Climate Change and Health Seminar: Attribution of Health Impacts to Climate Change

Transcript

• 00:00<v ->Hi, everyone, as we have long been</v>
• 00:03waiting for this time,
• 00:05Dr. Ana Vicedo-Cabrera will talk about
• 00:09the most advanced sciences in how to attribute
• 00:13the health impacts to climate change.
• 00:16Also Vicedo-Cabrera's current research
• 00:19develops along the intersection of epidemiology
• 00:22and climate sciences to understand
• 00:25how different climate factors
• 00:26and other related environmental stresses
• 00:29affect health in the context of climate change.
• 00:32So she has many, many excellent publications
• 00:37in the climate epidemiology field,
• 00:39including using the Multi-Country Multi-City Network
• 00:43data to look at the health impacts also,
• 00:46and the health impact to heat-related mortality
• 00:49including the one study
• 00:50that she will be sharing with us today.
• 00:54But before we hand it over to Dr. Ana Vicedo-Cabrera,
• 01:01I want to just mention that housekeeping rules
• 01:06that if you do have any questions,
• 01:08especially our online audience,
• 01:10please feel free to type in your question in the Chat box,
• 01:14and we will have all the questions answered at last.
• 01:19So thank you, and without further ado, Ana.
• 01:23<v ->So just give me a second, sorry.</v>
• 01:34Do you hear me?
• 01:36<v Host>Yeah, okay.</v> <v Attendee>Yes.</v>
• 01:37<v ->Yeah, perfect, so welcome, everybody,</v>
• 01:40and thank you very much
• 01:42for being here in this webinar.
• 01:44And of course, thank you for the invitation
• 01:46to contribute to this event today.
• 01:49It's a great pleasure for me being here
• 01:52to talk about a topic that in a way,
• 01:54has been a bit of my nightmare, I must say,
• 01:58or a bit of my priority during the last,
• 02:01I would say, two, three years.
• 02:04And I believe that it might be one
• 02:07of my main research fields in the coming years as well.
• 02:13So I hope that basically, at the end of my presentation,
• 02:18you might have already some insights about this topic,
• 02:23and probably you might get inspired as well
• 02:28about the specific topic of attribution.
• 02:31So as you could see from the title, we'll talk about
• 02:35"Attribution of Health Impacts to Climate Change",
• 02:37and now, you will see that mostly of my presentation
• 02:40will be focused on heat and health as an example.
• 02:44So let's start from there, from the very beginning.
• 02:48See if I can, okay?
• 02:49Yeah, so basically, heat is considered nowadays,
• 02:57an important environmental stressor.
• 02:59Very recently, it was estimated that
• 03:01around 1% of, sorry, 1% of all deaths globally
• 03:10can be attributed to heat.
• 03:11That translates around seven deaths
• 03:13per 100,000 population per year.
• 03:17And as well, it has been estimated
• 03:19that increase in morbidity, in particular,
• 03:22for cardiovascular, respiratory, and mental disorders.
• 03:26And as well, it has been identified several,
• 03:29let's say, vulnerability populations
• 03:35such as elderly, pregnant women,
• 03:37chronic patients, and children,
• 03:39and finally, it has been estimated an important burden
• 03:42in terms of economic costs,
• 03:44and also reduction in labor productivity.
• 03:46So every signal is taken together is considered
• 03:49that heat is an important element
• 03:52to be assessed in our field of climate change,
• 03:56epidemiologic impact in particular with regards,
• 03:58let's say, climate change research
• 04:00and what is gonna happen in the next decade?
• 04:03So let's say, how heat affects health?
• 04:10In a way, we can see that first,
• 04:14the mechanism by which heat likely
• 04:16impacts human health are complex
• 04:18and understanding, let's say, how the body
• 04:20reacts to heat has been the focus of decades of research
• 04:25in particular, in physiology.
• 04:28It stresses kind of the overarching term
• 04:31that is used for, let's say, to describe
• 04:34the response of human body to the exposure to heat,
• 04:37and usually happens when the body
• 04:40is overwhelmed by metabolic heat production.
• 04:42You can see here from this diagram,
• 04:44basically, our body, what it has to do
• 04:46is kind of react to the exposure to heat
• 04:48in several mechanisms to try to dissipate,
• 04:51or let's say, emit the overheating
• 04:52that we have in our body.
• 04:55And if in a way, this is not efficient,
• 04:58what we can create or let's say, can cost
• 05:00or through different mechanisms that eventually
• 05:03can damage different systems or organs in the body.
• 05:07In this nice review that was recently published,
• 05:12they summarized most of the areas,
• 05:15so the different mechanisms they have seen
• 05:16that actually, there are several,
• 05:19let's say, several organs that are directly
• 05:22affected by heat exposure through different mechanisms
• 05:25such as ischemia, heat cytotoxicity,
• 05:29inflammation, et cetera.
• 05:31This is from, let's say, physiological
• 05:34mechanism important view.
• 05:35But if we, let's say, us, epidemiologists
• 05:38working on climate change research,
• 05:40we know that we assess heat in a kind of different way.
• 05:44Basically, what we do is to have our wonderful,
• 05:47or let's say, so called exposure response functions,
• 05:51that in a way summarizes the association
• 05:53between the ambient temperature in this case,
• 05:57we do focus on heat, and specific health outcome,
• 06:00that is in this case, mortality.
• 06:02That basically, you see here
• 06:04in the y-axis is the relative risk,
• 06:07and in x-axis is the temperature.
• 06:09In this case, representing this, let's say,
• 06:11association that usually is non-linear for the City of Rome,
• 06:15and how we define heat is basically all temperatures
• 06:19above a specific threshold that we call
• 06:21temperature of minimum mortality
• 06:23that is tightly-defined from the curve
• 06:26that corresponds to the temperature
• 06:28for which the risk of dying is minimum.
• 06:30So every temperature above
• 06:32this threshold is considered heat.
• 06:35And we know that risk increases steeply
• 06:38from this point onwards, as you could see here
• 06:40in the curve, up to a maximum.
• 06:43So basically, this is how we assess heat,
• 06:46the effect of heat on health.
• 06:49But to make our life a little bit more complicated,
• 06:52we know that actually the effect of heat
• 06:55is very different across locations.
• 06:57I mean, we can assess this expression response function
• 07:01or curve from a specific population,
• 07:03but we know that we cannot extrapolate this curve
• 07:06to other locations because we know
• 07:08that vulnerability is very specific,
• 07:11very particular for a specific location.
• 07:14It's mostly because of the different combination
• 07:16of let's say, factors, so vulnerability factors
• 07:19or resiliency factors that make this population
• 07:22more or less resilient to an ambient temperature,
• 07:25in this case, for heat.
