As Perseus co-chair, members, stakeholders, and the broader community tell me that it is seen as a pioneering initiative, with a significant scale of opportunity (at least £5B+ in embedded sustainable finance), but there are still challenges in communicating what it is, and isn’t, and ‘why now?’.

“Collaborate on the rules, compete in the game.”

The course is set, now it’s time to shape how value is realised

Perseus is now recognised as a flagship exemplar under the UK Data (Use and Access) Act, supported by both the Smart Data Council (in its Smart Data Strategy for 2035) and the Net Zero Council. The regulatory current is moving in this direction, and the Perseus team is both in constructive conversations with regulators and code bodies, and at the table in creating the UK Smart Data guidebook. 

Perseus Members are defining where the rules of embedded sustainable finance are being written. The question isn’t whether this infrastructure gets built, it’s who helps shape it, and who arrives late.

To help better position what Perseus is, here are some of my reflections, based on 300+ conversations.

Getting the data to do the work: SME impact at market scale

SMEs are where the impact is needed (they are half of UK business emissions). For the vast majority, carbon reporting is a burden: manual, confusing, inconsistent, and disconnected from anything that actually helps them run their business better. 

Perseus flips this: with the SME’s permission, their energy data flows automatically into their accounting platform and to their lender. No spreadsheets, no data entry, no consultants: they get a verified emissions baseline, access to sustainable finance products they can’t easily reach, and a credible sustainability story they can use with their own customers and suppliers. 

Perseus meets them where they are, through the tools and relationships they already have, and costs them almost nothing to participate. Reducing friction and cost is the point of good data infrastructure, getting smart data to do the work so the SME gets the benefits, and the market gets the scale. 

Perseus is infrastructure, not a product

Most responses to addressing SME carbon emissions follow a familiar playbook: build an app, sign up users, grow a dataset, and sell reporting services. Some go further and package insights as a commercial proposition. Both hit the same ceiling: they create value for their own customers, but they don’t change the market.

“Carbon reporting can often be seen as a random number generator linked to compliance, not value.”

Data silos are no longer business moats

When data stays siloed and calculations stay inconsistent, every bank, accountant, lender, software provider keeps solving the same problem independently, at their own cost. Multiply that across the whole economy and you have a colossal, systemic waste of time and money: with no true comparability, little trust, and no efficiency of scale. As one senior expert put it, “it’s a random number generator linked to compliance, not value“.

“Perseus meets SMEs where they are, through the tools and relationships they already have, and costs them almost nothing to participate.”

Perseus takes a structurally different route (the same route Open Banking took). The design of Open Banking wasn’t to ‘make a better banking app’, it was that if you agree the rules by which data flows between any bank and any third party, every player in the market benefits simultaneously, and the infrastructure becomes self-reinforcing as more join.

Perseus applies exactly that logic to SME emissions data: not a pipe, not a platform, a Scheme. A Scheme is a shared rulebook that defines how the data flows, it is legally permissioned, technically assured, and provenance-stamped between energy data sources, carbon accountants, and lenders, regardless of which specific providers are involved.

Schemes are designed to ‘do as little as possible’ so that the heavy lifting that they do deliver, can deliver at scale. Perseus is not a database, or a calculator, or a portal. Instead it’s the trust layer that makes everyone else’s products work together, enables solutions to go to where the customer already is, and makes them credible due to the governance wrapped around its design.

“Perseus is not a database, or a calculator, or a portal. It’s the trust layer that makes everyone else’s products work together.”

No single organisation can build what Perseus builds collectively

Any carbon accounting platform can reach its existing customers, any energy data business can find organisations already looking for a data feed, any bank can bring these things together, but none of them can, on their own, shift the behaviour of 5.5 million SMEs and the financial system that serves them.

Perseus can because its Steering Group and commercial membership collectively represent the whole system: the banks, accountants, energy companies, trade associations, and SME platforms that already have the customer relationships. The joint communications that can flow from this coalition don’t just amplify awareness, or make ‘business today’ more efficient, it creates an addressable market that didn’t previously exist. By going far together, they can all reach SMEs who have never considered net zero was for them, through channels they already trust: their bank, their accountant, their software tools, and their trade association. Perseus is creating a route to market no individual organisation can replicate through its own sales effort, and this is estimated to be £5B-£10B by 2030 (see 2025 annual report).

Its benefits can compound in both directions: automating data flows that currently require manual effort, reducing the cost of compliance, reducing friction at every point in the chain and building customer trust not for one product, but at market scale.

“Perseus Members are defining where the rules of embedded sustainable finance are being written. The question isn’t whether this infrastructure gets built, it’s who helps shape it, and who arrives late.”

The value case for a Financial Services Provider (e.g. bank, lender)

There are reasonable objections a bank or lender might raise. Right now, Perseus is a UK SME Scheme, not where the biggest financed emissions numbers sit for most large institutions; they may have existing bilateral data arrangements they’re reluctant to revisit; and in a climate where public sustainability commitments are under scrutiny anything that looks ‘new’ can face internal resistance. 

These are valid questions, but they don’t change the underlying logic.

In impact, most initiatives measure engagement, they rarely measure or report on verifiable impact. Perseus enables continuous, assurable measurement, reporting and verification of impact. By harmonising the approach, the reporting is comparable across organisations.

On scale: the UK SME market is not a rounding error but half of all UK business emissions. Any lender with a material SME book has a financed emissions reporting problem that carries sufficient risk to increase their cost of capital. Perseus addresses this across the whole market at once. Perseus Members have indicated that ‘just’ energy (electricity and gas) addresses over 70% of their use cases, and the programme is designed to expand beyond energy based on Member needs (e.g. water). If we go far together, our collective impact is material and meaningful.

On existing bilateral arrangements: Perseus doesn’t replace them, it improves them through harmonisation of approach, liability and technical provenance. Joining doesn’t unwind existing relationships, rather it gives them an additional trust layer, aligned with the Data Act and endorsed by the Net Zero Council.

On the commitment: Perseus is not a ‘climate pledge’, but an action to deliver the data infrastructure for embedded sustainable finance. Operationally, it’s equivalent to joining any financial data scheme – a technical and commercial decision, not a public statement about net zero ambition. It supports diverse go-to-market impact messaging across cost savings, energy efficiency, energy security, net zero and transition planning. It’s not a campaigning approach, but rather a way to deliver measurable value to the market.

“Any lender with a material SME book has a financed emissions reporting problem that carries sufficient risk to increase their cost of capital.”

On governance and legal overhead: Perseus’ architecture is deliberately modelled on Open Banking. Its legal agreements, certificate infrastructure and KYC processes are designed to align with what regulated financial institutions already do (the path through legal and compliance is not trivial, but it is well-trodden).

Ultimately, the financial providers already in Perseus are sitting in the room where the rules of sustainable finance data infrastructure are being written. It is a choice to be a late adopter of a model that Perseus members helped design, for a membership fee and some internal process. The cost of joining later is accepting the rules written by others.

The value case for a Carbon Accounting Providers (whether financial or carbon management)

A CAP might ask: why do we need Perseus? (we already have integrations with energy data providers, have bank and lender customers, and are building the product that does this).

These are fair points, but miss what Perseus is.

“Perseus is not a database, or a calculator, or a portal. It’s the trust layer that makes everyone else’s products work together.”

Every CAP currently solving this problem is solving it alone: each has negotiated its own data access arrangements, built its own ingestion pipelines, made its own judgements about data quality, and written its own terms. The result is a market where every emissions calculation is done differently, every audit trail looks different, and no two outputs are directly comparable. That’s not a CAP problem to fix, it is a market structure problem, and no single CAP can fix market structure.

This has been the case for decades. Now the baseline calculation needs to become pre-competitive infrastructure (co-designed and delivered by the market) so that CAPs can compete on the value they build on top of it.

Collaborate on the rules, compete in the game

Perseus addresses this by establishing a common trust layer (common legal agreements, provenance standards, assurance levels, harmonised calculations) so that data flowing into any Perseus-connected CAP is verified, traceable, and comparable to data flowing into every other. This doesn’t commoditise the CAP’s product, but rather makes the CAP’s product something an SME or bank can actually rely on, report against, and put in front of an auditor with confidence.

