Using the STIRPAT model to explore the factors driving regional CO2 emissions: a case of Tianjin, China

In order to curb anthropogenic carbon emissions and achieve the carbon intensity reduction target in China, it is crucial to shed light on influencing factors of carbon emissions at the city level. This paper selects Tianjin, one of the largest economic centers in northern China, as a study case. An extended stochastic impact by regression on population, affluence, and technology model is conducted to systematically identify the determinant factors driving CO2 emissions in Tianjin during the period 1996-2012. To eliminate multicollinearity problems, partial least squares regression is applied to improve this model. Empirical results show that the rapid process of urbanization has the greatest impact on the increase in carbon emissions, while the industrialization level has the least impact. Affluence level, population size, and FDI also play important roles in CO2 emission growth. The outcome of the FDI-emission nexus supports the pollution haven hypothesis, which shows that the inflow of foreign capital harms the local environment. Improvement in energy intensity is the major inhibitory factor and partially offsets the increase in carbon emissions. Finally, policy recommendations for carbon emission reduction plan in Tianjin have been given. Moreover, the approach developed in this research is transferable and can be utilized to analyze driving factors of CO2 emissions and formulate sustainable development strategies in another region.