Access TNFD’s full report here

What does good nature data look like?

Working across multidisciplinary stakeholder teams, we identified seven key principles for high-quality nature datasets, acknowledging that nature data should be:

• Transparent and reproducible
• Credible
• Accurate and complete
• Relevant and decision-useful
• Accessible and usable
• Legal, ethical, privacy protecting
• Networked and compatible

Our Process

TNFD provided a roadmap of use cases, which we used as a foundation to work from. From this, our user-needs led approach helped define who the data users were as we developed a set of recommendations and principles that could lay the groundwork for nature data that supports financial decision-making.

“The Taskforce is very clear that its focus and contribution is on addressing use cases specific to corporations and financial institutions”

TNFD: A roadmap for upgrading market access to decision-useful nature-related data

Findings from the pilot testing of proposed nature data principles:

After evaluating 40 existing datasets against these principles, we confirmed that none fully met the standards required for global reporting or investment use. This exposed a critical gap for the financial sector: the absence of reliable, comparable, and standardised nature data needed to direct capital toward nature-positive outcomes.

“We tested 40 datasets against robust criteria for decision-useful nature data and found that none of them fully met the principles required for global reporting and investment needs.” Lewis Just, Lead Researcher

Understanding the barriers of nature-based data

A key reason for this gap lies in the complexity of nature data. Unlike carbon, which can be measured through a single metric such as tonnes of CO₂, nature spans many interconnected systems including water, soil, species, forests, and many more. Each uses different metrics, standards, and methods of measurement, making it extremely difficult to compare results across regions, sectors, or reporting frameworks.

Without harmonisation, financial institutions face a fragmented landscape where nature-related risks are hard to identify or value, and progress toward biodiversity goals is difficult to measure.

IB1’s approach brings structure and clarity to this complexity: developing guiding principles to support better quality, comparable, and decision-ready nature data that can help direct financial flows toward positive environmental outcomes.

Nature loss isn’t just an environmental issue, it’s an economic one

“Reducing nature data barriers is key to enabling effective nature-related reporting and accelerating action to halt and reverse nature loss.” (TNFD)

The impact of nature-related financial risk is widespread and financial institutions currently lack the data needed to measure and manage their nature-related financial risks. For instance, UK Banks could avoid potential losses of 4–5 % of loan-book value if they’re able to anticipate and price risks more accurately. For governments, making nature risk visible could help to avert $2.7 trillion annual GDP loss by 2030.

To unlock the power of nature data and positively shift financial investment, we need better access to high-quality, trustworthy nature data. But access alone isn’t enough, how that data is shared and governed is equally important.

Balancing openness with responsible governance

At first glance, it seems there’s a simple solution: make all nature data open and accessible, right? Unfortunately, it’s not that simple. And, in fact, not all data should be open.

For instance, sharing the precise locations of endangered species could make them more vulnerable to poaching. For example, GPS tracking data intended to help conserve rhinos could be exploited by poachers to locate the animals. Similarly, satellite-derived coral reef maps — created to support conservation — could be misused by developers and industrial fishing fleets to identify and exploit those same ecosystems.

That’s why responsible governance is crucial. Data providers must retain control and ownership over their datasets to prevent misuse and ensure that data serves its intended purpose: protecting and restoring nature.

NOVA (Networked, Open, Verifiable Architecture)

Our NOVA principles were developed to guide us to outcomes that are interoperable, scalable, and aligned with existing standards, ensuring that data can be shared and used responsibly.

Applied through the lens of nature data, this means that sensitive information, such as species locations, is only accessible to those granted explicit permission, safeguarding wildlife while still enabling actionable insights for conservation and investment.

COP30 Brazil

IB1’s principles and recommendations for this project were formed to help TNFD understand what good nature data looks like, so that financial flows can shift towards investments that help, not harm, the planet.

With COP30 in Brazil now underway, the spotlight turns to a country that holds some of the world’s richest biodiversity and largest nature datasets. At its launch event in São Paulo on Thursday, November 6th, TNFD published its recommendations for upgrading the nature data value chain for market participants. This included a blueprint to govern, launch, operate and finance a Nature Data Public Facility (NDPF).

Data has a huge role to play in delivering net zero by 2050.

Reliable data is vital for verifying that organisations are meeting their sustainability commitments. Investors depend on it to shift their investments towards greener companies, while innovation in energy production will hinge on the smart use of data.

