1 00:00:00,640 --> 00:00:03,120 Speaker 1: This is Tom Rowlan's Reese, and you're listening to Switched 2 00:00:03,160 --> 00:00:06,320 Speaker 1: on the podcast brought to you by Bloomberg NIF. Climate 3 00:00:06,360 --> 00:00:09,520 Speaker 1: adaptation is no longer just about protecting assets. It's become 4 00:00:09,520 --> 00:00:14,400 Speaker 1: a question of financial resilience. Rising insurance costs, shifting property values, 5 00:00:14,440 --> 00:00:17,480 Speaker 1: and the growing impact of extreme weather are increasingly flowing 6 00:00:17,520 --> 00:00:19,840 Speaker 1: through to city and county balance sheets, with knock on 7 00:00:19,880 --> 00:00:24,079 Speaker 1: effects from municipal bond markets. Across the US, climate risk 8 00:00:24,160 --> 00:00:28,000 Speaker 1: is already reshaping local finances. Counties with high exposure are 9 00:00:28,000 --> 00:00:31,960 Speaker 1: seeing insurance premiums rise, property markets soften, and tax revenues 10 00:00:32,000 --> 00:00:35,199 Speaker 1: come under pressure, particularly in regions heavily reliant on real 11 00:00:35,320 --> 00:00:38,360 Speaker 1: estate for funding. At the same time, differences in climate 12 00:00:38,360 --> 00:00:41,120 Speaker 1: exposure are beginning to show up in municipal credit profiles, 13 00:00:41,240 --> 00:00:44,840 Speaker 1: creating clear winners and losers. So how are climate risks 14 00:00:44,840 --> 00:00:47,440 Speaker 1: filtering into public finances and what does this mean for 15 00:00:47,479 --> 00:00:50,840 Speaker 1: investors watching the municipal market. On today's show, I'm joined 16 00:00:50,840 --> 00:00:54,000 Speaker 1: by Daniel u, a specialist in climate resilience and adaptation 17 00:00:54,120 --> 00:00:57,240 Speaker 1: here at BNEF, to discuss her note climate risk in 18 00:00:57,360 --> 00:01:01,280 Speaker 1: US municipal finances. Storm Ahead NIF clients can find this 19 00:01:01,440 --> 00:01:04,720 Speaker 1: note along with other related research by heading to BNIF 20 00:01:04,760 --> 00:01:07,600 Speaker 1: go on the Bloomberg terminal or BNIF dot com. If 21 00:01:07,680 --> 00:01:10,280 Speaker 1: you'd like to learn more about how BNIF approaches strategy 22 00:01:10,319 --> 00:01:14,040 Speaker 1: research on the energy transition, including developments in commodity markets, 23 00:01:14,080 --> 00:01:17,199 Speaker 1: trends across different sectors, and the cross cutting technologies shaping 24 00:01:17,240 --> 00:01:20,480 Speaker 1: the future, you can find more information on BNIF dot com. 25 00:01:20,600 --> 00:01:22,080 Speaker 1: And if you'd like to speak with a member of 26 00:01:22,080 --> 00:01:24,720 Speaker 1: our team about becoming your client, email us at sales 27 00:01:24,720 --> 00:01:27,800 Speaker 1: dot BNF at Bloomberg dot net. And with that, let's 28 00:01:27,800 --> 00:01:29,920 Speaker 1: take a closer look at what this means for cities, 29 00:01:30,000 --> 00:01:42,880 Speaker 1: counties and investors. Hi, Danielle, Welcome to the show. 30 00:01:43,360 --> 00:01:43,839 Speaker 2: Hey Tom. 31 00:01:43,880 --> 00:01:49,320 Speaker 1: Thanks so those of you who are subscribers to bn 32 00:01:49,320 --> 00:01:52,400 Speaker 1: EF will have access to a note that daniel published recently, 33 00:01:52,680 --> 00:01:58,280 Speaker 1: and it has the most beautiful colored maps out of 34 00:01:58,320 --> 00:02:00,880 Speaker 1: any I can ever remember us wishing at BNF and 35 00:02:00,960 --> 00:02:03,880 Speaker 1: we love at BNF a beautifully colored map. We nerd 36 00:02:03,920 --> 00:02:05,720 Speaker 1: out for that sort of thing. I'm telling you all 37 00:02:05,720 --> 00:02:07,480 Speaker 1: this because I know that some of the people on 38 00:02:07,480 --> 00:02:09,840 Speaker 1: the call are not be ANYF subscribers, so you just 39 00:02:10,000 --> 00:02:12,239 Speaker 1: have to trust me when I tell you that Dania 40 00:02:12,320 --> 00:02:15,720 Speaker 1: produced these absolutely beautiful maps, And I suppose my first 41 00:02:15,800 --> 00:02:19,480 Speaker 1: opening question is just broadly, what are these maps trying 42 00:02:19,480 --> 00:02:21,920 Speaker 1: to show? Thanks? 43 00:02:22,040 --> 00:02:24,640 Speaker 2: What a compliment although this is a this is an 44 00:02:24,680 --> 00:02:26,080 Speaker 2: audio forum. 45 00:02:26,280 --> 00:02:28,320 Speaker 1: Yeah, I know. That's that's why I you know, I'm 46 00:02:28,520 --> 00:02:30,480 Speaker 1: in in a way. We're trying to generate business here 47 00:02:30,520 --> 00:02:32,800 Speaker 1: because some people will have heard about the maps and 48 00:02:32,840 --> 00:02:34,760 Speaker 1: they will be getting in touch with our sales team 49 00:02:34,840 --> 00:02:36,160 Speaker 1: because they'll want to see them too. 50 00:02:36,680 --> 00:02:38,880 Speaker 2: Yeah, go look at the maps. They really are beautiful. 51 00:02:40,120 --> 00:02:45,880 Speaker 2: The maps show the propagation of climate risk into fiscal 52 00:02:45,960 --> 00:02:50,800 Speaker 2: risk in very real terms. What we did is used 53 00:02:50,880 --> 00:02:55,360 Speaker 2: these different beautiful colors to show how each and every 54 00:02:55,400 --> 00:03:00,440 Speaker 2: single county in the US is experiencing climate risk that 55 00:03:00,600 --> 00:03:05,919 Speaker 2: then pushes up insurance prices, which then pushes down property values, 56 00:03:06,360 --> 00:03:13,520 Speaker 2: which then endangers local government fiscal revenue because as you know, 57 00:03:14,120 --> 00:03:18,280 Speaker 2: many many county governments really depend on property tax revenue. 