1. Introduction
China has become the largest energy consumer in the world, leading to the environment coming under strain [
1]. As the largest developing country, China is still in the middle stages of industrialization and urbanization [
2]. Its economic development is difficult to realize without energy consumption [
3]. Therefore, it is critical to coordinate its economic growth and energy consumption. Currently, residential energy consumption (REC) is the second largest energy consuming sector in China, accounting for around 12% of the total energy consumption in 2015 [
4,
5]. However, the average share of REC to the total energy consumption is about 31% in the world [
6]. With further development of urbanization and industrialization expected, China’s REC is likely to increase in the future [
7,
8,
9]. Therefore, it is crucial to accelerate the decoupling process between REC and economic growth.
Previous studies that investigated the REC of China have grown numerous over the past few decades [
10,
11], and they mainly focus on calculating the REC and exploring the factors affecting REC from the perspectives of nation, region and urban–rural contrast [
12]. The source of the REC is often studied by many researchers, finding that REC includes direct and indirect REC. The former refers to energy consumption in a residents’ daily life, such as cooking, lighting, heating and private transport, etc. [
13]. The latter describes energy consumption caused by the embodied energy consumed during the various stages of the non-energy goods and services, such as production, transportation and marketing [
14]. In general, there are two mainstream methods to calculate the indirect REC, namely, the consumer life approach (CLA) and input–output analysis (IOA) [
15,
16]. Using these methods, many researchers quantified the direct REC [
7,
17,
18] and the indirect REC in China [
12,
19,
20,
21].
When it comes to the factors affecting China’s REC, the main drivers, such as the energy structure, energy intensity, population, income, urbanization and so on, are usually discussed [
7,
22]. There are two widely used decomposition methods to explore the issue, namely, structural decomposition analysis (SDA) and index decomposition analysis (IDA) [
23]. The SDA is based on IO tables, which yields more accurate decomposition results. However, the IO tables are only available in specific years, thus limiting the application of SDA [
1,
24]. Conversely, IDA can analyze changes between any years based on aggregate data, as well as providing some optional indices [
25]. Among all of these indices, Log-mean Divisa Index (LMDI) is the preferred decomposition method that is widely utilized because it has no unexplainable residuals and its being able to handle zero values problems [
26,
27,
28]. Zhao, et al. [
29] employed the LMDI to decompose the REC in urban China between 1998 and 2007 from the perspectives of disaggregated product and activity. The LMDI was utilized by Nie and Kemp [
6] to research the drivers of the REC in China between 2002 and 2010. Zhang and Bai [
17] also used LMDI to investigate the factors governing the REC in Shandong urban and rural regions from 1995 to 2013.
While many studies have extensively studied the REC of China with robust results, little attention concentrates on exploring the decoupling relationship between the REC and economic growth. For example, Zhang and Bai [
17] explored the decoupling state of REC in Shandong, which mainly focused on quantifying the decoupling status. To understand this issue in-depth, several questions need to be answered. These include the following: What is the decoupling status between REC and economic growth? What are the underlying factors affecting the decoupling? How can the specific decoupling efforts made by each factor be understood?
Under such a circumstance, our study selects Guangdong Province as a presentative case.
Figure 1 shows its location. As the most developed province in China, Guangdong accounts for 10.85% of national gross domestic product (GDP) and 8.03% of population in 2017, leading to about 7.25% of China’s total final energy consumption [
30]. To our knowledge, Guangdong was chosen by the National Development Reform Commission (NDRC) as one of the first batch of low-carbon provinces on 18 August 2010 [
31]. Thus, it is important for Guangdong as well as the national level to coordinate the economic growth and energy consumption. Meanwhile, Guangdong residents’ per capita income and life quality are higher than those of the national average, which may lead to more REC. Nowadays, the REC in Guangdong is the second largest energy consumer, occupying around 15.78% of the total energy consumption from the end-use perspective in 2017 [
32]. With further development of urbanization and industrialization expected, the REC in Guangdong is expected to increase in the future. Although there are many studies on Guangdong seeking to decouple economic growth from carbon emissions or energy consumption [
33,
34,
35], few of them focus on REC. Thus, the aim of this paper is to analyze the decoupling relationship between REC and economic growth (residential income) in-depth in urban and rural Guangdong during 2000–2017. We expect that this study can help Guangdong coordinate their economic growth and REC, as well as provide some important insights and references for researching the REC in China at a national or provincial level. The Tapio decoupling model was applied to analyze the decoupling states in Guangdong urban and rural regions. Then, the main factors (energy structure, energy intensity, per capita income, family size and household) governing the decoupling process are explored by combining the Tapio decoupling model with the LMDI method. Finally, the decoupling effort model is introduced to assess the contribution of each factor to the decoupling.
