Post-COVID era sees surge in AI adoption for green corporate strategies

Companies across industries are increasingly turning to AI to balance productivity with ecological responsibility, a trend underscored by growing global emphasis on green growth and sustainable development. A new study published in the journal Sustainability offers robust empirical evidence that the adoption of AI is reshaping how Chinese businesses allocate resources, innovate, and enhance their green performance, setting a strong precedent for corporate sustainability in the country.

The peer-reviewed study, “The Impact of AI on Corporate Green Transformation: Empirical Evidence from China,” analyzes a comprehensive dataset of A-share listed firms in China spanning from 2015 to 2022, providing a detailed view of how AI adoption influences green total factor productivity (GTFP) - a key metric that captures eco-efficiency by balancing output performance with environmental impact.

AI as a catalyst for sustainable operations

AI adoption is now a key driver of corporate green transformation, improving both efficiency and innovation capabilities. By processing large datasets and identifying inefficiencies, AI enables firms to optimize energy management, enhance supply chain resilience, and improve waste management systems. These operational improvements directly contribute to higher productivity while reducing environmental footprints.

The study finds that companies integrating AI technologies show a measurable increase in green total factor productivity. This positive relationship holds across different model specifications and remains consistent even after addressing potential biases, such as endogeneity concerns and sample selection effects.

Mechanism analysis reveals that AI contributes to sustainability through two primary channels. First, it increases firms’ emphasis on research and development. Companies adopting AI allocate more resources toward innovation projects that focus on energy efficiency, emissions reduction, and clean energy adoption. Second, AI enhances green innovation capabilities by streamlining both independent and collaborative innovation processes. By reducing costs and shortening R&D cycles, AI empowers companies to develop new sustainable solutions faster and more efficiently.

Regional, industrial, and temporal dynamics

The study uncovers significant variations in the impact of AI adoption across regions, industries, and time periods. In eastern China, where infrastructure and technological readiness are more advanced, the benefits of AI-driven green transformation are particularly pronounced. This regional edge underscores the role of economic development and digital infrastructure in facilitating AI integration.

Industry-specific patterns also emerged. Firms operating in low-pollution sectors demonstrated stronger green performance gains compared to their counterparts in high-pollution industries. Researchers attribute this to the lower costs and fewer barriers associated with implementing AI-driven solutions in industries that already have a relatively low environmental footprint. In contrast, companies in high-pollution sectors face greater structural challenges, higher transformation costs, and regulatory hurdles that slow down the pace of green innovation.

The COVID-19 pandemic in 2020 marked another turning point in AI’s influence on corporate sustainability. Post-pandemic data revealed a surge in AI adoption, driven by the acceleration of digital transformation initiatives across industries. As companies sought to build resilience in the face of economic uncertainty, AI became a strategic tool not only for operational efficiency but also for aligning with green development goals.

Market conditions amplify AI’s impact

The moderating role of market conditions in amplifying AI’s effect on corporate green transformation. The development of the non-state-owned economy and the competitiveness of product markets significantly enhance the positive outcomes of AI adoption. Firms in regions with more dynamic and open markets tend to adopt AI more effectively, achieving greater improvements in green performance.

On the other hand, indicators such as the government–market relationship, the development of factor markets, and the maturity of intermediary organizations show limited moderating effects. The findings suggest that while supportive regulatory frameworks are important, it is the dynamism of the market and the presence of competitive pressures that truly drive firms to integrate AI into their sustainability strategies.

The authors argue that fostering an environment conducive to innovation is essential for maximizing AI’s potential. This includes reducing market entry barriers, encouraging competition, and strengthening intellectual property protections to promote technology adoption across sectors.

Policy and strategic implications

The study offers several policy recommendations to strengthen AI’s role in green transformation. Policymakers are urged to design differentiated strategies that address regional and sectoral disparities. In less developed regions, investment in digital infrastructure and targeted subsidies for AI integration can help close the technological gap with the eastern provinces.

For high-pollution industries, financial incentives for AI-driven energy-saving upgrades and integration of emission reductions into trading schemes for pollutant discharge rights could accelerate the adoption of green technologies. Low-pollution industries, on the other hand, could benefit from shared AI diagnostic platforms and cloud-based models to lower the cost of implementation.

The research also stresses the importance of talent development. Building a workforce skilled in AI and green technologies is critical to sustaining momentum. Short-term efforts should focus on educational investment and curriculum development in fields such as artificial intelligence, big data, renewable energy, and environmental management. In the medium to long term, stronger collaboration between educational institutions and businesses is recommended to bridge skill gaps and enhance practical training programs.

Limitations and future directions

While the findings present compelling evidence of AI’s transformative role, the authors acknowledge several limitations. The analysis is based on data from listed firms in China, which may limit its applicability to smaller enterprises or other economies. Future research should expand the dataset to include non-listed companies and conduct cross-country comparisons to validate and generalize the results.

The study also notes the need for more advanced methodologies to establish stronger causal relationships between AI adoption and green performance outcomes. Decomposition models could further clarify the distinct pathways through which AI influences green technical efficiency and green technological change.