Disentangling the carbon emissions impact of digital consumer innovations

Pettifor, H., Agnew, M., Wilson, C. ORCID: https://orcid.org/0000-0001-8164-3566, & Niamir, L. ORCID: https://orcid.org/0000-0002-0285-5542 (2024). Disentangling the carbon emissions impact of digital consumer innovations. Journal of Cleaner Production e144412. 10.1016/j.jclepro.2024.144412. (In Press)

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Abstract

Digital consumer innovations provide functionality to consumers through different impact mechanisms. These act indirectly on carbon emissions by shaping behaviour. Outcomes include energy/emissions mitigation or, growth through rebound effects, where energy savings are offset by increasing demand for energy.
In this study we use meta-regression techniques to quantify the relative strength of different impact mechanisms on emissions for a diverse set of digital innovations. We use data from two key synthesis studies, providing 135 estimates of impact across 22 different digital consumer innovations. We measure impacts using different metrics including activity, energy use, or carbon emissions (CO2/CO2 eq). We refer to these as “emissions-related outcomes”.
We find strong evidence that impact mechanisms explain differences in emissions-related outcomes between digital consumer innovations. Digital consumer innovations that influence behaviour by technology ‘substitution’ e.g., food gamification apps, have a significantly larger impact (44% reduction) than those that ‘coordinate’ e.g., food pairing apps (17% reduction) or those that that improve ‘control’ e.g., smart home appliances (20% reduction).
Estimates of impact included in energy studies are highly sensitive to boundary decisions and assumptions made by researchers, introducing further uncertainties into their magnitude and direction. When we control for variation in study design such as whether emissions-related outcomes data were collected using field experiments, or simulations we find that differences between impact mechanisms are amplified. A further key finding is that impact mechanisms explain more of the difference between-innovations than deployment context.
Our novel approach of classifying innovations by the underlying causal mechanism through which they change user behaviour and so energy emissions adds a new dimension to methodological work on indirect impacts for which system boundary and variable definition are not fixed. Identifying causal mechanisms with the largest benefits for emissions reduction also guides policy, innovators, service providers, and digital users concerned with carbon footprint.

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