WEBVTT - Climate Risks and Rewards: Rating Companies’ Exposure 0:00:00.080 --> 0:00:03.000 This is Dana Perkins, and you're listening switched on the 0:00:03.040 --> 0:00:07.680 BNEF podcast. With around ninety percent of global emissions covered 0:00:07.720 --> 0:00:10.799 by some sort of net zero target, the transition to 0:00:10.880 --> 0:00:14.840 a less polluting, greener economy is somewhat inevitable. What's more 0:00:14.840 --> 0:00:17.959 hotly debated is the magnitude and speed. So as the 0:00:17.960 --> 0:00:22.160 transition takes place, asset values will invariably shift, and some 0:00:22.360 --> 0:00:25.799 industries and companies will be more exposed to this risk 0:00:25.960 --> 0:00:29.040 than others. On today's show, we discuss some of the 0:00:29.120 --> 0:00:33.040 ways BNF is approaching transition risk across various sectors of 0:00:33.040 --> 0:00:35.920 the economy. The show gets into two pieces of data 0:00:35.920 --> 0:00:39.960 analysis at BNF. One is the Clean Energy Exposure Ratings, 0:00:40.200 --> 0:00:43.519 which help us to look at clean energy specifically and 0:00:43.600 --> 0:00:46.880 assess the percentage of a company's revenues that come from 0:00:46.920 --> 0:00:51.640 clean energy. We also discuss the Transition Risk Assessment Company Tool, 0:00:51.840 --> 0:00:56.120 which helps us understand company specific transition risk and leverages 0:00:56.200 --> 0:01:00.560 climate scenarios, company level financial data, and key transition assets 0:01:00.600 --> 0:01:04.319 across nine sectors to help us navigate what these tools 0:01:04.360 --> 0:01:08.400 are telling investors and the industries undergoing transition. I speak 0:01:08.440 --> 0:01:11.760 with two members of b and EF's sustainable finance team 0:01:11.800 --> 0:01:16.520 specifically focused on transition risk, Tiffin Brandily and Mike Daily. 0:01:17.160 --> 0:01:20.360 We discuss methodology and how to go about ranking a 0:01:20.400 --> 0:01:24.480 company's clean energy revenue. We also discuss the transparency of 0:01:24.560 --> 0:01:29.240 revenues generated by different industries, including oil and gas, and 0:01:29.480 --> 0:01:33.680 how to assess just how clean their energy portfolios really are. 0:01:34.000 --> 0:01:37.760 We also discuss which sectors are most exposed to transition 0:01:37.920 --> 0:01:41.000 risk and why. If you want to receive alerts on 0:01:41.040 --> 0:01:44.360 your device when future episodes of this podcast are released, 0:01:44.720 --> 0:01:46.800 make sure to subscribe. And if you give us a 0:01:46.840 --> 0:01:49.760 review on Apple Podcasts or Spotify, that is going to 0:01:49.800 --> 0:01:52.800 make us more discoverable by others. But right now, let's 0:01:52.840 --> 0:01:56.440 jump into our conversation with Tiffin and Mike about clean 0:01:56.560 --> 0:02:10.000 energy exposure ratings and transition risk. Tiffin, thank you for 0:02:10.080 --> 0:02:13.040 joining today, thanks for having us. And Mike, thank you 0:02:13.120 --> 0:02:13.640 for joining. 0:02:13.960 --> 0:02:15.320 Yeah, thank you very much for having me. 0:02:15.639 --> 0:02:18.280 So we've got two analysts here today to talk. Well, 0:02:18.280 --> 0:02:20.720 we're going to talk about data, but it's the story 0:02:20.760 --> 0:02:24.040 that the data tells, and I think it makes the 0:02:24.040 --> 0:02:25.680 most sense for us to start in a bit of 0:02:25.680 --> 0:02:29.680 a chronological order in terms of what's happening now versus 0:02:30.000 --> 0:02:32.799 how is this impacting the future and how companies think 0:02:32.840 --> 0:02:35.799 about the future. So, in the spirit of what's happening now, 0:02:36.040 --> 0:02:41.240 let's start with our clean energy exposure ratings and definitionally, Mike, 0:02:41.639 --> 0:02:42.360 what are these? 