Mining projects around the world often promise development and economic growth, yet their legacies reveal a far more complicated story. Sites like Cerrejón in Colombia, Córrego do Feijão and Samarco in Brazil show how environmental, social, and human rights risks can ripple through communities for decades. Rivers are poisoned, soils contaminated, and ecosystems devastated, while thousands of residents face health crises, displacement, and loss of livelihoods. Legal actions, class lawsuits, and ongoing remediation efforts illustrate that the consequences of these operations do not end when production stops. Communities continue to grapple with the aftermath, from toxic waste and tailings spills to the psychological scars of displacement and conflict.

What are the most pressing ESG challenges currently facing the mining sector? Read on to find out.

Córrego do Feijão Mine: ESG Challenges and Ongoing Controversies

The Córrego do Feijão Mine is facing serious ESG controversies following a dam collapse that resulted in 270 fatalities and contamination of the Paraopeba River. Indigenous villagers still lack safe land and access to clean water and food. The operating company, Vale, is dealing with ongoing legal challenges, including fines and criminal charges for negligence and bribery. Environmental and social impacts persist, with continued monitoring of water quality and safety risks from existing dams. Governance issues remain significant, highlighted by employee arrests and scrutiny over manipulated audits, prompting calls from local and international NGOs for improved remediation efforts and oversight of the mine’s operations.

Key Controversies:

• Prosecutors Appeal Supreme Court Over Córrego do Feijão Mine Dam Collapse
• Unsafe Dam at Córrego do Feijão Mine Kills 270 as Operator Is Convicted of Homicide and Bribery
• Vale Fined US$17 Million for Brumadinho Tailings Dam Disaster at Córrego do Feijão Mine
• Indigenous Communities Still Lack Safe Land, Clean Water, And Food, Following The Córrego Do Feijão Mine Dam Collapse
  

Cerrejon Mine: Human Rights, Health, and Environmental Impacts

Cerrejón Mine faces significant ESG issues, particularly in environmental and social areas. Its expansion has displaced over 20,000 indigenous people, especially from the Wayúu community, leading to severe health problems and the deaths of 5,000 children. Environmental concerns include the diversion of the Bruno Stream, excessive water use during droughts, and significant air and water pollution. Labor unrest persists, with over 4,600 unionized workers striking over job cuts and unsafe conditions. The mine continues to face international criticism for violating local community rights and harming regional ecosystems.

Key Controversies:

• Cerrejón Coal Mine Expansion Sparks Protests Over Air Pollution and Water Diversion Affecting Indigenous Communities
• Cerrejón Coal Mine Poses Serious Threats to Indigenous Wayúu Community’s Health, Water, and Livelihoods
• Ecocide and Child Deaths Ravage Wayúu Community at El Cerrejón
• Cerrejón Mine Workers Strike Against Job Cuts, Dangerous Shifts, and Benefit Reductions

Samarco Mine: Escalating ESG Challenges and Corporate Accountability

Samarco Mine faces intense ESG scrutiny nearly a decade after the 2015 Fundão dam collapse, with over 700,000 claimants in a $44 billion lawsuit for contaminated water and pollution. The operator risks bankruptcy due to environmental liabilities and failed debt restructuring, exacerbating financial instability. Ongoing issues include heavy metal contamination in wildlife and dust pollution exceeding health standards, prompting NGOs to demand the operator's removal from the UN Global Compact. Governance concerns have risen with employee arrests linked to safety protocol violations and falsified audits, raising questions about corporate accountability.

Key Controversies:

• Samarco Mine Disaster Lawsuit Escalates to $44 Billion with Over 700,000 Claimants
• Samarco Mine’s Operator Faced Bankruptcy Risk Due to Environmental Liabilities, Compensation Claims, and Operational Shutdowns
• NGOs Urge Removal of Samarco Mine Operator from UN Pact Over Dam Disaster
• Samarco Mine Faces New Legal Actions and Investigations Over Contaminated Water Crisis

Conclusion

The ongoing ESG challenges in the mining sector highlight significant environmental, social, and governance failures that profoundly impact affected communities. The cases of Córrego do Feijão, Cerrejón, and Samarco reveal the dire consequences of prioritizing profit over sustainability. Mining companies must embrace accountability, transparency, and community engagement to rebuild trust and ensure a positive impact.