• 07:27So in a way, you could see that during the last years,
• 07:31there has been a lot of, let's say,
• 07:34developments in the field of climate change epidemiology,
• 07:37and to clarify how heat affects health.
• 07:42But if we have to define specific moments in time
• 07:45in the past but in a way constitute an important,
• 07:49let's say, kind of turning points,
• 07:52how we assess the effect of temperature on health,
• 07:56in what particular in public health
• 07:58is this event that happened in Europe in 2003
• 08:02is this massive European 2003 heat wave
• 08:06that affected very heavily Central Europe
• 08:11on the Southeast in over the Mediterranean area.
• 08:15And basically, what happened is that
• 08:18it was so massive, so unique that made
• 08:22that everybody turned their, let's say,
• 08:25focus, in particular, on public health on heat.
• 08:29Actually, a few years after that,
• 08:32there was an assessment in which they estimated
• 08:37that around 70,000 deaths could happen
• 08:44during this massive event.
• 08:46So in a way, it gave a kind of very clear idea
• 08:50about the severity of the event.
• 08:52And more importantly, what's at that time
• 08:54is really to say, "Okay, if this is happening now,
• 08:57"what would happen in the future?"
• 08:59So we know that probably due to climate change,
• 09:01these events will be much more frequent.
• 09:04So while the epidemiology is, let's say,
• 09:07as we were assessing what were the impacts
• 09:09of that event at that time, in particular,
• 09:13try to understand how we can implement,
• 09:15probably, health measures,
• 09:16how we can protect population from future events,
• 09:20climate science community, they were more thinking,
• 09:25"Okay, and what could have, in a way,
• 09:28"what was the role of climate change in this event?"
• 09:33In a way, there's this kind of very,
• 09:37not very good question,
• 09:39or let's say, an imposed question
• 09:41whether this 2003 heat wave was actually caused,
• 09:44in a very simplistic or deterministic sense,
• 09:47by a modification of external influences on climate.
• 09:49Basically, as we said it, due to the increase
• 09:52in concentration of greenhouse gases in the atmosphere.
• 09:55Because we know that almost any such weather event
• 09:59might have occurred by chance in our world
• 10:01without climate change.
• 10:04In a way, what we have to do is to think
• 10:05that in another perspective,
• 10:07or let's say, to put that question in a different way.
• 10:10Basically, it's how much did human activities
• 10:13increase the risk of occurrence
• 10:16or probability of this event?
• 10:17Or more specifically, did actually climate change
• 10:21alter the severity, frequency, or duration of the event?
• 10:25So this is exactly what attribution
• 10:28and detection studies does.
• 10:30It's a field that it has been, in a way,
• 10:33developed in the last years,
• 10:37in a way, it's more traditional
• 10:38from the climate science community,
• 10:40but not much on the epidemiological side.
• 10:43In a way, one of the first example
• 10:45was actually this study led by Peter Stott from the UK,
• 10:49in which actually they assessed
• 10:52what happened in this 2003 heat wave,
• 10:54and what they came up from this study
• 10:56is that it is very likely that human influence
• 11:00has at least doubled the risk of a heat wave,
• 11:03exceeding this threshold magnitude.
• 11:05So in a way, it's already posting,
• 11:07putting a certain name into this event,
• 11:10saying that probably climate change
• 11:13have altered the, let's say,
• 11:15probability of the occurrence of this event.
• 11:18So how this attribution and detection studies work?
• 11:22So basically, in a very simplistic way,
• 11:25what happens is that we model, let's say,
• 11:30we compare our current climate
• 11:32in presence of climate change
• 11:35that we can actually, in a way, estimate
• 11:39or let's say, mimic or get simulations
• 11:42based on these kind of climate models,
• 11:45in which they kind of try to mimic current conditions
• 11:49based on the what we know
• 11:51in terms of greenhouse gas emissions,
• 11:54and we compare it with a world without climate change.
• 11:58So basically, it's what you can see here.
• 12:00We compare it, here is in this curve
• 12:03is kind of simulated data just for you
• 12:05to illustrate this comparison
• 12:07in which we have our warming climate
• 12:10that is increasing in red
• 12:11compared to a kind of climate-free,
• 12:14climate change-free environment
• 12:16or what they called a naturalized scenario,
• 12:20or let's say, without anthropogenic forcing.
• 12:24So in a way, the difference
• 12:25between these two scenarios would give us
• 12:28what is actually the contribution of anthropogenic forcing,
• 12:34that eventually is what we want to know,
• 12:35what is the human influence in the climate
• 12:37that actually might have altered the climate
• 12:44in current period or historically,
• 12:45during the last decades.
• 12:47So as I said, it's a field that has been developing
• 12:53in the last years in particular
• 12:54for the climatological statistics
• 12:56because of course, we know what is happening now.
• 12:59I mean, we can see whether these simulations
• 13:03from these climate models really mimic
• 13:06what we are experiencing today based on observations,
• 13:10but we don't have data what would have been
• 13:13the world without climate change.
• 13:14So in a way, we have to rely on these models
• 13:18that eventually, when you reduce this,
• 13:20let's say, forcing, so these inputs in your model,
• 13:23you're actually mimicking what would have been
• 13:26the world without climate change.
• 13:28So in a way, you can see that
• 13:30there are a lot of uncertainties.
• 13:31And of course, one thing
• 13:33that I would like you to put in your,
• 13:36kind of your front, in your forefront,
• 13:39is that when we talk about attribution
• 13:41and detection studies,
• 13:42we talk about, basically, probability.
• 13:45This is a term that you will say
• 13:47that it's very pivotal in this story
• 13:50because in a way, it's not a matter of,
• 13:52okay, yes or no climate change
• 13:54have caused this event.
• 13:57It's whether climate change
• 13:58has altered the probability of this event.
• 14:01So basically, what we, let's say,
• 14:05people working or researchers working in,
• 14:07in climate science, mostly on the part
• 14:10on attribution and detection studies,
• 14:12what they do is to compare probabilities.
• 14:15As you could see here in red,
• 14:17the probability of an event happening
• 14:19above a specific temperature threshold
• 14:22compared to the same, let's say, threshold,
• 14:24what would have been the probability
• 14:25in a world without climate change
• 14:27in this counterfactual scenario.
• 14:31Let's say, in this nice review,
• 14:35this researcher, Fredi Otto from Oxford,
• 14:40she very well described in this paper,
• 14:43in this review, in which basically,
• 14:45what she said is that out of this exercise,
• 14:47we would have four different outcomes.
