How better data leads to better solutions
Environmental Finance: How have the events of 2020-21 driven the impetus for sustainable finance solutions?
Rahul Ghosh: Due to the Covid-19 pandemic, we’ve seen renewed impetus among market practitioners to consider systemic risk in a robust and material way. The past year has also brought social dimensions to the fore. For instance, global issuance of social bonds reached $90 billion during Q1 2021 alone – an eight-fold increase from the previous year.
Meanwhile, assets under management globally – that are managed sustainably – are expected to increase to 40% by 2025 – compared with less than 20% today. Against this backdrop, we are seeing enormous demand for ESG data and content. To make better decisions, market demand for transparent and useful information is surging.
EF: How can ESG content and data providers support market alignment with the evolving regulatory landscape?
RG: The regulatory developments underway will likely result in more consistent disclosure requirements. Among other advantages, EU-led initiatives such as the EU Taxonomy and Sustainable Finance Disclosure Regulation (SFDR) will help to build a common language among investors – thereby helping to galvanise investment flows and increase market trust in ESG-labelled opportunities.
ESG data and content providers can play a pivotal role by helping market practitioners identify the extent to which companies or portfolios are closely aligned with emerging regulations. For example, we have recently launched a dataset covering 2,500 entities and 11 mandatory indicators to help companies respond to SFDR requirements. Our coverage will expand to all 18 mandatory indicators later this year – ahead of SFDR reporting requirements coming into effect from 2022. We are also building a comprehensive framework to assess the extent to which companies globally align to the EU Taxonomy.
EF: How far can standardised data take us in solving some of the challenges of ESG data?
RG: It’s clear that investors need granular, standardised ESG data and insights. Yet herein lies the challenge: the ESG data available today are vast and take various forms. Some metrics are inconsistent, and sourcing accurate historical data can be challenging. More rigorous standards for data collection and disclosure, therefore, form part of the virtuous cycle that will reinforce market confidence in ESG analytics.
Standardised data and disclosure practices alone are not enough, however. Take climate risk disclosure, for example. Our coverage for alignment to the Task Force on Climate-related Financial Disclosures (TCFD) spans 3,100 companies globally. We have seen progress on the disclosure of climate metrics – including scope 1, 2 and 3 – but the translation of climate risk into financially-relevant information isn’t quite so advanced. Companies are still wrestling with how to integrate and report on climate-related information relevant to their business strategies, models and future resilience strategies – particularly for physical climate risk. Looking ahead, the application of data into new risk measurement techniques, benchmarking analytics and models will be critical.
EF: Can you provide examples where new risk measurement techniques are emerging?
RG: Among banks, stress testing requirements are increasing. However, challenges arise when assessing an entire sector’s exposure to physical climate risk, compared to identifying transition risk by sector. To help banks respond to stress testing requirements, we have location-specific data on more than two million commercial facilities globally, enabling us to score companies based on forward-looking exposure to physical climate hazards at the facility level.
The European Central Bank (ECB) recently used our data to help assess the climate exposure of four million companies and 2,000 banks across Europe, as part of its preliminary climate stress tests. The ECB concluded that, without effective climate policy, physical climate risk will increase substantially. Depending on their location, the ECB found that the firms most geographically vulnerable to physical risk could have up to four times as much climate risk as the average firm.
Another innovation centres around helping investors, large companies and banks overcome ESG disclosure gaps among small- and medium-sized enterprises (SMEs). Since ESG disclosure among smaller companies is almost non-existent, we frequently hear that larger organisations are struggling to identify and report on ESG vulnerabilities within supply chains or within portfolios. Working with our partners in Moody’s Analytics, we are developing an SME Score Predictor Tool that calculates ESG scores via a model-driven machine learning algorithm. By analysing company size, location and industry characteristics, the tool can also create more than 50 standardised ESG and climate-related metrics for 100,000 SMEs.
EF: How will better sustainability data and performance measures feed innovation in sustainable finance going forward?
RG: We are all familiar with the phrase, “you can’t manage what you can’t measure” – but soon it may be a matter of, “you can’t finance what you can’t measure”. Take the rise in popularity of sustainability-linked bonds. These instruments offer a dynamic vision of sustainability. However, they do require issuers to define several key performance indicators (KPIs). Most KPIs today – around 60% according to our analysis – are greenhouse gas emissions-related. But we are seeing targets related to renewable energy, water use, waste management and even community involvement or gender diversity.
Targets will need to be based on clearly defined pathways and robust assumptions – and that’s where disclosure around historical performance, science-based criteria, and transparency on the scope and coverage of different commitments will be important. As a result, the availability of more ESG data could be a vital enabler for innovation – allowing for more ambitious targets and, in turn, greater investment within the ESG domain.