Using Geospatial Big Data for Climate, Finance and Sustainability

Disponible en anglais seulement

Jamie Herring:

The risks of climate change and the impacts of our economic activities on the climate, they're happening here and now, but they're hidden from us, we don't see them. When we drive we don't see the emissions, But what I really truly believe is with satellite data and just a mass amount of information about the planet happening now in the planetary systems, we can start to see those things. And so the trick is going to be taking what is normally hidden and making them visible and then tying those to actual financial instruments.

Michael Torrance:

Welcome to Sustainability Leaders. I'm Michael Torrance, Chief Sustainability Officer with BMO Financial Group. On this show, we will talk with leading sustainability practitioners from the corporate, investor, academic and NGO communities to explore how this rapidly evolving field of sustainability is impacting global investment, business practices and our world.

Speaker 3:

The views expressed here are those of the participants and not those of Bank of Montreal, its affiliates or subsidiaries.

Michael Torrance:

Today I'm joined by Justin Huntington and Jamie Herring of Climate Engine to discuss the evolving topic of spatial finance and the use of geospatial modeling and climate science to better understand the impacts of physical climate change and their economic implication. Climate Engine is a firm that specializes in the unique delivery of satellite based data insights to support climate resilience and sustainability. They're also a partner of BMO's Climate Institute.

Michael Torrance:

Justin and Jamie's innovative approach to mainstreaming climate science has led them to work with the US and Canadian governments. As Chief Science Officer of Climate Engine, Justin's research interests are on satellite remote sensing, drought modeling, agriculture and risk analysis. A key focus of Justin's work is stakeholder engagement and outreach to better understand and communicate science needs and support research for operations using Earth observation, Cloud computing and modeling approaches. Jamie Herring is CEO of Climate Engine, and in his work, he supports a number of firms and organizations in the public and private sectors to better understand their near term climate risks and the sustainability of their operations.

Michael Torrance:

Hi, Jamie and Justin, thanks so much for speaking with me today.

Justin Huntington:

Thanks for having us.

Jamie Herring:

Thanks, Micheal. I really, really appreciate the invitation.

Michael Torrance:

All right, so let's get right into the discussion. I mean, it's a really exciting business that you're building and it's great for us to be able to work with climate scientists like yourselves to really figure out how to integrate climate science into our business. But let's maybe just give the audience a little bit of background about your work. And I'll start with you, Jamie, can you tell us about yourself and how you got involved in the climate field?

Jamie Herring:

Yes, for sure. I started really back in my undergrad when I was really involved in environmental activism and then got a degree in philosophy, quickly realized that there wasn't a huge job market for people with degrees in philosophy. So went into the working world and became a designer, I did that for a few years but the environmental side was always just so important to me. so I decided to go back to school and do a PhD in the sciences

Jamie Herring:

I went to Cornell University in the Department of Natural Resources and got tapped into a number of different projects as almost a communication bridge between science organizations and the public or decision makers. And so doing that work, that really got me into the research community and by proxy the climate community, and talking to scientists in the climate world, got really, really concerned. They started sharing the data with me and I started working with some groups at NASA, and they had recently done a global climate projection downscale which was really mindbogglingly scary. And so with that, that really got me really honed in and focused on climate as a specific issue.

Michael Torrance:

Thanks, Jamie. Justin, tell us a little bit about yourself and how you got into the climate field.

Justin Huntington:

Thanks for the question, Michael. So I was born and raised in Las Vegas, in Nevada. And Nevada is the driest state in the US. I came to college at University of Nevada, Reno . And there was a class in the Environmental Science Department called Snow Hydrology, , so really got into the science of snow and quickly realized just how important snow is for water resources,

Justin Huntington:

And just realizing, wow, climate change is really going to, at that time, potentially change the way that hydrology works in these Alpine and subalpine snow dominated areas where runoff is going to happen earlier, 

Justin Huntington:

And so that really got me interested in hydrology and ended up pursuing a Master's degree in hydrology, and then got into the realm of water resource availability. And I did an internship with a local county here and they had a Master's project for me to basically help them better understand how much water is coming into a basin and going out of the basin, And the question was, how much water is available to appropriate? And will we be appropriating more water than what's coming in and going out naturally? and I was able to focus my studies on that.

