There has been an explosion of terms, initiatives and acronyms over recent years as climate change has risen to the top of the agenda, accompanied by confusion between concepts such as ESG (‘Environmental, Social, Governance’), ‘sustainability’ and ‘net zero’. There is confusion about what these acronyms and terms mean, who is involved and how they all relate to each other. 

Icebreaker One is working to enable net zero data to flow, and in doing so, help to bridge the gaps between organisations, initiatives and their outputs. We have a common goal to make data more discoverable, accessible and usable to support net-zero decisions.

In many conversations, we find there is a lack of clarity about what is happening, who is involved and how things are evolving in a highly diverse and complex ecosystem.

The aim of this post is to shed light on “What is going on? Who’s who and what’s what?”. 

With a bias towards sustainability finance within the UK and the EU (and this is by no means exhaustive) we highlight some of the significant actors, acronyms and activities currently in motion as the world scrambles to achieve net zero by 2050. 

Climate disclosures and frameworks

Part of the commitment by the EU to limit and neutralise carbon emissions (European Green Deal), the SFDR (Sustainable Finance Disclosure Regulation) is a broad ESG disclosure requirement for companies and products in the EU. It uses the language of the EU Taxonomy regulation, which provides the classification system needed to describe climate and nature-related economic and investment activities. For corporates, the EU’s Corporate Sustainability Reporting Directive (CSRD) lays out which organisations need to comply and what to disclose in order for more capital to be invested in climate-positive, sustainable and net-zero activities. The EU Sustainability Reporting Standard is being developed by the European Financial Reporting Advisory Group, EFRAG, which provides the technical expertise and guidance needed for the reporting standard. 

Using input from discourse frameworks such as the globally adopted GHG Protocol for calculating carbon emissions (built on the Kyoto Protocol classification system of greenhouse gases), as well as TCFD (Taskforce for Climate-related Financial Disclosures, a  global framework for disclosing climate risk and opportunities, which is now mandated by law to qualifying organisations) and TNFD (Task Force for Nature-related Financial Disclosures), which will provide a global framework that addresses risks and opportunities for the natural world and biodiversity. 

Regulatory requirements and climate change law

A regulatory requirement since March 2021, SFDR forms a core pillar of the EU Sustainable Finance agenda, a pan-European policy objective to create a more sustainable future and reach the legally binding international treaty on climate change (the Paris Agreement), brought to the forefront by the scientific and socioeconomic analysis by the Intergovernmental Panel on Climate Change, IPCC. While the IPCC is an independent scientific, technological and socioeconomic institute formed by the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP), the goal of the Paris Agreement is to limit global warming to “well below 2, preferably to 1.5 degrees Celsius”, compared to pre-industrial levels by countries to decarbonise their economies and reach net-zero emissions by 2050, many countries of which have now enshrined into law.

Standards Boards for disclosures

The current leading internationally adopted sustainability disclosure standard is from The Global Reporting Initiative (GRI), established in 1997 after the Exxon oil spill disaster to create more transparency and accountability around organisational activities and their impacts on the environments they operate in. More recently, the ISSB, the International Sustainability Standards Board, is creating a global disclosure standard for sustainable finance disclosures, published by the IFRS (International Financial Reporting Standards) foundation. It was formed through a consolidation of the IFRS foundation, the Climate Disclosure Standards Board (CDSB) and the Value Reporting Foundation (VRF) outputs. CDSB provided the foundation for FSB’s (Financial Stability Board) TCFD framework while the Value Reporting Foundation VRF spearheaded the Integrating Guiding Principles, Integrated Reporting Framework and the SASB (Sustainability Accounting Standards Board) standard. The International Sustainability Accounting Standards Board (IASB) is also working in collaboration with SASB (all under the ISSB banner) to drive forward the Integrated Reporting Framework. GRI and ISSB are also collaborating to align both objectives and accelerate the adoption of sustainability reporting. 

How does this relate to net zero?

We’ve mentioned the GHG Protocol and how the GHG calculation framework inputs into regulatory reporting such as SFDR and TCFD. Net zero is mainly captured within the “Environmental” category of ESG and sustainability disclosures (the others being Social and Governance), so “carbon accounting” primarily involves accounting for carbon emissions, which is the first step needed to develop a plan to decarbonise to net zero emissions. Carbon accounting is different to “financial accounting” in the traditional sense, although accounting standards boards and authorities (e.g. ICEAW), and the recommended frameworks mentioned above are working to make carbon accounting, and associated climate risk, an integral part of overall financial accounting disclosures and processes.  

