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Artificial Intelligence and Corporate Green Innovation: Evidence from Pakistani Listed Firms

Abstract

The fast-paced advancement of disruptive technologies like Artificial Intelligence (AI) has opened new pathways for promoting corporate green transformation. Using panel data from Pakistani listed firms between 2010 & 2023, this research reveals that: (1) A higher level of corporate AI adoption significantly enhances the scale of green innovation activities. (2) AI primarily drives substantive green innovation rather than strategic green innovation indicating that AI improves the overall quality of corporate green efforts. (3) The mechanism through which AI fosters green innovation includes better data-driven decision-making, smoother human–AI collaboration, & reduced information asymmetry. (4) The influence of AI on green innovation notably differs across companies with varying ownership types, industries, & regions. (5) Ultimately, AI contributes to increased corporate value through its positive effect on green innovation.

These findings provide important insights into how AI investments shape economic performance and support the green transformation of businesses in Pakistan.

Keywords: Artificial Intelligence; Corporate Green Innovation; Data-Driven Decision-Making; Human-AI Collaboration; Information Asymmetry

JEL Classification: O32; Q55

1. Introduction

Global issues such as resource depletion, pollution, and climate change are becoming increasingly significant concerns. Around the world, countries are emphasizing sustainable economic growth and green development. Pakistan has also aligned its long-term national strategy with global sustainability goals by encouraging low-carbon initiatives, renewable energy adoption, and eco-friendly industrial practices. To achieve these objectives, every sector must pursue decarburization, recycling, and enhanced efficiency in energy and resource utilization. National frameworks and policies highlight low-carbon and green technological innovation as key drivers of progress toward these environmental goals.

Enterprises play a vital role in realizing these sustainability ambitions. The development of green innovation capabilities not only strengthens corporate competitiveness but also contributes to ecological transformation. However, businesses often face a dilemma in pursuing green innovation. On one hand, compared to traditional innovation, green innovation carries strong externalities and involves high capital intensity, alongside risks of knowledge spillover and uncertain financial returns — resulting in limited motivation for private investment. On the other hand, as the complexity of required knowledge increases, implementing green innovation becomes more challenging. Therefore, enterprises urgently need new technologies to address the imbalance between costs and benefits in green innovation.

As a General-Purpose Technology (GPT), the rapid advancement of Artificial Intelligence (AI) offers new opportunities to tackle the challenges of green innovation. Guided by human-defined objectives, AI has the ability to make predictions, generate recommendations, and assist in decision-making—thereby accelerating technological upgrades and fostering innovation across industries. Over the past decade, AI adoption and investment have expanded rapidly, finding applications in nearly every sector. In emerging economies like Pakistan, AI plays an increasingly important role in achieving a balance between economic growth and sustainable development.

However, an important question remains: are firms undergoing technological transformation prepared to leverage AI effectively for green investment and sustainability-oriented innovation? Using panel data of Pakistani listed firms from 2010 to 2023, this study investigates how the application of AI influences corporate green innovation.

2. Literature Review and Hypothesis Development

Green innovation refers to technological progress aimed at conserving energy and resources while reducing pollution and environmental degradation. Existing research explores the determinants of corporate green innovation from both macro and micro perspectives. From a macro perspective, scholars generally agree that corporate green innovation is closely influenced by government environmental policies and green financial initiatives. However, uncertainty in environmental regulations tends to discourage corporate green investment and weaken innovation efforts.

From a micro perspective, effective corporate governance has been found to significantly increase the number of green patents. Recent studies also indicate that firms led by locally networked CEOs tend to demonstrate lower levels of green innovation compared to those with more independent leadership. Meanwhile, digital transformation enhances corporate green innovation by improving environmental disclosure transparency and reducing environmental uncertainty. Similarly, intelligent transformation—the integration of AI, automation, and digital systems—further promotes corporate green innovation.

