Digital finance and the energy transition: Evidence from Chinese prefecture-level cities

Global Finance Journal, Год журнала: 2024, Номер 61, С. 100987 - 100987

Опубликована: Май 4, 2024

Язык: Английский

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Financial misallocation and green innovation efficiency: China's firm-level evidence

Energy Economics, Год журнала: 2024, Номер 136, С. 107697 - 107697

Опубликована: Июнь 15, 2024

The prevalent financial misallocation phenomenon appears to restrict firms' ability be improve green innovation efficiency. Our theoretical model yields a testable hypothesis, which the empirical analysis validates. We consider economic implications, channels, and countermeasures that emerge from impacts promoted by on use firm-level dataset 2008 2021. findings suggest hinders Chinese show in supply chain concentration is most important channel for negative influence misallocation. also find firms face discrimination having access resources based type of ownership size. results should enable policy makers clearly see green-innovation gains can produced China eliminating

Язык: Английский

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Climate risk, digital transformation and corporate green innovation efficiency: Evidence from China

Technological Forecasting and Social Change, Год журнала: 2024, Номер 209, С. 123777 - 123777

Опубликована: Сен. 26, 2024

Язык: Английский

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The impact of artificial intelligence on the energy consumption of corporations: The role of human capital

Energy Economics, Год журнала: 2025, Номер unknown, С. 108231 - 108231

Опубликована: Янв. 1, 2025

Язык: Английский

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The impact of artificial intelligence on corporate greenwashing: evidence from the Chinese listed firms

Journal of Accounting Literature, Год журнала: 2025, Номер unknown

Опубликована: Янв. 28, 2025

Язык: Английский

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From polluter pays to polluter reborn: Exploring the economic and green implications of corporate carbon risk exposure

Energy Economics, Год журнала: 2025, Номер unknown, С. 108317 - 108317

Опубликована: Фев. 1, 2025

Язык: Английский

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AI in Companies' Production Processes

Journal of Global Information Management, Год журнала: 2025, Номер 32(1), С. 1 - 29

Опубликована: Янв. 9, 2025

The accelerated integration of Artificial Intelligence (AI) in comprehensive organizational management has marked a significant milestone enhancing efficiency and productivity across all sectors. However, the effective adoption this emerging technology faces challenges, such as ethical dilemmas, barriers, notable deficit relevant technological skills. This study embarks on detailed analysis crucial determinants influencing AI by companies, UTAUT model with four new variables: Response Costs, Trust AI, Anxiety, Environmental Sustainability. Through surveys directed at over 400 CEOs work reveals that facilitating conditions, performance expectancy, response costs, trust anxiety determine their companies. These findings contribute to identifying which factors, from managerial perspective, should be considered more than sufficient reasons for implemented production processes.

Язык: Английский

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Harnessing digitalization to enhance private finance in resource-rich industries

Resources Policy, Год журнала: 2025, Номер 101, С. 105371 - 105371

Опубликована: Янв. 10, 2025

Язык: Английский

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The impact of artificial intelligence on corporate green innovation: Can "increasing quantity" and "improving quality" go hand in hand?

Journal of Environmental Management, Год журнала: 2025, Номер 376, С. 124439 - 124439

Опубликована: Фев. 10, 2025

Язык: Английский

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AI and Green Efficiency in Technological Innovation: A Two-Stage Analysis of Chinese Rare Earth Enterprises

Systems, Год журнала: 2025, Номер 13(3), С. 176 - 176

Опубликована: Март 4, 2025

As a scarce strategic resource, the efficient utilization of rare earth resources is crucial for ensuring national economic security and promoting sustainable development. AI, core engine Fourth Technological Revolution, provides favorable opportunity to drive green technological innovation. Green efficiency in innovation has not been adequately studied, relationship between era AI still unclear. Based on above research gap, this study employs slack-based measure model perform both static dynamic evaluations during technology development transformation phases eight listed Chinese enterprises from 2017 2021. This aims provide policy basis improving industry application industrial chain. The findings reveal following: (1) among these remains low phases, with pure technical being key factor contributing overall efficiency; (2) total productivity phase exhibits fluctuating upward trajectory while demonstrating general downward trend achievement phase; (3) significantly enhances phase, more pronounced impact compared phase. contributes existing literature by extending previous efficiency, particularly within context industry. empirical results offer valuable recommendations enhancing through integration.

Язык: Английский

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