Cleaner energy conversion and household emission decomposition analysis in Indonesia

Accepted Manuscript Cleaner energy conversion and household emission decomposition analysis in Indonesia

Robi Kurniawan, Yogi Sugiawan, Shunsuke Managi PII:

S0959-6526(18)32385-0

DOI:

10.1016/j.jclepro.2018.08.051

Reference:

JCLP 13835

To appear in:

Journal of Cleaner Production

Received Date:

14 May 2018

Accepted Date:

05 August 2018

Please cite this article as: Robi Kurniawan, Yogi Sugiawan, Shunsuke Managi, Cleaner energy conversion and household emission decomposition analysis in Indonesia, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2018.08.051

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

Cleaner energy conversion and household emission decomposition analysis in Indonesia Robi Kurniawan1, 2 Yogi Sugiawan3, 4, and Shunsuke Managi 4 1

2

Graduate School of Environmental Studies, Tohoku University, Aramaki-aza-Aoba, Aoba-ku, Sendai, 980-8579 Japan. E-mail: [email protected]. Ministry of Energy and Mineral Resources, Jalan Pegangsaan Timur No. 1a, Jakarta 10320, Indonesia

3

Planning Bureau, National Nuclear Energy Agency of Indonesia (BATAN), Jl. Kuningan Barat, Mampang Prapatan, Jakarta 12710, Indonesia

4

Urban Institute and Department of Urban and Environmental Engineering, Graduate School of Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 8190385, Japan. E-mail: [email protected] (*Corresponding author)

Acknowledgements This paper was supported by Grant-in-Aid for Specially Promoted Research (26000001) by Japan Society for the Promotion of Science. Yogi Sugiawan was supported by Research and Innovation in Science and Technology Project (RISET-PRO), Ministry of Research, Technology, and Higher Education of Indonesia [loan number 8245-ID]. Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of the institution’s and funding agencies.

1

ACCEPTED MANUSCRIPT

Cleaner energy conversion and household emission decomposition analysis in Indonesia

ABSTRACT Increasing the efficiency of the household sector’s energy consumption plays a significant role in reducing CO2 emissions, particularly for Indonesia, the world’s fourth most populous country. However, there is a lack of analytical studies on the driving forces of emissions from the household sector in Indonesia, including the contribution of one of the world's largest efforts to promote a cleaner cooking fuel program. We intend to examine the characteristics of the Indonesian energy matrix and its evolution in the household sector alongside the impact of kerosene to the Liquid Petroleum Gas (LPG) conversion program to the Indonesian emissions change in the sector. We also investigate the underlying determinant of emissions change, both directly and indirectly, from household energy consumption in Indonesia from 2000 to 2015. For this purpose, we conduct the Logarithmic Mean Divisia Index (LMDI) decomposition analysis. We found that population and income led to increases of both direct and indirect energy emission, while the impact of energy intensity was the opposite. The fuel mix and carbon intensity effect, which reflects the conversion of kerosene to LPG, contributes to reducing direct emissions with limited effect. High share growth of coal in electricity generation led to increasing indirect emissions for the period. Our findings have important policy implications, particularly for increasing the share of new and renewable energy in the national energy mix and for intensifying energy efficiency in the household sector.

1

ACCEPTED MANUSCRIPT

1. Introduction It is crucial to have an energy policy that not only ensures energy security but also maintains environmental sustainability and capability to provide non-declining future utility. Energy consumption, on the one hand, is essential for generating human wellbeing; on the other hand, increasing the level of energy consumption leads to new socioeconomic problems. For instance, Fankhauser and Jotzo (2018) find that the energy sector accounted for about two-thirds of all greenhouse gas emissions (GHG) worldwide. This has created serious environmental problems, including the threat of global warming, which, according to Kurniawan and Managi (2017), may have a significant impact on countries’ performance and is considered a threat to intergenerational human well-being. Efforts for reducing carbon dioxide (CO2) emission from energy consumption might be effectively achieved by encouraging energy efficiency and conservation in the household sector, which largely dominates the total energy consumption worldwide (Zhou and Yang, 2016). Furthermore, this sector is characterized by a specific CO2 emission, which is not likely to be displaced to or influenced by other countries (Pablo-Romero et al., 2017). Along with the growing population, rapid urbanization and economic growth, the final energy consumption of the residential sector has led to an increasing trend of CO2 emission, particularly in developing countries (Nejat et al., 2015). As one of the world’s largest developing countries, according to the Ministry of Energy and Mineral Resources (MEMR), the household sector in Indonesia accounted for approximately 36% of its total energy consumption, the largest share in this respect (MEMR, 2017). Inefficiency in domestic energy consumption was believed to be driven mainly by the highly subsidized household’s kerosene consumption. In 2006, the total amount of subsidies for petroleum products was estimated to be approximately 18% of total government spending, 57 percent of which was allotted for kerosene (Pertamina, 2012). To reduce the financial burden for Indonesia’s state budget, the Government of Indonesia had initiated the massive kerosene to Liquid Petroleum Gas (LPG) conversion program in 2007. This program has been considered one of the world's largest efforts to promote cleaner cooking fuels (World-Bank, 2013). Approximately 58 million conversion LPG packages would be distributed and the kerosene subsidy for the household sector consumption would be gradually removed. In 2012, the program was fully implemented in 23 provinces, with 2

