How businesses in Asia are harnessing AI to meet growing ESG obligations

Artificial Intelligence (AI) – and generative AI in particular – has an interesting role to play in the net zero transition and the wider drive for improved sustainability. Faced with the increasingly complex shifting environmental, social and governance (ESG) landscape, organisations are looking to leverage AI to enhance their ESG strategies, yet the use of AI can also give rise to potential ESG litigation and regulatory scrutiny and much focus has been on the vast quantities of energy needed to power AI systems.

In this short blog, we look at some of the issues that businesses in the region are navigating when considering the interplay of ESG and AI.

Evolving climate and sustainability disclosure landscape

Numerous regulatory regimes and soft law standards have been introduced in recent years globally which ask businesses to collect data and report on an increasingly broad range of sustainability-related topics. Disclosure regimes can impact businesses directly and indirectly and can vary across jurisdictions creating challenges for global corporations and requiring a consistent and strategic approach to reporting. Over the last few years, we have seen key developments in the ESG disclosure landscape with very clear implications for 2025, including: 

• adoption of the first two global sustainability disclosure standards by the International Sustainability Standards Board (ISSB) globally, with more than 20 jurisdictions deciding to use the ISSB standards, or take steps to introduce the standards, in their own frameworks, including Japan, Singapore, Hong Kong, South Korea, Malaysia, and mainland China (see our blog posts here and here);
• adoption of the European Corporate Sustainability Directive (CSRD) and the European Sustainability Reporting Standards (ESRS), impacting business in the EU but also non-EU companies with businesses operating in the EU (see our blog post and CSRD Demystified webpage); and 
• the publication by the Taskforce on Nature-related Financial Disclosures (TNFD) of its final recommendations on a framework to identify, manage and disclose nature-related issues (see our Biodiversity and Nature webpage).

Another ESG regulatory regime that is set to have a very significant impact on providers and users of AI is the EU’s new environmental and human rights due diligence rules under the Corporate Sustainability Due Diligence Directive (CSDDD or CS3D) – which is set to revolutionise corporate responsibility in global supply chains and requires companies to adopt climate transition plans (see our CSDDD Explained podcast series). Many companies are turning to cutting-edge compliance technologies to meet regulatory requirements, fulfil net zero commitments, and make necessary disclosures.

AI use cases in ESG 

AI will have significant and far-reaching effects for most businesses, allowing existing activities to be automated and new business models to be created. In the context of ESG and sustainability, its capabilities include:

Data integration and reporting tools

AI has now become a significant player in ESG reporting. It can help organisations make sense of vast quantities of ESG data to help them comply with the increasing sustainability disclosure requirements discussed above, help compare a company’s climate/sustainability performance against its peers, and help the financial sector make better-informed investment decisions. AI can process significant quantities of ESG data by aggregating information from internal documentation, historical reports, and public data from third parties along the value chain. It helps organisations prepare mandatory reporting requirements and voluntary disclosures, streamlining the collation, review, and approval of ESG reports. 

It has been reported that Microsoft announced Project ESG Reporting – a new tool aimed at enabling companies to create, review and approve ESG reports across multiple standards and frameworks – so designed to help organisations solve the challenge of reporting against a variety of voluntary and regulatory reporting frameworks. 

Performance comparison

AI is also being employed to evaluate natural capital risks and generate exposure scores for individual companies within investment portfolios. For example, BlackRock leverages AI to analyse company publications using large language models, assessing their management of nature-related risks and opportunities. 

Climate modelling and operational efficiencies

AI is already proving to be a game-changer in climate monitoring and modelling and in increasing operational efficiencies from smart cities to electricity grids – to name just a few examples.  Integrating AI into data centre activities, for example, can help automate and enhance operational processes, such as resource allocation, security, server maintenance, and monitoring and reporting. AI can optimise energy usage (which is one of the largest operational expenses for a data centre) by dynamically adjusting cooling systems and power distribution based on real-time demand and environmental conditions.

Challenges and regulatory concerns

However, the widespread use of AI comes with its own set of challenges:

Energy consumption

Much has been said about the vast quantities of energy needed to power AI systems. For companies (not just tech companies) under pressure to reduce their carbon footprints and meet their climate commitments, this can prove a serious challenge, including feeding into the debate about if and when to use carbon offsets and the risk of potential liability if a company misses its climate targets or does not make accurate or sufficient disclosures about lack of progress towards meeting its publicly disclosed climate targets.

Liability for inaccuracies

The use of AI to help comply with ESG disclosure requirements also has its challenges – for example, using AI modelling to fill in gaps in information (for example, gaps in Scope 3 emissions data from a company’s value chain). The risks include liability for inaccuracies or lack of transparency when using AI for ESG data collection and analysis, with regulators like the European Securities and Markets Authority indicating that where AI is used in sorting data, for example assessing the ESG credentials of investment data, proper management oversight of the AI system is required for regulatory compliance (see our blog post). 

Litigation and regulatory scrutiny

The use of AI can give rise to potential ESG litigation and regulatory scrutiny, including in respect of greenwashing claims, human rights or discrimination claims due to algorithmic biases or under emerging supply chain due diligence regimes, as well as liability for inaccuracies or lack of transparency when using AI for ESG data collection and analysis to comply with ESG disclosure requirements, or using AI for automated decision-making which could result in adverse ESG outcomes.

Mitigating Risks 

Emerging technologies like distributed ledger technology (DLT) can mitigate the risks associated with AI. DLT provides traceability, enabling auditors and automated reporting via Internet of Things to verify compliance with ESG requirements. Evidence can be presented “on chain,” reducing the risk of greenwashing.

Additionally, companies should engage legal experts at every stage of the development of AI tools in ESG to ensure efficacy and legality. Based on our experience with our clients, advice from legal experts is most frequently needed in the following steps:

• Algorithm Design: Incorporate accurate and up-to-date legal requirements into the design of ESG monitoring and filing algorithms.
• Data Training: Ensure the use of non-disputed datasets with clear ownership and authorisation.
• Tool Usage: Oversee access control, ensure appropriate filing procedures, and conduct general risk control training.

In summary, companies must implement careful management and regulatory oversight to harness AI’s benefits while mitigating associated risks in navigating the complexities of ESG compliance and reporting, to take a step closer to a future defined by sustainability and transparency.

For more information:

Watch our webinar - TMT Meets ESG: Key Trends and Opportunities in Asia

See our Contentious AI webpage.

See our updated AI Toolkit.