Getting to net zero 

The Science Based Target Initiative, SBTi, a science-based net-zero pathways initiative, uses the carbon footprinting calculated through the GHG Protocol framework and provides the scientific pathway and framework for planning an organisation’s pathway to net zero through sector-based scenarios, helping guide planning and reduction strategies for all types of organisations. These science-based targets are based on scenarios derived from a variety of academic and related sources and provide YoY reduction targets in line with “Well Below 2℃” (WB2C) and 1.5℃ scenarios. A collaboration between Initiatives such as CDP (the global carbon disclosure and reporting mechanism for organisations, cities and countries which holds a variety of disclosure data from these actors), WRI and UN Global Compact, SBTi provides the authoritative guidelines and disclosure mechanisms for companies for emission calculations, Green House Gas reporting, and forward-looking target setting.

Other net zero initiatives and guidance

SBTi provides validation for organisation’s net zero commitments and targets for certain sectors. While there is guidance for financial institutions, the UN-approved Race to Zero is a coalition of different net zero initiatives, many of which focus on different sectors and industries not covered by the SBTi. Some of these financial services net-zero initiatives involved include the Net Zero Asset Managers Initiative (NZAM), Net Zero Asset Owners Alliance (NZAOA), and Net Zero Banking Alliance (NZBA), some of which have united under the banner of the Glasgow Financial Alliance for Net Zero, GFANZ. Others in GFANZ span insurance, financial service providers as well as investment consultants. These initiatives are forums for financial services actors to make commitments, receive guidance on action and share knowledge on how to decarbonise their activities in line with net zero targets and channel their allocations (or business activities) towards sustainable and net zero investments. Almost all initiatives do however reference SBTi (and therefore GHG Protocol) as an authoritative way to account for GHG emissions, as well as the above-mentioned TCFD and sustainability accounting standards for climate risk and accounting disclosures. 

The data ocean 

There seem to be many frameworks and disclosure standards, so what’s the data issue? 

Within the financial marketplace, data is used not just for regulatory disclosures, but also for investment analysis, which requires a variety of data including mandatory and voluntary disclosures, financial performance and ratings as well as external data (e.g. from company-specific and national energy consumption, product materials and embodied carbon information, the government published data such as from the Office of National Statistics ONS in the UK, and even weather and imaging data from earth observation from sources such as the Met Office, European Space Agency, NASA, amongst other). Vast data coverage is required for financial institutions to assess where emissions come from and where to action, as well as for assurance and validation, to realistically and demonstrably channel capital towards achieving net zero. 

There are specialised third-party data services (such as Bloomberg’s BNEF, S&P’s TruCost or Morningstar’s Sustainalytics) to benchmark and verify ESG or sustainability claims, but there is often a need for organisations to access the ‘raw data’ that some third parties and companies use for their calculations or to make disclosures due to insufficient data transparency, availability and/or quality. These data providers also amass a wide variety of other data, such as company and country performance and ESG indices provided by the likes of MSCI, and Bloomberg Indices, as well as ratings from rating agencies such as Moodys and S&P amongst others to verify financial stability and benchmarking for to financial institutions such as banks, credit providers and insurance companies. Public listed organisations’ performances and rankings are also provided by some of these indices providers above, as well as stock exchanges such as NASDAQ and London Stock Exchange (part of LSEG, which is also now the parent company of index provider FTSE Russel as well as specialist ESG analytics provider Refinitiv), all which are significant disclosure and third-party data consumers. There are multiple and differing methodologies and frameworks even across organisations, with information silos and financial barriers to appropriate data. 

If we look deeper, other types of data providers offer specialist risk/exposure analytics and loss or catastrophe modelling, used by banks and other financial institutions such as re/ insurers to calculate financial risks which look at hazards (flood, fire, storms, earthquakes, etc, all of which are increasing in frequency due to climate change) for exposure and vulnerability assessments. These include service providers such as Ambiental (now Twinn by Royal HaskoningDHV), RMS (now part of Moody’s) and AIR Worldwide (now part of Verisk) as well as the open source OASIS Loss Modelling Framework. (If you want to learn more about the insurance ecosystem, look here).

