The Canadian government has invested $5.2 million in 70 adaptation projects across the country. One of the beneficiaries is the community of Munster, which received funding to assess the state of its assets. Mayor Sylvestre stated that the community is always looking for government grants, as its small taxation base of around 200 houses limits its project funding. The assessment will help the community prioritize projects and apply for more grant streams in the future, such as the Green Municipal Fund and the Canadian Housing Infrastructure Fund (CHIF). Currently, there are no major projects planned, but the community is focusing on regular maintenance. Sylvestre emphasized the importance of these funds in visualizing future spending and planning. The community is hopeful to apply for the next round of funding and has a list of potential projects in mind, but nothing is set in stone yet.
Disproportionate Impact of Climate Change on Human Populations
A recent study published in Nature Communications highlights the unequal distribution of human exposure to future climate extremes. The research combines climate modeling and socio-demographic data to project how severe climate events will become and who will be most affected. The study reveals that regions with historically marginalized populations, such as parts of Africa, South Asia, and Latin America, will experience the harshest increases in climate damage risk. These areas face limited adaptive capacity due to economic constraints, fragile health systems, and infrastructure ill-equipped to handle extreme events. The research also shows that socioeconomic status is a key determinant of climate exposure inequality, with poorer communities residing in hazard-prone areas with limited access to protective infrastructure. The study calls for targeted interventions grounded in robust science and equity considerations to address climate inequality and ensure that no population is left behind in the face of climate change.
Opposition parties, including the Coalition and the Greens, unite to investigate the delayed release of a climate risk assessment report.
Liberal and Nationals senators have unexpectedly joined forces with the Greens to launch a parliamentary inquiry into the delayed publication of the National Climate Risk Assessment. This report highlights 11 significant climate risks affecting Australian industries, households, and the broader economy. The assessment is crucial in informing a separate report on adaptation measures needed to mitigate these climate risks. The inquiry aims to investigate the reasons behind the delayed publication of the report, which is expected to provide valuable insights into the climate challenges facing Australia. By working together, the senators hope to shed light on the issue and push for the report’s release, ultimately contributing to the development of effective strategies to address the climate risks and protect Australia’s economic and environmental interests. The inquiry’s outcome is anticipated to have significant implications for the country’s climate change mitigation and adaptation efforts.
Enhancing Climate Resilience: Strategic Approaches to Mitigate Asset and Portfolio Vulnerabilities
The magnitude of climate-related risks has tripled to 27 in 2024, making it crucial to incorporate climate risk assessments and resilience measures into asset management plans. A panel of experts discussed this topic at the 2025 ULI Resilience Summit in Denver. They emphasized that climate risk assessments are no longer just about satisfying investor demands, but also about protecting assets and adding value. Companies like JBG Smith and Global Infrastructure Partners are using tools like S&P Global’s Climanomics and Ramboll’s HazAtlas to identify climate risks and develop mitigation strategies.
The experts stressed the importance of making climate risk assessments actionable, involving insurance teams, and using frameworks to evaluate potential risk reduction measures. They also highlighted the need for companies to educate their board members about climate risk resilience and integrate risk management with capital expenditures planning. By building a framework for climate risk assessment and resilience, companies can better prepare for future climate events and drive value. The 2026 ULI Resilience Summit is scheduled for May 8 in Nashville, Tennessee.
The FMA’s 2025 review to put climate risk reporting under the microscope
The Financial Markets Authority (FMA) has outlined its focus areas for 2025-2026 reviews of climate-related disclosures (CRD). The key areas include addressing prior feedback, disclosing material climate-related risks and opportunities, complying with requirements, and obtaining independent assurance over greenhouse gas emissions disclosures. The FMA will provide verbal feedback to climate reporting entities (CREs) to help them improve their disclosures. The goal is to enhance the quality of CRD reporting, which will improve transparency of borrower climate risk exposures and support risk-based decision making for lending and investment. This will also mitigate greenwashing risks as assurance and regulatory scrutiny increase. The FMA expects CRD reporting to reach a “steady state” by 2026 and will release its next monitoring report in mid-2026, reflecting its third year of reviews. Overall, the FMA’s efforts aim to improve the quality and transparency of CRD reporting, which will benefit lenders, brokers, and institutional investors.
Climate Risk Assessment with ESG AI
Earth is changing in each aspect. From ecology to natural resources, to even cities. Need for sustainable practices is at peak.
In-Depth Analysis and Future Prospects of the Climate Risk Market
The Climate Risk Market is expected to grow significantly, with an estimated value of $42.9 billion in 2025 and $66.8 billion by 2032, at a compound annual growth rate (CAGR) of 6.52%. The market is driven by increasing demand for climate risk assessment and mitigation solutions, stringent regulatory frameworks, and technological advancements. The insurance and financial services sectors offer the largest growth opportunities due to escalating climate-induced risks. The market is dominated by players such as Munich Re, Swiss Re, and Aon plc, who are driving innovation and growth through collaborative innovation strategies and enhanced offerings. The adoption of AI-powered analytics, integration with ESG frameworks, and growth of cloud-based deployment are expected to evolve over the next five years. The market is fragmented, with challenges including data standardization and integration complexities. To succeed, market players must focus on collaborative innovation, enhancing AI capabilities, and aligning offerings with emerging regulatory requirements.
Does the latest National Climate Risk Assessment reveal more through what it leaves out than what it actually includes?
The Australian government is set to release its first National Climate Risk Assessment (NCRA) in 2024, which aims to identify and prioritize climate risks to Australia’s environment, biodiversity, health, infrastructure, agriculture, and economy. However, the assessment’s approach has been criticized for being narrow and focused on individual risks rather than systemic climate risks. The government’s method involves asking stakeholders to identify risks relevant to them, which may lead to a partial and incoherent picture.
Experts argue that a comprehensive risk assessment should start with a big-picture understanding of existential and systemic climate risks at a global scale, considering factors such as tipping points, non-linear change, and cascading risks. The NCRA’s focus on adaptation and resilience responses, without discussing the need for stronger emission-reduction policies, has also been criticized.
The interim report released in 2023 was deemed poorly conceived and narrow in its approach. The final report’s prospects are not promising, given the Department of Climate Change, Energy, Environment and Water’s lack of experience and depth in climate science. The Australian Academy of Science has recommended the establishment of an Australian Institute for Earth System Science to address the fundamental understanding of climate and provide a unifying agency for climate intelligence. The effectiveness of the NCRA will depend on its ability to provide a systemic and comprehensive assessment of climate risks, which is unlikely given the government’s track record of underplaying climate risks.
Insurance actuaries unveil innovative climate risk assessment tool
The Actuarial Society of South Africa (Assa) has introduced a framework to help life and health insurers evaluate the potential effects of climate change on policyholder outcomes. The Mortality and Morbidity Impact Assessment Framework provides a structured approach for assessing climate-related health risks across different product lines and demographic groups. This tool will enable insurers to better understand the implications of climate change on claims, reserving, and long-term solvency. The framework is designed to be adaptable, evolving with advancements in climate science and the changing needs of the insurance industry. By using this framework, insurers can make more informed decisions and better manage climate-related risks, ultimately protecting their policyholders and maintaining their financial stability. The framework is a significant step forward in helping the insurance industry address the challenges posed by climate change. It will support insurers in South Africa and potentially globally.
Global Economy or Climate Emergency. Is that our choice?
If you want to know the truth…FOLLOW THE MONEY! The people who control the world’s money rely on financial risk assessors to …
in October 2023 we took a look at some new analysis by a team of experienced climate scientists and actuaries who’ got together to apply the hard-nosed real world principles of Financial Risk Management to the rather more cautious and consensus-driven world of climate projection the report made for a pretty St juer position of approaches that suggested that the climate bods were being a bit hamstrung in their efforts to highlight the seriousness of our situation by policy makers who essentially didn’t want to overly concern their citizens and cause them to vote for someone else who might tell them a nicer story well now that original piece of work has been followed up by this latest report which adds a bit more context and color to the Actuarial appraisal and the conclusions are challenging before we Harriet hello and welcome to just ever think for this latest piece of work two of the original paper authors Sandy trust and Timothy Lenton have been joined by actuary and chair of the resources and environment board Oliver Bettis deoe sustainability actuary Lucy SE Georgina bham from the UK government actuaries Department exitor University maths and stats pH HD grad Dr Jesse abon and Dr Luke Kemp senior research associate of The nraam Institute of advanced studies in the previous video we took a deep dive into the differences between Actuarial conservatism and climate science conservatism and we discovered the potential dangers of scientific averaging when it comes to climate modeling so I won’t go back over that here but if you haven’t seen that video and it is worth watching it first because it provides the context for what we’ll talk about today and you can do that by picking up there somewhere or by following the link in the description section below the authors of this latest research point out that the rate of global warming accelerated in 2023 and despite a well publicized El Nino event during that time there are nevertheless some early indications that the acceleration is not temporary this chart shows monthly temperature anomalies relative to pre-industrial levels from 1940 to today the last record El Nino year was 2016 and you can clearly see a spike up well above 1.5° C in February of that year but then temperatures settled back down again towards June and stayed relatively flat thereafter that didn’t happen in 2023 though temperatures reached about 1.5° celsus in March last year and then just kept going it’s really not clear at this stage whether that rise will also settle down or whether we’ve reached a new Baseline for warming in which case the climate models need to be revised and so to our Global mitigation and adaptation policies climate risks are increasing globally say the papers researchers and events that used to be regarded as very rare are now becoming increasingly common this chart from the website carbon brief shows all the extreme weather events that are being exacerbated by climate change all over the planet anything in red has The Telltale fingerprint of human influence so you know the vast majority basically according to the consultancy firm V risk total global economic losses from these events now average $400 billion US a year with a likelihood that future infrastructure losses will be in the region of a trillion dollars annually with as little as a quarter of that being covered by any kind of insurance actuaries tend to use charts like this one to provide strong visualization of where we’re currently at this blue curve represents the probability of severe flood events back in 1980 it’s a classic normal distribution curve really so the most severe flooding events are all in this tail bit over here on the extreme right hand side in other words not impossible but very unlikely to happen and here’s where the world is projected to be in 2030 what was the extreme tale 50 years previously will be right slap bang in the middle of the curve by the end of this decade and extreme events that weren’t even on the scale of possibility when I was just starting out at high school are now in scope and represent a much more severe taale to the new curve one of the biggest concerns raised by this paper is the risk of climate tipping points the authors highlight Recent research by D Armstrong McKay at L suggesting that climate tipping points may be triggered at lower temperatures than previously estimated with several at risk in the 1.5 to 2 degre range that we’re now entering things like ice loss in Greenland West Antarctica and the Himalayas permafrost melt Amazon dieback and the halting of major ocean current circulation are projected to interact causing a very unwelcome Cascade effect that could be irreversible from an Actuarial point of view these things can no longer be considered as high impact low likelihood tail risk events which should instead be factored into risk assessments as high impact High uncertainty and increasingly likely events actuaries are acutely conscious of something called Model risk either as a result of using the completely wrong model in the first place which I would characterize as total Muppet or using the right model but implementing it in the wrong way which reminds me of dear old Eric morham I’m playing all the right Ms but not necessarily in the right order or simply misinterpreting the data that the model provides if you’re a bean counter or risk assessor then an estimated probability that turns out to be lower than the real probability is a way bigger problem than an estimated probability that’s higher than reality so actores tend to make conservative worst case estimates with the word conservative in this case meaning conserving your client’s money conservative estimating in climate science by STK contrast means using the lowest common denominator of data averaging based on geopolitical consensus if political action on climate was instead decided from the perspective of financial solvency the first question wouldn’t be what’s the least bad thing we can get away with telling our public it would be how bad can it get under the European solvency regime insurance companies are required to hold enough Capital to survive an unlikely but possible one in2 200e set of Adverse Events this papers authors strongly suggest that society as a whole might reasonably expect a similar standard from our global leaders when it comes to climate mitigation and adaptation policies essentially what these industry and climate experts are saying is that planetary warming above 1.5° C is dangerous not to the planet obviously the planet will be fine so we can all put away our Save the Planet placards because that is most definitely not what we’re talking about here we’re talking about the existential risk to life on the planet not the planet itself which will keep rotating and recalibrating its balancing systems over geological time scales for several billion more years whether we humans and all of Earth’s other species are still breathing or not the point is we humans have never in all our history ever known the planet at the levels of warming that are coming our way in the next 70 years and beyond all these intricately interconnected functions and consequences are being tested and stressed in ways that we simply have no precedent for or experience of and currently no mechanism for dealing with on any kind of globally coordinated basis right now according to this report we’re metaphorically sleepwalking blindfolded with our arms tied behind our backs into an arena filled with a bunch of very hungry Lions so how do we take the blindfold off and untie ourselves then I hear you cry well a proper realistic climate risk assessment is urgently needed according to the paper’s authors maybe get the insurance risk assesses to do it instead of the political leaders a that way we might all be provided with a slightly clearer picture orbe it a more terrifying one the author suggests using a Financial Risk Management technique called reverse stress testing where ensur companies ask themselves the simple question what would ruin us and work out their action plans from there the paper also concludes that we desperately need a mechanism that enables long-term policy decisions and ideally not one that involves an autocratic dictatorship a the author’s solution to that conundrum is something they call a planetary soleny commission so what do they mean by that then well to quote them directly risk management techniques from a variety of disciplines should should be used to develop a global risk management framework that explores the interconnected societal natural climate and economic risks we face and recommends actions to address them planetary solvency should be complemented with long-term governance and risk management this could include radical actions to reduce global temperatures and govern large scale displacement these need to be undertaken carefully democratically and through holistic risk assessments for example comparing the risks of unmitigated climate change versus geoengineering now of course there’s a great deal more data and commentary in the rest of this report that put a lot more flesh on the bones of the bullet points that I’ve highlighted today and far more than we could hope to cover in a short little video like this but I’ve left a link to the paper in the description section below so you can take a deeper dive if you wish and of before I go I must just let you know that we’re already hurtling towards the second everything electric show of the year how time flies the next event is being held at the wellestablished and much loved Yorkshire Event Center up in Harriet from Friday the 24th to Sunday the 26th of May you never know we might actually be having some some decent weather by then and the site has lots of outdoor space for a wider range of new electric vehicles to be on show and of course with the usual attractions for families and space for the kids to run around and generally have a bit of fun I’ll be hosting another six discussion panels too so it’d be great to see you if you can make it the ticket discount code for just have a think viewers is on the screen now along with the website where you can grab your tickets there’ll also be a link to that website on the end screen of this video and in the description section below Harriet is a popular one folks so if you’re thinking of coming along I’d recommend grabbing your tickets early and I’ll hopefully see you there that just leaves a massive thank you as always to my friends over at patreon.com have athink who helped this channel stay independent and a big thank you to you for watching up until now if you like this video and you want to keep up to date on new content then don’t forget to hit the Subscribe button and the notification Bell that way you won’t miss out and you’ll be massively helping the channel get in front of the dreaded algorithm whichever way you choose to support the channel though that support is absolutely crucial and very much
LyondellBasell Earns Notable Climate Change Score in CDP’s 2024 Evaluation, Demonstrating Significant Advances in Sustainability Initiatives
LyondellBasell, a global chemical industry leader, announced that it has improved its climate change score from A-minus to A in the CDP’s 2024 assessment. This places the company in the leadership category for the second consecutive year. The CDP is a leading environmental disclosure platform used by investors and stakeholders to evaluate companies’ climate-related risks and performance. LyondellBasell’s Chief Sustainability Officer, Andrea Brown, attributed the improvement to the company’s strong strategy and momentum in scaling circular solutions, advancing low-carbon innovation, and embedding sustainability into its operations. The company also raised its water security score to a B and received its first-ever forests score, demonstrating its commitment to nature-related disclosure and environmental stewardship. The improved score reflects LyondellBasell’s continued progress in climate risk integration, energy performance, and environmental transparency. The company aims to unlock value for its customers, investors, and society by enabling a circular and low-carbon economy through advanced technology and focused investments.
