So when the EU does something bold, we should give it its due. The EU’s boldness in addressing a host of environmental problems head-on is unmatched. In December, it unveiled the European Green Deal (EGD), a 1 trillion euro ($1.13 trillion) investment program to use the free market to accelerate the green transition. Not content to “merely” mitigate climate change, the EGD will also aid in protecting water and marine resources, transitioning to a circular economy, restoring biodiversity and ecosystems, preventing pollution and adapting to the effects of climate change that we cannot mitigate.
If the 1 trillion euros did not provide enough of a signal to the market, the EU’s Covid-19 recovery program, Next Generation EU, created a special bonds program to make a further 750 billion euros ($846 billion) available to companies to make Europe’s economy more sustainable.
Yet coming up with nearly 2 trillion euros was, in a way, the easy part. EU residents overwhelmingly regard climate change as a “major threat,” and the economic consensus says Europe needs substantial stimulus to prevent the worst effects of a Covid-19-precipitated recession.
The hard part will be ensuring this capital — whether public or through private financial institutions — goes to the companies and projects that have the greatest impact. And the hardest part will be collecting and using data to ensure those investments have lasting effects for combating climate change and even for return on investment. Given the sheer volume of data, we will need to rely on artificial intelligence (AI) and the internet of things (IoT) to guide our approach.
To its credit, the EU has created a useful taxonomy providing guidance to all involved. The so-called Taxonomy Regulation (TR) is effectively a “common language” that financial institutions, companies seeking green funding, issuers and other investors will use to formally disclose the environmental sustainability of their investments and offerings. This has broad implications for both environmentally conscious private investors as well as much larger institutions like the European Investment Bank, whose disbursement of EGD funds will be stipulated by adherence to the TR.
Indeed, this encyclopedic classification system lays out metrics by which companies and projects will be judged for meeting the six major sustainability criteria for EGD funds. The taxonomy provides guidance as to the types of projects that will qualify, as well as guidance for how those projects should qualify. Recipients of EGD funds must state their project’s sustainability effects throughout their entire life cycle, and they must “substantially” contribute to one or more of the six major criteria.
Critically, the European Commission will put out official requirements for institutions receiving EGD funds by the end of the year. That should leave little room for ambiguity when it comes to eligibility. It should also enable companies to put together compelling bids for EGD investment.
That, however, leaves the hardest problem. Ongoing data collection and measurement against core metrics is going to be exceptionally difficult. There will be countless input factors across countless projects, with information required in real time. After all, a major project that is underperforming could squander hundreds of millions of euros in just a few months. How, then, do we correct for this problem?
Similar to how the TR will be critical in helping set the proverbial rules of the road for EU sustainable finance, AI and IoT will be critical in helping market players assess and verify their compliance with those rules.
Take infrastructure equity funds. These institutional entities invest in a range of companies and projects that typically have significant environmental impacts that are sometimes difficult to quantify, let alone align with TR criteria. Yet, a fund manager could use AI and IoT to collect, aggregate and structure enormous amounts of asset data that can then be studied for TR compliance.
Were managers of an infrastructure fund to invest in distributed energy resources (DERs) such as wind or solar photovoltaic farms, or even something more complex, such as a virtual power plant platform, then the contribution of those investments toward climate change mitigation and adaptation would need to be continuously verified to ensure the associated activities — e.g., increased renewable power generation — did not run afoul of TR screening criteria.
IoT and AI make this possible. IoT provides the investor with relevant, real-time data, such as technical asset performance, and AI supplies the analytical capacity needed to calculate a given activity’s impacts toward climate change mitigation and adaptation, which will ultimately be used to verify compliance with the TR. These technologies can mitigate compliance risk for companies, too. Independent power producers seeking EGD or privately sourced funds to finance the development of a new generation scheme may, for instance, leverage IoT and AI to analyze the performance of their existing assets and then use those findings to model the proposed scheme’s anticipated TR alignment.
This is especially noteworthy for the EGD’s administrators. The same key performance indicators that underpin both the investment in and management of DERs, for example, could feasibly be standardized and tied to the European Commission’s various eligibility criteria for receiving EGD funds.
Allocating nearly 2 trillion euros may have been the “easy” part, but it is still a once-in-a-lifetime investment. I believe that, more importantly, it is our last, best chance to slow or reverse climate change. As I’ve argued before, we do not have time to get this round of funding wrong. No matter how disagreeable, the EU rules must set high standards.
Critics are loath to admit it, but the EU has done its part and come up with the investment necessary to combat climate change. The question now is if we will use AI and IoT to do ours.