What makes a context? Let me put it this way. I like talking about climate change because for me it’s a pretty big deal (frankly, it should be to every single one of us) but I know that not everyone else does. So, I make certain that when I’m engaged on the topic, I use keywords, phrases and anecdotes that reflect the values of people I’m chatting with or the environment in which the conversation is happening. At the micro level, then, all I’m trying to do is empathise and engage with those who are trying to understand the topic I’m on in a way that holds true to the meaning but in a relevant and applicable way. Should it be any different when it comes to ESG data collection and disclosure?
The importance of contextualising Environmental, Social and Governance (ESG) data stems from the fact that ESG factors are numerous, highly diverse and encompass nearly all conceivable sectors of the global economy. Therefore, broad-brushing all business sectors with the same ESG criteria could lead to the dilution and eventual erosion of meaning and relevance of ESG information. So, it becomes essential that ESG data ties to sector specificity and collection of ESG data mirrors the requirement for sector-relevant, material disclosures.
Within the global economy, various sectors will differ greatly in their operations, impacts, priorities, targets, legislation, concerns and opportunities. Consequently, different sectors will respond to ESG changes and challenges in different ways. This is reflected by their respective ESG requirements for emerging issues; technological, environmental or beyond.
Take artificial intelligence (AI) as an example of an emerging technology that will plausibly impact most, if not all, conceivable sectors of the economy. The ESG factors of AI will be exceedingly diverse and the relevance of different ESG factors to different sectors will be correspondingly vast. For example, stakeholders in the extractives and mineral processing sector will be most concerned with ESG factors relating to material procurement for AI, compared to the healthcare sector which would likely prioritise ESG factors relating to data protection and security.
Likewise, emerging environmental issues, such as the projected increases in extreme weather events will impact ESG decision making across sectors in different ways. For example, the projected increases in flooding associated with climate change, will be particularly of concern to agricultural and infrastructure sector, specifically presenting risks of resource and material acquisition. Stakeholders in these sectors would therefore require ESG data on factors that will help safeguard these industries against long term trends to flooding.
These examples depict just how varied ESG data requirements can be across different sectors. Evaluating diametrically opposed sectors under the same ESG criteria would be invidious and counterproductive. Instead, personalised sector specific ESG data is essential for companies to make effective and dynamic ESG decisions.
To ensure the most appropriate and relevant data is collected, the user must be clear on their specific sectorial ESG challenges (present or future) and their principle targets for collecting data- risk reduction, performance enhancement or ensuring compliance.
However, this is easier said than done. Forecasting future trends and determining likely ESG challenges is inherently challenging and complex. Even the most sophisticated predictions are surrounded in uncertainty and should be approached cautiously.
One avenue for predicting risk and opportunity, specifically related to climate change, is through scenario analysis from the Task Force on Climate-related Financial Disclosures (TCFD). This provides stakeholders specific information on potential risks and opportunities to industry through climate change. This framework can be used for companies in various sectors to outline and distinguish which climate change impacts are most relevant to them and thus can act accordingly. Scenario analysis is already being applied by a number of companies in various sectors of the economy to evaluate and improve their long-term investment opportunities.
Another approach is through the Sustainability Accountants Standards Board (SASB) materiality matrix. This shows which ESG factors are most likely to affect each industry. Analysis of this matrix can provide for more informed and relevant ESG data collection which results in lower risk profiles and improved ESG decision making.
The bottom line is that choosing the best and most relevant ESG data specific to each sector will yield better and more useful results for all ESG ‘data consumers’. Thus, it is essential that data gatherers, preparers and reporters implement purpose driven ESG data collection that is derived from their key values and strategies.