On distribution: joining Perseus is not just a technical integration but access to a network of lenders, trade associations and SME platforms that collectively reach the entire UK SME market. This is a route to market no CAP can replicate through its own commercial efforts. Perseus-connected CAPs are not just selling software but access to a trusted, standards-aligned data flow that their competitors outside the scheme cannot match.

On the competitive question: the CAPs already building Perseus integrations reach hundreds of thousands of UK SMEs today. They are not waiting before positioning themselves within it. Waiting until Perseus is ‘already proven’ before engaging will find the integrations, the relationships, and the market positioning is already occupied.

On effort: Perseus adds a compliance overhead, but this is inversely proportional to scale. The cost of integrating once (which can be done in under a month) with a common framework is substantially lower than maintaining multiple bespoke bilateral arrangements as the market grows. Perseus reduces long-run complexity, it doesn’t add to it.

Spend-based estimates or manually uploaded spreadsheets are no longer fit-for-purpose. Perseus provides the foundations that CAPs can build on top of, creates trust, defensibility, reduces long-term costs, increases market engagement and innovation.

To go far, we go together.

Join Perseus today

So what is a smart meter?

A smart meter is a device that records and transmits your utility usage directly to your supplier, while also giving you insights into your own consumption. Most people are familiar with these household energy smart meters but much less so with water smart meters, which differ significantly in their design, purpose, and implementation. 

Not all smart meters are created equal

Smart water meters record a household’s water use and automatically send this information to the water company. According to Anglian Water, they can help customers detect leaks early, monitor their consumption, and receive alerts if their bill is unusually high. And, by encouraging more efficient water use, smart water meters play a valuable role in helping the UK move toward its net zero goals.

However, compared to energy smart meters, water smart meters typically offer less granular data and limited historical records – factors that can reduce their overall impact. You can explore the key differences between the two systems in the table at the bottom of this page.

Why does the Water Sector matter for net zero?

The water sector is an energy intensive one. In fact, the movement and treatment of water is said to create around 3 million tonnes of greenhouse gas emissions each year. This is because every time someone uses water, whether it’s turning on a tap, flushing a toilet, or doing laundry, energy is used to pump and treat drinking water, distribute it through the network, collect and treat wastewater. 

The more water we use, the more energy is needed, which leads to higher carbon emissions. And, we’re seeing a rising demand and consumption of water in the UK, with seven regions in England on track to become severely water stressed by 2030. If we are to reach our net zero targets, curbing our water consumption and preventing water wastage, should be top of the agenda. 

What can we learn from energy smart meters? 

While the water sector faces its own unique challenges, it can draw valuable lessons from the energy sector’s experience with smart meters. The rollout of energy smart meters is further advanced but has not been without difficulties – ranging from incomplete deployment to inconsistent functionality. Both the successes and the setbacks in this journey could provide the water sector with a useful blueprint to follow.

These lessons also hint at a larger problem that the technology itself isn’t enough. To fully unlock their benefits (whether in energy or water) we need a way to make smart meter data more accessible, usable, and secure.

That’s where smart data schemes come in. And, propelled by the recent passing of the Data (Use and Access) Act, smart data schemes could unlock the value of smart meters, paving the way for a much smarter energy system. 

But what exactly is a smart data scheme?

A smart data scheme is a framework that enables secure, customer-authorised data sharing between organisations. It supports Smart Data, which is “the process of sharing customer data, upon a customer’s request, with authorised third parties in a secure way. The term ‘Smart Data’ is often used interchangeably with ‘open X’, where X is banking, finance or any other sector”. (Department for Energy Security & Net Zero)

A prime example of a smart data scheme, already in action, is Open Energy. You can think of Open Energy as a smart data scheme, like Open Banking, but for the energy sector. It allows consumers and innovators to securely access and share energy data – unlocking better services, smarter tariffs, and encouraging more sustainable behaviour. 

To learn more about our work in Open Energy follow this link: https://ib1.org/energy/uk/ 

What’s the connection between smart meters and smart data schemes?

To put it simply: smart meters are the source of the data but smart data schemes are the key to creating meaningful impact with this data. 

“These meters create datasets that could accelerate energy efficiency and help encourage sustainable behaviours, but the data is currently challenging to access. With the ability to see exactly how much energy they use and when, consumers can optimise their habits and take advantage of smart tariffs that incentivise energy use during off-peak periods. This creates immediate financial benefits for households and drives the adoption of “smart” energy systems across the country. 

A Smart Data framework leveraging Smart Meter data could amplify these benefits by accelerating the use of flexible energy tariffs and technologies. Empowering consumers with real-time energy insights ensures that the transition to clean power is not just a policy objective but a grassroots movement supported by informed citizens.” Startup Coalition and TBI project – Smart Data Report.

Smart Data in action

A live example of a cross-sector smart data scheme is our Perseus project, which connects half-hourly smart meter data – with permission from SMEs – to the financial sector. This helps to unlock green financing from banks to accelerate SME decarbonisation efforts. In other words, it links real economy data to the financial economy through a smart data scheme. 

“Our work in Open Energy has led, directly, to initiatives like Perseus which is taking smart meter data, with permission from SMEs into the financial sector. It is Data Act ‘ready’ and I believe is the first national cross-sector Smart Data Scheme in the country” Gavin Starks, CEO, IB1. 

If you’re interested in being part of a smart data scheme, whether its Open Energy and Perseus, then please get in touch via: icebreaking@ib1.org 

Similarities and differences between household energy and water smart meter systems 

Aligned with our ethos of collaboration: ‘to go far, we go together’, they contribute to our mission of making data work harder to reach net zero. Now we want to highlight some of the important work they do for both people and the planet.

Icebreaker One employees make up the foundation of our constellation and in this week’s Q&A, I speak with our very own Chris Pointon, Product Manager, Data Services. We delve into the world of Open Net Zero, before gaining a deeper understanding of assurance and its impact in securing confidence and trust along the net zero value chain.

Ross: Hi Chris, thanks for taking the time to do this.  It would be great to start by talking about Open Net Zero. Could you explain what it is and how it’s developed over the last year?

Chris: Of course. So Open Net Zero is an index. You could think of it like Google in the sense that Google doesn’t contain all of the content of all of the websites. It simply turns them into an index so you can find them. And that’s exactly what Open Net Zero does. It doesn’t store any data, it just finds it and points to it.

Last year, the IB1 data services team spent a lot of time on Open Net Zero, dramatically increasing the number of organisations and datasets that it can cover. This was because we added some capabilities to our indexing that allowed us to harvest the data catalogues of other people’s data portals and add them to our index, so you can find them all in one place. This meant that we went from a few hundred datasets at the beginning of 2023 to about 55,000 data sets, currently. Now one of the transitions we’re focused on is looking at how we join up these 55,000 datasets. One dataset is not usually the answer to a question. It usually takes several datasets that are connected together, for instance, because they’re in the same geography or they’re related to the same theme. 

Ross: Why is it so important to have these datasets in a machine readable format?

Chris: We’ve still got a backlog of dozens of really good data websites that we can’t index using standard mechanisms. The reason we can’t index them is because they’ve built a website with data on rather than building a data portal. So when it comes to best practice in publishing data, organisations should provide a machine readable catalogue, meaning a file that a computer can parse which describes the data in their data portal or on their website. Typical formats for this file include DCAT, INSPIRE and GEMINI, but there are other options. When they publish that, we have the simple job of including the URL of the catalogue in the index and keeping up to date with it over time. We’ve been prioritising finding websites that publish data relevant to people who want to get to net zero, but also have this well-structured publishing technique. 

Ross: You also launched ‘assurance’ last year, can you explain exactly what this is and why it’s important?

Chris: Assurance gives confidence to people inside companies that they’re allowed to share data externally. It also gives confidence to external users, as they know that they have permission to access what is being supplied, and can rely on the data management practices of the publisher. There are two types of assurance. First, we assure organisations. As a minimum, we ask, does this organisation meet some basic identity requirements and have they signed an agreement that means people can rely on the assurance they provide? Basically, it means that they’ve got some skin in the game.

