The relationship between artificial intelligence and environmental performance: the mediating role of external environmental factors – Nature

Report on the Relationship Between Artificial Intelligence and Environmental Performance in SMEs: Emphasizing Sustainable Development Goals

Abstract

This report examines how artificial intelligence (AI) influences environmental performance (EP) in small and medium enterprises (SMEs) in Pakistan, with a focus on the mediating role of external environmental factors such as carbon emission strategies and sustainability regulations. Data from 387 SME employees reveal a positive relationship between AI use and EP, intensified by external factors. Grounded in dynamic capability theory (DCT), the findings highlight AI’s role in enhancing SMEs’ capabilities to achieve Sustainable Development Goals (SDGs) and improve environmental outcomes. The study provides practical insights for SME owners, policymakers, and managers in emerging economies to promote sustainable business practices aligned with SDGs.

Introduction

SMEs in Pakistan face challenges including limited resources, outdated technologies, and weak infrastructure, which hinder their environmental performance (EP). These challenges affect their ability to implement eco-friendly operations and align with the United Nations Sustainable Development Goals (SDGs). AI technologies, particularly green AI, offer solutions by improving energy efficiency, waste management, and carbon emission reduction. The integration of AI with external environmental factors can significantly enhance SMEs’ EP and support SDG achievement.

Key Challenges for SMEs

• Inadequate resources and outdated technologies
• Inefficient energy utilization and waste management
• Lack of awareness and alignment with SDGs

Role of Artificial Intelligence

• Automated monitoring and predictive analytics
• Enhancement of energy efficiency and waste reduction
• Alignment with carbon emission reduction and sustainability goals

Theoretical Framework

The study adopts Dynamic Capability Theory (DCT) to explore how SMEs adapt and integrate AI to improve environmental performance under changing external pressures. Unlike other theories, DCT focuses on organizational agility and the ability to transform resources to meet sustainability objectives, including SDGs. AI is conceptualized as a dynamic capability that, when combined with external environmental factors, enhances SMEs’ capacity to achieve improved EP.

Hypotheses Development

Artificial Intelligence and Environmental Performance

AI, especially green AI, is posited to have a significant positive impact on EP by enabling better resource management, energy efficiency, and waste reduction. This supports SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production).

1. H1: AI significantly impacts EP in Pakistani SMEs.

External Environmental Factors and Environmental Performance

External factors such as carbon emission policies and sustainability regulations are critical in shaping EP. These factors align with SDG 13 (Climate Action) and SDG 7 (Affordable and Clean Energy).

2. H2: External factors significantly affect EP.

Integration of AI, External Factors, and Environmental Performance

The interaction between AI and external environmental factors is expected to strengthen EP outcomes, emphasizing the importance of regulatory and strategic alignment for sustainability.

3. H3: AI significantly influences external environmental factors.
4. H4: External factors mediate the relationship between AI and EP.

Methodology

A quantitative approach was employed, collecting primary data via structured questionnaires from 387 employees across manufacturing, textile, and pharmaceutical SMEs in Pakistan. The study used Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze relationships among AI, external factors, and EP.

Results

Demographics

• 59.95% male and 40.05% female respondents
• Educational qualifications ranged from matriculation to master’s degrees

Model Measurement

• Construct reliability and validity confirmed (Cronbach’s alpha and composite reliability > 0.70)
• Convergent validity established (Average Variance Extracted > 0.50)
• Discriminant validity confirmed (Fornell-Larcker criterion and HTMT ratios)

Hypotheses Testing

1. AI positively impacts EP (β = 0.269, p < 0.000), supporting H1.
2. AI significantly affects external environmental factors (β = 0.273 and 0.260, p < 0.005), supporting H3.
3. External factors significantly influence EP (β = 0.259 and 0.201, p < 0.001), supporting H2.
4. External factors mediate the AI-EP relationship (β = 0.217 and 0.201, p < 0.004), supporting H4.

Discussion

The findings demonstrate that AI is instrumental in enhancing SMEs’ environmental performance by improving resource management, energy efficiency, and waste reduction, thereby contributing to SDGs such as SDG 9, SDG 12, and SDG 13. The mediating role of external environmental factors underscores the necessity of regulatory frameworks and sustainability policies to maximize AI’s impact on EP. SMEs integrating AI with external sustainability measures can improve their environmental footprint and resilience, aligning with global sustainability agendas.

Conclusion

This study confirms that AI positively influences environmental performance in SMEs, with external environmental factors playing a crucial mediating role. The integration of AI and sustainability regulations enables SMEs to enhance resource efficiency and reduce environmental impacts, supporting the achievement of the United Nations Sustainable Development Goals. The research validates Dynamic Capability Theory in the context of AI-driven sustainability and highlights the importance of external factors in realizing environmental benefits.

