Residential Energy ‐ Related CO 2 Emissions in China’s Less Developed Regions: A Case Study of Jiangxi

: The residential sector is the second ‐ largest consumer of energy in China. However, little attention has been paid to reducing the residential CO 2 emissions of China’s less developed or undeveloped regions. Taking Jiangxi as a case study, this paper thus aims at fully analyzing the difference of the residential energy ‐ related CO 2 emissions between urban and rural regions based on the Log ‐ Mean Divisia Index (LMDI) and Tapio decoupling model. The main results are showed as follows: (1) Since 2008, residential energy ‐ related CO 2 emissions have increased rapidly in both urban and rural Jiangxi. From 2000 to 2017, the residential energy ‐ related CO 2 emissions per capita in rural regions rapidly increased and exceeded that in urban regions after 2015. Furthermore, the residential energy structures had become multiple in both urban and rural regions, but rural regions still had room to optimize its energy structure. (2) Over the study period, consumption expenditure per capita played the dominant role in increasing the residential energy ‐ related CO 2 emissions in both urban and rural regions, followed by energy demand and energy structure. Energy price had the most important effect on decreasing the urban and rural residential energy ‐ related CO 2 emissions, followed by the carbon emission coefficient. However, urbanization increased the urban residential energy ‐ related CO 2 emissions but decreased the CO 2 emissions in rural regions. Population made marginal and the most stable contribution to increase the residential energy ‐ related CO 2 emissions both in urban and rural regions. (3) Overall, the decoupling status showed the weak decoupling (0.1) and expansive negative decoupling (1.21) in urban and rural regions, respectively.


Introduction
The Intergovernmental Panel on Climate Change (IPCC) suggests that climate change is a great threat for human survival and development owing to the anthropogenic greenhouse gas (GHG) emissions [1,2]. Since 2006, China has become the largest CO2 emitter globally [3]. Currently, its emissions account for approximate one third of the global total emissions [4,5]. In this context, China has made commitments and formulated a series of policies to reduce emissions. Meanwhile, there is a consensus that reducing the fast increase of residential CO2 emissions is a significant pathway to achieve energy-saving and emission-reduction targets in China [6,7]. For China, the residential sector is the second-largest energy consumer category as well as primary CO2 emissions source [8,9]. Specifically, the residential sector is responsible for 11.7% of energy consumption and 12.6% of CO2 emissions in 2015 [10]. On one hand, after 40 years of "reform and opening up", China steps into the new development phase. In addition, with its further development of urbanization and industrialization, the residential CO2 emissions are expected to increase continuously. Thus, it is necessary and significant to study the key driving forces to help raise reasonable and effective mitigation policies to combat climate change [9]. On the other hand, it is widely appreciated that investment, consumption and export are the troika for economic growth, and it is not wise to simply restrain consumption to mitigate the CO2 emissions [11]. In addition, as reported by a recent study that China's economic prosperity had been coupled with environmental degradation, and suggested that decoupling economic growth from ecological impact had been central to achieve the national sustainable development [12]. Therefore, it is also necessary and imperative for China to discover a green, low-carbon and sustainable path to coordinate the link between developmental and environmental protection [13].
In reality, plenty of studies have been conducted to respond to the above concerns for decades in China. They often focused on exploring influencing factors of the CO2 emissions or energy consumption in the residential sector, based on national or regional perspectives, or analyzing the relationship between economic growth and CO2 emissions from residential sector. When exploring the drivers affecting residential CO2 emissions or energy consumption, many factors such as income [14][15][16], population [9,11,17], urbanization [18,19], energy intensity (or energy price) [10,20,21], energy structure [6,22,23], floor space (per capita) [24,25], education level [26], etc., were discussed. For example, based on structural decomposition analysis (SDA), Zhu, et al. [11] studied the indirect residential CO2 emissions during the period 1992-2005. They argued that population size was not the main reason for the emissions growth anymore, although it promoted the indirect emissions to a certain extent. Using Log-Mean Divisia Index (LMDI), Zhao, et al. [27] decomposed the residential energy consumption in urban China from 1998-2007 at a disaggregated product level. The results demonstrated that energy price had the positive effect on the emission reduction, while population and income played the key role in the growth of the CO2 emissions. Simultaneously, more scholars were also awareness of the urban-rural gap or dual society in China, thereby they attempted to research Chinese residential CO2 emissions or energy consumption based on the perspective. Based on Sato-Vartia index, Liu, et al. [17] analyzed the impact of China's increased urban and rural residential consumption on CO2 emissions from 1992-2007. Furthermore, Fan, et al. [6] studied the residential CO2 emissions evolutions in urban-rural divided China and explored the underlying driving forces from the perspective of end-use and behavior by applying Adaptive Weighting Divisia (AWD) decomposition. With the further study on the issue, some researchers extended their scopes and took into account Chinese regional disparity in their related studies. Taking Guangdong, the most developed province in China, as a case study, Wang, et al. [21] decomposed the influencing factors of direct residential energy-related CO2 emissions into eight factors by using LMDI. The results showed that residential living standard had the largest contribution to the increase of CO2 emissions, while energy price was the first inhibiting factor. Similarly, taking Liaoning, a coastal province in northeastern China, as an example, Tian, et al. [9] explored the driving forces of the residential CO2 emissions from 2002-2007 based on LMDI. The results suggested that population and per-capita consumption were the main factors to the increase of residential CO2 emissions, whereas carbon intensity had the negative effect on the residential CO2 emissions growth. Moreover, based on spatial-regional level, Yuan, et al. [22] applied LMDI to analyze the drivers of urban and rural residential CO2 emissions in China's 30 provinces between 2007 and 2012. They found that population and income-per-capita effects were the main drivers of high urban residential CO2 emissions in most of the coastal provinces. There were so many studies conducted in many other regions of China. The explanations for these studies can be similarly illustrated, here; we condense some explanations and list some presentative references in Table 1 to save space.
In the analysis of the pathway to coordinate CO2 emissions and economic growth, numerous studies were performed using the decoupling analysis. Generally, there are two main decoupling methods, i.e., OECD indicator and Tapio decoupling indicator [28,29]. Compared with OECD indicator, the latter was more accurate and flexible, which greatly improved OECD indicator. Therefore, Tapio decoupling indicator was more popular and widely used to analyze the relationship between CO2 emissions and economic growth. In addition, in China, most of these previous studies focused on the main emission sectors: industry [13,30]; transportation [31,32]; construction [33,34]; agriculture [35,36]. To the best of our knowledge, a few scholars applied the Tapio decoupling indicator to analyze the nexus between CO2 emissions and economic growth from the residential sector. Based on the Tapio decoupling indicator, Ye, et al. [20] conducted the study to analyze the relationship between residential CO2 emissions and economic growth in China from 1994-2012, which ignored the regional disparity and urban-rural gap. Using the OECD indicator, Yuan, et al. [22] studied the decoupling status in China's 30 provinces from the residential sector with little content.
Therefore, from Table 1, we can conclude that to date, researchers have mainly focused on developed regions especially coastal provinces and cities such as Guangdong, Liaoning, Jiangsu, Shandong and urban agglomerations, or on national and its 30 provinces when they conducted the studies on exploring the driving forces of CO2 emissions in the residential sector. Moreover, few studies were conducted on decoupling analysis of the relationship between CO2 emissions and economic growth from the residential sector compared with other main sectors. The existing related decoupling analysis studies on the residential sector were far from straightforward. As we all know, there is a consensus that China is a multiple-regional and heterogeneous country with the regional disparity and urban-gap [10,23], such as socio-economic, consumption pattern, urbanization, natural resource endowment, energy use, etc. Indeed, China's mitigation targets need all regions' efforts. However, this country's less developed and underdeveloped regions have not yet received academic attention, and few findings can be found in the existing studies worldwide. It is thus crucial to study these less developed and undeveloped regions. This can complete CO2 emissions profile and help those regions' decision-makers make reasonable mitigation policies on residential sector by fully considering the local situations. Under such a circumstance, this paper aims to make up for the above-mentioned deficiencies by fully considering urban-rural gap and regional disparity; we take Jiangxi, a typical less developed province in central China, as a case study. Figure 1 shows its location. Jiangxi is in central China with an administrative area of 166,900 km 2 . Statistically, its total gross domestic product (GDP) reached 2001 billion RenMinBi (RMB) in 2017 [37], which ranked 16 among all 31 regions of China. To the best of our knowledge, its capital city, Nanchang, was assigned the task of pursuing a low-carbon economic transformation in China's first "Low-Carbon Pilot Cities" national project in 2010 [9,38]. More recently, Jiangxi, Fujian, and Guizhou are selected as the first national ecological civilization test beds in 2016, aiming to set up the relatively sound ecological mechanism for China. According to these facts, we can know that Jiangxi had the huge potential of increasing its CO2 emissions on residential sector with its further development. However, little academic attention was paid to Jiangxi, especially its residential sector. Thus, based on data availability, we first attempt to overview the situation of residential energy-related CO2 emissions in Jiangxi urban and rural regions. Then, the driving forces affecting the residential energy-related CO2 emissions in urban and rural regions are explored by using LMDI. In addition, the Tapio decoupling model was applied to analyze the relationship between the residential energy-related CO2 emissions and economic growth (consumption expenditure). We believe this study has some innovative significance to help this province make mitigation policies and provide important insight for China's other less developed or undeveloped regions to reduce emissions.
The remainder of this paper is organized as follows: Section 2 depicts the methodology and data description. Results and discussions are given in Section 3. The main conclusions and corresponding policy implications are provided in Section 4.

