximum of them because we had several physical events in that specific year. Now in terms of contributing these data from Sustainable1, the data set will provide us with more precise data exposure across various hazards. This in turn helps us have an informed discussion with the management in terms of understanding their adaptation and resilience plans and its potential impact on credit. So the use of data in itself is not expected to lead to a credit rating action. However, the data and scenario analysis can provide us a starting point to understand what was the exposure of this risk, perform period comparisons, and that in turn informs to enhance our forward-looking views. And when we're looking at a risk, we always look at the risk exposure and its risk management efforts to determine the impact on credit. Next slide, please. According to our research, the sustainable bond market is going to grow to $1 trillion this year. And if you see in the middle boxes, that will be around 14% of the overall market. And on the right dials, you will notice that most of it -- most of the issuance is coming from Europe, followed by APAC and North America. In the bottom bar that you see there, out of the total sustainable bonds, green bonds are the ones that are most popular, followed by social and sustainability bonds and some sustainability-linked bonds. On the right dials, you will notice that the sustainable bond issuance has increased the most in the sovereign sector in 2023. Next slide, please. So within S&P Ratings, we perform second-party opinions for all sustainability bonds, which is green, social, sustainability and sustainability-linked ones. Here are some snippets of some of those analysis. If you noticed, some of them also have Shades of Green because Shades of Green is a company that S&P Ratings acquired at the end of 2022. And now we have an integrated methodology between Shades of Green and legacy Ratings. We cover more than 660 second-party opinions with a team of 70-plus analysts and have received several external recognition in the form of awards. Next slide, please. The key features of our SPO field includes issuer sustainability context, which considers the sustainability factors that are most relevant for the issuer and how they are addressed in the financing, while we also review the issuer strategy for sustainability. In project analysis, this looks at how green, socially sound and sustainable the project categories are. And for green projects, we also have Shades of Green, which depicts what role does an activity play in the transition to a low-carbon and climate-resilient future. Climate principles, we assess whether the financial documents aligns with certain principles, like ICMA LMA. In the formal request, we will also perform alignment with EU Taxonomy, or ICMA's Climate Condition Finance Handbook, and mapping to the UN Sustainable Development Goals. Next. So this is for the spectrum of our shades, from dark green to all the way to red. And what the shades really represent are qualitative opinion of how consistent an economic activity or financial investment is with a low-carbon, climate-resilient future, which is aligned with Paris alignment. Next slide. So on the sustainability-linked financing, some of the key features include how relevant the KPIs are, how ambitious the sustainability performance targets are, instrument characteristics, reporting, and post-issue review, along with mapping to UN SDGs. That is all I had from Ratings today, and I'll hand it over back to Bob.

Thanks, Rohan. That was great. It's really cool to see kind of the -- how the physical risk content is being integrated into the Ratings process. Fascinating to see that.

[Operator Instructions] I'm going to jump to a few of the questions that are available now to give you all a little bit more time also to put those in. So Therese, I want to come back to you and just kind of talk about, what are you planning in terms of how to incorporate financial impacts, climate financial impacts, for munis in the near to longer term?

Great. So this is the -- kind of the, I think, especially alluding to the way that you framed the initial setting, I think there's kind of a very multidimensional question of how does this data set, for instance -- can this help us think about the downstream effects on, let's say, pricing or financing costs? And can we somehow tie these all together? And first thing I would say is that, keep in mind, this is an exposure data set, so therefore it's a really key input, but obviously not the only input. And there's many different steps along the way. So one of the things that's really key and in fact, we're working on this now is to look at how does adaptation affect both climate hazard exposure, which can be quite significant. And also, how does it affect entity vulnerability? So as you think about the kind of the kind of chain of causation, there's exposure vulnerability and then kind of impact, which could be fiscal or economic impact. And then there is finally kind of the -- kind of how that enters into either financing costs or pricing. And I think as Rohan spoke, there's clearly several different factors also weighing in on how you would look at a specific municipality's kind of sort of situation and how climate enters into its -- both its kind of status and also what this means for the instrument side of issues. So we're starting with this exposure, important input. We are also looking at adaptation, and that actually is something we're focused on, looking at state adaptation to start. But just understanding how is this going to change climate exposures and also how is this going to change specific entity vulnerability. Vulnerability itself also varies a lot. You can have a very wealthy community that can afford to put -- invest a lot. Or you can have a community that is, let's say, much -- an elderly, less -- let's say not so well-off community. And they may not have the financial resources or they may need to lean on external financial resources. So anyway, the vulnerability can also vary a great deal. And then also the exact kind of fiscal and economic situation for that locality can also differ. So with all those factors in mind, what I would say is that we're focusing first on looking at adaptation to understand fully how that weighs in. We will also be looking at vulnerability of different localities and the different variables that -- the range of variables that can exist to affect vulnerability, whether it's socioeconomic, demographic or economic. And then I think just to add one more dimension, which is that I think there's a lot of interest in some dynamic processes, which basically include how are property markets and insurance companies and so on responding to this acceleration of acute events. That's a super important dynamic. It basically feeds into how property markets are evolving and then what that means for localities which depend on property taxes for revenues. A related kind of dynamic processes is, can we expect that migration within the U.S. can change over time as people, say, they decide we don't want to live in a place that is too hot or has too many floods or whatever, or the threat of wildfire is too high. So I think we'll be looking at all the elements along this chain, starting off with a focus on adaptation, but then also really looking at the dynamic processes as well because I think that's such an important factor. So that's my initial take. And please check back in a year or sooner to see what progress has been made.