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In theory, a portfolio with no ESG controversies signals low risk. In practice, experienced analysts treat it as a warning sign. The absence of alerts often reflects not resilience, but limited coverage, fragmented data, or incomplete aggregation. What looks like reassurance may instead point to a gap in visibility.

This dynamic came up repeatedly during our recent webinar, Private Markets in 2026: Macro Trends, ESG Shifts, AI Innovation and What It Means for Deal-Flow. The discussion highlighted how gaps in coverage and aggregation can shape investor perception, particularly when portfolios appear “quiet,” not because risks are absent, but because relevant information is not being captured.

This dynamic matters more than ever as private market due diligence intensifies. With fewer deals, longer holding periods, and higher selectivity, investors are spending more time scrutinizing assets before acquisition and monitoring them for longer after entry. Yet the informational foundation behind many ESG assessments has not caught up with these expectations.

When "No Data" Becomes "No Risk"

Private assets operate under persistent disclosure constraints. Unlike public companies, most private firms do not produce standardized, recurring ESG disclosures, nor do they benefit from consistent analyst coverage. These gaps are structural and unlikely to disappear in the near term.

In this context, silence is ambiguous. A clean ESG screen may indicate the absence of material issues, but it may just as easily signal that no relevant information was captured. Language limitations, fragmented sources, and uneven coverage across geographies and asset types all contribute to this uncertainty.

This dynamic is particularly visible in secondary transactions. Deal teams often need to assess large portfolios under tight time pressure, with limited access to management and incomplete identifiers. In such cases, relying on the absence of signals can create false confidence rather than reduce risk.

How Weak Coverage and Duplicated Signals Create Blind Spots

Even when information exists, it is not always immediately actionable. Adverse media has become a valuable substitute where structured ESG data is limited, offering outside-in visibility into private assets. However, it is not without challenges. Without robust aggregation and cross-language consolidation, the same issue can appear repeatedly across multiple articles, jurisdictions, and languages, creating duplication rather than clarity. At the same time, gaps in coverage or weak filtering can allow other material risks to go undetected.

At the same time, some portfolios appear unusually quiet simply because the underlying assets fall outside the scope of traditional datasets. ESG and reputational expectations in private markets remain fragmented, with bespoke workflows driven by LP-specific requirements. This lack of convergence makes it difficult to distinguish between genuinely low exposure and analytical gaps.

More data does not automatically resolve this problem. Without traceability, source quality, and a way to assess financial, legal, or operational materiality, increased volume can add noise without improving decisions. In that environment, silence can be just as misleading as signal overload.

What Meaningful ESG Visibility Looks Like Under Disclosure Constraints

A core takeaway from the webinar was that point-in-time ESG assessments are no longer fit for purpose in private markets. A single diligence exercise conducted at entry cannot capture emerging governance failures, litigation, reputational issues, or supply chain risks over multi-year holding periods.

Instead, meaningful ESG visibility combines three elements:

• Broad coverage, to avoid portfolios appearing "low risk" simply because assets are not captured.
• Aggregation and severity assessment, to separate isolated news from controversies with real financial or operational implications.
• Continuous monitoring, so the original risk thesis evolves as new information emerges rather than remaining static.

This approach reframes ESG from a compliance exercise into a source of informational advantage. Rather than concluding that no alerts mean no risk, investors use ESG signals to guide follow-up questions, prioritize deeper diligence, and identify issues that were not visible at entry.

Replacing False Comfort with Informed Uncertainty

Private markets will continue to operate with imperfect information. Disclosure gaps, opaque supply chains, and bespoke reporting demands are inherent to the asset class.

Treating “no issues detected” as a conclusion creates false comfort. Treating it as a hypothesis, contingent on coverage quality and monitoring depth, aligns ESG analysis with how risk actually emerges in private assets.

SESAMm’s AI Technology Reveals ESG Insights

Discover unparalleled insights into ESG controversies, risks, and opportunities across industries. Learn more about how SESAMm can help you analyze millions of private and public companies using AI-powered text analysis tools.

Barcelona, QuantMinds International, November 2022

CEO Sylvain Forté joins QuantMinds correspondent Joanna Simpson in an interview highlighting the use of AI in ESG Investing and how we use it to detect greenwashing practices.