Understanding the Intersection of Climate, Space, and Security | Open Access Government
The European space sector is vulnerable to climate risks, which can have cascading effects on peace and security. The Guiana Space Center, Europe’s main launch site, is critical to the continent’s space sovereignty, but it is located in a region prone to climate-related hazards such as sea-level rise, heat stress, and coastal erosion. Climate change can disrupt the launch facility’s operations, compromising the deployment of satellites and the provision of essential services such as navigation, Earth observations, and telecommunications.
GERICS scientists have identified the space sector as underestimating climate risk and advocate for operationalized climate services to support the sector’s resilience. This includes regular production of high-resolution climate change projections and co-development of sector-specific climate impact indices. The European Commission’s Directive on the Resilience of Critical Entities recognizes the importance of protecting critical infrastructure, including space infrastructure, to ensure reliable access to vital services.
Addressing climate-related risks to the space sector is crucial to maintaining peace and security in Europe, as it can have far-reaching consequences for the economy, environment, and societal stability. By integrating climate change information into practice, the space sector can build resilience and ensure the continued provision of essential services.
Climate Risk Assessment in Credit Management
This webinar provides practical insights, equipping viewers with the knowledge to enhance their climate risk management using …
Hello, everyone, and
welcome to today’s webinar on climate stress
testing for credit risk, where we’ll take a deep
dive into the evolving landscape of financial risk management
amidst the growing concern of climate change. My name is Akshay Paul, product
manager for the Quant Finance Products at MathWorks. Financial institutions worldwide
are facing a new frontier of challenges, with climate
risk at the forefront of emerging threats to credit
risk assessment and management. Our session today is
dedicated to addressing some of the challenges
associated with navigating these uncharted waters. I’m joined by two of my
colleagues, Elre Oldewage and Eduard Benet,
both application engineers at MathWorks
and experts in this field. All right. So today, they’ll be
going through the details of climate stress
testing by touching on four key challenges– data management,
model development, deployment and reporting,
and overall governance. Throughout this webinar,
we aim to provide you with practical
examples and tools for developing climate
stress tests, leveraging both open source and
commercial data sets. Whether you’re just
beginning to consider the implications of
climate risk or looking to enhance your existing
models, today’s session is designed to equip you
with the knowledge and tools necessary for success in this
new era of financial risk management. A couple of quick logistics
before we get started– firstly, please use the
Q&A panel on the Teams chat or on the Teams application
to ask any questions. While we have a Q&A
session at the end, we’ll go through
all those questions and answer any new
ones you might have. Secondly, the webinar
is being recorded and will be available on
our website after the event. So I’d like to start
off with a quick poll to get a sense of where
folks are currently at in their assessment
of climate risks within their organization. I’m sorry, there is a small
technical problem with the poll, so we’re going to have
to skip the polls. OK, all right. So the polls,
we’ll skip for now, but we’ll be able to– if
the problem gets resolved, we’ll post them in the chat,
and then you can answer those a little later. All right. So before we get into the
details of the challenges involved, I’d like to set the
stage a little bit with a primer into the traditional financial
stress tests and climate stress tests, highlighting some of the
differences between the two. So as most of you all know,
traditional stress tests and climate stress tests, while
sharing the overarching goal of evaluating the resilience
of a financial institution under adverse conditions,
they diverge significantly in their scope, time horizon,
and the nature of risks they assess. Traditional stress
testing primarily focuses on short- to
medium-term economic scenarios, such as market crashes, interest
rate hikes, or recessions that could impact the
bank’s capital adequacy. These tests are grounded
in historical data and economic cycles,
aiming to ensure that institutions can withstand
acute financial shocks. Climate stress tests,
on the other hand, introduces a novel
dimension by assessing the long-term financial risks
associated with climate change, including both physical risks,
like floods, hurricanes, wildfires, cyclones,
and transition risks as economies shifts towards
low-carbon alternatives. Unlike traditional stress
tests, climate stress tests relies less on
historical data and more on predictive models
and scenarios, spanning multiple decades. It requires integrating
complex climate models with financial forecasting,
addressing uncertainties inherent in long-term
climate impacts, and assessing how
these risks cascade through economies, markets,
and specific portfolios. All right. So to summarize, the key
differences between the two are in the time horizon,
the data requirements, scenario design, and
assessment of exposure. So with that, I’d
like to hand it off to Elre, who’s going to go into
the details, starting with data. Take it away, Elre Thank you, actually. Hi, everyone. I’m Elre Oldewage an application
engineer here at MathWorks. And I’m going to
be talking to you about the challenges involved
in climate stress testing. So there are many
different dimensions to consider when performing
climate stress tests, but the process will follow
this general pattern. First, you need to collect
relevant climate data. This can be information
like the risk of natural disasters, energy
demand, or policy requirements. Then you need to do
some kind of modeling to process that data into
a form that can be ingested by your financial models. For example, wind
speed in kilometers per hour for hurricanes
or megajoules of energy, if you’re considering
energy demand, is not directly consumable by
a probability of default model. This needs to be transformed
into financial shocks in some way so that
you can compute the impact on your portfolio. As we’ll see, the data
and modeling steps can be closely intertwined
because the kind of processing you do on the data depends on
what you want to use it for. We’ll discuss
strategies to do this in a way that is modular and
reusable so that you can easily apply different models
to the same data source or apply the same model
to different data sources. Once you’ve
developed your model, you need to share the outcome
of the stress test in some way. This may be by making the model
accessible to other members of your team via
deployment, or it might be by generating some
sort of climate impact report. Throughout, we’ll see
MathWorks Modelscape platform, which supports the full
climate risk modeling workflow. Modelscape is built on top
of our core capabilities in computational
finance and other tools, like our image processing
and mapping toolboxes. And it addresses many of the
challenges we talk about today. We will take a look at each
of these four key issues– data, models, deployment and
reporting, and governance, in turn. I will be discussing
the data portion, and my colleague Eduard will
discuss the modeling deployment and governance aspects. So before talking too much
about the nitty gritty details of climate data, it’s important
to understand that there are two kinds of climate risk. One is physical
risk, which refers to the risk caused by
the increasing frequency and severity of natural events. The other is
transition risk, which refers to the risk
caused by our response to climate change issues, like
requiring a minimum energy efficiency standard
for rental properties or developing new technology. These kinds of risks expose
you to economic losses, stranded assets, and issues
with inaccurate valuation, which can then, in turn,
have knock on effects to downstream models. Another important concept
when considering climate data is that of climate scenarios. So as actually mentioned,
where regular stress testing is based on historical
data, climate stress testing relies on scenario analysis. Scenario analysis is a
well-established method for developing
strategies that are robust to a whole range
of plausible future states if you don’t know what
future is going to hold. As an example, the
NGFS, or Network for Greening the
Financial System, formalized these possible
scenarios in 2019. The scenarios vary
along the two dimensions of physical and transition risk. And whether or not
climate targets are met is the physical access,
and the orderliness of the transition policies is
the transition risk access. So this gives rise to
four possible scenarios, each describing a
potential future with accompanying
predictions for variables like CO2 emissions,
carbon price development, changes in energy generation
technology, and so on. So this is just one example
of climate scenarios. There are other
versions of this, and there are other
frameworks as well, like MIT’s FS scenarios. The scenarios vary a little bit
in terms of their definitions and assumptions, and they might
track different variables and so forth. So armed with this knowledge, we
can now talk about climate data. It comes from many
different sources, both commercial and open source
and in many different forms. We just talked about the NGFS
and other climate scenarios. For physical risk, you often
end up working with maps. One example on the left here
is showing average rainfall per six hours on the left. Or you might work
with map layers like the one on the from BRGM. BRGM is France’s reference
public institution for Earth science data. And what we’re
showing you here is exposure to different
kinds of flooding events and also the reliability
of that estimate. You might need to use government
policy data, like minimum energy efficiency requirements. And also you’d need the
corresponding efficiency ratings for all the properties
in your portfolio. You might need outputs of
natural catastrophe modeling frameworks, like
Climada and Oasis. And there are a whole host
of online data providers that you may need
to pull data from. Now what we’re hearing
from our customers and what roles we’re
seeing in our own projects is that dealing with all of
this data is a challenge. Specifically, there
are two issues here– acquiring the data and
preprocessing the data, especially if you want to
do this in an automated and repeatable way. Getting hold of data
requires some work. You don’t want to be downloading
zips manually from a website. It’s difficult to
track where you put it, and you end up with
multiple copies scattered across different machines. And you’d need to repeat
this process manually every time the data changes. If you’re leveraging
online data providers, you may not have the in-house
capacity or enthusiasm to build custom APIs
to interact with them. We have a suite of tools
to help you acquire open source and commercial data
in an automated and repeatable way. We partner with all
the data providers listed here and many more
to make it easy for you to get the data you need. We also have the
expertise and enthusiasm to build custom APIs
if that’s required. The other challenge here
is data preprocessing, partially because the data
formats can be so different. You may be comfortable
dealing with time series data from the climate scenarios, but
what if the data is categorical, like energy efficiency ratings? What if it’s a data
format that requires domain-specific expertise,
like maps or satellite images? Handling this kind
of data correctly can be especially
difficult if you don’t have in-house expertise
with techniques like image processing and geoprocessing. You might encounter
even more exotic, unstructured data,
like this example, where we needed to
incorporate commuting routes to compute scope 3 emissions. So here, we aren’t even
dealing with proper maps. The routes are just
a series of points that indicate commuter routes. And this sort of thing can
be even trickier to work with if you’re not a geo data expert. And this example is
not even too esoteric. We might encounter similar data
if we were considering supply chain problems or,
as we’ll see later, the path of a tropical cyclone. Let’s consider an example to
make all of this more concrete. So I’ll be using an
example that computes the impact of increasing flood
risk on a mortgage portfolio. This is based on
a bespoke solution we built for one
of our customers in France using components from
our Mapping Toolbox and Image Processing Toolbox. Suppose you have a portfolio
of mortgages located in an area that is increasingly
prone to flooding due to climate change. You want to know what impact
that flooding risk has on your mortgage portfolio. Now, if you’re considering
something like flooding risk, then that usually
simplifies quite easily to finding a trustworthy
data provider, potentially using one of our
nice data connectors, that can provide you with
regions or polygons, these blue shapes here, that
indicate different levels of flooding risk. All we need to do,
in theory, is check whether the latitude and
longitude of our property is located within a particularly
risky flooding region. But really, the story
can be more complicated. For example, the BRGM
API that we mentioned before provides data at
various precision levels, depending on the size
of the region or tile that you request
the information for. And it considers multiple kinds
of risks in a single tile. So for example here, red
is water table flooding. Orange indicates
cellar flooding. Gray indicates no flooding risk. And the hue of each color
indicates the reliability of the information. So really, we’re capturing
multiple dimensions of flooding risk on a single
tile, both the kind of flooding and the reliability of the data. So to be able to
use this API, you need to choose, firstly, an
appropriate region or tile size to get good precision. And then you need to
do further processing to map each of these risk
types to a different layer to get to your polygons
of risk on the right here. So you can see the
data processing can get quite complicated. And what we found in working
with customers on these projects is that the best way to
handle this is to treat the preprocessing as models. This makes the preprocessing
repeatable, for example, if you want to do the same
preprocessing on a new version of the underlying data. And it also makes it modular
so that you can reuse and tweak parts of the
preprocessing as you need. Now, these preprocessing steps
can happen one after the other. For example, you
start with your tile, capturing all the different
kinds of flooding. You need to extract
the kinds of flooding that you’re interested
in as a layer and then convert
that to a polygon. On the other hand, for
each of your properties, you need to map the street
address to a latitude and longitude, project that
into the same coordinate system as your polygon, if you
hadn’t done that already. And only then can you
compute the flooding risk for the property. So as you see here, we
end up with these series of small modular data models
dependent on one another in a flow like this. And the smaller you can
break up these steps, the easier they are to reuse. So let’s take a step back. We’ve talked about the
benefits of modular data models to help with data acquisition
and preprocessing. In our example,
these steps amount to determining the flooding
risk of a particular property, in other words, going from our
BRGM tile and property address on the left to the actual
risk polygons, the probability of flooding on the right. But having this risk
measure doesn’t yet tell us anything about
its impact on the numbers we care about, like
the financial loss we expect to actually incur
over our portfolio. We need additional modeling
to go from our process data to expected credit loss,
lifetime PD, and other models from our risk
management toolbox. And we’ll talk about
this in just a minute. But first, let’s take this
example one step further. So suppose that
our loan portfolio is global and also contains
properties in Florida in the US. To get an accurate impression of
the physical climate risk posed to these properties,
we’d also need to consider the impact of
tropical cyclones, which are a real risk here. In our case, the input data is
synthetic tropical cyclone paths and the maximum wind speed
along this path, which you can see visualized
here in the bottom left. We can use this,
along with information about the Floridian
properties in our portfolio, in much the same way as
the French flooding example to come up with a risk
measure about the likelihood of your properties
being hit by a cyclone. We would need a bit more mapping
expertise to handle the paths instead of 2D layers. But with our mapping
toolbox, that’s easy. Notice that we’re still
interested in exactly the same financial models as we
were for the flooding example. We still want to compute the
impact of the physical risk on our portfolio. We just changed
the physical risk. Here, we see the benefit
of modularity again. If our data processing
steps and models are built in a
modular way, then it becomes easy to consider
different risks. So to recap, data
acquisition and preprocessing can be quite complicated
because the data comes in many different forms,
from many different places and can be tricky to
work with if you don’t have domain-specific expertise. The best way to cope
with this complexity is to encapsulate these
steps in data models so that the preprocessing is
modular, repeatable, and can be done automatically. We at MathWorks have
experiencing in establishing processes like these. Modelscape, seen here, offers
a comprehensive platform and a suite of tools from
our core capabilities to help you with this. We also have expertise in
handling the trickier data formats that would
typically require domain specific experience. I’ll now hand over to
my colleague Eduard, who will talk more about
the models, deployment, and governance aspects. Take it away, Edu. Thank you, Elre. My name is Eduard. I’m another engineer
in Elre’s team. And what we’ll do
is we’ll take it a bit further down the path
of modeling to show you or to emphasize, if possible, a
bit more the importance of being very modular when
approaching these climate stress testing problem. So let’s go back to the
example that Elre, you showed. So in the end, if we