0:02:42.800 --> 0:02:46.360 So, the Keen Energy Exposure ratings are a classification on 0:02:46.639 --> 0:02:49.680 a company's revenue where we use a combination of BNF 0:02:49.760 --> 0:02:53.760 data sets like EV sales or country level generation to 0:02:53.840 --> 0:02:56.760 determine whether or not those revenue streams are in fact 0:02:56.840 --> 0:02:58.880 clean full you in on what our model can do 0:02:58.960 --> 0:03:02.000 is that we've ever saying like over fifty thousand companies 0:03:02.200 --> 0:03:05.720 and from those we've identified over eight thousand companies with 0:03:05.800 --> 0:03:08.400 some level of keleen energy exposure. So we think about 0:03:08.400 --> 0:03:11.320 what that kind of means. In twenty twenty two, those 0:03:11.400 --> 0:03:14.720 eight thousand plus companies tracked more than two point five 0:03:14.800 --> 0:03:17.600 trillion dollars in clean energy revenues and that's about two 0:03:17.600 --> 0:03:21.799 point six percent of global GDP. With the exposure ratings, 0:03:21.360 --> 0:03:24.440 there's two parts what we're offering. We're offering the clean 0:03:24.560 --> 0:03:28.040 energy exposure percentage as well as the exposure ratings, So 0:03:28.360 --> 0:03:31.400 the percentage of themselves take those company revenues and it 0:03:31.680 --> 0:03:35.320 tells you what percentage of a company's revenues are from 0:03:35.400 --> 0:03:39.400 clean energy sources, whereas the exposure rating is a less 0:03:39.480 --> 0:03:42.200 granular view. So this is where we group companies into 0:03:42.520 --> 0:03:45.200 a one to A four buckets, where an A one 0:03:45.280 --> 0:03:47.960 type rating means that what more than fifty percent of 0:03:47.960 --> 0:03:51.200 a company's revenue is derived from clean energy, and an 0:03:51.240 --> 0:03:53.800 A four is where less than ten percent of that 0:03:53.840 --> 0:03:55.920 revenue comes from clean energy. 0:03:56.040 --> 0:03:58.760 This is going to lean very heavily towards some industries 0:03:58.800 --> 0:04:01.080 over others. So what are thees that we tend to 0:04:01.120 --> 0:04:03.200 find in that A one categorization? 0:04:03.520 --> 0:04:05.960 So our A one companies on the peer player side, 0:04:06.000 --> 0:04:10.440 we've mostly seen our renewable manufacturers and developers, I guess 0:04:10.440 --> 0:04:13.840 fit into these sort of categories, So typical companies that 0:04:13.880 --> 0:04:16.080 we've seen a lot of. It's been in the automotive side. 0:04:16.160 --> 0:04:18.400 Companies like Tesla feature quite strongly. 0:04:18.680 --> 0:04:21.240 That's because it's on a percentage term, right, So a 0:04:21.360 --> 0:04:25.520 Tesla is inherently itself an electric vehicle. But how about 0:04:25.520 --> 0:04:29.839 companies like BW where they're definitely looking at that space, 0:04:29.920 --> 0:04:32.599 definitely involved in selling quite a few electric vehicles, but 0:04:32.640 --> 0:04:35.080 also have their internal combustion vehicles. That's where you end 0:04:35.160 --> 0:04:37.320 up finding them further down the track on a two, 0:04:37.360 --> 0:04:38.200 a three, or a four. 0:04:38.320 --> 0:04:41.160 Right, yeah, exactly. I think the automotive industry is going 0:04:41.200 --> 0:04:43.360 to be something super exciting to watch over the next 0:04:43.360 --> 0:04:45.760 ten years because it's very much a divided market. On 0:04:45.800 --> 0:04:48.320 the one side, you have your pure player electric vehicle 0:04:48.360 --> 0:04:50.720 companies like Buid and Tesla. But then on the other 0:04:50.760 --> 0:04:53.040 side you have your more traditional auto makers, which are 0:04:53.279 --> 0:04:55.640 you know, they're playing catch up, right, and they've also 0:04:55.839 --> 0:04:59.480 committed to some pretty bold pledges. I think BMW Over 0:04:59.480 --> 0:05:02.760 the past year, I guess we've seen their EV sales 0:05:02.960 --> 0:05:06.599 increase from about thirteen percent up to about twenty five percent. 0:05:06.720 --> 0:05:08.840 Companies like Volvo have gone from about twenty five percent 0:05:08.920 --> 0:05:11.520 up to thirty eight percent, and these companies are looking 0:05:11.520 --> 0:05:14.800 to either partially or fully electrify their EV sales by 0:05:14.960 --> 0:05:17.240 by twenty thirty. So that's going to be something really 0:05:17.240 --> 0:05:18.000 interesting to watch. 