58 00:03:18,480 --> 00:03:21,519 Speaker 2: So we use these maps to just show how precarious 59 00:03:21,680 --> 00:03:25,440 Speaker 2: each county is to this chain of events that directly 60 00:03:25,480 --> 00:03:28,959 Speaker 2: links climate risk to fiscal risk and eventually to MEUNI 61 00:03:29,040 --> 00:03:31,560 Speaker 2: bond markets, which are a huge, huge outs of the class, 62 00:03:31,560 --> 00:03:34,160 Speaker 2: not just in the US but globally, so. 63 00:03:34,120 --> 00:03:38,840 Speaker 1: They're really Dania's technicolor maps of doom in a way, 64 00:03:38,720 --> 00:03:42,240 Speaker 1: they're not representing, you know, who's going to get most 65 00:03:42,280 --> 00:03:45,840 Speaker 1: candy next year, They're they're really highlighting real world risk. 66 00:03:45,920 --> 00:03:49,960 Speaker 1: So you kind of have described an entire domino effect 67 00:03:50,200 --> 00:03:54,600 Speaker 1: of consequences, as you say, of climate risks translating into 68 00:03:54,880 --> 00:04:00,200 Speaker 1: fiscal impacts. So let's start. I believe at the beginning 69 00:04:00,240 --> 00:04:01,680 Speaker 1: of that domino tabe well the end, I didn't know 70 00:04:01,680 --> 00:04:03,640 Speaker 1: which way around it is. It's all instantly. But let's 71 00:04:03,680 --> 00:04:07,240 Speaker 1: start talking about property tax. Obviously, property tax is something 72 00:04:07,320 --> 00:04:10,040 Speaker 1: that affects us. All we'll pay it. But you've been 73 00:04:10,120 --> 00:04:12,800 Speaker 1: diving in a little bit deeper. So explain to us 74 00:04:12,800 --> 00:04:17,120 Speaker 1: why that is significant in this whole equation you've just described. 75 00:04:17,520 --> 00:04:23,040 Speaker 2: Yet for me, the property tax map is our last domino, 76 00:04:23,120 --> 00:04:26,719 Speaker 2: and that it really brings this whole storyline together. I 77 00:04:26,960 --> 00:04:31,120 Speaker 2: didn't know how critical property taxes were to local governments. 78 00:04:31,760 --> 00:04:35,920 Speaker 2: Maybe I was naive, but I pulled up the raw 79 00:04:36,000 --> 00:04:39,560 Speaker 2: data set from the Census of Governments and I ran 80 00:04:39,600 --> 00:04:44,039 Speaker 2: it through script and found that there's actually a significant 81 00:04:44,120 --> 00:04:48,400 Speaker 2: number of counties, especially in this middle band up and 82 00:04:48,440 --> 00:04:51,120 Speaker 2: down the middle of the country, that takes seventy five 83 00:04:51,160 --> 00:04:55,880 Speaker 2: percent or more of their annual general revenue from property 84 00:04:55,920 --> 00:05:00,119 Speaker 2: taxes alone, which is huge. I mean, imagine something that 85 00:05:00,520 --> 00:05:05,760 Speaker 2: comes in and deprecates property values, even a tiny bit 86 00:05:06,080 --> 00:05:10,720 Speaker 2: property tax revenue. Then is how that tiny deprecation in 87 00:05:10,839 --> 00:05:15,520 Speaker 2: property values then reverberates on local government balance sheets? 88 00:05:16,240 --> 00:05:19,080 Speaker 1: And so what we're saying is if anything, and we're 89 00:05:19,120 --> 00:05:22,320 Speaker 1: going to talk about what anything could be negatively impacts 90 00:05:22,600 --> 00:05:26,039 Speaker 1: property values in certain counties that are very dependent on 91 00:05:26,040 --> 00:05:29,640 Speaker 1: this revenue, suddenly that means things like local services aren't 92 00:05:29,640 --> 00:05:32,840 Speaker 1: getting funded, schools aren't getting properly funded, and it seems 93 00:05:32,880 --> 00:05:36,000 Speaker 1: like you're maybe on a slippery slope at that point, 94 00:05:36,080 --> 00:05:40,360 Speaker 1: because I can't imagine people exactly being excited about buying 95 00:05:40,400 --> 00:05:44,000 Speaker 1: property in a county that's going through that sort of process, 96 00:05:44,120 --> 00:05:48,000 Speaker 1: which I also imagine doesn't help property values and therefore 97 00:05:48,080 --> 00:05:51,480 Speaker 1: the tax revenue either. Is that a fair characterization of 98 00:05:51,520 --> 00:05:53,520 Speaker 1: what this means when this happens. 99 00:05:53,400 --> 00:05:57,080 Speaker 2: Yeah, that's really fair. I mean, once you have something 100 00:05:57,320 --> 00:06:02,600 Speaker 2: endangering local government fiscal stability, then the repercussions from there 101 00:06:02,640 --> 00:06:06,880 Speaker 2: become very very clear. Like I said, municipal debt from 102 00:06:06,920 --> 00:06:10,440 Speaker 2: those governments, from those issuers could be called into question. 103 00:06:11,360 --> 00:06:15,159 Speaker 2: You mentioned local public services could be called into question. 104 00:06:15,680 --> 00:06:18,880 Speaker 2: I have a selfish view on this because my coverage 105 00:06:18,880 --> 00:06:23,520 Speaker 2: at BNF is climate adaptation and resilience, which is mainly 106 00:06:23,560 --> 00:06:27,800 Speaker 2: going to be funded by probably smaller local governments initially, 107 00:06:27,960 --> 00:06:31,360 Speaker 2: and you can imagine that if their top line revenue 108 00:06:31,880 --> 00:06:36,600 Speaker 2: is endangered, then probably climate adaptation, these long term, big 109 00:06:36,640 --> 00:06:40,279 Speaker 2: infrastructure public good projects are probably one of the earliest 110 00:06:40,560 --> 00:06:43,240 Speaker 2: or easiest to be on the chopping block. And if 111 00:06:43,240 --> 00:06:45,960 Speaker 2: that happens, that really feeds into the beginning of this 112 00:06:46,200 --> 00:06:51,000 Speaker 2: climate fiscal recycle, which is the dangerous part. If governments 113 00:06:51,040 --> 00:06:55,640 Speaker 2: are having their budget squeezed and their climate adaptation plans 114 00:06:55,720 --> 00:06:59,000 Speaker 2: then get chopped, then that makes this climate fiscal risk 115 00:06:59,040 --> 00:07:02,880 Speaker 2: pathway even stronger. It's a positive feedback loop, a dangerous one. 116 00:07:03,000 --> 00:07:06,080 Speaker 2: We found that close to four hundred counties across the 117 00:07:06,240 --> 00:07:11,080 Speaker 2: US take seventy five percent or more of their general 118 00:07:11,160 --> 00:07:14,720 Speaker 2: annual revenue from property taxes alone. So you can imagine 119 00:07:14,720 --> 00:07:17,560 Speaker 2: that if that is endangered in any way, there's no 120 00:07:17,760 --> 00:07:20,600 Speaker 2: other viable source to make up seventy five percent or 121 00:07:20,640 --> 00:07:22,120 Speaker 2: more of your revenue. 122 00:07:22,240 --> 00:07:25,080 Speaker 1: I mean, and I saw that that number in the notes, 123 00:07:25,280 --> 00:07:27,400 Speaker 1: and so I just put in context for those of 124 00:07:27,400 --> 00:07:30,160 Speaker 1: you not familiar with the US, the US has just 125 00:07:30,200 --> 00:07:33,800 Speaker 1: over three thousand counties, so if we're talking around four 126 00:07:33,880 --> 00:07:37,640 Speaker 1: hundred counties, it's more than ten percent of counties. So 127 00:07:38,440 --> 00:07:41,520 Speaker 1: this collectively, if this is you know, the final domino 128 00:07:41,600 --> 00:07:45,720 Speaker 1: you're saying, is the falling property values to fall in 129 00:07:45,720 --> 00:07:48,680 Speaker 1: in county revenues. This is kind of like one of 130 00:07:48,720 --> 00:07:52,240 Speaker 1: the first really tangible effects. If people are in denial 131 00:07:52,280 --> 00:07:54,840 Speaker 1: about the weather and as they know, it's it's all 132 00:07:54,840 --> 00:07:58,240 Speaker 1: made up, this climate change stuff, then something that is 133 00:07:58,360 --> 00:08:01,680 Speaker 1: very tangible is these potent Actually, you know, if there's 134 00:08:01,720 --> 00:08:05,280 Speaker 1: a massive county level bankruptcies, I suppose is where this heads. 