recap everything we did, we have two separate
physical risks. One is cycling. The other one is flooding. And then we have
an objective, which is the impact on our
property portfolio. Now, on the one side,
this require data APIs. Requires mapping tools,
image processing tools. On the other side, it requires
the standard risk processing tools, like expected
credit loss, probability of default
models, and whatnot. Now, the only chunk
that’s really new or here is that, what
is your– how do you adjust with PD with
your flooding risk? How do you adjust with PD this
transition risk adjustment and whatnot? And that’s what we will
talk about a bit more. These out of steps in
building these models. And they get more and
more complicated the more you look at these climate risk. Now, Elre talked
about physical risks. Now, we’ll take a bit of
turn, and we’ll start– the examples I’ll show will be
more focused on the transition risk aspects, but the process
or the concepts would apply. So let’s look at the
standard model development. You normally gather data
and then you build a model. And after you build the
model, you test the portfolio, and then eventually
you build a report. Now, if you try to do
that in the climate space, very soon, you’ll find that
this is a very inefficient way of doing things. Because when you start putting
all the data providers you need, when you start putting all the
models you want to look at, the process explodes,
and this is not scalable. This is, for example,
the current set of models that we have working
together in a single project. On the top, right, you can see
the hurricane and the flooding models that we showed you,
alongside the different models that take care of the
post-processing steps of the data, which eventually
lead to the mortgage portfolio climate impact. So the first thing
that comes to mind is, like, why would you
ever want to do that, or you want to modularize
the problem so much? So if you look specifically
at just one of the problems, lie the flooding problem,
we built this tool. And as Elre was saying,
it was a bespoke project we built for a
customer, specifically on French data provider. But eventually, if you’re
building this capability, you don’t want to
stick with France. You want to look at flooding at
many, many different regions. So this model in the top
right, the flooding data model, eventually,
what will be is we have multiple versions
of the model, right? There’ll be a model for the US. There’ll be a model for the
UK, another one from Spain, for France, for every
single region where we can get flooding data. And each of these regions–
because this is normally governmental data, we’re
going to give us the data in different format, right? Some of them will
give us an API. Some of them you’ll be
able to download some zip files with some, like, I don’t
know, Geo mapping files inside. And some of them will give you
a client in its own language to manage the model. And you need to be able
to ingest all that. And eventually, you just pass
your event to your PD model. So what I’m going
to do is I’m going to move to a different
example, transitionry space. What I’m going to
try to emphasize, how important it
is to be modular when approaching these climate
stress testing problem. So for that, I’m going to look
first at some climate scenarios. The ones you see on screen, this
is the EPPA model from the MIT. For those of you
familiar with them, it’s similar to an
integrated assessment model, but there’s some key
differences in there, right? So this model gives you the
projection of certain variables in some years to come. So in this case, you
see from 2020 to 2100. But unlike the normal
integrated assessment model, this model gives you multiple
paths for each variable. So eventually,
what you can do is you can compute a mean value
and some confidence intervals on what it’s
actually going to be the evolution of that variable,
according to the model. So what you see on screen? In the screen, you see, first
of all, two separate regions. I pick the United States,
and I pick Europe. I’m looking at three
different variables. I’m looking at the emissions
coming from agriculture, coming from electricity, and
coming from transportation. And then you see
three different colors we state for the
base scenario, which normally stands
for, we do nothing and we hope for the best. And then two more
restrictive scenarios, where the end policy is that
the global temperature gets below a certain value. And as you can see, then it
requires more immediate action to achieve these policies. Right, so we want
to use this model. We want to make sure that we
incorporate this into a PD model and see how our
portfolio behaves on these different scenarios. So now that we have the
data coming from MIT, we need to build a model. So the first
problem we’ll see is that the data coming
from these models is not just something that
we can consume, right? In this case, for example,
it’s in tons of CO2. If you look at the
energy, it’s going to be in megajoules, right? So it’s not a value that
you can simply utilize. And then you have to start
looking at what models are available in the literature. So when we started
this process– so, to look at the breadth
of models available– one that came up
initially was this one. They said the change in
probability of default can be adjusted for climate
by multiplying this value by a shock, which they name u. And this shock is the
change between being on the response or the
baseline scenario B to moving to the more
restrictive scenario P. So then you need the
formula for that, right? And then if you look, for
example, at this paper– that’s from a very
interesting group– what they say is that,
OK, you can define the shock as the change between
the variables between scenario P and then the baseline
scenario, right? If you at the change
sometimes in the variables, sometimes in the
market share, you can define these shock, right? And then you eliminate this
problem of having the variable in a market that
you cannot consume. You actually have some
actual magnitude, right? How much the emissions or how
the market share of this energy is changing. And then you can apply this
value to each asset, right? Of course, you have to map the
sectors of the variable you’re looking at to your assets. You have to map the
regions to your assets. But eventually, this is
not a complicated problem. You can say, OK,
let’s go and calculate the change in our portfolio. And you can see here, this
has just been anonymized. But this is the example
on a couple large banks. And this distribution
you see for the value is because the scenarios
used are not a fixed path, but they’re giving me a breadth
or a statistical distribution and on how these
variables evolve. Right. So let’s say you build this
model and you’re quite happy with it. Well, normally what happens
is that when we discuss this with customers is
that immediately, we see that this is not
scalable, because nobody wants this as a single model, right? And the reason for that
you’ll see very quickly. When you build this model,
immediately someone’s going to say, yeah,
but we decided that we want to compare
maybe these scenarios with some other scenarios. This is the immediate
thing that will come up. What if instead of
using the MIT model, I use some other
integrated assessment model, for example, the NGFS? So essentially,
what I want to do is I’m going to swap this thing. And immediately, you see that
this is actually a much trickier problem than it seems. So to show you that,
let’s look at what this NGFS-equivalent
data would look like. So, first of all,
this is closest– it’s the closest value
to the NGFS scenario that we saw before. Three scenarios below 2 degrees. One is a baseline,
and one that’s a much delayed transition. And the first thing that you
notice is that in this case, there is no statistical
distribution. There’s one path per variable. That, however, is actually the
easiest part of the problem that you have in
dealing with that. Second problem is that if
you look at the regions where these variables come,
one side is the United States. That’s the same
way we had before. But the second one is
28 European countries. So probably the European
Union posts across the UK. It doesn’t include
Eastern Europe. And the MIT scenarios did. So if you want to
compare apples to apples to see how these scenarios
compare to the other ones, you probably have to do
some region adjustment to combine this
region here at 28 plus some other
additional regions. Usually, when they
provide these values, they also provide the
Eastern Europe counterpart. So you have to be able
to match those two. And this is not a
trivial problem anymore. This one, however,
is actually not bad compared to the
other problem, which is that the variables
that you’re looking at, they’re not the same. So in the previous
slide, we saw emissions. And you can see that in
the bottom two variables, we also have emissions. We have tons of emissions of
CO2 coming from the electricity and from the demand
of transportation. Maybe not exactly the same, but
that’s actually fairly close. For by these standards,
you will be quite happy. However, if you wanted to look
at agricultural production, that’s a different
story entirely. In this case, you can see how
the only variable that comes in this model is the energy. It’s not CO2 anymore. So that is unfortunately not
directly comparable to CO2 because there’s multiple
ways of generating energy. So if you want to use
this model and compare it to the previous one and
see if your portfolio was behaving the same way, you need
to do a lot of transformation. You need to
transform the region. You do transform the variables. And luckily, once you’re
done, the final model is actually the same. So in that aspect, you
you’re pretty good. So what would that mean? Well, it means that if you
want to switch scenarios, what will happen is that the
simple model that you had to calculate
the shock will now become a collection of models
aimed to deal with the data switch that you unify
regions, unify variables, and eventually
compute the shock. So at this point,
you’re pretty good. You managed to
stretch your portfolio using two different scenarios
and finally get a value. But now we just look at the
very top left of the problem. We just looked at the data. So when we start
plotting or talking about more people
about this problem, this thing just keeps growing. I’m just going to
say, well, yeah, we can use either of these two. We actually use the scenarios
provided by the Bank of Canada. Well, if you do
that, in this case, you’d see that you’re pretty
lucky because those scenarios are fairly close to the NFTS. So all these framework
you produce actually can be reused for
the Bank of Canada. But they can even increase
the complexity of the problem and say, this
model you’re using, this is too simple for us. We decided to use a
different methodology. For example, the one provided
by the Bank of Canada. They probably said
methodology to change– or approach this
change in a paper called Assessing
Climate-Related Financial Risk. So how does this work? So they start from
the scenarios, and you saw how they look like. And from the
scenarios, they say, well, you can compute a shock
on some of the variables that we provide on the scenarios
called the risk factor pathways. And when you look at that,
you’re like, pretty good. That’s actually the same
equation I was using before, right? So I can reuse the model. So again, this modularity
that we built is helping us. This process is fairly
straightforward. It’s just a time
series refactoring. But when you look
at the PD model, it’s actually more complicated. They said, no, look, the pretty
model that you need to use is entirely different one. It’s this one here. And what this model does is
very different approach, right? So they defined a
adjusted model that depends on obviously the
original one you had, plus some factor values,
alpha, beta and S. Alpha is a value that needs
to be fitted, depending on or according to the different
sectors of the economy that they provide. And S is a sensitivity that
depends not on the sector, but on the segment
of the sectors. And you can see that some of
them is a one-to-one mapping, but some of those have
subsegments in there that need to be fitted. And finally, F is just
this risk factor pathways that come from the scenario
itself, which they call RFPs. And they’re variables like the
projected revenue, projected indirect costs, and whatnot. So what do you do with that? So what you can do is it’s
actually fairly straightforward. If you ahead– and
well, straightforward as in if you’re familiar
with portfolio optimization. It’s fairly straightforward
to go and calibrate the values of alpha and
beta for the sector level. And once you have those, you
can actually go and calibrate the values of the sensitivities
at the segment level. So eventually, you
get with a matrix that looks like that, right? On the bottom part, you have
these RFPs or these variables, like revenues, low carbon,
indirect cost, and whatnot. And on the right, you
have the actual sectors of the economy like air
transportation or electric power transmission. And you can see how the
sensitivities is quite– seems to be correct, as in air
transportation is very direct. It’s very sensitive to climate. So eventually, after you
do this for all sectors and for all variables, you
get with a much bigger matrix, right? Again, the one I was
showing you was just a sharp bit, or an
extract a bit of this one. And having alpha
S and these RFPs, it’s easy to adjust the
probability of default for each of the assets
within the portfolio. From there, it just goes back
to the standard risk management problem, right? You can just pack
your portfolio. You can prove the loss given
default and expected shortfalls and whatnot. It’s an economics or a
risk management problem. Again, there’s no
self-complexity. But having explained
this example, if we now go back to our
overall overarching problem and how this looked
like, well, now we swap the bottom right part
of the problem, right? We use a different
set of scenarios, and we change the model. So all in all, if we
continued, somebody said, well, why
don’t use this model? But no, I don’t want to use
And I want to just go back to the MIT or the NTFS models. So you can see how
the problem needs to be split up in order to
be efficient in rerunning it. So the key concept we want to
emphasize is the modularity. If you’re not modular–
and it happened to us at the beginning–
it’s very difficult to scale up these problems. These data providers
change quite often. And it’s hard to deal with
them if you have to rebuild everything from scratch. So just to give you
a bit of a sense, all the models that we described
look more or less like that. And if you actually put
them on the model scale or on a real model
management platform, this is the actual dependencies
just for this subset of problems that we looked at today. If you look at the right, you’ll
see the ones I just showed you. There’s the integrated
assessment models like the NGFS or the Bank of Canada. There’s the EPPA model from MIT. There’s certain sub models to
manage and unify these data. There’s finally a climate shock
model, which, for some reason, this seems to be quite
unified and most of them use. But we expect that
at some point, somebody’s going to come
up with a different one. And then these are
the two PD models that we showed you at the end. And of course, for
each of them, you can have multiple versions,
multiple states of the code. But this is just the
overall top level view on how the models
depend to one another. And again, just for
this particular project. So now that we see how the
ordering process end-to-end works, let’s look at how
we actually start deploying and start managing or
running the process in So we have our diagram of
models and how they depend. So the first step, if you
want to perform the task, you have to pick a model
and stress your portfolio. A model means you have to
pick the entire path what you have to pick. What’s the final model
you’re going to use? What is the region where
you’re going to apply the model or map the region to each of
the assets in the portfolio? You have to pick what
climate scenarios you want to stress test to. And again, each
of the models will have their own set of
scenarios that are, again, similar, but not quite. And finally, you have
to assess the climate impact of the portfolio. You have to build
a pipeline that concatenates all these models
together and eventually gives you– or speaks a final answer. Now, the key thing here
is what LG was saying. You will have to
repeat this process because it’s rare that somebody
uses the same scenarios. Even if you do, to give you an
example, when we started looking at this problem three or four
years ago, from that point till now, the NGFS
scenarios, which is one of the most common
or well-known set of data, have changed three
or four times. I think they’re now
in version 4 or 5. And that means that
even though you would think that, because
it’s the same scenario, the values wouldn’t change
that much, they actually do. The regions change. The variables change. So repeating this process,
even though it seems simple, it’s actually not quite. And being able to just simply go
and swap a box in this diagram and just submit the pipeline
and get the results again is actually quite powerful. So how easy is to
deploy, how easy is to rerun this
process actually comes down to how is this
to concatenate this model? How easy is to build a pipeline
from your inventory of models? And finally– I think I already
mentioned at the beginning– your final goal is
probably to build a report. And maybe or maybe not, but
this is fairly common process. And again, same problems apply. You are using various
different models. And that means you have to have
probably various different types of reports. And even if you use, for
example, different data sets, like, different
scenarios, you probably have to switch entire sections
of the model explaining why is one use or why
is the other one used. And different templates
are going to be necessary. Different requirements
are going to be needed to be linked to the report. So eventually, there
is a clear need for a tool that allows
you to automate and adapt to the rapid turnaround
that these models have. And that’s how a report or
automated reporting tool really helps in this space. It lets you automate
building these reports while easily simply switching
the models within the platform. So we saw the entire end-to-end
process right from data to model and reporting. Now we’ll see– well,
we propose as a solution or as a platform to
run all these steps. And that’s something
we called ModelScape. ModelScape is our own
proprietary risk management tool. So why we want to