For the whole financial services industry, The Network for Greening the Financial Services initiative, NGFS has provided future scenarios for different use cases for financial services, including macroeconomic modelling, exposure, stress testing, etc. The Bank of England via the Prudential Financial Authority of England (PRA) is responsible for publishing annual financial scenarios for banks, building societies and insurers for future planning and stress testing, including the Climate Biennial Exploratory Scenarios (CBES) exercise for climate change risk, which was developed together with the Financial Conduct Authority via the Climate Financial Risk Forum (CFRF) that convened senior authorities within large financial organisations including asset managers and banks. They have been working with the NGFS to integrate climate change considerations into these scenarios. 

Where are all the underlying data coming from?

These reporting frameworks and standards have been instrumental in instigating action toward net zero, however, “net zero” is still a fairly new and bewildering concept to the many players that need to make net zero happen. How do we appropriately satisfy the guidelines and disclosure requirements? What data for this information is sufficient, of high enough quality (i.e. reliable) and in sharable or interoperable formats to be able to achieve this mammoth task of industrial and global decarbonisation? Are these specialist data providers sufficiently addressing the growing need for net zero data? 

As mentioned above, the GHG protocol was established to address the need to calculate and clearly understand how greenhouse gases are produced and from where through business and regional-level activity. But this framework came from a global collaboration of nations, scientists and academia as well as economic actors over years of action to bring to light the existence and severity of how our fossil fuel-based economy has led to climate change and its devastating impacts. There is still a pressing need for underlying data to be made available and shared which can help all actors to understand, manage, monitor and most importantly collaborate and act on their net zero goals. 

Carbon or GHG accounting – estimating GHG emissions 

The GHG protocol provides guidance on how to calculate emissions from specifically categorised ” “scopes ” (activities)through the use of conversion or “emission factors”. Government agencies and industry bodies (such as the UK’s Department for Environment, Food and Rural Affairs, DEFRA and the Internation Energy Agency, IEA), as well as the global IPCC, publish recognised, authoritative emissions factors to calculate emissions from these activities (such as the ‘average’ carbon emissions from electricity drawn through the UK energy grid, or the emissions produced by the processing of materials such as steel and cement). Other databases such as Ecoinvent and Sphera’s GaBi LCA databases also exist for more specific conversion factors. All of these databases use a variety of sectoral and scientific data to publish these factors, and some are accessed through a paywall whereas some are publicly available (e.g. DEFRA and IPCC). 

DEFRA also publishes the UK’s carbon footprint through consumption data and derives emissions factors from a variety of sources such as industry or sectoral data, Energy Network Operators and energy consumption, scientific institutions and other international agencies or academies. DEFRA also provides information on air quality, food, farming and biosecurity and other data, such as that collected and published by the Environment Agency and the US Environment Protection Agency, US EPA. Equivalent bodies in other jurisdictions also publish this data but use a variety of data sources and information to do so, with the frequency of such updates varying widely, and always backwards-looking (for example, DEFRA publishes emission factors once every year). Additionally, these factors (and other such data used for emissions estimations) to be used with actual consumption or industry-averaged data are sometimes not relevant or even available for the multitude of activities that exist and therefore need more information, of higher quality and better frequency to be made available. 

Data frameworks, standards and net-zero data 

The task of bringing all of these data points and information together cannot be underestimated. One of the biggest challenges is where this data is stored, how to access it (paywalls and security), whether they are sharable and comparable, how these data are presented, the differing formats, taxonomies and frameworks as well as differing data standards involved. 

The solution to this big challenge is to make existing data sources searchable, accessible and sharable to all actors in order to achieve net zero. Established and widely adopted data standards, such as the leading standard for business reporting, XBRL, could be leveraged to create a common language for net zero data. Organisations such as Linux foundations’ OS-climate are using the principles and best practices of open source and interoperability to share climate models and tools for a variety of stakeholders (governments, regulators, academics, corporations and others) in order to achieve net zero. The EDM Council is a global membership association that is elevating data management within organisations in order to make data integrity and interoperability possible across industries, countries and beyond. Its Financial Industry Business Ontology (FIBO) can be adapted to relate net zero within business and finance terms and corresponding relations, which drives forward machine-readability, interoperability and thus increase efficiency, sharability and federated data systems. The data and systems exist, but there needs to be a much greater focus on user needs and use-cases to unlock and bridge the data flows necessary for cross-industry and cross-border net zero action. 

Who is addressing these challenges and driving net-zero forward?

