Since our initial blog on climate change risk, much has happen in the world in terms of both physical risks and transitional risks.
In Australia physical risks have materialised as catastrophic bush fires. We’ve had severe flooding in Europe and hurricanes and volcanic eruptions across Asia/Pacific. Lives and livelihoods have been lost; billions of dollars have been lost; ecosystems have been destroyed. Temperatures are at seasonal highs and ocean temperatures have scored their 10 highest levels in the last 10 years.
As a result, the World Economic Forum’s Global Risk Report 2020 has signalled that climate risk change makes up all of the top five risks (in terms of likelihood).
What a wake-up call. On the transitional risk side alone:
- the new EU commissioner has ratcheted up her climate agenda via the European Green Deal;
- the US has established a new CFTC climate-related subcommittee to capture and mitigate financial risks;
- the Bank of England has introduced new climate change stress testing for 2021;
- and the European Banking Authority is also outlining plans for its climate stress test this year.
Given that many of these incoming requirements will be foreign for many financial institutions, you could be forgiven for not knowing where to start. Just like many of the financial regulations of the past decade, banks and financial institutions will need to develop an array of scenarios, stresses, calculations, metrics and reports to address climate change risk. And ultimately, these will all be based on the same critical source – data.
Data is the bedrock that allows banks and financial institutions to meet regulations and make better decision; to identify risks and develop ideas which in turn facilitate business growth. But with Environmental, Social, and Corporate Governance (ESG) investments accelerating and supervisors preparing for a new wave of ESG and climate change regulations, is there enough data available to satisfy banks, investors and supervisors?
In many banks and financial institutions, the overarching view is that data remains a significant barrier to greater adoption of both climate change risk and ESG. Criticisms include poor quality data, insufficient track records, lack of standardisation and gaps in the data with too few companies reporting the information necessary to assess and quantify risks then generate reports, particularly in certain regions or sectors of the economy. But it’s also important to remember that these are new data sets and similar challenges could still be said for many types of data sets today.
And it’s not all doom and gloom. We are witnessing an industry of data providers (both old and new) springing up to service the demand for high quality data that can help align to the climate change risk challenges (such as the Task Force on Climate Related Financial Disasters, or TCFD) and inform investment decision making. These range from the established ESG ratings agencies, such as MSCI and Sustainalytics, to smaller providers focusing on climate risks via satellite imagery, through to an NGO that explores companies’ lobbying activities, screening and metrics.
Total spending on ESG data, including indices, was estimated at $505 million in 2018, according to a report by Opimas. It is predicted that this figure will reach $750+ million in 2020.
- Good examples include the Carbon Disclosure Project, now known as CDP, which is a platform through which companies can report their carbon emissions. The number of companies submitting greenhouse gas (GHG) reporting information to the CDP has ballooned from 220 in 2003 to 6,937 in 2018. That now represents over 50% of the global equity market capitalisation, according to CDP.
- CDP data is also available on leading platform providers and CDP claim it forms the basis for much of the environmental analysis done by other data providers who license the data.
- The same data sets are also being created across water, forests, energy through to individual sectors, although that data quality is not (yet) at the same level across all these domains.
While developing company CSR and ESG disclosures will be essential to enhance data quality, the requirements don’t stop there. Given the geographical reach of many companies today, it’s just as important to also understand your supply chains, how they operate and their impact on the environment. Indeed the environmental impacts and exposures of a majority of companies over the world are found in their supply chains. According to the CDP Supply Chain Program 2019/20, companies’ supply chains produce on average more than five times more emissions than their own direct operations.
Importantly, all this climate and ESG data (sometimes called non-financial data) is being created from both structured and unstructured data to better align to banks, investors and supervisors’ needs (given the unique challenges that physical risks present). Whereas structured data is created by companies’ disclosures and leading governmental / non-for-profit bodies, unstructured data is linked to media coverage, social media or satellite imagery. This means that powerful new sets of climate / ESG data can be created via artificial intelligence (AI)-type techniques, such as machine learning and natural language processing (NLP), to help make big data easier to collect, process and analyse.
The growing levels of internet connectivity and the accompanying data deluge has countless applications which can influence how finance impacts the environment, and vice versa. One of the main advantages of this type of data is that it can provide information based on current events, whereas reported climate / ESG data is, by its very nature, backwards-looking.
Both historic and real-time data sets have pro and cons:
- Structured data on individual stocks (exchange prices as an example) is progressively covering more companies across more regions and looking to become more standardised, but still contains a bias towards large-cap stocks.
- Non-reported unstructured data (unlisted stocks have no official market value but social media coverage could provide useful information) ‘could’ help eliminate this bias.
Although many financial institutions are starting to build climate change models, they need to know where the company’s assets are in order to access location-based data for physical climate risk and their degree of sensitivity to that type of risk. Banks and investors know where these companies sell into, but have little visibility on where they are manufacturing. For example: what is each factory or warehouse’s exposure to flooding?
Efforts are currently underway by industry bodies and supervisors to make climate disclosures mandatory and to create industry standards with an underlying data taxonomy, but this is still a work in progress. That shouldn’t stop banks and financial institutions collecting more data and reporting on climate change related matters to help better identify, manage and monitor the key risks to which they are exposed.
The financial services industry is currently in a period of transition for climate change, regulators and supervisors understand that this is work in progress and that the ESG data output cannot be perfect.
But both financial change and regulatory scrutiny is imminent. No matter what the challenges are today around climate change data, it is imperative that all banks and financial institutions start to build their ESG datasets and think strategically about the type of data they need in order to complement their financial data for better risk management.
The overall objective should be to have all companies report ESG data in a standardised and timely manner(across as many sectors and countries as possible in a similar way to their financial data reporting). While the financial sector still has some way to go to achieve this, the view is that there are many data sources (both structured and unstructured) from which banks and financial institutions can start to work right now.
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You can also catch up with our previous blog: Déjà vu for 2020? Regs, Geopolitical Change, ESG & Tech Innovation to Continue to Dominate Agendas
