The Case for Standardizing the Way We Report Climate and Environmental Data

This episode of Data for the People! explores a problem with climate and environmental data that burdens public agencies and the private sector: Currently, federal and state regulators have a host of different reporting requirements for climate and environmental data, such as data on greenhouse gas emissions. As a result, the data get reported in multiple ways to multiple regulatory entities, which hampers the public's ability to use the data while making it hard to monitor trends and evaluate the effectiveness of policies and programs. Private organizations also pay a price in terms of time and money to report similar information in different ways to comply with a patchwork of state and federal regulations. 

A recent paper by XBRL.US proposes that the public and private sectors adopt a structured standardized data format to simplify reporting and improve government's ability to measure trends across jurisdictions and data sets. 

The episode features three guests: 

• Liv Watson, a Senior Fellow with the Data Foundation's Climate Data Collaborative and a co-founder of the Digital Global Single Market Data Alliance
• Catherine Atkin, a co-chair of the Law x Climate project at Stanford Codex as well as a Senior Fellow with the Data Foundation's Climate Data Collaborative and a co-founder of the Digital Global Single Market Data Alliance
• Michelle Savage, Vice President of Communications for XBRL.US

They discuss XBRL.US’s proposal to adopt structured, standardized, machine-readable reporting via a semantic data model to improve interoperability, support investors and policymakers, enhance AI use through better context, and reduce a growing patchwork of regulations.

We're dropping this episode during the week of DC Climate Week (April 20-24, 2026) when we are co-hosting a full-day event on April 21 about climate and environmental data. Learn more about the event's programming and speakers here.

The Data Foundation's Climate Data Collaborative makes climate and environmental data work better for decision-makers across sectors. Our vision is a federated and interoperable data ecosystem where decision-makers have the data needed to drive markets, mitigation, conservation, finance and compliance—one where public agencies provide foundational data and private actors are motivated to contribute. Learn more about the Climate Data Collaborative at www.ClimateDataCollaborative.org. 

Want to be part of a national community that promotes policies that enable government data to be high-quality, accessible, and usable? Join our Data Coalition: https://datafoundation.org/pages/join-the-data-coalition

The Data Foundation is a 501(c)3 nonprofit, nonpartisan think tank. All contributions may be tax deductible. We appreciate all charitable contributions towards fulfilling our mission to make democratic society better for everyone by championing the use of open data and evidence-informed public policy. Donate: https://datafoundation.org/supportus

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Transcript

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[00:00:08] J.B. Wogan: 

Welcome back to Data for the People, a podcast from the Data Foundation. I'm your host, JB Wogan, and on this episode we'll be discussing a problem with climate and environmental data that burdens public agencies and the private sector. Currently, federal and state regulators have a host of different reporting requirements for environmental data, such as greenhouse gas emissions data. As a result, the data get reported in multiple ways to multiple regulatory entities that hampers the public's ability to use the data while making it hard to monitor trends and evaluate the effectiveness of policies and programs. It also costs private organizations time and money to report similar information in different ways to comply with a [00:01:00] patchwork of state and federal regulations. A recent paper by XBRL US proposes that the public and private sectors adopt a structured standardized data format to simplify reporting and improve government's ability to measure trends across jurisdictions and data sets. To discuss the current problem with climate and environmental data reporting standards, we have three guests, Lee Watson, Catherine Atkin, and Michelle Savage. Liv and Catherine are senior fellows with me here at the Data Foundation, and they are part of the Data Foundation's Climate Data collaborative. Catherine also co-chairs the Law X Climate Project at Stanford Codex. Michelle is the Vice President of Communications at XBRL us. Welcome Liv, Catherine and Michelle.

[00:01:52] Michelle Savage: Thanks for having us.

[00:01:53] Catherine Atkin: be here.

[00:01:54] J.B. Wogan: Alright, so Michelle, I wanna start with you. Your organization supports the implementation of digital [00:02:00] business reporting standards across the United States from private industry's point of view. What are the current pain points for complying with state and federal climate and environmental data reporting requirements?

