--- title: "Artificial Intelligence and Green Total Factor Productivity in Manufacturing: An Empirical Analysis · AI Fault Line" url: https://aifaultline.com/research/p/doi-10-17632-g8zygxps2x/ description: "AI Fault Line is a neutral, evidence-cited field guide to why experts looking at the same developments disagree about AI's effect on productivity, jobs, inequality, regulation and the pace of change." --- [Research](https://aifaultline.com/research/) # Artificial Intelligence and Green Total Factor Productivity in Manufacturing: An Empirical Analysis Muratbek · Mendeley Data · 13 Jul 2026 [Read original source](https://doi.org/10.17632/g8zygxps2x) Open access Where this sits in the debate - [Will AI actually boost productivity and growth?](https://aifaultline.com/debate/productivity/) · its finding: it depends ([why studies differ](https://aifaultline.com/debate/productivity/#why)) Direction of the finding as read from the abstract by an AI model ([AI-written](https://aifaultline.com/about/)). ## Abstract Research hypotheses: H1: The development level of AI has a significant positive direct effect on the green transition of manufacturing. H2a: AI indirectly drives the green transition of manufacturing through green technology innovation. H2b: AI indirectly drives the green transition of manufacturing through improved digital infrastructure support. H3a: The impact of AI on the green transition of manufacturing exhibits a threshold effect contingent on government R&D investment. H3b: The impact of AI on the green transition of manufacturing exhibits a threshold effect contingent on economic development. H4: The effect of AI on the green transition of manufacturing exhibits a positive spatial correlation effect. The data are sourced from the China City Statistical Yearbook, provincial statistical yearbooks, the EPS database, and the National Enterprise Credit Information Publicity System. Missing values for individual observations are imputed using linear interpolation. To mitigate the influence of extreme values, the raw data are winsorized at the 1% level. The study sample comprises 281 prefecture-level and above cities in China, and it covers the period from 2009 to 2023. This yields a total of 4,215 observations. ## About this publication **Academic journal.** Published in an academic journal — typically peer-reviewed before publication, though indexing alone doesn't guarantee review depth. Published 2026 (3 months ago) Citations 0 (OpenAlex) 0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison. [Chart: published 2026] ## More on this topic Related by theme, not necessarily by method or conclusion. - [Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality](https://aifaultline.com/research/p/doi-10-2139-ssrn-4573321/) SSRN · 2023 - [Generative AI at Work](https://aifaultline.com/research/p/doi-10-1093-qje-qjae044/) The Quarterly Journal of Economics · 2025 - [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://aifaultline.com/research/p/doi-10-48550-arxiv-2302-06590/) arXiv · 2023 - [The Productivity J-Curve: How Intangibles Complement General Purpose Technologies](https://aifaultline.com/research/p/doi-10-1257-mac-20180386/) American Economic Journal Macroeconomics · 2020 Overview assembled automatically from collected metadata. [How this site is built](https://aifaultline.com/about/).