Although Artificial Intelligence (AI) has been advanced as a lever for corporate decarbonization, evidence on whether and how AI-enabled carbon reduction strategies (AICRS) translate into superior firm performance remains thin and theoretically under-specified. Integrating the natural-resource-based view with dynamic capabilities theory, we theorize AICRS as a strategic orchestration process through which firms combine AI assets with environmental routines to sense carbon risks, seize low-carbon opportunities, and reconfigure operating models. Using a panel of 1,842 large multinational listed firms from 14 industries over 2014-2024, we construct AICRS via transformer-based text classification of disclosures, triangulated with AI patents and validated against cloud-expenditure and green-AI job-posting microdata. Two-way fixed-effects, system-GMM, and staggered difference-in-differences estimators consistently show that AICRS improves financial performance (ROA, Tobin’s Q) and carbon performance, with dynamic capabilities partially mediating and AI capability amplifying the effect. Supplier-facing (Scope 3) AICRS initially attenuates financial returns owing to coordination costs but converges to comparable returns over five years while delivering persistent Scope 3 abatement, and netting out the energy footprint of AI deployment yields attenuated but still significant carbon gains. We reframe decarbonization as an AI-mediated dynamic-capability problem, deliver a replicable operationalization of AICRS, and surface contingencies that reconcile conflicting evidence on the sustainability performance link.
Cite this paper
Dilane, M. P. J. and Xu, R. (2026). Orchestrating Intelligence for Decarbonization: AI-Enabled Carbon Reduction Strategies, Dynamic Capabilities, and Firm Performance. Open Access Library Journal, 13, e15659. doi: http://dx.doi.org/10.4236/oalib.1115659.
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