Justin Huntington:

I further went on and realized that the way that we can answer a lot of these questions is through the use of remote sensing, analyzing weather data and climate data to get at what's really happening on the ground and better understand these water budgets, and then make future projections.

Michael Torrance:

And then from there, where did you go? How did you end up working with Climate Engine?

Justin Huntington:

Yes, so I quickly realized, doing my research, that we were spending most of our time as scientists wrangling data and not answering these questions that were being posed to us about, how much water do we have? How much water are crops using? To answer those questions, we were endlessly downloading massive data sets off government FTP sites, unpacking these zip files and writing local Python codes.

Justin Huntington:

So back in 2014, I applied for a Google Earth Engine faculty research award grant that was offered through Google for Good, and basically the call was to get scientific algorithms into the Cloud computing environment called Google Earth Engine, And so I wrote a proposal, and they funded that. And so our very first really use case, if you'll call it, of climate Engine was on a use case focused around drought and drought projections using Cloud computing and Google Earth Engine.

Justin Huntington:

So that really framed Climate Engine. I didn't want to have people have to learn how to code in Python and JavaScript on the Google Earth Engine application programming interface that they provide. And so we instantly got a lot of interest from different government agencies on using this new technology where we could process petabytes of information that used to take months in seconds now. So that's that's how Climate Engine was originally developed, was as a no-code solution to Google Earth Engine so that anybody can go do this type of massively parallel Cloud computing on climate and Earth observations.

Justin Huntington:

I'm one of 21 international members of the NASA and USGS Landsat Science Team. And basically, my role on that science team is really to help USGS and NASA think about and implement new technologies on how we can use Landsat Earth observation information to support the water resources community And for us to better manage our water resources, we really do need to understand how much water we're actually consuming and withdrawing from our river systems in our aquifers.

Michael Torrance:

That's fascinating. And Jamie, you told us about your early journey to the climate field, but can you tell us about Climate Engine? What is Climate Engine? Maybe you can explain that to our audience. And just who the users are.

Jamie Herring:

I was involved really heavily in my work prior to Climate Engine in helping design, develop and deliver climate projection data to a number of organizations around the world. So I founded a company called HabitatSeven, and we built data pipelines and data systems around climate projections for organizations like the IPCC, the World Bank, the White House, NASA, NOAA, And I got really distressed a couple years ago with the idea that despite the amount of effort, and time, and science, and rigor, greenhouse gas emissions still go up

Jamie Herring:

And so I really started thinking about why that was. And whether it's cultural or whether it's biological, but certainly, cultures in the West are not good at long term planning or long term risk management, we're really good at short term risk management.

Jamie Herring:

So what really got me into Climate Engine was this idea that what we need to do to tackle the climate crisis is to make the distant here and now. And so instead of focusing on the 2050, 2070, 2080, which are all really important, , but really this idea that climate change and the changes in those systems and extreme weather events, they happen to us now, right. and they're happening more frequently and more severely. And that will continue to happen regardless of what we do in terms of curbing our emissions where the climate change is baked into the climate system.

Jamie Herring:

So this really led me to working with Justin. And Justin and I had been working on a project together, and he really started introducing me to satellite data and just the incredible things you could do about understanding the earth systems. And we really connected on this idea that what we needed to do was to bring these Earth insights, right, these things that were happening in the planet, into the realm of decision making in the here and now. The risks of climate change and the impacts of our economic activities on the climate, they're happening now as well, they're happening here and now, but they're hidden from us, we don't see them. When we drive we don't see the emissions, we take a flight we don't see the impact of that flight on the environment,  all these things are really hidden from us.

Jamie Herring:

But what I really truly believe is with satellite data and just a mass amount of information about the planet happening now in the planetary systems, we can start to see those things. And so the trick is going to be taking what is normally hidden and normally invisible to us, in terms of our impacts, and making them visible, and then tying those to actual financial instruments like what we're trying to do with the Bank of Montreal, with BMO. I think that just has an enormous amount of power and really changed the direction of my focus in terms of my work.

Michael Torrance:

And Jamie, can you tell us a little bit more about your work with HabitatSeven? You said you worked with the White House, with the World Bank. What exactly were you doing?

