Environmental, social and governance aspects have been getting a bad rap in some corners of the business world. Advocates identify the problem with ESG as the excessive emphasis placed on the environment and not enough on social and governance issues. Ricardo Vargas and Antonio Nieto-Rodriguez write that, when used correctly, artificial intelligence may hold the answer to this problem, helping organisations emphasise all aspects of ESG in their projects.
The term “ESG” (environmental, social and governance), traditionally a core issue in the World Economic Forum summit of top business leaders in Davos, was not even mentioned in the 2024 meeting. Instead, and not coincidentally, this year’s theme was Rebuilding Trust, which applies directly and appropriately to the crises in confidence enveloping ESG.
As delegates gathered in Davos, bond rating agency Morningstar issued a report showing that 2023 was the first year that ESG funds suffered a net outflow of capital. Investors pulled more than US$13 billions out of sustainable funds due to concerns about underwhelming results, increasingly shrill political scrutiny and “the absence of clear, cross-border regulation for environmental, social and governance, and sustainable investing.”
All those concerns are real, but they don’t get to the bottom of the crisis facing ESG: it has lost focus and impact because of an obsession with environmental impacts, with little or no scrutiny of the broader issues of social and governance. The “green blind spot” represents a significant and possibly existential threat to ESG. Without an equal emphasis on all three letters in its initialism, we will edge ever closer to the end of ESG and its promise to make the world cleaner, more resilient and more just.
How the green blind spot undermines ESG
Let me give an example. The United Nations received directives from donor countries that all new public infrastructure projects (such as schools and hospitals) should come equipped with solar panels to make them energy-self-sufficient. Theoretically, it was a great idea, particularly for projects in countries with little or no established power grid.
However, the local environment was ignored. In some Saharan countries where this strategy was being considered, the persistent presence of dust and sandstorms is a fact of life, and they can be extremely destructive for glass solar panels, even with constant cleaning and maintenance. When the panels began to succumb to the ravages of the environment, the projects became technological white elephants, symbolic of first-world hubris and waste. These were realities that donor countries, in their rush to embrace what they deemed was an obvious environmental win, did not consider.
It’s not hard to see how the green blind spot drags down the entire issue of ESG. Despite increasing attention to this deficiency in ESG thinking, we’re simply not learning from our past mistakes and continue to emphasise only one of the three letters in what is a potentially world-saving strategy.
There is hope, however, that artificial intelligence will help us give equal time and effort to all three letters in ESG and finally deliver on its enormous potential. But to use this technology to make the next quantum leap in ESG, we have to take a holistic approach to ensure that AI is a net positive.
The danger of the blind spot
A 2020 joint endeavour between Cambridge University and the University of Melbourne, the Green Algorithms project has created calculators that “researchers can use to estimate the carbon footprint of their projects [and] tips on how to be more environmentally friendly.” The publicly accessible Green Algorithms Calculator has already been used in groundbreaking research into the carbon footprints of technology that requires massive computer power.
For example, a landmark study published in 2022 by Cambridge and the Baker Institute in Melbourne, Australia, found that the enormous energy needs of “large-scale computational infrastructure” required keen emphasis on using leading-edge software and hardware in data centres to avoid creating unsustainable and unjustifiable carbon emissions.
The study also found that some of the solutions being applied to date are making the problem worse. Some AI researchers were turning to “faster processors, or greater parallelisation”, to reduce running time and energy usage. But while running time is shorter, using these faster machines “can lead to [a] greater carbon footprint.” Conversely, applying more efficient data centre software and hardware can reduce carbon footprints by more than 30 per cent, putting data mining and analysis on the right side of the ESG equation.
However, in their current form, these green algorithms – entirely focused on environmental impacts – don’t necessarily help the ESG cause, nor ensure that our urgent need to reduce greenhouse gas emissions won’t have unintended negative consequences.
For project managers, the real challenge is how we can use AI to check all three boxes in ESG: showing us how to get more of what we need over a much longer period while using less energy and with a guarantee that the benefits are made available to as many people as possible.
For projects in developing countries, donors and international agencies are attuned to using advanced, recycled and environmentally safe materials to build things like bridges. However, these projects typically require the materials to be imported, cutting local businesses out of procurement. Advanced materials also come with the need for workers with advanced skills who are not in ample supply in the recipient nation. So, entire workforces are imported, skewing local labour markets.
The way forward
We need a way to model and calculate each project’s ESG consequences. But most of the tools associated with green algorithms focus almost entirely on environmental impacts. Whether using an algorithmic calculator or performing manual data analysis, a more holistic and comprehensive approach is needed. We suggest six steps to get there:
Define sustainability objectives
In modern project management, well-defined social objectives can act as a roadmap, helping to guide the AI-driven solutions you may employ.
Leverage frameworks
Frameworks like the United Nations’ Sustainable Development Goals (UN SDGs) can be used to identify sustainability and social objectives that may be lost in an assessment focusing solely on the environment. Asana or similar project management software can be tailored to include sustainability, social and governance metrics in your objectives and key results (OKRs).
Centralise data
For green algorithms to function optimally in a project management context, having strong, centralised data is vital. This ensures that algorithms and other tools have real-time, comprehensive data to make sound decisions. Use data management platforms or data lakes to store all markers. Open-source platforms like CKAN can be customised for sustainability, social metrics and data tracking.
Customise algorithms
The essence of effective project management lies in customisation. Projects often have unique sustainability and governance challenges, which generic algorithms can’t address effectively. Adapt prebuilt algorithms to meet your sustainability and social objectives, whether it’s reducing emissions or enhancing energy efficiency. TensorFlow and scikit-learn are machine-learning libraries that offer prebuilt algorithms that can be customised to meet your project’s sustainability and social criteria.
Perform pilot testing
Before any algorithm can be integrated into the larger project management framework, it must be tested in real-world conditions. A well-executed pilot test provides insights into how well the algorithm serves the project’s sustainability and social goals and what fine-tuning may be needed. Use simulation techniques to model your project’s ecosystem, allowing you to rigorously test your algorithms under various scenarios. Simulation software like Simul8 can help you create a digital twin of your project, facilitating the pilot testing of your green algorithms.
Apply full-scale implementation
This is where the algorithm moves from being a theoretical concept to a practical tool that helps the project meet its goals. Use a phased approach, gradually incorporating the algorithm into different aspects of the project while monitoring performance metrics closely. Software platforms like Jira offer functionality to track the implementation process across multiple departments or subprojects.
Fears that ESG is failing or has already failed are well founded. In the rush to claim ESG credentials, we’ve cut many corners and made many mistakes. And the biggest mistake right now is putting sole emphasis on environmental concerns to the detriment of social and governance.
The good news is that modern tools can give us total insight and clarity about the full range of impacts of any one project. We need only ensure that we take a wide view and not succumb to the temptation to focus on, and be satisfied by, checking only one box.
- This blog post represents the views of the author(s), not the position of LSE Business Review or the London School of Economics and Political Science.
- Featured image provided by Shutterstock
- When you leave a comment, you’re agreeing to our Comment Policy.
Print Friendly
