ESG: how to transform ESG with technology
At most companies, ESG discussions typically focus on issues such as pollution control, biodiversity, health and safety, business ethics and board diversity. Technology-related risks and opportunities do not receive adequate attention. But three areas that business leaders need to start focusing on now are green software, AI bias, and trusted data. In the future, they will have enormous implications in the three components – E, S and G – of the organization.
It has come to the fore due to the exponential increase in cloud adoption by global enterprises, fueled by the pandemic. Data centers already account for more than 1% of total global electricity consumption each year. This is expected to skyrocket to 8% of total global electricity demand over the next 10 years.
Data centers not only use a lot of electricity, they also need a lot of water to stay cool. The environmental footprint of data centers is becoming a significant concern around the world. Optimizing hardware and using solar or other renewable sources for electricity helps to reduce the carbon footprint to some extent. But one area that can also help immensely is green software – where the software algorithm ensures maximum energy efficiency. This is essential because the electricity consumed in data centers directly depends on how efficiently software applications manage hardware resources.
In simulations conducted at the University of Washington, green software development techniques reduced energy consumption by up to 50%. Earlier this year, the Green Software Foundation — founded by corporations and nonprofits including Microsoft and the Linux Foundation — took on the task of bringing the sustainable coding movement on board. It is currently establishing green software standards and practices in various IT disciplines and technology fields. Going forward, sustainability leaders will want to ensure that software developed by their employees and suppliers includes green practices that are subject to energy monitoring, peer benchmarking, and performance reviews.
As companies increasingly harness the power of artificial intelligence in everything from hiring decisions to customer service, concerns about AI bias are also being flagged. Algorithmic or AI biases can have profound implications in almost any area of deployment. For example, this bias could lead to discrimination against minorities and women, and raise questions about privacy, especially about the amount of data needed to be collected to make decisions. If AI is used to make decisions about people who may have an undesirable impact, how do companies deal with this? How much information about people is appropriate to capture? What decisions are we going to let a machine make? All of this could lead to a bigger social governance problem. For example, a major conglomerate recently apologized for an “unacceptable error” in which its AI-based algorithms classified a video about members of a minority community as being about primates. Businesses need a plan to mitigate these risks. To ensure social fairness, it is essential to have strong governance controls for the development and deployment of AI solutions.
Currently, investors rely on two primary sources of information to make funding decisions. The first concerns a company’s self-reported quantitative and qualitative data regarding ESG impact. The second is peer-to-peer benchmarking of a company’s ESG performance, for which third-party ESG ratings are leveraged. Unfortunately, the plethora of scoring methodologies often hampers objective decision making. This issue can be addressed by triangulating the above data points leveraging natural language processing (NLP) techniques that can help perform sentiment analysis on stakeholder perceptions of company ESG policies and practices. a company. This involves analyzing online news and social media posts for positive attributes, as well as potential controversies, complaints and legal actions. NLP enables the real-time conversion of millions of structured and unstructured pieces of information – including text, images, and videos – into a structured, intelligent dashboard that can help “unify” disparate metrics. This can be aligned with various ESG frameworks and performance standards and ultimately used by investors to make more informed decisions.
Investors can also benefit from technologies such as blockchain that enable reliable and standardized ESG data collection and reporting. For example, a global phone manufacturer uses this technology to trace the origin of raw materials and work-in-progress inventory throughout its global supply chain. It has a real-time view of quality-related compliance certifications, as well as labor and environmental permits. This facilitates the transparency and auditability of the ecosystem of suppliers, contractors, distributors and service providers, which investors seek when assessing companies’ ESG practices and impact.
Organizations operate within a complex ecological system and ESG metrics serve as an indicator of the quality and impact of their interactions with various stakeholders. In this regard, leveraging emerging technologies can help companies create transformational solutions to address the ESG challenges we face today.
The author is the leader of the technology sector and Chaitanya Kalia is the leader of climate change and sustainability services at EY-India.