Commentary

How to Handle Natural Growth

James Phare, CEO of Neural Alpha, describes how AI can be used to understand the system-level risks arising from the UN’s Global Biodiversity Framework.

Last week over 5,600 delegates from 190 countries gathered at the headquarters of the UN Environment Programme (UNEP) in Nairobi for the sixth session of the United Nations Environment Assembly (UNEA-6).

As the world’s top decision-making body on the environment, UNEA’s role is to keep momentum behind all countries’ efforts to tackle the triple planetary crisis of climate change, nature and biodiversity loss, and pollution, while also encouraging a multilateral approach.

While the agenda was wide-ranging, one area of focus across the week was the targets of the Global Biodiversity Framework (GBF), signed by 196 countries at the breakthrough Kunming-Montreal COP 15 Biodiversity summit in 2022.

In just six years, signatories are expected to have put in place actions to deliver the GBF’s 23 global targets for urgent action. The 2030 targets are the first step to achieving the GBF’s outcome-oriented goals for 2050 and cover three broad areas: reducing threats to biodiversity, meeting peoples’ needs, and developing tools and solutions for implementation and mainstreaming.

System-level risks

For investors and asset managers, the focus to date has been on GBF Target 15 that encourages countries to put in place the legal, administrative or policy measures to enable businesses to assess, disclose and reduce biodiversity-related risks and negative impacts.

One initiative Target 15 supports is the adoption of the recommendations of the Taskforce for Nature-related Financial Disclosures (TNFD) to identify and report on both physical and transition-based risks, dependencies, and impacts.

While Target 15, and TNFD, support greater understanding of nature-related risk at a company level, there are also system-level risks created by the 2030 GBF targets that investors and asset managers must take account of for effective investment decision making.

Some of the GBF targets that have the greatest potential to impact whole sectors and industries are:

Target 2: Restore 30% of all degraded ecosystems;

Target 3: Conserve 30% of land, water and seas;

Target 7: Reduce pollution to levels that are not harmful to biodiversity; and

Target 18: Reduce harmful incentives by at least US$500 billion per year and scale up positive incentives for biodiversity.

Let’s take a closer look at Target 3. To conserve 30% of all land and water environments and classify them as protected areas by 2030 presents a transition risk for all agricultural and food production companies. For investors in these sectors, it is critical to understand how aligned, or not, portfolio companies are to these future scenarios.

Analysing this type of nature-based risk in a holistic way is highly complex due to the unique and location-specific relationship of each company’s operations, and its supply chains, with the ecosystems in which they operate or are located.

To achieve a full and accurate picture, reliable data sources covering physical assets, water pollution, air quality, latest news or controversies, legislation, spatial layers, invasive species and more need to be sourced, filtered, connected, and placed in context.

An unprecedented opportunity

Fortunately, the opportunity to leverage AI-based technology to meet the challenges of nature risks, impacts, dependencies, and opportunities is unprecedented. AI and large language models are well placed to accelerate efficiencies around data acquisition and collection, identify exposure to material nature impacts and nature risks in portfolios, and then identify potential solutions.

Driven by initiatives such as the TNFD Data Catalyst there has been huge innovation in the data and software landscape in recent years. This is enabling leading investors and lenders to move beyond purely qualitative scorecard-type assessments of nature-related risks and opportunities to more quantitative and scientifically rigorous models.

There are now dozens of different global models which can be used for baselining portfolios and to account for Scope 3 nature and biodiversity dependencies, risks and impacts in the absence of or supplementing corporate nature disclosures. These include multi region input output (MRIO), environmentally extended input output (EEIO) and lifecycle assessment (LCA / LCIA) models which investors are adapting to begin to measure factors such as the embedded carbon, water, biodiversity and land use footprints of financial portfolios.

It’s important that asset owners and managers start to use these tools now to be able to thrive in the new world of enhanced biodiversity and nature protection required by the GBF.

Investors need to shift their analysis from a purely qualitative disclosure-centric approach to more of a mixed quantitative / qualitative approach by starting to gather meaningful data about nature related dependencies, impacts, risks, footprints, about supply chains, and about market structure in order to understand their transition risks. There is a long way to go – indeed recent Neural Alpha research using our Responsible Capital AI Disclosure Assistant revealed that 66% of S&P 500 companies had no formal policy on biodiversity.

Exponential growth

As the volume of corporate disclosures on nature and biodiversity topics continues to grow exponentially, it will become increasingly challenging for investors and lenders using traditional tools and approaches to keep up.

Large language models offer enormous potential for extending capacity and upskilling ESG teams in developing custom sector benchmarks, conducting due diligence and in identifying innovative solutions being adopted within invested industries.

Overall, these technologies will also allow us to meet Target 21 of the GBF – “Ensure that knowledge is available and accessible to guide biodiversity action”, a cross-cutting target that facilitates all other goals.

There is plenty of debate about the positive or negative impact of AI in wider society. For asset owners and managers wrestling with the complexity of biodiversity investment risk and opportunity, it can only be a good thing.

The practical information hub for asset owners looking to invest successfully and sustainably for the long term. As best practice evolves, we will share the news, insights and data to guide asset owners on their individual journey to ESG integration.

Copyright © 2024 ESG Investor Ltd. Company No. 12893343. ESG Investor Ltd, Fox Court, 14 Grays Inn Road, London, WC1X 8HN

To Top
Share via
Copy link
Powered by Social Snap