ACCEPTED MANUSCRIPT

53.9 million conversion packages distributed to households, reaching 93% of the target (Pertamina, 2012). Regarding the environmental aspect, the Government of Indonesia’s policy to ascend to a higher energy ladder by switching from kerosene to LPG was expected to have a positive impact on CO2 emission mitigation, particularly from the energy sector. By 2030, GHG emissions were expected to be reduced by at least 29% through Indonesia’s own efforts, of which the energy sector is expected to contribute approximately 314 million tons in CO2 emission reduction (MEMR, 2016). Some previous studies have highlighted the beneficial impacts of kerosene to the LPG conversion program, such as reducing CO2 emission, improving community health level and reducing government subsidies for kerosene (Budya and Yasir Arofat, 2011; Permadi et al., 2017). Furthermore, in investigating the linkage of the program to poverty, Andadari et al. (2014) argue that the energy conversion has managed to alleviate extreme energy poverty. They also find that the suburban population, especially medium- and higher-income groups, are the stakeholders who benefit the most from this conversion program. However, to the best of our knowledge, there have been no comprehensive studies on the impacts of Indonesia’s kerosene to LPG conversion program on CO2 emission change in the household sector. Having advantages such as perfect decomposition, easy interpretation, and consistency with aggregation, the Logarithmic Mean Divisia Index (LMDI) has been widely used for the index decomposition analysis (Ang, 2015). For instance, Zhao et al. (2017) conducted a decoupling analysis for China’s five major economic sectors during the period 1992–2012 by using the LMDI method. They find that economic growth was weakly decoupled from CO2 emissions, to which the industrial sector contributed significantly. By using a similar approach, Lin and Ahmad (2017) find that for the case of Pakistan, CO2 emission reduction is likely to be achieved by combining both diversifications of cleaner energy supply and energy conservation policy. Additionally, focusing on energy consumption decomposition in the specific industrial sector, Wang and Feng (2017) find that the energy intensity effect cannot completely offset the increasing energy consumption in China. Nevertheless, only a few empirical studies have focused on decomposition analysis with a specific reference to Indonesia. For instance, in decomposing the consumption side of the electricity sector, Tanoto and Praptiningsih (2013) find an addition effect from the energy intensity effect on energy consumption in 3

ACCEPTED MANUSCRIPT

Indonesia. Additionally, Chen and Pitt (2017) find that demographic characteristics, economic development, and the shift of supply-demand play significant roles as the main driving forces for the change in energy demand in Indonesia. However, none of the aforementioned studies has analyzed the emissions change from the household sector, particularly on how it might be affected by the massive kerosene to LPG conversion program. This has been the primary motivation for our empirical study. In this study, we contribute to the existing literature from the empirical side by investigating the underlying determinant of emissions change from household energy consumption in Indonesia so as to draw implications for Indonesia’s future energy and environmental policies. Our study focuses on assessing the impact of Indonesia’s massive kerosene to LPG conversion program, which has been considered one of the world's largest efforts to promote cleaner cooking fuels, on emission change in the household sector. Additionally, it is likely to maintain its domination as a major CO2 emitter in the near future since the household sector portion of energy consumption grows at a relatively high rate of 3.8 percent per annum (NEC, 2014). For this purpose, we utilize the LMDI decomposition approach in the Indonesia household sector over the 2000-2015 study period. Our study is organized as follows. Section 2 provides the research background, which gives a brief overview of Indonesia's energy profile and the kerosene to LPG conversion program. Section 3 describes literature review concerning the theoretical background and the application of LMDI, especially for analyzing changes in household sector CO2 emission. Section 4 explains the methodology and data. The results and discussion are presented in section 5. Section 6 presents the study’s conclusions and offers policy recommendations. 2. Overview of Indonesia’s energy consumption Indonesia’s rapid economic growth and increasing demographic pressure have led to an upward trend of energy demand, with an annual average growth rate of 2.5 percent being observed over the last decade (BPPT, 2014a; BPPT (2014b)). Unfortunately, this was followed by massive extraction of natural resources, which threatened its sustainable development path (Kurniawan and Managi, 2018a). Furthermore, Hwang and Yoo (2012) 4

