We have an entered an age of ubiquitous information and, potentially, ultra-transparency. New sensors, whether cube satellites in space or those contained in our smartphones, are generating constant streams of data.

Analysing this vast amount of information can be too difficult for people using traditional systems. Every point on earth is now photographed in high resolution every single day from space. It would be extremely challenging to process all that information effectively using traditional forms of analysis. Similarly, analysing the daily social media interactions of millions of people would be impractical, even for the largest and most capable teams.

Machine learning, and other forms of artificial intelligence, unlock the ability to process information incredibly quickly. When combined with pervasive data capture, they allow us to generate insights into the behaviours of individuals, the activities of companies and the actions of governments, without any of these actors necessarily wanting to share this information.

The capacity to reveal what is going on anywhere and everywhere in near real-time could easily lead to a dystopian future. We are already seeing this in some parts of the world – particularly in authoritarian states, who use these capabilities to decide when and how to change the rights and privileges of citizens.

But machine learning and AI are also, of course, a massive opportunity for improving governance, and a critical tool for tackling the great social and environmental challenges we face around the world. We must, therefore, use these technologies responsibly to achieve positive outcomes.

In the realm of sustainability and finance, these capabilities will allow us to upend the current information asymmetries that exist between companies and their investors, and between financial institutions and their regulators.

Information is the lifeblood of financial markets. The systems that collect, collate and disseminate financial market information are a key component of well-functioning capital markets. Developments in data capture and data processing will have profound implications for these information markets and associated systems.

The current focus on companies disclosing more and better information, for example, as recommended by the Task Force on Climate-related Financial Disclosures (TCFD), is an attempt to remedy information asymmetries for climate change-related risks and opportunities. Such efforts should be lauded.

But the idea that we will secure comprehensive or accurate disclosures globally anytime soon is naive. It will take many years to achieve anything like global coverage for some of the most basic disclosures required. In an area where time is of the essence – we must achieve net zero emissions by mid-century to meet the terms of the Paris Agreement, and physical climate-related risks are already having a material impact – this is deeply unsatisfactory.

Companies reporting climate disclosures to the non-profit CDP since 2003.
Companies reporting climate disclosures to the non-profit CDP since 2003.
Image: CDP

The only plausible pathway for securing the data required to make a meaningful difference is not disclosure, but developments in data capture and data science. Bottom-up approaches to analysing climate-related risks and opportunities, based on accurate and timely asset-level data, is already possible for key polluting sectors. The work can be easily accelerated and made much more readily available.

Not only would actors within the financial system then be able to see the risks and opportunities facing highly exposed sectors globally and for both listed and non-listed companies, it would also allow regulators, policymakers and civil society to see what is happening and what that means for their respective responsibilities.

We must move the focus of the conversation about climate-related information sparked by the TCFD away from disclosure and towards how we get the data required quickly through new technologies. Waiting for disclosure to work is not the right strategy.

We need a data-focused approach, which would also support other objectives. For example, it could enable a comprehensive Measurement, Reporting and Verification (MRV) system for global greenhouse gas emissions.

Ultra-transparency and the associated earth observation and data science capabilities we need, as well as the balance between public and private contributions to make them widespread, must be figured out now. The Global Climate Action Summit in San Francisco taking place this week is the perfect moment. It is, after all, where so many of the technologies required have been conceived and developed. Without progress on data, the TCFD and so many other related processes will not succeed.

This article is part of the World Economic Forum’s Fourth Industrial Revolution for the Earth series, which explores how innovative technologies are beginning to transform the way we manage natural resources and address climate change and other environmental challenges caused by industrialization.