With COP26 concluded, waves of new and updated climate action plans from countries across the world have been a welcomed (if still insufficient) sight. But the gap between country-level ambition and “safe” (<1.5°C) levels of global heating remains large. To address this gap, city and regional governments and companies from around the world have been pledging their own emissions reduction targets and climate action plans, seeding hope that this groundswell of non-state activity can “bridge the emissions gap.”

But understanding the content of these pledges presents a huge technical challenge. A centralized body for non-state actor pledges or a harmonized set of standards does not yet exist. Some of these climate action plans can take the form of long official planning documents or even codified legislation, while others could look like a simple press release. Compiling, aggregating, and analyzing these different types of subnational climate commitments to figure out how they interact with country-level policy is a huge task, one traditionally accomplished with hundreds of manual searching, coding, and cleaning hours. By the time these processes are accomplished, the information released for the public might already be out of date, presenting a major stumbling block for climate transparency and accounting.

The Data-Driven EnviroLab has recently teamed up with Arboretica, an open-source intelligence technology startup, to utilize next-generation digital approaches to push the frontier of climate action tracking forward. The two groups have been collaborating on an ambitious end-to-end automation of climate action tracking – from searching and identification of new or updated climate action policies, to extraction and coding of the relevant information needed to assess their quality. This work has been trialed already in support of the Net Zero Tracker project, a recently-launched initiative to track the proliferation of so-called “net-zero” climate commitments in an open-source and transparent way. 

Automated searching and target identification, powered by our proprietary algorithmic processes, were utilized in compiling the database for the Net-Zero Tracker. This process drastically reduced manual searching and coding time, and allowed team members to instead shift attention to quality assurance and vetting of data. Additionally, the integration of algorithmic data processing helps the Net Zero Tracker to be “refreshed” in a frequent and replicable manner, ensuring that future versions of the database will be up-to-date and comparable over time. 

DDL and Arboretica plan to continue to work together on pushing forward this technology to provide an end-to-end solution for the Net-Zero Tracker and other climate action tracking initiatives, working towards the goal of real-time, intelligent climate tracking platforms that can provide solutions for the sticky problem of climate accounting. 

 

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