Jamie Herring:

I was really leaning on my design background. So the idea behind HabitatSeven was to really bring together world class designers and developers with world class scientists, and try to produce products around climate data to communicate the climate risks to different stakeholders.

Michael Torrance:

And did HabitatSeven work on the, is it climatedata.ca website for the Canadian government?

Jamie Herring:

Yes, and we continue to do that. So the first big one was the National Climate Assessment for the US government, which was the White House project under Barack Obama. And that was a huge hit, and it was much bigger than I had anticipated. We were a Canadian company working in the White House delivering this product, I had no idea how big the splash was going to be. This was just around the time that people really started to tune in to climate changes as an issue on a global scale.

Jamie Herring:

So we built the website, did all the visualizations, basically did all the digital work for that rollout, and it was a massive hit. We were on the front cover of The New York Times, The BBC, we were on The Daily Show, we were on every late night show, I think we might have been mentioned in Saturday Night Live. I mean, it was this strange nexus of media and climate change and it all just popped at the same time.

Jamie Herring:

And so that was really exciting and that led us into the world of climate and really at a global scale. So with the recognition we got from doing the climate assessment, we started working with the UN and the IPCC, other World Resources Institute, the World Bank. And I think it helped people think about the science side of the work as being something compelling and being something worth putting the effort into actually communicating in a more interesting and maybe more nuanced way.

Michael Torrance:

You'll have to send me the clips of the Saturday Night Live and Daily Show references, that's pretty cool.

Jamie Herring:

I will. Yes, for sure. I'll track them down for you.

Michael Torrance:

So Justin, maybe pivoting to you, I mean, am I right to say then it sounds like, from your backgrounds, you were bringing a big data Landsat geospatial data lens and Jamie was bringing modeling and visualization lens? I mean, is that fair to say? You're bringing the two together under Climate Engine.

Justin Huntington:

Yes, that's a fair way to characterize this. When I first met Jamie, I was really inspired by his vision that he just described in terms of where we've been with developing climate change projections and how, as he said, they're really useful for planning purposes and to get a good understanding of what's to come. And starting this work 20 years ago around modeling the decline and snowpack, for example, in the Sierra Nevada and around Lake Tahoe, what we modeled has come true. I mean, it's full stop come true. We have now this thing called snow drought, and back then there was no such thing, there was no such term as a snow drought. Who would have thought that above average precipitation in the winter but no snowpack? And that's exactly what's happened and the models projected that 20 years ago when we were first setting up our hydrologic models to do that.

Justin Huntington:

But in terms of what we can do here and now to make an impact now, is to really focus on just that, the here and now. What are our current conditions? And then what's the short term forecast? If we show, for example, in the Saskatchewan area that this last drought that you guys have had, there's a really big impact of the drought in terms of impacting wheat prices, it's one of the lowest wheat production and canola production years on record and that's made prices soar. We can observe those things using satellite imagery. And in fact, we can get early warning by combining Earth observations with satellites with the climate information, and really combining those two worlds together, the Earth observations with satellites versus the gridded weather data and short term sub-seasonal and seasonal forecasts of weather to talk about, here's the current risk and here is the near future risk. And then from there, integrate that information into the supply chain in the economic world.

Justin Huntington:

So bringing together Jamie's experience with this longer term view and his passion for the shorter term here and now with what we have within the Earth observation community, these petabytes of satellite data to where we can observe long histories of what's happened in the past and how climate has been related to those historical events to develop machine learning models, for example, based on past history and then use these short term to even long term projections to talk about risk in the future.

Michael Torrance:

And I mean, to build on that, who are your clients? Who are you working with now in terms of leveraging this type of analysis?

Jamie Herring:

So Climate Engine as a commercial entity is relatively new. So Climate Engine as a nonprofit, and there's part of Climate Engine that will continue to be a nonprofit, as Justin mentioned, started in 2014 with a grant from google.org. It's only been recently that Google has agreed to commercialize Earth Engine, and mostly because there's just become this wide interest from the commercial sector, both in the public and private sectors, to get at this information and to really operationalize this information. So trying to move beyond research, and the research is obviously imperative, it's the core of everything that we do, our colleagues at Google also recognize that to have change and affect real change in of the kinds of speeds and scale that we need them just to survive, I think we need to operationalize this data. And by that we mean, we need to have people making decisions about what they're doing to either reduce their risks of these extreme climate events or to reduce the negative impacts of any economic behaviors.

