Summary
Sustainable investing is entering a new phase. Policy architecture is largely in place. The conversation has shifted from what rules should exist to whether firms are actually implementing them, and whether that implementation is producing the outcomes regulators and investors intended.
The picture emerging across the industry is mixed. Engagement with sustainability themes is deeper and more sophisticated than it was three years ago, but structural gaps remain. Consumer awareness of sustainability labels is low. Physical climate risk remains materially underpriced across asset classes. Sustainability data is fragmented and difficult to audit. And stewardship is operating under significant tension between what it can realistically deliver and what clients and policymakers expect.
This piece sets out the key themes defining sustainable investing in 2026, and what firms need to do about their data, governance and product oversight before the regulator does it for them.
1. The Regulatory Phase Has Changed
The most important context for everything that follows is that sustainable finance regulation has moved from a standard-setting phase into an implementation and scrutiny phase.
Over the past five years, the priority across major jurisdictions was establishing frameworks: SFDR in Europe, SDR in the UK, TCFD reporting mandates and taxonomy development. The primary challenge for regulators has now shifted from whether standards exist to whether they are being embedded meaningfully in practice.
For the FCA, this means an increasingly supervisory and evidence-focused posture. Recent publications have emphasised that good regulation produces behavioural change, addressed through supervision, engagement and ongoing guidance, not just enforcement actions. The FCA is signalling that it is watching how sustainability claims translate into investment decisions, governance structures and disclosed outcomes.
Internationally, the UK SDR regime is feeding into the development of SFDR 2.0 in Europe, with lessons flowing in both directions. IOSCO’s role is shifting towards monitoring implementation rather than issuing new standards. Global alignment remains an aspiration, but the direction of travel is toward greater consistency in what substantiated sustainability claims need to look like.
What this means in practice
- Data: Evidence of sustainability claims must be documented and traceable. The FCA’s supervisory posture means firms need data trails, not just position-level data, but the governance trail behind claims.
- Governance: Internal governance frameworks must connect investment decisions to sustainability disclosures. A structure that produces claims without documented oversight is a supervisory risk.
- Transformation: Firms that built sustainability processes for compliance-only purposes need to rebuild for demonstrable outcomes. The regulator is now asking ‘what actually happened’, not ‘what did you intend’.
- Review: Review whether your sustainability disclosures can withstand evidential challenge, not just whether they are technically compliant. If the answer is uncertain, that is the starting point.
2. SDR: Adoption Has Lagged and the Awareness Gap Is Material
The UK’s Sustainability Disclosure Requirements regime launched with significant ambition. Eighteen months on, the gap between those ambitions and market reality is difficult to ignore.
Label adoption has substantially undershot expectations. According to the Investment Association’s SDR Implementation Survey (June 2025), only 110 funds had adopted an SDR label by the time of surveying, less than half the 216 funds that the IA expected to be labelled based on its April 2024 survey. Labelled fund FUM stands at £39.5 billion, equivalent to just 2.5% of UK domiciled FUM. The Sustainability Mixed Goals label has seen particularly low take-up, with only 5 labelled funds. One third of firms that intended to adopt a label in 2024 had not done so by the time of the 2025 survey. Only 38% of surveyed firms have adopted an SDR label for at least one fund.
Consumer awareness compounds the problem. Research conducted by the FCA as part of its SDR development process found that retail investors’ knowledge of the four FCA sustainability labels is extremely limited. Investors were unable to reliably distinguish genuine FCA labels from invented alternatives in testing, underscoring that the label names and the distinctions between them are not landing with the people the regime was designed to serve.
There are structural reasons for this. Language has been consistently identified as a barrier. The regime is also described as too narrow in scope: exclusions alone are insufficient to achieve a label, and a significant proportion of funds with genuine sustainability characteristics remain ineligible under current criteria.
What this means in practice
- Data: Label eligibility depends on sustainability data that is auditable and consistent. If the data underpinning a claim cannot be reproduced and traced, the label is at risk, both at application and during subsequent FCA review.
- Governance: The bottleneck to SDR label adoption is governance, not investment strategy. Who owns the label logic? Who reviews it? Is it embedded in product oversight processes and documented in governance records?