[00:02:14] Michelle Savage: yeah. Today we have a number of different regulators just within the United States are collecting. Very similar data you see, you know, greenhouse gas emissions is one really good example. So the EPA has been collecting greenhouse gas emissions from certain facilities for decades. they've been collecting facility level greenhouse gas emissions for any source that emits 25,000 or more metric tons of CO2 equivalent in the us. So that covers around 6,000 or so reporting entities. And, um, they have actually halted this program with an end date of 2024. but you know, who knows if it'll be revisited in the future? the data is made available to the public through Excel files. it's publicly accessible and [00:03:00] it's very complex dimensional data. it's reported by address, by geolocation, by industry, by the type of gas, by facility types. So very complicated data, but. It's, you know, a, a wealth of data for researchers. And so for facilities that fall into this category and have to report, they have a particular tool that the EPA provides and, creates for them that allow them to submit this data. Now the state of California has a different regulatory authority and they also collect greenhouse gas emissions data.

So, and again, at a facility level, they have a different cutoff. they require facilities that emit 10,000 more metric tons of CO2 equivalent to report. And they have their own separate, you know, kind of submission, protocol and, and methodology. that covers about 800 facilities. So. They also make this data available.

It's in spreadsheet form and it breaks emissions data down into dimensional characteristics. Washington State has a similar program [00:04:00] and New York State just passed one too, passed a a, um, legislation where they're gonna begin collecting their own greenhouse gas emissions data. So, okay, so you've got all these different facilities reporting into multiple regulatory authorities. You can imagine the duplication of, of effort on, on many fronts. first from the standpoint of the facilities and you talked about like how does this affect the issuers? they've gotta report different ways, different data at maybe at different time periods. So that's a huge duplication of, of effort on the part of the reporting entities. And from the standpoint of the users of the data, it's, it's kind of even worse. they have these separate data sets that are structured differently. They follow different semantic models. there's gonna be overlap between, entities that report, and in order to use the data, like let's say you're a researcher and you want to bring all of this data together into a single data set so that you can perform comparisons, you need to map the [00:05:00] data. You know, one data set to the next data set, you need to review the data manually. So there's a huge amount of work that has to be done each year when a new data set is available from each of these regulators. You have to go through that mapping process again, and you think about multiplying that by the hundreds or potentially thousands of individuals or organizations that wanna do that kind of research. So. the cost, the inefficiency is huge when it comes to this issue, and that's why we strongly feel that adopting a single standard is gonna make it easier for everybody, for the reporting entities and for the, the users of the data. If you really wanna make an impact on climate, you, you need to understand the data, and it needs to be structured in a way that can be easily, consumed and analyzed.

[00:05:48] J.B. Wogan: Okay, great. I'll note in the paper you foreground greenhouse gas emissions, but there is thought given to other kinds of environmental data, like and trade data or air [00:06:00] quality data. So the ideas we're discussing here, while we may, we may focus on greenhouse gas emissions that. This same, conversation would apply to other kinds of climate and environmental data.

[00:06:11] Michelle Savage: Absolutely, absolutely. I mean, air quality data, you know, there's collections of this, kind of data. By all of these different regulators, and you know, everybody needs to understand this, but you can take air quality data. It still has the same kind of structure and parameters. Air quality data collected by the California Air Resources Board is gonna have the same, should have the same structure as that collected by New York or by the EPA. You know, you see this duplicated over and over and over in different types of climate related data, And if you establish a single data model, it's it. makes it easier, makes it more efficient to collect all of this data and have a much better understanding of air quality, of emissions data, of cap and trade data.

You know, these are sa the same programs all over the country and and overseas as well. [00:07:00] not put it in the same structure? It just, it just makes sense.

[00:07:03] J.B. Wogan: Okay, so that's, private industry. Catherine and Liv, I wanted to turn to you now. How does the matrix of data reporting requirements affect groups outside of private industry? Like, who else should be concerned about the lack of unified digital data reporting standards? And I'm thinking about listeners who perhaps don't work for a facility that is reporting this data, but may still have an investment in the broader issue of, climate data or its impacts. maybe we'll start with you 

Liv.

[00:07:35] Liv Watson: Yeah, I, I always go to say that, the standard setters that are developing the standards are the ones that really need to make these standards, machine readable. and develop their standard with the taxonomy for disclosure. So for example, being part of the European Commission recently developing the climate standard, it was a [00:08:00] very early on in the initiatives.

We work to make sure that these standards were built to be digitally represented in, I know we call it, taxonomies or, structured data, however you wanna say it, but that it really, from the beginning, not wait to the end of the developmental standard. Really think about how these standards are gonna be disseminated and how it impacts the companies to report them.

