Other's shoes also fit well: AI technologies contribute to China's blue skies as well as carbon reduction

Unveiling the effects of artificial intelligence and green technology convergence on carbon emissions: An explainable machine learning-based approach

Green technology and artificial intelligence (AI) are playing a positive role in reducing carbon emissions. Technology convergence, as a typical form of technological innovation, can expedite the realization of low-carbon goals through the outcomes of AI and green technology convergence (e.g., the smart home system and smart transportation system). To investigate the mechanisms within AI and green technologies that affect carbon emissions, this study extracts convergence features from convergence attributes and convergence networks, based on panel data from Chinese prefecture-level cities spanning the period from 1997 to 2019. By combining the eXtreme Gradient Boosting (XGBoost) algorithm and the Shapley Additive Explanations (SHAP) value method, the study explains the individual effects and interaction effects of each feature on carbon emissions. The research findings reveal that technology convergence generality and innovation team scale have a significant impact on carbon emissions, with the latter exhibiting a U-shaped effect. Cities with high convergence network efficiency are found to influence suppressing carbon emissions positively. This study and its findings provide insights for policymakers to develop AI and green convergence technologies to reduce carbon emissions.

Government environmental governance, fiscal decentralization, and carbon intensity of the construction industry

In recent years, China's economic development is dominated by an eco-friendly and low-carbon transition, making the low-carbon advancement of the construction sector urgent. Local governments play a crucial role in this process. This paper, utilizing provincial panel data from 2007 to 2021, empirically analyzes the impact of government environmental governance (EG) on the carbon intensity of the construction industry (CCEI) through the panel regression, spatial econometric and dynamic threshold models. The findings indicate that (1) EG has a significant inverted U-shaped effect on CCEI, with initial increases in carbon intensity followed by reductions once EG intensity surpasses a certain level. (2) Significant spatial spillover effects reveal that increased EG in one region exerts a similar inverted U-shaped impact on both local and neighboring CCEI. (3) Under China's fiscal decentralization framework, two dimensions-vertical decentralization and horizontal competition-serve as forms of fiscal decentralization, each with dynamic threshold effects: EG's influence on CCEI turns negative under high vertical decentralization, and positive under intense horizontal competition. These results could offer insights from China's emission reduction experiences in the energy-intensive sector, serving as a valuable reference for environmental decision-makers worldwide.

The influence of digital intelligence transformation on carbon emission reduction in manufacturing firms

Severe global climate change has resulted in the focus of social attention shifting to the manufacturing industry's low-carbon transformation. Digital intelligent transformation (DIT) in enterprises is identified as a crucial driver in mitigating carbon emissions. An estimation of DIT's impact on manufacturing industries' carbon emission intensity (CEI) and its underlying mechanisms was conducted using data from Chinese A-share listed companies. Research findings indicate that DIT significantly reduces corporate CEI. Robustness tests, such as the instrumental variable method and variable substitution method, confirm this conclusion. By enhancing labor productivity and accelerating capital renewal, DIT indirectly lowers CEI. Furthermore, non-state-owned enterprises with superior market competitiveness have been observed to be markedly adept at harnessing DIT for CEI. The heterogeneity test found that DIT has a considerably significant effect on reducing CEI in enterprises that are not low-carbon pilots, non-broadband pilots, smart pilots, non-provincial capitals, and eastern cities. This study provides new evidence supporting the promotion of enterprise DIT for achieving green development, offering insights for corporate policy making.

Revolutionizing energy practices: Unleashing the power of artificial intelligence in corporate energy transition

Corporate energy transition is crucial for long-term sustainable development. The widely discussed Artificial Intelligence (AI), as a disruptive technological innovation, is highly potential for enhancing environment performance. However, the specific impact of AI on the process of corporate energy transition and its underlying mechanisms have not been fully explored. This study focuses on A-share listed corporates in Shanghai and Shenzhen stock markets in China spanning from 2011 to 2021. Based on corporate annual report information and information from over 200,000 patent application texts, we innovatively construct indicators for corporate energy transition and AI technology application. Furthermore, we empirically investigate the impact of AI technology on corporate energy transition and its potential mechanisms through combining information asymmetry theory and institutional theory. The empirical results indicate that: 1) AI can drive corporate energy transition and the promoting effect of AI collaborative innovation on corporate energy transition should not be ignored. 2) AI can help corporates achieve energy transition through pathways such as mitigating information asymmetry, reducing financing constraints, adjusting sustainable development concepts and practices. 3) The driving effect of AI on corporate energy transition varies depending on the characteristics of different types of corporates, industries, and regions. This study provides strategic guidance and decision support for business managers and policymakers, assisting both corporates and governments in better utilizing AI technology during the social energy transition process to achieve a dual optimization of environmental and economic goals.