Estimation of the Residential Energy-Related CO2 Emissions
The residential energy-related CO2 emissions was caused by the residential energy consumption, e.g., lighting, cooking, heating, using household appliances and private transport, in addition, including electricity and heat [9]. Along with these activities, quantities of fossil fuels (e.g., coal, oil, natural gas) and secondary energy sources (e.g., electricity, heat) were consumed, which resulted in CO2 emissions directly. According to IPCC [39], the residential energy-related CO2 emissions can be calculated by the following equation: where i represents the resident type, i.e., urban residents and rural residents.  [38], the carbon emission coefficient of heat can be measured as 0.11 t-CO2/GJ by the equivalent calorific value. As for the carbon emission coefficient of electricity, here, we calculate and list the coefficient in Table A1.

Decomposition Method
Decomposition analysis has been widely applied to explore the impact factors affecting the changes of CO2 emissions in the economic and environmental field. To date, there are two mainstream factor decomposition methods [25,36], viz., SDA and index decomposition analysis (IDA). Generally, SDA depends on input-output (IO) table and produces more accurate decomposition results. However, the IO tables are not available every year so that SDA has the limitation to the annual analysis. Inversely, IDA is easier to acquire data and more alterative to use aggregated data to analyze any years' changes [10]. Hence, IDA is widely applied to decompose the driving factors of energy-related CO2 emissions. In addition, IDA provides many different indexes to choose [38]. In particular, among these indexes, LMDI has the incomparable advantages for its ease of use and no unexplainable residuals [42]. Moreover, it also provides eight effective strategies to handle zero values problem [43]. In view of these advantages, LMDI has been regarded as the most perfect decomposition method and widely used by many researchers [44,45]. Thus, in this paper, we apply the LMDI to explore the influencing factors affecting the residential energy-related CO2 emissions in Jiangxi urban and rural regions.
The Kaya identity was widely used to reveal the influencing factors of CO2 emissions. With the further development of the studies, more influencing factors are discussed. According to Kaya [46], the extended determinants of the residential energy-related CO2 emissions can be expressed as: (2) can be written as: where the variables are defined in Table 2. Thereafter, to explore the contribution of the influencing factors to the total CO2 emissions, next, we use LMDI, and it has two formations, i.e., additive LMDI (LMDI-I) and multiplicative LMDI (LMDI-II). Based on the additive LMDI, the changes of the total CO2 emissions ( TOT C ) from the baseline period ( 0 C ) to the target period ( T C ) can be decomposed as the following seven effects: Each effect can be calculated as follows: In addition, to present the decomposition results clearer, here, we also use the multiplicative LMDI. According to Shao, et al. [47], the corresponding multiplicative LMDI can be written as: and P .  TOT means the changes of the total CO2 emissions between the baseline period and the target period with the corresponding multiplicative formation of the LMDI.
To the best of our knowledge, the CO2 emission coefficient is usually assumed as a constant value, which has no contributions to the changes of CO2 emissions. However, in this paper, the CO2 emission coefficient of electricity changes every year, and it will lead to the changes of CO2 emissions. Therefore, we finally analyze the following seven effects: the carbon emission coefficient effect ( 