From a preparer perspective, I mean, they need to, and I think Michelle, you talked about that, but what they don't realize is that the data that they collect to try to make these report are generally often stored in multiple different data format and proprietary system inside the organization. So to them it becomes very important that they start asking themself, how are we storing and collecting this [00:09:00] data to be prepared to produce?

And then I wanna say the software vendors. The software vendors are very important part of this puzzle because they need to be at the table to be able to integrate these taxonomies and digital standards into their solution because at the end of the day, the preparers are the one that buys a solution to be able to produce those document.

And then let's not forget the regulators and I, Michelle, you talked about that and over the years we've been talking to regulator for a long time, is that they need to start thinking about a common language, a controlled vocabulary, a dictionary so that we don't call the same thing that means something different or, or something different.

The same thing. Right? So it really. Goes back to also a controlled vocabulary and that really needs the government and policy makers to really understand, [00:10:00] not just to develop policies, which Catherine is, you know, expert on, but really how is this implemented? In these policies that's being developed. So there is not just a, uh, technical solution.

This is really a community solution and consensus building that really takes place. And I think that's one of the key roles that XBRL US does with the community, is bringing those different constituents together to make sure that we drive to a more common controlled language. Develop consistent taxonomies.

[00:10:39] J.B. Wogan: Catherine, uh, leave was just singing your praises there. I heard leave mention a few different, potential interest groups like, or not interest groups, but groups who'd be interested in this issue, like, standard setters or regulators. the problem of data not being standardized. It's probably a little bit below the radar, I imagine.

when people talk about [00:11:00] being interested in climate change, they're thinking more about, emissions going up. what does it mean for my health? What does it mean for the environment? Why is it that they should care about this specific kind of sub-issue of, data standards?

[00:11:13] Catherine Atkin: You know, and I'll just say great to be here today and, just privileged to work with the data foundation. and really kind of the, the work I did in California, I'm sort of the poster child for somebody who didn't really understand the importance of having machine readable data and, putting XBRL into this policy.

I, I know I look back on this and I'm like. Michelle, we'd have a lot less work right now in the regulation process if we just gotten this into the statute. But I was like many other people, I, I really understood the importance of data access for, you know, investors for the companies themselves, but also for consumers.

And that was, you know, one of the highlights of, of the bill in California that requires, GHG emissions reporting [00:12:00] by, You know, US companies with over a billion dollars in revenues that do business in California. Which also happens to be pretty much every large corporation in the United States of America.

So we have, you know, great coverage here in California. Um, but you know, really we felt like this is something that consumers also care about. They care about this data. They wanna make good decisions as consumers. policy makers wanna understand, you know, in California, sort of the genesis of this policy was the recognition that.

We can do all the leading work we want to in California, and we have, right? California has a great history of being, you know, sort of at the vanguard of environmental regulation, but when it comes to GHG emissions, they don't stop at the border. We can't just reduce our own GHG emission emissions and really protect our communities and, you know, the flora and fauna of California.

So we have to be about the global, carbon footprint. And so in, developing this policy. We knew that consumers, [00:13:00] investors and the companies need access to the data of, of the, of the global carbon footprint of these companies. And so I think one of the things that's important to realize and why interoperability is, is so important is because we all have a value of not creating a patchwork quilt of different. Reporting obligations. Right? And so the fact is we can all work, to create the same policies, but at the end of the day, it's really the data. Interoperability and the tagging in these taxonomies that are gonna kind of go that last mile, because we're not always gonna say it exactly the same way.

But if we have these as, as Liv has, uh, and Michelle have taught me these concordance tables, and we can, we can have confidence that when we, when a company reports that data, it can then be transferred and understood and discoverable, in many different jurisdictions. So if we really care about the burden on corporates, which we do. Then we need this, interoperability. if we also care about [00:14:00] consumers being able to compare apples to apples, then we need it. and certainly, for policy makers, these are, you know, we have scarce public dollars in our budgets and we've gotta make good decisions. And so making sure that we have access to high quality data, is really important.

And so in, in the. Disclosure law in California, we said the duplication of effort by companies should be minimized. That consumers, investors, and policymakers should have maximum access to data, you know, available to them in different ways, disaggregated. And so we're gonna have this cool digital platform, but we need to make sure that we get this part of it right in the regulation process.

[00:14:39] J.B. Wogan: Michelle, anyone we've left out, we've talked about private industry, we've talked about stand. Or setters, regulators, anyone else that, is affected by this problem of, you know, different data formats, lack of standardization across climate data.