But despite generating huge quantities of data every day, we’re not making the most of it. Take data about companies’ emissions. It’s languishing in spreadsheets, carbon calculators, smart meters and other siloes. Even when organisations do share their emissions data, it’s generally seen as an exercise in after-the-fact reporting.

Databases of low quality, out-of-date information are not a foundation for developing new products or technologies, or unlocking new markets.

When it comes to net zero, data is everywhere, just not where we need it.

At IB1, we don’t try to bring ‘all the data into one place’, as others attempt. Nor do we host data or seek to provide analysis services using it.

Instead, we [orchestrate] [schemes] of [data governance] that enable groups of organisations to share continuous flows of well-structured, assurable data with one another.

Governance

Our focus on [data governance] is driven by the view that making data work harder for net zero isn’t a technology challenge.

Rather than a deficit of data or technologies to manage it, it’s a deficit of effective processes for groups of organisations to come together, cooperate on and set the terms of data sharing that’s really holding us back.

“Incentives in our organisations and society prompt us to beaver away on our own. Collaboration is the catalyst of innovation, [but] we often struggle to practice it when it comes to overcoming complex challenges and making efforts towards positive social progress”. – Miranda Dixon, Brink

As with other collective action problems, only good governance can align fragmented interests, enable collaboration and facilitate shared investment.

In our work, governance is an ongoing process. The execution of this process produces decisions that enable data sharing to take place. In practice, this involves establishing principles, defining clear roles and responsibilities, and agreeing priorities and tasks. It also involves collaborating to create artefacts to express and enforce these decisions, such as legal agreements and technical standards.

We have a particular approach to organising data governance at IB1. We use a tiered system of Steering, Advisory and Working Groups to bring organisations together. These groups work together to agree and adopt:

• User needs & impact: commercial priorities, business cases, and prospective new products and services.
• Technical infrastructure: shared ontologies, APIs, schemas and standards to support data exchange. 
• Licensing & legal: data sharing agreements, modes of redress and liability frameworks.
• Engagement & communications: common language, stakeholder engagement and recruitment.
• Policy: alignment with corporate policy and industry regulations.

Participation in this process can be either voluntary (initiated by the market), or mandatory (demanded by regulators). 

Our approach is inspired by the UK’s Open Banking ecosystem, which enabled data to be shared in new ways across banks and other financial services. It now has 10 million users and is projected to sustain a $12bn market of data-driven products and services. This change has been achieved not by building a big, centralised database of customer banking data, but by governing who should access it and how it should flow. 

Schemes

As well as neglecting governance, attempts to build databases of net zero data fail because they try to be all things to all people.

In a 2024 talk, Building scalable public data sets for scientific innovation, John Wilbanks described how effective data systems generally begin life by addressing a small set of very specific primary uses, before evolving to enable more over time:

“No one has ever built a complex data system by setting out to build a complex data system [from day one]. You build one by answering five questions at a time, using a standards based approach… And then when you’re able to answer twenty, you’ll have a functioning complex data system”.

We agree that specificity is a necessary condition for effective data sharing. We enable groups of organisations to come together around tightly-focused challenges or use cases related to net zero, which we refer to as [schemes].

Our flagship scheme, Perseus, enables small-and-medium sized businesses to share granular emissions data from their smart meter systems with banks and other lenders. By providing lenders with the accurate and assurable data they need, the scheme enables participating businesses to access loans and other finance to help reduce their emissions.

Perseus isn’t trying to cast a net around all sustainability data, or work for every company. It demonstrates how good governance—anchored around a very specific goal —can unlock data from the real economy and put it to use for net zero.

Orchestration

We don’t have a monopoly on this view of data governance. But we think groups of organisations can go further, more quickly with our [orchestration].

We provide and maintain the following Trust Services to enable schemes like Perseus to function:

1. A machine-readable rulebook that codifies how data can be shared within the scheme.
2. An approach for verifying which organisations can take part in the scheme.
3. An open directory of the organisations that have been verified to take part in the scheme.
4. An approach for monitoring and assuring that access to data within the scheme adheres to the agreed rulebook.
5. An open catalogue of the data that is made available within the scheme.

None of the services we provide rely on particular software or a singular technology vendor. What we deploy depends on the needs of the scheme. Data access can be enabled by API, more advanced privacy enhancing technologies… even fax machine. (Although we wouldn’t recommend the latter.) What’s important is that the solution meets our NOVA principles: a Networked, Open, Verifiable Architecture. 