0:05:18.160 --> 0:05:19.880 So from one year to the next, you're going to 0:05:19.920 --> 0:05:22.800 see companies, well you're mentioning a bunch of them moving 0:05:22.960 --> 0:05:25.680 kind of up the rankings into more exposure. But can 0:05:25.720 --> 0:05:28.120 this also be used as a way to identify from 0:05:28.160 --> 0:05:30.159 one year to the next if companies are moving in 0:05:30.200 --> 0:05:33.960 the opposite direction and deemphasizing. Is that a use Is 0:05:33.960 --> 0:05:35.159 that a proper use case? 0:05:35.279 --> 0:05:38.479 You think it'll definitely point out or expose I guess 0:05:38.520 --> 0:05:42.120 companies that are not committing to their pledges. One company 0:05:42.120 --> 0:05:44.479 that I've kind of noticed has a pretty strong pledge 0:05:44.520 --> 0:05:46.560 as well, that was to fully electrify their sales by 0:05:46.640 --> 0:05:48.880 twenty thirty. But what I've seen in the past year 0:05:48.960 --> 0:05:52.200 is that the EV sales have not increased at all, 0:05:52.480 --> 0:05:55.279 or their percentage of EV sales of about five percent. Yeah, 0:05:55.320 --> 0:05:57.320 the company was in ascent for lack of you know, 0:05:57.360 --> 0:05:58.080 exposing them. 0:05:58.400 --> 0:06:01.440 Okay, we'll see maybe over the next couple of years 0:06:01.520 --> 0:06:04.560 how they move from one category to the next. Let's 0:06:04.600 --> 0:06:06.640 talk a little bit more about these industries, and thank 0:06:06.640 --> 0:06:09.159 you for going into some detail on the automakers. But 0:06:09.400 --> 0:06:11.880 how what sort of trends do you end up seeing 0:06:11.920 --> 0:06:15.040 from one industry to annex to the next in terms 0:06:15.040 --> 0:06:18.159 of which ones seem to have more versus less exposure. 0:06:18.560 --> 0:06:20.719 Definitely noticed a few trends. When we look at the 0:06:20.720 --> 0:06:24.440 top twenty largest revenue generators from last year, electric utilities 0:06:24.480 --> 0:06:26.839 has come out on top. They've definitely had more of 0:06:26.920 --> 0:06:29.720 like a mix to their portfolio. Right, their mix will 0:06:29.760 --> 0:06:31.880 consist of, you know, some of the long established clean 0:06:31.920 --> 0:06:35.359 technologies like nuclear or large hydro, as well as some 0:06:35.400 --> 0:06:38.160 of the more fast growing ones like wind and solar. 0:06:38.279 --> 0:06:41.560 So electric utilities i'd say have topped with the automotive 0:06:41.560 --> 0:06:44.320 industry coming in second. I think it was Tesla have 0:06:44.400 --> 0:06:47.320 raised about eighty one billion dollars last year in clean 0:06:47.400 --> 0:06:50.000 energy revenues. You know, ninety five percent came from the 0:06:50.080 --> 0:06:52.760 ev stuff and five percent from their their solar type industry. 0:06:52.800 --> 0:06:56.320 So definitely trends in terms of industries clean energy exposure, 0:06:56.480 --> 0:06:59.240 and for the actual model that we built, we've factored 0:06:59.240 --> 0:07:01.600 that in. Right, We've got different methodologies for how we're 0:07:01.600 --> 0:07:05.000 approaching the automotive industries versus the electric utilities, versus the 0:07:05.080 --> 0:07:06.880 renewable energy developers, et cetera. 0:07:07.240 --> 0:07:09.680 Are there any geographical trends that you end up seeing 0:07:09.720 --> 0:07:12.920 across these industries and do you see it being emphasized 0:07:13.000 --> 0:07:17.480 more on certain continents or perhaps even more granular level 0:07:17.640 --> 0:07:18.680 in certain countries. 0:07:19.080 --> 0:07:19.360 Yeah. 0:07:19.440 --> 0:07:21.640 I think one of the things that we identified pretty 0:07:21.680 --> 0:07:24.200 early on is we tracked the most clean energy revenues 0:07:24.440 --> 0:07:27.240 from APAC, and that was mostly from China and their 0:07:27.240 --> 0:07:30.000 dominance of unclean energy supply chains. I think it was 0:07:30.120 --> 0:07:32.720 particularly in the solar as well as the energy storage 0:07:32.760 --> 0:07:35.440 type industries. I think it was Emia came in second, 0:07:35.440 --> 0:07:37.760 but there was a huge gap between the revenues in 0:07:37.840 --> 0:07:41.000 APAC and EMEA, and then the US came in in last. 0:07:41.000 --> 0:07:43.760 So there are some u notable trends