[00:14:54] Michelle Savage: I guess one, you know, I know Liv talked about software companies and I think that's really important. I think the other audience [00:15:00] for this is investors. And, you know, at the end of the day from the standpoint of the regulators and the governments that are involved in this, we need to get data that that can actually set efficient policies and the companies are gonna look at the data so they can set their own targets.

That's really important. investors are using this data too, and frankly, they were one of the first. Audiences that, you know, kind of a fuss about this and said, Hey, this is material information, this is data that we need. And, you know, regardless of, you know, the SEC's pull back on this, the states see that investors need this data.

You know, CalPERS needs that. Councilors need it. It's, investors are looking for this data so they can better understand the, the climate footprint of the companies in which they invest because. They know it's gonna have an impact on their financials down the road. It's not just altruistic, you know, they, they know that this is affecting the companies. And so, at the end of the day, if we really wanna make a change, then you need to be able to measure what the [00:16:00] data is reporting and, and measure it consistently and efficiently. And that's the only way it's really gonna work. So I, I, I think investors are a critical audience 'cause they're one of the first ones that stepped up and said, Hey, this is material information.

We need it.

[00:16:15] J.B. Wogan: Got it. All right, so the paper from XBRL US proposes something called a semantic data model, which is not term I was familiar with before You were one of the original developers of XBRL, which for listeners who don't know, is an open international standard for digital business reporting. Could you explain what a semantic data model is and how, in the context of. Climate and environmental data, it might help different user groups in the public and private sectors.

[00:16:47] Liv Watson: I think we need to look back at history, right? When we develop XBRL, some HTM was. Visualize information on the web. Then [00:17:00] XML came and it was a way to describe each data point, right? And making that data machine readable and exchangeable. So when we developed XBRL, it was really built on, XML as far as, developing, the data standard.

Well, we realize now that the internet and data is, you know, accessible is that XML data is heavy. It's, doesn't, allow you to kind of separate the technical format from the meaning of the business, right? Business terms. So what does it really mean with a semantic model is that if we don't go down to the really technical detail, it's gonna allow us to turn this data into usable information and be able to be able to.

Analyze a lot more larger dataset. So what I kind of look at it is, is [00:18:00] moving away from digital reporting to liquid data, it really allows the data to be used where it doesn't take a long time to get. Insight into the data, so it separates. XML was very, and specifically exec XBRL, we used what we called link basis.

And so it linked to a lot of additional information, like what is the authoritative literature around that. And, uh, using these link paces, it separates that from, so that we can make data. Now XBRL international has been working. To develop something called the open information model. OIM model, which is basically then allowing XBRL to come into the 21st century where the data economy really needs data quickly and fast.

And I'm [00:19:00] sure that what we really mean by Symantec data modeling is that the data. Like revenue or carbon emission definition, identical, whatever they are sitting is the same in Excel sheet, a cloud database, or in an AI prompt. And I also wanna emphasize something with ai. AI is only as good because a lot of people are talking about why, what do we need the XBRL now, right?

Or do we need an open information model for business reporting data? Because we have. Ai. Well, AI hallucinates, right? It's only as good as the data that it comes in, and it's quite proven that if we have taxonomies and if we now move that into semantic models that make data flow like liquid and not so heavy, we are actually being able to advance the XBRL specification [00:20:00] into improving.

AI information and AI prompts that will be, now sit sitting on this information. So if I will say something about what Semantic data model is, it is just a strategic layers that separates the business, meaning from the technical format, but more important, making data at scale usable. A very timely fashion.

Michelle, I'm sure you want to maybe talk more technically about it, which we can, but I wanted to make sure that we understood that it's just not replacing XBRL. It's just advancing, and expanding on advancing advanced technology today and making sure that XBRL is not just. Heavy burden on the user, [00:21:00] but now is able to what I call liquid data.

[00:21:04] J.B. Wogan: I love the, uh, the metaphor of liquid data and heavy data. Michelle, is there anything you'd like to add in terms of. Helping users, helping listeners understand what we mean when we say a semantic data model.

[00:21:17] Michelle Savage: Yeah, I mean, I think, I think, you know, Liv expressed it very well since she's been in this world for a million years. Um, like me, You know, semantics are

just, is just meaning. It's, a way, you know, the semantic data model is, is just a way of expressing in a consistent, structured, standardized way, the meaning of what, what the data is that you're trying to represent.

