As we mark Earth Day 2025, it’s clear we’re at a defining moment. Despite decades of activism and innovation, the ecological crisis continues to accelerate, driven by climate change, biodiversity loss, and unsustainable resource use. But alongside this growing threat is a growing opportunity: the ability to harness artificial intelligence (AI) and data to drive more responsible business practices and environmental stewardship.

Learn “How Organizations Are Using NLP To Detect Greenwashing.”

Escalating Environmental Pressures

The warning signs are everywhere. By early 2025, Earth’s average surface temperature reached 13.0°C (55.4°F)—pushing us closer to climate tipping points. The first three months of this year were among the hottest on record, with unprecedented temperature anomalies in both the Arctic and Antarctic regions.

Global greenhouse gas emissions reached 37.8 billion metric tons in 2024, driven largely by fossil fuel use, agriculture, and industrial output. Meanwhile, biodiversity is in sharp decline, with over 46,000 species threatened with extinction due to climate-related stressors like habitat loss, pollution, and extreme weather.

Plastic waste continues to acidify oceans, while urban sprawl and overconsumption accelerate deforestation. From fast fashion to factory farming, human activity is pushing planetary boundaries—and the consequences are becoming harder to ignore.

The Human Toll: Environmental Anxiety on the Rise

This crisis isn’t just environmental—it’s deeply personal. Younger generations are increasingly affected by eco-anxiety, a psychological response to fears of environmental collapse. Studies warn that by 2050, billions could face water scarcity, food system disruption, and mass migration from climate-affected regions. Overheated ecosystems, wildfires, and resource scarcity are not abstract threats—they’re the lived reality of millions.

The Role of AI and ESG in the Fight for a Livable Planet

Fortunately, powerful tools are emerging. Artificial intelligence is transforming how we track and respond to environmental, social, and governance (ESG) risks. Natural language processing (NLP) can detect greenwashing, monitor corporate behavior, and surface early warning signals of environmental harm. AI is also helping companies reduce emissions, optimize energy use, and act on regulatory and reputational risks in real time.

Learn “How Successful Investors Are Using AI to Get ESG Data: A Quick Guide.”

By integrating ESG strategy with AI-powered insights, businesses are no longer passive observers but active players in shaping a sustainable future.

Hope for Change

Despite the scale of the crisis, momentum is building. From robotic wildlife conservation to AI-enabled recycling innovations, new technologies offer hope. Companies are aligning with the UN Sustainable Development Goals (SDGs), and governments are responding with new ESG regulations and global climate pledges. But regulation and innovation are not enough—collective action is still key.

Conclusion

There’s no better time than Earth Day to commit to change. Whether it’s supporting environmental nonprofits, reducing your consumption, investing in sustainable products, or advocating for better policies, every action counts. Our window to act is narrowing, but it’s still open.

Happy Earth Day!

The private markets secondaries space has entered a new chapter. What was once a niche corner of alternative investments, used primarily by limited partners (LPs) seeking early exits from fund commitments, has grown into one of the most dynamic segments of global private capital. The market has tripled in size since 2019 and grown by approximately 50% between 2024 and 2025 alone, reaching an estimated $230 billion in annual transaction volume and now representing around 5% of all global private equity assets under management. 

This piece examines the forces behind that expansion, the structural shifts redefining the market, and the operational and regulatory challenges participants will need to navigate as the asset class continues to scale.

Market Growth and Shifting Deal Dynamics

Several converging factors have driven the secondaries market to its current size. A prolonged slowdown in IPO activity and traditional exits has created a liquidity bottleneck across private markets, leaving many LPs over-allocated to alternatives and constrained in their ability to make new commitments. The secondary market has become a primary mechanism for these investors to rebalance portfolios and free up capital.

Deal structuring has grown more sophisticated in step with market volumes. Ropes & Gray has observed a continued expansion in the use of purchase price deferrals and earnouts, and more recently, the introduction of deal-specific funding caps, limits on how much capital a buyer can be called to deploy before a specified date. These mechanisms allow sellers to achieve higher reference-date pricing while enabling buyers to manage capital deployment pacing and portfolio composition. In Q1 2026 alone, institutions initiated new secondary sales processes totaling north of $20 billion, some linked to denominator effect concerns as declines in public market portfolios pushed private allocations above target levels. Whether this proves a sustained driver of supply will depend on how institutional portfolios weather current market conditions.

The Three Transaction Types

Secondary transactions fall into three main categories: 

• LP-led transactions, the original form, involve an LP selling existing fund interests, sometimes across a broad portfolio of hundreds of positions, typically through competitive auction processes with tight timelines. 
• GP-led continuation funds, the fastest-growing segment, involve a sponsor transferring select assets into a new vehicle, giving existing LPs the option to cash out or roll forward. As of 2025, GP-led and LP-led volumes are roughly evenly split at around $115 billion each. GP-led buyout fund volume grew 39% year-over-year, while private credit secondaries saw nearly 300% year-over-year growth in GP-led activity. 
• The third category, structured solutions, provides capital to a GP collateralized by existing fund assets and can take a wide variety of bespoke forms.

What Are the Operational and ESG Challenges in the Market?