• 14:50First, could have been made, let's say,
• 14:53that this event could have been made more likely
• 14:55because of the anthropogenic climate change, let's say,
• 14:59or it could have been made, let's say, less likely,
• 15:03or there is no detectable influence
• 15:05from anthropogenic climate change.
• 15:07And the last one, with our current understanding
• 15:10and available tools, we are unable to analyze
• 15:12the role of external drivers in this event.
• 15:15So basically, as you could see,
• 15:17is to see whether climate change
• 15:20altered the probability to make this more or less likely.
• 15:23And then we have resources,
• 15:25or let's say, our models can help us to really clarify
• 15:29whether these differences in probability is meaningful.
• 15:33So to understand the world, this work of attribution,
• 15:37is we have to talk about,
• 15:40and usually, you will see this plot
• 15:43in every study in this field.
• 15:46For us, epidemiologists, it's basically
• 15:49something that we, I mean, honestly,
• 15:51when I saw it, I don't know what the hell is this,
• 15:53but at some point, I found it very, very interesting.
• 15:56So I will try to guide you through this plot.
• 16:00You could see here in the y-axis
• 16:03is the monthly temperature equivalent.
• 16:07And basically, in the x-axis,
• 16:10you have what they call return time.
• 16:13It's a measure of, let's say,
• 16:15of probability or severity of an event.
• 16:17It's basically, you put in here in this point,
• 16:20what it's saying is an event
• 16:22has a return kind of time,
• 16:23or basically, it already happens 1 in 10 years.
• 16:27If you go in here, it's an event happening 1 in 100 years.
• 16:32So in a way, you could understand
• 16:34that the far you are from the origin,
• 16:38the more extreme an event could be
• 16:41or the less likely, as you could see here
• 16:45on the other side of the axis,
• 16:47less probable an event is.
• 16:49So basically, what they do, in this case,
• 16:51it's an example of the 2010 heat wave in Russia.
• 16:55And it's basically to compare this blue line,
• 16:59that in a way, is used based on this counterfactual world
• 17:03in which you have, basically,
• 17:05this is the probability of a specific,
• 17:06let's say, event happening at different times.
• 17:09And in red, the same,
• 17:11but in our world currently, in our world,
• 17:13let's say, in our world current conditions,
• 17:15so we don't have to worry.
• 17:16So if you go in this line,
• 17:19this line is basically corresponds to this threshold
• 17:25that was, let's say, defined during this heat wave.
• 17:27But basically, this heat wave reached,
• 17:29I think, it was 24.5 degrees.
• 17:33So in a way, according to these dimensions,
• 17:36what they say is that in current times,
• 17:40this event corresponds to approximately 1-in-50-years event,
• 17:46while in a world without climate change,
• 17:50this event would have corresponded to 1 in a 100 years.
• 17:54So basically, what has happened
• 17:56is that climate change has made the event more likely,
• 18:00let's say, from an event in a hypothetical world,
• 18:031 in 100 years has become 1 in 50 years.
• 18:07So as you could see again,
• 18:10is the EDL changes in probability,
• 18:12making a specific event or a specific temperature threshold,
• 18:16make it more probable, let's say,
• 18:18going from 1 in 100 to 1 in 50 years.
• 18:23So as you could see, it's kind of something
• 18:28that is not very, for us as epidemiologists,
• 18:31a little bit difficult because in a way,
• 18:33it's just talking about probabilities,
• 18:35but for us, translating this into health impacts,
• 18:38it requires a bit of work.
• 18:40But let's say that we're working on it.
• 18:42The idea is that this work of attribution
• 18:46has gained kind of a lot of attention
• 18:49during the last years.
• 18:51In particular, thinking about
• 18:52what happened this last summer.
• 18:54Surely, you might have heard,
• 18:56or even lived there, or suffered this event,
• 19:00this massive heat wave that happened in last summer
• 19:03in West, North in America.
• 19:05So in a way, what happened,
• 19:08we know there were few days,
• 19:11the group came with temperatures above record,
• 19:15and it was a lot of attention in media, et cetera.
• 19:18So while all this was happening,
• 19:21let's say, that there was an initiative
• 19:23from this World Weather Attribution initiative
• 19:27that it's a kind of, again,
• 19:30it's an initiative in which different researchers
• 19:33working on attribution studies put together
• 19:35and try to give answers about whether climate change
• 19:39might have had some role
• 19:41in a specific extreme weather events.
• 19:44Not saying that to provide this evidence
• 19:48a year or two years after,
• 19:49it's really to provide this evidence in the coming weeks,
• 19:53because we know that times matter.
• 19:55If we have suffered a heat wave like this,
• 19:58it would have much more impact
• 20:00if this answer comes earlier in time
• 20:05rather than wait years ahead
• 20:08that people might completely forget
• 20:10about the severity of this event.
• 20:12So the idea is that this group of researchers
• 20:15and they did this analysis,
• 20:16and basically, what they came up with this
• 20:19is that it would be virtually impossible
• 20:22without human-induced climate change.
• 20:24At this event, it's estimated to be about
• 20:271-in-1000-year event in today's climate.
• 20:30So you can have from this sentence,
• 20:33that this event was kind of unique,
• 20:35very extreme, 1 in 1,000 year, it's a lot.
• 20:39And actually, they provided this plot.
• 20:40You will see that it's very similar
• 20:42to what I just shown.
• 20:43And actually, this event that was in here
• 20:47around 40, almost 40 degrees,
• 20:50they saw that it was actually even outside
• 20:53the probable range of events within a year,
• 20:57within let's say, in our current climate.
• 21:00So in a way, it's saying already about
• 21:02this huge severity of this event that happened.
• 21:05So this study and the savings they provide at this time,
• 21:10I said it was a couple of weeks after the event
• 21:14was very, very powerful because it's giving clear,
• 21:19let's say putting the finger into the idea
• 21:22of the role of climate change
• 21:24and the human influence in this event.
• 21:26So the message was very, very, very strong.
• 21:29At the same time, we have to bear in mind
• 21:31that surely you know that there was this,
• 21:34the new report of the IPCC,
• 21:38that part on "The Physical Science Basis"
• 21:41was already published in August.
• 21:43And the difference, let's say,
• 21:47of this report compared to previous reports,
• 21:49really to put in more weight into the influence
• 21:54of the human activities on carbon,
• 21:56let's say, extreme weather events
• 21:59happening in today's world.
• 22:02So again, you can see that the idea of attribution
• 22:07is gaining much more attention lately,
• 22:10but more importantly, because we know
• 22:14that evidence from attribution studies
• 22:18can be used in lawsuits.