So here, you know, we're talking about greenhouse gas emissions or air quality data, or financial statement data. And the meaning of the data is kind of embodied in the context around it. Like, what's the definition? What's the label? What's the data type? Is it monetary? If it's monetary, is it in euros? Is it in US dollars? how do da, how do [00:22:00] know? Facts in a report relate to other facts in the same report, like assets and liabilities that the semantics or the meaning of the data is what XBRL brings together. And like Liv was saying, you know, we're working on this enhancement to the existing specifications so that we're making it more efficient so that it, it, you know, I think it's a really good example of like the water flowing, you know, the, the data will flow more easily with this enhancement that we're making to the specification, it's still, it's all about like identifying the meaning.

And the cool thing about how XBRL works with AI is that. It needs context. contextual information so that it can accurately discern the meaning of that data. And that's exactly what XBRL is all about. It's always been about that. And so that context that's available through a semantic data model that concretely defines it in a consistent, structured way. what makes the AI able to say, oh, I know [00:23:00] exactly what this means. I mean, we've done, you know, I'm, I'm no techie, but I, we've done some analysis to just. compare using structured XBRL data versus non-structured data like HTML or text and the differences are so clear because it's like, it's like for the, for the AI model, sort of an aha moment. Oh wow. I understand exactly what this means because the data has context. And that just makes it much better for any kind of large language model.

And obviously that's the route we're going down these days. So we use AI in everything we do. I'm sure all of you guys do too.

[00:23:39] J.B. Wogan: Perhaps not surprising, but this year AI seems to be coming up a lot on the podcast, and I'm glad we were able to find a connection for this episode as well. Catherine, you referenced earlier that you helped write. California's new corporate greenhouse gas emissions reporting law. Several other states are taking steps to regulate greenhouse gas emissions. [00:24:00] Now, I think Michelle referenced some of those states, as well. Given your experience and lessons learned, how would you like to see states incorporate this idea of digital business reporting standards into their regulation of greenhouse gas emissions?

[00:24:17] Catherine Atkin: I mean, it wouldn't take much, it's about 10 words, right? That these reports should be, or the data should be disclosed or submitted in structured machine readable XBRL format. I think that's really all it would take. and I do think, you know, I'll let Michelle, uh, I know you've been working Michelle. Uh, in New York, and I don't know, I, you know, the, uh, the two states that have the most kind of adherence to similar policy language to SB 2 53, which is the GHG Emissions Disclosure Bill are New York and Illinois. And I think we were trying to do our best to say, the point of this is not to create sort of a, Pick your own GHG adventure. Every state does its own thing. [00:25:00] California does its thing. New York has to be different. The idea was let's actually take the leading global standards. Let's look at what else is happening globally. And I think it would be, you know, interesting to also understand for the listeners what's happening globally with, you know, the EU and other countries, but. You know, in California we were really, aware of how to write the policy in a way that was gonna be, really all us all rowing in the same direction. And I think, making it machine readable is, as I said, the Kind of that extra mile that that goes. You know, we we're not always gonna get the standards or the policy directive in exactly the same language. I mean, we're looking at New York and Illinois, but we've also got, Washington and New Jersey and Colorado, with, pieces of legislation as well. And so I think it's our job as, you know, policy makers and those, you know, important stakeholders around it to say, we really don't wanna create a patchwork quilt of reporting. [00:26:00] But I think, the machine readable format is also gonna help us, in ensuring that we actually don't create, a thicket of regulation, which isn't what we're about.

[00:26:09] J.B. Wogan: Does it need to be explicitly addressed in the language of the legislation or is that something that can be handled at the 

[00:26:17] Catherine Atkin: regulation 

[00:26:18] J.B. Wogan: stage? 

[00:26:19] Catherine Atkin: you know, when you get into the regulation process, and I, I know that Michelle and Liv know this. know, it's always easier when it says it in the law so that you don't, you know, then nobody gets to de debate whether it should be in a regulation. So it's not necessary for it to be in a, in the law, but I just think it, it creates, um, it to, to me, we should be valuing.

If we're saying that data is important and data, integrity and comparability is important, then why wouldn't. I mean, we, I don't know if you have to put XBRL in the language, but you should be saying it should be in a format that is machine readable. and I do think that to me, that that would be, the best fix at the beginning.

And I, I mean, I'm, uh, [00:27:00] but I do think obviously the regulations can also be a place to work on that.



