The research findings show that at the initial application stage, artificial intelligence can facilitate both substantive and strategic green innovation simultaneously, and such impacts present significant heterogeneity across industries and enterprise property rights.
Abstract
The rise of artificial intelligence offers a critical opportunity to drive green innovation and advance sustainable development. However, the existing literature lacks sufficient systematic exploration of the relationship between the two from the resource allocation perspective. Taking initial application scenarios of artificial intelligence among Chinese enterprises as the research context, this paper employs a multiple mediation and threshold model to systematically examine its linear and nonlinear influencing mechanisms, so as to further enhance the green innovation-driven effect of artificial intelligence. The research findings show that (1) at the initial application stage, artificial intelligence can facilitate both substantive and strategic green innovation simultaneously, and such impacts present significant heterogeneity across industries and enterprise property rights. (2) Mechanism analysis verifies the existence of parallel and chain mediating effects of financing constraints, R&D investment, and labor force quality between artificial intelligence and green innovation. (3) The threshold effect test suggests that the impacts of artificial intelligence on green innovation and substantive green innovation feature a single threshold centered on corporate ESG performance, while no threshold effect is observed for strategic green innovation. This study provides a reliable theoretical and practical reference for enterprises to leverage artificial intelligence to advance green transformation and sustainable development.
Artificial intelligence investment lies at the core of enterprise digital transformation, and its influencing mechanism on green innovation efficiency urgently needs to be explored. Taking China's A-share listed companies from 2007 to 2023 as samples, this paper measures the development level of enterprise artificial intelligence from two dimensions: disclosure frequency of artificial intelligence-related words and investment level, and uses the random-effects panel Tobit model to examine its impact on green R&D efficiency and green achievement transformation efficiency. The findings are as follows: First, each logarithmic unit increase in the frequency of artificial intelligence words raises green innovation efficiency by approximately 0.042 units (about 8%), and a one-standard-deviation increase in investment level improves green innovation efficiency by about 4%, both of which are significant at the 1% level. Second, the conclusions still hold after robustness tests using the high-dimensional fixed-effects model and the DID policy shock of the National New-Generation Artificial Intelligence Innovation and Development Pilot Zones, and pass the parallel trend test. Third, the asset-liability ratio significantly positively moderates this effect, highlighting the key role of financing capacity. Fourth, the promotion effect is more significant in state-owned enterprises, large-scale enterprises and manufacturing enterprises. This paper provides empirical evidence for optimizing enterprise artificial intelligence investment strategies and promoting green transformation.
Qianhao Feng· Advances in Economics, Manag...· 0 citations
Micro-level evidence is provided on AI-driven green transformation in manufacturing enterprises from the dynamic capabilities perspective, offering theoretical and practical insights for advancing high-quality transformation of China’s manufacturing sector in the digital-intelligent era.
This study aims to explore the impact of energy enterprises’ implementation of artificial intelligence (AI) strategies on green total factor productivity (GTFP) and analyze their influencing mechanism and heterogeneity. By constructing a fixed-effects model and using A-share listed energy enterprises from 2013 to 2023, this study draws the following conclusions. The benchmark regression shows that AI promotes GTFP, and the results remain valid after endogeneity and robustness tests. The advancement of green technology innovation and the alleviation of financing constraints mediate the effect of AI on GTFP. Heterogeneity tests indicate that state-owned enterprises, enterprises with high levels of digital infrastructure, and new energy enterprises significantly benefit from the positive effect of AI on GTFP. This study contributes by breaking the traditional application framework of corporate strategy theory by focusing on industry structure and integrating it into the emerging field of AI technology. By analyzing the role of AI in enabling energy enterprises to gain competitive advantage and cost-effectiveness, this study reveals a new path for intelligence-driven strategic planning to enhance GTFP. These findings have practical significance for the green transition of energy enterprises in the digital era.
Cui-Ping Wen, Kaihua Wang, Zhengzheng Li· Energy & Environment· 0 citations
This study investigates the impact of Artificial Intelligence (AI) on sustainable innovation and its influence on entrepreneurial risk management in the Business Sector of Oman. AI technologies enhance resource efficiency, optimize energy usage, support eco-friendly product design, and enable data-driven environmental decision-making, promoting green innovation across sectors such as renewable energy, smart manufacturing, and sustainable finance. Using a structured questionnaire, primary data were collected from entrepreneurs, managers, and innovation professionals in technology-driven and green sectors. Quantitative analyses, including descriptive statistics and regression, were applied to examine the effects of AI adoption on sustainable innovation performance, entrepreneurial growth, and environmental outcomes.
The results reveal that AI adoption significantly improves operational efficiency, environmental performance, and the scalability of green ventures, generating new entrepreneurial opportunities in line with Oman Vision 2040. At the same time, challenges such as high implementation costs, technical complexity, skill gaps, data security issues, and regulatory uncertainties were identified. The study provides empirical evidence from a developing economy context and underscores the importance of supportive policies, responsible AI governance, and capacity-building initiatives to maximize the benefits of AI-driven sustainable innovation.
Keywords: Artificial Intelligence; Green Innovation; Sustainable Entrepreneurship; Eco-Innovation; Entrepreneurial Opportunities; Risk Management; Empirical Analysis; Environmental Sustainability; Oman Vision 2040
Mohammed G. Ali· International journal of re...· 0 citations
Under the dual carbon targets, green finance faces growing demand as a key channel for financing and low-carbon transformation, while Artificial Intelligence (AI) technologies are growing quickly with financial services. This gives AI opportunities to strengthen green finance operations. This paper systematically studies the empowerment mechanism of artificial intelligence in the development of green finance. From the three dimensions of information empowerment, risk empowerment and regulatory empowerment, it reveals the adaptability of AI technology and green finance and its role, and puts forward the practical path of AI to empower green finance in the future. Through the combination of case analysis and quantitative and qualitative methods, this study draws three main findings: AI can integrate dispersed environmental information to provide investors with a clearer decision-making basis; AI model evaluates environmental risks and credit risks more accurately than traditional tools; automated monitoring can find greening faster and at a lower cost. Behavior. At the same time, the study points out the technology, policies and market conditions needed to maintain these results, which provides a practical reference for making green finance more transparent, accurate and reliable with the help of AI.
Jinqiu Wang· Advances in Economics, Manag...· 0 citations
Artificial intelligence entrepreneurship has increasingly become an important part of urban innovation in the digital economy. At the same time, reducing carbon emissions remains a central issue for sustainable development and carbon neutrality. Using panel data from Chinese cities from 2008 to 2023, this study investigates how AI entrepreneurship influences carbon emission intensity and through which mechanisms this influence occurs. The empirical results reveal a nonlinear inverted U-shaped relationship. At the early stage, the expansion of AI entrepreneurship tends to increase carbon emissions, while after a certain development threshold is reached, AI entrepreneurship begins to contribute to emission reduction. Further mechanism tests show that digital technology innovation strengthens the positive effect on emissions in the early stage because it is associated with higher energy demand. In contrast, fiscal expenditure on science and technology helps reduce emissions in the later stage by supporting green technology development and industrial upgrading. The heterogeneity analysis indicates that this nonlinear effect is more evident in cities with higher levels of industrialization. This study provides city-level evidence for understanding the environmental consequences of AI entrepreneurship and offers policy implications for coordinating digital entrepreneurship with low-carbon development.