The second level of organisational assurance is about entering into a secure data publishing environment. Level three and four are about indicating greater certainty of the identity of who’s involved. So instead of them self-asserting who they are and providing their own assurance measures, you bring in third-party auditors to provide additional assurance about how the organisation is managing its data. 

Ross: What about assurance on the data side?

Chris: This is the other type of assurance. Again we have multiple levels. At level one, the data’s got to have a licence. The metadata has to be machine readable, publicly on the web, with no personal data and for open data there needs to be a clear open data licence. Level two starts to bring in tighter requirements on the licensing. The licensing has to have certain features that outline what people’s rights to reuse are. Level two also requires additional metadata to cover the timeframe and geographical aspects of the datasets, and the publication of documentation for them. 

Level three builds on the above by requiring documentation on the data provenance and processing, open standards for data formats and machine-readable definitions of the fields in the data. Finally, at level four, we require that all of the information above  – licences, provenance, quality parameters – are provided in machine-readable formats, so they can be passed along a chain.

We had been consulting about assurance over the summer last year and came up with these definitions. Then in September we launched an update to Open Net Zero that showed the assurance level of assured datasets. At roughly the same time, SSE launched their open data portal, showing assurance on their site. And so when we index their data portal, it shows their organisational assurance and their dataset assurance. From a technical perspective this is just a little data field, but from an organisational and internal processes point of view, it’s really an important step.

There will be a new version of these assurance levels coming soon. We’ve had some feedback and we’ll incorporate that before putting it out for another public consultation. It’s important to note that the purpose of assurance is not ‘we’ve got assurance and everyone has to put up with it’. It’s that we’ve got assurance and we want to know whether it works for people, whether it actually helps them feel more confident, have a greater trust in data and to what extent can it be improved. We’re all ears to hear ideas for making it better and better over time.

Ross: So how does assurance tie into the wider goal of net zero?

Chris: One of the fundamental things we talk about is the data value chain. And, the data value chains we want to bolster are the ones where the data becomes more and more valuable to getting to net zero. The way the data value chain works is data gets generated from all sorts of activities and sources and once it’s published, it’s then available for people to process and turn into information. 

Take our Perseus project for instance. It covers the data governance needed to access half hourly electricity readings, and turn them into the carbon footprint of your electricity consumption using grid factors. So in that journey from electrical consumption to carbon emissions, you’re moving along the value chain. Next up in the chain the banks say ‘we’re going to use this information to prioritise lending to organisations that take steps to reduce their carbon footprint’ and that data has become more valuable because it’s been aggregated by a lending institution. Then finally the banks use all of that and another level of aggregation to say to the government – ‘we’re on track as a public limited company in the UK to meet the UK’s net zero requirements’. 

Now going back to the question of assurance, you need to have confidence at each step of the value chain that the data is being handled properly and that the organisations are reputable, and by reputable it also means they are part of a legal framework that allows you to hold them to account. You can audit them too, which gives everybody confidence along the value chain. Compare this to the current reality, in which the government asks the banks ‘what are you doing to reduce the carbon in your lending portfolio’ and they can only talk in sectoral language, such as we’ve got 50,000 small office-based businesses on our books. The average footprint for an office-based business based on ONS is this and so they provide a rough estimate. 

Animated slides below:

[open-to-comment Google slides version]

I believe we need to come up with some kind of umbrella language that isn’t about “climate” [tired] or “oceans” or “plastics” and… it’s not the word ‘sustainability’ [expired] or the SDGs [too big]. Instead, could we be outcomes-based? I know my science friends won’t jump at this, but it’s not for them. The financial economy and industry already have an operating model: can we align around redefining value?

Stills (in case Google is blocked in your organization)

Now, in the midst of COP28, as we await the first Global Stocktake of Paris Agreement emissions targets, our attention is fixed firmly on world leaders, in the hope that real tangible action will be taken.

Navigating a water-stressed world 

The discussion of water in reaching our net-zero goals is a timely one, not least because COP28 is taking place in one of the most water-stressed regions in the world. Indeed, high on the agenda this year is a focus on conserving and restoring freshwater ecosystems, enhancing urban water resilience, and bolstering water-resilient food systems.

This is underscored by the fact that the water sector constitutes approximately 10% of global greenhouse gas emissions, with an investment gap that, according to the World Resources Institute, would require $1.04 trillion annually from 2015 to 2030 to fill.

Icebreaker One’s role

There are a number of data-driven levers we could pull on to help curb emissions in the water sector. But, to really understand its impact, it’s first useful to understand the broad spectrum of data at play. This ranges from data on drinking water quality, which is open and can be accessed by anyone. To data on customer’s financial details, which is closed and can only be accessed internally. In between these, we have data on water network maps which is limited to named access only. 

There’s a clear variability in levels of accessibility when it comes to water data, and Icebreaker One has a firm grasp on this. As part of the Stream initiative, we’ve been working to establish a widely accessible open data platform along with the associated governance and data standards that underpin it. The potential benefits of this are far reaching, unlocking water data for customers, society, and the environment. 

By making UK water industry data openly available, backed by clear data licences and data standards, the Stream initiative will aid academics, innovators and campaigning organisations to analyse and better understand the relationship between water use and climate change. This can then be translated into tools and initiatives to support consumers and businesses to reduce water consumption, minimise water wasted through leaks, and identify and implement solutions for water pollution’. Ceri Stanaway, User needs Researcher, on the Stream programme

Unlocking water data

Making water industry data openly available can improve transparency in the sector, which can help spur on more data-driven decision making. Unpacking this; if all stakeholders across the water sector’s ecosystem can access the necessary data required to understand the potential challenges they face, then new and innovative solutions of ways of working can come to the fore. Collaboration will also be encouraged as policy makers, regulators and businesses gain a better understanding of their shared challenges. 

Collaboration is also a critical component when creating a standardised approach to reporting greenhouse gas emissions in the water sector. Data on greenhouse gas emissions can vary both in terms of availability and accessibility, which can give rise to unreliable emissions targets that are difficult to act upon. More than this, it can lead to organisations underestimating their carbon impact. A standardised approach will help to address these variabilities, pushing the sector to more accurately measure its emissions and achieve its net-zero targets. 

All eyes on COP28

Now, as the UN pushes for more significant emission cuts by 2030, to align with the Paris Agreement, the spotlight intensifies on COP28. We hope as the conference unfolds, more attention will be given to decarbonising the water sector. But more importantly, we hope to see a growing recognition that data is the tool to get us there. The realisation of these net-zero ambitions are not achievable without a tangible blueprint. Icebreaker One and the Stream consortium certainly understand this, with the Stream Open Data Platform (Minimum Viable Product) to launch on 18th December 2023.

Now, in the lead-up to COP28, with a climate emergency on our hands, we want to reinforce this common thread, highlighting why our work is more pertinent than it has ever been. In order to achieve this, we’ll be revisiting past use cases. These demonstrate our action-led work, showing the potential impact better access to data can have, and its critical role in keeping us within the boundaries of the Paris Agreement. 

Insurance

Data lies at the heart of the insurance industry because insurance is at its core, a business of risk management. With data playing a central part in assessing, pricing, and managing this risk. But, data in the insurance industry is often siloed or isolated to bilateral contracts between the insurance company and the policyholder. What’s more, in order to share data, stakeholders in the insurance industry need to navigate issues of trust regarding how their data will be used as well as issues of commercial sensitivity. All this, while operating within the labyrinth of regulations that underpin the industry. Our work with the Standard for Environment, Risk and Insurance (SERI) programme, aimed to bridge these gaps and siloes by establishing a foundation for net-zero underwriting, aligning insurance practices with sustainable objectives.

Building a case for retrofitting

The energy consumption of existing buildings accounts for around 34% of the UK’s annual carbon emissions. What’s more, 80% of buildings in use today will still be in use by 2050, the same year we have pledged to reach our net zero emissions target. Even if we focus on residential housing, of the 28 million homes in the UK, only 40% of these have an EPC rating of C or higher. There is a stark need to retrofit our existing buildings, in order to improve their energy efficiency, decarbonise and reach our net-zero targets.

