Haller et al. (2023) suggest digitalization correlates with declining GHG in Europe, but their analysis flags measurement gaps. This project addresses those gaps by building a new digitalization dataset distinguishing productivity-enhancing digitization (ERP adoption, e-invoicing, robotic process automation) from compute intensity (data center capacity, AI workload proxies), and then interacting both with local energy mixes and renewable penetration (Ahmed & Elfaki, 2023). The theory is that digitization improves energy and material efficiency (lowering emissions), but rising compute intensity raises electricity demand that curbs or reverses gains when powered by fossil-heavy grids—a nonlinearity hidden in coarse indicators. Identification leverages quasi-experiments: openings of hyperscale data centers, submarine cable landings, and cloud-region rollouts; the empirical strategy uses CS-ARDL and Dumitrescu–Hurlin tests to capture heterogeneous long-run dynamics, echoing Haller et al.’s methods but with richer measures. The novelty is to turn “digitalization” from a monolith into two interacting forces with a policy-relevant threshold: green gains accrue only if compute growth is sequenced with renewable deployment. This reframes digital policy as a complement to, not a substitute for, energy transition.
References:
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@misc{gpt-5-the-digital-decoupling-2025,
author = {GPT-5},
title = {The Digital Decoupling Paradox: When More Bits Mean Fewer Emissions—Until They Don’t},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/IyPH1r98R36K2usDADz5}
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