135 00:08:05,560 --> 00:08:07,440 Speaker 1: I know you say it's a final domino, but maybe 136 00:08:07,440 --> 00:08:11,480 Speaker 1: it's a final domino in the first really big financial 137 00:08:11,560 --> 00:08:13,440 Speaker 1: impact of climate change. 138 00:08:13,680 --> 00:08:17,840 Speaker 2: Yeah, I think it's the final domino in this pathway, 139 00:08:18,120 --> 00:08:21,120 Speaker 2: but it's in a way the first domino in a 140 00:08:21,200 --> 00:08:24,440 Speaker 2: series of other pathways. Like you say, it could lead 141 00:08:24,520 --> 00:08:29,200 Speaker 2: to local bankruptcies in a worst case scenario, but before then, 142 00:08:29,480 --> 00:08:32,960 Speaker 2: it could in a very real way affect the performance, 143 00:08:33,120 --> 00:08:37,960 Speaker 2: the stability, the investor perception of MUNI bonds from these regions, 144 00:08:38,400 --> 00:08:40,480 Speaker 2: so that, yeah, there's a lot. 145 00:08:40,360 --> 00:08:43,679 Speaker 1: To care about on the pathway to the worst case scenario, 146 00:08:43,960 --> 00:08:46,720 Speaker 1: even if the worst case scenario can be averted. So 147 00:08:46,800 --> 00:08:49,640 Speaker 1: let's get into a little bit more about the different 148 00:08:49,760 --> 00:08:54,280 Speaker 1: factors that can combine to impact property values in this way. 149 00:08:54,360 --> 00:08:59,800 Speaker 1: I mean, at firstly, how does climate risk translate into 150 00:09:00,320 --> 00:09:01,360 Speaker 1: the housing market. 151 00:09:02,080 --> 00:09:06,040 Speaker 2: There's a couple of ways. There's an acute way, which 152 00:09:06,400 --> 00:09:08,800 Speaker 2: I think is pretty easy to imagine. Right, a natural 153 00:09:08,840 --> 00:09:12,200 Speaker 2: hazard comes in and tears through the town and in 154 00:09:12,280 --> 00:09:15,680 Speaker 2: one go rex property values across the board because the 155 00:09:15,679 --> 00:09:21,200 Speaker 2: buildings just simply aren't there anymore catastrophic losses. Yeah, so 156 00:09:21,280 --> 00:09:23,600 Speaker 2: that's an acute pathway. But what we did in this 157 00:09:23,720 --> 00:09:27,520 Speaker 2: note is focused on a chronic pathway that in some 158 00:09:27,559 --> 00:09:31,120 Speaker 2: ways is more pernicious because it can happen anywhere, and 159 00:09:31,200 --> 00:09:35,800 Speaker 2: it is happening everywhere, and because it's chronic and maybe incremental, 160 00:09:36,080 --> 00:09:39,440 Speaker 2: it's not flashy, even though it is very dangerous. And 161 00:09:39,520 --> 00:09:44,679 Speaker 2: so this chronic pathway is climate risks, natural hazards accumulating 162 00:09:44,840 --> 00:09:48,520 Speaker 2: creating a lot of losses. Insurance providers are one of 163 00:09:48,559 --> 00:09:51,640 Speaker 2: the earliest and probably one of the best at pricing 164 00:09:51,679 --> 00:09:54,800 Speaker 2: in climate risks. To their financial instruments. Right now, there's 165 00:09:54,840 --> 00:09:57,920 Speaker 2: a lot of talk about, you know, private insurers pulling 166 00:09:57,960 --> 00:10:02,200 Speaker 2: out of these very risky regions altogether, like you here 167 00:10:02,280 --> 00:10:05,400 Speaker 2: in Florida and parts of California, because they just can't 168 00:10:05,440 --> 00:10:08,640 Speaker 2: bear the cost of the natural hazard downside anymore. So 169 00:10:08,800 --> 00:10:12,640 Speaker 2: these climate risks are being priced in to insurance premiums. 170 00:10:12,840 --> 00:10:16,520 Speaker 2: As your homeowner's insurance gets more and more expensive. That 171 00:10:16,640 --> 00:10:20,400 Speaker 2: obviously has an effect on the performance of the local 172 00:10:20,440 --> 00:10:23,120 Speaker 2: housing market, the local real estate market. And that's a 173 00:10:23,200 --> 00:10:27,640 Speaker 2: known correlation between insurance prices and housing values. As insurance 174 00:10:27,920 --> 00:10:30,960 Speaker 2: goes up, housing values tend to go down. And so 175 00:10:31,040 --> 00:10:34,160 Speaker 2: that's a chronic pathway that is. It is happening. It's 176 00:10:34,160 --> 00:10:37,400 Speaker 2: not flashy, but it is, like I say, dangerous. 177 00:10:37,600 --> 00:10:39,280 Speaker 1: I mean, I don't know why we would expect it 178 00:10:39,320 --> 00:10:43,439 Speaker 1: to be flashy. Where we were looking about this sort 179 00:10:43,440 --> 00:10:46,000 Speaker 1: of doom and doom. Here we expect it to be uh. 180 00:10:46,120 --> 00:10:48,480 Speaker 1: I know what you say, the mundanity of evil. I mean, 181 00:10:49,760 --> 00:10:53,520 Speaker 1: it's it's wrong to call just the physical environment doing 182 00:10:53,559 --> 00:10:56,640 Speaker 1: what it does evil, but the mundanity of destruction. 183 00:10:56,840 --> 00:11:00,600 Speaker 2: I suppose I like the mundanity of evil, and you've 184 00:11:00,640 --> 00:11:03,800 Speaker 2: called me the purveyor of gloom already. I like these terms. 185 00:11:04,280 --> 00:11:06,920 Speaker 1: I mean, Danielle, you are you know, maybe we need 186 00:11:06,920 --> 00:11:09,160 Speaker 1: this someone who's keeping it real with us. And I 187 00:11:09,200 --> 00:11:12,040 Speaker 1: honestly I've read your note and I've thought very hard. 188 00:11:12,160 --> 00:11:14,760 Speaker 1: If I do decide to settle in the US, where 189 00:11:14,840 --> 00:11:16,280 Speaker 1: I'm likely to invest in. 190 00:11:16,240 --> 00:11:19,000 Speaker 2: Property, I know, maybe we have a lot of listeners 191 00:11:19,000 --> 00:11:20,240 Speaker 2: with high risk appetites. 