I think geographically 0:07:43.800 --> 0:07:46.040 we've seen. I'd say, like when we talk about the 0:07:46.080 --> 0:07:49.280 type of sectors, different regions had different strengths in the 0:07:49.280 --> 0:07:52.240 sectors that they were covering. So like on the APAC 0:07:52.360 --> 0:07:54.480 side that was more as I mentioned, like solar and 0:07:54.720 --> 0:07:57.640 energy storage, whereas the US there was more stuff around 0:07:57.680 --> 0:08:01.960 biofuels and electrified transport, and in Amea we saw many 0:08:02.080 --> 0:08:04.800 legric utilities from the nuclear side adding to that level 0:08:04.800 --> 0:08:05.880 of clean energy exposure. 0:08:06.000 --> 0:08:08.000 We've seen well, and we're going to go to transition 0:08:08.120 --> 0:08:10.160 risk in a second, but before we get there, let's 0:08:10.200 --> 0:08:12.680 talk a little bit about one sector in particular that 0:08:12.880 --> 0:08:15.800 is very much in transition. So you've already established the 0:08:15.920 --> 0:08:19.200 electric vehicles and automotive that moves you hire up the ranking, 0:08:19.280 --> 0:08:22.080 and then in utilities you're seeing a lot of this electrification. 0:08:22.440 --> 0:08:25.960 But let's talk specifically about oil and gas, who in 0:08:26.000 --> 0:08:29.080 many respects, a lot of these companies are referring to 0:08:29.440 --> 0:08:32.760 themselves as energy companies in a much more holistic way 0:08:32.800 --> 0:08:35.000 because that's their plan is to be a much more 0:08:35.120 --> 0:08:39.560 diversified business. Where do you find the oil sector on 0:08:39.880 --> 0:08:43.600 this list and are they moving around? I guess which 0:08:43.600 --> 0:08:45.600 categorization do they tend to fall into? 0:08:46.559 --> 0:08:49.680 The short onswer is oil and gas companies have dominated 0:08:49.720 --> 0:08:52.200 the A four type rating, so that's less than ten 0:08:52.240 --> 0:08:54.960 percent clean energy exposure. But one of the huge difficulties 0:08:55.000 --> 0:08:58.719 that we've seen with these majors is through transparency in 0:08:59.160 --> 0:09:02.280 the company revenue reporting. What they tend to do is 0:09:02.280 --> 0:09:05.400 that they tend to group these revenue segments into phrases 0:09:05.520 --> 0:09:07.680 which make them sound a lot cleaner than they really are. 0:09:07.760 --> 0:09:10.480 So two examples that kind of jump to mind. Shell 0:09:10.720 --> 0:09:15.000 has reported on their Renewables and Energy Solutions type division, 0:09:15.160 --> 0:09:19.600 which mostly includes electricity generation, marketing and trading of power 0:09:19.720 --> 0:09:23.320 and pipeline gas. And another example is Repsol reported on 0:09:23.559 --> 0:09:27.199 their commercial and renewables activities, but these mostly include the 0:09:27.240 --> 0:09:29.679 sale of electricity and gas and the sale of oil 0:09:29.720 --> 0:09:33.840 products and liquified petroleum gases. So for this whole piece, Like, 0:09:33.880 --> 0:09:35.720 our main goal for the exposure ratings is not to 0:09:35.800 --> 0:09:38.079 blame these oil majors, but more to point out the 0:09:38.160 --> 0:09:41.840 nuances that we're seeing across industries and how we're able 0:09:41.840 --> 0:09:44.480 to cater in our methodology. We've got an awesome team 0:09:44.600 --> 0:09:47.880 of BNF sector experts who are doing the research on 0:09:47.960 --> 0:09:51.760 these companies to make sure that exposure ratings are reflecting accurately, 0:09:51.920 --> 0:09:54.720 not purely based on what's being reported, but also adding 0:09:54.760 --> 0:09:57.559 that additional layer to make sure that we are accurately 0:09:57.640 --> 0:09:59.679 I guess, mapping out the energy transition. 