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This study addresses the question of why industrial restructuring towards light industries or services sometimes fails to achieve carbon emission mitigation goals. By employing a new perspective of dividing industry segments into emission-dominating and non-emission-dominating ones based on Logarithmic Mean Divisia Index (LMDI) decomposition method, this paper analyses city-level carbon dioxide emission reduction performance under three distinctive industrial restructuring directions. Results indicate that carbon dioxide emission dominating segments are relatively fixed across cities, regardless of the various city types in China. The key point to achieve emission reduction through industrial restructuring is to identify and control the emission-dominating segments, instead of economic-leading ones. Besides, emission reduction performance of industrial restructuring from emission-dominating industry segments to services is better than that to non-emission-dominating ones. More importantly, industrial restructuring not involving output scale controlling of emission-dominating segments, or that recklessly rushing towards services are likely to fail the emission mitigation goal. This paper presents a strong international reference that merits cities facing policy hesitation over industrial restructuring directions while in pursuit of emission mitigation. It suggests that cities first focus on identifying the carbon dioxide dominating segments, of which the output scale should be controlled. For cities whose emission-dominating segments are not economic-leading ones, it is necessary to carry out industrial restructuring towards services or non-emission-dominating segments; while for cities heavily dependent on emission-dominating segments, energy efficiency should also be improved. • Identifying CO 2 -dominating segments at city level with LMDI method. • Comparing CO 2 mitigation effects among different industrial restructuring directions. • Providing feasible restructuring paths for distinct cities to reduce CO 2 emissions. [ABSTRACT FROM AUTHOR]

Considering the enlarging inter-provincial disparities in China as regards carbon emissions and carbon intensity (carbon emissions per unit gross domestic product), this paper is the first study to investigate the inter-provincial carbon imbalance by constructing and employing the Kaya-Zenga index. We use China's panel data of provincial-level carbon emissions over 1995–2016 to quantitatively measure the levels of inter-provincial imbalance and polarization in carbon emissions and carbon intensity. Further, we decompose the Kaya-Zenga index into different contributing factors both regionally and structurally and perform a scenario analysis to identify the corresponding regionally differentiated countermeasures regarding carbon emission reduction. The results show that the imbalance in carbon emissions is mainly caused by imbalances in population scale and income level, while the imbalance in carbon intensity predominantly results from imbalances in energy efficiency and energy mix. In addition, for heavy manufacturing provinces, the respective emission-reduction strategy should aim at lowering energy intensity through local technology improvement and inter-regional technology transfer. For light manufacturing and high technology provinces, carbon emission reduction is harder to be achieved; however, a mix of policies of improving energy efficiency, optimizing energy mix, and industrial upgrading should be implemented. The results of the scenario analysis indicate that reducing imbalance in carbon intensity under different scenarios can lead to a substantial reduction in carbon emissions (up to 10%). • We investigate the inter-provincial carbon imbalance within China. • We construct the Kaya-Zenga index and decompose the index into four contributors. • We use a panel data set of 30 provincial-level regions in China over 1995–2016. • Carbon imbalance is mainly caused by imbalances in population and energy efficiency. • Reducing intra-group imbalance in carbon intensity can abate carbon emissions. [ABSTRACT FROM AUTHOR]