192 00:11:20,720 --> 00:11:24,559 Speaker 1: Oh yeah, so how I mean, well, we can get 193 00:11:24,559 --> 00:11:26,480 Speaker 1: into that. Where are the places that are risky? Then 194 00:11:27,000 --> 00:11:29,240 Speaker 1: the property market doesn't seem to be impacted. We'll get 195 00:11:29,280 --> 00:11:30,760 Speaker 1: to that. But I feel like there's a question that 196 00:11:30,800 --> 00:11:33,000 Speaker 1: I should ask before that, which is, you know, are 197 00:11:33,040 --> 00:11:36,760 Speaker 1: we seeing you mentioned you're seeing insurance premiums being impacted? 198 00:11:36,920 --> 00:11:41,040 Speaker 1: So are we seeing like a divergence between markets that are, 199 00:11:41,400 --> 00:11:44,760 Speaker 1: due to their location high risk from a physical climate 200 00:11:44,840 --> 00:11:47,240 Speaker 1: risk point of view and markets that are not? Are 201 00:11:47,240 --> 00:11:49,640 Speaker 1: we seeing a divergence in the housing markets? 202 00:11:50,240 --> 00:11:52,840 Speaker 2: I think so based on the data that we analyze. 203 00:11:52,880 --> 00:11:55,480 Speaker 2: So one of those colorful charts that you mentioned at 204 00:11:55,520 --> 00:11:59,199 Speaker 2: the beginning is our insurance map. So our insurance map 205 00:11:59,360 --> 00:12:02,560 Speaker 2: categorized is each county in the US into one of 206 00:12:02,640 --> 00:12:06,960 Speaker 2: four buckets high or low climate risk and then rising 207 00:12:07,080 --> 00:12:10,400 Speaker 2: or decreasing insurance premiums over the past couple of years, 208 00:12:10,679 --> 00:12:14,920 Speaker 2: the vast, vast majority of counties fall into the high 209 00:12:14,960 --> 00:12:20,360 Speaker 2: climate risk increasing insurance premiums bucket. On property values, we 210 00:12:20,440 --> 00:12:27,000 Speaker 2: did a similar analysis categorized counties into how quickly their 211 00:12:27,040 --> 00:12:33,080 Speaker 2: property values are increasing or decreasing so hotter cold property markets, 212 00:12:33,320 --> 00:12:36,160 Speaker 2: and then on a different access looked at higher low 213 00:12:36,160 --> 00:12:40,320 Speaker 2: climate risk. So among hot property markets in the US, 214 00:12:40,480 --> 00:12:44,679 Speaker 2: those that are growing very very quickly relative to their 215 00:12:44,720 --> 00:12:48,560 Speaker 2: statewide average. Among those hot property markets, three out of 216 00:12:48,640 --> 00:12:52,760 Speaker 2: four are located in low climate risk zones, which tells 217 00:12:52,840 --> 00:12:56,840 Speaker 2: us that, yeah, home buyers are already factoring in climate 218 00:12:56,920 --> 00:13:00,959 Speaker 2: to some extent into their buying and decisions. 219 00:13:01,360 --> 00:13:04,440 Speaker 1: So when you say growing property market, you mean where 220 00:13:04,840 --> 00:13:08,160 Speaker 1: property prices are going up more than say, the less 221 00:13:08,160 --> 00:13:10,040 Speaker 1: hot markets. That's what you mean by that. 222 00:13:10,320 --> 00:13:16,199 Speaker 2: Yes, hot markets are counties where the local property prices 223 00:13:16,520 --> 00:13:20,559 Speaker 2: have grown at least fifty percent faster than the statewide average. 224 00:13:20,920 --> 00:13:23,959 Speaker 1: So is there anywhere where you find the hottest property 225 00:13:23,960 --> 00:13:27,079 Speaker 1: markets also have lower climate risk? And I'm presuming the 226 00:13:27,120 --> 00:13:30,240 Speaker 1: inverse is true, that you'll find a higher climate risk 227 00:13:30,360 --> 00:13:32,760 Speaker 1: with some of the cooler property markets are the only 228 00:13:32,840 --> 00:13:35,400 Speaker 1: exceptions to that rule that are notable. 229 00:13:35,880 --> 00:13:40,360 Speaker 2: We found some clusters of low climate risk, fast growing 230 00:13:40,400 --> 00:13:45,400 Speaker 2: housing markets in the Pacific Northwest and in the Upper Midwest. 231 00:13:45,960 --> 00:13:48,240 Speaker 2: If you consult the map, you see some of this 232 00:13:48,520 --> 00:13:51,199 Speaker 2: around Michigan, around Colorado. 233 00:13:51,800 --> 00:13:56,000 Speaker 1: All right, so those markets are in a good place 234 00:13:56,240 --> 00:13:59,000 Speaker 1: relatively speaking, but there are I mean, I think I 235 00:13:59,080 --> 00:14:02,240 Speaker 1: remember that there were. There is also some some markets 236 00:14:02,240 --> 00:14:06,120 Speaker 1: that are hot but are higher risk from a climate perspective. 237 00:14:06,160 --> 00:14:07,080 Speaker 1: So how can that happen? 238 00:14:07,760 --> 00:14:13,000 Speaker 2: Yeah, there's obviously many, many, many other factors at play 239 00:14:13,320 --> 00:14:18,880 Speaker 2: that affect property markets, not just climate risk. Climate risk 240 00:14:18,960 --> 00:14:21,960 Speaker 2: maybe is a growing small one, but you know there's 241 00:14:22,000 --> 00:14:27,400 Speaker 2: everything from the local economy to the views and you 242 00:14:27,440 --> 00:14:31,040 Speaker 2: know the weather, how close yard to work. So yes, 243 00:14:31,080 --> 00:14:34,600 Speaker 2: there certainly are markets that are still very fast growing 244 00:14:34,960 --> 00:14:39,280 Speaker 2: but are in very very climate risky zones. And you've 245 00:14:39,320 --> 00:14:42,400 Speaker 2: probably seen a lot of these articles about beachfront properties 246 00:14:42,600 --> 00:14:45,960 Speaker 2: in the southeast that keep going up even though you know, 247 00:14:46,000 --> 00:14:49,280 Speaker 2: we're dealing with more storms, more hurricanes, maybe some sea 248 00:14:49,360 --> 00:14:51,880 Speaker 2: level rise that is putting the value of these properties 249 00:14:51,920 --> 00:14:52,360 Speaker 2: at risk. 250 00:14:53,160 --> 00:14:55,680 Speaker 1: So for now it might not be showing up in 251 00:14:55,720 --> 00:15:00,920 Speaker 1: the property market. Assuming that climate change continue us to 252 00:15:01,040 --> 00:15:05,240 Speaker 1: create unstable weather and climate risk in those regions. At 253 00:15:05,280 --> 00:15:07,920 Speaker 1: some point you would see the climate risk playing more 254 00:15:07,920 --> 00:15:09,400 Speaker 1: of a factor than it does currently. 255 00:15:09,800 --> 00:15:13,640 Speaker 2: You would be Yeah, you would think that's still to 256 00:15:13,760 --> 00:15:14,200 Speaker 2: be seen. 257 00:15:14,520 --> 00:15:18,160 Speaker 1: I think so in a way, what your your research 258 00:15:18,240 --> 00:15:20,440 Speaker 1: on this area is captured is the kind of the 259 00:15:20,480 --> 00:15:23,720 Speaker 1: beginning of what might be a broader trend. You know, 260 00:15:23,760 --> 00:15:26,480 Speaker 1: you're seeing a bit of a correlation here between climate 261 00:15:26,600 --> 00:15:30,040 Speaker 1: risk and the property market. It's not universal. 