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CO2 emissions and growth: A bivariate bidimensional mean‐variance random effects model

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  • Antonello Maruotti
  • Pierfrancesco Alaimo Di Loro

Abstract

We introduce a bivariate bidimensional mixed‐effects regression model, motivated by the analysis of CO2$$ {\mathrm{CO}}_2 $$ emission levels and growth on OECD countries from 1990 to 2018. The model is able to capture heterogeneity across countries and allows for a full association structure among outcomes, assuming a discrete distribution for the random terms with a possibly different number of support points in each univariate profile. We test the behavior of the proposed approach via a simulation study, considering several factors such as the number of observed units, times, and levels of heterogeneity in the data. Empirically, we define an extended version of the STIRPAT model where all model parameters, and not only the mean, vary according to a regression model. Our empirical findings provide evidence of heterogeneous behaviors across countries and suggest the need of a flexible approach to properly reflect the heterogeneity in both the emission levels and the growth processes.

Suggested Citation

  • Antonello Maruotti & Pierfrancesco Alaimo Di Loro, 2023. "CO2 emissions and growth: A bivariate bidimensional mean‐variance random effects model," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.
    • Handle: RePEc:wly:envmet:v:34:y:2023:i:5:n:e2793
    DOI: 10.1002/env.2793
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