• 22:20Basically, has been used for a specific,
• 22:23let's say, companies, individuals, et cetera,
• 22:27to kind of give some complaints,
• 22:31or let's say, to ask for some compensations
• 22:34of a specific losses to, let's say, governments
• 22:37or companies emitting greenhouse gases.
• 22:40So the idea is that during the last years,
• 22:43it has been a tremendous increase in,
• 22:46let's say, different lawsuits
• 22:48that have been implemented against climate change
• 22:51using evidence from attribution and detection studies.
• 22:54In this plot, you can see that it actually
• 22:56was mostly during the second half
• 22:59of the previous decade that actually went super up.
• 23:02And most of these, let's say,
• 23:04these initiatives happened in the US.
• 23:08Most importantly, it's like, okay,
• 23:12we know that there's this tool,
• 23:13but we need scientific, robust scientific evidence
• 23:18that could help us to gain or let's say,
• 23:21to win these different, let's say,
• 23:23initiatives in the courts.
• 23:25At the same time, we know that,
• 23:28let's say, that the idea of these initiatives
• 23:31is that beyond individual litigant,
• 23:33but it is seek to advance climate policies,
• 23:36drive behavioral shifts by key actors,
• 23:39and or create awareness,
• 23:41and encourage public debate.
• 23:43So it goes beyond the idea of compensation.
• 23:45It's really to gain more weight,
• 23:48or let's say, to put more emphasis
• 23:50on the role of climate change on the different,
• 23:53let's say, events, extreme weather events
• 23:54that are happening.
• 23:57At the same time, it's something
• 23:59that has been in a way highlighted.
• 24:03That's why that nowadays, it's not an easy task.
• 24:08There are variants such as accessing to justice,
• 24:10and difficulties in dealing with scientific evidence,
• 24:15and the conservatism of many courts
• 24:19that eventually confronted the different policy issues.
• 24:22So in a way, the idea is that
• 24:25there's a lot of now, research,
• 24:27going on, putting together climate science
• 24:31and low, try to gain or let's say,
• 24:35to create some synergies that eventually
• 24:37would help advance this field on climate litigation.
• 24:41And one important, let's say,
• 24:43call that I take from a recent publication
• 24:45of a colleague of mine, of Rupert Stuart-Smith,
• 24:50they say that greater appreciation
• 24:52and exploitation of current methodologies
• 24:54in attribution science could address obstacles to causation
• 24:58and improve the prospects of litigation.
• 25:01So in a way, it's really saying,
• 25:02"Okay, we know that we can do something.
• 25:05"Law can be a very good path for doing that,
• 25:08"but probably, science is we need to, in a way,
• 25:11"advance knowledge in this field
• 25:12"and try to provide better, let's say,
• 25:15"scientific evidence that could help, let's say,
• 25:17"winning on these different initiatives in courts."
• 25:21So let's say that so far,
• 25:23we have been working more on the part on climate events,
• 25:29more on the weather events,
• 25:31whether one weather event can be attributed,
• 25:33attributed or let's say,
• 25:34how was the role of climate change.
• 25:37But what about health impacts?
• 25:39Okay, we know that one event
• 25:42might have been more frequent or more, let's say,
• 25:45the probability has increased because of climate change,
• 25:47but at some point, we would like to know
• 25:49what this translates into health impacts.
• 25:52So, as I said, the idea is how much
• 25:54of the observed health burden during an extreme event
• 25:57can be attributed to human activities?
• 26:00Or more broadly, how much of the historical
• 26:04health burden of a climate-sensitive outcome
• 26:07can be attributed to climate change?
• 26:09So it's not an easy task.
• 26:11I mean, we know that in there,
• 26:14there might be some kind of different, let's say,
• 26:20developments in terms of methods, et cetera.
• 26:22And actually, one example, for example,
• 26:24you know that I found this article in The New York Times
• 26:28that was basically, they showed some calculations
• 26:31based on a recent report of the CDC,
• 26:35based on that, let's say, what has happened
• 26:37in these massive heat waves in the Northwest in the US.
• 26:42And actually, they just did a very simple estimation
• 26:46on the let's say, estimated the number of deaths
• 26:49that were kind of excess,
• 26:51or let's say, more than normal
• 26:53during that period of time, during the heat wave.
• 26:56Attributing that, let's say,
• 26:57that during this heat wave,
• 26:59more than 600 people died because that in a way,
• 27:03one could attribute to this heat wave.
• 27:08But the other question is how much actually
• 27:10of this burden can be attributed to human activities?
• 27:14Again, talking about the probabilities,
• 27:16not to say yes or no, is to how much of this burden
• 27:19can be kind of attributed to these events.
• 27:23So one of the first exercise that has been done
• 27:28in terms of attribution of health impacts
• 27:29was this study done by Dann Mitchell
• 27:33in which they assessed what was the impact
• 27:36of the 2003 heat wave in London and in Paris.
• 27:39And actually, what they found
• 27:40is that anthropogenic climate change
• 27:42increased the risk of heat-related mortality
• 27:44in Central Paris by 70%, and by 20% in London.
• 27:49So eventually, what is really in here
• 27:51is saying that how much human
• 27:55or anthropogenic climate change
• 27:57has either the severity of this event
• 28:00in terms of how much to really put the value,
• 28:03a number on this contribution in terms of health impacts.
• 28:06So in a way, you will see that
• 28:09it's clearly a different message
• 28:10compared to what I said before.
• 28:12It's not about the excess debt during that period,
• 28:15it's really to say how much,
• 28:16how many beds can be attributed
• 28:17to anthropogenic climate change.
• 28:21So let's say that traditionally,
• 28:22the way how we have assessed this
• 28:25is more into the future.
• 28:27Say compare in what has been there,
• 28:31the health burden attributed to heat in current times
• 28:34compared to what will be in the future
• 28:37using climate change scenarios,
• 28:38assuming that the difference between today
• 28:41and the future can be attributed
• 28:42to anthropogenic climate change.
• 28:44But you will see that from this idea
• 28:47of attribution studies is not about future,
• 28:49it's about present, okay?
• 28:51This is something that you should be reminded,
• 28:53is really to use historical events
• 28:55and try to see what has to be the footprint
• 28:58of human activities in historical events.
• 29:01So when we talk about the tradition, as I said,
• 29:04one could focus on, let's say,
• 29:07on a specific event to say,
• 29:09what they call event attribution
• 29:10for individual extreme weather events
• 29:12as this example of Dann Mitchell,
• 29:15but another example is more on the trend attribution.
• 29:19Basically, for long-term changes
• 29:21in the mean of climatological statistics.