Below is an approximation of this video’s audio content. Watch the video for a clearer understanding of the topics discussed during the interview.

Joanna:  I'm Joanna Simpson here at QuantMinds International in Barcelona. Joining me now is Sylvain Forté, CEO of SESAMm. Thank you very much for being here.

Sylvain: Thank you.

Joanna: Tell me, how does it feel to be here at QuantMinds International?

Sylvain:  It feels very good, actually. We've been to the conference a couple of times already, so it's not our first year, and this time we brought several people from our team. We're all here together, presenting our technology and discussing some of the novelties in the space. It's very exciting and personalized.

Joanna: Great. And what role does artificial intelligence have to play in the future of ESG and ESG investing, in particular?

Sylvain:  ESG is a massive trend in the industry right now, not just in asset management and the quant space but also in private equity, in corporate space like tracking suppliers, clients, etc. And one of the key problematic themes that we see is data gaps. There's a lack of data in terms of coverage; small caps, mid caps, or even private firms are not well covered. The frequency of information tends to be lagging. There's a very low frequency, like quarterly updates or so. There's also a lack of transparency and the like.

So, I believe that AI is primarily a tool that can help build that information gap and, for example, cover millions of companies instead of just a few tens of thousands of companies manually. What we do at SESAMm is leverage a technology called natural language processing (NLP), where we screen text automatically to understand potential ESG controversies or positive impact events. This leads us to have a coverage of around 5 million companies, meaning every publicly listed company out there and private firms that no one else would cover otherwise. This enables many use cases.

There's also frequency; you can generate indicators every single day, more like a quantitative time series that people are used to, and this enables clients to get access to information even locally, like Raiffeisen, one of our clients, is tracking clients in Poland, in Austria, in Germany, or in Ukraine using NLP which would not be possible with traditional ESG metrics. I think that the key topic of AI is expanding the use, expanding the coverage in terms of ESG data, and making sure that data is systematic, follows a good process, and is transparent.

Joanna: What examples are there of ESG investing being enhanced by AI?

Sylvain:  We see two primary use cases.

The first one is more quantitative, where people are looking to leverage ESG NLP data in their systematic trading process. It's either for alpha generation; for example, we work with LFIS, an asset manager in France that created a fund based on ESG NLP data. Their primary goal is to enhance their strategy to generate outperformance, which is really a good use case in that space. This is the quantitative use case where you can use higher frequency data like daily data to leverage ESG like any other kind of alternative dataset and derive superior returns.

Then we have more discretionary use cases where we see large asset managers or private equity shops which are looking to perform risk management tasks or help their team prioritize the scoring of assets. Say they have a team that does their own proprietary scoring on assets with regards to ESG, but how do I prioritize? I have 3000 assets to follow, I need some kind of alert on that whole universe to make sure that I focus on the assets that could be most controversial today. That's one of the things that we provide; daily alerts using natural language processing where people can say okay, there is a massive shift right now; as an ESG analyst, I'm going to make a decision to look at this asset specifically to help cover it and update the score.

Joanna:  Can AI help with greenwashing in ESG investing, and if so, how?

Sylvain:  Yes, it's one of the other kinds of problems that you have in ESG is the lack of transparency on the methodology creates some anomalies in some cases. And one of the big anomalies is that there's this averaging effect where a firm that has both positive actions and negative topics is going to be, on average, neutral, which is really problematic.

We had a big example like this in France recently with Orpea, a listed company of nursing homes exposed to a massive scandal with regards to mistreating patients—so more like social washing than greenwashing. And the problem is their scores were pretty high because, at the same time, they had some positive impact. They were implementing new diversity policies and the like, so it was averaging up.

At SESAMm, we leverage NLP to completely differentiate positive and negative topics. So if a firm is doing good stuff that is aligned with SFDR, and they have positive actions, etc., great! That's going to be one score. But if, at the same time, they have very negative topics, there are a lot of risks we're going to still detect that's not going to be averaged. It's going to be very specifically focused on.

Joanna: Sylvain Forté, thank you for your time.

Sylvain: Thank you very much.

To learn more about how SESAMm uses Text Reveal to find ESG data, contact a representative today.