A domain-specialised AI assistant can unlock barriers to nature finance by systematising available data and processes, write a team of experts spanning MIT, X (the Moonshot Factory), and global finance.
As delegations gather for Climate Week in London, New York, and elsewhere, one number keeps surfacing: $200 billion a year. That is what the world will need in private capital for nature by 2030. Almost none of it is flowing today.
In a new working paper, we argue that one of the core obstacles is not ambition or cost. It is something far more tractable: nature is hard to underwrite.
Climate finance has a single fungible metric in tons of carbon dioxide-equivalent, but natural capital has no such common language, even though it is broader than carbon alone, underpinning climate stability while delivering value far beyond it.
A wetland in Louisiana, a mangrove in the Sundarbans, and a watershed in the Cotswolds do not roll up into a comparable risk-return signal, so they cannot be allocated to like any other asset class.
We propose a way to close that gap: a domain-specialised AI assistant that can systematically translate nature data into a financial signal, designed to sit inside the workflows institutional investors already use.
This platform would help enterprises (asset managers, hedge funds, and private credit) underwrite nature-positive infrastructure, like water-resilient data centres, while also helping last-mile institutions (impact funds, microfinance providers, and philanthropies) better support nature investments in many ways e.g., to extend nature-linked credit to smallholder farmers and small- and medium-sized enterprises in developing economies.
It can also unlock financing for Nature-based Solutions (NbS) for climate resilience, which may be one of the first application areas for such a platform.
The empty intersection
Until now, the market has innovated at the bilateral intersections of nature, finance and AI. Multilateral banks and philanthropies have channelled capital into nature and climate. AI labs have produced strong predictive models for forests, oceans, and the atmosphere. FinTech has reshaped parts of financial markets.
What is missing is the tri-sector overlap, i.e. AI applied to the financial underwriting of natural capital. That is the space we are trying to unlock and advance.
Figure 1: The tri-sector overlap remains the open frontier
The timing finally works. AI, as a field, stretches back to the Perceptron in 1958, but applied AI for natural ecosystems – capable of ingesting diverse data sources such as satellite imagery, bioacoustics, and Internet of Things (IoT) sensor data in a usable form – has only developed recently, in the last few years.
Recent joint work by Google and the World Resources Institute (WRI) documents this surge. What is striking is what this literature lacks: a finance pillar.
To our knowledge, our research is among the first to explicitly link AI with the financial underwriting of natural capital, allowing nature-based ecosystem services to be treated as a quantifiable, investable asset class, as well as bolstering its role in the treatment of all types of investments that are touched by nature.
Figure 2: The 70-year build-up of AI capabilities is finally meeting the needs of natural capital
Where the value lands
The Nature AI Assistant would be designed to embed seamlessly within traditional financial workflows. Every mainstream institutional investment process moves through roughly seven stages, from strategy and business development through due diligence, investment review, legal agreement, commitment, portfolio monitoring, and exit.
By overlaying where nature assessment is most critical onto that cycle, in particular, the AI Assistant would target three critical bottlenecks:
"What is missing is the tri-sector overlap, i.e. AI applied to the financial underwriting of natural capital to standardise, simulate, and integrate these assets into core decision-making"
- Due Diligence: baseline analysis, feasibility assessment, and live scenario generation;
- Investment Review: eligibility screening, alignment scoring, nature cashflows, and nature modelling; and,
- Portfolio Management: continuous monitoring, reporting, verification (MRV), and disclosure.
Unlike a general-purpose large language model, the Nature AI Assistant would operate strictly within institutional financial guardrails and with a deep knowledge base at the intersection of nature and finance.
It is fine-tuned by domain experts on rigorous frameworks (multilateral nature taxonomies, IUCN, TNFD, climate and weather models, among others) and wired directly into diverse biophysical data streams, including IoT sensors, satellite APIs, and proprietary ecological data rooms.
To make this concrete, we developed a two-dimensional evaluation matrix that maps seven of the Assistant's core capabilities against each stage of the deal lifecycle. Each cell is scored 'High', 'Medium' or 'Low' impact.
Figure 3: Mapping seven AI capabilities against the institutional deal lifecycle such that impact concentrates precisely where deals get stuck
| Industry Segment | Strategy | Business Development | Due Diligence | Investment Review | Legal Agreement | Commitment | Portfolio | Hold to Maturity/ Exit/ Secondaries |
|---|---|---|---|---|---|---|---|---|
| Precedent Replication | HIGH | HIGH | HIGH | MEDIUM | MEDIUM | LOW | LOW | MEDIUM |
| Internal Policy Alignment | HIGH | HIGH | HIGH | MEDIUM | LOW | LOW | LOW | MEDIUM |
| Technical Eligibility | LOW | MEDIUM | HIGH | HIGH | MEDIUM | MEDIUM | LOW | LOW |
| MRV & Disclosure | LOW | LOW | LOW | MEDIUM | HIGH | LOW | HIGH | HIGH |
| Nature Score (N-Score) | LOW | LOW | HIGH | HIGH | MEDIUM | MEDIUM | MEDIUM | HIGH |
| Modelling & Optimisation | LOW | LOW | MEDIUM | HIGH | LOW | LOW | HIGH | MEDIUM |
| Dynamic Tracking Nature Covenants | LOW | LOW | LOW | MEDIUM | HIGH | HIGH | HIGH | MEDIUM |
A few patterns stand out. Precedent Replication and Internal Policy Alignment deliver the most value in the earliest strategy and due diligence stages, where teams need to rapidly screen past projects and regulatory frameworks.