Decoupling Model
The decoupling theory initially originated from the field of physics, which denotes the delinkage relationship between two or more variables. Organization for Co-operation and Development (OECD) first categorized the decoupling indicators into relative decoupling and absolute decoupling [28]. However, there are some limitations and shortcomings of the OECD decoupling theory. Specifically, it is liable to be affected by the decoupling elasticity and lacked obvious criteria for choosing appropriate factors [36]. In view of this, Tapio [29] subdivided decoupling indicators into eight sub-categories to analyze the relationship between the road traffic and CO2 emissions in the EU 15 countries from 1970-2001, which greatly improved the framework of OECD decoupling indicators. Since then, the Tapio decoupling model (see Table 3) was widely used to explore the dynamics nexus between economic growth and environmental protection. However, based on the context above, the decoupling analysis of the CO2 emissions in the residential sector was far from straightforward in China, especially in less developed regions. Therefore, in this paper, the Tapio decoupling model was selected to analyze the decoupling status and the underlying drivers of the residential consumption expenditure from the residential energy-related CO2 emissions in Jiangxi urban and rural regions.
According to Tapio [29], the decoupling indicator of residential energy-related CO2 emissions of the i type resident from residential consumption expenditure can be expressed as:

Data Description
Based on data availability, in this paper, the study period ranges from 2000 to 2017. To the best of our knowledge, since 1953, with a gap from 1963 to 1965, Chinese government has formulated plans for national economic and social development every five years, namely "Five-Year Plan (FYP)". To make the related results and analysis clearer, here, the study period is consistent with the FYP and subdivided into four stages, i.e.  [37]. It should be noticed that consumption expenditure is subdivided into eight categories (e.g., food, cigarettes and wine, clothing, residence, household appliances, and services) in the JSY [37]. Additionally, among these categories, the data of residence expenditure (including the expenditure of housing, electricity, water, fuel and others) is the main source of the residential energy-related CO2 emissions [21]. Thus, we split the residence expenditure from the consumption expenditure to make the analyses clearer. The share of residence expenditure to consumption expenditure is defined the residential energy demand in this study.

The Trends of Residential Energy-Related CO2 Emissions
As shown in Figure 2, the urban residential energy-related CO2 emissions grew from 540. 38 [38]. Specifically, the urban residential energy-related CO2 emissions were not more than 600 × 10 4 t. After 2008, the urban residential energy-related CO2 emissions grew to 615.17 × 10 4 t in 2009, then it steadily and quickly increased to 1112.84 × 10 4 t, with an annual growth rate of 7.47%. The rapid growth of the residential energy-related CO2 emissions might be explained by the recovery from the crisis and the improvement of economy and living standard for urban residents. This situation was mainly caused by the growing demand of high-carbon appliances and private transport (Table 4), consuming large quantities of oil and electricity [23]. For example, the number of family cars, air conditioners and computers in 2017 reached 40-fold, 1.9-fold and 2.3-fold than that in 2005, respectively. By contrast, the rural residential energy-related CO2 emissions grew from 210.13 × 10 4 t to 954.82×10 4 t between 2000 and 2017 ( Figure 2), with an annual growth amount of 43.81 × 10 4 t and a growth rate of 9.31%. However, the growth rate of rural residential energy-related CO2 emissions was quicker than that of urban Jiangxi. Here, we also subdivided the rural residential energy-related CO2 emissions into three phases: a rapid growth phase   Figure 2 also showed the ratio of the residential energy-related CO2 emissions for urban and rural regions widened and narrowed, felling from 2.57 in 2000 to 1.17 in 2017. Over the study period, the urban-rural ratio of residential energy-related CO2 emission most remained the interval 1.0-1.2 except 2000-2005. Specifically, the ratio was more than 1.3 between 2000-2003. To the best of our knowledge, urban residents owned more energy equipment and consumed more commercial energy than that of rural residents, leading to more residential energy-related CO2 emissions [17,48]. The ratio was 0.84 and 0.96 between 2003 and 2004, respectively. The reason might be that the large-scale reduction of coal use in urban regions as well as the growing popularity of appliances in rural regions (Table 5). From 2006-2017, the ratio of urban-rural always over 1.0, which mainly resulted from the development of urbanization. The changes of residential energy-related CO2 emissions per capita in Jiangxi urban and rural regions were presented in The decrease of the urban residential energy-related CO2 emissions per capita could be explained by the following reasons. The proportion of high-quality energy types, such as oil, natural gas, and electricity greatly improved due to the shift of energy structure and the improvement of appliances' energy efficiency [24]. Additionally, urban residents reduced some unnecessary energy consumption when the financial crisis occurred. The urban residential energy-related CO2 emissions per capita grew from 361.35 kg-CO2 to 440.96 kg-CO2 from 2010-2017, this might be explained by the increase of appliances and private transport (Table 4). By contrast, the rural residential energy-related CO2 emissions per capita rapidly increased from 70.05 kg-CO2 to 455.02 kg-CO2 from 2000-2017 ( Figure 4), with an annual average increase amount of 22.65 kg-CO2 and a growth rate of 11.64%. In particular, the rural energy-related CO2 emissions per capita were 401.41 kg-CO2 and 455.02 kg-CO2 from 2016-2017 which exceeded that of urban regions. Compare to urban regions, the rural residential energy-related CO2 emissions presented a tedious upward trend. This situation could be explained by the traditional biomass was gradually outpaced by the commercial energy. Moreover, with the improvement of living standards, rural residents had the capacity to pursuit high-quality life, such as buying more energy-intensive appliances and private transport tools (Table 5). However, many of them lacked the consciousness of environmental protection, resulting in more residential CO2 emissions [49].
As presented in Figure 4, the gap in residential energy-related CO2 emissions per capita between Jiangxi urban and rural regions decreased from 6.72 to 0.97 from 2000-2017. In addition, it could be divided into two phases: a rapid decrease phase in 2000-2005 and a slow decrease phase between 2005-2017. The approximate reason might be owing to the development of urbanization and the rural economy [23].

