Dr. Sandy Smith, EHS&S Vice President – EMEA & APAC, Sphera, Brian Payer, Vice President, EHS&S, Sphera
Sustainability is rapidly becoming a prime driver behind consumer and investor decisions, transforming Environmental, Social and Governance (ESG) factors from an optional bolt-on into a key financial risk. An increased spotlight on corporate ESG performance, especially in the finance sector with the publication of the EU Sustainable Finance Strategy, means that companies with unsustainable practices can face greater regulatory risks, higher costs to access capital, lower returns on investment, higher operating expenses and lower top-line growth. With rising environmental and social consciousness among regulators, consumers, and financial institutions alike, ESG is increasingly critical to everything from talent retention and customer acquisition to investment. Yet, even though ESG now represents a business risk equivalent to other financial risks, it is thus far not on a par with traditional risk management and accounting.
Quantifying ESG risks across end-to-end product lifecycles and multinational supply chains represents a major challenge in a field that has yet to see the same digitalization and standardization as other forms of financial accounting. The risk is compounded by the competing and contradictory array of ESG standards, and ratings agencies using proprietary, mysterious models of accounting that can conceal or confuse the calculations and uncertainties behind ESG scores. There is a similarly disparate array of standards for capturing, curating, aggregating, and communicating the exponentially expanding array of ESG data. And there are no universal ways of weighting different environmental impacts such as land use versus carbon emissions to help companies make the appropriate trade-offs.
Despite the myriad of models and methods at play, we are seeing an insatiable appetite for ESG investment, which is tipped to represent $53 trillion in assets under management by 2025. This creates an urgent imperative to consolidate sustainability data and coalesce around common methods of quantifying and controlling ESG risks. Below are some of the emerging technology trends that could soon enable the first standardization of ESG reporting and information.
Cloud tech will drive transparent, traceable ESG data
Crude, simplistic generalized single ESG scores and input/output models will likely be replaced with transparent, holistic ESG ratings. For example, universal single scores conceal key differentials in performance across various ESG components and fail to offer a granular understanding of individual metrics such as land or water use. Similarly, input/output models that calculate CO2 generated for each amount of revenue offer a narrow, short-term view that fails to account for wider environmental impacts across entire product lifecycles. And private, proprietary methods of modelling ESG performance that do not reveal the uncertainties around the data reinforce allegations of greenwashing by concealing the calculations behind different ESG scores.
Instead, we will increasingly see industries rally around more nuanced, granular models and metrics such as Science Based Target initiatives (SBTi). Simplistic, short-term calculations will be replaced by holistic accounting methods such as environmental lifecycle impact assessments that record cradle-to-grave ESG performance. Cloud technology and cross-sector standards for collating, aggregating, and communicating ESG data across value chains will enable an infinitely scalable, standardized ESG data trail across entire sectors. This will far surpass the current status quo tracking abilities of spreadsheets and linked documents that simply do not have the capabilities to handle nuanced metrics in the complex, scalable manner that is now a fundamental requirement. Revolutionising this process will ultimately support a holistic and accurate view of ESG risk across organizations and industries.
Data analytics will help us to move beyond single scores towards individual ESG impact categories and smart, standardized ways of weighting performance between them. For example, we use smart data to measure performance across numerous environmental impact categories from land use to ozone depletion and enable companies to strike the optimal balance between them. Cloud based data displayed on digital dashboards will enable differential performance to be monitored and managed across myriad environmental categories in real-time.
Internet of Things, data analytics and AI will drive predictive ESG
Current ESG reporting often measures past performance. While this is useful for auditors, a lack of comprehensive, current data hampers organizations from measuring and managing ESG risks in real-time and improving future performance. Without this, they cannot make data-driven decisions or strike the optimal balance between different considerations such as costs and carbon emissions. Crucially, without forward-looking data, companies cannot accurately model, predict and plan for future ESG performance across different business scenarios. No company would set profit or turnover targets without being able to forecast future performance or even track current performance. Yet, many are currently setting ESG targets without the ability to analyse and optimize their existing ESG practices.
Instead, technologies such as the ‘Internet of Things’ (IoT), data analytics and AI will soon allow companies to analyse, optimize and predict international ESG performance based on live data. For example, we use digital dashboards drawing on live data feeds that enable companies to tweak business decisions to ESG parameters in real-time. Managers can access live dashboards tracking everything from location-based energy use to employee satisfaction levels across multiple divisions.
Live data will enable companies to quantify and control environmental, social or governance performance and make data-driven decisions that balance bottom line costs against ESG. One such use-case could see businesses visualising how investing in carbon capture and storage versus cleaner fuel will impact both its ESG rating and operating costs.
This would help forecast the effect of future investments on sustainability across both optimistic and pessimistic scenarios. Future investment decisions will be based not only on various geopolitical scenarios but also all the possible energy mixes in a new target geography. Digital twins could facilitate remote, real-time interventions to avert ESG risks across all infrastructure and supply chains. And in future, AI algorithms could autonomously adjust corporate risk management practices based on smart data, predicting and preventing ESG risks before they materialise.
Opening up the black box of ESG accounting
ESG continues to be perceived as a risky field of ‘black box’ accounting, exposing financial institutions and investors to potentially dangerous and damaging oversights. In this confusing landscape, many firms have their own in-house ESG metrics and models but these lack credibility without universal gold standards to act as an external reference point.
There is an urgent imperative for greater trust, transparency and traceability around sustainability data across product lifecycles and value chains. New ESG standards could provide a universal stamp of quality assurance for ESG practices across sectors. From data analytics to the predictive power of AI, new technologies are now bringing us a step closer to giving ESG true parity with other forms of accounting. The question is, will the financial sector rise to the occasion in 2022?
Global Banking & Finance Review
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