• 29:23So not really to assess specific events,
• 29:25it's to see how much the observed trend
• 29:29can be attributed to human activities.
• 29:32So basically, using this approach,
• 29:35not really to focus on extreme events,
• 29:37but on the trend during a period of time
• 29:40is we did this analysis that it was, I mean,
• 29:46I had the pleasure to lead together with my colleagues
• 29:48of the Multi-Country Multi-City
• 29:49Collaborative Research Network
• 29:51was recently published is here.
• 29:53And this is the reason why I'm talking today
• 29:54about this topic, because thanks to this opportunity,
• 29:57I had really the option to dig a bit into this topic.
• 30:02So in a kind of general terms,
• 30:05this study like the general framework
• 30:10was about combining data and methods in epidemiology
• 30:15with modeling, let's say, climate projections,
• 30:20climate, let's say, simulations of the past years,
• 30:25we were able to estimate how much
• 30:28of the observed heat-related mortality
• 30:31can be attributed to human-induced climate change.
• 30:35So I will go step by step.
• 30:38First, as I said, we used data from the
• 30:40Multi-Country Multi-City Collaborative Research Network
• 30:42in 732 locations in 43 countries in the world.
• 30:48Here, you can see the different location
• 30:50of the different places.
• 30:51And the idea is that we combine,
• 30:54let's say, we took all this data
• 30:56on observed temperature and mortality,
• 30:59and we derived this, the vulnerability function
• 31:03or the exposure response functions of each city.
• 31:06You've seen the state of the art methods
• 31:09in climate change epidemiology
• 31:10is basically to a stage and serious analysis
• 31:13with distributed lag non-linear models
• 31:15and multivariate multilevel meta-regression.
• 31:17Yeah, it sounds super fancy,
• 31:19but in a ways, it's not as complicated,
• 31:22and you'll have all the information
• 31:24on the methods in the paper.
• 31:26I invite you to have a look,
• 31:27review if you would like to learn more
• 31:29about the methodological part.
• 31:31So basically, what we did, as I said,
• 31:33is to estimate the vulnerability of each city,
• 31:37which in a way, was already kind of an advancement
• 31:41compared to previous assessments.
• 31:43Because again, here, the idea is that we clearly
• 31:45or we aim to assess the specific vulnerability
• 31:50of each population to have a better estimation
• 31:53of heat-related mortality in each location.
• 31:56And you see here that it was clearly heterogeneous.
• 31:59We saw as we found in previous assessment,
• 32:02that actually, most of higher risks
• 32:05are usually found in Europe, in the Mediterranean area,
• 32:09and other locations in Asia.
• 32:12So as I said, we combined these exposure response curves
• 32:16with moderate climate data
• 32:18that we got from our colleagues from there,
• 32:21the DAMIP Project, this is
• 32:22the Detection Attribution Model Intercomparison Project
• 32:24that is based on the last CMIP6 simulations.
• 32:29And idea is that for each location in this assessment,
• 32:34we derive a series, let's say, of moderate pairs
• 32:39of moderate climate on daily temperature
• 32:43under current conditions
• 32:45and under our without climate change,
• 32:48that is our counterfactual scenario.
• 32:50Here, you have a kind of illustration of the trends.
• 32:54Basically, in red, you have the observed trend
• 33:00with a warming trend.
• 33:01That it mimics current conditions with climate change
• 33:04while the orange one mimics somewhere
• 33:07without climate change in the absence of warming.
• 33:09So basically, we focused in this period here
• 33:12between 19, yeah, 1990, oh, 1990, oops,
• 33:19I forgot, 1991 to 2006, 2018.
• 33:24Actually, sorry about the numbers,
• 33:25I'm very bad with that.
• 33:26And basically, what we did is as I said,
• 33:28for each location, we obtained these pairs,
• 33:33and we translated these observed,
• 33:36or let's say, simulated temperature
• 33:39into hypothetical excess mortality
• 33:43under these two scenarios.
• 33:45And this is basically what you can see here
• 33:49in this panel A.
• 33:52In solid, you have the anthropogenic,
• 33:58let's say, the heat-related mortality
• 34:01under current condition, let's say,
• 34:03in presence of anthropogenic climate change,
• 34:05while in light here, these bars,
• 34:08you have what would have been heat the excess,
• 34:11or let's say the heat-related mortality
• 34:14in a world without climate change.
• 34:16So basically, we estimated on this
• 34:18for each of the 700 something locations,
• 34:21and we aggregated by country,
• 34:23and this is what you see here.
• 34:24And eventually, we estimated the difference
• 34:27in terms of excess mortality between these two scenarios.
• 34:31That is basically what you find here.
• 34:33So what we saw overall
• 34:35is that 0.98% of excess mortality,
• 34:41heat-related excess mortality
• 34:43in the counterfactual scenario,
• 34:45and of course, more excess mortality
• 34:47in the factor is null,
• 34:49that is with anthropogenic climate change
• 34:51that is currently slipping to 1.56%.
• 34:56So the difference between the two
• 34:58that is basically, this number here is 0.58%.
• 35:03It represents the all-cause mortality
• 35:06that can be attributed to heat induced
• 35:08by anthropogenic climate change.
• 35:11So the idea is that in a final step,
• 35:13what we did is to kind of rescale this difference
• 35:17over the observed, or let's say, the impact
• 35:21or the excess mortality in anthropogenic climate change.
• 35:25In a way to estimate what is the proportion of this,
• 35:30the excess mortality happening today,
• 35:33that can be attributed to human-induced climate change.
• 35:37So it's basically, what we try to illustrate here,
• 35:41and we found that overall, 37% of heat-related deaths
• 35:46can be attributed to human-induced climate change
• 35:49in this assignment, these locations that we included.
• 35:52And in a later step, what we did
• 35:54is to kind of extrapolate this
• 35:57and compute what would be the mortality rate
• 36:00attributed to heat-related or let's say,
• 36:04heat-induced climate change.
• 36:06So in here, what we observed that on average,
• 36:092.2 deaths per 100,000 population per year
• 36:14can be attributed to heat induced in human influences,
• 36:18and let's say, of climate change.
• 36:21So as you could see in this assessment,
• 36:23it had very powerful message.
• 36:27It's really we provide evidence on the clear
• 36:31to tell the impacts of climate change
• 36:34over health burden that we observed today.
• 36:38And you can see that, of course,
• 36:39this evidence can be very, very useful
• 36:42for let's say, to support policy-making processes.
• 36:45And more importantly, I think,
• 36:46it was about the key message about the relevance
• 36:49of these findings in terms of to put
• 36:52a little bit more attention to what is happening,
• 36:55saying that climate change is not something
• 36:57that will happen in the future,
• 36:59it's already happening today.
