M&G Investments: Investing in nature: Identifying leaders and laggards

By Giorgis Hadzilacos, Nature & Climate Modelling lead, Stewardship & Sustainability and Christian O’Dwyer, Head of Nature Solutions at Bloomberg

With integration of climate factors increasingly becoming standard practice for financial firms, investors are increasingly paying attention to nature – and with good reason. Nature is no longer an overlooked issue, it’s an emerging pillar of fiduciary duty with real-world financial materiality. However, much like the early work in climate, this shift brings complexity, uncertainty, and opportunity. This joint blog demonstrates how data-driven insights can help assess financial materiality of nature-related risks and opportunities in investment portfolios leveraging the full wealth of available data. 

Globally, asset managers representing €23 trillion AUM are committed to assessing, engaging and reporting biodiversity-related risks¹. Although there are similarities, nature is inherently different from climate. It is multifaceted and hyperlocal. Unlike climate data, nature metrics are non-fungible; there is no single “nature equivalent” to a carbon footprint. A data centre in Texas may face water stress, while a packaged food manufacturer in Southeast Asia could be exposed to deforestation risks. 

Nature metrics need to relate to companies’ operations and – ultimately – their valuation. Lacking straightforward methods to link company valuations with nature-related metrics, the investment world has typically responded with two approaches: 

1. Wait for climate accounting-style standards and frameworks like the Taskforce for Nature-related Financial Disclosures (TNFD) to mature at the cost of continuing to “fly blind” while we wait; or
2. Simplify the complexity by developing scores or footprinting metrics which are relevant to the investment process but lack some comparability and consistency in the short-term.

We would like to propose a third route moving forwards, which doesn’t attempt to solve everything at once, but focuses on analysing high-impact themes using company specific data, such as water and deforestation. This enables investors to focus on relevant dependencies and risks that vary meaningfully across companies.

Why these two priority themes?

• Water is a local constraint with global financial materiality. It affects sectors from agriculture to semiconductors and is tied directly to operational resilience. Tightening water-use regulations and community scrutiny expose firms to compliance costs and brand damage, while droughts and water pollution increasingly threaten agricultural and industrial supply chains and production continuity.
• Deforestation — driven largely by seven agricultural commodities known as Forest Risk Commodities (FRCs), — carries regulatory and reputational risk, and is increasingly linked to supply chain exposure. For instance, the EU’s new Deforestation Regulation (EUDR) requires goods sold in the EU to be deforestation-free. Non-compliance introduces real risk: penalties, loss of market access, and reputational harm.

Nature data is available at the company level, even if imperfect. With thoughtful proxies and disclosure analysis, we believe it's possible to separate signal from noise.

Case Study: Detecting deforestation and water risk in a sector

To illustrate the value of company-specific nature data, we worked together to apply a practical screening to the 30 largest firms in the Bloomberg World Packaged Food Index, with focus on deforestation and water. The analysis was based on M&G quant modelling and investment expertise, combined with Bloomberg nature-related data and analytics.

On deforestation we assessed two dimensions:

• Exposure to deforestation risk: based on revenue ties to FRCs, commodity production and sourcing, supply chain exposure and operating in areas of high biodiversity intactness.
• Mitigation efforts: using publicly disclosed policies, governance, and FRC-specific certifications (eg RSPO - Roundtable on Sustainable Palm Oil) and measures of traceability.

Deforestation outlier identification for packaged food index 

Source: Bloomberg L.P.

The chart above identifies the highest exposure and weaker mitigation on the top right, while the bubble size denotes capitalisation. The two anonymised examples in the chart illustrate the range of outcomes:

• Company A: An Asian meat processor with exposure to two FRCs but minimal disclosed mitigation. A large player in a high-risk, low-action quadrant – raising red flags for engagement or risk pricing.
• Company B: A European packaged food company with high exposure across six FRCs – but strong mitigation including clear governance, traceability, and policy commitments. Despite exposure, mitigation reduces its risk profile.

On water we assessed two dimensions:

• Water inefficiency: using publicly disclosed information, we assessed water consumption efficiency, expressed as the inverse ratio of water consumption over withdrawal². 
• Water stress: The percentage of a companies physical assets that are located in areas of high or extremely high water stress, measured by combining Bloomberg physical asset locations with the World Resource Institute’s water stress data.

The second chart below identifies companies operating at high water stress and least efficient in utilising the freshwater they extract on the top right.

Water outlier identification for packaged food index 

Source: M&G Investments using Bloomberg L.P data.

The two anonymised examples illustrate the range of outcomes:

• Company A: An Asian-based multinational food processing and retailing company whose operations include water-intensive dairy processing. Its plants operate in catchments where limited water resources compete with agricultural activities. 
• Company B: A south-east Asian multinational food and beverage conglomerate with diverse operations including consumer food products. Its operations include plants in the tropics were water supply stress is less material.

From complexity to actionability 

While outlier identification adds value to the investment process, it comes with limitations. Nature-related data remains unevenly reported. Companies that disclose such metrics tend to be those actively measuring and managing their nature-related impacts, which can bias results. As a result, negative outliers may reflect poor performance – or simply a lack of disclosure. Though estimates help bridge data gaps, they are no substitute for a true understanding of company-level nature dependencies and impacts. By factoring in disclosure quality, the model implicitly penalises non-reporting and supports stewardship efforts to encourage greater transparency.

While a degree of caution is therefore recommended, imperfect datasets shouldn’t be a barrier to using what’s currently available, so action does not have to wait. By focusing on company-specific data aligned with financially material themes, investors can begin identifying potential nature-related leaders and laggards and leverage these insights to inform engagement priorities.

With the right framing, investors can simplify complexity, build relevance into analysis, and take the first steps towards real integration of nature risk.

In our view, investors should:

• Choose a starting point: Pick a theme that’s material to your portfolio and assess in depth – don’t try to solve everything.
• Work with what's available: The absence of perfect data, useful insights can still be found.
• Push for better disclosure: Engagement works best when rooted in tangible, company-specific insights.

¹ Finance for Biodiversity Foundation, (https://www.financeforbiodiversity.org/finance-for-biodiversity-pledge-hits-200-signatories/), 2025.
² For instance, if a company is using 20% of the water it withdraws, their water inefficiency is 80%. 

By Giorgis Hadzilacos, Nature & Climate Modelling lead, Stewardship & Sustainability and Christian O’Dwyer, Head of Nature Solutions at Bloomberg

The views expressed in this document should not be taken as a recommendation, advice or forecast.

The value of investments will fluctuate, which will cause prices to fall as well as rise and investors may not get back the original amount they invested. Past performance is not a guide to future performance.