An aggregate and disaggregate energy consumption, industrial growth and CO₂ emission

Abstract

Abstract Purpose This study aims to examine the relationships between aggregated and disaggregated energy use in the industrial sector, carbon emissions and industrial output in China. Design/methodology/approach The study utilizes annual frequency data for the period of 1984-2015. The unit root properties of data are tested using augmented Dickey–Fuller and Phillips and Perron unit root tests. Furthermore, the Zivot–Andrew structural breaks unit root test is used to detect the structural breaks steaming into series. The autoregressive distributed lag bound test and newly developed Bayer–Hanck combined cointegration are used to check the existence of a cointegration relationship between underlying variables. Last, the direction of causality is determined applying vector error correction model (VECM) Granger causality. Findings The results confirm the existence of a long-run relationship in the presence of structural breaks. The authors conclude that aggregated and disaggregated energy consumption in the industrial sector increases CO2 emission in both long and short run. The VECM Granger causality analysis indicates the bidirectional relationships between CO2 emission, industrial growth and aggregated and disaggregated (coal, oil and natural gas) energy consumption. Research limitations/implications Based on the empirical results mentioned above, the study proposes the recommendation that China should focus on the use of natural gas in the industrial sector instead of coal and oil consumption. The most potent reasons for such a transformation are twofold: natural gas is much more environment-friendly, thus being a much lesser polluting source of energy, and, most significantly, such a change would have no adverse impact upon the output level. Originality/value This study contributes to the existing literature on estimating CO2 emission by using aggregated and disaggregated energy consumption in case of China. Notwithstanding, it also adds to the existing applied literature by using newly developed combined cointegration to confirm and substantiate the cointegration relationship between the underlying variables. Moreover, this study incorporates the role of structural breaks while investigating CO2 emission function, which helps in providing more valuable policy suggestions.