ACCEPTED MANUSCRIPT

and Shahbaz et al. (2013) reveal a bidirectional causality between energy consumption and CO2 emissions in Indonesia, suggesting a feedback effect between energy consumption and CO2 emissions. Their findings imply that with the current national energy mix policy, which is characterized by the high share of fossil fuel, it is rather challenging for Indonesia to decouple its CO2 emissions from energy consumption. However, the level of CO2 emissions might be reduced by increasing the share of renewable energy in the national energy mix and improving the efficiency of energy consumption (Dutu, 2016). A high dependency on fossil fuels can also be found in the electricity sector. Indonesia has relied on coal as the primary option for boosting its electricity generation capacity due to its abundant reserves, lower price, and ease of use. In 2014, more than 50 percent of the electricity was generated from coal, while the share of electricity generated from renewable energy was only approximately 11 percent (NEC, 2014). Kumar (2016) argues that the level of CO2 emissions might be significantly reduced to more than 80% by utilizing a massive scale of renewable energy for electricity generation. However, the development of renewable energy in Indonesia slowed due to the highly subsidized electricity sector. In 2014, the government of Indonesia spent more than 6.5 billion USD for electricity NEC (2014). Therefore, phasing out the energy subsidies might be beneficial for triggering energy efficiency in Indonesia (Burke and Kurniawati, 2018). Furthermore, when evaluating from the demand perspective, Azam et al. (2015) point out that urbanization also significantly contributes to the increasing level of both energy consumption and CO2 emissions in Indonesia. Household sector energy consumption also received a huge subsidy from the government. From 2001 to 2008, the subsidy for petroleum fuels ranged from 9%-18% of the government expenditure, equal to 1.9% - 3.7% of the Indonesian GDP, in which kerosene was the largest contributor (Budya and Yasir Arofat, 2011; Pertamina, 2012). As kerosene was the most popular fuel for household cooking, its demand increased rapidly along with the population growth. In 2004, kerosene was consumed by more than 90 percent of households, which were classified as low- and middle-income households, for daily cooking (Pertamina, 2012). Consequently, the subsidy for kerosene had become a massive financial burden for Indonesia’s state budget. Additionally, the amount for the kerosene subsidy was volatile because it was highly influenced by the changes in world’s 5

ACCEPTED MANUSCRIPT

oil price. Motivated by this economic issue, the government of Indonesia had implemented a policy for promoting cleaner fuel by conducting the National Program for the conversion of kerosene to LPG, which was stipulated in Presidential Decree No. 104/2007. This program has been considered one of the largest energy conversion efforts in the household sector. Additionally, from an environmental perspective, LPG is considered a cleaner energy than kerosene since it has higher calorific value and efficiency (Maes and Verbist, 2012). For daily household cooking, 1 litre of kerosene is equivalent to 0.39 kg of LPG. Additionally, in terms of global warming commitment, LPG produces only 139 grams of CO2 equivalents per MJ of delivered energy, which is 20 percent lower than that of kerosene (Maes and Verbist, 2012). After completion of the program, 6.5 Tg of kerosene for household cooking would be replaced by 3.3 Tg of LPG (Pertamina, 2012). 3. Literature review Finding out the main drivers of changes in energy consumption and CO2 emissions is essential and has important implications for energy policy. For this purpose, the decomposition analysis method has been widely used. According to Soldo (2012), the literature on decomposition analyses are generally divided into two approaches, i.e., structural decomposition analysis (SDA) and index decomposition analysis (IDA). Relying on an input-output analysis, the SDA method enables us to differentiate between a range of technical and final demand effects both directly and indirectly. For instance, Mi et al. (2017) utilize the SDA method to investigate the major determinant of CO2 emission changes in China during the post-global financial crisis. They find that the structural changes in China’s economy after the global economic recession have led to significant changes in CO2 emission patterns, for both domestic and foreign trade. However, this method requires laborious input-output tables and involves a complex economic model. For these reasons, the SDA method is less popular compared to its counterpart (IDA), particularly in the field of energy consumption and CO2 emissions studies, although both methods involve simple algebraic operations (Wang and Feng, 2017). The IDA method relies on the index number theory involving sectoral-level data, which is widely available and less demanding. Implementing this 6

