As countries prepare to report on their NDCs, it is evident that a lack of integrated data-management systems, in addition to the varying measuring methodologies, will pose challenges related to accurately measuring progress. At the national level for example, existing data-sharing infrastructure for climate change tends to be inadequate – when, for example, the data reported is typically shared manually, in bespoke formats, or the data flows across a complex web of stakeholders, with varying control and visibility.

This reality calls for another type of collective action – the need to foster a climate-focused digital public infrastructure (DPI) that is interoperable and open source to enable transparent measurement, reporting, verification (MRV) and aggregation of NDCs at scale across countries. Climate-reporting truth lies in unified data connectivity, which provides universal visibility, communication and actionability while ensuring alignment with evolving UNFCCC guidance.

An MRV-DPI that is built responsibly could potentially:

● Enable collection and aggregation of high-quality data on NDCs within each country, providing an accurate and timely handle on our collective progress across sectors and regions.

● Help scale fledgling carbon markets, as high-quality and real-time reporting and verification of emissions is key to a cross-country carbon market that is trusted by buyers and sellers.

● Integrate emerging and innovative data capture efforts, such as the Land & Carbon Lab initiative, which uses geo-spatial technology to track land-related carbon stocks and flows around the world. While the methodologies are still being developed, creating a ground for easy comparison can encourage exploration by academic, non-profit and private sector players.

A recent impact study by the Digital Public Goods Alliance and UNDP estimates that such a MRV-DPI system – particularly one that creates a replicable digital NDC monitoring system and enables interoperability between reporting mechanisms and various climate finance platforms across countries – could lead to reduced carbon emissions by 2030 (in amounts that are at minimum 3-4% of low and middle-income countries’ targets).

Given the plethora of data analytics and monitoring systems that have emerged in recent years, a reasonable question is: How is a DPI approach different from any tech platform that collects emissions and other NDC data? Most importantly, what would make a system built with a DPI approach truly useful and effective at a global scale?

First, the core underlying tech platform for such an MRV-DPI system would be open source, with the code for building country-level platforms available freely to every country. This would enable standardization and rapid, cost-effective adoption by countries. Countries could avoid getting locked into disparate proprietary tech systems of different tech vendors. This approach is similar to DHIS2, the world’s largest Health Information Management System platform, in use by ministries of health in 73 low and middle-income countries. DHIS2 is an example of a DPI that has seen massive uptake and sharing among countries. Built into this approach would be common standards and schemas for data collection and data exchange, to tackle data connectivity and interoperability towards ensuring consistency on what is measured across countries, while respecting the national contexts.

Second, such a MRV-DPI system would have open application programming interfaces, often referred to as APIs in tech-speak, which basically means it will be easy for other tech systems to plug in to it. This could enable smooth aggregation of data across countries, and the integration of other project-level MRV tools and platforms into the system (such as the Land & Carbon Lab data example referenced above). It could potentially spur more innovations on top, such as tools that would enable advanced analytics.

Third, while openness is at the heart of a DPI approach, equally critical is ensuring there are adequate safeguards, such as the incorporation of Privacy-by-Design principles, so that household and firm-level data is not put at risk of being compromised, misused or double-counted.

In addition to these core features, other desirable features would include: agility, so additional modules and features can be added easily as more becomes known on what needs to be measured and how; intelligence, so rapid analytics can be conducted and reports can be generated automatically at various levels of the chain, flagging errors, outliers, etc.; and future readiness, so that emerging technologies such as AI and machine learning, blockchain, internet of things, etc. can smoothly be deployed to speed up and improve the quality of data collection, processing and verification, as well as analytics to better prioritize actions.