In the context of insurance for the built environment, data capture often focuses on assessing a building’s resilience to current and future climate risks. In fact, insurers have a very solid understanding of the risks associated with climate change and how those risks can impact buildings and infrastructure. They do this by evaluating how well a building can withstand or adapt to extreme weather events, rising sea levels, increased temperatures, and other consequences of climate change. 

And yet, current short term insurance risk transfer measures are not capable of covering large scale risks caused by long term climate change, in short, the insurance industry lacks a focus on climate change mitigation. And, to adopt this focus, the systemic risks posed by climate change needs be taken into consideration during policy making. This should ensure the industry takes the route of net zero underwriting which, in turn, should incentivise net-zero behaviours from their customers through rewarding better building efficiency and performance. Reciprocally, if policyholders then share data relating to their retrofitting efforts, this information can then be used in underwriting to determine more accurate risk profiles and potential premium reductions. 

A climate-ready building passport

The key outputs of our SERI programme were the creation of an insurance product that would provide value across the insurance ecosystem, helping it factor in climate change mitigation. The insurance product created was a climate-ready building passport (C-RBP). It was designed to address industry shortcomings, paving a way for insurers to bring net zero underwriting into their repertoire and ultimately incentivise net zero behaviours like retrofitting. 

The C-RBP pools together the physical, environmental, financial, risk and regulatory data of a building in digital form. But more than this, it incorporates data points not widely used in building insurance or in pricing risk. These include the likes of Building Information Modelling (BIM) data, Building Renovation Passport (BRP) data including logbook & roadmap (e.g. retrofitting records) and Energy Performance Certificates. 

Merging the untapped data from the C-RBP with existing data, has the potential to give insurers a more well-rounded, accurate and timely view of risk, leading to improved risk pricing. They can, in turn, incentivise net-zero behaviours from their customers by rewarding climate change mitigation efforts like retrofitting. What’s more, building owners can use the C-RBP to gain access to open exposure data and, in turn, better understand their assets’ climate risks and greenhouse gas emissions. 

Finally, the C-RBP also has far reaching benefits when it comes to regulation and standard setting. Using a C-RBP could aid regulators in examining a company’s environmental impact by creating a more structured and efficient disclosure process with standardised data up-front. Companies can use the passport in their ESG disclosures for example, in order to demonstrate that it has accurately incorporated climate risks into its business strategy. 

Future of the industry 

Data could provide key solutions for the insurance industry, especially in the face of challenges it has encountered since our SERI project in 2021. In June this year, the Net-Zero Insurance Alliance (NZIA) – created to reduce greenhouse gas emissions in the industry – saw seven of its members leave. This was amidst growing political opposition from a group of Republicans in the United States who claimed the group might be violating antitrust laws by working together. 

The potential benefits of our Climate-Ready Building Passport (C-RBP) could serve as a beacon in these dark times, illustrating how the strategic use of data can significantly advance our mission toward net-zero objectives. By equipping insurers with a comprehensive understanding of risk, the passport becomes a valuable tool for integrating climate change mitigation efforts within the industry. Having said this, these efforts alone may not be enough. Combining government subsidies, green financing, insurance rebates, and retrofitting roadmaps with net-zero building insurance strengthens the case.

Now, in the lead-up to COP28, with a climate emergency on our hands, we want to reinforce this common thread, highlighting why our work is more pertinent than it has ever been. In order to achieve this, we’ll be revisiting past use cases. These demonstrate our action-led work, showing the potential impact better access to data can have, and its critical role in keeping us within the boundaries of the Paris Agreement. 

Impact investment & the built environment

For this programme, our focus was the development and improvement of data infrastructure and practices for the sharing of impact investment data. Impact investments are defined here as ‘investments made to generate positive social and environmental impact alongside financial return’. We therefore needed a use case that would hold significant environmental weight, while also providing a financial impetus for investors. And, given the breadth of impact investment, we also wanted to refine the focus of our use case, looking to the built environment as a suitable lens for doing so. 

Europe’s building sector alone is responsible for 40% of energy consumption, more energy than any other sector. It also accounts for 36% of the EU’s GHG emissions. This of course is not isolated to Europe, the same can be seen in the United States where buildings are the single largest energy user, responsible for a third of national GHG emissions. To meet the EU’s 2030 climate target, €3.5 trillion of total investment will be needed this decade to decarbonise Europe’s buildings through renovation. Based on Member States’ current plans, the investment gap to 2030 is estimated at €2.75 trillion. 

Icebreaker One’s role

Icebreaker One, set about finding a point of leverage that would help plug this investment gap, mobilising finance while moving the built environment closer to net zero. We focused on long-term risk data as a way of homing in on a particular data point in the sector. Long-term risk data is a crucial component in the sustainability and resilience of the built environment as it includes data on environmental risks such as climate change exposure as well as operational risk relating to an asset’s performance. Investors need to know the long-term risk associated with an asset in order to make more well-informed decisions. With this front of mind, we arrived at our use case question: How do organisations and investors currently access reliable, standardised long-term risk data for the built environment, globally?

Data granularity 

Part of the information used to evaluate long-term risks is asset-level data. We discovered that access to granular asset-level data is proving to be a serious issue for the industry, with the data that is available, mostly regionalised. Not only this, but our research uncovered a fundamental disconnect between the increasing demands of regulators and investors for more detailed asset-level data when it comes to ESG reporting and the data that is actually available to satisfy these demands. 

José Cordovilla, Director of Infrastructure Advisory at Typsa, echoed the challenge of accessing granular asset-level data. He cited flood risk data as widely available in Europe and the US but scarce in other countries, highlighting a significant issue in the industry’s data flows. According to José, a lot of building pre-design data is government-owned, not public and can only be accessed once an organisation has won a building contract.

The lack of data granularity can be damaging for the industry, leading to inaccurate asset valuations and potentially deterring investment. But, if improvements can be made in accessing more granular asset-level data, investors can more accurately assess the performance and energy consumption of their assets. This could save investors money while ensuring they’re aligned with ESG reporting standards. 

Standardisation 

Many ESG reporting frameworks are not mandatory however, and those that are, lack standardisation. This leads to inconsistencies and difficulties in comparing organisations. Having a standardised method of ESG reporting that provides guidelines or best practices for reporting could lead to improved data quality and better long-term risk assessment. Standardisation has the potential to create consistency in the market. This increased consistency could make it easier to assess and compare the long-term risks associated with assets, a powerful tool for investors. One existing solution for comparing organisations is the free-to-access Built Environment Carbon Database, designed to become the main source of carbon estimating and benchmarking for the industry. 

Retrofitting & whole-life carbon assessment

Retrofitting is one example of how access to reliable long-term risk data can be used to provide value for investors and the planet. By retrofitting an asset, asset owners can align themselves with ESG regulations as well as enhancing the long-term value of the asset. On top of this, long-term risk data can provide a more accurate cost-benefit analysis, helping to justify investment in retrofitting efforts by demonstrating the potential long-term savings.  

‘ESG initiatives present an opportunity for investors, owners and occupiers to focus on value creation and mitigation of risks. Much of this opportunity is centred around the management, retrofit and refurbishment of existing real estate assets.’ (Carl Brooks Global Head of ESG, Property Management, CBRE). 

Long-term risk data can also help to provide a comprehensive whole-life carbon assessment (WLCA) of a building. Conducting a thorough WLCA can provide a more accurate figure on the carbon emissions of an asset and is a crucial step in reducing GHG emissions in the built environment. In turn, a WLCA can help investors set more accurate emissions targets and help them comply with government regulations. 

Industry challenges 

Despite deep-rooted challenges, regulatory hurdles and a tension between financial growth and sustainability, we believe that improving access to reliable and standardised long-term risk data in the built environment could act as a driving force for decarbonising the industry while mobilising finance. This can best be seen through the potential benefits that retrofitting and a WLCA of a building can have. 