ACCEPTED MANUSCRIPT

approach, a variable factor linked to the aggregate energy or emissions should be defined at the beginning (Ang, 2005; Yeo et al., 2015). Afterwards, a different approach can be utilized to quantify the effect of each factor change on the aggregate variable (Ang, 2004). The IDA method mainly consists of two main groups: the the Laspeyres index and Divisia index. The Divisia index is divided further into arithmetic mean Divisia index method (AMDI) and the logarithmic mean Divisia index method (LMDI). In recent years, the LMDI approach has dominated the IDA method due to its superiority. For instance, the LMDI method provides perfect decomposition without creating any unexplained residual terms, which makes the interpretation of the decomposition output relatively easier (Ang, 2005). Furthermore, Ang (2001) argues that the LMDI approach is also consistent with aggregation. As a result, the LMDI approach has been widely used in decomposition analysis studies, particularly for identifying the influencing factors of changes in energy consumption (Torrie et al., 2016) and CO2 emissions from fuel combustion (Chapman et al., 2018; Mousavi et al., 2017; Sumabat et al., 2016). The LMDI approach has also been applied for analyzing the driving forces of emission changes in specific sector, such as the residential (Yeo et al., 2015), industrial (Shao et al., 2016); energy saving of buildings (Ma et al., 2017) and transportation sectors (Achour and Belloumi, 2016). Xu and Ang (2014) argue that a decomposition analysis in the household sector involves numerous determinants of energy consumption, which will significantly influence the results of the analysis and what they infer. For instance, examining household energy consumption in China, Nie and Kemp (2014) show that the increasing energy demand of residential appliances is responsible for the dramatic increase of energy consumption. Similarly, Nie et al. (2018) identify the effect of income growth on an increasing trend of energy consumption for both urban and rural areas in the residential sector of China during the 2001-2012 period. Both studies suggest that increasing income leads to an increasing utilization of electrical home appliances, which eventually leads to higher energy consumption. In addition to the transformation of the social structure, increasing demand of energy consumption in the residential sector might also be related to the demographic changes in the country, such as urbanization (Fan et al., 2017). On the other hand, the growth of energy consumption might be restrained through the energy 7

ACCEPTED MANUSCRIPT

intensity effect (Liu and Zhao, 2015) and/or by increasing the quality of energy sources (de Freitas and Kaneko, 2011). The summary of some previous literature concerning the decomposition approach in the household sector is provided in Table 1. Table 1. Summary of decomposition approach in the household sector (sample) References Nie et al. (2018)

Methods LMDI

Periods 2001– 2012

Country China

Chen and Pitt (2017)

Econometric analysis and index decomposition Divisia decomposition

1980– 2002

Indonesia

1996– 2012

China

Liu and Zhao (2015)

LMDI

19932011

China

Yeo et al. (2015)

LMDI

19902011

China India

Nie and Kemp (2014) Tanoto and Praptiningsih (2013) de Freitas and Kaneko (2011)

LMDI

2002– 2010 2000– 2010

China

1970– 2009

Brazil

Fan et (2017)

al.

LMDI LMDI

and

Indonesia

Outcomes Energy prices and energy expenditure mix effects decrease residential energy consumption. Demographic change alone has had only a modest effect on energy demand. Urbanization makes a significant contribution to the increasing energy consumption, Although the prices of energy declined, the price effect was negative. The energy intensity effect had the largest effect on CO2 emissions reduction in residential sectors. The dramatic increase of energy related to the residential appliances Energy efficiency failed in the household electricity sector. Reducing the emission level is linked with the increasing quality of the energy source.