show you ModelScape? So if we recap
all the challenges we saw– we saw that the
data had multiple APIs, had these regions. You needed to account for
different regions and variables. And you need to
account or you need to have different tools
that are maybe less standard in the finance space. You have models that
are rapidly evolving. You need to constantly
adapt these models or constantly allow these
models to ingest data coming from many different places. Because of how
broad the space is, the language where
these models is built on is probably going to be
different, especially in the data side because
the different providers will give you in different formats
and different languages. And finally, you want to build
pipelines on top of all that. And that’s where models helps. You saw it through all
the entire presentation. That’s where I was–
or we were using to show the inventory
of the models. This is the front page. It’s fully
customizable, but this is the one we’ll be
using as an inventory to store all of
our climate models. And you can see that most
of them– because it’s me and Elre usually managing
this process– have me listed as an owner
because I’m usually the one entering the model. And this platform, or
the advantage of it, is that you manage the
end-to-end workflow that we described today. So ModelScape is a
cross-language platform to manage the full
end-to-end workflow from development of the
model to the deployment and the monitoring. So the governance
piece or the inventory is what you’ve been seeing. It’s the list of models
or the database of models with all the
metadata and built-in to know who owns the
model, where’s the code, and how to run it and whatnot. And it also supports the
development of the model. Some of you already have parts
of this process built in-house. But some of these
parts, sometimes you need to build them or you
need help building them. That’s what we bring– where development pitch in. You don’t have to build
everything from scratch. We can just use reuse
the ready-made tools that we have for a lot
of these processes. Like for example, the Mapping
Toolbox or the image processing bit. This is a build environment. You’ll be building. A lot of models will be
changing quite often. Being able to quickly
review and gather the latest version of the
code, get a quick review, see if it actually works, is
giving you the expected results and quickly submit an assessment
to it, it’s quite necessary. You have to evaluate
the models, right? Some of the models are
built with a simple API when you can just pass
the inputs and outputs. But some others are
more complicated. They require a visualizing
app like the MIT scenarios, especially when you
still searching for data to be processed or what
fields you can use. You will then need
automated reporting. And again, most of these models
will have a set of requirements either from governmental
requirement– from government requirements
or internal company ones. And it’s usually very
easy to simply check whether the model fits the
requirements that were set and link those to the final
report that’s being built. And as we said, it’s
language agnostic. Each of the models can be
built in its own language with its own framework. And what ModelScape does, it
just brings everything together. And as you saw, especially on
the data side, we get models or we build models in
many different languages. And we simply put
them all together so that the various models can
consume the data in a combined way. There’s no need
to recode or unify everything in one single place. And you see here
just one dashboard is the one that we
use for the climate when we deal with
the climate space, but it’s fully customizable. So you would eventually have
your own dashboard adjusted to your own to your own. But anyway, we just
give you a glimpse on how complex the climate
justice problem is. I hope or we hope
that we made the point that the key point or
the key aspect of it is to be very modular and break
the problem in as much pieces as possible so that
you can actually reuse and swap the
bits as necessary and do it in a really– with a really quick turnaround. And finally, we want
to position ModelScape as a tool or the place
to do all this process in the end-to-end
workflow regardless of what is your current
development language for each of the models. So with that, I’m going to send
the presentation back to Akshay, who’s going to host
a bit of a Q&A. And I encourage any of you who
have questions to please ask it. Awesome. Thank you so much,
Elre and Edu, for going to the details of the different
aspects involved in climate stress testing and
specifically demonstrating how our platform addresses
some of the challenges faced throughout the process. So we have been
monitoring the questions. And before we
actually get to those, it looks like we do have
the polls up and running. So we wanted to run
a quick audience poll to see where your current
capacity building is at in your organization. So the question is around
climate-related expertise and capacity building
at your organization. Do you have a team of
climate experts in-house? Are you looking to
build a team in-house? Or are you either
working or looking to work with external
consultants on this? So, feel free to
make selections. It’s multiple choice. And once you’re done submitting,
just x out of the, the dialogue, and it’ll stay around
in the chat window. If you want to make a change to
your selection, you can do that. But the results
come in live and it seems like it’s a little
weighted towards folks who already have experts in-house. But otherwise, pretty
close for the other two. So I’ll let that poll
run in the background. And we can switch over to the
Q&A part of this webinar now. So as I mentioned, we’ve
been looking at the questions and trying to compile similar
ones so that we don’t need to– don’t need to repeat a
question and also making sure that we get everyone’s
questions answered. So, feel free to keep using the
chat window or the Q&A panel to ask questions. And let’s get started. So the first one is
around an assumption of static balance sheet for
regulatory stress testing. And this was asked a
couple of times, actually. So I can get started
and then Elre and Edu can add their thoughts if they
have anything extra to add. So, yes, traditionally–
and regulatory stress tests have been conducted for the
last four, five years maybe now, and there’s been tens
of them, like high tens, I think, by now
across the world. For the most part, it has
been under the assumption of a static balance sheet, but
that’s a little unrealistic. And going forward,
there is more of a shift towards having a dynamic
balance sheet assumption in some of the regulatory stress tests. So, personally, at MathWorks,
what we’re doing is we have a number of
academic partnerships that we currently have in place. And we’re engaging with
modelers in academia to look into this area
and working with them to figure out what that
would mean in software. So, Elre, Edu, would you
like to add anything to that? Well, yeah, no, maybe
when we initially looked at this problem– I think it was around
three or four years ago– all the models that initially
included climate on them were assuming a
static balance sheet. But more and more,
we’re starting to see the new models do
assume a dynamic one, right? So I think the couple ones– definitely the
first one we showed was assuming a balance sheet. I think that for the
second one, there’s an option already to assume a
dynamic one if I’m not mistaken. But anyway, yeah, the
original models, yeah, are definitely static. But more and more
nowadays, the news that are showing up to
assume a dynamic one. Awesome. The next question
is around data. And specifically, do you provide
the data to your customers? Do customers need a separate
relationship with data vendors? So we do not provide
our own data. We do, however, have connectors
to a number of leading climate data providers, which
can be accessed directly through our tools. So customers would license
directly with data providers either on the physical or
transmission risk side. And then through
that partnership, we can integrate
the data directly in our tools with the connectors
that we already have built. Actually– Sure, go ahead. –on this climate space,
though, a lot of the data comes from
governmental agencies. And oftentimes, the connect
that we provide is enough. There’s no need for an
additional agreement with because there’s no– data provided by the government. It’s usually free of access
or free to navigate, right? So especially on the physical
risk side, flooding data, European data is something that
is provided by public agencies, and it’s fairly straightforward
to access and manage. And we’ll help you with that. If it’s connecting to
a proprietary vendor, that’s a different story. But on the climate space
so far what we’re seeing is that a lot of the data is
actually on the public domain. That’s a good point. Thanks, Edu. All right. Do you have tools for managing
different geolocation coordinate systems? And I can let Elre
take this or Edu. Yeah, absolutely. So we have a whole
Mapping Toolbox to help you visualize your
data in a geographic content. You can build map displays
for more than 60 different map projections. So if you have a specific
map projection in mind, we’ve probably got
something for you. And it can also help you do
things like import raster data, vector data. We support a wide
range of data formats and can connect to
several web map servers that people typically use. And then, of course,
it’s a mapping toolbox, so it’s got some nice
complicated mapping functions as well– stuff like resampling,
interpolation, trimming, whatever your heart would
desire for geocoding and that sort of thing. Awesome I think the next one’s
also around one of the maps that you showed in
your slides, Elre. So it’s around what the map
of the cyclones represents. I’ll let you take that. Yeah, sure. So what we were seeing there
were simulated paths of tropical cyclones over a 1,000-year span. And then the color was
showing the maximum wind speed for that cyclone. So the reason that we’re
using simulated data here is that tropical cyclones
are firstly quite rare. And secondly, our record keeping
is pretty spotty until like the late 1800s. So if we were trying to do
risk prediction for cyclones, we’d only have about 100
years’ worth of good data, and not all of that
data is consistent. So to be able to do anything
useful with these sorts of models, what they
do is they simulate cyclones over a much
longer period of time. And obviously, the models
doing that simulation is trained on what
data we do have, and then we draw conclusions
from those simulated paths. Thanks, Elre. The next question is actually
very relevant for work that Edu recently did. So do you have tools for running
integrated assessment models? Edu, you want to talk a bit
about the dice 2023 model work that you just did? Yeah. so first of all, these
models are big, right? And when we started
looking at them, we realized that running
them might not be the most straightforward thing. So we recently started
with one of the smaller versions of these models,
which is called dice. It’s a model produced
by Professor Nordhaus. And they just– I mean with the
whole hype of the climate space in finance was a model that was
initially developed in the ’90s. And then they got it
got revamped in 2016, and it got ramped again on 2023. And we just got a bunch
of people interested in us giving them an
implementation that can be quickly run and
can be quickly changed. So the interesting
thing about these models is that they combine
what’s basically the economy today in a country
with some sort of climate model at the simplest– or the simplest space. And then you just run
an optimization problem that tells you if you want to– depending on how much
you want to leave for your great
grandchildren, how much you need to consume today. So because these models,
they grow quite a bit, there’s a trend from
bigger institutions to rerun run these models
and then just provide you with the results. And that causes
that these models keep having different versions
where they add new variables. They add new regions. And it’s a bit harder to
keep track to manage them. And more and more,
people are asking them if you can just provide
them with a runnable version that they can just
tweak and run locally with their own
assumptions and with– yeah, basically with
their own assumptions. And it’s something
we’ve been doing. Most commonly, we get asked
about the smaller versions of these models because
they’re easier to absorb, and easier to understand
what’s going on. There’s less moving parts. And eventually, the
results, they do vary, but it’s not like you get
absolutely different results, which usually are
on the same lines. And for all these models,
which are just projections into the future, that’s
oftentimes good enough. Being able to run your own
model with your own assumptions in a reasonable time frame
is probably the best option. Perfect. All right, next
question is about whether the model
considers wildfires. So I can take this one. The platform itself is more
of a is more plug and play. So it really– we can
incorporate or develop whatever models are needed
for the particular risk. Physical risks are
very different, depending on what region
you’re talking about. So we work closely
with the customer and where their assets are
located in order to develop, validate the most appropriate
model for their risks. OK. Next, we have a question on. validation of
climate risk models, given that there is
no history to go on. Elre, can you take that? Yeah. So I’d say in the
first instance, that’s a very good point. There is no historical data when
we’re doing these climate stress tests, but that’s
why it’s important that we use multiple
scenarios, and that we choose our scenarios carefully. Because if you’re considering
multiple possible scenarios, then that gives you– it
kind of hedge you against– hedges, you against
various plausible futures that could happen. So you’re safe against
the worst-case scenario. You’re doing very well in the
best-case scenario and so on. And especially, if you’re
using something like MIT’s EPPA scenarios with multiple
paths per possible future, then that gives you
uncertainty balance on what you expect
to happen as well. So, that’s really quite useful. In a different sense,
ModelScape actually has a way for you to do model
validation, which is generally used for credit risk models. But we’re starting to
work with customers to understand the extent to
which these sorts of climate models can actually be
validated and what that means. Perfect. The next couple are more Well,
they should be pretty quick. So this one’s around IAM models. Says, “IAM models provide a
bunch of climate variables and economic data at
the macro level at most. Still, we need to convert the
results into financial risk variables with new tools such
as climate risk credit, right?” Yes, that is absolutely correct. And that was shown
in the slides. I think Edu, it was, who
went over that in the demo. So again, the recording
and the slides will be shared afterwards, so
feel free to review it again. And reach out if you have
any questions or feedback. It’s a good point, actually. But yeah, let me say
something about that, though. Yes, it’s actually something
that surprised us quite a bit when we initially
looked at these models. The variables that
come out from them, they’re usually unconsumable
on a finance space. But I think most
people developing these models have realized
of that limitation and how hard it is to transform
megajoules per year for x sector to something you
can actually use. And nowadays, most
of the models, beyond the normal variables
that you would produce– like an energy consumption or
like CO2 emissions or whatnot– they also provide
derived variables like projected interest rates
or projected income value or projected unemployment
and stuff like that. So more and more, if you
look, for example, NGFS data, or you look at the Bank
of Canada scenarios, they do provide variables
of this type that are directly pluggable
in a much easier way because they’re the common
economic variables that can be used. If you look at the earlier
versions, definitely not, right? But the more you look
at the newest ones, there’s always a
subset of variables that the pure interest
rates, projected interest rate in the next 100 years. Here you go, according
to the scenario. And that actually makes
them more usable and more attractive to everybody. Yeah, that’s correct. All right, “Is this
platform-installed software? Or does it run as a
service in the cloud?” So the short answer is, both. Parts of it can be
usually hosted somewhere. And it’s pretty easy to
share models within Teams using our platform. And then, there’s
one last question, and that’s around modeling
available for chronic climate risks. So again, this goes back
to the data and the models that we develop. And it differs based on where
your geographic location is, what the most pertinent
physical risks are to you and your organization. But short answer is yes, chronic
climate risks can be modeled and are being modeled as well. Let me quickly do a scan– I know we have
three minutes left– to see if there are
any more questions. All right. I think we covered
almost all the questions. There have been questions
answered in the chat while the talk was going on. So I think we’ve gotten through
all the questions there. So thank you, everyone, again
for all your great questions. And yeah, so as
you’ve seen, we’ve– as we presented in
the examples today, our team has been
working on this space with a number of customers
for the last few years now. And to do this, we’re using
an existing library of APIs to connect the
commercial and open data or building new ones
to local data sources. We’re leveraging
our existing suite of models across the
computational finance suite of products
or building new ones as required by engaging
our network of leading academic partners in the
climate risk modeling space. And finally,
integrating the solution into your existing
enterprise risk stack. So if there’s a project that
you’re currently working on and have run into some
or all of these problems, we’d like to hear from you. If this is an area you’re
looking to do work in but not sure where
to start, again, feel free to reach out to us. You can either contact us on our
email, which is on the slide– [email protected],
or reach out to Elre, Edu, or me through LinkedIn. Our LinkedIn profiles have been
posted on this slide as well. So with that, I’d like to thank
everyone who attended today for your time and attention. And I look forward to
seeing your participation in one of our future webinars. Bye bye.