262 00:15:29,800 --> 00:15:31,760 Speaker 2: Yet, yes, I think that's right. 263 00:15:32,280 --> 00:15:36,000 Speaker 1: So when property values weaken, you know, how does it 264 00:15:36,120 --> 00:15:39,240 Speaker 1: impact you know, the broader financial systems. I mean, we 265 00:15:39,320 --> 00:15:42,360 Speaker 1: talked about property tax, you know, I imagine there's broader 266 00:15:42,440 --> 00:15:46,120 Speaker 1: ramifications than just the property tax and the local government revenue. 267 00:15:46,680 --> 00:15:49,160 Speaker 2: Yeah. I mean there's a reason why housing markets are 268 00:15:49,200 --> 00:15:52,400 Speaker 2: always in the news, right. They underpin so much economic 269 00:15:52,440 --> 00:15:56,000 Speaker 2: activity and financial systems. I mean, you think about the 270 00:15:56,080 --> 00:15:59,720 Speaker 2: mortgage books that are tied to these, and how mortgages 271 00:15:59,760 --> 00:16:03,240 Speaker 2: and being repackaged and traded. You think about how important 272 00:16:03,320 --> 00:16:08,520 Speaker 2: housing affordability is to a number of economic indicators. Anything 273 00:16:08,560 --> 00:16:11,760 Speaker 2: that affects the housing market, I think is of generally 274 00:16:11,800 --> 00:16:15,479 Speaker 2: great concern to many many economists and financial analysts. 275 00:16:15,720 --> 00:16:18,440 Speaker 1: So there's a real vulnerability here, you know, not just 276 00:16:18,520 --> 00:16:21,200 Speaker 1: in impacting local governments and property taxes, but maybe the 277 00:16:21,200 --> 00:16:25,000 Speaker 1: broader investability of certain areas. So we talked a little 278 00:16:25,040 --> 00:16:29,400 Speaker 1: bit about the regions that are maybe less exposed, and 279 00:16:29,680 --> 00:16:32,880 Speaker 1: we're seeing the hotter property market which parts of the 280 00:16:33,000 --> 00:16:36,200 Speaker 1: US would you say is most exposed. But both because 281 00:16:36,240 --> 00:16:38,840 Speaker 1: of the kind of the climate risk, but also, you know, 282 00:16:38,880 --> 00:16:41,120 Speaker 1: we were talking about a big part of this is 283 00:16:41,160 --> 00:16:44,840 Speaker 1: how much of local government revenue comes from property taxes. 284 00:16:45,120 --> 00:16:48,920 Speaker 1: That's the kind of where the combination leads to most vulnerability. 285 00:16:48,920 --> 00:16:49,880 Speaker 1: Where are you seeing that. 286 00:16:50,320 --> 00:16:55,000 Speaker 2: Yeah, on the property tax specifically, it's this north south 287 00:16:55,120 --> 00:16:57,600 Speaker 2: band running up and down the middle of the US 288 00:16:57,800 --> 00:17:01,240 Speaker 2: that is most vulnerable because those ties tend to depend 289 00:17:01,400 --> 00:17:05,000 Speaker 2: most on property tax for a majority of their revenue. 290 00:17:05,200 --> 00:17:06,040 Speaker 1: But we did. 291 00:17:06,080 --> 00:17:11,719 Speaker 2: Compile a an aggregate analysis of all of these risk factors, 292 00:17:11,800 --> 00:17:14,680 Speaker 2: all of these dominoes, if you will. We looked at 293 00:17:14,920 --> 00:17:19,199 Speaker 2: each county's vulnerability not just to climate risks outright, but 294 00:17:19,400 --> 00:17:22,680 Speaker 2: climate risk as it relates to insurance prices, like we said, 295 00:17:23,040 --> 00:17:28,199 Speaker 2: housing markets, like we said, property taxes, and dependency on 296 00:17:28,440 --> 00:17:30,160 Speaker 2: federal disaster aid as well. 297 00:17:30,440 --> 00:17:33,240 Speaker 1: So in a way, you're looking at where the dominoes 298 00:17:33,240 --> 00:17:36,040 Speaker 1: are most unstable, you know, not just where the first 299 00:17:36,040 --> 00:17:38,439 Speaker 1: domino is, like it's a fall, if the first domino 300 00:17:38,600 --> 00:17:41,680 Speaker 1: is climate event, and you're looking at where those dominoes 301 00:17:41,760 --> 00:17:46,159 Speaker 1: are least resilient. If that's a good metaphor let's just 302 00:17:46,240 --> 00:17:50,360 Speaker 1: run with it. Yeah, less regilliant dominoes, and so where 303 00:17:50,480 --> 00:17:52,960 Speaker 1: stands out. You know, when you think of all of this. 304 00:17:53,320 --> 00:17:56,480 Speaker 2: In our composite analysis, the first thing that you'll notice 305 00:17:56,480 --> 00:17:58,840 Speaker 2: when you open this map is this is the true 306 00:17:58,920 --> 00:18:02,480 Speaker 2: technic color map. There's no broad general trend really, and 307 00:18:03,359 --> 00:18:06,440 Speaker 2: that was interesting because that highlighted to us that there's 308 00:18:06,480 --> 00:18:11,000 Speaker 2: a lot more factors at play. And that's why hyperlocal 309 00:18:11,080 --> 00:18:15,600 Speaker 2: analysis is so so key, because you get this patchwork 310 00:18:15,840 --> 00:18:20,040 Speaker 2: of relatively safe, relatively stable counties right next to a 311 00:18:20,119 --> 00:18:22,480 Speaker 2: cluster of more risky ones. So that's the first thing. 312 00:18:22,560 --> 00:18:25,160 Speaker 1: It's interesting. So we can't make you know, broad sweeping 313 00:18:25,200 --> 00:18:28,600 Speaker 1: statements really that when you consider all the factors that 314 00:18:28,880 --> 00:18:32,480 Speaker 1: make particular areas risky from the fiscal impacts, when you 315 00:18:32,520 --> 00:18:35,719 Speaker 1: consider everything, you can't just say, oh, yeah it's the Northwest, 316 00:18:35,760 --> 00:18:38,040 Speaker 1: or oh it's Texas. It's more like you have to 317 00:18:38,119 --> 00:18:42,320 Speaker 1: look at every single county as an individual entity. Yeah. 318 00:18:42,480 --> 00:18:46,920 Speaker 2: On our composite map, that's absolutely true. It is a mosaic. 319 00:18:47,240 --> 00:18:50,200 Speaker 2: You can kind of make out general patterns, so you'll 320 00:18:50,200 --> 00:18:53,560 Speaker 2: see that there's more red in the south and red 321 00:18:53,600 --> 00:18:57,520 Speaker 2: in our coloring. There was high risk across all of 322 00:18:57,560 --> 00:19:01,800 Speaker 2: those dominoes as well as low climate resilience, and then 323 00:19:02,240 --> 00:19:08,879 Speaker 2: one region of green green being low aggregate risk and 324 00:19:08,960 --> 00:19:14,720 Speaker 2: high resilience start to cluster around Michigan, Ohio, parts of Pennsylvania, 325 00:19:14,840 --> 00:19:17,720 Speaker 2: parts of New York, so Ross Belt in general. 