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In recent years, the global greenhouse effect caused by excessive energy-related carbon emissions has attracted more and more attention. In this paper, we studied the dynamic evolution of factors driving China's energy-related CO2 emissions growth from 2007 to 2015 by using energy consumption method and input-output analysis and used the IO-SDA model to decompose the energy carbon emissions. Within the research interval, the results showed that (1) on the energy supply-side, the high carbon energy represented by raw coal was still the main factor to promote the growth of energy-related CO2 emissions. However, the optimization of energy consumption structure is conducive to reducing emissions. Specifically, the high carbon energy represented by raw coal exhibited a downward trend in promoting the increment of energy-related CO2 emissions, while the clean energy represented by natural gas showed an upward trend in promoting the increment of CO2 emissions. It is worth noting that there is still a lot of room for optimization of China's energy consumption structure to reduce emissions. (2) On the energy demand-side, the final demand effect is the main driving force of the growth of carbon emissions from fossil energy. Among them, the secondary industry plays a major role in the final demand effect. The "high carbonization" of the final product reflects the characteristics of China's high energy input in the process of industrialization. At the same time, since the carbon emission efficiency of the tertiary industry and the primary industry is better than that of the secondary industry, actively optimizing the industrial structure is conducive to slowing down the growth of carbon emission brought by the demand effect. (3) The input structure effect is the main restraining factor for the growth of energy carbon emissions, while the energy intensity effect has a slight driving effect on the growth of energy carbon emissions. The results show that China's "extensive" economic growth model has been effectively reversed, but the optimization of fossil energy utilization efficiency is still not obvious, and there is still a large space to curb carbon emissions by improving fossil energy utilization efficiency in the future. [ABSTRACT FROM AUTHOR]

• We measure the green growth degree of China's industrial sectors by considering the constraint of total energy consumption. • We proposed a new common equilibrium efficient frontier, which considers each DMU's own adjustment strategy on fixed-sum energy input. • The proposed approaches are applied to analyze the green growth and energy and environmental efficiency of 36 of China's industrial sectors. Tight energy constraints and severe environmental pollution are two main problems affecting economic growth and sustainable development. As an important economic growth mode, green growth seeks to achieve both economic growth and environmental sustainability. Previous studies on the efficiency evaluation of green growth ignored fixed-sum energy consumption; this is a significant omission because this constraint is general in China: the government has set targets to reduce energy consumption by 20% and 16% per unit of GDP during its the eleventh (2006-2010) and twelfth (2011-2015) five-year plans. To fill this gap, we propose a new data envelopment analysis (DEA) model to analyze the green growth degree of China's industrial sectors. The proposed DEA model considers the constraint of total energy consumption in order to conform to the policy of energy and emission reduction. Previous theoretical studies on fixed-sum output construct the common equilibrium efficient frontier (CEEF) only by considering the minimal reduction strategy for the fixed-sum output of all decision making units (DMUs), while ignoring each DMU's adjustment strategy for fixed-sum input/output. In this paper, we construct a new CEEF by considering each industrial sector's adjustment of energy consumption. The new CEEF not only guides the industrial sectors to alter their energy consumption structure to achieve green growth but also allows a more accurate evaluation of the energy and environmental efficiency (EEE) of industrial sectors. Last, our proposed model is applied to analyze the green growth and EEE of 36 of China's industrial sectors. [ABSTRACT FROM AUTHOR]