Icebreaker One is convening stakeholders to understand which data are required and how data can flow into these requirements using data sharing standards and principles established by existing mechanisms (such as the Open banking standard), to understand the data gaps and holes (e.g. the Future of Sustainable Data Alliance, FoSDA that explores what current financial data flows are needed, and the green taxonomies that exist or are lacking) that financial institutions can use for investment analysis and investment decision making for a net zero future.  Icebreaker One launched Open Energy through working with the UK’s energy industry to solve the need for better data availability, accessibility and data sharing as recommended by the Energy Data Taskforce. Open energy is based on the principles of data sharing highlighted via the success of Open Banking, through the establishment of a Trust Framework which has grown to become an example of how secure and scalable data sharing is possible. 

Many organisations are work to fund this complex ecosystem (e.g. through governments and research funding such as the UK Research Insitute, UKRI and EU’s Climate-KIC, or corporation-established foundations such as Bloomberg Philpanthropies, Climate Works, and Climate Arc) and helping to convene actors, produce research and insights and incubate or test solutions around critical topics.

What’s next?

The vast ecosystem described in this post is but a snippet of the nascent and continually evolving net zero and climate action landscape. It is still unclear whether the multiple standards, frameworks and initiatives can demonstrate real net zero action without the provision of the data necessary (or access to it) to quantify and verify claims. A clear way to address this is through the availability of net zero data — searching a trusted data ecosystem where any and all actors can search for, identify and access trustworthy data needed to operationalise, achieve and verify their net zero goals. By demonstrating such a data-sharing ecosystem can not only work but can drive innovation and change forward (c.f. Open Banking and Open Energy), Icebreaker One’s ambitions with net zero data may well be the change we need to genuinely achieve net zero in the brief time we have left to act. 

Linked initiatives

• Carbon call
• Future of Sustainable Data Alliance (FoSDA)
• Climate Arc (workshop outputs)
• Bankers for Net Zero (UK All-party Parliamentary Group)
• Net-Zero Data Public Utility
• Climate Action Data 2.0
• IB1 Constellation

Project NIMBUS will revolutionise the way detailed meteorological data and models are used in the design and decision-making of electricity assets, through innovative uses of the data and predictive modelling techniques.

In the 8-week long Discovery Phase, NIMBUS will develop business-driven use cases for the application of detailed meteorological data as a proof-of-concept and allow for a clear cost-benefit analysis to be taken into the Alpha phase of Ofgem’s Strategic Innovation Fund. To develop this use case, Icebreaker One conducted research and held a workshop with SSEN Transmission, SSEN Distribution, and the Met Office. The three finalist use case ideas that came out of the workshop are:

1. Historical accident data to identify “high risk/high incident” parts of the system that need investment.
2. Model weather-related degradation to Probability of Failure (PoF) for assets connecting large volumes of generation to the grid
3. Predictive hazard identification for extreme weather events using remote monitoring

Through further research and stakeholder engagement, use case 2, model granular weather-related degradation to Probability of Failure (PoF) for assets connecting large volumes of generation to the grid has been chosen as the Project NIMBUS Use Case.

Use Case Development Process

As part of the use case development process, Icebreaker One research team compiled a long list of use cases that use meteorological data to build asset resilience such as using weather data in the design and decision-making process for an energy Systems Operator. This was developed through desk research, and interviews with key SSE stakeholders including asset risk and data scientist experts, as well as meteorological academics.   

Icebreaker One held a workshop to narrow down from nearly 40 to the top three impactful use cases to be taken forward in the Discovery phase of this project.  The workshop was attended by a range of participants from across SSE and the Met Office. These included members from the Asset Risk and Asset Management teams from SSE Transmission, data scientists and system managers from SSE Network Distribution, as well as asset policy, strategy and innovation specialists. 

The Met Office domain experts were invited to participate in the discussion by providing important context and meteorological acumen. They did not participate in any voting to ensure the use cases were driven by the needs of the network operators.

The Use Case Prioritisation Workshop

The aim of the session was to narrow down the focus of NIMBUS in a clear, transparent and methodological way. It presented a chance for various members of the team to collaboratively agree on what to consider while choosing which use cases to focus on for the current project phase. 

Participants were presented with the long list of use cases that were put together by the Icebreaker One team. 