0:10:00.040 --> 0:10:02.920 Yeah, because what I expect to see is movement across 0:10:02.920 --> 0:10:05.480 these categories and that this particular data set and this 0:10:05.559 --> 0:10:09.240 analysis will become increasingly useful over time as we think 0:10:09.280 --> 0:10:12.880 about net zero targets and how these companies in transition 0:10:13.200 --> 0:10:16.240 really change. I mean, that is the definition of a transition, 0:10:16.400 --> 0:10:18.199 right Like, that is what we're here to talk about, 0:10:18.320 --> 0:10:21.640 is change within the oil and gas space and the 0:10:21.679 --> 0:10:24.680 company specifically exposed here. What I'm hearing from you is 0:10:24.720 --> 0:10:27.160 you've really got to look at the specific activities and 0:10:27.200 --> 0:10:29.240 take a look under the hood, if you will, to 0:10:29.320 --> 0:10:32.400 understand what's happening. Oftentimes, you also end up finding because 0:10:32.400 --> 0:10:35.520 these companies are so big, so many different activities. Do 0:10:35.640 --> 0:10:38.480 you look at the company as one company and it's 0:10:38.600 --> 0:10:42.000 this percentage of activities as you've already outlined, or would 0:10:42.000 --> 0:10:45.679 you for any sort of let's say publicly listed oil company, 0:10:45.720 --> 0:10:50.000 would you actually have multiple different subsidiaries in the ranking 0:10:50.240 --> 0:10:54.160 evaluated differently because the business unit looking at hydrogen is 0:10:54.240 --> 0:10:56.040 going to be very different than the business unit that 0:10:56.200 --> 0:10:57.840 is doing oil exploration. 0:10:58.320 --> 0:11:01.720 Yeah, I'd say when we're evaluate companies, we're looking at 0:11:01.760 --> 0:11:03.000 that company, right. 0:11:02.880 --> 0:11:04.800 Like saying a holistic sense, well. 0:11:04.640 --> 0:11:07.040 Not just in realistic sense, but at every level. So 0:11:07.120 --> 0:11:09.719 if we're looking at a subsidiary company and we're looking 0:11:09.760 --> 0:11:12.320 at their exposure, you may have a subsidiary such as 0:11:12.360 --> 0:11:15.480 like Brookfield Renewables right where we're evaluating them on their 0:11:15.480 --> 0:11:18.160 cleenage exposure and they would rank very highly, right, But 0:11:18.200 --> 0:11:20.760 at the parent level they're involved in a bunch of 0:11:20.800 --> 0:11:23.600 other operations where at the parent level they wouldn't have 0:11:23.800 --> 0:11:26.560 I guess as much clean energy exposure. So at every level, 0:11:26.760 --> 0:11:30.000 we're trying to gauge what level of exposure those companies have. 0:11:30.360 --> 0:11:33.640 And you mentioned disclosure being really important, So the question 0:11:33.720 --> 0:11:36.920 I have is on this same industry. Do we get 0:11:37.000 --> 0:11:39.239 much information from national oil companies. 0:11:39.880 --> 0:11:41.760 I mean, we do, and we don't. One of the 0:11:41.800 --> 0:11:44.600 things that that we're leveraging, right is an in ours 0:11:44.640 --> 0:11:48.800 team that looks at industry type taxonomies and classifying those 0:11:48.840 --> 0:11:52.920 revenue streams to specific industries and sub industries and subjectivities. 0:11:53.320 --> 0:11:55.360 So we're leveraging a lot of that. But I would 0:11:55.360 --> 0:11:57.440 say when it comes to the oil industry, we are 0:11:57.440 --> 0:11:59.760 doing a lot of research at a company by companies 0:12:00.080 --> 0:12:02.080 level to make sure that we are capturing the best 0:12:02.120 --> 0:12:04.240 sort of view on that company. And this is also 0:12:04.320 --> 0:12:06.400 one of the reasons why we do have both exposure 0:12:06.480 --> 0:12:08.800 rating and the percentage, because often we don't have that 0:12:08.920 --> 0:12:11.680 level of granularity, in which case we're happy to say 0:12:11.679 --> 0:12:13.800 it's in a four type company, but we may not 0:12:13.880 --> 0:12:18.120 necessarily be able to distinguish the split between the gas 0:12:18.160 --> 0:12:21.360 portion versus the electricity type generation portion. 0:12:21.559 --> 0:12:23.760 It doesn't tell the whole story. So that's why you 0:12:23.800 --> 0:12:25.559 look at it in a couple of different ways. 0:12:25.679 --> 0:12:27.360 Yeah, yeah, exactly, exactly. 