• 37:02We can talk about the projections,
• 37:04but we cannot focus on your projections
• 37:06in terms of impacts of climate change
• 37:07is really that already we are suffering.
• 37:11So it's really to say, "Okay, we need to do,
• 37:14"or put more emphasis in terms of implementing
• 37:18"a strong mitigation policies to abate
• 37:20"this warming in the future,
• 37:22"but more importantly, to implement adaptation strategies
• 37:27"that would help us to reduce our vulnerability,
• 37:31"in this case, for heat."
• 37:33But of course, we had to acknowledge several limitations,
• 37:37and understood that, although it was one of the biggest,
• 37:39let's say, assessment on this field
• 37:41in terms of attribution of health impacts,
• 37:44we know that for example,
• 37:45it was cannot be considered a worldwide study
• 37:48because we focused our assessment on the locations
• 37:51that were already included in the MCC,
• 37:53and we know that there are important regions
• 37:55in the world that were not covered.
• 37:57This is an important limitation
• 37:59that we have in our study environment directly
• 38:01because we are very much aware
• 38:04that vulnerability is very heterogenous
• 38:07and changes from one location to the other.
• 38:09So at some point, we can extrapolate risk
• 38:12that we observed in Europe
• 38:14into places like Africa or Asia.
• 38:17So at some point, we need better data
• 38:19that would help us to better identify
• 38:22or let's say, assess what is the vulnerability
• 38:25of these locations that currently,
• 38:26are unobserved or unexplored.
• 38:28On the other side as well,
• 38:30something that we have to bear in mind,
• 38:31we have to do a simplification in terms of risk.
• 38:34We assume that, in a way,
• 38:38we did a cultural adaptation in the sense
• 38:40that we assumed a kind of average risk
• 38:44across the 20 years, 30 years that we assessed.
• 38:47And the idea is that okay, it's likely,
• 38:50and we know that as you could see here in this plot
• 38:53that actually, there might have been
• 38:57a partial adaptation of the population to heat.
• 39:01Though at some point, we don't know
• 39:04which impact this could have had
• 39:05because probably, the idea is that probably,
• 39:08at the end of the period, the risk
• 39:10might have been lower compared with the beginning.
• 39:12So eventually, as you could see,
• 39:14we had to do a kind of group simplification
• 39:17and something as well that we have to bear in mind
• 39:19is that the risks that we applied to both scenarios
• 39:22is the observed risk.
• 39:24That is the one that we estimated
• 39:26in our world with climate change.
• 39:28So we don't know what would have been the risk
• 39:31without climate change.
• 39:33So again, it's very difficult,
• 39:35and I expected in the future,
• 39:37it's something that we will implement in them,
• 39:40in this field or in climate change epidemiology.
• 39:43And finally, the lack of epidemiological causal basis.
• 39:46This is important because this assessment
• 39:48is purely based on an ecological design
• 39:51that as most of the climate change
• 39:53and epidemiological studies.
• 39:56So at some point, if we want to talk about the attribution,
• 39:58we have to improve our way,
• 40:01how we can assess causal links in this field.
• 40:06So just as a kind of final wrap-up on this subject,
• 40:10and as I said, I really want you
• 40:11to make it fun about this kind of a study,
• 40:14is first, because as we know,
• 40:18it can be a powerful tool for climate change policy,
• 40:22and as well, it can help understanding the mechanism
• 40:25by which climate change effects have.
• 40:28Can support in finding projections
• 40:29of future health effects of climate change,
• 40:32and as well, improve adaptation to climate change impacts
• 40:35and disaster recovery.
• 40:36As well, it can increase motivation
• 40:39for climate mitigation, as I said,
• 40:41just learning about what is happening today
• 40:42and the urgency to really do that.
• 40:46And also, demonstrate causal links
• 40:48between greenhouse gas emissions
• 40:49and climate change impacts
• 40:51that serve as a basis of evidence
• 40:52underpinning climate-related losses,
• 40:54as I said, previously.
• 40:56And finally, what I believe is also very, very attractive.
• 40:59It's an excellent platform for interdisciplinary research,
• 41:03really to put together experts from different fields,
• 41:05from climate science, working more on the modeling side,
• 41:09climate epidemiologists, working
• 41:11on the ascertain the health impacts.
• 41:13And at the later stage, other experts in other fields
• 41:16like the economy, law, et cetera,
• 41:18can take part on these investigations.
• 41:20So definitely, it's an excellent platform
• 41:22for latching our kind of research area,
• 41:26grab information, address knowledge from other fields
• 41:29and reach our risk portfolio,
• 41:31which I think is also very relevant for young researchers.
• 41:35And just as our final point,
• 41:39something that I think it has to do, bear in mind,
• 41:41and as I said for me, this research field
• 41:45can be considered kind of very powerful research line
• 41:48in the future in climate change epidemiology.
• 41:52Let's say, climate attribution
• 41:54is something that has been developed
• 41:56for years in climate science sphere,
• 41:58but not much in epidemiology.
• 42:00And if we really want to advance in climate litigation,
• 42:06really advance on the fight against climate change,
• 42:09we have to put a value on what is happening
• 42:12in terms of extreme events,
• 42:13in terms of X is that burden, economic cost, et cetera.
• 42:17And all this can help people change your mind,
• 42:21and as well, help, advancing or let's say,
• 42:24winning different initiatives in courts, et cetera.
• 42:27So as important elements that I believe
• 42:30we should focus in the future,
• 42:31is first assess causality, use advanced methods
• 42:35in environmental epidemiology
• 42:37that help us to clarify causal links.
• 42:39Second point, to provide innovative frameworks
• 42:43that probably, I mean, you think about
• 42:46as the world attribution initiative,
• 42:48they provided this evidence on the role of climate change.
• 42:52If we can couple this with health impacts,
• 42:55that could be even much more powerful.
• 42:58And finally, we have to think
• 42:59about how we can address this research question
• 43:02in a more broader perspective
• 43:04and provide probably, global estimates
• 43:06that are closer to the, let's say,
• 43:08the real, what is happening today.
• 43:11So yeah, that's all.
• 43:13Thank you very much for your attention,
• 43:15and I'm happy to take questions, thank you.
• 43:19<v ->Thank you, Ana.</v>
• 43:19Thank you for the wonderful presentation.
• 43:21I think you gave a superb view
• 43:24like an introduction from kind of science, how to tackle
• 43:27and attribute extreme weather events,
• 43:29and these type of extreme events attribution
• 43:32to the trend attribution,
• 43:34and to the landmark study that you have,
• 43:36the MCC quality you've been working on.
