The Role of Technology in ESG Reporting

Jan 20, 2023

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Technology’s role is increasingly becoming important in the world of Environment (E), Social (S), and Governance (G) and is useful in different aspects of a company’s ESG journey. From the accurate collation and analysis of ESG data to helping a company gain deep insights into their ESG performance and therefore improve. Furthermore, technologies such as Artificial Intelligence (AI) and Machine Learning (M/L) combined with software platforms can help with ESG Investing and ensuring transparency. Let’s dive deeper into the role of technology in ESG reporting and analysis.

Advancements in technology in ESG Reporting 

Artificial Intelligence

AI is the science and engineering of creating intelligent machines, most specifically, intelligent computer programs. This technology encompasses various branches of computer science, cognitive science and engineering, which focus on the development of intelligent systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language understanding.

AI can generally be classified into two main categories: rule-based systems and machine-learning systems. Rule-based systems use a set of predefined rules to solve problems, while machine learning systems use algorithms to learn from data and make predictions or decisions.

Technological advancements in the analysis of data, with the help of artificial intelligence, have allowed data providers to increase the amount of data that is accrued and therefore, enhance the actionable insights provided to companies. These actionable insights are crucial for companies to improve their ESG framework.

Natural Language Processing

Natural Language Processing refers to the branch of artificial intelligence concerned with giving computers the ability to understand text and the spoken word, like human beings.

One of the key technologies under the ‘umbrella’ of AI is the use of Natural Language Processing (NLP) and Machine Learning (M/L) algorithms in ESG analysis. These technologies can be used to analyze large amounts of unstructured data, such as thousands of company annual reports, sustainability reports, integrated reports, impact reports, and news articles – to identify relevant information about a company’s ESG performance.

For example, NLP can be used to automatically extract information about a company’s environmental impacts, such as its greenhouse gas emissions, while M/L can be used to identify patterns and trends in this data.

A specific instance of the use of NLP and M/L would be NN investment partners who studied ESG across thousands of reports and found that the social pillar was being discussed significantly more in recent years. They used NLP to study thousands of reports and articles, which equates to 4 million paragraphs and 330 million words to achieve their results.

It's safe to say, this would have been impossible for humans to read the information, let alone analyze it to get deep insights into the momentum of ESG amongst corporates. All these insights and data can then be used for investment purposes or to inform investment decision-making.

Data Providers

Essentially, data providers can be classified into three types and are done so by the type of data that is collected and analyzed. The 3 types are as follows:

- Market-based: This type of data provider measures industry trends and investment performance notwithstanding ESG status.

- ESG Generalist: The data provider would measure all 3 aspects of ESG – i.e. Environment, Social and Governance

- ESG Specialist: This type of data provider specializes in collecting and measuring a single pillar of ESG. i.e. either the environment or social or governance. Popular ESG data providers include Sustainalytics, Bloomberg, FTSE Russel, and MSCI.

Predictive Modelling

Another area where AI is being used in ESG analysis is predictive models. Predictive models can be used to forecast a company’s future ESG performance, based on historical data and other information. Therefore, predictive modeling can use data on the company’s current emissions, energy consumption, water consumption, and production processes and run it through the model to estimate the company’s future greenhouse gas emissions.

Improving ESG transparency

AI is also being used to improve transparency and accessibility of ESG information. For example, companies can use AI-powered chatbots or virtual assistants to provide stakeholders with real-time access to information about their ESG performance. Additionally, AI-powered visual analytics tools can be used to present ESG data in an interactive and user-friendly format, making it easier for stakeholders to understand and interpret the information.

Benefits and challenges of AI technology in ESG reporting

The benefits of using AI technology in ESG reporting include improved efficiency and accuracy, as well as the ability to identify patterns and trends in data that may not be immediately apparent.

More specifically, consider the difficulties companies have in the measurement of Scope 3 emissions. Scope 3 emissions data is amongst the hardest to track and validate as it would require companies to get this data from their suppliers (if they have it) or from customers. A nearly impossible task for humans but within the abilities of a sophisticated AI-based platform.

However, there are also some challenges and limitations to consider, such as the quality of the data used to train and test AI models and the lack of standardization in the use of AI technology in ESG reporting. Significantly, humans are behind the development of AI algorithms and therefore, inherent bias is a risk at many levels thereby potentially skewing the credibility of insights gained.

Examples

Some companies that use AI to analyze data on companies’ ESG performance including data on environmental sustainability, social responsibility, and governance – are as follows:

BlackRock: BlackRock uses AI to analyze data on companies’ ESG performance such as climate change, water usage, and human rights. BlackRock can then identify companies that are likely to perform well on ESG issues.

Microsoft: Microsoft has been using AI to track and report on its carbon emissions and energy usage, as well as to develop new products and services that can help reduce their environmental impacts. Additionally, Microsoft uses AI to analyze data on its supply chain and to identify and address issues related to labor rights and human rights.

Unilever: Unilever uses AI to analyze data on its operations and supply chain, to identify and address issues related to environmental sustainability and social responsibility.

Goldman Sachs: Goldman Sachs uses AI to analyze data on companies’ ESG performance, including data on environmental sustainability, social responsibility, and governance. This allows Goldman Sachs to identify the companies that are likely to perform well on ESG issues and develop investment strategies that incorporate ESG performance.

S&P Global: S&P Global uses artificial intelligence to analyze data on companies’ ESG performance. S&P Global uses this data and the deep insights gained to develop investment strategies.

Conclusion

While companies can take huge steps forward by adopting emerging technology and software platforms to ensure that accurate ESG data is collected and analyzed – this is just the beginning. Sure, these platforms would be able to generate ESG reports as per specific frameworks and international standards but your message and overarching story must be reflected in your annual report, integrated report, sustainability report, impact report, or ESG report – in a manner that is easy to read and understand.

Hence, companies hire specialized corporate reporting agencies, such as Report Yak. We have a combination of skill sets including a dedicated content team that has experience and expertise in getting our client’s message across to investors and stakeholders. Check out some of our award-winning reports and please feel free to reach out!