0:12:29.040 --> 0:12:31.760 Also, then let's pivot a little bit to the transition 0:12:31.920 --> 0:12:35.800 risk part of it, which is really around well, how 0:12:35.920 --> 0:12:37.640 things are going to play out for some of these 0:12:37.720 --> 0:12:40.840 companies in the future if they don't move themselves necessarily 0:12:40.920 --> 0:12:44.360 up this ranking. So Tivin, can you explain what the 0:12:44.360 --> 0:12:47.439 transition risk analysis is that we do on our side? 0:12:47.600 --> 0:12:50.120 Sure, So, first of all, transition risk is the risk 0:12:50.200 --> 0:12:54.920 arising from climate policies, technology disruption, are shifting consumer patterns 0:12:55.160 --> 0:12:57.760 and if you think about the discipline of transition risk, 0:12:57.880 --> 0:13:00.440 we have to go back to twenty fifteen. That time 0:13:00.559 --> 0:13:03.560 Mark Corney was Governor of the Bank of England and 0:13:03.600 --> 0:13:06.240 he gave a speech at Lloyd's in London and the 0:13:06.240 --> 0:13:09.720 speech was called The Tragedy of Horizon, essentially explaining that 0:13:09.840 --> 0:13:12.760 the financial markets have a very short term view on 0:13:12.880 --> 0:13:16.440 returns and risk while climate change is essentially a problem 0:13:16.520 --> 0:13:18.320 for the next generations. 0:13:17.880 --> 0:13:20.760 Which is a very famous speech in the takeoff of 0:13:20.840 --> 0:13:23.520 the Tragedy of the Commons right exactly. 0:13:23.679 --> 0:13:27.440 And in this speech he provided two recommendations. Number one 0:13:27.840 --> 0:13:31.599 was around disclosure, making sure that corporates and financial institutions 0:13:31.640 --> 0:13:35.280 have provided enough transparency on their activities and that would 0:13:35.320 --> 0:13:38.600 have a financial market to price the risk in the transition. 0:13:38.800 --> 0:13:40.880 So that's what we discussed about with Mike. And the 0:13:40.880 --> 0:13:43.920 second recommendation was around stress testing. So you got to 0:13:43.960 --> 0:13:46.560 remember that twenty fifteen were still in the aftermath of 0:13:46.640 --> 0:13:50.280 the suprime crisis, and we're just one year away from 0:13:50.440 --> 0:13:54.079 the European Death Crisis twenty fourteen, and so central bankers 0:13:54.120 --> 0:13:58.080 are still thinking about how to strengthen the financial system 0:13:58.200 --> 0:14:02.440 from a micro prudential perspective, so that's financial institution level, 0:14:02.480 --> 0:14:05.000 but also from a macro perspective, and that is the 0:14:05.040 --> 0:14:08.520 resilience of the whole financial system. And so at BNF 0:14:08.559 --> 0:14:13.000 we've worked on a tool so Tracked is BNF's Proprietary 0:14:13.360 --> 0:14:16.520 Transitioners Risk Tool, and it is looking at the revenue 0:14:16.600 --> 0:14:21.280 projections for more than eleven thousand companies across ten climate scenarios. 0:14:21.320 --> 0:14:23.680 And so what we're looking at is really trying to 0:14:23.760 --> 0:14:28.120 understand the whole sensitivity of revenues to the temperature outcome 0:14:28.160 --> 0:14:30.880 of the scenario, all the way up from three degrees 0:14:30.920 --> 0:14:33.480 of warming by twenty one hundred this is for the 0:14:33.520 --> 0:14:36.800 baseline scenarios, all the way down to one point four, 0:14:36.840 --> 0:14:39.280 one point five, one point seven degrees for the New 0:14:39.360 --> 0:14:42.400 Energy Outlook and the NGFs scenarios, and so you have 0:14:42.520 --> 0:14:46.880 this whole temperature sensitivity on revenues, but also each scenario 0:14:46.920 --> 0:14:50.080 has its own characteristics in terms of which technologies are 0:14:50.080 --> 0:14:52.640 deployed to solve the climate equation, and so the tool 0:14:52.720 --> 0:14:57.360 allows investors to explore these risk and opportunities across NGFs 0:14:57.360 --> 0:15:00.480 and BNF scenarios. So trying to understand whether they have 0:15:00.520 --> 0:15:03.680 exposures to China where the transition is going very fast, 0:15:03.760 --> 0:15:06.840 to the US, where we've seen recently policy package being 0:15:07.000 --> 0:15:10.280 passed into Congress, or to Europe where first of fil 0:15:10.320 --> 0:15:13.160 demon is already going down, and so it's very important 0:15:13.200 --> 0:15:17.240 to understand what exposures these firms have in their balance sheet. 