A Vision for Computational Decarbonization of Societal Infrastructure

Modern society is at a critical inflection point with rapidly accelerating demand for energy due to growth in domestic manufacturing, datacenters, artificial intelligence (AI), electric vehicles, and electric heat pumps. Sustaining this growth while also reducing society’s carbon emissions will necessitate a shift beyond our long-standing focus on improving energy-efficiency to optimizing carbon-efficiency. This paper lays out a vision for a new field of Computational Decarbonization (CoDec), which focuses on optimizing and reducing the lifecycle carbon emissions of complex computing and societal infrastructure systems. We identify an important class of decarbonization problems that arise from interdependencies across multiple infrastructure domains, including computing, transportation, the built environment, and the electric power grid. As we discuss, solving these problems will require developing novel computational techniques, algorithms, systems, and AI methods that sense, optimize, and reduce the operational, embodied, and lifecycle greenhouse gas emissions of societal infrastructure over long temporal and spatial scales.

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Publication Details

Journal
IEEE Internet Computing
Published
2025-03-01
DOI
https://doi.org/10.1109/mic.2025.3575016
Citations
4
Primary Topic
Distributed systems and fault tolerance
Type
article
Field-Weighted Citation Impact
5.95

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article

A Vision for Computational Decarbonization of Societal Infrastructure

Line Roald, Alı Hortaçsu, Geoffrey J. Gordon, John R. Birge et al.
4 citations
IEEE Internet Computing
Distributed systems and fault tolerance
5.95
article

A Vision for Computational Decarbonization of Societal Infrastructure

Line Roald, Alı Hortaçsu, Geoffrey J. Gordon, John R. Birge, Prashant Shenoy, Ramesh K. Sitaraman, Mohammad Hajiesmaili, Jimi Oke, Deepak Rajagopal, David Irwin, Vivienne Sze, Yuvraj Agarwal, Mani Srivastava, J. Zico Kolter, Priya L. Donti, Andrew A. Chien, Walid A. Hanafy
article en
4 citations

Abstract

Modern society is at a critical inflection point with rapidly accelerating demand for energy due to growth in domestic manufacturing, datacenters, artificial intelligence (AI), electric vehicles, and electric heat pumps. Sustaining this growth while also reducing society’s carbon emissions will necessitate a shift beyond our long-standing focus on improving energy-efficiency to optimizing carbon-efficiency. This paper lays out a vision for a new field of Computational Decarbonization (CoDec), which focuses on optimizing and reducing the lifecycle carbon emissions of complex computing and societal infrastructure systems. We identify an important class of decarbonization problems that arise from interdependencies across multiple infrastructure domains, including computing, transportation, the built environment, and the electric power grid. As we discuss, solving these problems will require developing novel computational techniques, algorithms, systems, and AI methods that sense, optimize, and reduce the operational, embodied, and lifecycle greenhouse gas emissions of societal infrastructure over long temporal and spatial scales.

IEEE Internet ComputingVol. 29(2)
University of Wisconsin–Madison (US), University of California, Los Angeles (US), University of Massachusetts Amherst (US), University of Illinois Chicago (US), University of Chicago (US), Massachusetts Institute of Technology (US), Carnegie Mellon University (US)
National Science Foundation
Industry, innovation and infrastructure
Openalex Percentile: Top 4%
Distributed systems and fault tolerance
5.95
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