326 00:19:17,800 --> 00:19:20,440 Speaker 1: I mean Detroit for the whin. You know, if we're 327 00:19:20,480 --> 00:19:24,920 Speaker 1: talking about a city that's had to endure that fiscal 328 00:19:24,960 --> 00:19:28,680 Speaker 1: slippery slope, it's good to know that Michigan is maybe 329 00:19:29,000 --> 00:19:32,440 Speaker 1: on the right end of this particular equation. I'm curious 330 00:19:32,440 --> 00:19:34,840 Speaker 1: to know, do you see a big difference between urban 331 00:19:34,920 --> 00:19:38,840 Speaker 1: and rural counties in terms of the composite risk. 332 00:19:39,359 --> 00:19:42,440 Speaker 2: In a couple of the underlying data sets that we use, 333 00:19:42,520 --> 00:19:46,920 Speaker 2: you can see an urban rural divide. So, for instance, 334 00:19:47,359 --> 00:19:50,720 Speaker 2: urban counties more metropolitan counties, they tend to have more 335 00:19:50,920 --> 00:19:54,280 Speaker 2: diverse economies and therefore more revenue streams coming in, so 336 00:19:54,320 --> 00:19:57,440 Speaker 2: they tend to be less dependent on property tax alone, 337 00:19:57,920 --> 00:20:02,720 Speaker 2: and the opposite is often true for rural counties also 338 00:20:02,760 --> 00:20:07,120 Speaker 2: when you look at the underlying climate risk layer alone. 339 00:20:07,640 --> 00:20:10,640 Speaker 2: The data set that we use came directly from FEMA, 340 00:20:10,760 --> 00:20:16,439 Speaker 2: the Federal Emergency Management Agency, and their data set quantifies 341 00:20:16,720 --> 00:20:21,520 Speaker 2: natural hazard risk via expected dollar losses, So you can 342 00:20:21,760 --> 00:20:24,639 Speaker 2: you can see how that shows up differently in urban 343 00:20:24,680 --> 00:20:27,679 Speaker 2: and rural counties. Urban counties tend to be show a 344 00:20:27,680 --> 00:20:30,040 Speaker 2: little bit higher because you know, they tend to have 345 00:20:30,119 --> 00:20:34,600 Speaker 2: denser infrastructure, denser population. Well, rural counties typically are a 346 00:20:34,600 --> 00:20:38,480 Speaker 2: little bit lower expected loss due to sparser density. 347 00:20:39,440 --> 00:20:41,920 Speaker 1: You know, I know have been jokingly saying you've created 348 00:20:42,000 --> 00:20:44,560 Speaker 1: the you know, the technical and map of doom, but 349 00:20:45,480 --> 00:20:49,480 Speaker 1: you know, jops aside, the research you've done really highlights, 350 00:20:49,640 --> 00:20:54,320 Speaker 1: as we say, a kind of a domino of effects 351 00:20:54,440 --> 00:20:59,439 Speaker 1: where climate impacts the world of financing government in a 352 00:20:59,440 --> 00:21:03,680 Speaker 1: way that could have very serious consequences. So apart from 353 00:21:03,720 --> 00:21:07,119 Speaker 1: you know, making personal investments in the places on the 354 00:21:07,160 --> 00:21:11,560 Speaker 1: map that you rate as being relative safe havens, I mean, 355 00:21:11,560 --> 00:21:17,360 Speaker 1: what would your recommendation be on how to address this 356 00:21:17,359 --> 00:21:21,560 Speaker 1: this potential domino effect that is waiting to happen. What 357 00:21:21,720 --> 00:21:23,760 Speaker 1: actually and is this something for the federal government to 358 00:21:23,760 --> 00:21:26,639 Speaker 1: be addressing. If you can see that ever happening, some 359 00:21:26,800 --> 00:21:29,560 Speaker 1: state government should be thinking about. Is it something that 360 00:21:29,680 --> 00:21:34,840 Speaker 1: local jurisdictions can be taking action on? Insurance companies? Should 361 00:21:34,880 --> 00:21:37,280 Speaker 1: they be doing something different? Do you have any view 362 00:21:37,400 --> 00:21:40,960 Speaker 1: on what can be done other than, you know, personally 363 00:21:41,000 --> 00:21:43,920 Speaker 1: trying to avoid putting one's investments at risk. 364 00:21:44,400 --> 00:21:45,480 Speaker 2: Yeah, it's a big question. 365 00:21:46,760 --> 00:21:47,919 Speaker 1: Yeah, I mean, I don't want to put you on 366 00:21:47,960 --> 00:21:49,760 Speaker 1: the spot. You know how to know it's okay. 367 00:21:50,320 --> 00:21:51,600 Speaker 2: I think it's a small. 368 00:21:51,480 --> 00:21:54,520 Speaker 1: Climate change when you can't solve climate change. I suppose 369 00:21:54,800 --> 00:21:56,159 Speaker 1: that is the question. 370 00:21:56,720 --> 00:22:02,320 Speaker 2: I think through this research we wrive ABT two takeaways. 371 00:22:02,480 --> 00:22:05,679 Speaker 2: At least I arrived at two takeaways. One that is 372 00:22:05,680 --> 00:22:10,760 Speaker 2: more physical, so physical climate adaptation, physical climate resilience, whether 373 00:22:10,800 --> 00:22:14,600 Speaker 2: it be in the form of a seawall or a 374 00:22:14,640 --> 00:22:19,240 Speaker 2: better drainage system, or protecting and restoring wet lens. Those 375 00:22:19,720 --> 00:22:25,320 Speaker 2: resilience services really do have an economic benefit. Just thinking 376 00:22:25,320 --> 00:22:28,240 Speaker 2: about this pathway that we've outlined. If you have any 377 00:22:28,280 --> 00:22:31,760 Speaker 2: of those wet lens or drainage systems or seawalls protecting 378 00:22:31,920 --> 00:22:36,720 Speaker 2: your region, then you're dampening the wobbliness of your domino system. 379 00:22:37,040 --> 00:22:42,119 Speaker 2: You're protecting more properties from flood or from wildfire or 380 00:22:42,160 --> 00:22:45,280 Speaker 2: whatever it may be, and therefore you're not triggering the 381 00:22:45,400 --> 00:22:53,240 Speaker 2: downstream effects of destabilized government income, destabilized financial system, broken 382 00:22:53,280 --> 00:22:56,720 Speaker 2: mortgage books, what have you. So's that's one thing is 383 00:22:56,920 --> 00:23:00,919 Speaker 2: we can be investing more in climate adaptation and resilience, 384 00:23:00,960 --> 00:23:03,600 Speaker 2: and we should be seeing it as an investment in 385 00:23:03,800 --> 00:23:09,080 Speaker 2: economic protection. The other takeaway is more I suppose for 386 00:23:09,119 --> 00:23:12,200 Speaker 2: the analysts, and it is to make the case that 387 00:23:12,600 --> 00:23:17,080 Speaker 2: these pathways are real and they're entrenching, and they're acting everywhere. 