Municipal Bond Market Affected By Climate-Related Threats: Rising Flood And Wildfire Warnings
Municipal bonds in the US are facing increased scrutiny due to their vulnerability to climate-related risks such as floods and wildfires. New data from ICE Climate Data highlights the severe risk scores of bonds from regions like Nahant, Massachusetts, and Banning Unified School District, California. These scores, ranging from 0.0 to 5.0, indicate the level of threat from environmental events that could impact the financial health of these investments. For example, Nahant’s $6 million bond has a flood score of 5.0, indicating extreme risk. As climate concerns grow, investors are likely to take these scores into account when making investment decisions. The integration of climate risk assessments is crucial for understanding potential vulnerabilities and safeguarding portfolios against future climate events. This shift highlights the importance of incorporating climate resilience into nationwide economic strategies, and municipalities may need to factor these risks into financial planning, affecting borrowing costs and infrastructure priorities.
Lithium Ionic Releases 2024 Environmental, Social, and Governance Report Highlighting Sustainability Efforts
Lithium Ionic Corp. has released its 2024 Sustainability Report, ESG Scorecard, and Climate Risk Assessment. The reports highlight the company’s commitment to environmental stewardship, social responsibility, and governance. Key milestones include planting over 2,300 seedlings for environmental restoration, sourcing 100% of electrical emissions from renewable hydroelectric energy, and prioritizing sustainable design principles in the Bandeira Feasibility Study. The company has also launched initiatives to support local communities, foster gender diversity, and enhance workplace diversity.
Lithium Ionic has developed and implemented new corporate policies, including an ESG policy, and has become a signatory of the UN Global Compact. The company’s CEO, Blake Hylands, expressed pride in the progress made and commitment to responsible business practices. The reports are available on the company’s website. Lithium Ionic is a Canadian mining company exploring and developing lithium properties in Brazil, with a focus on sustainable development and environmental responsibility. The company’s feasibility-stage Bandeira Project is situated in a mining-friendly jurisdiction with significant lithium deposits.
Economic Climate Study – News Publication
The Pakistan Economic Survey 2024-25 has been criticized for failing to address the pressing issue of climate change. Despite the country’s vulnerability to climate change, which could reduce GDP by 20% by 2050, the Survey treats climate change as a peripheral concern. The current framework isolates climate change in a separate chapter, rendering the Survey’s conclusions unreliable for long-term planning. The Survey’s treatment of agriculture, manufacturing, and energy sectors illustrates this “climate blindness,” failing to consider the impact of climate change on these sectors.
The Survey excludes climate analyses across all chapters, reducing its utility for evidence-based policymaking. Climate integration can eliminate the separation between environmental and economic analyses, providing a comprehensive assessment of climate-economy interactions. The writer argues that the Survey should be transformed to incorporate climate considerations across all chapters, using standardized risk assessment methodologies and impact quantification protocols. This would enable policymakers to understand the economic implications of climate change and make informed decisions to build resilience and promote sustainable development.
New Climate Risk Assessment Agency to Evaluate Environmental Impact on Infrastructure Investments
Scientific Climate Ratings, a firm created by French business university EDHEC, has developed a system to estimate financial losses for infrastructure assets under various climate change scenarios. The firm’s CEO, Rémy Estran-Fraioli, noted that climate risks are accelerating, but most financial decisions overlook them. The ratings system will initially cover 6,000 assets, expanding to 5,000 listed equities in 2026. The methodology involves two stages: assessing an asset’s exposure to future climate risks and estimating the potential financial impact of climate scenarios from 2035 to 2050.
The firm found that 1,088 assets are expected to experience losses of at least 24% by 2035 and over 50% by 2050 if no action is taken. However, assets with the best ratings (A or B) account for only 2% of the expected loss, while those with the worst ratings (F and G) account for nearly 50% of the expected loss. The ratings aim to provide granular data to improve financial risk assessment and decision-making for investors and companies, highlighting the importance of considering climate risks in investment decisions. This is particularly crucial as trillions of dollars are invested in infrastructure vulnerable to extreme weather events.
Australia is bracing for a climate crisis it is not equipped to handle.
Australia is experiencing climate-related extreme weather events, including floods, fires, heatwaves, drought, and cyclones. Despite the federal government’s efforts to assess national climate risk, the report is delayed, raising concerns about outdated information. Former Defence chief Chris Barrie warns that the longer the wait, the higher the chance of an out-of-date report that fails to embed the latest science on warming. The delay is particularly concerning given that last year was the first to exceed pre-industrial temperatures by more than 1.5C.
Climate change and disaster researcher Rebecca McNaught notes that communities are taking proactive measures to build resilience, but investment in prevention is falling short. Only 3% of disaster-related funding is spent on preparedness and resilience building, compared to 97% on response and recovery. Experts argue that spending on prevention pays off, with a 10% return on investment anticipated for every dollar spent on disaster risk reduction. The government is under pressure to deliver on the climate risk assessment and adaptation plan, particularly as it bids to co-host the COP31 summit alongside Pacific nations.
DAS introduces a new climate dataset to assess rural property risk across Australia
DAS has introduced a new climate dataset package to help organizations manage long-term climate risks in rural Australia. The dataset, accessible via the DAS Rural Platform, provides climate risk assessments at the property level, focusing on variables such as rainfall variability, temperature, and drought index. This data aims to give organizations a more accurate understanding of climate vulnerabilities affecting agricultural assets and regional property portfolios. The dataset is designed to support frontline risk assessments, climate stress testing, and resilience planning for institutions with rural asset exposure, including insurers and banks. DAS’ CEO, Anthony Willmott, emphasizes the importance of accurate and timely data for informed decision-making, and the dataset’s high spatial resolution and geospatial standardization address traditional data limitations. The dataset is targeted at a range of stakeholders, including banks, insurers, and real estate firms, to inform policy pricing, risk mitigation strategies, and investment decisions. By providing reliable, property-specific data, DAS aims to support sustainable investment, risk assessment, and resilience planning in the face of a changing climate.
Physical Climate Risk Assessment for Your Company’s Annual Report
Meet Matt Macunas, Folmer Krikken and Elena Maksimovich, founders of Weather Trade Net ! The European Commission …
Evaluating and Implementing Climate Change Mitigation Strategies through Stakeholder Engagement on Seaport Infrastructure Resilience in Comoros (Note: I made the sentence more concise and changed some words to make it more elegant and formal, while maintaining the same meaning as the original sentence.)
The Global Center on Adaptation (GCA) is providing technical assistance to the Comoros government to integrate climate adaptation and resilience into the rehabilitation and expansion of port infrastructure. The project aims to strengthen maritime infrastructure, improve logistics, and enhance regional trade while promoting inclusive socio-economic development and gender inclusion. A stakeholder consultation workshop is being held to present the findings of a climate risk assessment and proposed adaptation options for the ports of Moroni, Boingoma, and Mutsamudu. The workshop will include a discussion of port climate stress tests and the cost-benefit analysis of adaptation options, which range from engineered to nature-based solutions. The objectives of the consultation are to present the findings and gather feedback from stakeholders on the proposed adaptation options.
Cytora and Vāyuh Collaborate to Elevate Climate Risk Analytics for Property Insurers
Cytora, a digital risk processing platform, has partnered with Vāyuh, a pioneer in AI-powered weather forecasting and climate analytics. The collaboration aims to integrate Vāyuh’s sophisticated climate and weather data models into Cytora’s platform, providing property insurance underwriters with improved risk analytics and more precise risk scores, especially for climate and weather-related risks. The partnership addresses the increasing frequency and severity of natural disasters, such as wildfires and severe storms, which property insurers must better assess and manage. Vāyuh’s technology aggregates data from thousands of sources to create detailed risk perspectives and provides precise forecasts and risk models for various weather-related risks. This integration will enable insurers to streamline workflows, reduce underwriting delays, and enhance profitability by making more informed and precise risk assessments. The partnership aims to enhance market resilience by providing a robust new resource for understanding and mitigating climate and weather-related risks.
Cornell University Partners with Industry to Advance Drought Risk Analysis
A global advisory and brokerage firm, WTW, has partnered with Cornell University’s Toby Ault, an expert on future drought, to better advise clients on drought-related risks and losses. The collaboration aims to provide actionable and usable information to the insurance and reinsurance industries. Ault’s research focuses on year-to-year and multi-year variations in drought, and the project seeks to develop tools and datasets to predict single and multi-year droughts. The partnership will also identify areas where climate may amplify drought, estimate drought risk more accurately, and create new tools and datasets to predict drought. The research aims to understand how climate models can be used to improve drought risk assessments and to develop products that can be used by the insurance industry to anticipate and price risks. The long-term goal of the project is to catalyze industry-wide transformation in how drought risks are understood, measured, and mitigated. The partnership will also explore the downstream effects of drought and its impact on various sectors, including energy, finance, and public policy.
Indonesia’s vulnerability to climate-related security threats in the Indo-Pacific by 2035
A new report from the Australian Strategic Policy Institute (ASPI) warns that Australian policymakers are underestimating the impact of climate change on national security and regional stability in the Indo-Pacific. The report assesses the risks of climate-driven disruptions in Indonesia, one of Australia’s closest neighbors, and suggests that these disruptions could have far-reaching consequences across the region. The report identifies three key pathways to compounding and destabilizing climate disruptions in Indonesia: food insecurity, population displacement, and slowed economic growth. It recommends that Indonesia and its regional partners, including Australia, work together to anticipate and prepare for these disruptions through research, policy, and diplomatic efforts. The report also highlights the need for a deeper public discussion in Australia about cross-border climate risks and the need for further analyses and strategic public conversations on climate issues. It emphasizes the importance of prioritizing national efforts to adapt to climate change and the high costs of inaction.
Please stand by for just a moment
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Practical Solutions for Mitigating Climate and Environmental Impacts in Mine Action
The event, moderated by Mine Action Review, brings together the CEOBS, Mines Advisory Group, Norwegian People’s Aid, and the UK Foreign, Commonwealth and Development Office to discuss the new Green Field Tool and research findings on the long-term environmental impact of mine action. The event aims to explore how the Green Field Tool can align mine action programming with the updated IMAS 07.13 on environmental management and climate change. The agenda includes:
* Utilizing an open-source data platform to understand the environmental setting
* Conducting environmental risk assessments and implementing mitigation measures
* Developing environmental reporting indicators
* Presenting findings from post-clearance impact assessments on the environment and land use
The event aims to provide insights and best practices for effective environmental management in mine action programs, ultimately promoting sustainable development and reducing environmental risks.
Here are a few rewritten versions of the line: * A Record-Breaking Number of Students Take On the Global Risk Management Challenge * Global Risk Management Challenge Sees Record Student Participation * Students Around the World Contribute to New Record in Global Risk Management Challenge * The Global Risk Management Challenge Attracts a Record Number of Student Participants
The 2025 Spencer-RIMS Risk Management Challenge has set a record with 244 student teams from 61 universities across 16 countries competing to develop climate risk management strategies for Huntington, West Virginia. The competition has seen a significant increase in participation from new institutions and underrepresented groups, with 38 first-time applicants and 9 Minority-Serving Institutions (MSIs) participating. Eight finalist teams have been selected to advance to the final stage at RISKWORLD in Chicago, including teams from DePauw University, University of New South Wales, and University of the Witwatersrand. The challenge requires teams to identify the impacts of climate change on Huntington’s infrastructure, economy, and residents, and develop proactive recommendations to address these vulnerabilities. A panel of six risk management professionals will judge the teams, with the top three performing teams announced on May 5.
Seven localities in the Western Balkans join forces to boost climate resilience through the CLIMAAX initiative.
The European Union’s Mission on Adaptation to Climate Change (CLIMAAX) has selected 69 cities and regions in 23 countries, including seven in the Western Balkans, to receive financial and technical support to enhance their climate resilience. The selected cities and regions will conduct multi-risk climate risk assessments and will be eligible for support from the CLIMAAX consortium. The program aims to support the implementation of the EU Adaptation Strategy and the Horizon Europe Mission Adaptation, which focuses on preparing and planning for climate resilience. The selected cities and regions will receive a total of over EUR 12 million in financial support, with the maximum amount ranging from EUR 115,227 to EUR 300,000.
Diginex Limited and Mazars forge strategic alliance to boost supply chain risk assessment capabilities with diginexLUMEN.
Diginex Limited, a leading impact technology company, has announced a strategic alliance with Forvis Mazars Global to bring its supply chain due diligence platform, diginexLUMEN, to Forvis Mazars’ client base. The platform uses “cutting-edge technology” to provide insights into supply chain risks related to climate and social issues, enabling businesses to assess and manage these risks. The alliance combines Diginex’s technology with Forvis Mazars’ expertise in ESG advisory, climate risk management, and business strategy to help clients navigate the demands of sustainability and regulatory compliance. DiginexLUMEN is a scalable and affordable SaaS solution that provides unparalleled insight into supply chain risks by leveraging robust governance processes, multilingual worker voice surveys, and algorithm-based risk scoring. The platform is designed to help companies identify, prioritize, and address issues such as forced labor, climate impacts, and other social vulnerabilities across their global operations. The alliance is aimed at helping businesses of all sizes tackle the critical challenges within their supply chains and drive meaningful change.
