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

The latest episode of Data for 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 data related to climate and the environment, from greenhouse gas (GHG) emissions to air quality to carbon credits. As a result, data is reported across a disparate range of data formats, time series, and submission modes to various regulatory bodies. This fragmentation hampers public accessibility and forces private organizations to navigate a costly, redundant patchwork of state and federal requirements. 

A recent white paper by XBRL US proposes that the public and private sectors adopt a semantic data model to standardize reporting. By ensuring data is machine-readable, users can eliminate manual transcription and enable automated analysis for everything from jurisdictional trend monitoring to AI-driven policy evaluation. 

Guests Liv Watson, Catherine Atkin, and Michelle Savage join the podcast to discuss the current problem with climate and environmental data reporting standards. 

Watson and Atkin are Senior Fellows with the Data Foundation’s Climate Data Collaborative and Co-Founders of the Collaborative initiative called the Global Digital Single Market Data Alliance, which supports the green transition in emerging economies with investor-grade data. Atkin, who helped write California’s Climate Corporate Data Accountability Act, also is a CodeX fellow co-chairs the Climate Data Policy Project at Stanford Law School. Savage is the Vice President of Communications at XBRL US, a non-profit organization supporting the implementation of digital reporting standards for business and government.  

On the episode, Savage explains that businesses currently report information to state and federal regulators multiple times and in multiple ways.  “That's a huge duplication of effort on the part of the reporting entities,” she says. Because the datasets are structured differently, anyone interested in using the datasets for analysis first has to combine them, which can be time- and labor-intensive. “The inefficiency is huge when it comes to this issue,” Savage says. “Adopting a single standard is going to make it easier for everybody, for the reporting entities and for the users of the data.” 

In our 2026 Advocacy and Policy Agenda, the Data Foundation supports the government-wide adoption of Standard Business Reporting, where businesses report once using standardized, machine-readable formats that serve multiple regulatory requirements. In the context of climate policy—and reporting requirements for GHG emissions in particular—such efficiencies could benefit regulators, researchers, and other data users who need the data provided to different agencies and levels of government to be interoperable and share a common language. On the episode, Atkin draws from her experience shaping California’s law requiring large corporations to report their GHG emissions to illustrate how the data need be as borderless as the emissions themselves. 

“ When it comes to GHG emissions, they don't stop at the border. We can't just reduce our own GHG emissions and protect our communities and the flora and fauna of California.” In developing California’s law requiring corporations to report their emissions across the entire value chain, “we knew that consumers, investors and the companies need access to the data of the global carbon footprint of these companies,” she says. 

A semantic data model, the solution proposed in the XBRL US white paper, would  make “data at scale, usable, in a very timely fashion,” Watson says. It would also benefit AI applications that rely on high quality data.  ”AI hallucinates, right? It's only as good as the data that comes in,” she says. 

AI “ needs contextual information so that it can accurately discern the meaning of that data,” Savage adds. A semantic data model provides context “in a consistent, structured way. That’s what makes the AI able to say, ‘oh, I know exactly what this means.’”

Another benefit of structuring the data in the same way? Atkin argues it could restore public trust in the data reported by private entities and collected by government agencies. 

 “We need to be able to articulate the need for interoperability and machine-readable data as an element of data sovereignty and democracy,” she says in the episode. In the context of growing concern about “greenwashing,” where companies that exaggerate or misrepresent their environmental, social, and governance efforts, making data more readily available and easier to analyze can increase transparency and confidence in the data, benefiting everyone from private investors and companies to consumers and policymakers. “We have to take this issue of transparency seriously [...] to build back that trust we so desperately need.”

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Learn more about the Data Foundation's 2026 Advocacy and Policy Agenda, including our support for government-wide adoption of Standard Business Reporting to allow businesses to report once using standardized, machine-readable formats that serve multiple regulatory requirements.