2.1 AI and Corporate Green Innovation

According to Schumpeter's Theory of Innovation, technological progress serves as the fundamental driving force of economic growth. Under the global movement toward sustainable development, green innovation has become vital for enterprises seeking to overcome environmental limitations and achieve long-term competitive advantages. In recent years, an increasing number of Pakistani enterprises have begun to adopt Artificial Intelligence (AI) to strengthen green investment and promote eco-innovation.

AI enables a cascading empowerment effect by transforming the enterprise innovation ecosystem and enhancing its capacity for sustainability-oriented technological progress. First, AI facilitates technological advancement by accelerating research and development in green technologies. Through machine learning, big data analytics, and simulation modeling, AI can optimize experimental design, shorten testing cycles, and improve the precision of innovation outcomes.

H1: The application of Artificial Intelligence (AI) significantly promotes corporate green innovation in Pakistani enterprises.

2.2 AI and Innovation Quality

Corporate innovation behavior can be classified into two main types—substantive innovation and strategic innovation. Substantive innovation refers to high-quality innovation aimed at achieving genuine technological breakthroughs and tangible improvements in productivity, environmental performance, and sustainability. In contrast, strategic innovation represents low-quality innovation activities undertaken mainly to comply with policies or meet regulatory expectations, without generating meaningful technological progress.

From the Resource-Based View (RBV), differences in innovation quality stem from firms' heterogeneous capabilities. Achieving substantive innovation requires enterprises to develop strategic resource combinations that possess the VRIO attributes—value, rarity, inimitability, and organization. Artificial Intelligence (AI) enhances substantive innovation through its distinctive capabilities in resource optimization and technological integration.

H2: Artificial Intelligence (AI) enhances the quality of corporate green innovation, with a more significant effect on substantive innovation than on strategic innovation.

2.3 Mechanisms of AI Impact

Dynamic Capabilities Theory explains how firms sustain a competitive advantage in a rapidly changing environment. Artificial Intelligence (AI) strengthens these capabilities by enhancing the firm's ability to sense opportunities, seize resources, and transform innovations—thereby promoting corporate green innovation.

First, AI enhances a firm's ability to sense green innovation opportunities. With increasingly stringent environmental regulations, AI enables real-time monitoring of policy and market dynamics. Second, AI enhances firms' ability to seize green innovation opportunities by facilitating knowledge integration and collaboration. Third, AI improves the transformational capacity of corporate green innovation by enhancing transparency and standardization of corporate data, minimizing information asymmetry.

H3: Artificial Intelligence (AI) promotes corporate green innovation by empowering data-driven decision-making, enhancing human–AI collaboration, and reducing information asymmetry.

3. Research Design

This study uses panel data of Chinese listed companies from 2010 to 2023 as the research sample. The year 2010 is chosen as the starting point because the Chinese government began placing stronger emphasis on Artificial Intelligence (AI) in that year. After data cleaning, the final dataset includes 32,969 firm-year observations. Firm-level patent data are obtained from the CNRDS database, AI-related data from annual reports are sourced from the CSMAR database, and additional financial and firm-level information is collected from CSMAR, Wind, and CNRDS databases.

3.1 Variables and Measurement

The dependent variable is corporate green innovation, measured by green patent applications (GreenApply1), substantive green innovation (GreenApply2), and strategic green innovation (GreenApply3). The independent variable is AI application intensity (AImda), measured through the frequency of AI-related keywords in annual corporate reports. Control variables include firm size, age, leverage, cash holdings, profitability, growth, tangibility, management shareholding, ownership concentration, board independence, and board size.

4. Empirical Results

The results demonstrate that corporate AI application significantly increases the quantity of green innovation. The coefficients for AI intensity are significantly positive at the 1% level, indicating that corporate AI application significantly increases the quantity of green innovation (GreenApply1). In terms of economic significance, after controlling other factors, for every 1-standard-deviation increase in AI application, the quantity of green innovation will increase by 4.57%, which is equivalent to 4.91% of the mean value.