Ensuring a fair and equal approach to mitigating urban heat stress through climate-resilient urban planning I removed some technical terms and rephrased the line to make it more concise and accessible, while still conveying the main idea. Let me know if you’d like me to make any changes!
The article describes a study that analyzed climate data, population data, green infrastructure data, and survey data to investigate the impact of green infrastructure on residential choices and gentrification in Vienna. The study used a range of data sources, including climate models, population data, and survey results. The study also employed a variety of analytical steps, including the use of regression models and hierarchical logistic regressions.
The study used climate data from the ÖKS15 time series and the MUKLIMO_3 model to analyze the impact of climate change on heat-risk indices in Vienna. The study also used population data from the Statistical Department of the City of Vienna and the Austrian Statistical Office to analyze trends in population growth and social vulnerability.
The study used survey data to investigate the role of green surroundings in residential choice and gentrification. The survey results showed that residents who lived in areas with more green spaces were more likely to move to a new location. The study also found that gentrification was more likely to occur in areas with higher levels of green infrastructure.
Overall, the study used a range of data sources and analytical techniques to investigate the impact of green infrastructure on residential choices and gentrification in Vienna. The study provides insights into the relationship between green infrastructure and gentrification, and highlights the importance of considering these factors in urban planning and policy-making.
Amid mounting climate concerns, investors demand more transparent and accurate physical risk data.
A recent paper by Nest, UBS Asset Management, and the University of Oxford highlights the need for investors to better integrate physical climate risk into their strategies due to the “limited, incomplete and inadequate” corporate disclosures on physical risks. The report notes that climate change is already causing significant economic damage and that carbon emissions and land-use change continue to worsen the physical risk. The study argues that third-party data providers should improve the clarity and consistency of analytical models and data on physical risk events, and that listed companies should provide granular, location-specific information on physical risks, including insurance availability, asset geolocations, and past and potential future risks. The researchers recommend that regulators and capital markets adopt uniform frameworks for integrating climate risk data into financial decision-making, and that investors engage with companies to encourage improved climate risk disclosure and adaptation efforts. The paper warns that without improved data and clearer methodologies, investors risk misallocating capital and failing to protect their portfolios from climate-induced disruptions.
Here is a rewritten version of the line without adding any new words: Leading C-Suite Insights on Climate Resilience from London This rewritten line maintains the core message and structure of the original, but in a more concise and clear format.
The London Councils’ Climate team has released a new report, “London Leading: Case Studies in Climate Resilience Leadership,” which showcases how London boroughs are prioritizing climate adaptation and making progress in this area. The report identifies five areas of Climate Resilience Leadership and provides recommendations for implementation. The report highlights 12 case studies from London boroughs, demonstrating how they are embedding resilience, collaborating, using data, and empowering leaders. The findings suggest that mainstreaming climate adaptation across all council levels and services is crucial, and that every officer should be considered a climate change officer. The report emphasizes the importance of senior leadership in ensuring that climate adaptation and resilience become a priority. The case studies demonstrate innovative approaches, such as developing climate resilience and adaptation strategies, conducting climate risk assessments, and engaging citizens in heatwave planning. Overall, the report aims to inspire and support London boroughs in their climate adaptation efforts.
OroraTech harnesses the power of NVIDIA Earth-2’s cutting-edge AI to empower climate risk assessments through advanced thermal intelligence.
OroraTech, a company that operates a space-based wildfire detection system using thermal imaging satellites, has partnered with NVIDIA’s Earth-2 digital twin platform to improve climate risk assessment and disaster response. The partnership allows for the integration of OroraTech’s thermal intelligence with Earth-2, enabling researchers and developers to build more accurate models for predicting extreme weather events and natural disasters. The system, which includes 27 satellites, can detect wildfires in near real-time and provide critical fire detection data to emergency responders. The integration with Earth-2 expands the potential applications of this technology in climate resilience efforts, including refining predictions for extreme weather events and improving emergency response strategies. The partnership combines OroraTech’s thermal intelligence with NVIDIA’s edge computing capabilities, enabling real-time data transmission and analysis. This collaboration has the potential to improve climate resilience and disaster response worldwide.
In a bid to attract climate finance, India’s central bank advocates for the pooling of viable projects
Here is a summarized version of the article in 200 words:
The Reserve Bank of India (RBI) Governor, Sanjay Malhotra, has proposed creating a shared pool of bankable projects to increase investment and funding for climate-related projects. This idea aims to address the significant funding gap for climate finance, with India needing trillions of dollars by 2050. Experts acknowledge that the proposal has potential, but concerns such as taxonomy, risk management, and project viability need to be addressed.
A shared pool of projects could offer multiple benefits, including providing experienced financial institutions with a collection of financially viable and investment-ready projects. However, experts caution that success depends on agility, taxonomy, and risk management. The RBI has taken steps to address climate-related financial risks, including creating a dataset repository and issuing guidelines on climate-related financial risk disclosure.
Experts also emphasize the need for banks and non-bank finance companies to develop skills and knowledge to assess and finance climate change mitigation projects. The idea of pooling non-return-oriented projects with return-oriented ones raises concerns about cross-subsidization and may only work if both types of projects belong to the same entity. Overall, the RBI’s proposal aims to address the significant funding gap for climate finance, but more work is needed to ensure its success.
The council endorses a comprehensive, five-year strategy to combat climate change.
Staffordshire County Council has released a new Climate Change Action Plan, which focuses on reaching net zero emissions by 2050 and preparing for the changing climate. The five-year plan aims to reduce carbon emissions and adapt to the effects of climate change. The plan includes a stronger focus on climate resilience, with a comprehensive risk assessment identifying 55 climate-related risks. The council has made good progress, reducing emissions by 52% since 2019, through initiatives such as using HVO fuel in fleet vehicles and LED lighting upgrades. However, the council recognizes that reducing emissions alone is not enough, and must also prepare for the impacts of climate change on communities, infrastructure, and the natural environment. The plan outlines new actions to enhance climate change resilience within services, including a detailed risk assessment. The plan was approved at the council’s Cabinet meeting on March 19th. The council’s cabinet member for environment, infrastructure, and climate change, Cllr Simon Tagg, emphasized the county’s mission to become sustainable and the importance of preparing for the changing climate.
-$4 million seed funding secured for pioneering projects that reimagine property carriers’ scalability in high-risk regions vulnerable to climate-related disasters This rewritten line aims to: * Use a more active and concise way to convey the information * Change from a straightforward statement to a more narrative-driven one * Use more descriptive language to make the concept more engaging and clear * Use action-oriented words like reimagine and pioneering to convey a sense of innovation and progress.
ResiQuant, a company that uses AI and structural engineering expertise to assess and manage catastrophe risks for property insurers, has raised $4 million in seed funding led by LDV Capital and other investors. The funding will accelerate the company’s mission to provide accurate and granular data for property carriers, helping them to make more informed underwriting decisions. ResiQuant’s platform combines AI with site inspection photos, aerial imagery, and publicly available data to identify critical structural weaknesses in buildings, allowing insurers to evaluate risk with greater accuracy. The company’s founders, Dr. Issa and Dr. Galvis, have backgrounds in structural engineering and earthquake research, and have gained valuable experience conducting post-disaster inspections following major events. The funding will be used to expand the company’s platform and grow its engineering and AI units to support carriers in all major US property markets. The founders believe that their approach can help to transform the insurance industry and reduce the impact of natural disasters.
ESG and Climate Risk Intelligence Platform for Seamless Tracking and Reporting
The Komunidad Climate Action Suite is an all-in-one platform that helps organizations assess, track, and report on their climate risk, sustainability performance, and ESG data. The platform offers three key features: Climate Risk Insights for real-time environmental data and risk assessment, Sustainability and ESG Management for monitoring carbon emissions and social impact, and Reporting and Compliance for automated reporting aligned with international sustainability frameworks. The platform also features Collaboration Tools for engaging stakeholders across the organization and with partners. The suite is highly flexible and adaptable to different regions and industries, with a granularity of 27x27m. By using the Komunidad Climate Action Suite, organizations can transform raw data into actionable insights, making better decisions about climate resilience and sustainability. The platform helps organizations measure, manage, and report on their environmental, social, and governance performance, aligning with sustainability frameworks and goals.
According to experts, the NZ First bill will not prevent banks from evaluating climate-related risks
Federated Farmers has opposed banks’ efforts to reduce their exposure to fossil fuels and set climate targets, claiming it’s driven by non-commercial factors. However, Deloitte’s Will Symons, who met with CFOs, found no evidence that organizations are making decisions based on a “woke agenda.” Symons argues that companies are driven by commercial considerations, such as the need to reduce emissions to meet government targets and respond to shifting market conditions. The bill, proposed by MP Andy Foster, aims to require banks to make lending decisions solely on a commercial basis. However, some experts believe the bill may be unworkable, as climate change poses real commercial risks. DLA Piper’s Daniel Street notes that some US banks may have pulled out of climate accords due to concerns about litigation or reputational risks, not a change in their approach to climate risk. The bill’s passage could increase the cost of borrowing by introducing risk and uncertainty.
Rapid Climate Risk Assessment: Mukuru, Kenya
Flooding and extreme temperatures were identified as the major climate change hazards in the Mukuru informal settlement in a …
Breaking: Classified intelligence reveals the alarming threat climate change poses to global national security
Senator David Pocock, an independent, has described the national security threat of climate change as “frankly terrifying” after being briefed on a secret report from the Office of National Intelligence (ONI). The report, which was delivered to the government in 2023, is only available to the government and crossbenchers, and is not publicly available. The crossbenchers, including Pocock and others, are gagged from speaking publicly about the report’s contents. The article argues that the government’s secrecy around the report is “recklessly negligent” and that the country is “woefully underprepared” for the impacts of climate change. The report is said to link climate change to national security, with experts warning of increases in global unrest, supply chain disruptions, and the potential for climate refugees. The government has been accused of sitting on the report, which has sparked criticism from the Greens and other independents. The article concludes that the country needs to take immediate action to address the threat of climate change, with Senator Pocock calling for regular, independent, and public national climate risk assessments.
Offset by 2025, the reliability of current climate change risk assessments could be called into question.
The Chief Investment Officer of the British Business Bank, Leandros Kalisperas, stated that current climate risk assessments on pension investments may not be credible. He acknowledged that current assessments have inaccuracies, but emphasized the need for practical solutions to address climate risk. This comes as climate protesters gathered outside a pension conference, urging delegates to update risk assessment methodologies, citing a report that found climate and nature-driven risks have been underestimated due to flawed economic modeling. An LCP investment partner, John Clements, suggested that instead of divesting from fossil fuel companies, pension schemes should set clear expectations for them and threaten divestment if they fail to meet targets. Many pension funds, including 65%, have committed to net zero, with 22% of those who do not expect to adopt a commitment within five years.
Mitigating the effects of climate change is a pressing concern for the NEN – North Edinburgh News.
Extinction Rebellion Scotland, Divest Lothian, Friends of the Earth Scotland, and Protest in Harmony held a protest outside the Pensions and Lifetime Savings Association Investment conference in Edinburgh, calling on pension fund leaders to acknowledge the underestimation of climate risks and take bold action. The protesters, including a performance with singing and a “Big Oil Funk” dance, drew attention to the fact that pension funds invest over £1.3 trillion on behalf of 30 million people, yet many are still investing in fossil fuels despite the growing threat of climate change. A report by the Institute and Faculty of Actuaries and the University of Exeter found that climate risks are being underestimated by pension funds, which could lead to catastrophic consequences, including a 50% contraction of the global economy. The campaigners are calling for pension funds to conduct robust climate risk assessments, divest from fossil fuels, and advocate for policy changes to accelerate the energy transition. They warn that if pension funds continue to invest in fossil fuels, there will be no liveable planet or positive future for pensioners to retire into.
Strengthening Collective Fortitude: A Collaborative Journey Towards Climate Resilience in the Northwest Territories
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The government of the Northwest Territories (GNWT) is working to address the challenges posed by climate change, prioritizing adaptation, resilience, and sustainability for future generations. Over the past year, significant progress has been made in Indigenous engagement and partnership on climate adaptation initiatives. This includes developing flood maps for at-risk communities, flood mapping for ten towns, and mapping water level fluctuations. The GNWT has also expanded water monitoring networks, partnered with researchers, and provided water monitoring bulletins.
Adaptation planning is another key area of focus, with the government working with Indigenous governments, community representatives, and federal partners to conduct climate risk assessments and identify vulnerabilities in areas such as housing, infrastructure, and ecosystems. The government is also investing in training programs to build climate adaptation skills in communities, empowering them to respond effectively to climate impacts.
The government’s efforts aim to deliver real benefits, including enhanced safety and preparedness through updated flood maps and expanded water monitoring. By integrating Indigenous knowledge, the GNWT is working towards an inclusive, collaborative approach that reflects northern values. The government is committed to securing a sustainable future for the Northwest Territories.
In the path of the gust: Adapting to the evolving threat of hurricanes
The year 2024 was the warmest on record, exceeding 1.5°C above pre-industrial levels. This warming may lead to more intense hurricanes, as seen in the recent Hurricane Beryl, which was fueled by record-high sea surface temperatures. Traditional natural catastrophe (nat cat) models are based on historical data, but climate change is altering the world, making these models outdated. To address this, reask, a nat cat modeller and data provider, has developed a methodology that combines machine learning with advanced stochastic simulations to provide a climate-informed, forward-looking risk assessment. This approach can help quantify uncertainty in hurricane formation and risk. The model can simulate millions of hurricanes and provides a robust database for training machine learning algorithms. By forcing the model with different climates, the industry can assess how climate change affects the entire risk curve, including the tail. This methodology can be adapted for other climate-driven perils, such as wildfires and floods, to facilitate better risk management, pricing, and assessment. By anticipating non-linear shifts in risk distributions, the industry can avoid future surprises and be better prepared for a rapidly changing climate.
Ocean Ledger secures €900,000 in funding to enhance the accuracy of its coastal risk management systems.
