Gross Domestic Climate Risk
XDI now has global capability to analyse the risk posed by extreme weather and climate change on the built environment and infrastructure in cities, provinces and countries around the world using consistent and comparable metrics.
What is the Gross Domestic Climate Risk ranking?
The Gross Domestic Climate Risk ranks states, provinces and territories around the world according to the probable impact of eight climate change hazards on their built environment.
What metric is the analysis based on?
The dynamic table on the XDI Insights webpage offers three ways to compare states, provinces and territories:
(i) Aggregated Damage Ratio: the total extent of building stock damaged
(ii) Average Damage Ratio: damage as a proportion of building stock, and
(ii) degree of damage escalation from 1990-2050.
Damage Ratio is an expression of the Annual Average Loss from extreme weather damage to a property as a fraction of the replacement cost of that property, using a standard archetype. It is expressed as a ratio to enable comparability of physical risk unaffected by exchange rates, inflation and other variables. In other words, it is possible for two properties to have the same Damage Ratio, despite having different replacement values.
Damage Ratio is used in the XDI analysis as it allows risk to be assessed across a range of property types and across different countries where the value of property or the cost of repair may vary considerably in order to make a comparable estimation of extreme weather and climate physical risk.
Aggregated Damage Ratio
This measures the annual sum of damage in a state or province. It adds up (aggregates) damage to all of the buildings. High ranking for this metric reflects states, provinces and territories where extensive built-up areas coincide with exposure to climate change and extreme weather hazards. Larger territories tend to be higher in this ranking because they have a greater extent of built-up areas.
Average Damage Ratio
Average Damage Ratio looks at the proportion of buildings at risk of damage in a state or province. High ranking for this metric reflects states, provinces and territories where a larger proportion of total built-up area will be subject to damage from climate change and extreme weather, even if the extent of that area may be small. As a result, small territories with fewer buildings but elevated risk from climate change and extreme weather tend to rank highly here.
How did you calculate this?
The dataset is based on a data pool representing the built environment of the terrestrial world, with an asset level, bottom-up analysis of human and economic activity. The built environment is represented by a vast global sample of data points at a sample resolution equivalent to a tenth of all buildings in the world. This method ensures high resolution global coverage aggregated to the sub-national level of states and provinces. Small countries are represented as one territory.
The system uses global climate models, combined with local weather and environmental data and engineering archetypes to calculate the damage to the built environment from eight different climate change extreme weather hazards under the IPCC’s Representative Concentration Pathway (RCP) 8.5 – consistent with average global warming over 3 degrees above pre-industrial temperatures by the end of the century.
What is included?
The Gross Domestic Climate Risk profiles reflect risk to the built environment by climate change hazards: riverine and surface flooding, coastal inundation, extreme heat, forest fire, soil subsidence (in drought), extreme wind (synoptic and tropical cyclones) and freeze thaw.
What is not included?
Because it focuses on damage and failure of features in the built environment, this analysis does not include the social, environmental and economic effects of climate change – such as water shortages, or impacts on agricultural production, biodiversity or human wellbeing. Growth of building stock is not included and nor is human population density.
Are protections or loss constraints included?
The models include known protections for riverine and surface water flooding – as provided by leading flood modelling companies. XDI also has techniques to identify the likely presence of municipal or property level defences for riverine, surface and coastal flooding which are included in these results.
There are limited assumptions built into the modelling relating to adaptation responses, and limited inclusion of built defences. The model allows for damaged buildings to be repaired and rebuilt and damaged again in subsequent events. In practice, there will be a threshold of return frequency or impact intensity in some locations beyond which it becomes implausible to assume rebuilding and where it would be reasonable to expect a change in the built environment, property level adaptation or even “planned retreat.” For this reason a cap on damage frequency is imposed for coastal inundation.
XDI’s Climate Risk Engines can apply constraints on rebuilding to reflect plausible limits of social tolerance for ongoing damage; these constraints have been applied in this dataset to coastal inundation only.
How do you define states, provinces and territories?
The states, provinces and territories in this dataset are the first sub-national administrative jurisdiction for most countries, where that data could be located. Where that was not available, the largest possible sub-national regional boundaries have been selected. The areas treated vary greatly in size and this variation influences the result for Aggregated Damage Ratio. The 115 countries that are less than 25,000 square kilometres in land area, or have a population below 5 million people have been represented as a single territory and are labelled ‘whole country’ and this includes some external and special territories of other nations. Some administrative boundary changes may post-date the data used in this analysis.
Why are you using RCP8.5?
Evidence indicates that greenhouse gas emissions are flattening and annual emissions are not tracking RCP8.5. This is a good sign, but RCP8.5 is still an appropriate scenario to use in a prudent risk assessment, given that it remains a feasible bound of future levels of warming and impact. RCPs are based on cumulative greenhouse gases in the atmosphere, rather than annual emissions levels and this concentration tracked closest to RCP8.5 at least up to 2020.
Feedbacks remain highly uncertain and aren’t included in all models, so using a higher carbon emission scenario can be used as a proxy to capture low likelihood high-end impacts.
XDI has used advanced proprietary computational techniques applied to satellite and high resolution spatial data from commercial and non-commercial sources for urban and industrial developed areas across the globe.
Get in touch
Please get in touch if you want to learn more about this data set or our work. A wider data set is available for researchers and public policy development. Commercial data sets for sovereign and sub-sovereign bond markets are available.