Now, in the lead-up to COP28, with a climate emergency on our hands, we want to reinforce this common thread, highlighting why our work is more pertinent than it has ever been. In order to achieve this, we’ll be revisiting past use cases. These demonstrate our action-led work, showing the potential impact better access to data can have, and its critical role in keeping us within the boundaries of the Paris Agreement. 

Shipping & Net Zero

Shipping plays a crucial role in the world’s economy, with 80% of the world’s merchandise trade transported by sea. However, the shipping industry was responsible for emitting 1.07 billion tonnes of CO2 in 2020, according to The International Maritime Organization (IMO), with this figure projected to surge by 130% by 2050. 

Navigating the murky waters of decarbonisation in the shipping industry has proven difficult. Not least because shipping was omitted from the Paris agreement in 2015, due to the difficulty in regulating ships, which were often owned by one country but registered with another. Following this, in 2018, the industry agreed to cut carbon in half by 2050 but these targets faced widespread criticism. Since then the IMO has revised its goal to net-zero emissions by or around 2050, as well as committing to an uptake of alternative zero or near-zero GHG fuels by 2030. Sadly, the vague language and absence of concrete targets indicate an uncertain future for the industry. 

Icebreaker One’s role

In partnership with Lloyds Register, Icebreaker One undertook a project aimed at giving initiators and investors an understanding of how data sharing and risk model changes can expedite zero-emission ship financing. Not only did Icebreaker One uncover a severe lack of open data during this research period but we also uncovered a significant level of uncertainty in regard to local government policies. Namely, information of future green fuels and a lack of visibility on available funding. 

Sheree Hellier, Lead researcher on the project states: ‘We interviewed a number of industry stakeholders to uncover what they perceive as the main challenges and opportunities for the decarbonisation of shipping. From a legacy-driven, fragmented industry to a lack of data sharing and transparency, the challenges are widespread. We explored how better access to data could drive investment in net zero shipping. Increased collaboration and transparency between major ship owners, particularly in regards to innovation and data sharing, underpinned our recommendations’.

Data to decarbonise 

The potential benefits of enhanced data can be found, first of all, within vessel supply chains. Parallels can be drawn with the built environment, where adopting a whole lifecycle view of the asset or in this case, a vessel, can prove instrumental. It does, however, require a deep understanding of the shipping industry’s complex supply chain, comprising numerous stakeholders, geographical locations and logistical challenges. But, better access to data could enable end-to-end visibility of the supply chain, meaning stakeholders are better able to track cargo, creating market efficiencies. This improved transparency also has the potential to build trust amongst consumers. 

Another potential avenue where data can accelerate decarbonisation in shipping is through Port grid connections. These aid the reduction of emissions while the ship is at berth, as well as providing power to those ships with batteries. With better, real-time data on vessel arrivals, departures and congestion, Port operators can optimise how they allocate power resources. Equally, shippers can plan their routes to be more energy efficient. Despite this, Icebreaker One discovered two key areas of contention with Port grid connections. The first being the disparity in infrastructure across Ports, with some lacking the funding and investment needed to provide the appropriate grid connection for vessels. The second issue is that some of the energy supplied by the grid is still derived from fossil fuels, undermining emission reduction efforts.

We have identified a lack of granular, real-time data as a common issue in many of the projects we work on. But, in the context of shipping, improvement in the granularity of data and more consistent metrics would enable stakeholders to fully assess their emissions and more accurately report their environmental performance, ensuring alignment with reporting frameworks. For example, under the IMO’s new certification scheme, all ships need to report energy efficiency and carbon intensity data, with ratings to be issued in 2024. For shipping companies, having the granular data to prove their commitment to decarbonisation may allow them to access debt instruments at preferential rates such as sustainability-linked loans. 

Developments

The lack of hard targets poses a real problem for the decarbonisation of the shipping industry, especially given the United Nations recently pitting the potential investment required to do so at $100 billion. What’s more, the industry is not accustomed to sharing data, in particular, not with the end consumer. Shipping companies often retain data internally due to the need for frequent negotiation of commercial contracts and the sensitivity of such information.

Yet, the Energy Efficiency Existing Ship Index (EEXI) regulations (which will require ships to report on their energy efficiency from 2025) may serve as a catalyst. Enhanced data granularity holds the potential for shipping companies to prepare for impending regulations and align with net-zero goals, even if it requires a cultural shift in data sharing practices.

As we head toward COP28, the role of data in the shipping industry’s decarbonisation efforts remains more critical than ever. Data illuminates the path, providing transparency, accountability, and spurring innovation, vital components in our shared mission to combat climate change and reach net-zero. 

We urgently need to take action to mitigate climate change. While there is clearly a need for bold leadership, no single organisation or sector can act entirely in isolation. Energy is a complex, interconnected system – bringing with it risks of unintended consequences and of significant inertia. Closed datasets impede the coordination and collaboration we desperately need, prolonging the status quo and locking out new entrants and insights.

At the Centre for Net Zero, our mission is to realise faster, fairer and more affordable paths to net zero. At the heart of our work is a not-for-profit Open Research Lab. We lead groundbreaking global research on the biggest questions in the energy transition, and make our data, models and reports freely available for everyone to understand, challenge and build on.

One of the questions we’re exploring is how the structure of our energy markets can support the transition to net zero. In order to make sensible changes to the way that our energy markets work, we need to think about the whole system – identifying impacts and feedback loops that might not be intuitive. Data is one of the essential ingredients to this, but no one party has all the data they need to think about the system as a whole. Relatedly, a key part of our mission is supporting the fairness of the energy transition. Access to wider datasets helps protect against implicit biases within the data we work with, and safeguard against unintended impacts on particular parts of society.

The Open Energy programme offers exciting new possibilities for thinking about the energy system in a more joined up way. At the moment it’s hard to imagine the impact of new policies or market structures because it’s hard to track how people, markets and physical systems all interact. But underpinned by shared data, it’s possible to stitch together real-world measurements and models of the various parts of the energy system to create what’s called a Digital Twin. 

With that, we can start to ask insightful questions about what we might do to change the rules of the system. For example, what if we were to dramatically cut the cost of adopting heat pumps, or make unit energy costs reflect the dynamic utilisation of the local network, or introduce a new way to reward electric vehicles for the flexibility they offer. Answering any one of these questions requires data from across the energy system (and beyond) – from the physical networks and assets through to the way we expect individuals and businesses to behave. Making it easier to share, discover and link data are the first steps along that path.

“[Our] new modelling strategy will increase transparency and collaboration. This will improve our insights and increase confidence in policy.” 

UK Government’s Energy White Paper, December 2020

The benefits to our net-zero transition could be enormous. There is great potential to underpin broader and more productive research collaborations. I see a growing number of passionate and intelligent people coming to this mission, and an urgent need to find effective ways for them to contribute and collaborate. I also believe there is a real opportunity for the UK to establish a leadership position around energy data, which can facilitate change on a global scale.

For the Centre for Net Zero this means the opportunity for greater impact – through richer analysis for policy makers, business leaders and investors, which in turn allows them to make bolder decisions about their role in the transition.

About the Centre for Net Zero

Backed by Octopus Energy, the Centre for Net Zero is an Open Research Lab focused on the energy transition and working closely with governments, cities, investors and businesses around the world to get us to net zero quickly, fairly and affordably. Find out more at CentreForNetZero.org.

About Icebreaker One

Icebreaker One is a UK-based non-profit making data work harder to deliver net-zero, working across agriculture, energy, transport, water and the built world. Its Open Energy project aims to revolutionise the way energy data is shared in the UK and create an energy data ecosystem that works for everyone. More information at IcebreakerOne.org

Durante la Cumbre COP25 en Madrid, Solvere organizó una mesa redonda para presentar Icebreaker One. Esos son casos de uso desarrollados con los asistentes, en que debatimos las necesidades y posibles usos del estándar abierto de datos sobre riesgo climático. 

CASO 1. Transporte urbano: En colaboración con UN1, y a partir de la sensorización de las plazas de carga y descarga, optimizar rutas y horarios de reparto de mercancías.