0:15:17.360 --> 0:15:17.520 Now. 0:15:17.560 --> 0:15:20.040 The third element in order to build our transition risk 0:15:20.120 --> 0:15:23.360 research is really to look at the changes in the 0:15:23.440 --> 0:15:27.080 demand for commodities and products in different climate scenarios. And 0:15:27.120 --> 0:15:30.240 so if you consider baseline scenario where the world would 0:15:30.240 --> 0:15:33.160 be headed to two degrees or three degrees of warming 0:15:33.200 --> 0:15:35.760 by twenty one hundred, what you'd have is essentially the 0:15:35.840 --> 0:15:40.040 flattening of oil, DeMont, gas, DeMont and other commodities, while 0:15:40.040 --> 0:15:43.200 in net zero transition scenario you would have demond destruction 0:15:43.360 --> 0:15:45.960 coming from oil gas and so this would have impact 0:15:46.040 --> 0:15:48.560 on the holy ecosystem of companies in the oil and 0:15:48.560 --> 0:15:50.600 gas sectors, but also in mining. 0:15:50.800 --> 0:15:54.400 What time horizon are you looking at when you're evaluating 0:15:54.640 --> 0:15:58.000 the risk and how far into the future can somebody 0:15:58.160 --> 0:16:00.600 look when they're thinking about this analysis. 0:16:00.760 --> 0:16:04.080 So we're looking at from now to twenty to fifteen. 0:16:04.400 --> 0:16:07.600 And obviously the issue is that most of the financial 0:16:07.640 --> 0:16:11.240 products have a short lifespan, so that might be two years, 0:16:11.280 --> 0:16:14.280 might be five years, maximum ten twenty years. But most 0:16:14.480 --> 0:16:16.960 of what we call physical risk, which is the risk 0:16:17.080 --> 0:16:20.920 arising from extreme weather events, these are likely to materialize 0:16:20.960 --> 0:16:24.560 over the next twenty thirty, fifty sixty years. And so 0:16:24.800 --> 0:16:28.240 this is coming back to mcconnie's speech here. How the 0:16:28.280 --> 0:16:30.920 real question is how do we price climate risk as 0:16:30.960 --> 0:16:33.520 a whole into the decisions that the finance industry is 0:16:33.560 --> 0:16:34.240 making today. 0:16:34.600 --> 0:16:37.080 I imagine there's a good deal of overlap with the 0:16:37.160 --> 0:16:40.160 industries that you're covering Tiffin and the ones that Mike 0:16:40.240 --> 0:16:42.760 is looking at from the clean energy exposure space, and 0:16:42.800 --> 0:16:46.120 I'm thinking in particular of oil and gas. But really, well, 0:16:46.160 --> 0:16:49.560 let's take a step back and which industries have you 0:16:49.680 --> 0:16:53.640 started with your analysis looking at, because well, presumably you've 0:16:53.680 --> 0:16:56.560 selected them because you think perhaps there's the most to 0:16:56.680 --> 0:16:59.840 find out regarding their exposure to this risk. 0:17:00.120 --> 0:17:03.600 I think oil and gas utilities and automakers are the 0:17:03.680 --> 0:17:05.720 name of the game in terms of transition risk, and 0:17:05.760 --> 0:17:08.480 they are very interesting developments that are happening right now. 0:17:08.640 --> 0:17:12.120 So in September twenty twenty three, you've had the International 0:17:12.240 --> 0:17:15.480 Energy Agency IE that published a report saying that peak 0:17:15.560 --> 0:17:18.760 them on for fossil fuel, what's going to happen prior 0:17:18.840 --> 0:17:21.280 to twenty thirty. So this is a view that we've 0:17:21.520 --> 0:17:23.679 had a been a for the past three years, and 0:17:23.720 --> 0:17:26.480 we're calling oil picked them on by twenty twenty eight. 0:17:26.680 --> 0:17:29.639 And in other words, we were saying transition risk for 0:17:29.760 --> 0:17:32.280 the oil and gas sector organ to materialize within this 0:17:32.359 --> 0:17:35.399 business cycle. So this is not a matter of twenty fifty. 0:17:35.400 --> 0:17:39.120 It's very much a matter of today's board decisions and 0:17:39.480 --> 0:17:43.640 how fast these companies might diversify away from these revenue sources. 