The rest of this paper is organized as follows:
Section 2 gives the literature review.
Section 3 presents the methodology and data description.
Section 4 shows the results and discussion.
Section 5 displays the main conclusions, policy implications and perspective for future work.
2. Literature Review
The term “decoupling” initially originated from the field of physics, meaning a separation of two or more physical variables [
1]. Zhang [
36] first introduced the concept in the environmental field. Until 2002, the Organization for Economic Co-operation and Development (OECD) categorized decoupling into absolute and relative decoupling [
37]. The OECD decoupling indicator was easily influenced by the decoupling elasticity and lacked explicit criteria [
2]. Later, Juknys [
38] put forward three types of decoupling, namely, the primary, secondary and double decoupling. Tapio [
39] built upon that study and studied the EU 15 countries’ transportation, to refine the decoupling elasticity and divide the decoupling states into eight logical categories. Since then, the Tapio decoupling model has been widely applied to analyze the dependence of energy consumption or carbon emission on economic growth [
40,
41]. For instance, the Tapio decoupling model was used by Luo, et al. [
42] to analyze the relationship between CO
2 emissions and economic growth in the agriculture sectors of 30 Chinses provinces during 1997–2014. Wu, et al. [
43] also utilized that method to research the decoupling status between CO
2 emissions and economic output in the Chinese construction industry during 2000–2015. Following the Tapio decoupling method, Wang, et al. [
3] conducted a comparative study on the decoupling performance between China and the United States.
In fact, the isolated Tapio decoupling method has typically been utilized to quantify the decoupling elasticity, which often failed to capture the effects of environmental externalities and indicate related information for improvement [
43,
44]. As the research in this field developed, many researchers also combined the Tapio decoupling model with the IDA to explore the factors affecting decoupling and assess the effectiveness of real efforts in decoupling progress [
40]. For instance, by combining the Tapio decoupling model with the LMDI method, Dong, et al. [
45] decomposed the decoupling indicator between energy consumption and economic growth in Liaoning Province from 1995 to 2012 into five decoupling indicator effects. They found that the energy intensity effect had a positive influence on decoupling, whereas the effects of economic structure and investment exerted a negative influence on decoupling. Similarly, Zhao, et al. [
46] explored the influencing factors of the decoupling of China’s industrial growth from CO
2 emissions during 1993–2013. The results showed that the investment efficiency was the most important factor accelerating the decoupling, while the investment scale played the most significant role in inhibiting the decoupling. As for assessing the effectiveness of environmental policies in decoupling efforts, focusing on the manufacturing sectors of 14 EU countries, Diakoulaki and Mandaraka [
44] assessed the effectiveness of efforts in decoupling the economic growth from CO
2 emission by combining the decoupling index with the refined Laspeyres decomposition model. The results suggested that most of these countries made the desirable decoupling efforts. Integrating the decomposition results of LMDI with the decoupling index, Román-Collado, et al. [
47] assessed and analyzed the progress in decoupling energy consumption from economic growth in Columbia. Similarly, Wang, et al. [
40] evaluated the decoupling efforts of India and China to achieve decoupling during 1990–2015 by employing the decoupling effort model.
As mentioned above, the existing studies on decoupling analyses were mainly conducted from sectoral, national and regional perspectives [
3,
41]. At the sectoral level, major sectors like agriculture, industry, construction and transportation were studied by using decoupling analyses [
42,
43,
46,
48]. Nevertheless, few studies were performed to analyze the decoupling relationship between REC and economic growth, especially the in-depth decoupling analyses of the Chinese residential sector. The main contribution of this paper is to conduct a comprehensive decoupling analysis between the REC and residential income by quantifying the decoupling status, exploring the factors affecting decoupling and measuring the effectiveness of decoupling efforts. We use a representative province in China (i.e., Guangdong) as the case, and conduct a comparative study between urban and rural regions from the perspectives of regional disparity and the urban–rural gap.
5. Conclusions and Policy Implications
5.1. Main Conclusions
Presently, the REC has become the second largest source of energy consumption in China. However, previous efforts paid little attention to studying the decoupling relationship between the REC and economic growth. This paper conducted comparative decoupling analysis between REC and economic growth (residential income) in urban and rural Guangdong during 2000–2017. First, we overviewed the situation of the REC in Guangdong urban and rural regions. Secondly, the Tapio decoupling indicator was used to analyze the decoupling status. We then further decomposed the decoupling index to explore the main factors affecting the decoupling process by combining the Tapio decoupling model with the LMDI method. Finally, the decoupling effort model was used to assess the effectiveness of the efforts towards achieving decoupling in urban and rural Guangdong. The main results are as follows:
Over the study period, the REC showed a similar trend of rapid increase in both urban and rural regions of Guangdong, as the residents’ living standards improved. However, the growth rate of the rural REC (8.86%) was faster than that of the urban REC (7.55%). The urban–rural gap of REC became narrow, though it was more than 1.0 for most years. The energy structure of the REC was dominated by oil in both urban and rural regions. In terms of the energy intensity of REC, overall, it decreased in urban regions, whereas it increased in rural regions.