- Transformation: Treat SDR label eligibility as an operating model question. The investment thesis may be sound; the infrastructure to document and evidence it consistently across regulatory cycles may not be. This is the gap to close.
- Review: Audit the gap between your current sustainability strategy and your label eligibility position. If eligible funds have not applied, the barrier is likely operational, and fixable before SFDR II implementation raises the bar further.
3. Physical Climate Risk: Underpriced, Underestimated and Now Unavoidable
If there is a single theme that has moved most dramatically up the sustainable investing agenda in the past two years, it is physical climate risk. The transition risk narrative has been the dominant framework for most of the last decade. Physical risk was acknowledged but treated as a longer-term concern. That is no longer a tenable position.
The damages from climate-related events are consistently exceeding model predictions. Real estate, infrastructure, supply chains and corporate revenues are all exposed in ways the financial system has not adequately priced. The catastrophe bond market, a direct mechanism for pricing physical risk, has seen issuance yields remain elevated, and the market has grown at a compound annual growth rate of 9.7% over the past five years, reaching nearly USD 48 billion in outstanding notional by end of 2024. Insurance and reinsurance markets are reassessing their exposure to climate tail events far more rapidly than public equity markets, a pricing signal public market participants cannot ignore.
The challenge for investors is compounded by the state of available data and modelling tools. Most existing frameworks rely on linear models for events that are fundamentally non-linear. Location-specific risk data for supply chains and listed companies remains incomplete. Capital markets assumptions have largely not been updated to incorporate physical climate scenarios.
The sectors identified as systematically under-examined include:
- Semiconductors and data centres – vulnerable to water risk and heat stress, with operational downtime directly affecting revenues
- Coastal real estate – where valuations have not adjusted to flood risk trajectories, despite a well-developed evidence base
- Utility infrastructure – where the interdependence of substations, grid assets and water systems creates cascading exposure that most asset owners have not mapped
Private markets have generally made more progress on physical risk integration than public markets, partly because asset-level granularity is more naturally available in private real assets. For public market investors, physical risk data is increasingly being used for stewardship and engagement, even where it cannot yet be embedded in systematic strategies.
What this means in practice
- Data: Asset-level physical risk data is available and being used for stewardship now. For real assets, start with location data and map exposure. For public markets, identify the companies with highest physical risk concentration in the portfolio.
- Governance: Physical risk needs formal ownership within risk governance frameworks. It should appear in risk committee papers, investment governance reviews and capital assumptions documentation, not sit outside existing structures as a separate workstream.
- Transformation: Update capital markets assumptions to incorporate physical climate scenarios. For firms with significant real assets, asset-level mapping is the immediate priority. For public market investors, build structured stewardship engagement programmes around physical risk.
- Review: Review whether your stewardship programme includes documented engagement with the companies most exposed to physical climate risk. If it does not, this is a gap regulators and clients are increasingly likely to ask about.
4. Data, AI and the Operational Gaps Beneath the Surface
The industry consensus on sustainable investing data in 2026 can be summarised simply: there is a lot of it, most firms are using too many sources of it, and almost no one has the internal infrastructure to govern it properly.
Across major asset managers, the majority rely on multiple sustainability data providers to meet their various regulatory and reporting obligations. Nature and biodiversity data alone draws on dozens of distinct sources, with coverage ranges varying so dramatically that drawing consistent conclusions across providers is genuinely difficult. The result is fragmented, expensive to maintain, difficult to audit, and increasingly unsustainable as reporting requirements multiply.
AI is increasingly being applied to this challenge, and there are genuine efficiency gains available. Automated extraction of ESG metrics from corporate sustainability reports, AI-assisted mapping of portfolios against regulatory classification frameworks, and natural-language summarisation of physical risk data for investment committee use are all live use cases with meaningful results.
But AI is not a shortcut to operational maturity. The firms making it work have invested in the governance infrastructure that makes AI outputs trustable: traceable data lineage, documented assumptions, defined human review checkpoints, and executive-level accountability for AI-derived conclusions. Firms that layer AI onto fragmented, poorly governed data will produce faster bad answers, not better outcomes.
What this means in practice
- Data: Before investing in new data sets or additional providers, map what you have. Where does sustainability data live across the firm? Who owns each element? How does it flow between investment, ESG, compliance and disclosure functions? Resolve structural fragmentation before procurement.
