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China is currently confronted with the dilemma of achieving its green development goals while maintaining economic growth. The National Ecological Demonstration Zones (NEDZs) represent an innovative attempt by local governments to balance economic development with ecological civilisation, potentially offering a solution to this issue. This study calculated the Green Total Factor Productivity (GTFP) for 1925 districts and counties from 1999 to 2018. Using the selection of NEDZs as a quasi-natural experiment, a difference-in-differences model was employed to empirically analyse the impact of NEDZs on GTFP. The results indicated that the establishment of NEDZs led to an average increase in GTFP of 0.2175 compared to the control group. The primary mechanisms involved are innovation, structure, and enforcement. Moreover, the green development effects of the NEDZs exhibited regional heterogeneity. Compared to the western regions, the central and eastern regions showed limited green development effects. Areas with smaller populations, lower human capital, and lower administrative levels demonstrated significant improvements after the implementation of the NEDZ. Further analysis reveals a significant spatial agglomeration pattern of GTFP and the NEDZs exert a strong 'siphon effect' on the GTFP of neighbouring areas. This study provides a new perspective on the GTFP research and offers theoretical and practical evidence for assessing the impact of NEDZs. • The green total factor productivity (GTFP) of 1925 counties are calcuated. • The construction of NEDZs significantly enhances GTFP. • The green development effect of NEDZs exhibits heterogeneity in different types of regions. • Innovation effect, structural effect, and enforcement effect are the main mechanisms. • The NEDZs can reduce GTFP in nearby regions. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has been proved to be an important engine of green economic development, yet how it will affect the internal structure of green economy is unknown. The aim of this study is to examine the impact and its mechanism of AI on green total factor productivity (GTFP) from the internal-structure perspective, by using provincial panel data of China from 2009 to 2021 and global Malmquist index. The main research results show that: (1) the development of AI contributes to China's GTFP growth. And this effect is more significant in undeveloped areas; (2) AI promotes China's GTFP growth mainly by improving resource allocation efficiency, while it exerts little impact through the paths of technological progress and scale efficiency; (3) the transmission mechanism of AI on GTFP varies greatly among China's three main regions. In the eastern region, AI improves GTFP mainly by both advancing technological progress and improving resource allocation efficiency, while in central region AI contributes to GTFP growth mainly through technological progress. Compared with the eastern and central regions, AI in the western region plays a stronger impact on GTFP through the channel of improving scale efficiency. This study helps to understand the pathways of artificial intelligence affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions. • This paper studies the effects of AI on China's GTFP from internal-structural perspective. • GTFP is decomposed into three internal factors by the global Malmquist index. • AI has positive effect on GTFP through different impacts on internal factors. • Improving resource allocation efficiency is the most critical channel of AI on GTFP. • AI plays a more significant role in promoting GTFP in less developed areas. [ABSTRACT FROM AUTHOR]