[00:27:05] Liv Watson: So I wanna wrap up with a kind of forward looking question for listeners who share in this vision of greater interoperability and standardization of digital climate data, can they do? We've talked about different kinds of. Users, lots of different groups who should have some kind of investment, who should care, for, you know, regardless of whether you're working at a facility, you're a standard setter, you're a regulator, you're an investor, what is one thing that, a listener could do to help in this effort?

[00:27:37] J.B. Wogan: And why don't we start with Michelle.



[00:27:39] Michelle Savage: I, I would just say. get educated, know, learn more about what we mean when we say digital reporting. And, you could find information on, you know, at the data foundation at xbrl us@xbrl.org. and then also support it, you know, actively support supported to regulators, to legislators, [00:28:00] to software companies. Help, people understand that this is something that's really important and that it is going to be, have a big impact on the downstream usage of the data and efficiencies and eliminating that patchwork is of different reporting requirements. So I would say support it and make sure that people, you know, make sure people are aware of that.

[00:28:20] J.B. Wogan: Catherine, what would you say? what can listeners do to help with this issue of, interoperability and data standardization?

[00:28:28] Catherine Atkin: You know, I think we obviously, as we know, we do have a language problem, right? we need to be able to articulate, the need for interoperability. And machine readable data as an element of data sovereignty and democracy. And I think that we're seeing, not only in the environmental and climate space, but certainly in that space, an erosion of trust which has tremendous consequences. And so I think that we have to, really take this issue of, [00:29:00] transparency. seriously and know that not only investors, and companies, but it's also consumers and policy makers this is real for them. And the extent to which we're seeing, you know, allegations of greenwashing and. the kind of implosion of some of the markets that are so important for us to develop is I think certainly a cautionary tale and, and we have to start talking about this and, and not only talking about it, but then delivering it. And that is what's gonna really start to, build back that trust We so desperately need.



[00:29:36] Catherine Atkin: 

[00:29:36] J.B. Wogan: Got it. Liv. I'll let you have the final word. What can users do? What can listeners do to help in this issue?

[00:29:42] Liv Watson: Well, first of all, standardization is like team sport, right? It takes, everybody to collaborate. But I think number one, what corporates should do, or anybody listening here is really take a look inside their own organization and say, do we [00:30:00] have a digital first policy? And align that and start, getting.

This at the top of the organization as something that's very important. They need to start looking at their own data supply chain. How are they collecting the data? So. Taking really an internal look at their own organization. But then also, and I mean this from the bottom of my heart, since I'm one of the founders of XBRL, there are many that has been part of this journey.

Join. XBRL US and XBRL International, and they do, as I say, play team sport, right? They bring everybody together and they need that kind of glue to be able to coordinate this work and make sure that we do educate and bring policies. So yes, join the organization, but more important, start asking the vendors.[00:31:00] 

Are supporting your organization? Do they have semantic interoperability? Are they adopting these standards so that you can create liquid data? Right? So you as an individual listening in on this podcast. You need to put on, I can do hat and start actually looking internally how your own data supply chain and join these organizations that are creating these function.

Like XBRL US is very focused on the implementation in the us but XBRL International is working on, you know, the technical standard and then I have one. Vision that one day we don't just create data that is digital, but we actually also make data that is discoverable because many companies report not to just one repository.

In California, multi international [00:32:00] companies can report to hundreds of different repositories around the world, and how can we make that data discoverable?

[00:32:08] J.B. Wogan: I think that's a really good note to end on. Liv, Catherine, Michelle, thank you so much for talking with me today.

[00:32:14] Michelle Savage: Thank you. Thanks.

[00:32:15] Liv Watson: JB has been an honor and thank you.

[00:32:19] J.B. Wogan: again to our guests, Liv Watson, Catherine Atkin and Michelle Savage. And thank you for listening to Data for the People, a podcast from the Data Foundation. our show notes, we'll provide a link to the XBRL US White Paper. digitizing data to combat climate change. We will also have links to the Data Foundation's Climate Data Collaborative, where Liv and Catherine are senior fellows and the Global Digital Single Market Data Alliance, an initiative of the Climate Data Collaborative that Liv and Catherine Co-founded. If you like this episode, please find us wherever you listen to podcasts, subscribe, and if you're feeling really generous, [00:33:00] leave us a rating and review. more about the data foundation by going to data foundation.org. 

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