London-based Ocean Ledger, a geospatial analytics startup, has raised €900,000 in pre-seed funding led by Ananda Impact Ventures and Silverstrand Capital to scale its solutions for predicting coastal risks and identifying interventions for engineering firms, municipalities, environmental services, and insurance companies. The company offers high-resolution geospatial analytics for coastal risk assessment and environmental impact analysis, detect anomalies in shoreline movement, underwater topography, and natural defenses to enable proactive risk management and continuous monitoring. With the funding, Ocean Ledger aims to improve the accuracy and usability of coastal risk assessments, which are crucial for evaluating and managing the risks associated with coastal erosion and flooding. The company’s solutions can help reduce the estimated €3.7 trillion in climate-induced infrastructure losses over the next 15 years. It has already collaborated with satellite data provider Planet Labs PBC and is exploring partnerships with marine drone and sensor companies to further enhance its capabilities.
A pioneering climate risk model revolutionizes real estate investment strategies.
Aisix Solutions Inc. has partnered with Stessa Real Estate to integrate its Climate Genius platform into real estate investment assessments, providing property investors with climate risk data. This integration aims to enhance transparency in property investment and help investors make informed decisions. Aisix Solutions’ CEO, Mihalis Belantis, views real estate as a highly vulnerable asset class to climate-related financial risks.
The company is also expanding its climate risk insights across multiple industries, including finance, insurance, and government, to address rising regulatory demands. Its Climate Genius platform leverages advanced modeling and geospatial data to provide property-specific climate risk assessments. The company has also partnered with Triomphe Holdings Ltd.’s Capital Analytica for investor relations and communications.
The financial sector is adapting to rising climate risks, with institutions prioritizing climate risk assessment and integration into financial models. Regulatory frameworks, such as IFRS S2 and OSFI B-15, require financial institutions to assess and report on climate-related risks. Aisix Solutions aims to facilitate this process through data-driven climate insights, generating revenue through subscription-based access to climate data, analytics, and premium features.
Here is a rewritten version of the phrase: Understanding climate-related risks as a hidden factor in financial analysis Let me know if you’d like any further assistance!
Oxford Economics has identified a “hidden” risk in financial analysis related to climate change, which is often overlooked. This risk arises from the indirect impact of climate change on global supply chains, rather than the direct impact on individual companies. This “mesoeconomy” is the blind spot in climate risk analysis for the finance sector.
Oxford Economics has developed a new approach to measure these indirect climate risks, which assesses the vulnerability of supply chains to climate risks. They tested this approach by analyzing financial market data over a 10-year period and found that portfolios that incorporated this indirect climate risk performed better than those that did not.
The study shows that there is a correlation between indirect climate risk and lower total returns to equity, and that considering this risk can lead to improved portfolio performance. To access the full whitepaper, which includes the detailed methodology and findings, readers can complete a form. The report highlights the importance of considering indirect climate risk in financial analysis and provides a new approach to do so.
Building a stronger future: The emergence of climate-resilient construction in Canada
The Insurance Bureau of Canada reports that combined damages from summer 2024 extreme-weather events reached over $7 billion, with flash floods in Toronto accounting for a significant portion of the losses. To address this growing concern, the construction of St. Paul’s Hospital in Vancouver’s False Creek Flats neighborhood is incorporating climate-resilient design features. The hospital’s entrances will be designed to withstand flood levels up to 2100, and it will have backup systems for power and water, as well as permeable ground and a tree canopy to reduce the heat-island effect. This design is a result of a climate-risk assessment by PCL Construction and environmental consultants at Stantec, who identified coastal flooding, intense rainfall, heat domes, and poor air quality as the most likely risks. This initiative, according to Bruce Norman, project director for PCL Construction, demonstrates a change in how buildings are designed, with a focus on forward-thinking and resilience. The Canadian Home Builders’ Association is also working to incorporate climate adaptation and resilience into new builds and rebuilds, leveraging advanced climate risk modeling and alternative building materials to make homes more resilient without increasing costs.
Every region in the state is at high risk of experiencing severe climate-related issues.
As of September 30, 2024, extreme weather events have affected 3.2 million hectares of crop area across 35 states in India, with India’s National Innovations in Climate Resilient Agriculture (NICRA) assessment finding that 255 of the past 274 days have been impacted by extreme weather. In Meghalaya, a state in northeastern India, every district is categorized as either “very high” or “high” risk, with Ri-Bhoi district specifically identified as facing “high” climate risk due to rising minimum temperatures, increased drought, and erratic rainfall. The assessment found that rising minimum temperatures are a critical risk factor, particularly in Meghalaya where a slight increase can disrupt crop cycles and diminish yields. Without immediate intervention, farmers in Meghalaya may face worsening crop losses and food insecurity. To mitigate the impact, efforts are needed to improve irrigation infrastructure, introduce climate-resilient farming techniques, expand crop insurance coverage, and strengthen early warning systems and sustainable agricultural practices.
Minimizing vulnerabilities: How central banks navigate the intersection of climate and energy transition challenges
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The IPCC Climate Change 2022 report highlights the urgent need for mitigating climate change. This report discusses the role of central banks and financial institutions in addressing climate change. Several studies, including those by Bolton, Despres, and Svartzman, emphasize the importance of green finance in achieving a sustainable future. They argue that central banks can play a crucial role in promoting sustainable finance through monetary policy, supervision, and climate stress testing.
Other studies, such as those by Gabor and D’Orizio, explore the relationship between central banks and financial stability in the context of climate change. They suggest that central banks can use their mandates and instruments to promote green finance and mitigate climate risk.
The paper by Battiston et al. highlights the need for a systemic approach to addressing climate-related financial risks, while that of Oman et al. discusses the importance of policy coordination between central banks and financial supervisors.
Overall, the report emphasizes the need for a coordinated effort by central banks, financial institutions, and policymakers to address the challenges posed by climate change. It provides insights into the role of green finance, financial regulation, and policy coordination in mitigating climate change.
Ethiopia’s climate resilient infrastructure fund receives backing from EIB
The European Investment Bank (EIB) has committed to join Africa Finance Corporation (AFC) in financing the $750 million Infrastructure Climate Resilient Fund (ICRF). The fund is aimed at accelerating climate adaptation and sustainable infrastructure development in Africa, focusing on climate-resilient infrastructure, such as transport and logistics, clean energy, digital infrastructure, and industrial development. The EIB will invest $52.48 million in the fund, which is managed by AFC Capital Partners, the asset management arm of AFC. The Green Climate Fund (GCF) has committed $253 million to the fund, marking its largest-ever equity investment in Africa. The fund will use blended finance to de-risk private investment and integrate innovative tools, such as climate risk parametric insurance, to enhance protection against climate-related risks and losses. The EIB’s investment is aimed at attracting additional investors, reducing risk, and promoting best practices in climate finance. The fund is aligned with the European Union’s Global Gateway initiative and the United Nations’ Sustainable Development Goals.
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New technology from Bloomberg helps investors mitigate climate risk by providing a cutting-edge portfolio risk management solution
Bloomberg has launched MARS Climate, a new tool within its Multi-Asset Risk Management (MARS) suite, to help portfolio managers and investors assess and manage the financial risks associated with climate change. The tool is designed to aid in evaluating the impact of climate-related risks on portfolios and helping firms comply with regulatory reporting requirements. The launch of MARS Climate comes as regulators and central banks worldwide are increasingly asking financial institutions to assess their exposure to climate risks. The tool is backed by BloombergNEF’s Transition Risk Assessment Company (TRACT), which evaluates the financial impact of climate-related risks on company revenues and opportunities. Bloomberg’s Global Head of Risk Product, Dharrini Bala Gadiyaram, notes that portfolio managers are seeking to conduct climate risk analysis alongside financial risk assessments to manage risk and regulatory reporting. With MARS Climate, financial professionals can assess portfolio vulnerabilities and opportunities related to climate change, helping them make informed investment decisions and comply with regulatory requirements.
MSCI and Swiss Re Join Forces to Boost Financial Institutions’ Climate Risk Evaluation Capabilities
MSCI and Swiss Re Reinsurance Solutions are partnering to enhance the financial sector’s approach to physical climate risk management. The collaboration combines MSCI’s GeoSpatial Asset Intelligence with Swiss Re’s proprietary natural catastrophe and climate risk data. The initiative aims to provide financial institutions with detailed, asset-level physical risk insights, enabling them to develop strategies to assess and mitigate risks across individual assets, companies, and global portfolios.
Richard Mattison, Global Head of ESG and Climate at MSCI, sees this collaboration as a significant advancement in physical risk insights, empowering clients to navigate the evolving risk landscape with confidence. Ali Shahkarami, Global Head of P&C Solutions at Swiss Re, highlights the broader impact, stating that the cooperation will benefit financial institutions globally and align with Swiss Re’s vision of making the world more resilient.
The partnership is seen as a critical advancement in building robust, climate-resilient portfolios for C-suite executives, investors, and financial leaders. By leveraging Swiss Re’s CatNet data and other natural catastrophe risk insights, the financial sector can better assess and manage physical climate risks, ultimately leading to more resilient investment strategies.
Bloomberg Expands Its Sustainability Efforts with MARS Climate, a New Tool for Investors to Analyze the Financial Risks of Climate Change
Bloomberg has launched a new module, MARS Climate, as part of its Multi-Asset Risk Management (MARS) suite. This new feature helps portfolio and risk managers analyze the financial impacts of climate change on investment portfolios, enabling them to better manage climate-related risks and aligned with regulatory requirements. The MARS Climate module uses BloombergNEF’s Transition Risk Assessment Company Tool (TRACT) to evaluate portfolios against multiple climate scenarios, providing transparent financial impact reports that categorize risks into physical, acute, chronic, and transition risks. This tool is accessible through the Bloomberg Terminal, allowing for easy integration with other sustainable finance tools. The launch of MARS Climate responds to regulatory pressure on financial firms to evaluate climate-related risks and meet compliance requirements, while also identifying new opportunities in the transition to a greener economy. The module is designed to support firms in their efforts to manage climate risks and improve their risk reporting, ultimately helping them stay ahead of the curve in the rapidly changing financial landscape.
Bloomberg Unveils Innovative Tool to Enable Investors to Quantify ESG Risks and Opportunities in Their Portfolios
Bloomberg has launched MARS Climate, a new solution that enables financial firms to quantify and manage climate-related risks and opportunities across their portfolios. The solution is part of the Multi-Asset Risk Management (MARS) suite and aims to help investors address regulatory demands to assess their exposure to climate risks. Climate change is having a significant impact on the economy, with natural disasters causing $280 billion in damages globally in 2023, according to Swiss Re. The MARS Climate module uses integrated assessment models and the Transition Risk Assessment Company Tool (TRACT) to analyze climate scenarios and project company revenue risk and opportunities. The solution provides a report assessing the financial impact of climate-related risk down to the security level, split out by physical acute, physical chronic, and transition risk. Bloomberg’s Global Head of Risk Product, Dharrini Bala Gadiyaram, said that the new solution will enable users to assess portfolio vulnerabilities and opportunities related to climate change.
At the recent Growth Conference, AISIX Solutions unveiled its innovative AI-driven climate risk platform, empowering businesses to navigate the complexities of environmental sustainability.
AISIX Solutions, a leader in climate risk assessment and modeling, will present at the Centurion One Capital 8th Annual Growth Conference from March 3rd to March 6th, 2025, at the Four Seasons Hotel in Toronto’s Yorkville neighborhood. CEO Mihalis Belantis will deliver a presentation on March 6th at 10:45 AM and participate in a panel discussion at 1:45 PM.
AISIX Solutions recently launched Wildfire 3.0, a latest wildfire prediction and risk management tool that incorporates advanced AI, machine learning, and probabilistic modeling algorithms to assess wildfire probability, intensity, and expected losses under various climate conditions.
The conference will feature presentations, panel discussions, and individual investor meetings from 8:00 AM to 5:00 PM EDT. The event is expected to provide an excellent opportunity for stakeholders to engage with Mihalis Belantis and explore the company’s innovative climate risk solutions.
AISIX Solutions is committed to empowering organizations to protect their property, assets, and infrastructure from climate-related risks through its cutting-edge data analytics and risk assessment solutions. The company is dedicated to fostering resilience and sustainability in the face of climate change.
Empower Your Business for a Sustainable Tomorrow: Mastering Climate Resilience and Strategic Leadership through our Executive Education Certificate Course Let me know if you’d like me to make any further changes!
The topic of climate change and its impacts is a pressing concern globally. This analysis delves into the effects of climate change on various industries through real-world case studies. The discussion begins by exploring the physical risks associated with climate change, including floods, heatwaves, droughts, and other extreme weather events. It also examines the transition risks that come with policy shifts, carbon pricing, and regulatory changes. The next step is to integrate sustainability principles into daily operations, such as adopting a circular economy, improving energy efficiency, and implementing green supply chains. Additionally, the analysis assesses vulnerabilities in critical infrastructure and assets, with the goal of building resilience and minimizing the impact of climate-related disasters. By understanding these risks and challenges, industries can better prepare for and mitigate the effects of climate change, ultimately ensuring business continuity and long-term sustainability.
The majority of US state pensions neglect to consider climate-related risks in their proxy voting decisions.
A recent report by the Sierra Club assessed the proxy voting guidelines and practices of 32 US state pensions for their effectiveness in addressing climate-related financial risks. The report found that only one pension fund, the Massachusetts Pension Reserves Investment Management, received an A grade for its guidelines and voting, while two-thirds of the pensions received D or F grades. The report praised the New York State Common Retirement Fund for its proactive approach to risk mitigation, while seven other funds received B grades for their solid performance on climate-related votes. The report also highlighted that eight pensions had transparent voting records, with a minority having incomplete or non-existent records. Overall, the report suggests that many state pensions are still failing to effectively address climate-related financial risks in their proxy voting practices.
According to a comprehensive report, India ranked sixth globally in terms of its vulnerability to extreme weather events, highlighting a pressing need for urgent climate action.
India’s Climate Risk Index (CRI) rank has improved from 7th worst to 49th in 2022, although it remains among the top 10 most affected countries historically. A new report by Germanwatch analyzed extreme weather events in 1993-2022, finding that India reported 80,000 fatalities and $180 billion in economic losses due to 400 events. Globally, over 765,000 people lost their lives and $4.2 trillion in damages during the same period. Storms, heat waves, and floods caused the most fatalities. The report highlights the increasing climate risks globally and suggests that high-income countries must increase their climate risk management. The report criticizes the failure of rich nations to provide adequate finance for climate actions in developing countries, citing the lack of an ambitious New Collective Quantified Goal (NCQG) on Climate Finance at the 2022 UN climate conference. The report emphasizes the need for increased financial support to developing countries to address climate impacts and adaptation financing gaps. Experts warn that climate change is becoming a global security risk and that bold multilateral actions are necessary to address it.