The long list spanned a vast number of ideas, which were roughly broken down into three types:

• Historical or event-based
• Real-time or near-term close predictions
• Longer-term predictions and planning

While meteorologists prefer to categorise the ideas in terms of hazards (such as flooding, heatwaves, windstorms or wildfires), the initial exercise focused on simple elimination through a Google form. This allowed for discussion, merging to remove duplicates, indicating overlapping projects, and adding new thoughts. Each SSE member was given a maximum number of votes per use case type. All use cases scoring 2 or less thus did not make it through to the shortlist. 

After a quick break, the 10 ideas with the highest number of votes from vote one were gathered into a separate spreadsheet for vote two.

Voting on the Project NIMBUS Alpha Use Case 

Vote two consisted of five rounds to deprioritise use case ideas to arrive at three shortlist “finalists”. The team were asked to consider the following criteria while deciding which use cases to progress: 

The strategy used included picking from use cases that received less votes to survive the current voting round, with the opportunity for individuals to discuss and support their choices with the rest of the team during each stage of elimination. An example of how this method works is shown below using fruit:

As pairs and then individual use cases survived the consecutive polling rounds, all popular use cases were eventually compared to arrive at a final three. 

 The three finalist use case ideas that came out of the workshop are as follows:

4. Ref: NIM 4 Historical accident data to identify “high risk/high incident” parts of the system that need investment
5. Ref: NIM 30 Model weather-related degradation to Probability of Failure (PoF) for assets connecting large volumes of generation to the grid
6. Ref: NIM 31 Predictive hazard identification for extreme weather events using remote monitoring

The next steps will be using the Icebreaker One use case framework to further flesh out these use cases through desk research and targeted stakeholder engagement for the then for the NIMBUS team to perform a cost/benefit analysis on the Project NIMBUS Alpha Use Case. 

[if you would like to speak to us about your work and how it relates to Project NIMBUS, please get in touch with vichi@ib1.org]

Background

As part of the Modernising Energy Data Access (MEDA) competition, a core use case was developed to demonstrate the benefits of an Open Energy Search and Access Control. This focused around a Local Authority looking to understand the impact of retrofitting Low Carbon Technologies and Electric Vehicle (EV) charging points across a large estate. 

As part of our user needs-based approach, two further use cases are being developed. These will further develop the value of better data access: making energy data more robust, shareable, and easily accessible, and outlining the benefits of the Open Energy approach to stakeholders across the market.

Use case prioritisation process

Step 1

Members of the Open Energy Steering and Advisory Groups, including representatives from government, regulators, consumer bodies, trade associations and industry, identified potential areas of focus for new use cases.

Broad areas were considered during this process, including flexibility, electric vehicles (EVs), fuel poverty, heat pumps, smart meter adoption rates, and the transition away from gas boilers/heating.

Step 2

Following prioritisation discussions with the Steering and Advisory Groups, focus was narrowed to two candidates: 

• Electric Vehicles (EVs)
  The switch from conventionally-fuelled to emission-free vehicles forms a core part of the UK government’s Net Zero strategy, with the adoption of EVs expected to increase at an exponential rate over the next decade. To support this uptake, it is essential that a comprehensive charging infrastructure is in place. New legislation requiring all new building developments to have EV charging points will contribute to this.
  
  However, as it stands, it’s felt that the grid lacks enough capacity and/or flexibility to support the anticipated increase in electricity demand as a result of the mass adoption of EVs. Better, and better access to, data will help ensure that an increase in the number of EVs – and EV charging points – does not place unsustainable demands on our energy resources. Our EV use case will explore this opportunity through the lens of a specific stakeholder, map the data value chain required and engage with actors within it.

• Flexibility (‘flex’)
  The way the UK sources its energy is continuing to evolve away from the certainty of electricity supplied by a relatively small number of large power stations, to the distributed energy supply of multiple low-carbon technologies. The ability of these distributed resources to supply electricity at given times of day or year is more variable and less linear and the costs of managing this variability are high. In parallel,  customer demand patterns are changing as the UK electrifies its heating, transport, and related areas. This will require a move to a far more flexible system – in both supply and demand. As with EVs, our flex use case will demonstrate how Energy Search and Access Control could support this flexibility, from the perspective of a particular stakeholder (for example, a flexible asset owner), map the data value chain required, and engage with actors within it.

Step 3

Members of the Steering and Advisory Groups have put forward specific problem statements in each of these areas that improved access to energy data could help address. Over the period from December 2021 to February 2022, the Open Energy programme will work with stakeholders in each of these priority areas to detail the specific use cases for development, with a view to publication by the end of February 2022. 