Both urban and rural regions experienced four kinds of decoupling states: WD, EC, END and SD. WD occurred in most years (52.94% of the whole period) for urban regions, while END was dominant for nearly 60% of the whole period for rural regions. Overall, the decoupling state in urban regions was more ideal overall than that in rural regions, representing a WD (0.54) and an END (1.82), respectively.
Over the study period, the per capita income effect was the leading factor restricting the decoupling, followed by the household and energy structure effects, while the effects of energy intensity and family size accelerated the decoupling process in urban regions. In rural regions, per capita income was the biggest contributor to inhibiting the decoupling, followed by the effects of energy intensity and energy structure. Conversely, the household and family size effects had a positive effect on the decoupling process. Overall, the decoupling efforts in urban regions were mainly due to the improvement of energy efficiency, whereas the decoupling efforts in rural regions were mainly due to the reduction in family size.
5.2. Policy Implications
First, the per capita income effect was the biggest contributor to inhibiting the decoupling process in both urban and rural regions of Guangdong. As the most developed province in China, it is not wise for Guangdong to restrain residential consumption to achieve the decoupling, because consumption is the important impetus for economic growth, while a high-energy consumption model can no longer satisfy the needs of residents and the government for an environmentally friendly society. Therefore, the Guangdong government should seek a trade-off between economic growth and energy consumption. Increasing the investment and R&D (research and development) in products and services to foster new residential consumption spots can be a smart choice, such as smart home appliances, new energy vehicles, low energy consumption public transport and green residential buildings. Additionally, macro-control policies can also be applied to cut down the corresponding REC. For example, taxes could be increased for energy-intensive appliances, while subsidies and tax reductions could be granted for energy-saving appliances.
Secondly, the energy intensity effect made effective decoupling efforts and promoted decoupling in urban regions, whereas it made no decoupling efforts and inhibited the decoupling in rural regions. This suggests that the energy efficiency in rural regions is lower than that in urban regions. Thus, it is imperative to improve energy efficiency in rural regions so as to promote decoupling. On one hand, Guangdong rural regions can further eliminate the usage of outdated energy types (e.g., straw, firewood) to improve the energy efficiency of REC. On the other hand, the Guangdong government at all levels should actively guide rural residents to shift their lifestyles or behaviors to a low-carbon pattern. The specific measures can include public propaganda and education as well as drafting corresponding laws. Furthermore, rural residents should also enhance their awareness of energy conservation and environmental protection in their daily life.
Thirdly, the family size effect played a positive role in the decoupling process in both urban and rural regions. However, the decoupling effort of family size shifted from a weak decoupling effort state to a no decoupling effort state after 2013, indicating that the effect of family size on achieving decoupling will be weaker in the future. Presently, the family size is two to three persons and three to four persons in urban and rural Guangdong , respectively. To the best of our knowledge, the continuously shrinking family size may cause a series of unforeseen future problems, such as labor shortages and pension burdens, etc., while the expansion of family size will put the environment under strain. Therefore, the government should evaluate the population carrying capacity of Guangdong’s environment and resources, and put more effort into further adjusting the population structure by drafting reasonable and adaptive population policies.
Finally, the energy structure effect played only a minor role in inhibiting the decoupling process in both urban and rural Guangdong, suggesting that the energy structure of the REC was less than ideal and had enormous room for achieving the desired decoupling state. Thus, the Guangdong government should devote itself to further optimizing the energy structure and supporting related innovative energy technologies. Specifically, it should be reducing the proportion of thermal power and vigorously developing renewable and clean energy types, e.g., natural gas, nuclear technology, wind power, solar thermal power generation, hydro power, etc.
5.3. Limitations and Perspectives for Future Work
Some limitations are inherent within our study. We only researched the direct REC, while the indirect REC was excluded. This underestimates the total REC in Guangdong. Thus, further research is needed to analyze the indirect REC with more effective methods. Furthermore, both urbanization and economic growth had a great impact on REC, while this study mainly explored the relationship between REC and economic growth (residential income). Therefore, a future research direction will be to investigate the relationship between urbanization and REC in more detail.