Thanks. That's great. Clearly, we're looking at it from all angles. There's a lot to incorporate there. That's great. So Rohan, I want to come back to you a little bit and maybe ask this question here about, how is this content being leveraged within the Ratings analysis? And how have you seen kind of the uptake in usage amongst our user base?

Yes. So I think a couple of weeks ago, we published a paper, white paper, called Assessing How Megatrends May Influence Credit Ratings. Within that, we have 5-step framework. And in the framework, the first step is to understand the potential magnitude of the megatrend's impact on credit. And the sustainability is one of the biggest megatrend in today's time and fully the most impactful. Second is to understand the transmission channels on how it would impact credit. And third is to define some plausible scenarios. And then we will conduct a scenario analysis using those scenarios. And all of that should help us determine the potential future Rating impact on entities in highly exposed sectors and geographies. So this data set in specific helps us look at -- gives us all the risk exposure data for physical hazards across several -- or 9 hazards that Tim and Therese explained. It really informs our analysts to ask the right questions with the management on what the asset locations are and if there are any adaptation or resilience plans. And if the assets are pretty fixed, then how those risk exposures can really materialize in impact and transmit to credit, really. So that's the process. And if you want to learn more, I think our colleagues from Ratings analytics can -- also did a webinar which talks about navigating uncertainty in U.S. governments and physical climate risks. And that will provide much more details. But at a broad level, we would really look at this as an insight to begin our analysis.

Excellent. Thank you. That's great. So it's clear this is starting to be applied within that Rating analysis. And it's clearly one of the big global metrics that we see here. So Tim, I want to come back to you. So we've got uncertainty on the socioeconomic front, the ability to pay, the beginning, getting -- incorporating this into the range. And then there's the uncertainty in the underlying science. There was one thing that I saw about that, that actually was a bit counterintuitive. Where we've got regions of decreased tropical cyclone hazard, how does that happen? So it seems counterintuitive. It seems like every time I read about something, it's increasing strength.

Yes. Thanks, Bob. That's actually a great question. It's true that it's -- if you just sort of peruse headlines in the popular press, you will see that hurricanes are getting stronger. And so you naturally might jump to the conclusion that therefore the hazard is everywhere increasing. But actually, while it's true that hurricanes, tropical cyclones generically are getting stronger in the sense that storms that form are very likely becoming more intense, a lot of other things are changing as well. For example, one of the important things is that the tracks they take, how they propagate through the ocean before they get to land, are changing as well. And so even if the storms are getting stronger, if there -- if you are lucky enough to be in a region where the tracks are preferentially being slightly steered away from you by climate change, you actually might have reduced hazard. Because it's a stronger storm, but it doesn't make landfall near you, you're not going to have hazard. So we actually model all of those features, the changing tracks, the changing intensity of the storms, how many storms form each year, where they form. All these things are changing in climate with distinct signatures. And when you put them together, it's a pretty complex pattern along the coasts for the impact.

Thanks, Tim. Yes, that's -- it's great to hear and really gives some kind of insight into those nuances that we're able to model and capture. So I'm going to end there and kind of give the audience some time here before their next meeting or whatever they're off to next. I want to thank all of my presenters, and thank the audience for listening. Again, there is a survey that, if you have the time, we would love to hear from you. Please go in and fill that out and give us as much feedback as possible. And again, thank you all for your time, and thank you to my presenters.

Thank you, Bob, and thank you, everyone.

Thank you, everyone.

Thanks, everyone.