Jamie Herring:

So what we're seeing is just a flood of interest. And it really has been since we turned on the switch and started to go commercial, it's just like drinking from the fire hose. So there's all kinds of different organizations, really a widespread grouping, including financial institutions, insurance companies, packaged goods companies, agricultural producers, I mean, you name it, there's a really huge spectrum of organizations and groups. And I think it's because those organizations and those groups are seeing what we're seeing, is that the planet is changing and that the changes to the planet are unprecedented, and that's starting to have material impacts on business and finance.

Jamie Herring:

I think financial institutions are really waking up to the idea that they're holding the bag, at the end of the day, they're holding the money bag and they're holding all the risk. And so if we are to manage that risk, we need to understand where those risks are. And we need to understand when those risks are going to happen and when those impacts are going to happen, and then what the impacts of those impacts are. And so, to me, that's the really exciting piece in terms of building climate resilience, because this technology really allows us to understand what's happening on the planet. And if we can ladder up those impacts to financial assets, I think we can better manage the coming onslaught of climate extremes that we are inevitably going to be facing.

Michael Torrance:

Are you able to illustrate for us a couple of use cases about how you're seeing this technology applied?

Justin Huntington:

A really great research operations example that we've been really excited about is our work with NOAA here in the US. And in particular, [drought.gov 00:26:31] and the National Integrated Drought Information System, NIDIS. And they approached us a while ago asking for a operational way to both create and deliver current and historical drought conditions that are place based and then also provide sub-seasonal forecasts of different drought metrics. So they saw a real opportunity to use Climate Engine to help them be able to produce these data in more near real time and in an easier way, where they weren't having to basically process all this information on their "hard iron" at NOAA, which was taking a long time and had dedicated staff to create these drought indices, and then spatially average all the data to the counties, and then create these county level maps, and it would take a lot of time both to process the gridded weather data and then do all that spatial averaging to the counties then to make these county level drought maps that they put on their website.

Justin Huntington:

And so now they are using the Climate Engine application programming interface, or what we call API, to both create these gridded drought maps that take a while to compute but now go really fast in the Google Cloud. And then once those are created, to spatially average all the results to their areas, their features of interest, whether it's a single agricultural field or whether it's a county, and then create this massive database for all fields, for all counties across the entire country, they are updated every single night and then put into their system and then displayed on a map on drought.gov. And so that was the challenge they posed to us, they said, "Well, help us do this from end to end. Make the data, do the post-processing, push it to a cloud bucket, and we'll pick it up and then put it on our web page." And so that's exactly what we've done over the last year and now it's running operationally in real time. And we've saved them a lot of time and money because now they have all this processing and pos-processing off their hard iron or local computers."

Michael Torrance:

That's a great example, is there any other use cases you'd be able to speak about?

Jamie Herring:

So one really interesting use case is around logistics, and it's not something that I had ever thought about until they reached out to us, but it makes sense. When you think about the logistics and the shipping and routing organizations, they have massive exposures to extreme weather events. And up until now, they really haven't been incorporating the extreme weather risks into their algorithms for their shipping, what they're working on with us is building early warning systems across a number of variables, including wildfire, so that they can better route their shipping routes both for the safety of their employees, but also for the safety of the surrounding communities, because there are those interactive risks, right, in terms of activities and especially with something like wildfire. So we're working with them on building these early warning systems that provide decision-ready insights into the logistics organizations themselves, so that they can better manage their routing based on extreme weather events. And I think that's something that's going to be more and more required as these extreme weather events become more frequent and more severe.

Jamie Herring:

We have another project we're working with a large produce distributor who functioned at a global scale and who they themselves didn't understand where their risk to drought was. And so this is another example of companies, and these are large companies, understanding that the environmental impacts and these environmental changes, including climate change, are having material impacts on their own business themselves. So they've been working with us to help identify regions and areas where suppliers might be at risk due to drought, so that they can basically contact them and find out what the risk is on the ground and whether there are alternatives to help them out. Those are two really interesting examples, they point to the idea that companies and institutions are really being hit by these impacts and we can actually track those and provide really interesting metrics to help them weather those storms.