Spain Joins Global Elite: Ranked Among the Top 10 Most Vulnerable Countries to Climate Change
Spain has been ranked as one of the 10 countries most affected by climate change, according to a report by Germanwatch. Between 1993 and 2022, extreme weather events linked to climate change caused 27,000 deaths and €25 billion in economic losses. The country has experienced severe heat waves, floods, and droughts, with the Mediterranean region being particularly vulnerable to these extreme conditions. The report highlights the need for Spain to improve its adaptation to climate risks, as it has a significant margin for improvement. Experts warn that the country must take urgent action to strengthen disaster preparedness, enhance climate resilience, and accelerate emission reductions. This includes investing in sustainable infrastructure, improving water resource management, and implementing stronger heat adaptation strategies. The report also emphasizes the urgent need for multilateral actions to address climate risks and secure funding for adaptation measures, particularly in vulnerable regions. The global climate crisis is a $4.2 trillion challenge, with extreme weather events resulting in over 765,000 deaths and significant economic losses worldwide.
Here’s a rewritten version of the given line: Planetary perils, revolutionary innovation, and seismic shifts in global politics
The 18th annual emerging risks survey by the Casualty Actuarial Society and the Society of Actuaries has identified the top emerging risks as climate change and geopolitical instability, including civil wars. The survey of 201 risk managers from around the world also highlighted the growing concern about disruptive technology, particularly artificial intelligence, cyber security, and manipulation. The survey results show a shift away from concerns about failed and failing states, which were more prominent in earlier surveys. The top emerging risks reflect recent events, including conflicts in Ukraine and the Middle East, hurricanes, and changes in inflation and interest rates. The survey aims to provide a long-term view of risk and to mitigate recency bias, with a mid-year flash survey planned for May 2025. The results highlight the need for risk managers to consider these emerging risks and take a proactive approach to managing them.
API data access for CSRD, ESRS and IFRS-aligned climate risk reporting
At Weather Trade Net we offer climate risk assessment products at various levels: asset-level, portfolio-level, company-level, and …
Unlocking the Secrets of Weather and Climate: A Comprehensive Guide to Navigating the Turbulent Terrain of Climate Change Research
In today’s climate, credible research and data are essential for making informed business decisions. WTW, a company with a track record of 20+ years of working with climate scientists, believes that a transdisciplinary approach is key to making climate data accurate, accessible, and actionable. Collaboration between the insurance industry and academia is also crucial for translating complex data into practical solutions for adapting to climate change. To identify credible research, it’s essential to evaluate the source, accessibility, peer review, and relevance of the data. Additionally, research should be transparent, and the funding sources should be disclosed. The article highlights several areas of research that should be approached with caution, such as research that lacks transparency, questionable accuracy, and uncertain funding sources. The article also emphasizes the importance of integrating climate and catastrophe models, seasonal predictions, and transition and liability risks into risk assessments and planning frameworks.
Levelling up climate resilience in Cambodia: Combining the power of AI and geospatial data for more accurate flood risk assessments I made the following changes: * Simplified the language to make it more concise and easy to understand * Changed Harnessing to Levelling up, which is a more dynamic and impactful verb that better conveys the idea of leveraging technology to drive progress * Removed the hyphen between Assessment and in to make the sentence flow better * Changed Flood to Floods to make the language more accurate * Emphasized the idea of more accurate to highlight the benefits of combining AI and geospatial data for climate risk assessment.
The use of artificial intelligence (AI) and geospatial data is transforming the way we assess climate disaster risks in Cambodia, particularly floods and droughts. These advances provide critical insights that enable effective disaster preparedness and resilience-building. These insights are crucial for informed planning, budgeting, and targeting of disaster response efforts. By leveraging AI and geospatial data, it is possible to generate granular and location-specific information on disaster risks, which can be used to strengthen disaster risk reduction frameworks such as Implementing Frameworks of Disaster Risk Reduction (DRR), Early Warning for All (EW4All), Anticipatory Actions (AA), and Shock-Responsive Social Protection (SRSP). This enables the identification of high-risk areas and populations, allowing for targeted interventions and emergency response planning. By harnessing the potential of AI and geospatial data, Cambodia can enhance its capacity to prepare for and respond to natural disasters, ultimately reducing the impact of floods and droughts on communities and businesses.
Mitigating Malawi’s Financial Vulnerability in the Face of Climate Crisis: A Call for Economic Resilience
Malawi’s economy is heavily affected by agriculture, which is highly vulnerable to climate-related risks. A recent report highlights the urgent need for the country to address climate change, as extreme weather events have led to significant economic damage and widespread distress. The banking sector is particularly vulnerable to climate-related risks, with 71% of banks recognizing the importance of climate risk management but only 14% having integrated these risks into their governance frameworks. The insurance sector is also underdeveloped and underutilized, leaving rural communities without adequate financial protection. The report proposes a comprehensive financial resilience strategy, including regulatory reforms, capacity-building initiatives, and green tax incentives. It emphasizes the importance of climate risk management and climate-resilient infrastructure to ensure long-term financial stability. If climate risks are not proactively managed, Malawi could face a financial crisis marked by declining agricultural productivity, increased loan defaults, and systemic economic instability. The international community has a crucial role to play in supporting Malawi’s adaptation efforts, and the stakes are high for the country’s economic future.
New ASTM Standard Aims to Simplify the Integration of Climate Risk and Resilience Factors into Assessments
The American Society for Testing and Materials (ASTM) has published a new standard for property resilience assessments (PRAs), which aims to help parties identify and evaluate the potential risks posed by natural hazards, including those made more extreme by climate change. The standard, known as ASTM E 3429-24, provides a generalized and systematic approach for conducting PRAs, which are intended to be used in real estate transactions, investment, and lending decisions, as well as in property management and climate risk analysis. The PRA process involves three stages: hazard identification, risk evaluation, and resilience measures. The standard is intended to complement existing property decision-making processes, including the Phase I Environmental Site Assessment (ESA) and the Property Condition Assessment (PCA). While PRAs may be more expensive and complicated than traditional ESAs, they can be an important tool for identifying and mitigating climate-related risks. However, there are concerns about the potential challenges and limitations of PRAs, including their cost, timing, and the difficulties of dealing with subjective or future-oriented analysis.
Introducing the revised ASTM standard, a comprehensive framework for evaluating and strengthening property resilience against potential threats.
The ASTM International, a standards-setting organization, has released a new standard guide, Property Resilience Assessment (PRA), to help assess a property’s degree of resilience to physical climate hazards such as storms, wildfires, and floods. The PRA is designed to provide a consistent means of determining a property’s risk level and what measures can be taken to improve its resilience. The standard is intended to be conducted alongside other assessments, such as property condition assessments and environmental site assessments, and is expected to become widely adopted in the real estate industry.
The PRA guides property owners, developers, investors, lenders, and design teams through three stages: identifying hazards, conducting an on-site evaluation, and determining conceptual resilience measures. The standard aims to create a common language and framework for evaluating property resilience, which can enhance due diligence, investment decision-making, and risk management.
The PRA is expected to have a broad impact, from raising awareness of physical risk and resilience to standardizing definitions and creating a common language around exposure, vulnerability, and risk. It may also lead to cost savings from insurance premiums and insurer availability, and encourage lenders to require borrowers to conduct PRAs as part of their due diligence.
Despite decades of warnings, the full extent of climate change’s crippling impact on human health remains unclear.
As the planet continues to warm, the effects of climate change on human health are becoming increasingly concerning. The past two years, 2023 and 2024, have seen record-breaking temperatures, with the planet breaching 1.5°C of warming above pre-industrial levels. While some health impacts are easy to track, such as deaths from heat stroke, others are more insidious and long-term. Prolonged exposure to heatwaves and droughts can lead to kidney disease, poor sleep quality, and altered gene expression, which can have a cumulative effect on health. Research suggests that fetuses exposed to environmental stressors in the womb are more likely to develop high blood pressure as adults, decades later. The development of a warming global climate means that the health burden may be greater than current models can quantify, making it essential for researchers and public-health officials to re-evaluate their assessments. The human body’s limits in response to extreme heat are not fully understood, and more research is needed to address the unforeseen consequences of climate change.
January: Uncovering the Hidden Dangers of Climate Change
A leading climate scientist, Dann Mitchell, has emphasized the urgent need to understand and quantify the numerous adverse health effects of climate change, which will have a significant impact on current and future generations. Worsening climate change is linked to an array of health issues, including heat-related fatalities, hospital admissions, and long-term health consequences such as kidney disease, sleep disturbances, and compromised immune systems. Even subtle changes, like poor quality sleep, can have far-reaching effects on mental and physical health. Climate change can also alter fetal development and gene expression, leading to increased risk of chronic diseases in adulthood. The full extent of these health risks is difficult to quantify due to incomplete data and the varied ways in which people experience environmental stressors. Mitchell urges researchers and public health officials to explore four key areas: the timescales of health impacts, environmental health risks, socio-economic consequences, and incorporating findings into global climate risk assessments. By combining disparate data and insights, the health burden of climate change can be better understood, and effective strategies can be developed to mitigate its effects.
Strengthening Bhutan’s Foundations: Building Bridges to Seamless Crisis Response
The Bhutan Crisis Preparedness Gap Analysis, conducted by international organizations, assesses the country’s abilities to address crises. Bhutan is vulnerable to natural disasters due to its mountainous terrain, dependence on agriculture, and limited resources. The report identifies gaps in preparedness and provides recommendations for strengthening the country’s resilience. Gaps include a lack of financial readiness, inadequate early warning systems, and insufficient infrastructure resilience. The report also highlights social vulnerabilities, including rural isolation, aging population, and fragile food security.
The report recommends a comprehensive approach to disaster preparedness, including legislative reforms, risk assessment and monitoring, and financial preparedness. This includes the creation of a Disaster Risk Financing and Insurance Strategy and investments in emergency response systems, public awareness campaigns, and community-based preparedness initiatives. The report also emphasizes the need to strengthen social protection, infrastructure, and agricultural practices to reduce the country’s vulnerability.
Overall, the Bhutan Crisis Preparedness Gap Analysis provides a roadmap for building a resilient nation, highlighting the importance of cross-sector collaboration and international support. The report’s recommendations are practical guidelines for safeguarding lives, livelihoods, and development gains, and can help Bhutan lead by example in building a resilient country.
Biosphere report highlights the threats posed by climate change to marine ecosystems.
A new report by the Golden Gate Biosphere Network warns that coastal redwood forests, including Muir Woods, are at risk of water stress, mortality, and poor health due to climate change. The report assesses the vulnerability of 21 key species and habitats in the region, including Muir Woods, San Mateo to Mendocino counties, and finds that many are facing significant threats. The report recommends reintroducing prescribed burns in Muir Woods to increase forest resilience to drought, wildfire, and disease. Other habitats at high risk include freshwater and tidal marshes, riparian forests, and woodlands throughout Marin County, as well as coho salmon and steelhead trout populations. The report aims to prioritize restoration efforts and inform parkland managers on how to combat and adapt to climate change. The findings are based on climate change projections, which show a hotter future with changing precipitation patterns, leading to more frequent and intense droughts and floods. The report is available for review at goldengatebiosphere.org/ccva.
Californian wildfires wreak havoc: permanent vigilance crucial in the era of climate-led disasters
The recent Los Angeles (LA) fires are estimated to be the most expensive disaster in US history, with damages of $250-275 billion, or 24-26% of Australia’s annual GDP. While climate change is not the sole cause of the fires, it is a significant factor in their intensity and unexpectedness. LA, like many other cities, is vulnerable to climate-amplified disasters that can surprise authorities even in areas that know fire risks. The article suggests that insufficient preparation, inadequate staffing, and lack of investment in climate-resilient infrastructure contributed to the scale of the disaster. Urban development, such as building in high-risk areas, has also played a role. The article emphasizes the need for more investment in climate adaptation, including building codes, fire-resilient design, and infrastructure preparedness. It also highlights the need for better communication and data-driven decision-making, as well as preparedness planning to mitigate the impact of future disasters. Ultimately, the article concludes that these events are predictable and preventable, but only if we take climate risks seriously and invest in proactive measures to address them.
Integrating climate risk assessment into multifamily real estate investments optimizes returns and reduces vulnerabilities.
Real estate owners and investors are increasingly focused on climate considerations due to the rising frequency and severity of weather-related events. At Leo Impact Capital, a JBG SMITH subsidiary, they approach sustainability and resilience by integrating climate risk assessments into their investment and asset management processes. They analyze historical climate data, local infrastructure resilience, and regulatory frameworks to understand potential risks and opportunities. Energy audits are a critical component of their approach, helping them identify areas for improving energy efficiency and reducing costs. They have identified over $13 million in sustainability investments that will reduce energy consumption by 60% and result in total annual savings of over $500,000. By leveraging grant funding and private financing options, they have found that incorporating a sustainability lens into their work unlocks new opportunities for growth and innovation, reducing portfolio risks and creating win-win outcomes for residents and investors. By adopting a climate-conscious approach, they aim to maintain affordability, enhance economic mobility, and deliver solid financial returns.
Introducing the Climate Risk Hub by XDI, your go-to platform for on-demand, real-time physical climate risk analysis.
The XDI Climate Risk Hub is a platform that enables real-time climate risk assessments for assets worldwide, using a single auditable methodology. The platform is designed to support financial institutions, companies, and governments in due diligence, operational risk, and climate adaptation planning. The technology was initially piloted by the Hong Kong Monetary Authority (HKMA) and has now been expanded to serve all sectors. The XDI Climate Risk Hub offers features such as climate risk ratings, financial risk metrics, analysis up to 2100, and high-resolution spatial data. These features help institutions assess counterparty risk, plan climate adaptation strategies, and improve operational resilience. The platform’s capabilities include screening single assets or large portfolios, performing in-depth due diligence, and identifying high-risk subsets of assets. XDI plans to expand its adaptation-focused tools to further support banks, companies, and governments in building climate-resilient futures.