Legions of voluntary net-zero emissions pledges are now in place around the world to meet climate change goals — though ensuring companies and governments actually meet them is proving a much harder task.
But new automation and web-scraping techniques that aim to help tracking groups gather the data they need could soon help churn out almost real-time assessments of plans, curbing so-called green-washing opportunities among laggards, backers say.
One effort, run by the nonprofit Energy and Climate Intelligence Unit (ECIU), based in Britain, aims to be “the mothership where people come when they think about net zero,” said John Lang, the group’s Net Zero Tracker lead.
The tracker uses automated systems to extract, clean and export data from various publicly available sources, at a fraction of the time it would take humans alone to process the information.
It monitors roughly 4,000 entities globally, including close to 200 countries and, by revenue, the 2,000 largest publicly traded companies in the world.
The system, which went live in October, relies on a battalion of more than 150 volunteers, led in part by a team at the University of Oxford.
They sift through the more than 50,000 points of data gathered so far, as part of an ongoing effort to refine and develop the system.
Currently, assessing how genuinely ambitious pledges are and how well those who have made them are doing in meeting them is largely a guessing game, analysts say.
Data is patchy, hard to compare and often unreliable — something new tech-backed tracking efforts, which combine the efforts of researchers around the world, hope to address.
In February, researchers from the Germany-based NewClimate Institute, one of Net Zero Tracker’s partner groups, released an initial “corporate climate responsibility” report.
It found net-zero pledges by 25 top global companies added up to, at best, an average 40% cut in emissions, not the 100% promised.
Researchers noted that accessing and comparing data just for those companies had proved far more difficult and time consuming than expected — one barrier to scaling up regular assessment of progress on thousands of climate pledges.
Creating fast and reliable systems to document, track and compare action on net-zero commitments will be crucial to efforts to avoid the worst impacts of climate change, from rising hunger and migration to more extreme weather, analysts say.
Net-zero emissions pledges can be complicated to analyze and compare, for reasons ranging from dramatically different baselines to differences in whether companies, for example, include emissions from their products and supply chains.
Pledges can be met either by eliminating a company or a government’s own emissions — which scientists say should be the dominant goal — or by offsetting a share of those through projects such as protecting carbon dioxide-absorbing forests.
Many pledges currently rely far too extensively on offsets, with demand likely to far outstrip the supply, analysts say.
Lang said tracking large numbers of pledges is a huge task and even his group’s efforts will be limited.
Besides following countries and companies, Net Zero Tracker monitors cities with a population over 500,000 and individual regions of the top-25 emitting countries.
The tracker looks at the substance of net-zero targets and related goals, to try to provide a common set of benchmarks for public use by others who then track progress toward the goals.
Researchers are figuring out if they want to expand their efforts to track progress in more detail or leave those areas to other groups with different kinds of expertise, Lang said.
The ECIU group, for example, lists Climate Action Tracker, which monitors countries, as offering greater depth on national policy actions, as well as data converted into temperature rise projections.
Net Zero Tracker’s work uses an algorithm to scrape the web for key terms such as “net zero” or “climate neutral” while filtering out irrelevant information, to more easily track and document publicly available pledges.
Data is then turned into a spreadsheet people can review, said Zhi Yi Yeo of the University of North Carolina.
“The model is built to essentially go through the source and comprehend and understand the text very much how a human would,” said the data scientist with the University of North Carolina’s Data-Driven EnviroLab, one of NZT’s partner groups.
The algorithmic approach can be at least five to six times faster than a manual search by people, according to James Zhang, founder and CEO of Arboretica, a tech company involved with the automation part of the tracker.
Zhang said the concept is relatively novel for the field — though his company has also worked on another recent project tracking nature-based solutions for climate change using machine learning and automation.
“It’s not a new method, but it’s new to this industry,” he said.
The project joins a range of other efforts to hold climate polluters to account and press for the 45% cut in global emissions scientists say is needed by 2030 to remain on track for the more ambitious goal of the Paris Agreement.
Climate TRACE, a coalition of groups led in part by former U.S. President Al Gore, for instance, uses satellite imagery and other sources to calculate greenhouse gas emissions globally.
Though emissions from carbon dioxide are invisible, the group can track steam emanating from power plants, for example, and correlate such visible traces to emissions.
The aim is to track all emitters on the planet, not just those who “voluntarily wanted to report their emissions,” said Gavin McCormick, executive director of WattTime, a nonprofit leading the coalition’s work on modeling power-sector emissions.
He said typically only about 40% of private companies and a small minority of subnational governments voluntarily report those emissions — and there’s no quality control system in place to provide a measure of accountability.
When it comes to net-zero pledges, “because it’s unregulated, it’s a total grab bag — there’s no rules,” he said.
Lang, of Net Zero Tracker, said his team hopes to gradually improve the efficiency of its efforts to reflect changes in data for even smaller companies within 24 hours.
“We need more innovation — but I think it’s going to get there very, very quickly,” he predicted.
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