Michael Torrance:

Good metaphor to use in this context.

Jamie Herring:

Yes, just came up with that one.

Michael Torrance:

We've been collaborating to develop climate modeling capabilities in a banking and financial context at BMO, and it's part of BMO's broader net zero ambition and our launch of the BMO Climate Institute. And one of the ideas behind the Climate Institute is to create an analytical hub for all things climate within the bank and that we can use for our own purposes like risk management and we could be able to use it to help advise our clients. And your tool has become a pretty core pillar in terms of how we think about the Climate Institute.

Michael Torrance:

One of the things that led me to be so interested in the work that you're doing was the challenges that we had about thinking about how to integrate physical climate risk into what we're doing. Even though there's been a lot of imperatives for banks and financial institutions to consider climate change transition and physical risk, there really hasn't been a clear set of solutions that have emerged yet. But the thing I was really taken with your tool was that, and I think it sounds like now that you lay out the history it's the motivation that you had Justin in creating the company and has motivated you, Jamie, it's really to try to overcome the technological barriers, having to be a programmer to be able to use this data and to be able to leverage all of this available data, overlay it with modeling, and then generate output which can be decision useful, which is really the gold standard of what we're trying to do.

Michael Torrance:

And so we've worked on some experiments and test cases of how we could examine different types of physical hazards on our various lending portfolios, whether they're real estate related or otherwise, we've considered how we might be able to look at either risks or opportunities for the agricultural sector, and you touched on that, Justin. Like you say, Jamie, there's unlimited potential use cases and we're only just beginning to think about them. But just to focus in then the question for you both is, when it comes to the banking and financial context, and thinking about how we can integrate climate science there, either from a risk management perspective or to try to motivate the kinds of activity we need to address climate change through finance, what are your thoughts around that? What potential do you see there and where would you like to see that go?

Justin Huntington:

So we've been doing some research on just how to really lay this out nicely in a product development sense that is really focused on the agricultural sector with respect to illustrating financial risk within the agricultural sector. And by financial risk I mean production risk, agricultural production risk. And what parcels, for example, what fields are both resistant and resilient to water shortages? And developing models that will clearly illustrate per parcel, what areas or what fields, in particular, and crops are really impacted by seasonal and sub-seasonal weather and which ones aren't?

Justin Huntington:

So for example, if you're in an area and you're completely reliant on mother nature, it's a rain fed agricultural area, and you have no supplemental surface water supply from a river nearby or you don't have a well to irrigate during times of shortage, that particular parcel is going to be very well correlated with wet and dry cycles. And we can make those field level correlations, we can build machine learning models to show how well correlated or anti-correlated each parcel is with climate. And so, for example, in California the whole entire Central Valley was basically built to resist drought, the entire agricultural area in the Central Valley they have supplemental water, they pump groundwater, there's a bunch of dams all over the place. And so doing this type of analysis in the Central Valley, what will be shown and what we've seen already, in fact, is that you don't have a strong correlation between these wet and dry cycles with crop productivity, for example. And the reason is, is because when you're short on water you just pump groundwater and there's a lot of reservoirs they can pull water from.

Justin Huntington:

Now, over time, you start to see more and more impacts because certain farmers only have a supply from surface water and that reservoir has gone down enough to where they get curtailed and they don't have a well. So we can pinpoint and identify exactly what fields have supplemental water, IE. groundwater, versus not, or have supplemental surface water from a nearby river. So it's really fascinating that now we can, per parcel, map out what fields are at risk versus who is not so much at risk because they have supplemental supplies. And from a financial perspective and a banking perspective, if a financial institution has loans out to these growers that are at high risk because they're so reliant on mother nature, well then that's important for the bank to know. And so that's just something that we're getting deep into now and really excited to see these initial results coming out of our modeling team.

Michael Torrance:

That's really great. Actually, you mentioned the risk side, but is there also a way that, that could be used to help advise, in that case, farmers to maybe take different steps that would help mitigate their risks or improve their situation?