Our non-profit status is another key element of this work. There’s a risk that the direction of data use will be dictated by commercial actors, if schemes are left to the market alone. Our approach at IB1 ensures that no individual or entity can take disproportionate control of net zero data, and that end user needs rather than organisational agendas drive progress.

We’re glad the importance of this orchestrating role is now being recognised. A recent analysis of ‘data spaces’ being built across the European Union found that successful efforts have an independent organisation at the centre:

“It is crucial to have a neutral orchestrator facilitating the exchanges between participants before the operations and governance of a data ecosystem solidifies.

The orchestrator should prioritise use cases, map business value creation, test business models, and set up governance models. During the operation phase, the focus will shift toward onboarding, enforcing the rules, ensuring the governance works as it should, and scaling up”.

Infrastructure for real progress

Making data work harder for net zero ultimately depends on trust, coordination, and infrastructure that works across organisations. 

Ever-bigger, centralised databases won’t get us there. What we urgently need are well-orchestrated schemes of data governance that enable decision grade data to flow.

But making this shift requires resources—and partners.

If you’re working along the same lines, or if you’re looking to fund the infrastructure that underpins real progress on net zero, join us.

Reach out via:  icebreaking@ib1.org

Archive video:

What:

Recent developments at COP26 showed that data access is at the heart of our plans to get to net zero. This means organisations will need to prepare to accelerate plans for data sharing and data publishing. 

In our next 30 minute webinar, we’ll be exploring what COP26 announcements mean for businesses and their data, and what changes can be expected in the years to come.

We’ll discuss our main takeaways from COP26, and the actions that you can take now to ensure your data and digitalisation strategies will support the shift to Net Zero.

Speakers:

• Gavin Starks, Founder and CEO, Icebreaker One (Chair)
• Lee Wilson, Product Owner – Data & Analytics, ScottishPower
• Lucy Yu, CEO, Centre for Net Zero
• Ronan Palmer, Head of Clean Economy Programme, E3G

If you think a colleague or sector expert would be interested in joining this event, please share it with them.

Open Energy has been supported in-part with UK Government funding from UK Research and Innovation (UKRI) and BEIS.

All terms used in the blog are defined in the glossary here. All policies will also be published in the Phase 3 operational guidelines (forthcoming end July 2021). For any questions stemming from this blog, or materials within, please contact openenergy@ib1.org.

Data Sensitivity Classes

Open Energy facilitates sharing of a wide variety of data types, with varied levels of sensitivity. In order to handle this complexity, and to ensure data is appropriately protected, Open Energy has developed a system of data sensitivity classification. This policy details five Open Energy data sensitivity classes, graded across three dimensions of sensitivity: personal, commercial, and security. They are designed to complement Ofgem’s Data Best Practice guidance, by adding nuance to how Data Providers can classify types of Shared data.

Consultation during policy development informed a range of changes including:

• Reducing the number of classes from 6 to 5,
• Improving descriptions and example data types provided for each class; and
• Updating guidance regarding the classification of aggregated and anonymised personal data.

Details of public consultation feedback, and Open Energy’s responses, can be found here. A copy of the updated policy can be found here.

Data Access Conditions

Once Data Providers have allocated their datasets to appropriate sensitivity classes, they will then specify the access conditions for each dataset. To encourage the creation of  access conditions that are fair and proportionate to the dataset’s sensitivity profile, data sensitivity classes will be used as a guiding basis for considering access conditions, though not a complete determinant. As such, we define a standardised range of access condition types that Data Providers can associate with a particular dataset. This acknowledges the need for more nuance than would be captured under a ‘one size fits all’ approach for each sensitivity class, while still enabling standardisation of condition types. The policy focuses on access conditions for classes OE-SA and OE-SB only as personal data (OE-SP) are out of scope for Open Energy Phase 3. 

Consultation during policy development informed a range of adaptations including:

• Dividing group-based access conditions into externally defined and self defined types;
• Tightening the scope of use case-based access conditions to promote clarity and fairness; and
• Removing purpose-based access conditions to reflect coverage of these conditions elsewhere in ways that reduce implementation difficulties.

Details of public consultation feedback, and Open Energy’s responses, can be found here. A copy of the updated policy can be found here.