388 00:23:17,280 --> 00:23:20,840 Speaker 2: I mean I mentioned that we looked at this pathway 389 00:23:20,920 --> 00:23:23,960 Speaker 2: because it is chronic and it happens everywhere. It doesn't 390 00:23:24,040 --> 00:23:27,480 Speaker 2: need to wait for a storm or a fire to act. 391 00:23:27,600 --> 00:23:29,760 Speaker 2: It just acts on its own. 392 00:23:30,640 --> 00:23:35,240 Speaker 1: Even the potential for climate disaster is a disaster in 393 00:23:35,280 --> 00:23:35,600 Speaker 1: a way. 394 00:23:35,720 --> 00:23:40,600 Speaker 2: That's right. And so the takeaway is that climate risks 395 00:23:41,080 --> 00:23:44,320 Speaker 2: need to be taken more seriously in all types of 396 00:23:44,320 --> 00:23:47,600 Speaker 2: investment analyses, not just for the meuni markets, as we've 397 00:23:47,600 --> 00:23:50,800 Speaker 2: pointed out here, but really, like we said, for mortgage books, 398 00:23:50,800 --> 00:23:53,560 Speaker 2: for local banks, for local governments, and we need to 399 00:23:53,600 --> 00:23:57,880 Speaker 2: do better about illuminating these pathways and making the direct 400 00:23:58,000 --> 00:24:02,760 Speaker 2: link between climate risks and fiscal outcome very very clear, 401 00:24:03,040 --> 00:24:04,879 Speaker 2: so that that analysis can become better. 402 00:24:05,119 --> 00:24:08,399 Speaker 1: It's so interesting, and I know this wasn't necessarily in 403 00:24:08,400 --> 00:24:10,600 Speaker 1: the scope of the piece of research you've done, but 404 00:24:10,640 --> 00:24:12,800 Speaker 1: you may have a view it and it really relates 405 00:24:12,840 --> 00:24:16,120 Speaker 1: to the first of those questions. These investments in adaptation. 406 00:24:16,680 --> 00:24:18,679 Speaker 1: I can see how you can make the case that 407 00:24:18,720 --> 00:24:22,080 Speaker 1: they make sense, but it's I imagine that when you're 408 00:24:22,080 --> 00:24:24,080 Speaker 1: trying to make the investment case, and this is a 409 00:24:24,240 --> 00:24:28,639 Speaker 1: big investments of money of maybe not particularly wealthy counties, 410 00:24:29,440 --> 00:24:32,600 Speaker 1: and it might be invest in this versus some other 411 00:24:32,640 --> 00:24:36,520 Speaker 1: piece of infrastructure whose benefit is much easier to quantify 412 00:24:36,640 --> 00:24:39,240 Speaker 1: because you know you're building a I don't know, a 413 00:24:39,320 --> 00:24:42,000 Speaker 1: new bridge, well, lots of bridges have been built, and 414 00:24:42,160 --> 00:24:45,640 Speaker 1: is a kind of a well established way of measuring 415 00:24:45,800 --> 00:24:49,000 Speaker 1: the benefit of that bridge, whereas a sea wall or 416 00:24:49,000 --> 00:24:51,280 Speaker 1: something like that. The benefit when I mean, how do 417 00:24:51,280 --> 00:24:54,239 Speaker 1: you quantify that You're quantifying against what might happen. And 418 00:24:54,400 --> 00:24:56,679 Speaker 1: because we're at the really an early stage in the 419 00:24:56,800 --> 00:25:00,040 Speaker 1: sort of climate change journey, I imagine it's difficult. Is 420 00:25:00,080 --> 00:25:02,080 Speaker 1: there a way around that. I mean, firstly, is that 421 00:25:02,119 --> 00:25:04,919 Speaker 1: an actual problem I've just identified, and yeah, do you 422 00:25:04,920 --> 00:25:07,119 Speaker 1: have any view on how that can be addressed. 423 00:25:07,480 --> 00:25:10,359 Speaker 2: I think it is a problem. A lot of people, 424 00:25:10,680 --> 00:25:14,879 Speaker 2: when working with climate adaptation, point to this problem that 425 00:25:15,359 --> 00:25:18,800 Speaker 2: we have a hard time identifying bankability. We have a 426 00:25:18,800 --> 00:25:21,919 Speaker 2: hard time assessing when a project is bankable. Part of 427 00:25:21,960 --> 00:25:24,840 Speaker 2: the reason is, like you've identified a lot of these 428 00:25:25,000 --> 00:25:29,040 Speaker 2: protective infrastructure projects like a seawall, they don't come with 429 00:25:29,080 --> 00:25:31,320 Speaker 2: a revenue stream, So how do you make your money 430 00:25:31,359 --> 00:25:35,159 Speaker 2: back if there's no revenue associated with it. There's just 431 00:25:35,400 --> 00:25:39,080 Speaker 2: avoided lost revenue. So I think that is an issue 432 00:25:39,119 --> 00:25:43,320 Speaker 2: that many people are working on, and that's why more 433 00:25:43,400 --> 00:25:47,640 Speaker 2: data helps and more analyzes into how all of these 434 00:25:47,640 --> 00:25:51,200 Speaker 2: things are connected that really does help what you've described. 435 00:25:51,280 --> 00:25:54,439 Speaker 1: It really rings a bell for me because early on 436 00:25:54,440 --> 00:25:57,520 Speaker 1: in my benf career, my era of research was energy 437 00:25:57,520 --> 00:26:02,360 Speaker 1: efficiency and the kind of companies selling energy efficient products 438 00:26:02,359 --> 00:26:06,120 Speaker 1: and services trying to go to a building and say, 439 00:26:06,160 --> 00:26:09,560 Speaker 1: you know, we'll put in new windows or do all 440 00:26:09,600 --> 00:26:12,600 Speaker 1: sorts to reduce your energy bill and it's a total win. 441 00:26:12,760 --> 00:26:14,680 Speaker 1: The thing they always said was the struggle is that 442 00:26:14,840 --> 00:26:17,520 Speaker 1: although you can show that the financial benefit is real 443 00:26:17,600 --> 00:26:21,520 Speaker 1: on paper. In practice, there's no actual revenue stream. There's 444 00:26:21,560 --> 00:26:24,560 Speaker 1: a revenue stream versus a counterfactual where you didn't make 445 00:26:24,600 --> 00:26:28,480 Speaker 1: the investment, and that quickly evaporates in most sort of 446 00:26:28,520 --> 00:26:32,280 Speaker 1: fiscal analyses. Now in this case, it's even more complicated 447 00:26:32,320 --> 00:26:36,720 Speaker 1: because the counterfactual is something that might happen. I mean, 448 00:26:36,840 --> 00:26:39,080 Speaker 1: you don't invest in energy efficiency in a building, then 449 00:26:39,080 --> 00:26:41,560 Speaker 1: your energy bill will definitely be higher. If you don't 450 00:26:41,600 --> 00:26:45,560 Speaker 1: invest in climate resilience, you might be vulnerable to an 451 00:26:45,560 --> 00:26:49,440 Speaker 1: extreme climate event if it happens. So it's a possibility 452 00:26:49,480 --> 00:26:54,200 Speaker 1: within a counterfactual. So I guess answers on a postcard. 