One of the defining challenges in secondaries is the speed and scale of due diligence required, particularly in LP-led transactions. Buyers may need to evaluate hundreds, or in private credit secondaries, over a thousand, underlying positions with limited information and within windows of 24 to 48 hours. As Jessica Huang, Managing Director and ESG lead for private equity and secondaries at Ares Management, noted in a recent webinar:

Against this backdrop, LP expectations around ESG integration have risen sharply. LPs are now holding secondaries to a standard closer to that applied to direct investments, with requests for Article 8-classified funds, look-through exclusion lists, and UN Global Compact compliance screening becoming more common. Main exclusion categories include fossil fuels, controversial weapons, tobacco, and gambling, though definitions and revenue thresholds vary significantly across mandates. SFDR 2.0, currently in draft form, may introduce additional mandatory exclusion categories that managers are monitoring closely. In LP-led deals where buyers are inheriting a broad portfolio of assets, highly granular opt-outs can mean missing certain large transactions, a trade-off that must be clearly communicated to LPs.

The Role of Technology and AI

Technology has become central to the scaling of secondaries operations. AI tools are now applied across controversy screening, ESG data analysis, and emissions estimation, where direct disclosures are unavailable. A particular challenge in the asset class is coverage: many underlying companies are small or mid-market private businesses not captured in conventional databases.

Market participants consistently emphasize that AI outputs serve as inputs to human judgment, not as replacements for it. At Ares, screening results are reviewed by ESG specialists before being passed to deal teams for final decisions.

What the Future Holds

Transaction volumes are forecast to continue rising as both the seller and buyer universes expand. Private credit, infrastructure, and structured secondaries all represent areas of growing specialization and regional expansion, particularly in Asia, where secondary activity has been limited but is expected to grow as investment programs mature, broadening the market further. Capital supply dynamics bear watching: while dry powder remains substantial, deal volume growth has outpaced fundraising since 2023, which could create pricing or capital constraints. The entry of retail investors through evergreen vehicles adds a meaningful new source of capital but brings different liquidity expectations and regulatory considerations.

On the operational side, the sophistication of deal terms, the complexity of ESG compliance, and the volume of data processed per transaction are all increasing. Firms that can integrate technology into their diligence and monitoring workflows, while preserving the human judgment layer, will be best positioned to manage market growth. Secondaries are no longer a supplementary liquidity tool; they have become a structural feature of how private markets operate.

As Climate Week NYC approaches, artificial intelligence is emerging as a transformative force in sustainability reporting and ESG practices. This year's event preparations signal a fundamental shift from traditional manual processes to AI-powered solutions that promise to revolutionize how companies track, analyze, and report their environmental impact.

The Technology Focus

Among the notable events planned is "How AI is Disrupting Sustainability Reporting," hosted by Sustainserv in partnership with leading technology and sustainability firms, scheduled for September 23, 2025. This event exemplifies the industry's growing recognition that AI technologies may be key to solving persistent sustainability measurement and reporting challenges.

The focus on AI reflects urgent industry needs. Traditional ESG reporting has long struggled with data collection complexity, accuracy concerns, and the challenge of tracking Scope 3 emissions across global supply chains. Manual processes are proving inadequate for the scale and sophistication required by modern sustainability commitments.

AI's Transformative Applications

AI is addressing these challenges through several breakthrough applications. Advanced machine learning algorithms can now automatically extract and validate data from multiple sources, providing real-time monitoring of environmental metrics and cross-referencing information to identify inconsistencies that might indicate greenwashing.

Perhaps most significantly, AI is revolutionizing Scope 3 emissions tracking, the most complex aspect of carbon accounting. Machine learning algorithms can map intricate supplier networks, automatically calculate emissions factors, and use predictive modeling to estimate emissions even when supplier data is incomplete.

Computer vision technologies are opening new frontiers in environmental monitoring, from satellite imagery analysis for deforestation tracking to automated waste sorting optimization. Natural language processing enables automated analysis of sustainability reports and regulatory compliance monitoring.

Investment Implications

For investors, this AI-ESG convergence represents both opportunity and transformation. Enhanced due diligence capabilities allow for more sophisticated ESG analysis, including automated screening of potential investments and real-time monitoring of portfolio companies' sustainability performance.

The integration also creates new investment themes, from ESG technology companies developing AI solutions to traditional software companies pivoting to sustainability applications. Asset managers benefit from reduced costs for ESG research, faster regulatory response times, and improved accuracy in risk assessment.

Challenges and Considerations

Despite the promise, AI integration faces significant challenges. Data quality remains a concern, as AI systems are only as good as their underlying data. Historical ESG data may contain biases, and algorithmic bias could perpetuate existing inequalities.

Regulatory uncertainty adds complexity, with unclear guidelines on AI use in ESG reporting and potential liability issues for AI-generated recommendations. Implementation requires substantial organizational change, including staff training and system integration.

Looking Forward

The prominence of AI-focused events at Climate Week NYC signals that the sustainability industry is entering a new technological era. As AI continues to mature, the industry is likely to see more accurate, timely, and comprehensive sustainability data.

This evolution could accelerate the transition to more sustainable business practices by making environmental and social impacts more visible and actionable. However, success will ultimately be measured not by technological sophistication alone, but by the ability to drive real-world improvements in environmental and social outcomes.

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