**1. Introduction**

Fossil energy consumption is one of the main reasons for global warming [1,2]. As a major greenhouse gas (GHG) emitter, China aims to keep energy consumption within six billion tonnes of standard coal equivalent by 2030 for GHG abatement. Meanwhile, natural gas and non-fossil energy are targeted to have a 35% share of China's energy pie [3]. However, it has been announced that the commitment to the Paris Agreement of each country is not enough to achieve the 1.5 degree Celsius goal [4], which undoubtedly poses additional challenges to national climate policy. Therefore, it is important to quantify the driving factors related to energy saving and energy mix low-carbonization in order to shed light on the achievement of the pursuit of a higher GHG reduction. Also, the realization of national goals requires the joint efforts of all of the provinces. Given the provincial disparities, the provincial contributions to the energy saving and energy mix change are also worth estimating, for a policy tailored to each province.

Structural decomposition analysis (SDA) is usually used to evaluate the driving factors behind energy consumption or environment load change. An advantage of SDA is that a wide variety of the driving factors can be identified considering the economic linkages between sectors. However, the empirical studies are restricted by the availability of the monetary input–output tables of nations [5]. The energy consumption changes or environment load changes are often decomposed into three factors, namely: efficiency, the Leontief inverse effect, and the final demand effect. The final demand effect is further decomposed into commodity shares of final demand, destination share, per-capita total final demand, and population [6].

For the SDA studies focusing on energy change, Weber (2009) [7] used SDA to decompose energy growth between 1997 and 2002 in the United States, and found that rising populations and household consumption were the two drivers of energy demand growth, while being offset by the considerable structural change within the economy. A study of Italy from 1999 to 2006 showed that the final demand for goods and services was the main factor increasing energy consumption, but energy consumption was offset by the energy intensity and production structure [8]. In the case of Japan, the total energy demand dramatically increased, mainly as a result of the growth in the non-energy final demand from 1985 to 1990 [9]. In Brazil, from 1970 to 1996, the primary accelerator of energy use was population growth, and after 1980, the increasing demand for energy-intensive products also drove the energy demand increase [10].

Since China experienced an alarming average annual growth rate of energy consumption of 13% from 2002 to 2007 [11], many researchers have focused on an energy SDA analysis. Xie (2014) [12] examined the driving factors of China's energy use from 1992 to 2010. The study proved that after 2002, when China joined the WTO (World Trade Organization), 38% of its energy growth came from the increasing exports, 36% from the increasing fixed capital formation, while the contribution of household demand growth was only 15%. This was quite different from the household consumption-driven energy growth in developed countries such as the United States or Italy. However, between 2007 and 2010, the energy change induced by exports decreased because of the financial crisis. Fixed capital formation acted as the main reason for energy growth, accounting for 75% of energy growth, because of the "package plan" proposed by the Chinese government. The results of Mi et al. (2018) [13] agreed with those of Xie (2014) [12].

Moreover, Zhang and Lahr (2014), and Zhang et al. (2016) [14,15] quantified the energy change related to the regional demand in China. Meanwhile, efficiency played a critical role in offsetting energy consumption. However, from 2002 to 2007, the efficiency of construction, abnormally increasing the total energy use, resulted in a weakened energy saving effect of efficiency [12–14].

From the above-mentioned literature, we found that the decomposition of the final demand factor has already been well discussed, while the decomposition of technology factors was seldom deeply studied. The present research is intended to fill this gap. A novelty of this study is the development of a new refined decomposition framework to identify the technology role of each sector of each province in the change of energy-use in China.

Specifically, we propose an energy SDA based on a "hybrid" multiregional input–output (MRIO) table, expressed in both monetary and physical units. To the best of our knowledge, this is the first attempt to apply a hybrid MRIO approach to the provincial primary energy consumptions of China. It is well known that a hybrid input–output approach is superior to a monetary input–output approach, because energy sectors are frequently characterized by different pricing for different sectors, whereas the analysis based on monetary input–output tables assumes uniform pricing across all sectors [16–18]. However, the SDA described in previous studies is fit for the monetary approach, but not the hybrid approach [16]. Our new SDA framework applies to both hybrid and monetary cases.

In doing the hybrid analysis, we focus on the primary fossil energy (i.e., coal, oil, and natural gas) consumed in China. Hydropower, nuclear power, renewable energy, and others are not considered, because the proportion of non-fossil energy consumption in China was less than 10% during our study period [19]. The proposed energy SDA method is applied to the Chinese hybrid MRIO tables of 2007 and 2012, and the important driving forces of China's energy growth are identified at a sector and province level. Finally, we identify important stakeholder sectors and provinces that are important for energy saving in China, and sugges<sup>t</sup> that they should be environmentally monitored. Policy makers should commit to making important stakeholder sectors and provinces the highest priority.

This paper is organized as follows: Section 2 describes the methodology proposed in this study, Section 3 explains the data used in this study, Section 4 provides the results and discussion, and Section 5 concludes this paper.