4.1 Baseline Results

Further, the heterogeneous effect of AI intensity on substantive and strategic innovation shows that AI application only significantly increases substantive green innovation (GreenApply2) and has no significant effect on strategic green innovation (GreenApply3). In terms of economic significance, after controlling other factors, for every 1-standard-deviation increase in firm AI application, substantive green innovation will increase by 5.96%, which is equivalent to 9.32% of the mean value.

4.2 Endogeneity and Robustness Tests

To mitigate endogeneity, an instrumental variable approach is employed. The results confirm that AI enhances corporate green innovation quantity and substantive innovation after considering endogeneity issues. Additionally, propensity score matching (PSM) is used to address potential self-selection bias. The results remain consistent with baseline findings after various robustness checks including alternative variable measurements, multiple fixed effects, and sample adjustments.

4.3 Mechanism Analysis

Three key mechanisms are identified through which AI promotes green innovation:

• Data-Driven Decision-Making: AI significantly increases corporate data assets, enabling better decision-making for green innovation
• Human-AI Collaboration: AI increases the proportion of R&D personnel and strengthens human-AI collaboration, enhancing innovation efficiency
• Reduced Information Asymmetry: AI increases analyst attention and reduces information asymmetry, improving market expectations and facilitating green innovation funding

5. Heterogeneity Analysis

The impact of AI on green innovation varies significantly across different contexts:

5.1 Firm Heterogeneity

• Ownership: AI's influence is more significant in state-owned enterprises (SOEs) than in non-SOEs
• Firm Size: Larger firms benefit more from AI in promoting green innovation
• Female Executives: Firms with higher proportions of female executives show stronger AI-driven green innovation
• R&D Intensity: Firms with higher R&D investment ratios demonstrate greater AI impact on green innovation

5.2 Industry Heterogeneity

• Pollution Level: AI contributes more significantly to green innovation in heavily polluting industries
• Technology Intensity: High-tech industries show stronger AI effects on green innovation
• Competition: Highly competitive industries benefit more from AI-driven green innovation

5.3 Regional Heterogeneity

• Pollution Levels: Regions with higher pollution show more pronounced AI effects on green innovation
• Environmental Enforcement: Areas with environmental protection courts demonstrate stronger AI-driven green innovation
• Marketization: Regions with higher market development levels show greater AI impact on green innovation

6. Economic Consequences

The study examines whether AI-driven green innovation produces economic value for enterprises. The results show that the improvement of AI application promotes corporate green innovation, which in turn increases enterprise value as measured by Tobin's Q. This means AI produces positive economic value to enterprises. As improvements in AI application and green innovation capabilities reflect a firm's technological competence and commitment to social responsibility, such advancements attract greater market attention and enhance market expectations toward the enterprise, thereby increasing corporate value.

7. Conclusions and Policy Implications

Enterprise engagement in green innovation is crucial for achieving sustainable development goals. AI provides new opportunities for enterprises to realize the synergy between environmental protection and economic development. This paper finds that AI significantly promotes the green innovation quantity and substantive green innovation with no significant effect on strategic green innovation. AI effectively promotes corporate green innovation by empowering data-driven decision-making, promoting human-machine collaboration, and reducing information asymmetry.

7.1 Policy Implications

• Government Support: Implement policies to encourage AI technology application, including fiscal subsidies, tax incentives, and enhanced environmental regulations
• Regional & Industry Customization: Develop targeted measures based on regional and industry characteristics to promote AI in green innovation
• Enterprise Action: Actively apply AI technologies, invest in R&D, build cross-departmental AI teams, and increase proportion of experienced R&D personnel

7.2 Future Research Directions

Future research could focus on non-listed firms, explore more accurate methods for measuring enterprise AI application, and investigate the long-term effects of AI on different types of green innovation across various economic contexts. Additionally, examining the role of AI in facilitating international technology transfer and green innovation collaboration would provide valuable insights for global sustainability efforts.