Data Licensing 

This represents the final step of the journey that Data Providers must take in order to ready their datasets for sharing via Open Energy. It consists of two parts: creating access rules, then associating the grant of a set of capabilities and obligations with each rule to form the license. Our model proposes a system whereby access and capability grants are determined, for each request to a Data Provider’s API, on the basis of a set of rules defined and published by that Data Provider. This is different from the single licensing model that is commonly used in the sector, whereby one license is produced to cover all circumstances of the dataset’s use. It also responds to industry feedback regarding problems with the length and complexity of single licenses, which can increase cost, friction and risk associated with data use. By contrast, the Open Energy model permits reasonable multiple licensing within a set of transparent, standardised parameters that enable each license to be significantly simplified. 

Consultation during policy development informed a range of changes including:

• Sharpening the descriptions of certain capabilities; 
• Confirming use of the ‘data pyramid’ to support the definition of different levels of onward sharing permissions; and
• Honing a set of clarifications provided with the policy to support understanding.

Details of public consultation feedback, and Open Energy’s responses, can be found here. A copy of the updated policy can be found here.

What’s next? 

In the next phase of development, Open Energy will produce guidance and tooling designed to support Data Providers to comply with Open Energy policies. We aim for this to build trust and fairness in the ecosystem, while making it as easy as possible for Data Providers to get up and running. The next phase of the project will also explore policy development around dispute resolution and we expect to hold a further public consultation on this topic in due course.

Summary

We are seeking feedback on a system of data classification proposed for the Open Energy ecosystem. Comments are welcomed from all energy sectors organisations and users of energy data. Feedback will be used to shape the Open Energy data classes policy and inform subsequent development of data access and licensing policies (to be consulted on in due course). The consultation is open until 30 April 2021 and can be accessed here.

Background

Open Energy aims to modernise access to energy and related data and break down barriers to data sharing. Open Energy will make it easier to both share and access data, supporting the sector’s drive towards decarbonisation, as well as related social and economic benefits. The project aims to serve all energy sector actors looking to share data, access data, or both.

Open Energy builds on learning from Open Banking – identifying which elements are transferable to the energy sector, and which require adaptation or fresh thinking. Our ethos emphasises openness, transparency and sector engagement to ensure that the project meets the widest possible variety of needs. We are now seeking feedback on the first of a set of three policies aiming to navigate one of the most significant differences between Open Energy and Open Banking: the diversity of datasets shared within their respective ecosystems. 

Open Banking only handles two categories of data: open data and personal data. By contrast, Open Energy will incorporate more categories of data that have varied levels of sensitivity. In order to handle this complexity, and to ensure data is appropriately protected, Open Energy is developing a system of data classification. This consultation focuses on sharpening the descriptions, examples and criteria forming the data classes proposed. Follow-up consultations exploring data access and data licensing will take place later. 

Proposal

We propose a system of six data classes, graded across three dimensions of sensitivity: personal, commercial, and security. Proposed classes are presented in Table 1 of the consultation document. It is proposed that – once finalised – a definition, specification and dataset examples for each class will be provided in the Open Energy Operational Guidelines. Data Providers (organisations sharing data via the Open Energy ecosystem) will then assess their datasets and self-allocate them to a class, prior to sharing them via Open Energy. 

Open Energy data classes are designed to supplement, not replace, the Modernising Energy Data Best Practice Guidance (current version Point 12) determining whether data should be made Open, Shared or Closed. In particular, Open Energy data classes are designed to provide nuance to different classes of Shared data, with different sensitivity profiles.

How can you help?

We are seeking feedback on the proposal through our consultation here. It takes around 30 minutes to respond. The consultation explores questions including: 

• Are proposed data classes appropriate and clear?
• Are example datasets given for each class are accurate?
• Are any types of sensitivity missing from our analysis?

The consultation is open until 30 April 2021 and responses are encouraged from all actors in the energy sector, or who work with energy and related data. Any queries should please be directed to emily.judson@ib1.org. 

We at Icebreaker One are proud to be a partner of FoSDA, and it’s a community whose goals are very closely aligned with our own. In this blog post, we’ll be looking at a few highlights from FoSDA’s initial recommendations to the financial community, addressed to both regulators and financial market participants. There are 3 primary themes: 1) Defining and creating a path to filling ESG data gaps and data holes, 2) mapping data to sustainability taxonomies and 3) the need for ESG data talent development globally. At Icebreaker One, our focus is on the data and data infrastructure requirements that will underpin the successful transition to green finance and a green economy, and so we will look at FoSDA’s recommendations through this lens.  

What are data holes and data gaps?

