XDI Benchmark Series

XDI 1000

The XDI 1000 is the first release in the XDI Benchmark Series, a series of data sets providing insights into physical climate risk of selected samples of companies worldwide to be published throughout 2022.

The XDI 1000 is an independent analysis of the physical climate risk to more than 1,300 listed companies drawn from eight major stock exchanges.  This data set provides a like for like comparison between companies by ranking against three key metrics, using a single, consistent methodology and enabling investors to compare and contrast the current and projected impacts of physical climate risk to owned and leased operational assets world wide.


Ranking from 1 to 1000 with 1 being the highest risk.

Search by company name or sort by metric by clicking on column header.

Ex financials: The XDI 1000 presents data on the physical climate risk to owned and leased operational assets of the companies listed.  XDI works extensively with the financial sector to quantify the financial risks of of climate change however inclusion of financial assets is outside the scope of this analysis.

The confidence rating (low, medium, high) is based on the statistical accuracy associated with the number of assets identified in the asset discovery process.   Companies with higher asset counts have a higher confidence rating, as risk is averaged across the entire asset count.  Results for companies with a low asset count can be disproportionately influenced by high risk assets’ impacts on the overall risk profile.

The XDI 1000 does not provide complete coverage of each index, but has sufficient representation to enable insights.  Some companies have been excluded from this sample for reasons relating to results integrity.   See ‘Sample Selection” in technical specifications below. 

XDI 1000 companies, per index, over time

The graph below plots all companies in the eight indices over time.  Press play to animate, or select or deselect indices by clicking on the legend. 

Key risk indicators

Productivity Loss (PL)

Productivity Loss reflects the effects of different types of disruption, including periods of closure associated with different hazard events. Productivity Loss is the percentage of total productive availability of the asset for which the asset is assumed to be unfit to operate, for example, due to component failure, damage or repair.

Outage periods are assigned by hazard type. Thus, a heat-based non-damaging disruption to electronics may cause a day of outage representing a PL of 0.3%. Flood damage taking 4 weeks to repair would cause a PL of 8% for the year. PL is based on Failure Probability (FP)*, which includes both the annual average probabilities of event occurrence and the vulnerability of the asset and its components.

PL = FP x Disruption (days) / 365


Failure Probability (FP)

Failure Probability is the annual probability of a climate hazard causing the asset to stop working with or without damage. This is reliant on the vulnerability of an archetype’s element to a particular hazard. An element can fail without being damaged. A failed element may also cause other dependent elements to fail.


Value-at-Risk (VAR%) and Maximum-to-Date Value-at-Risk (MVAR)

Note: XDI’s MVAR is a derivative of Annual Average Loss, derived from an asset level assessment of extreme weather damage to properties, which is the engineering-based building block of the Climate Risk Engines computations. We acknowledge the investment community uses this term in reference to non-tangible assets with a different context, and as such bring your attention to this anomaly.  

The Percentage of Value-at-Risk (VAR%) is the Technical Insurance Premium (TIP)* expressed as a percentage of a single property’s replacement cost, specified for a one-year period with no discounting of the TIP or the property replacement cost.

VAR% = TIP / asset replacement cost

The VAR% can also be applied to a portfolio of assets, in which case average VAR% is the Total TIP divided by the total replacement value of all assets, making it a non-dimensional average for TIP.

Unless otherwise stated, for each analysed year, the highest VAR up to that date is used - Maximum-to-Date Value-at-Risk (MVAR). This is because climate models can have trend variability over time and some hazards may lower for some periods, which can cause misleading interpretations if only a single year is presented.

Therefore, the MVAR is used as default because it provides a single insight into the peak physically damaging stress placed on each asset from extreme weather and climate change observed in the modelling results up to that year.


Technical Insurance Premium (TIP)

The Technical Insurance Premium (TIP) is defined as the Annual Average Loss (AAL) per representative property for all hazard impacts combined. The TIP is based on the cost of damage to a property, expressed in current day dollars with no discounting or adjustments for other transaction costs.


High Risk Properties (HRP%)

XDI asset risk ratings are derived from US Federal Emergency Management Agency (FEMA) standards, which are used for pricing many insurance premiums in the USA. These have been augmented to allow for the resilience of a property. Rankings are based on TIP as a Percentage of Replacement Cost (VAR). XDI’s property risk ratings are shown below.

Low Risk: less than 0.2% VAR 

Moderate Risk: 0.2% to 1% VAR

High Risk: Above 1% VAR (may not be possible to insure)

For a given year, HRP% represents the proportion of an entire portfolio’s assets returning ‘high’ results. The number of High Risk Properties can also be expressed as a percentage of all properties in an area.

High-risk properties are usually created by substantial exposure to severely damaging hazards such as flooding or coastal inundation, as opposed to soil contraction or forest fire, which may only cause minor damage or where probabilities of loss are small. 