Analysis on the Residential Energy-Related CO2 Emissions Structure
As shown in Figure 3a, the urban residential energy-related CO2 emissions caused by coal waned from 324.57 × 10 4 t in 2000 to 65.64 × 10 4 t in 2007 and its corresponding share decreased from 60.06% to 5.90%, thereby indicating the reduction of coal use in urban regions. In addition, the residential energy-related CO2 emissions caused by heat also presented a decrease tendency (46.86 × 10 4 t in 2000 and 2.01 × 10 4 t in 2017). The reason was that Jiangxi reduced the supply of heat, eventually, cut it off after 2002. See other energy types, electricity, oil, and natural gas overall presented an upward trend. The residential energy-related CO2 emissions caused by electricity increased from 101.81×10 4 t to 605.55×10 4 t from 2000-2017, the corresponding share increased from 18.84% to 54.42%. It should be noteworthy that the residential energy-related CO2 emissions induced by electricity exceeded coal and ranked first, which became the most important energy for urban residents after 2004. Since 2007, the residential energy-related CO2 emissions induced by oil also exceeded the coal and became the second-largest energy type, and its corresponding share grew from 12.42% in 2001 to 29.24% in 2017. It is likely that the private transport increased rapidly (Table 4). However, starting from 2005, urban residents began to use natural gas. The share of residential energy-related CO2 emissions induced by natural gas increased quickly from 1.09×10 4 t in 2005 to 116.29 × 10 4 t in 2007, with an annual growth rate of 47.57%, the corresponding share was from 0.2% to 10.45%. Since 2012, the share of residential energy-related CO2 emissions induced by natural gas exceeded coal. At present, the residential energy-related CO2 emissions structure was dominated by electricity, followed by oil, natural gas, and coal in urban regions.
The residential energy-related CO2 emissions caused by heat was not found in rural regions according to JSY [37]. Here, we only considered coal, oil, natural gas, and electricity. As shown in Figure 3b, the residential energy-related CO2 emissions for rural residents mainly induced by coal, oil, and electricity, which accounted for over 99% over the study period. In 2000, the residential energy-related CO2 emissions induced by coal ranked the first, followed by electricity and oil. The share of residential energy-related CO2 emissions induced by coal decreased from 73.59% to 27.52% from 2000-2007. The residential energy-related CO2 emissions induced by electricity rapidly increased from 49.02 × 10 4 t to 513.47 × 10 4 t over the study period, with an annual growth rate of 14.82%. In 2009, the residential energy-related CO2 emissions induced by electricity exceeded the coal, which might be explained by the growing increase of appliances. From 2009-2017, electricity became the most important energy type of the residential energy-related CO2 emission for rural residents. For oil, the residential energy-related CO2 emissions induced by oil occupied from 3.08% in 2000 to 18.25% in 2017. In addition, its share was still lower than the coal, although the share of coal decreased. Therefore, it could be deduced that the energy structure had further space to optimize for rural regions. The residential energy-related CO2 emissions induced by natural gas was minimal among other energy types, which increased from 0.66 × 10 4 t to 4.37 × 10 4 t from 2008-2017. It should be noted that Jiangxi belonged to the "second pipeline of West-East Natural Gas transmission Project (To provide clean energy and shift energy structure in central and south China, the second pipeline of "West-East Natural Gas transmission Project" was launched in February 2008. Specifically, the pipeline went through Xinjiang, Gansu, Ningxia, Shaanxi, Henan, Hubei, Hunan, Jiangxi, Anhui, Jiangsu, Shanghai, Zhejiang, Guangdong, Guangxi, and H.K.)", thereby leading to the use of natural gas after 2008. With the implementation of the "coal to gas" project, the demand of natural gas of rural regions in Jiangxi might increase rapidly and expand the of share of residential energy-related CO2 emissions induced by natural gas in the foreseeable future. Correspondingly, the share of the residential energy-related CO2 emissions induced by coal would fall further. To the best of our knowledge, coal was a type of low-quality energy; the direct combustion of coal indoors was harmful to human health for rural residents, especially for women and children [17], which in turn led to air pollution even aggregated climate change. From the perspective of socio-development, if the energy structure were outdated, it would be bad for the development of rural economy and education due to the vulnerability of Chinese rural regions. To date, the share of residential energy-related CO2 emissions structure was electricity, coal, oil, and natural gas in sequence for rural regions.