0:17:43.880 --> 0:17:47.479 Twenty twenty two, we've had very high commodity prices and 0:17:47.520 --> 0:17:50.159 this is kind of hiding some of the risks inherently 0:17:50.320 --> 0:17:52.879 that they are. So we see this from the baseline 0:17:52.920 --> 0:17:55.840 scenarios all the way down to the net zero scenarios, 0:17:55.960 --> 0:17:58.560 where you would have essentially two percent of the market 0:17:58.640 --> 0:18:01.200 or three percent of the market removed on a yearly basis, 0:18:01.320 --> 0:18:03.920 So net zero scenario is very stressful and it would 0:18:03.960 --> 0:18:08.160 remove an equivalent amount of oid production as BPM share 0:18:08.200 --> 0:18:11.040 produced combined in a single year. So it's a very 0:18:11.080 --> 0:18:14.159 fast transition. And obviously the main cost for this is 0:18:14.280 --> 0:18:17.119 fuel economy standards on the one side, and on the 0:18:17.160 --> 0:18:19.919 other side, the outtake in electric vehicles. Now, there's a 0:18:19.920 --> 0:18:22.480 few markets that are very interesting to look at. China 0:18:22.520 --> 0:18:26.320 obviously is one of them. Sinopec, which is China's biggest 0:18:26.359 --> 0:18:30.359 fuel distributors. They've announced this year that they think peak 0:18:30.400 --> 0:18:33.760 demand has happened in terms of gasoline. So gasoline is 0:18:34.080 --> 0:18:36.919 let's say, the most vulnerable fuel out of the barrel, 0:18:37.119 --> 0:18:40.760 mainly because it is concentrated in lighter duty segments in 0:18:40.800 --> 0:18:43.639 the automotive market. And so this is not an obscure 0:18:43.720 --> 0:18:45.920 research house that is saying this. This is the largest 0:18:46.040 --> 0:18:48.879 a fuel distributor in China, and so this is very meaningful. 0:18:49.119 --> 0:18:51.640 From now on, the oil and gas industry in China 0:18:51.760 --> 0:18:54.560 has to deal with demand destruction. This is something that 0:18:54.680 --> 0:18:58.520 might be surprising for many people, but actually for electric 0:18:58.640 --> 0:19:02.680 vehicle analysts' experiencing this for a while in markets that 0:19:02.800 --> 0:19:05.560 are more heads in terms of their electric vehicle deployment. 0:19:05.640 --> 0:19:08.480 So if you think about Norway that has subsidized evs 0:19:08.520 --> 0:19:11.679 for a long time, since twenty fifteen, the gasoline demand 0:19:11.800 --> 0:19:14.240 in Norway has dropped by twenty five percent, and so 0:19:14.320 --> 0:19:17.439 this is something that will play out as governments and 0:19:17.560 --> 0:19:20.560 consumers shift towards and electrified transport. 0:19:21.160 --> 0:19:24.280 How about the data that we get regarding the company's activities, 0:19:24.440 --> 0:19:28.119 in particular for private companies, I imagine it's exceptionally hard, 0:19:28.200 --> 0:19:29.960 But all in all, are you able to get the 0:19:30.000 --> 0:19:32.600 information you need in order to make a fair assessment 0:19:32.760 --> 0:19:34.320 of rest to these companies? 0:19:34.480 --> 0:19:37.119 Yes, sore are different ways to slice this question. But 0:19:37.400 --> 0:19:40.240 you have a global data team at Bloomberg that looks 0:19:40.280 --> 0:19:42.520 at any type of disclosure, whether it's from a public 0:19:42.560 --> 0:19:45.200 company or private company. They would go out there and 0:19:45.280 --> 0:19:48.440 log whatever financial report they find, and then a team 0:19:48.480 --> 0:19:52.560 would classify revenues in standardized categories, and we use these 0:19:52.600 --> 0:19:57.200 categories to project our transition risk analy this forward looking 0:19:57.240 --> 0:20:00.359 at the revenue at risk across different climate scenarios. And 0:20:00.400 --> 0:20:02.920 so in terms of of these data sets, private companies 0:20:03.000 --> 0:20:06.040 might be captured, but the vast majority of the companies 0:20:06.040 --> 0:20:09.240 we have transparency on are really in the public domain. 0:20:09.720 --> 0:20:12.399 This is going to be an easier transition for some 0:20:12.800 --> 0:20:16.600 companies and industries in particular than others. So which industry 0:20:16.640 --> 0:20:19.600 is fair better than others when it comes to looking 0:20:19.680 --> 0:20:21.800 at well, what are the outputs and what is it 0:20:21.840 --> 0:20:22.400 telling us? 0:20:22.720 --> 0:20:26.040 So I think utility is really an interesting case because 0:20:26.359 --> 0:20:29.720 you have this shift from cool gas more traditional forms 0:20:29.920 --> 0:20:34.160