Sample selection

The XDI 1000 has been drawn from eight global indices: ASX200, CAC 40, DAX, FTSE 350, HSI, NI 225, S&P 500 and STI.

More than 1,300 companies’ owned or leased operational properties were analysed.  

Excluded companies 

  • Companies engaged in analysis with XDI
    The analysis underpinning XDI 1000 data set is based on our Company Intelligence analysis for investors, and involves assessment of third party data compiled through XDI's asset discovery process.  This differs to analysis undertaken with clients who provide their native data for a higher level of granularity (see archetypes) and decision making confidence. To avoid any real or perceived conflict of interest, such client companies have been excluded from the XDI 1000.
  • Lower asset count 
    Companies with less than 10 assets worldwide have been excluded.  This is because impacts on one asset of a low count asset base skews the average risk results, creating bias in final results.

Stress Testing under RCP8.5

This analysis uses an IPCC greenhouse gas emission scenario that follows business-as-usual (RCP8.5) emission scenario. Specific climate models are selected to ‘stress test’ each hazard - thus a model which tends to predict a drier future is used to consider drought, and a model which predicts a wetter future is used to test flood risk. This selection process avoids masking risks or diluting impacts through averaging an ensemble of models, however results should be interpreted as a stress-test, not a mean projection. 

Analysis using lower emission scenarios is possible in the Climate Risk Engines.  Results for these have not been included in this data set. Please contact XDI for Single Company Reports or Multiple Company Intelligence Analyses for additional emission scenarios.

Asset Discovery methodology

This analysis has been done without the participation of the companies analysed.  XDI uses an extensive asset discovery process to collate physical asset data, on which analysis is then run. 

Asset location data is drawn from numerous proprietary and public databases to search company names, trading names and subsidiaries, including both owned and leased operational assets worldwide. An extensive data grooming and Quality Assurance process follows to assess confidence in the resulting dataset.

XDI’s asset discovery process has been market tested in due diligence with major financial clients against their own internal native asset location data. In tender processes, XDI’s asset discovery methodology has consistently returned higher accuracy than competitor tenderers, and has been assessed as the most effective in identifying company assets amongst a range of key market players.

XDI has a number of asset discovery methods from narrow searches using exact name matching to interactive fuzzy matching and subsidiary surfacing. XDI uses a balanced method, consistently applied to all target companies.

Climate Risk Engines

The Climate Risk Engines are purpose built to compute hypothetical future risks to a modeled asset (synthesized with engineering data) that is designed to represent property and infrastructure. The system enables each such asset to be stress-tested against a wide range of extreme weather and extreme sea events typical of its location. A range of future-looking scenarios can be applied that are consistent with different greenhouse gas emission scenarios, atmospheric sensitivity and response, adaptation pathways, building standards and planning regimes. The Climate Risk Engines combine engineering analysis with statistical analysis of historical weather and climate projections, and probabilistic methods for financial analysis of risk and value. It’s important to note that these results apply to a synthetic ‘Representative Asset’ under a range of future scenarios. The results cannot therefore be taken as representations of the actual future risks to, or value of, a real or planned property or infrastructure asset.

Assets, hazards, models

The information presented in the XDI 1000 has been generated using an expert selection of the scientific methods and computational modelling techniques available at the time of creation. However, at any time, these calculations are subject to physical, political, regulatory, technological, stakeholder related variables and uncertainties that could cause results to differ materially. There are a number of limitations effecting this analysis of which users should make themselves aware. These are constantly refined and updated and more information on such variables is available on the XDI website.

  • Representative Assets
    The asset analysed in this report is a synthetic representation of a real or hypothetical asset placed at the nominated address which may include real estate properties, infrastructure or other physical objects. The analysis does not necessarily take into account the impact of any actual buildings, built infrastructure, modifications, adaptations or resilience building measures (public or private) that have been, or may be, applied that reduce (or exacerbate) the relevant risks.
  • Climate Hazards Covered
    The analysis covers riverine flooding (fluvial), surface water flooding (pluvial), coastal inundation, forest fires, extreme wind, soil subsidence, and extreme heat. The included hazards may increase or decrease over time, and/or for different locations at the sole discretion of XDI. The analysis does not cover any other hazards, such as coastal erosion, grass fires, land slip, cyclones/hurricanes/typhoons, hail or heat impacts.
  • Climate Change Scenarios
    The impacts of climate change analysed are based on greenhouse gas emission and global warming scenarios presented in the Intergovernmental Panel on Climate Change Assessment Reports (IPCC 2007; 2014). These models are only one possible view of the future and representation of climate change. No explicit or implicit assumption is made in relation to the current or future alignment of any climate change-related scenarios with climate related policies of any government at international, national or subnational level.

Company Level Insights and Multiple Company Data

Intelligence on your investments

The analysis underpinning XDI 1000 data set is based on our Company Intelligence analysis for investors.  Investors with a portfolio of equities can now obtain intelligence on the climate resilience of any number of companies world wide for due diligence, investment decision making, or to compile risk ratings.