Decomposition Analysis of Residential Energy-Related CO2 Emissions at Four Stages
As shown that the urban residential energy-related CO2 emissions increased by only 7.91×10 4 t with a growth rate of 1.46% during the first stage (Figure 5a and Figure 6a), and then rapidly grew by 162.15×10 4 t with a growth rate of 29.58% in the second stage (2005-2010) (Figure 5a and Figure  6b). During the third stage, the CO2 emissions increased by 215.42×10 4 t at a rate of 30.32% (Figure 5a and Figure 6c) and grew by 186.98×10 4 t at a rate of 20.19% during the fourth stage (Figure 5a and Figure 6d). Therefore, the conclusion could be drawn that the urban residential energy-related CO2 emissions presented a sequentially upward trend with a total growth rate of 105.94% over the study period (Figure 5a). With respect to rural regions, it could be easily observed that the rural residential energy-related CO2 emissions grew by 360.81×10 4 t at a rate of 171.71% in the first stage (Figure 5b and Figure 6a), and then increased by 69.11×10 4 t at a rate of 12.11% in the second stage (Figure 5b and Figure 6b). During the third and fourth stage, the rural residential CO2 emissions increased by 181.59×10 4 t with a growth rate of 28.37% (Figure 5b and Figure 6c) and grew by 133.18×10 4 t with a growth rate of 16.21% (Figure 5b and Figure 6d), respectively. Analogous to the urban regions, it could be easily noticed that the rural residential energy-related CO2 emissions also exhibited a sequentially increasing trend at a rate of 354.39% (Figure 5b).

Consumption Expenditure Per Capita (CE)
To the best of our knowledge, consumption expenditure per capita could reflect folks' living standards of a region to some extent. As shown in Table A5, it could be intuitively seen that consumption expenditure per capita always exerted a positive influence on the urban residential energy-related CO2 emissions, which was in consonance with the results in Figure 5a and Table 6.  Table A3). In addition, the consumption expenditure per-capita effect contributed to the largest annual growth rate of the CO2 emissions (11.77%), and the corresponding average annual contribution rates at four stages were 9.55%, 11.68%, 10.24% and 7.51% (Table 6), respectively. Thus, the conclusion could be drawn that the consumption expenditure per-capita effect played the most important role in promoting the urban residential energy-related CO2 emissions.
For rural regions, the consumption expenditure per capita also always had a positive effect on the rural residential energy-related CO2 emissions (Figure 5b and Table A6). At different four stages, the effect contributed to 166×10 4 t, 258.04×10 4 t, 472.22 × 10 4 t and 160.16 × 10 4 t, and the corresponding average annual contribution rates were 15.88%, 9.04%, 14.76% and 9.75% (Figure 5b and Table 7), respectively. Overall, the average annual led to the largest annual contribution rate (29.60%) over the studied decade, suggesting the effect of consumption expenditure per capita was also the most critical promoting factor to the rural residential energy-related CO2 emissions.
Therefore, simply stated, these results implicated that the effect of consumption expenditure was the first promoting factor to residential energy-related CO2 emissions for both urban and rural residents, which was in line with the previous studies [9,17]. In fact, with the development of Jiangxi's economy, both urban and rural residents' living standards had greatly improved. Specifically, the consumption expenditure per capita of urban and rural residents increased from 3623.52 RMB and 1642.66 RMB in 2000 to 19244.46 RMB and 9230.21 RMB in 2017 (Figure 7), with a growth rate of 10.32% and 10.69%, respectively. As a result, people had the capacity to shift their consumption patterns to buy more appliances and transport tools, resulting in more energy consumption and CO2 emissions. According to Pachauri [50], future increases in expenditure levels will lead to further increases in household energy requirement, in turn, a further increase in the urban and rural residential energy-related CO2 emissions caused by the steady increase of the consumption expenditure per capita in Jiangxi in the future.

Energy Demand (ED)
It could be intuitively observed that the additive decomposition effect of energy demand was -73.42 × 10 4 t and -0.11 × 10 4 t in urban regions (Figure 5a and Table A3), exerting a negative effect on decreasing the urban residential energy-related CO2 emissions during the first and second stages. However, the negative effect weakened year by year. This situation was mainly because the energy consumption growth lagged behind the growth of the consumption expenditure. However, during the third and fourth stages, the decomposition effect increased to 597.57 × 10 4 t and 98.05 × 10 4 t ( Figure  5a and Table A3), respectively. This could be attributable to the rapid growth of the energy demand, which increased from 10.45% in 2010 to 21.64% in 2015, and then to 23.85% in 2017 (Figure 7). In total, the effect of energy demand made the second-largest contribution to the total annual average rate of urban residential CO2 emissions (6.77%), and for each of the four stages, the corresponding average annual contribution rates were -2.72%, -0.01%, 16.82% and 5.29%, respectively (Table 6). Overall, the energy demand effect played a key role in increasing the urban residential energy-related CO2 emissions although it slightly mitigated the CO2 emissions in Stage 1 and 2 ( Figure 6 and Table A5).
Similarly, energy demand also had a positive effect on promoting the rural residential energyrelated CO2 emissions over the study period ( Figure 6 and Table A6). In total, it contributed to 427.17 × 10 4 t CO2 emissions, with an annual average contribution rate of 11.96% (Figure 5b and Table 7).

Population (P)
It was apparent the effect of population was the most stable and negligible factor to promote the urban and rural residential energy-related CO2 emissions (Table A5 and Table A6), which was consistent with the previous studies [11,20]. Specifically, the average annual promoting impact of population effect (0.76% for urban, 1.70% for rural) was much lower than those of consumption expenditure per-capita effect and energy demand effect (Table 6 and Table 7). See from urban and rural, at each of four stages, the additive decomposition effects were 19.25 × 10 4 t, 20.06 × 10 4 t, 18.29 × 10 4 t and 12.14×10 4 t for urban residents (Figure 5a and Table A3), similarly, 14.09 × 10 4 t, 19.62 × 10 4 t, 16.11 × 10 4 t and 10.77 × 10 4 t for rural regions (Figure 5b and Table A4). Over the study period, it could be found the population effect presented an inverted U-shape trend in urban and rural regions, implying the population effect would weaken in the future. The total population increased from 41.49 million to 46.22 million, with an annual growth rate of only 0.64% in Jiangxi over this 17-year period ( Figure 8). In fact, to the best of our knowledge, population was a stable element for a region during a certain period, i.e., population would increase with a stable growth rate.