El tipo de problema que nos encontramos en este caso es la falta de confianza y el recelo de las empresas transportistas en dar la información de sus rutas. Si se resolviera el problema se podrían optimizar las rutas de reparto de todas las flotas, reducir los trayectos de los reparto, evitando los tráficos de agitación para la búsqueda de aparcamiento y por tanto mejoraríamos también la calidad del aire y como no mejoraríamos la congestión.

CASO 2. Transporte urbano: Acceso por parte del planificador a datos de los patinetes eléctricos operados por empresas privadas para desarrollar y dar seguimiento a una regulación adecuada de este modo de transporte.

El problema que nos encontramos es que cada compañía usa su aplicación, y no tenemos datos homogéneos para poder comparar toda la información que nos suministran las empresas autorizadas. Si consiguiéramos tener toda la información homogénea, podríamos detectar las zonas de más demanda para el uso de patinetes eléctricos, podríamos zonificar mejor para otorgar las concesiones, incluir la información en una plataforma conjunta con el transporte público que permita a los ciudadanos elegir el modo de transporte que más le interesa en cada momento, etc.

CASO 3. Ciudad y zonas verdes: colaboración público-privada para la transformación y aprovechamiento de espacios públicos vacantes como zonas verdes, incluyendo el acceso a datos históricos de desarrollos anteriores.

El problema con el que nos encontramos es el de la falta de sistematización de los datos relativos a áreas o suelos vacantes en la ciudad de Madrid destinados a zonas verdes, suelo no urbanizable, infraestructuras y sus áreas de protección y dotaciones que pongan en relación las actuaciones llevadas a cabo en los mismos así como el éxito o no de las actuaciones y de las inversiones públicas o privadas en ese tipo de suelo y su repercusión en la ciudadanía. Si se resolviera podríamos reproducir esos casos de éxito a través de inversión pública reduciendo el gasto público y mejorando los espacios públicos lo que redundaría en el bienestar de la ciudadanía.

CASO 4. Agricultura: Acceso a datos sobre uso de agua y energía en las cadenas de suministro agrícolas, para reducir el impacto y mejorar la sostenibilidad.

Uno de los grandes problemas a la hora de cuantificar impactos ambientales en cadenas de suministro complejas es poder trazarlos desde el origen (la producción agrícola) y a lo largo de la cadena que muchas veces pasa por plantas de procesado y comercializadores intermedios antes de llegar a la compañía que elabora el producto (por ejemplo, el aceite de palma se recoge en pequeñas explotaciones, que luego se agregan y procesan en molinos locales, que a su vez se comercializan en mercados locales, luego exportadores, importadores y finalmente llegan a la empresa que elabora el producto con ellos). En estos casos es muy complicado saber qué impacto agregado tiene el producto, pues cada compañía tiene estándares distintos.

En este caso creo que un estándar abierto que permitiese la comunicación de impactos ambientales (huella de carbono, huella hídrica, deforestación, etc) no solo en la agricultura, sino en otros momentos de la cadena de suministro (transporte, procesado, envasado etc) sería muy útil.

Nosotros lo pensamos inicialmente para el sector alimentario, pero sería igualmente aplicable a otros como el textil, o materias primas como la madera.

En este sentido, en cadenas de suministro complejas, permitiría que los distintos actores a lo largo de la misma (productor agrícola, comercializador local, exportador…) pudiesen cuantificar impactos de manera agregada desde el origen hasta el consumidor. Además, con un diseño adecuado, permitiría a los actores compararse y hacer benchmarking sobre datos agregados de su mismo sector y de otros.

CASO 5. Energía: Acceso a datos detallados de propietarios individuales para redes de autoconsumo energético.

Problema/Reto: resulta muy complicado tener acceso a los perfiles de consumo (volúmenes y periodos de consumo) de consumidores tanto industriales/comerciales como domésticos. Tampoco es posible saber si disponen de capacidad para gestionar su demanda bien reduciéndola bien alterando los perfiles de consumo bien adoptando medidas de eficiencia energética.

Solución: teniendo acceso a información sería posible identificar dónde podría haber proyectos viables desde el punto de vista técnico y económico.

CASO 6. Sector Público: Diagnóstico de prácticas, capacidades y necesidades en el sector público para aumentar el acceso y sobre todo el uso de los datos abiertos por parte de los usuarios.

Las políticas públicas son cada vez más transversales y requieren una visión interdisciplinar sobre la realidad. Los datos ayudan a tomar decisiones, mejorar los indicadores de rendimiento y los criterios de evaluación.

Por tanto, si los datos públicos de las distintas administraciones estuvieran conectados y fueran accesibles a la innovación empresarial y social, podríamos valorar el diseño, la ejecución y la evaluación de las políticas públicas.

CASO 1. Transporte urbano: En colaboración con UN1, y a partir de la sensorización de las plazas de carga y descarga, optimizar rutas y horarios de reparto de mercancías.

El tipo de problema que nos encontramos en este caso es la falta de confianza y el recelo de las empresas transportistas en dar la información de sus rutas. Si se resolviera el problema se podrían optimizar las rutas de reparto de todas las flotas, reducir los trayectos de los reparto, evitando los tráficos de agitación para la búsqueda de aparcamiento y por tanto mejoraríamos también la calidad del aire y como no mejoraríamos la congestión.

CASO 2. Transporte urbano: Acceso por parte del planificador a datos de los patinetes eléctricos operados por empresas privadas para desarrollar y dar seguimiento a una regulación adecuada de este modo de transporte.

El problema que nos encontramos es que cada compañía usa su aplicación, y no tenemos datos homogéneos para poder comparar toda la información que nos suministran las empresas autorizadas. Si consiguiéramos tener toda la información homogénea, podríamos detectar las zonas de más demanda para el uso de patinetes eléctricos, podríamos zonificar mejor para otorgar las concesiones, incluir la información en una plataforma conjunta con el transporte público que permita a los ciudadanos elegir el modo de transporte que más le interesa en cada momento, etc.

CASO 3. Ciudad y zonas verdes: colaboración público-privada para la transformación y aprovechamiento de espacios públicos vacantes como zonas verdes, incluyendo el acceso a datos históricos de desarrollos anteriores.

El problema con el que nos encontramos es el de la falta de sistematización de los datos relativos a áreas o suelos vacantes en la ciudad de Madrid destinados a zonas verdes, suelo no urbanizable, infraestructuras y sus áreas de protección y dotaciones que pongan en relación las actuaciones llevadas a cabo en los mismos así como el éxito o no de las actuaciones y de las inversiones públicas o privadas en ese tipo de suelo y su repercusión en la ciudadanía. Si se resolviera podríamos reproducir esos casos de éxito a través de inversión pública reduciendo el gasto público y mejorando los espacios públicos lo que redundaría en el bienestar de la ciudadanía.

CASO 4. Agricultura: Acceso a datos sobre uso de agua y energía en las cadenas de suministro agrícolas, para reducir el impacto y mejorar la sostenibilidad.

Uno de los grandes problemas a la hora de cuantificar impactos ambientales en cadenas de suministro complejas es poder trazarlos desde el origen (la producción agrícola) y a lo largo de la cadena que muchas veces pasa por plantas de procesado y comercializadores intermedios antes de llegar a la compañía que elabora el producto (por ejemplo, el aceite de palma se recoge en pequeñas explotaciones, que luego se agregan y procesan en molinos locales, que a su vez se comercializan en mercados locales, luego exportadores, importadores y finalmente llegan a la empresa que elabora el producto con ellos). En estos casos es muy complicado saber qué impacto agregado tiene el producto, pues cada compañía tiene estándares distintos.

En este caso creo que un estándar abierto que permitiese la comunicación de impactos ambientales (huella de carbono, huella hídrica, deforestación, etc) no solo en la agricultura, sino en otros momentos de la cadena de suministro (transporte, procesado, envasado etc) sería muy útil.

Nosotros lo pensamos inicialmente para el sector alimentario, pero sería igualmente aplicable a otros como el textil, o materias primas como la madera.

En este sentido, en cadenas de suministro complejas, permitiría que los distintos actores a lo largo de la misma (productor agrícola, comercializador local, exportador…) pudiesen cuantificar impactos de manera agregada desde el origen hasta el consumidor. Además, con un diseño adecuado, permitiría a los actores compararse y hacer benchmarking sobre datos agregados de su mismo sector y de otros.