In the case of Indonesia, the LMDI approach has only been explored partially, and only a few applications are reported in the literature. Utilizing additive LMDI, Tanoto and Praptiningsih (2013) decomposed the household annual electricity consumption in Indonesia for the period 2000 – 2010. They found that the intensity effect still has a positive value, which implies that energy efficiency failed in the household electricity sector. Combining econometric analysis and index decomposition analysis, Chen and Pitt (2017) investigated the driving force of the household energy demand change in Indonesia. They found that the energy transition in Indonesia has been driven predominantly by demographics, economic development, and the shifting of supply and demand. Dealing with sustainable development and addressing emission change, a study that quantifies the driving force by decomposing aggregate emissions is needed. Even though 8

ACCEPTED MANUSCRIPT

information related to emissions in Indonesia’s energy sector abounds, the study that analyzes the driving force is still lacking. 4. Methodology and Data In this study, we employ the additive form of the LMDI model to analyze the driving forces contributing to changes in CO2 emissions from the household sectors in Indonesia. The aggregate household CO2 emissions can be decomposed into five factors as follows: 𝑛

𝑡 𝐶𝑂2 =

∑ 𝑗

𝑡 𝐶𝑂2 = 𝑗

𝑡

𝑛

𝐶𝑂2 𝐸𝑁𝑡 𝑗

𝑗

𝐸𝑁 𝐺𝐷𝑃

𝑡

𝑡

𝑗

𝑡 𝑗

𝑡

𝑡

∑ 𝐸𝑁 𝐸𝑁 𝐺𝐷𝑃 𝑃𝑂𝑃 𝑃𝑂𝑃 In Eq. (1), CO 𝑡

t 2

𝑡

refers to Indonesia’s (1)

aggregate CO2 emissions from the household sector, which captures the CO2 emissions from both direct CO2 emissions from household fuel use of type j. It represents both direct emissions (kerosene, LPG, and gas) and indirect CO2 emissions arising from electricity t

production. Then, we define the following variable of year t: EN 𝑗 , the household energy t

consumption for fuel type j; EN , aggregate energy consumption in household sector; t

t

GDP , Gross Domestic Product; and POP for the Indonesian population during the year. Then, Eq. (1) can be rewritten as follows: 𝑡

𝑛 𝑛 𝑡 𝑡 𝑡 𝑡 𝑡 𝑡 𝐶𝑂2 = ∑𝑗 𝐶𝑂2 = ∑𝑗 𝐶𝐼𝑗.𝐸𝑀𝑗.𝐸𝐼 .𝐻𝐼 .𝑃𝑂𝑃 𝑗

(2)

In particular, CO2 emissions are decomposed for 5 main energy sources of the household sector: Carbon Intensity (CI), Energy Mix (EM), Energy Intensity (EI), Household Income (HI), and Population (POP). Carbon intensity refers to the ratio of carbon emissions and energy consumed during the study period. This coefficient captures the quality of the energy mix consumed in the household sector. Lower emissions are generated from an energy mix composed of high embodied energy and lower carbon content. Energy mix corresponds to the energy component in the household sector during the study period. Measuring the effects of shifting energy consumption from available energy sources, this coefficient reflects the policies and consumer behaviour toward energy diversification. The energy intensity effect refers to energy consumed and the economic measure by sector. Energy intensity is also related to energy conservation policies, investment in 9

ACCEPTED MANUSCRIPT

energy efficiency, and deployment of energy efficient household appliances. Household income refers to per capita household income, which captures the sectoral income effect on CO2 emissions from energy consumption during a specific period. Population is associated with the effects of variable growth as a determinant of energy demand and CO2 emissions in the household sector. 𝑡

Based on Eq. (2), we can decompose the observed changes in CO2 emissions (𝐶𝑂2 ‒ 𝐶𝑂2

) from the household sector into five different factors: the change in emission

𝑡‒1

factors in carbon intensity, energy mix, energy intensity, household income, and population. Then, the changes of the aggregate household sector emissions in Indonesia between year (t-1) and year (t) can be expressed as: 𝑛

𝑡 𝐶𝑂2 ‒

𝐶𝑂2

𝑡‒1

= Δ𝐶𝑂2 =

∑Δ𝐶𝐼 + Δ𝐸𝑀 + Δ𝐸𝐼 + Δ𝐻𝐼 + Δ𝑃𝑂𝑃 𝑡 𝑗

𝑡 𝑗

𝑡

𝑡

𝑡

(3)

𝑖

In this study, we consider five-year time intervals to capture changes in the energy mix, policies and the Indonesian economy. Deriving the Eq. (3) function and following the Ang (2015) approach, we present Indonesia’s household emissions decomposition as follows:

𝐶𝑂2 - 𝐶𝑂2 𝑡

𝑡‒1

[ [ [

( )] [ 𝑙𝑛( )] + [ ∑ 𝑙𝑛( )]

4 ≡ ∑𝑗 = 1𝜈𝑗(𝑡)𝑙𝑛

∑4

𝜈 𝑗 = 1 𝑗(𝑡)

∑4

𝜈 𝑗 = 1 𝑗(𝑡)