Justin Huntington:

That's a good question. So with respect to evapotranspiration monitoring, basically monitoring in near real time the crop water consumption and then comparing that to their application rates and helping farmers really hit that target of, okay, this is how much you've been applying, this is how much you've been consuming, and we could show that, in a lot of cases, farmers have been over watering their crops because they don't want to take the risk of potentially not watering enough, because then they'll have crop stress and, of course, low productivity and less return. And so, typically, folks put on more water than they need to. And then what happens when you do that is you end up flushing your fertilizers down through the soil profile and you end up wasting a bunch of money because fertilizers are extremely expensive.

Justin Huntington:

And so we're trying to help farmers with irrigation scheduling and irrigation management by coupling our water use estimates from remote sensing with their application rates that they have on file. And you can imagine as like a cumulative plot where you have a cumulative over time, their application versus the crop water consumption. And ideally, we want those two lines to be as closest together as possible. And currently, a lot of times the application is far, far above what the crop is actually consuming. And so working with the farmers to close that gap will ultimately help the farmers use less water, use less power, less greenhouse emissions from the power consumption, which in large part come from coal fired power plants, and then also less fertilizer and pollution of the groundwater and which ultimately flows to the rivers in most agricultural areas. And so really trying to help the farmers optimize their farm management and ultimately optimize their production and operations.

Michael Torrance:

Jamie, what about you? I know you've given a lot of thought to this topic in terms of the role of this technology in finance, what are your thoughts?

Jamie Herring:

Yes, I've got a lot. So, I mean, this is pretty much the only thing I have been thinking about because for two reasons, one, I think the financial institutions and the financial industry hold the costs of climate change at the end of the day, right. They're the ones holding the mortgages, they're the ones holding all the loans, all the different financial instruments that at the end of the day are tied to the environment. And I think we've been living in a world where we're living this myth that the environment and the economy are separate, that somehow the economy lives outside of the environment, and that's a myth. And I think the impacts of living within that myth are starting to come to roost, and we're seeing this across all kinds of different areas and different variables, our water usage, to Justin's point, with climate change all the greenhouse gas emissions, right, the biodiversity crisis we're seeing ecological collapse, we're living through one of the largest reductions in biodiversity in the history of the planet, and not just humanity, but in terms of geological scales and timescales.

Jamie Herring:

So I think the time is now to really act in a way where we're connecting the economy to the environment. And what really excites me about this moment is there's just a massive amount of data about the planet being generated. And so we know more about what's happening to our Earth systems than at any other point in history. The challenge is really connecting those changes to the activities that are causing those changes. And I think that's really where banks and financial institutions have a lot of power. And encouragingly, a lot of institutions, including BMO and the Climate Institute, are really stepping up. And it's not just talk, it really is an earnest effort to do this, because it's in the financial institution's own best interest.

Jamie Herring:

So if we can start piecing those impacts on the environment, including greenhouse gas emissions, to assets on the ground, and then laddering up the risks to the owners of those assets and laddering up the actual impacts to the owners of those impacts, that's where I see wholesale change that can start happening as a result of the financial industry. In fact, I think it's the only way we're going to get out of this climate crisis. I'm really encouraged in a lot of ways by the financial industry now and I think what we need to do is really come together, the science community and the financial community really need to come together and start identifying where these insights can have the most power and where these insights can be best used to change the way we've been treating the planet and change the social-economic fabric of our world.

Michael Torrance:

That's great, Jamie. And one follow-on on that, biodiversity is something that you mentioned, how are you thinking about biodiversity in the context of Climate Engine, if at all?

Jamie Herring:

Yes, we are. So the way that we've been thinking about, especially in the context of financial institutions, is really in a geospatial way, right. At the end of the day, any climate risk for any activity that's causing environmental damage is happening in a place, it's geospatial, it has a location. So with all the different satellite systems that are going up and then the power of Cloud computing, specifically Google Earth Engine with its history of development and really the incredible contribution it's made to the scientific community, but marrying the power of Google Cloud and Google Earth Engine with these datasets, with the best available scientific models, we can start understanding all kinds of different systems on the planet, including ecological systems. And that's a really, really, I mean, critically important system for the health and maintenance of the planet, including our climate systems.