As an industry, we’re perhaps more familiar with the concept of data gaps – missing information related to a specific data point that has already been collected. An example might be data on greenhouse gas (GHG) emissions – it’s well-known and well-defined, there are plenty of standards out there, and the main challenge is that not enough firms are reporting it. Data holes, on the other hand, are potentially a much larger issue: entire datasets that are not collected at all, such as data on biodiversity risks.  FoSDA’s focus is on supporting regulators and the industry in identifying data holes and minimising data gaps, so that investors and regulators are able to have a better view of both specific climate risks as well as the overall systemic risks and challenges posed. 

Mapping and addressing data holes

The first step towards solving the problem of data holes is to identify where these holes exist. FoSDA has identified biodiversity data as a key initial area of focus, as nature-related and biodiversity issues are a new frontier in understanding climate and environmental risk. Regulators, data providers and investors should therefore come together and collaborate in determining the data sets that are needed to understand and address these risks. Where we collectively identify and map these holes, we then need to develop a plan of action to fill them. This can take the form of policy interventions – such as mandatory reporting requirements, or by interventions to open up public and private sector sources of such data – or through novel means of sourcing the data, such as geospatial data. 

Move away from binary reporting

By binary reporting, we mean reporting that requires a company to disclose whether or not it has certain policies in place. This is overly simplistic and does not provide any usable information on the robustness or actual implementation and outcomes of such policies. FoSDA therefore recommends a transition towards more standardised, metrics-based reporting. This could be implemented via the enhancement of existing reporting standards (such as the “Gang of Five”, comprising SASB, GRI, CDP, IIRC and CDSB), with financial institutions and data vendors, as consumers of this data, helping to determine the data points and level of granularity required.  

Increase focus on forward-looking data

It’s a well-known issue that ESG data – particularly environmental data – is near-useless if it is purely backwards-looking. That’s why we are huge supporters of the growing recognition of the role played by scenario analysis – a forward-looking tool for assessing climate impacts and risks – on the part of central banks, regulators and standards setters such as the Taskforce for Climate-Related Financial Disclosures (TCFD).  However, at Icebreaker One we also know that these types of forward-looking assessments also require common data inputs and base assumptions. Addressing this will also require multilateral cooperation and collaboration.

Standardise corporate ESG reporting

To be truly usable as means of internalising climate risk and ESG within the financial system, enabling the transition to a green and sustainable financial system and economy, we need ESG data standards and metrics that are internationally consistent, well-defined and produce meaningful, objectively comparable data outputs. 

As governments and regulators increasingly move towards mandating ESG disclosures and reporting, these will ultimately become costly but futile exercises for companies, unless they’re accompanied by clear standards. Existing standards setters will play a key role – but will require input from regulators and market participants globally as well.  

Sustainability taxonomies need to be mapped to underlying data

There are a number of initiatives across jurisdictions globally to develop taxonomies that define what is and is not “green”. These aim at helping financial institutions to better classify their products, investors to better understand the green credentials of their investments, and regulators to understand the relative exposures of the financial institutions that they oversee. In order to make the best use of taxonomy frameworks, they need to be mapped to the underlying data sets that enable taxonomy determinations to be made. Regulators therefore need to consider these data sets when developing their taxonomies.

Move away from singular dataset focus

Combining data sets from multiple sources, and starting to explore the potential for use of “alternative” data sources such as geospatial data, can provide powerful new mechanisms for understanding the exposures and risks of companies. There’s a need for governments, regulators and investors to all contribute towards understanding what these integrated data sets could look like and offer, and in creating the environments that will enable them. 

The need for ESG data talent development globally

As we’ve seen across these recommendations, we are going to need new skills and talent to drive ESG data forward and into the mainstream. Governments and regulators can play a role in helping to develop and nurture this talent, as well as in creating an environment conducive to innovation and testing of new ideas. 

Putting it all together

At Icebreaker One, our work with our insurance and financial partner network on initiatives such as SERI (the Standard for Environment Risk and Insurance) has highlighted many practical examples of the ways in which data gaps and holes are holding the industry back from developing the net-zero enabling financial products and services that are needed for the transition to a green economy. We’ve seen the consequences of these gaps in action, and their impacts. 

Even where data is available, it’s not always easy to find, or to access. We believe that the accessibility of data for decision-making is absolutely critical in the transition towards a green economy and a net-zero, sustainable future. Our vision is that a robust data infrastructure can address these challenges, and our goal is to work with our constellation of partners to develop this. 

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