Sustainability applications of AI
By Daulet Teginbayev, former Senior Analyst at Sancroft.
This year saw an unprecedented expansion of generative Artificial Intelligence (AI). OpenAI’s ChatGPT made history by becoming one of the fastest growing applications, reaching 100 million active users within two months of its release. The generative AI boom continues to take the world by storm as it enables individuals to perform routine tasks such as writing emails and creating digital art within seconds. This digital AI revolution is happening amidst rapidly accelerating and widespread changes in natural systems caused by climate change.
The ongoing fascination with AI largely revolves around its future effects on society, economy, and jobs. According to a report by McKinsey, the potential contribution of generative AI to the global economy is to range between $2.6 trillion and $4.4 trillion annually with applications across business functions. As businesses gradually incorporate AI capabilities, the question of how AI tools might help address complex sustainability issues in the near and midterm arises.
How can AI drive sustainability transformations?
Generative AI has numerous applications in sustainability-related projects and activities if it is implemented ethically and using representative and credible data sources. Research suggests that it can aid resource management and employee efficiency in sectors such as energy, agriculture, transportation, and conservation. Businesses are already beginning to reap the benefits of AI adoption in three major areas of sustainability.
1. Climate change
Decarbonisation and digitalisation are two disruptive yet interconnected forces. Over 80% of companies listed on FTSE 100 have made public Net Zero commitments. To comply with the objectives of the Paris Agreement and frameworks such as Science-based Targets (SBTi), businesses are expected to decarbonise not only their own activities but also their supply chains. However, gathering the necessary data to calculate emissions in a robust and consistent manner presents a substantial hurdle to a low-carbon transition due to the lack of widely adopted emissions tracking. According to CDP, less than half of businesses track and report on scope 3 emissions.
While gathering reliable, supplier-specific data is a time-consuming and costly endeavour, AI has a crucial role in automating data tracking and facilitating supply chain collaboration. Boston Consulting Group (BCG) created BCG CO2 AI, an AI-driven system, to assist businesses in measuring, simulating, tracking, and optimising carbon emissions at scale by enabling the exchange of Scope 3 data. The platform’s unique feature lies in helping companies collaborate on reduction initiatives and share abatement levers with suppliers, thereby fostering value chain decarbonisation. The tool also simulates various emission reduction scenarios and evaluates the possible effects of different interventions using real-time information powered by AI algorithms.
2. Biodiversity loss
The adoption of the Kunming-Montreal Global Biodiversity Framework in 2022 was an historic milestone, setting an international goal of restoring degraded areas and halting species extinction by 2030. Biodiversity is also gaining momentum among business, stakeholders, and investors. Consequently, there are growing expectations for corporations to create nature-positive outcomes and align with global targets for biodiversity preservation. To fulfil their biodiversity commitments, companies will need tools to transform business practices and manage risks more effectively.
Artificial intelligence is already being utilised for biodiversity loss monitoring and management: to identify species, map restoration areas, and help prioritise areas for protection. One notable example is the ARIES tool developed by Klab, which enables ecosystem accounting for terrestrial areas worldwide, such as countries, regions, or watersheds. This tool relies on publicly available remote-sensing data and models and has been used to support conservation planning and protection against illicit activities such as poaching. A recent study by PwC claims that similar AI solutions can help combat illegal deforestation, saving over 32 million hectares of forest globally by 2030.
3. ESG reporting and disclosures
The proliferation of sustainability frameworks, both at the international and country levels, means companies are required to report using multiple frameworks that serve different purposes and audiences. This poses a challenge for companies, especially smaller ones, as sustainability reporting demands extensive data collection and human resources, and can suffer from imprecision, lack of standardisation and overall unreliability.
AI-enabled data from satellites, sensors, and blockchain has given companies new tools to measure and report their sustainability metrics. AI tools can improve data tracking quality and reduce errors compared to manual data entry. Companies may also automatically generate performance reports by utilising modern NLP models like natural language generation (NLG).
Salesforce provides a notable illustration of incorporating AI technology into sustainability reporting. Within its Net Zero Cloud, Salesforce offers a tool utilising generative AI to optimise the reporting process for ESG data. This feature enables organisations to measure, manage, and disclose their ESG performance, ensuring alignment with recognised standards and frameworks such as SASB, CDP, TCFD and GRI.
Are you interested in exploring how AI tools can help support your sustainability targets? Get in touch with Ailsa Dormon.