Jamie Herring:

So I don't really see the climate change and biodiversity issues as being all that separate. I think the root cause of both is really the activities that we do every single day as a global community and just as humans in general, we don't see the impacts, right. If we build a house in the forest, it might just seem like one little house in the forest, but you add that up with the other 10,000 houses that are also going in the forest, you start having impacts on biodiversity, but we don't see them, right. So I think that's really the power of this geospatial data combined with the geospatial processing, is we can make these invisible impacts visible. And once they're visible and integrated into the financial system, that's really where I see the power of the financial system being able to affect change, right. And that's either through promoting environmental good, providing sustainability backed loans, green bonds, all kinds of different financial instruments or punishing organizations that are continuing to cause environmental degradation, including degradation to critical habitat. So I do see them really inherently connected.

Michael Torrance:

Thanks, Jamie. And so just final question to the two of you, what's next for you, Climate Engine? And then what do you see as being the next innovation from a technological perspective that you will be working with but you might just see more broadly with Google or anyone else?

Justin Huntington:

I feel like it is really getting these Earth observation and climate insights into operational decision making in an automated way where the decisions are made by ultimately computers. So as soon as we get a new satellite image or a new forecasts, for example, fire weather danger, we can, within a matter of minutes to hours, get that fire weather forecast information integrated into a client that is about transportation risk, for example, whether it's a train line or a highway, and having that information be integrated right into their decision making system that may, for example, control the speed of the train so that sparks don't fly in this area that's highlighted as high fire danger risk.

Justin Huntington:

So going from the raw imagery to processing in Google Earth Engine, doing all the spatial averaging or summarization for the place of interest, having that information be uploaded into, for example, the Google BigQuery platform, and then integrated into the climate's operational decision framework, that is also a computer based system, and then ultimately controls the speed of the train, that is I'm just thinking of a particular case where for us I feel like that's where the next big thing is, is going from the raw imagery into somebody's system that will ultimately control something as simple as the speed of a train and doing that in a matter of minutes. That's the next big thing and it's all built on AI and ML in the background. That's where I'm really excited to see these things and also with respect to early warning. I mean, it all is around early warning, but early warning for famine, early warning for conflict, early warning related to water abroad, for example, in the Eastern Horn of Africa.

Justin Huntington:

There's just a lot of real opportunities to get this information into so many different places and sectors to help out on the early warning side and decision making. So that's what I'm really excited about for what's next.

Michael Torrance:

Wow, that's super interesting. Jamie, how about you?

Jamie Herring:

Yes, I couldn't agree more with you, Justin. I think that the technology is here, we have the computing power. The imagery is here, we have more images about the planet than we've ever have. The science is also here as well, it's really robust. So I think, to me, the next steps and the next big horizon is working with organizations like BMO to develop these insights. And a lot of that is not necessarily technology, the technology's there, it's really the hard work of coming together and collaborating and identifying those business cases and the required insights. And that's really the hard work. It really is bridging those worlds together, bridging the financial world with the world of the Earth System Science, which are two worlds that normally don't cross, but they have to cross, right, it's absolutely imperative for both organizations, both sides, on the science side and on the financial sector side, to start merging together and start delivering these insights. And I think that's where we're going to see the fruit come to bear.

Michael Torrance:

Well, we've really enjoyed collaborating with you guys and we look forward to a lot more of that. So thank you very much for that collaboration and also for your time today, Jamie and Justin.

Jamie Herring:

Absolutely.

Justin Huntington:

Thanks for having us.

Jamie Herring:

Thanks, Michael. Really appreciate it.

Michael Torrance:

Thanks for listening to Sustainability Leaders. This podcast is presented by BMO Financial Group. To access all the resources we discussed in today's episode and to see our other podcasts, visit us at bmo.com/sustainabilityleaders. You can listen and subscribe free to our show on Apple podcasts or your favorite podcast provider. And we'll greatly appreciate a rating and review and any feedback that you might have. Our show and resources are produced with support from BMO's marketing team and Puddle Creative. Until next time, I'm Michael Torrance. Have a great week.

Speaker 3:

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