• 43:38So thank you very much.
• 43:40And I think there will be a lot
• 43:43of questions from our audience.
• 43:45So while our online audience is typing
• 43:48your questions in the Chat box,
• 43:51we do have already collect some questions from our students.
• 43:55So there are several types of questions
• 43:59that students are particularly interested in.
• 44:01For example, the first type,
• 44:03I think for some of the students still wondering,
• 44:06you have given this great example
• 44:09of attributing heat-related mortality.
• 44:12So they're wondering if this type of technique
• 44:14can be used to attribute other extreme weather events,
• 44:17like hurricanes or wildfires?
• 44:21<v ->Yeah, exactly, I mean, as I said,</v>
• 44:23in this assessment, in this presentation,
• 44:25I focused on heat on health,
• 44:27because in a way, I mean, of course,
• 44:28it's a bit biased because it has been
• 44:31my research field for already several years,
• 44:34but we know that within attribution science,
• 44:38it's not only about heat waves.
• 44:40Actually, there's also a very new report
• 44:44published by this Global Weather Attribution initiative
• 44:49on the floods happening in Central Europe even this summer.
• 44:54Again, put in, estimated that actually
• 44:56the role of climate change was very substantial
• 44:59in let's say, in increasing the probability of this event.
• 45:01So definitely, this kind of framework
• 45:05can be extended to other extreme weather events.
• 45:08Of course, with some caveats and some limitations,
• 45:12but I believe that if we try to, let's say,
• 45:17it would be easy to adapt this framework
• 45:21to other extreme weather events
• 45:22if data, of course, is available.
• 45:27<v ->Thank you, Ana.</v>
• 45:27I think we have a typo from our online audience.
• 45:31Exactly, the same question
• 45:34some of the students are also asking.
• 45:36But Mona is asking,
• 45:39"Why is the A and B data missing environments
• 45:42"from most of Africa?"
• 45:44And also, it's kind of related to the question
• 45:47student's asking in the Multi-Country Multi-City
• 45:50is that they only have South Africa,
• 45:54doesn't have much of Africa.
• 45:56And also, one of my students is asking,
• 45:59why there's no data from the South Pacific,
• 46:03where she have experienced doing this one
• 46:07and like fuzzy.
• 46:08So why there's no such coverage?
• 46:12<v ->Well, maybe I can give you a little bit of story</v>
• 46:15about how the MCC started.
• 46:17And basically, it was, I think in 2014
• 46:21during a conference, with a group of researchers
• 46:24working on climate change epidemiology,
• 46:28mostly on the temperature-related health impacts.
• 46:31They had an informal meeting,
• 46:34and they were discussing the possibility
• 46:36of probably putting together some data from their countries.
• 46:39For example, one have data on temperature mortality
• 46:42in the UK, other have in Japan,
• 46:45the other one had in Spain.
• 46:47So eventually, they realized that,
• 46:51"Okay, maybe we can start putting all this data together
• 46:54"instead of assessing our impacts
• 46:56"or let's say, our estimates in our country,
• 46:59"it would be nice to compare different locations
• 47:01"at the same time."
• 47:02Because as I said, the idea of,
• 47:06the peculiarity in a way of temperature-related
• 47:10health impacts is that this,
• 47:13the effect is very dependent on the location.
• 47:16So it's nice to compare these estimates across locations
• 47:19to understand vulnerabilities
• 47:21and potential vulnerability factors.
• 47:22So as I said, it started kind of informal way,
• 47:26and they started opening the door to other collaborators
• 47:32and colleagues to work in,
• 47:33and eventually, it grew, grew, grew,
• 47:35grew until nowadays that we are around, I think,
• 47:3870 researchers from 43 countries
• 47:43with all these bunch of locations
• 47:45with different data sets.
• 47:46And also, not only focusing
• 47:48on the idea of temperature mortality,
• 47:51but also, air pollution, on projections,
• 47:55on I mean, in a way, it's a project
• 47:58that greatly grow in an exponential way.
• 48:04But the idea how this, then the beauty of this project,
• 48:08how it's developed and how it started
• 48:11is that it works in a very informal way
• 48:13in the sense that the idea how you contribute,
• 48:16you take part of this consortium by providing data
• 48:20on a specific country that is missing
• 48:22because you had it because of your research or whatever.
• 48:27And it's surprising that it is not directly funded.
• 48:29I mean, it works, let's say,
• 48:31off each funds of each partner.
• 48:34The reason why there are some places in the world
• 48:37that is not, let's say, covered within this spread
• 48:40is basically, because so far,
• 48:43we didn't manage to get data from these locations.
• 48:46And I mean, it's a problem of course,
• 48:49of places like in Africa,
• 48:51where good quality on mortality,
• 48:54daily mortality in specific locations in Africa
• 48:58is very difficult to find.
• 48:59Especially because at some point,
• 49:00whether you need this data is somehow
• 49:03comparable in terms of quality and temporal scale.
• 49:07And especially, this idea
• 49:08that it should be daily mortality, et cetera,
• 49:12because the part on them, whether we know
• 49:14that is relatively easy to get it
• 49:16from the analysis data, et cetera,
• 49:20but the main limiting factor here is the mortality data.
• 49:23And that's why in a way,
• 49:25we didn't manage too far to kind of get this information
• 49:29here in terms of observed mortality in this assessment.
• 49:34However, very recently,
• 49:37as I mentioned in my first slide,
• 49:39we performed a global assessment
• 49:42in which basically, based on information
• 49:44of the observed locations, our colleagues in Monash,
• 49:48they managed to extrapolate
• 49:50the risk in an observed location
• 49:52and eventually, provide kind of comprehensive
• 49:55assessment on the team,
• 49:58non-optimal temperature-related mortality across the globe.
• 50:02I invite you to have a look in,
• 50:06I think, it was recently published in (indistinct).
• 50:10<v ->Thanks, Ana, I think,</v>
• 50:12if you collect it with the history
• 50:13and also development for MCC,
• 50:15why it's not covered?
• 50:16And what's the most recent
• 50:18that MCC predict in the temperature mortality
• 50:21association in places where you don't have mortality data.
• 50:25There are always a lot of questions,
• 50:27but I do have one kind of question
• 50:30related to your answer.
• 50:32This one student is kind of were astonished about
• 50:36since the heat-related mortality risk
• 50:38varies across places that you have shown me on slides.
• 50:42So the question is why do places have,
• 50:48even we have similar latitude,
• 50:50maybe even with the same organization level,
• 50:53why do we have different heat-related mortality risk?