Technical Eligibility and Financial Modelling peak in due diligence and investment review, where the deal economics are stress-tested.
MRV and Dynamic Tracking dominate the later stages, where disclosure obligations and covenant monitoring determine whether a nature-positive deal stays nature-positive over its life.
One capability worth highlighting is the N-Score (or Nature Score), a new rating system we introduce that synthesises diverse unstructured, non-stationary data, including satellite and sensor data as well as AI -based models and agents, into a single, comparable number or categorical band. It is designed to translate fragmented natural capital variables into the structured formats that investment committees already know how to read, and it maps onto a familiar credit-rating scale.
The true value of these AI-generated ratings lies in their direct integration into core financial models, moving far beyond standalone sustainability metrics. By translating ecological resilience into quantifiable risk reductions or yield enhancements, they provide the precise inputs needed to dynamically adjust Terminal Value calculations and discount cash flows.
This creates a direct mathematical linkage, transforming nature-positive actions from a costly compliance burden into improved financial outcomes that fit the parameters used by today's investment committees.
Four next steps for the industry
To stress-test the framework, we conducted structured interviews with dozens of leading institutions across the capital stack, including multilaterals, think tanks, financial institutions, and large asset managers. They were direct about what is needed to move from concept to scale.
A. Incentivise AI innovation: Leading asset managers, insurance companies, and financial institutions must be actively incentivised to integrate specialised Nature AI Assistant platforms tailored to their institutional finance needs. Off-the-shelf chatbots or direct applications of large language model (LLM) technology will not solve this problem.
B. Integrate the data: Proprietary data firms, public sector institutions, and ecological knowledge holders, including indigenous and local communities, must partner with technology providers to structure and integrate ecological data for seamless AI ingestion. These partnerships must respect and uphold the data sovereignty of the communities and public bodies that steward this knowledge. Public agencies or private actors with earned trust credentials should also publish clear, comparable project and nature ratings to avoid creating confusion among investors.
C. Upskill specialists: Domain experts like biodiversity scientists, AI engineers, and finance professionals must be systematically upskilled and collaborate to guide, orchestrate, train and validate the AI, maintaining a rigorous human-in-the-loop framework to ensure outputs match physical ground-truths and eliminate hallucinations.
D. Launch real pilots: Governments, investors, and industries must launch controlled pilots that unite private capital providers, institutional allocators, and blended capital facilities to underwrite actual infrastructure and financial market deals using these AI-generated tools and provide rigorous evaluations and reporting of their outcomes.
By integrating AI-driven baselines and monitoring capabilities directly into core investment and risk models, institutions can confidently underwrite nature-positive assets and scale natural capital allocations across global markets.
In 2030, the world will mark the 15th anniversary of the Paris Agreement. That same year is the target for achieving the Sustainable Development Goals, a universal call to action to end poverty, protect the planet, and ensure that all people enjoy peace and prosperity. The world is supposed to be mobilising $200 billion a year in capital for nature by 2030 as well.
These three critical deadlines stack on the same horizon. There is no realistic path to any of them without a step-change in how quickly capital can flow into nature-positive solutions, in addition to the important nature protection work underway.
That is the case for moving now. The technical building blocks for financing nature at scale finally exist. The remaining work is institutional, and largely overdue.
Climate Week is a deadline-forcing function, the moment each year when policy commitments, technology readiness, and capital intent are pushed into the same room. Used well, it can be where the next five years of nature finance is set in motion.
Gursimran Rooprai is an MBA and MPA student at MIT Sloan School of Management and Harvard Kennedy School.
Kazuaki Takagi is an MBA student at MIT Sloan School of Management.
Mohammad Fazel-Zarandi is a Senior Lecturer and Research Scientist at MIT Sloan School of Management.
Deborah Lucas is a Sloan Distinguished Professor of Finance and Director of the MIT Golub Center for Finance and Policy at MIT Sloan School of Management.
Peter Lindner is a former Senior Financial Sector Expert at the International Monetary Fund.
Rachel Payne is a Managing Director at X, the Moonshot Factory (advising contributor).
Ravi Jain is a Product and Technology Leader at X, the Moonshot Factory (advising contributor).
For more questions please contact: [email protected] and [email protected].