Urbanization (U)
The additive decomposition results of urbanization in urban regions were 145.86 × 10 4 t, 98.97 × 10 4 t, 125.77 × 10 4 t and 55.32 × 10 4 t at each of four stages (Figure 5a and Table A3), indicating the effect of urbanization always had a positive influence on increasing the urban residential energy-related CO2 emissions. The results were consonance with the results in Table A5. In total, the effect of urbanization made a specific contribution to the total urban residential energy-related CO2 emissions growth (4.64%) ( Table 6). Compare to urban regions, the additive decomposition results of urbanization effect in rural regions were -50.56 × 10 4 t, -66.74 × 10 4 t, -101.60 × 10 4 t and -55.66 × 10 4 t during the four stages (Figure 5b and Table A4), respectively. Eventually, the urbanization effect led to the total rural residential energy-related CO2 emissions decrease at an annual average rate of 7.69% (Table 7). Overall, the results showed the urbanization effect was the inhibiting factor to the rural residential energy-related CO2 emissions (Table A6). The difference between urban and rural regions could be explained by the followings.
As depicted in Figure 8, the population of urban residents increased from 11.49 million to 25.24 million with an annual growth rate of 4.74%, and the population of rural residents decreased from 30.00 million to 20.98 million with an annual change of -2.08%. Correspondingly, the urbanization rate steadily increased from 27.69% to 54.6% from 2000-2017. In light of the context mentioned above, the urbanization effect increased the urban residential energy-related CO2 emissions, but decreased the rural residential energy-related CO2 emissions. On average, when the urbanization increased by 1%, the urban residential energy-related CO2 emissions might have a growth of about 15.83×10 4 t, while the rural residential energy-related CO2 emissions might have a reduction of about 10.20×10 4 t. See from each of four stages, the annual average contribution rates were 5.40%, 3.61%, 3.54% and 2.99% in urban regions (Table 6), and -4.81%, -2.34%, -3.17% and -3.39% in rural regions ( Table 7). The urbanization effect presented a decline trend on the whole. In the future, this effect might be offset with the further development of Jiangxi's urbanization. It was demonstrated by the contribution rate of the urbanization effect in Guangdong, the most developed province of China, experienced an increase trend to 53.6% in 2003, then declined to 17.3% in 2012 with a diminishing trend over time [21]. Furthermore, Fan, et al. [19] also pointed out the urbanization contributed to the increase to Chinese residential energy consumption but with a diminishing trend over time.

Energy Structure (ES)
Energy structure adjustment played a minor role in increasing the residential energy-related CO2 emissions in urban and rural regions (Table A5 and Table A6), which was consistent with most relevant studies [21,51]. Over the study period, the aggregate changes of the energy structure effect were 94.29 × 10 4 t and 88.84 × 10 4 t (Figure 5), and the corresponding average annual contribution rates were 1.03% and 2.49%, respectively. Specifically, in the four stages, the corresponding annual average contribution rates were 6.34%, -2.23%, -0.82%, 0.72% for urban regions (Table 6), and 3.84%, 2.79%, -1.13% and 0.31% for rural regions ( Table 7). The usage of coal steadily decreased in both urban and rural regions, resulting in the share of the residential energy-related CO2 emissions decreased to 5.90%, 27.52% in 2017 from 60.06%, 73.59% in 2000 ( Figure 4). However, the share of residential energy-related CO2 emissions caused by oil and electricity rapidly increased, especially electricity. Furthermore, the share of natural gas was still low in Jiangxi, especially in rural regions. It was worth mentioning that the source endowment of China (including Jiangxi) was dominated by coal in the long term, and the electricity generation was mainly from coal [9,52]. The renewable and sustainable energy, such as wind, solar, nuclear, and biomass energy, had not been widely used [36]. Thus, it still required a longer time to thoroughly shift the energy structure to the low-carbon pattern to reduce the residential energy-related CO2 emissions.
Overall, the energy price effect made the greatest contribution to the total annual rates of urban and rural residential energy-related CO2 emissions mitigation (-17.55% for urban and -14.38% for rural) ( Table 6 and Table 7). This was mainly because the energy price increased from 2410.53 RMB/ ton-standard coal equivalent (SCE), 9855.43 RMB/ ton SCE in 2000 to 28078.53 RMB/ ton SCE, 16900.13 RMB/ ton SCE in 2017 for urban and rural regions, respectively (Figure 9), restraining the willing of people's consumption. In particular, it should be noted that the energy price rapidly grew from 9730.42 RMB/ ton SCE in 2012 to 24690.65 RMB/ ton SCE in 2015 because of the implementation of the "ladder electricity price plan (In November 2011, the National Development and Reform Commission (NDRC) issued the "residential ladder electricity price policy" to promote energy conservation and emission reduction as well as social equity. Specifically, residential electricity consumption was divided into three grades and a free grade for low-income households.)" in Jiangxi urban regions, making the energy price reached the peak value (-782.11 × 10 4 RMB/ton SCE). Moreover, during Stage 1, the energy price decreased from 9855.43 RMB/ ton SCE to 5226.89 RMB/ ton SCE, which stimulated the consumption of rural residents.

Carbon Emission Coefficient (K)
As mentioned above, in this article, the carbon emission coefficient of electricity was not a constant value. The carbon emission coefficient effect mainly derived from the change of the carbon emission coefficient of power generation (Table A1). In addition, according to Ren, et al. [53], if the contribution of the energy emission factor was negative, the residential energy-related CO2 emissions decreased. Otherwise, the contribution of the energy emission factor was positive and the residential energy-related increased.
The carbon emission coefficient effect had the least influence on inhibiting the residential energyrelated CO2 emissions in both urban and rural regions (Table A5 and Table A6). In total, the average annual contribution rates were -1.17% and -2.82% for urban and rural regions ( Table 6 and Table 7), respectively. See from each of four stages, the corresponding contribution rates were 1.36% and 1.92% in urban and rural regions from 2000-2005, indicating the carbon emission coefficient effect had a minor influence on increasing the CO2 emissions. This situation was likely that the carbon emission coefficient of electricity increased from 6.49 t-CO2/10 4 kwh to 7.99 t-CO2/10 4 kwh in Jiangxi (Table A1). However, in 2005-2010, 2010-2015, and 2015-2017, the average annual contribution rates decreased to -2.32%, -2.22%, -0.12% for urban regions, and -1.47%, -2.40%, -0.15% for rural regions, respectively. The results might be due to the improvement of power generation technology, updated generation equipment and other factors [53]. Specifically, the carbon emission coefficient of electricity decreased from 7.99 t-CO2/10 4 kwh to 4.84 t-CO2/10 4 kwh in 2017. On the other hand, the carbon emission coefficient effect represented an overall downward trend, resulting from the long-term coaldependent power generation in Jiangxi (Average=80.91%) ( Figure 10). Hence, the carbon emission coefficient effect eventually had a relatively marginal negative contribution to the residential energyrelated CO2 emissions reduction. To effectively enhance the effect of energy emission factor to mitigate CO2 emissions, it is necessary to boost the share of cleaner energy types such as hydropower, wind power, and solar power in Jiangxi's electricity generation structure.