In the first part of this mini-series, we looked at the sections numbered 1, 2 and 3 in the insurance landscape image to explore what types of insurance exist, the different structures and what insurance business models are based on. Here we look at sections 4 and 5 to explore how insurance relates to the rest of finance and how insurance is distributed to buyers of insurance.

The sections of the insurance landscape are discussed in this blog. You can access the full graphic here. To learn about sections 1, 2 & 3, read the first blog of this series.

4. Insurance and the wider financial services industry

As we have seen in the previous section, the insurance industry is closely linked to the wider financial industry through the investment of premiums. Furthermore, a stock insurance company can receive money from external investors (i.e. not only from premiums paid by policyholders) from the securities market through financial instruments known as insurance-linked securities (ILS). ILS are bought by investors who are willing to take on the potential risk of a catastrophic event for the reward of the guaranteed premiums. If the catastrophic event does not occur, then the investors make money. Creating, selling and trading these financial instruments require a high level of sophistication and industry know-how, and not surprisingly access to high-quality data.

What about regulation?

Insurance is a highly regulated industry within the wider financial marketplace with strict requirements to ensure insurance companies have the ability to fulfil their contractual obligations of servicing claims. Credit agencies play a significant role in the insurance market by assessing and monitoring an insurance companies’ solvency, or ability to make payouts. Hence, credit agencies are important data users in the insurance landscape and rely on data to accurately assess insurance companies’ credibility and fiduciary function, thus providing dependable ratings not just for the buyers of insurance but also for the insurance industry’s impact on financial markets.

5. Insurance distribution

Insurance brokers, intermediaries or insurance sales arms act as the interface between the customer or user of insurance and the insurers. Hence, the distribution channels hold key information about customer behaviour and industry demand. Technology companies are increasingly moving into these segments of the insurance marketplace with the growing digitalisation of insurance distribution and robo-insurance.

Technology uptake such as with smart devices, Internet of Things (IoT), wearables and even remote sensing data has led to innovations in insurance products and processes, but not without challenges relating to data use, ownership and privacy. Most disruption has been at the customer and operations segments of the insurance value chain; such as with the use of open and shared data from the financial industry to determine a policyholder’s ability to pay premiums, thus disrupting centuries-old insurance assessment processes. Promising structures that aim to streamline other segments of the market, such as claims processing and monitoring, are also emerging.

What next?

Insurance is a heavily synergetic marketplace due to the sharing or transferring of risk through invested capital, not just within the industry itself but across the wider financial marketplace and other industries such as energy, infrastructure and transport.

Establishing standards for data sharing to open up channels internally and across industry can facilitate interoperability to speed up attaining mandated net-zero targets. Here, we have explored the stakeholders and possible data segments in insurance for the SERI project, but the whole picture is not yet complete. Next, we aim to delve deeper into specific sections of the insurance data systems and data-flow landscape to understand more about data used in insurance to help us develop climate-ready insurance products.

Got feedback? Get in touch!

SERI is holding a series of stakeholder engagement activities over the coming months. If you have any questions, comments or would like to know more, please get in touch.

Insurance is a complex industry. In simple terms, paying small amounts, or premiums, over time to an insurer entitles us to claim large sums from the insurer if things go wrong. However, insurance business models depend on complex interactions of industry, data, mathematical modelling, investment structures, commercial and non-commercial stakeholders, and of course, changing circumstances caused by events around us.

Data is a key part of insurance, but the data flows and channels that exist are closed and/or high-friction. Data sharing is often carried out through expensive, bilateral agreements. Furthermore, there is a lack of standard formats across the insurance landscape for data sharing, and data quality widely differs depending on its uses.

As part of SERI, we mapped out the insurance industry landscape to clarify the various segments, how they interact, and the data channels that exist between market participants. By understanding the value chains, data layers and “gatekeepers” of the insurance ecosystem, we aim to identify levers that can facilitate decarbonisation across the industry through shared data. Creating a standard for a robust data-sharing infrastructure can facilitate cross-industry inoperability to reach our net-zero targets.

This is the first iteration of the insurance landscape based on findings to date. You can explore it in detail here. If you have any comments or feedback or would like to collaborate to take this work further, please get in touch.

Mapping the insurance landscape part 1

Here, we look at numbered sections 1, 2 and 3 of the insurance landscape.