CASO 5. Energía: Acceso a datos detallados de propietarios individuales para redes de autoconsumo energético.

Problema/Reto: resulta muy complicado tener acceso a los perfiles de consumo (volúmenes y periodos de consumo) de consumidores tanto industriales/comerciales como domésticos. Tampoco es posible saber si disponen de capacidad para gestionar su demanda bien reduciéndola bien alterando los perfiles de consumo bien adoptando medidas de eficiencia energética.

Solución: teniendo acceso a información sería posible identificar dónde podría haber proyectos viables desde el punto de vista técnico y económico.

CASO 6. Sector Público: Diagnóstico de prácticas, capacidades y necesidades en el sector público para aumentar el acceso y sobre todo el uso de los datos abiertos por parte de los usuarios.

Las políticas públicas son cada vez más transversales y requieren una visión interdisciplinar sobre la realidad. Los datos ayudan a tomar decisiones, mejorar los indicadores de rendimiento y los criterios de evaluación.

Por tanto, si los datos públicos de las distintas administraciones estuvieran conectados y fueran accesibles a la innovación empresarial y social, podríamos valorar el diseño, la ejecución y la evaluación de las políticas públicas.

Icebreaker One is one of 20 processes to be selected by a group of 70 international experts from the environment and technology sectors, that can help create this digital ecosystem across system architecture, applications and governance and policy.

With just 10 years left to meet the Sustainable Development Goals, the issue of innovating to meet climate challenges has never been more important and we’re thrilled to be recognised as being set to make a valuable contribution to those goals.

The full list and more information can be found here – https://medium.com/p/c802c259f8c1

Building a digital ecosystem for the planet to properly assess the risks and opportunities of trying to sustain our global population seems absolutely critical.

The good news is we’ve got some great prior work to build upon. We’ve built the Web (over a billion websites connect a lot of the worlds knowledge). We’ve got amazing technologies (analytics, machine learning, artificial intelligence, blockchain, etc) that can do great things — when fed. We’ve millions of highly skilled developers and professionals.

But: garbage-in, garbage out. We are crippling innovation by not feeding these technologies with good data.

A core blocker to this, from our perspective at Icebreaker One, is that we have not worked out how to share data at a non-technical level: intellectual property, security, privacy, liability, regulation, human processes, and organisational culture are all process-blockers to helping people find or share the information they need to make decisions.

To address this issue — commercial data sharing of data that cannot or should not be ‘open’— we are building on the work of Open Banking and Open Finance. Open Banking creates clear rules, principles and practice for sharing sensitive data between organisations, while robustly protecting the data owners.

We see ‘shared data’ as data that is pre-emptively licensed: you tell people in advance what they are allowed to do with it, rather than trying to negotiate every use-case [see here for more].

As David highlights, “for this vision to become a reality, public and private sector actors must take deliberate action and collaborate to build a global digital ecosystem for the planet — one consisting of data, infrastructure, rapid analytics, and real-time insights”

Are we at some tipping-points here?

• Will this catalyse a ‘coming of age’ for the Internet of Things?
• A clear and present purpose to apply machine learning and AI?
• A reason to finally sort out the really boring bit: getting the human and machine processes in place that will enable data to be shared at-scale, securely, with rights, social and business needs addressed?
• Do we need an equivalent of GDPR for things?
• A Hippocratic Oath for AI?

What are the business models? For example, with insurance, greater access to data (e.g. if assets are self-reporting) will fundamentally shift the way many existing processes operate. What role will (not might) regulation play?

There are hundreds of questions: we can only do this with silo-busting collaborations that work together to help bring us environmental intelligence for everything and everyone.

We’ve started to put together a list here of initiatives—please add your initiative to it and share far and wide.

Related links

UN Environment Programme post

Who’s who in Climate, Environment, Finance, Infrastructure and Data — the CEFID Directory

IcebreakerOne.org — bridging the data gaps between finance and climate change

Data sharing in government

“Leave no ecosystem behind” was, for me, the standout quote from Aromar Revi during the UN DRR Global Platform in Geneva. What he emphasised was that it’s not enough to discuss ‘leaving no one behind’ or ‘leaving no place behind’, rather we need to start thinking about wider, systems-level impacts, and how our ecosystems are connected. This was closely followed by the stark reminder of we have less than 4,000 days to do something about it.

What does leaving no ecosystem behind mean?

During the week I heard: “we’ve ended up in a car crash— which leaves us with the challenge of how we accept the inherent uncertainty that characterises human existence, and how we can do something with it” (Kirsten Dunlop). Or, as Mami Mizutori put it, “risk is growing in a shrinking world”.

This highlighted to me that  the long-standing issue is that systems-thinking isn’t baked into investment models.

How will we balance needs?

One example that surfaced throughout the week is the Tanzanian Selous Game Reserve. A hydroelectric power plant has been planned that will destroy at least 2% of the Selous Game Reserve, a Unesco World Heritage site and one of Africa’s last wilderness areas. The dam will be the largest in east Africa, and will inundate 1,200 sq km of land in the area. It will, undoubtedly, provide a large amount of ‘clean’ electricity. It will double Tanzania’s total power supply. This is an essential consideration in a country where ⅔ of the population currently resides without access to electricity.  

However, it will also vastly reduce biodiversity and accelerate species loss, at a time when 1 million species already face extinction, and affect the livelihoods of 200,000 people living downstream. The longer-term effects will result in greater downstream erosion, which will reduce the fertility of farmland and cause the Rufiji Delta to retreat. The result is that the OECD watch has declared the project unnecessary, and the economic and environmental costs too high to legitimately proceed.

This is without mentioning that the Reserve is home to elephants, rhinos, 350 species of birds and reptiles, and over 2000 species of plants. The elephant population alone has been dwindling since the 1980s (when it was at 100,000) – lower estimates now place the elephant population at 30,000.

What will the lasting effects be? Will the project contribute to the climate and biodiversity crises we face, or will it help us mitigate?

A fundamental question at the base of this is how do we incentivise a change in the way we model risk, to look be more forward-looking, to be more aware of the wider systems impacts?

To answer this question, I borrow again from one of the conference speakers: David Green of NASA. “We all own a piece of the solutions and we can make them stronger by bringing them together.” There is private sector applicability in the public sector, and vice versa. There is also a wide, immense base of information that could be used to improve our risk modelling, and bring in systems-thinking so that we can better understand the impacts. There are also places where there is no information at all.

The problem is that we don’t know where this information is, or if it even exists. There’s no easy way of searching for it, and if you do manage to find it, there is no easy way of understanding who can use it, and what for. Beyond that, there’s often no simple way of sharing it.

This need was echoed in all the conversations we had at Global Platform, by audiences, by exhibitors, by panelists and over casual coffees. We know the world is in climate crisis, and we’ve declared a data emergency. Icebreaker One is part of the emergency response.

We want to provide the collaborative framework to allow everyone to think about risk more systemically, so that we can, in the coming <4,000 days, leave no ecosystem behind.

We want to do this by inviting all stakeholders, from private, public, academic and NGO spheres to share their user needs and stories and collaborate to bridge gaps in information we are seeing.

The initiative is moving forward in a series of three steps:

• Step 1 (Global Programme Launch): initiate public conversation & alpha. Engage expert communities; draft principles, governance tools and technical standards.
• Step 2 (November 2019): public beta. Document and share standards and governance tools that are ‘good enough’; explore new data markets; engage countries to gather their requirements from a national strategic perspective.
• Step 3 (2020 – 2030): launch—put words into action. Build on open learning models to make sure stakeholders have the tools they need to discover, access and use data to address environmental risks, create and adapt financial instruments and invest in demonstrably more resilient infrastructure.

If you have a user story, or a user need, tell us your story. If you’d like to get involved, we want to hear from you – email me at gea@dgen.net.

On Wednesday 10th April participants from insurance, risk modelling, disaster risk reduction, policy, investment and infrastructure held a workshop to unpack user stories and validate economic cases for this project. For example:

• Maria is Director of a National Disaster Management Office who is overseeing recovery and reconstruction efforts from a recent hurricane. She is trying to overcome the critical funding gap that opens up after immediate response operations are complete, but cannot progress without a better shared understanding of the risks faced by governments, businesses and individuals.