Decoupling Analysis of Residential Energy-Related CO2 Emissions at Four Stages
As shown in Table 8, four decoupling states occurred in Jiangxi urban regions, namely SD, END, WD, and EC. From 2000-2005, the decoupling states switched every year, thereby implying that an unstable relationship between urban residential energy-related CO2 emissions and consumption expenditure. Eventually, the decoupling state showed WD, that is to say, the consumption expenditure increased a little faster than the CO2 emissions. During Stage 1 (2000-2005), both urban and rural residents' income increased with the development of economy. Correspondingly, the consumption expenditure grew by 135% and 37% (Table 8), respectively. To ensure the quick development of economy, the demand for urban residents was restrained, which caused the residential energy-related CO2 emissions increased by only 1%. Thus, the consumption expenditure increased much greater than the CO2 emissions, leading to an overall WD (0.01) in Stage 1 although EC and END occurred in 2001-2003 and 2004-2005 (Table 8). See from rural, decline of energy price stimulated the residents' consumption desire which made the energy demand increase (Figure 7). Meanwhile, it caused the CO2 emissions rose by 172% in 2000-2005 which was greatly higher than that of consumption expenditure. Consequently, the decoupling state was END in this stage.
During Stage 2 (2005Stage 2 ( -2010, the consumption expenditure rose by 114% (Table 8), which made urban residents pursuit more high-quality life and buy more high-carbon appliance and family cars (Table 4). Thus, the CO2 emissions grew by 30%, presenting the WD (0.26) ( Table 8) in 2005-2010. As to rural residents, on one hand, rural residential consumption expenditure further rose by 45%. On the other hand, traditional outdated energy gradually placed by the commercial energy in Jiangxi rural regions and the CO2 emissions increased by 12%. As a result, the decoupling state shifted from END to WD in Stage 2.
During Stage 3 (2010-2015), both urban and rural regions presented WD. The growth rate of the consumption expenditure continued to slow down and grew by 89% in urban regions. However, it continued to rise and grew by 74% in rural regions (Table 8). This could be explained by the longterm inequality and lag between urban and rural regions in China's economic development (including Jiangxi), namely duality of urban-rural. Moreover, in order to save energy and reduce emissions, Chinese government (Jiangxi included) formulated and implemented the "ladder electricity price policy" after 2012, which effectively restrained residential energy consumption, especially in urban regions. Thus, the change rate of the CO2 emissions in urban regions nearly had no obvious increase (30% from 2010-2015), and it grew by 28% in rural regions. Consequently, WD occurred. During Stage 4 (2015Stage 4 ( -2017, the relationship between consumption expenditure and residential energy-related CO2 emissions transformed from WD to EC in both urban and rural regions. Chinese economic development embarked on a new stage called "the new normal". Specifically, in this stage, China (including Jiangxi) shifted its development pattern from rapid growth to more inclusive and sustainable growth, including higher living standards, cleaner production [54]. Consequently, the growth rate of consumption expenditure and the CO2 emissions declined, to some extent. Consumption expenditure rose by 23% and 14% in Jiangxi urban and rural regions (Table 8), respectively, which was slower than that of other periods. Additionally, the CO2 emissions also showed a decline trend and grew by 20% and 16% in Jiangxi urban and rural regions, resulting from the improvement of energy efficiency. Therefore, they both presented EC in this stage, indicating there was insignificant decoupling between consumption expenditure and the CO2 emissions.

Main Conclusions
Presently, residential energy consumption has become the second-largest sector to the industrial energy consumption in China. However, little attention was paid to research the difference of the residential energy-related CO2 emissions between urban and rural areas in the less developed and undeveloped regions. Thus, based on the urban-rural duality, this paper aims at exploring the difference of the residential energy-related CO2 emissions in Jiangxi urban and rural regions from 2000-2017. We first overviewed the changes of the residential energy-related CO2 emissions in Jiangxi between urban and rural regions. Then, the LMDI method was introduced to distinguish the major factors affecting the residential energy-related CO2 emissions. In addition, the Tapio decoupling model was used to analyze the relationship between residential energy-related CO2 emissions and consumption expenditure. The main results were acquired as follows.
The residential energy-related CO2 emissions rapidly increased after 2008 in both Jiangxi urban and rural regions. However, the annual growth rate of urban regions (7.47%) was faster than rural regions (6.03%). In addition, the gap between urban and rural regions became narrowed and stable. The energy structures of both urban and rural residential energy-related CO2 emissions had shifted from coal-dominant to a multiple structure which consisted of oil, natural gas, and electricity. Specifically, the energy-related CO2 emissions structure was electricity, oil, natural gas, and coal in sequence for urban; it was electricity, coal, oil, and natural gas in sequence for rural. As for the residential energy-related CO2 emissions per capita, it showed a decline trend in urban regions, while an increasing tendency in rural regions over the study period. The gap in residential energy-related CO2 emissions for urban and rural residents narrowed from 2000-2007.
The energy price effect played the most important role in reducing the residential energy-related CO2 emissions in both urban and rural regions. In addition, the carbon emission coefficient had a minor effect on decreasing urban and rural residential energy-related CO2 emissions. The results showed that urbanization exerted a positive effect on increasing the urban residential energy-related CO2 emissions, but decreasing the rural residential energy-related CO2 emissions. Consumption expenditure-per-capita effect was the most important factor increasing the residential energy-related CO2 emissions in both urban and rural regions, followed by energy demand. From 2000-2017, population effect and energy structure played a minor role in increasing the urban and rural residential energy-related CO2 emissions.
Over the study period, four decoupling status (END, SD, WD, and EC) occurred in both urban and rural regions when analyzed the relationship between consumption expenditure and residential energy-related CO2 emissions. Overall, the decoupling state for urban and rural regions were the WD and END. This showed that residential energy-related CO2 emissions in urban Jiangxi was less depend on consumption expenditure, but rural residential energy-related CO2 emissions still depended on consumption expenditure.

