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Digitalization and CO2 emissions: Dynamics under R&D and technology innovation regimes

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  • Saia, Artjom

Abstract

New digital technologies help to curtail those that are carbon intensive but are accompanied by CO2 emissions due to their own energy demand. Whether digitalization relates to increased or decreased carbon emissions may depend on the sophistication of an economy's research and development (R&D) output. This study explores the R&D-induced regime transition that governs the relationship between digitalization and CO2 emissions. The study seeks evidence of the environmental Kuznets curve hypothesis and tests it with a panel smooth transition regression (PSTR). Employing this estimator with two R&D output regimes makes it possible to account for year- and country-varying effects of digitalization, human capital, and country income level on CO2 emission. The research covers 55 high- and middle-income economies from 1996 to 2019. The paper finds that the transition process is driven by R&D output level – measured in technology patents per country inhabitants. The findings support the environmental Kuznets curve hypothesis and confirm that CO2 emissions have an inverted U-shaped relationship with digitalization and income level. This nonlinear relationship transitions smoothly in the exogenous R&D output level. The digitalization indicator in the lower R&D regime has a significant point estimate of 0.07; in the higher regime, the estimate is −0.14. The R&D output threshold at which the transition function switches between the two regimes corresponds to a level of 39.9 technology patents per million inhabitants.

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  • Saia, Artjom, 2023. "Digitalization and CO2 emissions: Dynamics under R&D and technology innovation regimes," Technology in Society, Elsevier, vol. 74(C).
    • Handle: RePEc:eee:teinso:v:74:y:2023:i:c:s0160791x23001288
    DOI: 10.1016/j.techsoc.2023.102323
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    More about this item

    Keywords

    Digitalization; Carbon dioxide emissions; R&D regimes; Environmental Kuznets curve; Panel smooth transition regression (PSTR);
    All these keywords.

    JEL classification:

    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O44 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Environment and Growth
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

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