Contributions

• Validation of Dynamic Capability Theory in AI and environmental performance context
• Identification of external environmental factors as mediators between AI and EP
• Empirical evidence from SMEs in a developing country, expanding the literature on AI and sustainability

Implications

• For Businesses: Integrate AI with sustainability strategies and actively engage with regulatory environments to enhance EP and comply with SDGs.
• For Policymakers: Develop supportive policies, incentives, and infrastructure to promote green AI adoption and sustainability compliance.
• For Technology Developers: Design adaptable AI solutions that align with diverse regulatory and market conditions to support sustainable development.

Limitations and Future Research

• Study limited to Pakistani SMEs; results may not generalize globally.
• Cross-sectional data may not capture dynamic changes in AI adoption and EP.
• Convenience sampling may introduce bias; future research should consider longitudinal and broader geographic studies.

References

References are available upon request from the corresponding author.

1. Sustainable Development Goals (SDGs) Addressed or Connected

1. SDG 9: Industry, Innovation and Infrastructure
   • The article discusses the role of artificial intelligence (AI) in enhancing environmental performance of small and medium enterprises (SMEs) through technological innovation.
2. SDG 12: Responsible Consumption and Production
   • Focus on improving resource efficiency, waste management, and sustainable business practices in SMEs aligns with this goal.
3. SDG 13: Climate Action
   • The article emphasizes carbon emission strategies and sustainability regulations to reduce environmental impact and carbon emissions.
4. SDG 7: Affordable and Clean Energy
   • AI is used to improve energy efficiency in SMEs, which supports this goal.
5. SDG 8: Decent Work and Economic Growth
   • SMEs are highlighted as significant contributors to employment and GDP in Pakistan, linking economic growth with sustainable practices.

2. Specific Targets Under the Identified SDGs

1. SDG 9 – Target 9.4: Upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies and industrial processes.
   • The article’s focus on AI adoption in SMEs to improve environmental performance aligns with this target.
2. SDG 12 – Target 12.2: By 2030, achieve the sustainable management and efficient use of natural resources.
   • AI-driven improvements in resource utilization, waste management, and sustainability regulations support this target.
3. SDG 13 – Target 13.2: Integrate climate change measures into national policies, strategies, and planning.
   • The article discusses carbon emission policies and sustainability regulations as external environmental factors mediating AI’s impact on environmental performance.
4. SDG 7 – Target 7.3: By 2030, double the global rate of improvement in energy efficiency.
   • AI’s role in improving energy efficiency in SMEs supports this target.
5. SDG 8 – Target 8.3: Promote development-oriented policies that support productive activities, decent job creation, entrepreneurship, creativity and innovation.
   • Enhancing SMEs’ capabilities through AI aligns with this target.

3. Indicators Mentioned or Implied to Measure Progress

1. Environmental Performance Indicators:
   • Energy consumption levels and efficiency improvements in SMEs.
   • Waste management effectiveness.
   • Carbon emissions reduction and adherence to carbon emission policies.
   • Compliance with sustainability regulations and standards.
2. AI Adoption and Integration Metrics:
   • Extent of AI technology use in SMEs (e.g., automated monitoring systems, predictive analytics).
   • Impact of AI on resource utilization and decision-making processes.
3. External Environmental Factors as Mediators:
   • Implementation and effectiveness of carbon emission strategies.
   • Adoption and enforcement of sustainability regulations.
4. Organizational Capability Measures:
   • SMEs’ ability to adapt and integrate AI with external environmental factors.
   • Improvements in operational efficiency and alignment with SDGs.

4. Table: SDGs, Targets and Indicators

SDGs	Targets	Indicators
SDG 9: Industry, Innovation and Infrastructure	9.4: Upgrade infrastructure and retrofit industries to make them sustainable with increased resource-use efficiency and adoption of clean technologies.	AI adoption rate in SMEs Use of green AI technologies Improvement in environmental performance metrics
SDG 12: Responsible Consumption and Production	12.2: Achieve sustainable management and efficient use of natural resources.	Energy consumption and efficiency Waste management effectiveness Compliance with sustainability regulations
SDG 13: Climate Action	13.2: Integrate climate change measures into national policies, strategies, and planning.	Carbon emission levels Implementation of carbon emission policies Effectiveness of sustainability regulations
SDG 7: Affordable and Clean Energy	7.3: Double the global rate of improvement in energy efficiency.	Energy efficiency improvements in SMEs Reduction in energy consumption via AI
SDG 8: Decent Work and Economic Growth	8.3: Promote development-oriented policies supporting productive activities, decent job creation, entrepreneurship, creativity and innovation.	SMEs’ contribution to employment and GDP Integration of AI to enhance business capabilities

Source: nature.com