Policy Implications
First, since the residential energy-related CO2 emissions caused by coal was still high in rural regions, which accounted for 27.52% and was second only to electricity (53.78%) even in 2017. However, the corresponding proportion of natural gas was always low and took up less than 1% over the study period. Thus, the energy structure of rural regions had more space to optimize, and more efforts should be paid to further adjust the rural energy structure. For instance, promoting the proportion of natural gas and encouraging the use of renewable and clean energy in rural regions.
Second, since the rural residential energy-related CO2 emissions per capita had exceeded that of the urban region after 2015, thereby suggesting that the lifestyles in rural regions were more extensive and lower energy efficiency compared with urban regions. On one hand, it is urgent and imperative for the government at all levels to strengthen related public education and guide residents to shift their high-carbon lifestyles to a green and low-carbon pattern, especially for rural residents. In addition, the government should accelerate the elimination of backward production capacity to improve the efficiency in rural regions. On the other hand, residents themselves should also initiatively adapt to the transformation and cultivate the awareness of environmental protection.
Third, consumption expenditure-per-capita effect became the most factor increasing the residential energy-related CO2 emissions in both urban and rural regions. With the improvement of economic growth and living standards, people had the desire and capacity to pursuit the quality of life and buy more energy-intensive electricity appliances and private transport tools. However, it is not wise to reduce the CO2 emissions via restraining the consumption, this was because the consumption was a critical impetus of socio-economic development. Thus, the government could apply the fiscal and tax policy to cut down the residential energy-related CO2 emissions and accelerate the decoupling. Specifically, the government should continue to impose the "Home Appliances Subsidy Program" and monitor its proper extent of implementation. For the high-carbon and extensive goods and services, the government could add the tax to restrain the CO2-intensive commodities' consumption. Meanwhile for the energy-saving appliances, reducing the tax could stimulate the low-carbon consumption. Moreover, more investment and efforts were needed to improve public transport infrastructures and the energy conservation technology.
Fourth, considering energy price played the most important role in decreasing the residential energy-related CO2 emissions in both urban and rural regions, indicating tiered energy price was the effective and scientific pathway to achieve energy savings and emissions reduction. In light of the fact, the government needs further to uphold and ameliorate the energy price policy in the future. In particular, when applying the energy price mechanism, the duality as well as inequality of income level and economic development should be comprehensively considered in urban and rural regions.
Last, to the best of our knowledge, residential building energy consumption accounted for a large proportion of the total residential energy consumption, leading to enormous CO2 emissions. Therefore, it is necessary to improve residential building energy efficiency, especially for rural regions. For urban regions, the intelligent energy management technology in household could be popularized and applied to achieve energy conservation.

Limitations and Further Perspectives
There are limitations to this study. Based on the perspective of end-use of energy, only the residential energy-related CO2 emissions were estimated, while the indirect or embodied residential CO2 emissions from production perspective were excluded. This could likely underestimate the residential CO2 emissions in Jiangxi urban and rural regions. Therefore, the embodied residential CO2 emissions need further study. Moreover, this paper only explored the factors affecting the residential energy-related CO2 emissions and the decoupling relationship between consumption expenditure and residential energy-related CO2 emissions in Jiangxi urban and rural regions, specifically carbon emission coefficient, energy structure, consumption expenditure per capita, energy demand, energy price, urbanization, and population. However, family size, floor space of residential buildings and different income levels (an aspect of social inequality) also had the nonnegligible effect on the residential CO2 emissions. Thus, these influencing factors to the residential CO2 emissions should be paid more attention in our future work.
Author Contributions: Yong Yang, Junsong Jia and Chundi Chen conceived and designed this study; Yong Yang collected and analyzed the data; Yong Yang and Junsong Jia wrote and revised the paper; Chundi Chen contributed to progress of research idea. All authors read and approved the final manuscript.

Acknowledgments:
We thank the anonymous reviewers and editor for their constructive comments and suggestions to improve the quality of this article.

Conflicts of Interest:
The authors declare no conflict of interest.  [37], to date, the types of electric power generation include thermal power, hydropower, and wind power. Among these types of electric power generation, thermal power is the main source of CO2 emissions, and other contribute negligible CO2 emissions. Thus, here, we only consider the thermal power when calculating the CO2 emission coefficient of electricity in Jiangxi. In addition, CO2 emission coefficients of coal, oil, and natural are 2.7412, 2.1358 and 1.626 kg-CO2/ton-SCE, respectively. The results are listed in Table A1. Table A1. CO2 emission coefficient of electricity (t-CO2/10 4 kwh). Note: a The value is the IPCC recommended value; b The unit is KJ/m 3 ; c The unit is t-CO2/10 4 m 3 .

Abbreviation
Appendix B Table A3. Urban, LMDI additive: Detailed additive decomposition results of the residential energyrelated CO2 emissions of urban residents in Jiangxi (10 4 t).  Table A6. Rural, LMDI multiplicative: Detailed multiplicative decomposition results of the residential energy-related CO2 emissions of rural residents in Jiangxi.