1. Insurance types and structures

Insurance is broadly split into life and non-life company types, primarily structured such as mutuals or stock / proprietary companies. Other hybrid structures of insurance exist, such as Lloyd’s syndicates, P&I clubs and takaful insurance. Parametric or index insurance is yet another type of insurance and differs from traditional insurance in how the payout is structured.

Life insurance is similar to investment savings and pensions in that there is always a lump sum payment at some point in time. Non-life insurance provides coverage for risks that may cause significant financial loss such as in the event of flooding, fire or a motor accident. Insurance for specific risk themes is known as insurance products. For example, a goods company may purchase a marine insurance product to protect its cargo at sea. Policies are specific insurance agreements between a customer and insurer based on that customer’s needs.

The customer buys an insurance policy to off-load a potential expensive financial loss onto the insurer. The insured policyholder thus transfers an unknown financial loss amount to the insurer for a defined price. This is known as risk transfer and is an element of risk management.

Furthermore, insurance companies can purchase reinsurance. Reinsurance companies take on financial risks that may be too large for the primary insurance company to bear for a predefined price. Here, the primary insurance company is the insured, or policyholder and the reinsurance company is the insurer. Reinsurance firms can thus further off-load risk by buying their own reinsurance, which is known as retrocession. 

2. Insurance pricing

Insurance premiums and compensation limits are estimated through the pricing of risk. Risk pricing is derived mathematically from the probability of said risk, namely:

• the likelihood of its occurrence (will it happen?);
• the frequency (how often does it happen?); and 
• the severity (how bad is it?).

The above are estimated through a variety of macro and microdata inputs. Different types of data (such as historic, financial, and geographic data to name a few) are used for the pricing of risk, and thus to estimate general premium amounts and compensation amounts for a particular theme or insurance product. Actuaries in the back office estimate the general product pricing range, while the underwriter uses individual factors to accurately price the individual’s policy. This exchange of information or data between the underwriter and the back office is a crucial part of the insurance business model. The underwriter has the final say on what risks the insurance company is willing to insure for the individual and at what cost

3. The insurance business

Levels of risk and their associated premiums from numerous insurance policies diversified across customers, products and geographies are held in the insurance companies’ portfolios. The premiums paid by customers make up the portfolio, which is invested to grow. The portfolio monitoring teams ensure appropriate levels of liquidity with respect to risk and associated payouts are maintained. In the event that a policyholder experiences a loss, for example, due to extreme weather events that cause flooding damage, the insurer pays out the claims from its portfolio (or book) once the claim is verified.

Insurance business models are based on carefully balancing the levels of risks due to few loss events (and therefore the associated claims payout) with the premiums paid by all policyholders. Insurance companies make money managing the portfolio to get more from the premiums than the companies have to pay out to service claims from their policyholders.

Insurance profits are thus made up of money made “balancing the book” (underwriting profits) and profits from investing premiums in the financial markets, which is discussed in part 2.

• governance and regulatory entities
• agencies, consortiums and research institutions
• assets that generate, produce or supply energy to the country 
• manufacturers, equipment and infrastructure providers
• energy transporters, transmitters, operators and distributors
• energy suppliers
• service providers including brokers, consultants, technology, and other services

We included energy investors and the investment community given their role as facilitators of energy connectivity, especially with regards to the transition to renewable energy and our legal requirements to meet emission targets. 

Our map of this ecosystem shows its complexity and how large energy generating assets (e.g. coal or nuclear plants) centrally transmit and distribute energy to reach the end consumer. 

A number of monopolies are involved, such as the transmission (electricity & gas) and distribution systems (DNOs for electricity & DNs for gas). Ofgem along with other regulatory bodies provide governance to ensure price controls in the wholesale market so that energy suppliers can utilize the transmission and distribution networks fairly, and offer a competitive marketplace for consumers to purchase their energy.

As a result, the ecosystem contains vast amounts of data that are currently fragmented and often siloed.

We believe that harnessing this data through a robust, decentralised and federated data infrastructure is key to modernising the energy system in the UK and attaining our targets for a carbon-net-zero future—we call for the development of an Open Energy standard to help address this.

Acronyms unbundled

• DNO = District Network Operators (Also sometimes District Systems Operator DSO)
• ESO = Electricity System Operator
• NGESO = National Grid Electricity System Operator
• NGET = National Grid Electricity Transmission Plc
• TO/TNO/TSO = Transmission Operators (Network and Systems)