• 50:58<v ->Well, it's a very good question,</v>
• 51:00and I must say, difficult to answer
• 51:03in a very clear way.
• 51:11In a way, we know that vulnerability to heat
• 51:14or let's say, non-optimal temperature
• 51:16depends on a complex network of different factors
• 51:22that are highly interconnected.
• 51:24It's not like we know so far
• 51:26that what makes one city more vulnerable to the other
• 51:31is not because of one unique factor.
• 51:33It's because of combination of different factors
• 51:37that actually are very much dependent between each other.
• 51:40Thinking that, for example, we published,
• 51:42I think, it was in 2018, a study was led
• 51:46by our colleague, Francesco Sera,
• 51:49in which we tried to assess specifically this,
• 51:54to try to understand what were the contextual factors
• 51:58defined at city level that can give us some hints
• 52:01about which locations are more vulnerable
• 52:04in terms of higher excess mortality
• 52:06due to heat compared to others.
• 52:08And eventually, what we saw in this assessment
• 52:10is that it's not only one factor,
• 52:13it was a combination of probably cities
• 52:15that are more urbanized,
• 52:17but also more unequal are those
• 52:20with a higher heat-related burden
• 52:24compared to others with a lower level in this case.
• 52:27Well, for cold, the story was much more complicated
• 52:30with no clear patterns around.
• 52:32But again, the idea how all, let's say,
• 52:36the main factors driving this difference
• 52:38is nowadays, have very important
• 52:40or very crucial point that we are trying to disentangle,
• 52:44especially because we know that if we understand
• 52:47what are the mechanism, let's say,
• 52:48the reasons why one city is more resilient
• 52:52compared to other, this can help us
• 52:56to understand which mechanism in terms of adaptation
• 52:59we can apply to other places to try to protect
• 53:01to reduce our vulnerabilities in the future.
• 53:04So hopefully, if you ask me this question in a few years,
• 53:08I hope I will answer this question,
• 53:12but I think right now, it's very difficult to say.
• 53:15<v ->Yeah, yeah, I think it's excellent answer now.</v>
• 53:18So it's kind of related to one,
• 53:20our online audience questions
• 53:22regarding the difference in the heat-related mortality,
• 53:25whether it is rural or regional kind of communities.
• 53:28I think it's more related
• 53:30to Francesco Sera's paper you mentioned.
• 53:34<v ->Yeah, in a way, I mean,</v>
• 53:35it's still that in this assessment,
• 53:37and I must say that in the MCC,
• 53:39most of the locations that we have are cities.
• 53:43So in a way, the risks that we obtained
• 53:46are mostly represented for urban locations.
• 53:49This is one of our limitations in this assessment.
• 53:52And probably, if you don't,
• 53:53you need to go a kind of national level assessment
• 53:58in which you can better disentangle the different,
• 54:02let's say, patterns in terms of vulnerability to heat
• 54:06and cold in a rural versus urban.
• 54:09And as I said, it's also it's a story that needs to,
• 54:13we need to address in the next years.
• 54:16And I know there are initiatives in terms
• 54:17of nationwide assessments try to see patterns
• 54:22between urban and rural locations, et cetera.
• 54:27<v ->Yeah, I think kind of the final</v>
• 54:29group of questions students and also online audience
• 54:33is interested is adaptation.
• 54:36So I mean, the adaptation matters
• 54:41students are kind of wondering,
• 54:44how must immediate needs to deal
• 54:47with increasing temperature can be balanced
• 54:50against the long-term goals of emission reduction?
• 54:54Basically, asking using adaptation methods
• 54:59to talk to the long-term global warming paths.
• 55:04And also, if there are some studies like this,
• 55:07are there any practical suggestions
• 55:11on how local communities can do
• 55:14about the adaptation methods?
• 55:17<v ->Yeah, and I must say it was one of the key messages</v>
• 55:20of this assessment that yeah, I presented today,
• 55:23in this attribution study,
• 55:24because of course, we give a little bit,
• 55:28it gives them the message about the urgency
• 55:30in terms of abating or let's say,
• 55:35reducing the warming in the future.
• 55:38But more importantly, what it is saying
• 55:40is that we really need to reduce our vulnerability
• 55:44because the idea is that with mitigation,
• 55:46we know that these benefits will come
• 55:48in the next decades while with adaptation,
• 55:51these benefits can come earlier.
• 55:53And probably, this can be even more efficient
• 55:55compared to just waiting for, let's say,
• 55:57the mitigation strategies to have some impacts.
• 56:01And it's true that we have to think
• 56:02about even in the best of the scenarios today,
• 56:06in which we set emissions to zero,
• 56:08we will be any way exposed to warmer climate
• 56:11in the next decades.
• 56:13So it's about, really again,
• 56:14to put emphasis into the idea of adaptation
• 56:17that it might be the key on this story.
• 56:20And with regards on how we can counteract
• 56:24future warming in terms of how much
• 56:28we can decrease our vulnerability
• 56:30to counteract this warming.
• 56:31I know that there have been some initiatives
• 56:34of some studies published in the past.
• 56:37For example, there's a study by our colleagues in Romania
• 56:41that they simulate what this kind of how much
• 56:45we would need to reduce our vulnerability
• 56:47in the future to reduce or let's say,
• 56:51to keep our heat-related deaths in the future constant
• 56:56despite the global warming.
• 56:58So in a way, this is a very nice exercise.
• 57:00That is certainly something that as well, I'm leading,
• 57:02an initiative within the MCC to try to address this.
• 57:05Because as well, this can help us about them,
• 57:08how much we need to adapt to really do something,
• 57:13to have some impacts in terms of reduction
• 57:15of heat-related mortality.
• 57:17Because imagine that if warming continues,
• 57:21and let's say, the pace at which we adapt
• 57:25is not quick enough, let's say,
• 57:29to kind of counteract this warming,
• 57:31we eventually will have the same heat-related deaths
• 57:34today but in the future,
• 57:35which of course, it would be fine.
• 57:36But ideally, what we would like is that
• 57:39the heat-related deaths happening today
• 57:41won't happen in the future anyway.
• 57:45<v ->Thank you, Ana.</v>
• 57:46I think, I saw Tobias posts a comment,
• 57:49"A really fantastic talk."
• 57:51So I think is there are any final questions?
• 57:58If there's no final question,
• 58:01thank you, Ana, very much for this
• 58:03really, really amazing talk.
• 58:04And I think both the students
• 58:06and I'm sure, our online audience
• 58:08learned a lot from you, but thank you so much.
• 58:11<v ->Thank you, thanks a lot</v>
• 58:12for the invitation, my pleasure.