The long-term decarbonisation or net zero strategies based on Science Based Targets initiative (SBTi) or otherwise can also benefit from AI and analytics
Moving from profit to purpose has been accepted as the bedrock for sustainable future not just by regulators and society at large but also by businesses irrespective of their geographical presence and operational ecosystem.
Digital aid and deployment of data collection platforms have already become the new normal across large business, yet compilation of data alone is not serving the purpose. Data analysis, estimation of trends, forecast and its interplay with changing regulations, integration, and balancing act between investments and ESG priorities (goals and targets) require analytics and Artificial Intelligence (AI) support. This is specifically true for companies listed across geographies. For instance, the impact of European Union Corporate Sustainability Reporting Directive (EU CSRD) on Indian parent companies, or an investor from the US with an equity or quasi equity exposure in an Indian company needs specific forecast on environmental performance. Businesses are therefore unlearning and relearning in more than one way.
Access to quality data has started playing a major role in helping businesses understand and consolidate their positions pertaining to disclosures and build foresight as to how the changes will impact their businesses. For example, introduction of “EPR in tyre industry” in 2022 with a T-2 compliance requirement has resulted in additional unplanned investments for the tyres sold two years back. Now in this specific example, analytics would have helped Indian tyre manufacturers in developing an understanding basis EPR regulations across developed economies, which was the missing link that resulted in additional investment for waste management for the sales which were carried out two years back. Several AI applications can be calibrated to identify, monitor, and produce solutions for these challenges.
Artificial Intelligence (AI tools) and Analytics can facilitate a responsible approach towards creating value for the stakeholders. It has generated significant interest among organisations through improvements in financial, reputational, and operational matrices. The use of AI has offered significant flexibility to companies making public disclosures through:
-Identification of alternate approaches to fill in missing data, which may not be possible through traditional data collation platforms as it can only interact with data lakes available within business ecosystem (predictive analytics)
-Enabling development of scenarios through modeling of historical data and existing body of knowledge within a business to provide compelling information that may assist in financial as well as non-financial planning (descriptive analytics).
- Offering reverse analysis of outputs provided by ranking & rating agencies. The ESG ranking & rating happens both by active participation of businesses as well as passive collection of data by agencies. In addition to this, the more a company discloses, the more are the chances of disagreement across rating outcomes (descriptive analytics).
-Tracking resource consumption, changes in supply and demand across geographies and resource optimisation (image recognition)
Both active and passive ranking and rating agencies depend upon analytics and AI tools to generate opinion on the non-financial performances of companies. Though a large number of players collect data using natural language processing (NLP) tools, few still depend on alternate methods. Also, it is important to understand that NLP comes with its own set of disadvantages essentially when NLP captures data from social media as it begins to reflect sentiments, resulting in weight being assigned to a particular data set / indicator.
Though, it is possible to use large language analytics to understand stakeholder concerns, the biggest advantage of AI would be in establishing a level playing field for businesses when it comes to data collection, analysis and generate outputs / hypothesis which can facilitate decision making. In simple terms, it will help democratise the technology penetration. There may be no need to onboard a pool of data scientists.
Let’s take the example of Business Responsibility Sustainability Reporting (BRSR). The body of knowledge that will be available to Ministry of Corporate Affairs (MCA) from top 1000 listed companies can be sliced and diced to determine sector specific regulations and policies. It would be equally interesting to witness companies deploying analytics to carry out peer benchmarking.
Coming to the long-term decarbonisation or net zero strategies based on Science Based Targets initiative (SBTi) or otherwise can also benefit from AI and analytics. The three-step approach of measurement and analysis of existing environmental footprint, investments, and transition to renewable energy and finally optimisation and identification of emission avoidance opportunities with an overlap of complex regulatory expectations, access to finance and stakeholder expectations can be successfully delivered through analytics.
A live example of this could be large PEs who have started making use of analytics across their investment portfolios to ascertain their environmental footprint per dollar of investment (as they disclose their financed emissions) while approving fresh investment opportunities.
To summarise, AI & analytics can take the businesses to next level, wherein the tools will not just be analysing the data but also provide guidance on “what can” to “what should” and this foresight will help all stakeholders in making the right decisions to accelerate their sustainable growth.