Orchestrating Green Transformation: How <scp>AI</scp> Adoption Enables Corporate Carbon Neutrality

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🔬研究者:Provides a process-based theoretical framework (ROT, DCT, RDT) linking AI to carbon neutrality, with empirical evidence from Chinese manufacturing firms.

🏢実務担当者:Demonstrates that AI adoption can improve carbon neutrality performance by easing financing constraints and fostering green innovation, offering a strategic lever for sustainability.

🏛政策担当者:Highlights the role of green finance and supportive policies for AI-driven low-carbon transformation, relevant for designing industrial and environmental policies.

ABSTRACT As carbon neutrality has become a central goal of global climate governance, how firms achieve low‐carbon transformation has emerged as a critical research issue. However, prior studies have primarily focused on macro‐ or industry‐level analyses, offering limited and fragmented insights into how digital technologies—particularly AI—affect firm‐level carbon‐neutrality performance and the underlying process‐based mechanisms. To address this gap, this study adopts Resource Orchestration Theory (ROT) as the core analytical framework to examine how AI application is translated into firms' carbon‐neutrality performance and integrates Dynamic Capability Theory (DCT) and Resource Dependence Theory (RDT) as complementary perspectives—used to explain internal capability reconfiguration and the role of the external resource environment, respectively—thereby constructing a comprehensive analytical framework. Using panel data of Chinese A‐share listed manufacturing firms from 2018 to 2023, this study conducts empirical analysis based on a two‐way fixed effects model. The results indicate that AI application significantly enhances firms' carbon‐neutrality performance, and this finding remains robust after a series of robustness checks and controls for potential endogeneity. Further analyses reveal that AI exerts its effects primarily through alleviating financing constraints, enhancing R&amp;D vitality, and increasing green patent outputs. Moreover, green technological efficiency, which reflects firms' internal capabilities, and the level of green finance development, which captures the external resource environment, both exhibit significant positive moderating effects on the focal relationship. From the perspective of ROT, this study reexamines the environmental value of AI, demonstrating that such value does not stem solely from the technology itself but is shaped through managerial resource orchestration processes and the interaction between internal capabilities and external resource environments. By moving beyond the conventional view that attributes AI's environmental effects to mere technological inputs, this study extends the literature through a process‐based perspective. In the context of concurrent digital and green transformations, this research provides important theoretical insights and empirical evidence for understanding low‐carbon development among firms in emerging economies.

• openalex https://doi.org/10.1002/csr.70740first seen 2026-06-10 05:07:27