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ESG Analytics: AI for Corporate Sustainability

Updated: Feb 5

By Miguel Moreira da Silva

ESG targets are challenging, but the way organizations have been dealing with these indicators is based on a highly manual calculation and a total lack of predictive capacity. It is important to review this issue.

In the context of the climate, social and economic challenges, it is important to reflect on the role of advanced data analytics in accelerating sustainable growth.

Unlike in the past, today, in the vast majority of corporate boardrooms, green investment is perceived by executives as inexorable and cost-effective. All listed companies report ESG (Environment, Social and Corporate Governance) indicators, and the Sustainability Report is a key instrument of institutional communication and investor relations.

However, the collection, treatment, processing and modeling of data to calculate ESG indicators is a complex time-consuming process, requiring the engagement of a wide range of company areas for whom sustainability reporting is not a top priority.

The sustainability or ESG areas of organizations have the daunting task of ensuring harmonization of measurement methods (e.g. CO2 emissions, waste production, water consumption, biodiversity) and procedures for calculating indicators according to best practices. In a company with several business units, scattered across different geographies, there is the additional challenge of mapping and monitoring operational information and aggregating it in an accurate, consistent and systematic way in corporate reporting processes.

Companies are therefore faced with the challenge of combining environmental, social and governance excellence with operational efficiency and team productivity. Faced with this challenge, we recommend designing an ESG analytics roadmap that prioritizes digitization, automation, and artificial intelligence (AI) initiatives.

In achieving this roadmap, companies will benefit from the development of a unified database and platform for ESG intelligence & analytics, which not only ensures transparency in the lifecycle of indicators (from measurement to reporting) but also makes it possible to increase operational efficiency and generate simulation and foresight skills.

In addition to reporting, AI enables the creation of probabilistic planning models, scenarios and simulation of variables (e.g. GDP evolution) with impact on business and ESG indicators.

As an example, the war in Ukraine resulted in high pressure on the supply of natural gas to thermal power plants and pipeline networks. This new gas market context resulted in a setback in energy transition, materialized by the increased use of more polluting energy sources, such as coal and petroleum-based fuels. In short, the conflict has forced companies to change their business strategy, but the ESG targets remain (unrealistically) intact.

All things considered, challenging ESG targets have been addressed by organizations in what could be pictured as a "rear view mirror" control of indicators, and a total lack of predictive capability. ESG analytics models, on the contrary, allow companies to provide managers with an integrated business-ESG vision, bringing realism to strategic plans, indicators control capacity and ambition in the design of roadmaps for corporate sustainability.

Originally published in Jornal Económico (Portuguese version only)


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