- Governance: AI outputs used in a regulatory or disclosure context need governance infrastructure: traceable data lineage, documented assumptions, defined human review checkpoints, and named executive accountability. Without this, AI creates liability, not efficiency.
- Transformation: The shift from ad-hoc data management to defensible, scalable disclosure is an operating model transformation, not a technology procurement exercise. Technology is the last step. Governance and data structure are the first.
- Review: If using AI for ESG metric extraction or regulatory classification, review whether the governance framework around those outputs is documented and defensible. If not, this is the audit risk to address before your next regulatory engagement.
5. From Transition to Transformation: Private Markets and the Infrastructure Opportunity
The most energised conversation in sustainable investing at present is around private markets and the role they play in financing the energy transition at scale. Capital requirements are enormous, policy frameworks are actively being constructed, and transactions of genuine scale are beginning to close.
The UK’s clean power mission, targeting 95% of electricity from low-carbon sources by 2030, requires approximately £40–50 billion of investment per year between 2025 and 2030, according to the NESO Clean Power 2030 Action Plan. The offshore wind sector is being asked to scale from approximately 15 gigawatts of installed capacity to between 43 and 50 gigawatts by 2030, representing a near-tripling of capacity within five years.
The policy lesson from recent landmark transactions is important for private markets strategy: risk-sharing architecture matters as much as capital availability. The government’s toolkit, spanning grants, public finance institution co-investment, contracts for difference, regulated asset base models, and floor/ceiling revenue mechanisms, is being applied across a spectrum of technology readiness. For private capital to flow at scale, that policy architecture needs to be consistent, well-communicated, and resilient across government cycles.
Natural capital is an adjacent and growing space. River restoration, peatland recovery and nature-based infrastructure are beginning to generate investable cash flows, directly tied to flood risk reduction, water quality improvement and biodiversity outcomes. This is highly place-based investing, and it requires data, governance and evidence standards of exactly the kind discussed throughout this piece.
What this means in practice
- Data: New asset classes bring new data requirements. Construction and technology risk in offshore wind, biodiversity data for natural capital, these do not map onto existing data architectures and need to be assessed and built for specifically.
- Governance: Risk governance frameworks built around listed securities need extending for private markets. New committee terms of reference, new ownership of risk types, and new escalation pathways are needed before significant capital is committed.
- Transformation: Scaling into sustainable infrastructure requires risk management investment commensurate with capital deployment. The governance infrastructure needs to be in place before commitments are made, not retrofitted after.
- Review: Review whether your existing risk framework can adequately govern private markets exposures in transition and natural capital strategies. If it cannot, identify the gaps and the steps to close them as a priority.
What Firms Should Prioritise Now
Drawing these themes together, the sustainable investing agenda in 2026 creates a clear set of transformation priorities for regulated firms.
Governance and ownership first.
The most common failure mode across sustainability data, labelling, reporting and stewardship is unclear ownership. Before investing in technology or process, establish who is accountable for each element of the sustainability framework, and ensure those accountabilities are embedded in governance structures shared across investment, compliance and disclosure functions.
Treat operational maturity as the competitive advantage.
The regulatory direction of travel is unambiguous: substantiated claims, auditable evidence, demonstrable governance. Firms that achieve this rigorously will find it translates into client confidence, faster regulatory engagement, and the ability to label and market funds credibly.
Integrate physical risk into existing frameworks, not as a separate workstream.
Physical climate risk assessment does not need to wait for perfect data. Start with asset location data, incorporate physical risk into stewardship engagement frameworks, and begin updating risk management processes to reflect the frequency and severity of climate events already occurring.
Invest in data governance before investing in data.
The instinct to solve fragmentation by adding another data provider is rarely the right answer. The priority is structuring, governing and understanding the data already available, ensuring it can support auditable, consistent conclusions across the regulatory obligations the firm needs to meet.
Do not treat AI as a shortcut.
AI tools are genuinely useful in the sustainable investing context. But they produce enterprise value only when deployed on well-governed data, with defined human review processes and full traceability of conclusions. Firms that start with governance and build AI in on top of that foundation will create real advantage. Those that do not will create faster risk.
