engleski

Are we making progress on decarbonization? A panel heterogeneous study of the long-run relationship in selected economies

This study investigates the long-run relationship between carbon emissions (CO2) and technological progress, capital intensity, GDP per capita, population, and labor productivity for 23 selected economies from 1890 to 2019. The study's originality emerges from using the most comprehensive panel time-series data available (1890–2019), adopting the most recent panel empirical modeling techniques. We use different estimation techniques fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS), canonical cointegration regression (CCR) to evaluate whether the decarbonization process is significant and its progress comparing findings with mean-group error correction model with variable cross-sectional averages (CCEMG) for robustness. The results reported here underline that carbon emissions are slowing down, but the decarbonization process remains sluggish. Limiting factors for decarbonization are GDP per capita growth, population, capital intensity, total factor productivity, labor productivity, emissions inequality, high relative costs of renewables, economy structure (share of labor/capital intensive industries). The study results provide evidence countries with higher GDP per capita, total factor productivity growth, population growth, and increasing capital intensity drive global carbon emissions up. The findings of this study have several significant implications for global CO2 policymakers and carbon emissions reduction initiatives. It also has important implications for practitioners facing policy and technological choices toward corporate social responsibility management. This article provides new insights into empirical modeling for carbons emissions-effects under heterogeneity (endogeneity), non-stationarity, cross-sectional dependence, and unobservable technological progress effect.

Decarbonizations ; Carbon emissions ; Fully modified ordinary least square ; Dynamic ordinary least square ; Canonical cointegration regression ; Mean-group error correction model with variable cross-sectional averages

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