453 00:26:54,200 --> 00:26:58,080 Speaker 1: If anyone has a smart financing structure that can deal 454 00:26:58,200 --> 00:27:00,959 Speaker 1: with that and make it palitable, we can all agree. 455 00:27:01,320 --> 00:27:02,679 Speaker 1: You know, I'm not saying we do all agree, But 456 00:27:02,680 --> 00:27:04,760 Speaker 1: even if we were all agree that this makes sense, 457 00:27:05,000 --> 00:27:06,320 Speaker 1: it's still going to be hot. 458 00:27:06,480 --> 00:27:08,800 Speaker 2: I will say that we do have a little bit 459 00:27:08,880 --> 00:27:12,760 Speaker 2: of a sliver of light on what the counterfactual looks like. 460 00:27:12,840 --> 00:27:15,040 Speaker 2: Because we are starting this sliver of. 461 00:27:15,080 --> 00:27:17,959 Speaker 1: Light in this podcast. We are I want to hear 462 00:27:18,040 --> 00:27:18,760 Speaker 1: all about it. 463 00:27:18,840 --> 00:27:22,680 Speaker 2: We are starting to become much better at accounting for 464 00:27:22,840 --> 00:27:28,360 Speaker 2: the economic losses from climate damages alone. Our colleagues at 465 00:27:28,359 --> 00:27:30,800 Speaker 2: Bloomberg Intelligence do a great job of this. They have 466 00:27:30,880 --> 00:27:34,800 Speaker 2: a climate damages tracker globally and I believe by their 467 00:27:34,880 --> 00:27:39,600 Speaker 2: last accounting in twenty twenty five, globally we lost one 468 00:27:39,600 --> 00:27:44,879 Speaker 2: point three trillion dollars based on climate induced damages. So 469 00:27:45,440 --> 00:27:48,040 Speaker 2: there's a little bit of your counterfactual, right, and that's 470 00:27:48,160 --> 00:27:51,280 Speaker 2: kind of in a best case scenario, thinking that climate 471 00:27:51,359 --> 00:27:54,399 Speaker 2: change doesn't get any worse than it does today losing 472 00:27:54,400 --> 00:27:57,560 Speaker 2: one point three trillion dollars globally per year. And then 473 00:27:57,800 --> 00:28:02,040 Speaker 2: the other point is, I think when you bring up 474 00:28:02,119 --> 00:28:07,760 Speaker 2: adaptation and resilience investments, people tend to think about these huge, huge, 475 00:28:08,320 --> 00:28:12,760 Speaker 2: mega multi year projects, and in some cases that might 476 00:28:12,760 --> 00:28:18,120 Speaker 2: be needed, but there's also a wide variety of other technologies, 477 00:28:18,200 --> 00:28:21,320 Speaker 2: other solutions that can help and can make a difference 478 00:28:21,560 --> 00:28:24,800 Speaker 2: that are much easier to implement, cheaper to invest in 479 00:28:25,280 --> 00:28:29,040 Speaker 2: and fund than those types of projects. Right, there's a 480 00:28:29,119 --> 00:28:34,280 Speaker 2: lot of science behind how beneficial early warning systems are. Right, 481 00:28:34,359 --> 00:28:37,560 Speaker 2: if we can get a couple hours prior notice to 482 00:28:37,680 --> 00:28:39,880 Speaker 2: how bad a flash flood is going to be, or 483 00:28:39,920 --> 00:28:42,720 Speaker 2: a storm or a wildfire, that saves a lot. 484 00:28:43,000 --> 00:28:46,480 Speaker 1: That's a really interesting and I think important point, because 485 00:28:46,560 --> 00:28:48,360 Speaker 1: you know, when I was saying, what I was saying 486 00:28:48,520 --> 00:28:52,680 Speaker 1: is under the assumption that we're talking really big investments 487 00:28:52,800 --> 00:28:55,440 Speaker 1: that you know are going to be paid for over many, 488 00:28:55,520 --> 00:28:57,840 Speaker 1: many years. But what you're saying is there's actually some 489 00:28:58,000 --> 00:29:00,800 Speaker 1: pretty easy stuff that can be done so long as 490 00:29:00,840 --> 00:29:02,480 Speaker 1: there's the will to do it. I think. 491 00:29:02,520 --> 00:29:04,160 Speaker 2: So there is your glimmer of hope. 492 00:29:04,360 --> 00:29:08,600 Speaker 1: Well, let's end on that positive note that although there 493 00:29:08,720 --> 00:29:14,320 Speaker 1: is a stack of very wobbly dominoes waiting to fall, 494 00:29:14,480 --> 00:29:17,000 Speaker 1: there is plenty that can be done to fix them 495 00:29:17,040 --> 00:29:21,920 Speaker 1: in place and maybe mitigate or even avoid this potential 496 00:29:22,120 --> 00:29:26,280 Speaker 1: cascade of consequences from climate change on that the US economy, 497 00:29:26,320 --> 00:29:29,760 Speaker 1: and I'm sure that there's equivalent stories in other countries 498 00:29:29,800 --> 00:29:32,800 Speaker 1: as well. Dania, it's been an absolute pleasure having you 499 00:29:32,840 --> 00:29:36,240 Speaker 1: on today, and it really is a really new frontier 500 00:29:36,360 --> 00:29:38,640 Speaker 1: of research for US at bn EF, And so those 501 00:29:38,680 --> 00:29:41,840 Speaker 1: of you who do have access, I really strongly recommend 502 00:29:41,840 --> 00:29:44,880 Speaker 1: you check out Dania's research on this topic because I 503 00:29:44,880 --> 00:29:46,920 Speaker 1: think this is going to be a really important conversation 504 00:29:47,040 --> 00:29:49,160 Speaker 1: going into the future. So Dania, thank you so much. 505 00:29:49,360 --> 00:29:51,160 Speaker 2: Thanks Tom The Pleasure. 506 00:29:59,680 --> 00:30:00,080 Speaker 1: Today. 507 00:30:00,200 --> 00:30:03,120 Speaker 2: Episode of Switched On was produced by Cam Gray with 508 00:30:03,240 --> 00:30:07,120 Speaker 2: production assistance from Kamala Shelling. Bloomberg NEIF is a service 509 00:30:07,160 --> 00:30:10,440 Speaker 2: provided by Bloomberg Finance LP and its affiliates. This recording 510 00:30:10,440 --> 00:30:13,200 Speaker 2: does not constitute, nor should it be construed, as investment 511 00:30:13,240 --> 00:30:16,680 Speaker 2: in vice, investment recommendations, or a recommendation as to an 512 00:30:16,680 --> 00:30:19,400 Speaker 2: investment or other strategy Bloomberg. A NEIF should not be 513 00:30:19,440 --> 00:30:23,240 Speaker 2: considered as information sufficient upon which to base an investment decision. 514 00:30:23,320 --> 00:30:26,280 Speaker 2: Neither Bloomberg Finance LP nor any of its affiliates makes 515 00:30:26,320 --> 00:30:30,040 Speaker 2: any representation or warranty as to the accuracy or completeness 516 00:30:30,080 --> 00:30:33,080 Speaker 2: of the information contained in this recording, and any liability 517 00:30:33,120 --> 00:30:35,800 Speaker 2: as a result of this recording is expressly disclaimed.