• Julio, an urban planner in a rapidly growing city, trying to minimise disaster risk and optimise investment in improving critical infrastructure maintenance as well as improving day-to-day performance.

• Simone, a catastrophe risk model developer who is finding the costs inherent in making country-specific models too high. She would like to make more use of satellite data from private sector providers to customise the models, but lacks the technical capacity to work with satellite data and the commercial scale to negotiate with private providers, as well as needing a network of local collaborators to help her validate data points with ground surveys.

• Aziz, an employee in a resource-strained environment ministry who needs a framework and consistent data to be able to publish key environmental datasets required by the SDGs and implement the Sendai Framework target G to ‘substantially increase the availability of and access to multi-hazard early warning systems’.

• Philip, who works for a large logistics firm which recently suffered major losses when wildfires destroyed a warehouse and disrupted transit networks. Whilst the physical losses were insured, they are unable to insure against the losses from business disruption. As a result, Philip developed a pilot project with a government research laboratory model how the firm’s facilities, networks and operations could be affected – but the laboratory are not interested in turning this into a commercial project for future use.

• XYZ are Asset Managers.  They want to know the economic impacts of climate change on the companies they invest in – they want to compare companies, and know the impact of climate change on the physical locations/facilities and business operations.

• CCAT insurance is a company which provides insurance for households and businesses in a developing country where only 4% of the population take out insurance, they want to understand potential losses from a growing portfolio.

If you have a story to add, please contact gea@dgen.net

We created a diagram to help explain to newcomers how risk modelling works (click to comment).   

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[8 minute read]

An engineering company wants to measure the health (‘fitness’) of a bridge, but so do other people: the region and the country want to know the health of all their bridges.  People want to know if it’s safe to travel over them in advance. In a hurricane, your GPS should know if it’s safe to cross. An aid agency might want to know if they can take a heavy truck over it, or understand weak infrastructure points prior to a hurricane’s landfall to pre-plan stocking aid should critical infrastructure fail.

People designing bridges would like access to all the data about every bridge in the world so they can design them to use less material, be stronger and last longer.  Insurers want access to this same information to create a model for insuring it. Public and private financiers will want to invest wisely and maximise the value of building them.  And that value case also link to other economic benefits that a bridge can bring: for example, a reduced transit route reduces pollution and therefore consumption of fossil fuels.

~~~

Hello.

I’m Gavin, the instigator of Icebreaker One. I’ve been working with data, business, policy, licensing, science and the web for a long time. Along the way, this has involved me as co-chairing development of the Open Banking Standard, founding CEO of the Open Data Institute, founding venture-backed environmental data company AMEE, and founding CEO of digital supply-chain enabler CI.   This bridge story is one of many we are helping to surface.

Looking at the systemic economic, environmental and social challenges we all face, I believe there are a number of threads coming together that could enable us to collaborate at scale to tackle them. Perhaps, too, a ‘global coincidence of desires’ to affect change.

Icebreaker One is a global initiative to collaboratively create ways to publish public and private information online that will enable people to find it and use it for both public and private good.

Data is as important as our road, railway and energy networks and should be treated as such.  At the moment we either squirrel it away and worry about monetising it, or worry about privacy and security, or open it up for anyone to use.

Fundamentally our models for sharing information to help us solve problems, are still struggling with legacy thinking.  Our worries get in the way of sharing, when we could be making better decisions by providing access. Our concerns are also valid: if our global data infrastructure is as important as our physical infrastructure we must protect it, make sure it keeps working and enable access to those who need to use it.

We’ve made vast progress in creating an Open Web (which, broadly, ignored the © symbol to get going)—there are now a billion websites.  They’re all at least searchable. We’ve not yet managed this at scale with data—it’s not easy to even find if a particular bit of data exists, let alone use it. We have several billion web-enabled people: I wonder what we might all build?

We’ve also made huge progress on Open Data (data that is licensed for use by anyone for any purpose) on a global scale.  We’ve created groundbreaking changes in licensing to help people use content: Creative Commons demonstrates that there is a way to pre-authorise certain types of usage, at web-scale.  

What about all the other data?

Given the diversity and complexity of use-cases, this might seem an insurmountable challenge.  And, yes, if we were going to try and create a global licensing system for all the data that might exist (not that people haven’t tried), I’d recommend going for a quiet sit down somewhere.

What drives collective action? Common cause isn’t enough: humans need to have a visceral respones, driven by a powerful economic or social impulses.  This takes us to the great motivators of Risk and Fear. 

In banking, the UK has regulated the Open Banking Standard to mandate that banks open up information about their product (as Open Data) and, more significantly, regulated that banks open up access to private personal, customer account data.  By access we mean making things discoverable — for example Open Banking means services can access detailed product data to order to make it more findable.  It also means that, under certain government-enforced conditions, access can also extend into usage: regulated services can find and use confidential information that was previously not usable (for example, a service that is designed to find you the best mortgage across the whole market).

It is a seismic shift that a government mandated a specific sector must open up data access in a market.  At the same time, other regulation exists to protect rights (for example, GDPR, the General Data Protection Regulation).  We don’t (yet) have an equivalent of either Open Banking or GDPR for non-human data.

What are the drivers to collective action in the private and public sectors?

You can trace the origins of Open Banking back to the financial crisis — the need to ‘do something’ based on risk, fear, doubt and uncertainty.  

Today, people are exploring how the framework and principles of Open Banking might apply to other areas of the financial services sector, from insurance to pensions and beyond, into areas such as health & care.  

A central concept to take away from this is that banking products are digital products.

Another example to draw upon where digital products have had to go through this process, which may sound strange, to begin with, is the music industry.

Music has been a digital product for decades, pre-web.  The web brought a huge, systemic risk to the entire industry – their entire product base was being put online and made available for free.  The initial industry response was to panic: to ask to switch off the internet, to sue 18 year-olds for millions of dollars to try and scare people away, to add DRM (digital rights management) to attempt the control the use of assets.  

Fast forward and there is a thriving digital marketplace for music and a common standard for enabling the definition of digital licensing rules and standards to support an open digital supply chain (http://ddex.net/).  What enabled this? A collective action group formed around the needs of the industry.  

What drivers might trigger the risk and fear that could help us provide compelling private and public sector incentives to share environmental data?

As above, if we were going to try and create a global licensing system for all the environmental data that might exist, I’d recommend going for another quiet sit down somewhere—it’s still in the ‘boiling the ocean’ category for me.

However, even just framing around ‘risk’ introduces some really interesting and relevant bounding conditions.  

Firstly, risk is something that we like to measure and, in many cases, price.  Once we have a price incentive we can start to ask how we might make the price more accurate.  But again, market reality brings us back to the idea of what is ‘good enough’. Many companies don’t ‘need’ more data: business as usual is ‘good enough’ and we have quarterly results to hit.  

However, this isn’t the case any more:  business as usual isn’t an option. At scale.  

We have 10 years to decarbonise our global economy.  We have increasing disaster-level events – the once in a century weather events are happening every few years.  Millions are being displaced. Our air quality in cities is killing millions. We have a plastics crisis. We have a biodiversity crisis. We have many crises, all interlinked.

We need to build things better in the first place.  It’s not an option to get the ‘next billion’ people into a ‘modern’ economy with the same cars, fridges, power, gadgets as exist today.  We are also instrumenting the world. Embedded sensors in infrastructure will enable, for example, a bridge to report its own health or the observation of air pollution from space.  

If we see a collective need, stimulated by risk, what needs to happen next? One option is to formally convene public and private sector actors around the value of collective action and to formalise the ability to share data under certain conditions with specific rules.  In open banking there is an independent Trust; in open data there is the ODI; in music there is DDEX.

We think there’s an opportunity to affect change, building on what we have learned and combining the collective intelligence of humans and machines.

We are testing market needs, our social needs and our environmental needs.

We are working on the ‘what next’ conversation — if you would like to help shape it, join us.