𝐶𝐼𝑗,𝑡

𝐶𝐼𝑗,𝑡 ‒ 1

( )] + 𝑙𝑛( )] +

4 + ∑𝑗 = 1𝜈𝑗(𝑡)𝑙𝑛

𝐸𝐼𝑗,𝑡

𝐸𝐼𝑗,𝑡 ‒ 1 𝑃𝑗,𝑡

4 𝜈 𝑗 = 1 𝑗(𝑡)

𝐸𝑀𝑗,𝑡

𝐸𝑀𝑗,𝑡 ‒ 1 𝐻𝐼𝑗,𝑡

𝐻𝐼𝑗,𝑡 ‒ 1

(4)

𝑃𝑗,𝑡 ‒ 1

The term 𝜈𝑗(𝑡) refer to the additive weight function calculating the LMDI approach as proposed by Ang (2005). 𝜈𝑗(𝑡) =

𝐶𝑂2

𝑡‒1

𝑙𝑛𝐶𝑂2

𝑡‒1

(5)

‒ 𝐶𝑂2

𝑡

‒ 𝑙𝑛𝐶𝑂2

𝑡

Data

10

ACCEPTED MANUSCRIPT

To conduct the household decomposition approach, we utilize the related data*) concerning income, population, energy consumption, and emissions from the household sectors in Indonesia. The data for income have been converted to 2010 US$ constant prices to maintain comparability. The income and population data are derived from the statistics collected by the World Development Indicators (World-Bank, 2017). Concerning energy consumption in the household sector, we considered the Handbook of Energy & Economic Statistics of Indonesia (MEMR, 2017). Following the data, we split energy sources for the household sector into four main categories: kerosene, LPG, gas and electricity. By taking electricity into account, we will be able to analyze indirect effect CO2 emissions from electricity consumption. CO2 emissions from electricity have been allocated to household sectors in proportion to the electricity consumed. We derived the emissions data from Indonesia’s inventory of GHG emissions for the energy sector (MEMR, 2016). The estimation is based on the tier 2 approach provided by the Intergovernmental Panel on Climate Change (IPCC, 2006). Following the framework, the fossil fuel-related CO2 emissions are the function of the amount of fuel combusted and the Indonesia-specific emission factors. Particular specific emission factors are developed by considering the carbon content of the fuels utilized, fuel quality, carbon oxidation factors, the amount of carbon retained in the ash (for coal), and the state of technological development utilized in the country, which is updated regularly. *) We are willing to share the dataset utilized in this study with those who wish to replicate the results of this study.

5. Results and discussion The Indonesian household sector accounts for approximately 36% of the country’s total final energy consumption in 2015. As shown in Fig. 1, at the beginning of the study period, kerosene consumption constituted 72% of the energy source of the household sector, followed by electricity (21%). From 2000 to 2015, electricity consumption in the household sector increased at a rate higher than those for the population and GDP, which led to increases in the per capita electricity consumption over the period. In 2015, the bulk of the energy consumption in the sector is in the form of electricity (49%) and LPG (47%), while kerosene consumption is only 4 %. On the other hand, the gas share in the energy mix in the household sector is still considered low but is related to the limited 11

ACCEPTED MANUSCRIPT

infrastructure, such as piping for gas distribution. Figure 1. Household Sector Energy Consumption (MBOE) Fig. 2 presents the household consumption expenditure and the emissions change from each source. The reduction of approximately 59.32 million BOE kerosene and switching kerosene consumption to LPG would reduce CO2 emissions by 29% or 8.52 million tons of CO2 from 2000 to 2015. However, by including electricity consumption in the household sector, CO2 emissions increased by 86% or 45.2 million tons of CO2 during the study period. Figure 2. Household emissions by source (MT CO2) and household consumption expenditure (billion dollars) Given its significant contribution, it is also important to understand the electricity generation mix by fuel type for Indonesia. In Indonesia, electricity consumption by the household sector grew rapidly from 18.74 in 1990 to 54.36 BOE in 2015. This rapid growth is related to the electrification rates of Indonesia, which increased from 57.96% in 2000 to 88.30 % in 2015. As stipulated in their Energy National Plan, coal plays a significant role in Indonesia's electricity sector because of its reserves, ease of use and price. Indonesia has stipulated that the electricity fast-track program achieve a 100% electricity ratio by 2020. A coal-fueled power plant dominates the first batch of the program. Electricity consumption that is considered indirect emissions is also taken into consideration when analyzing changes of household CO2 emissions. As shown in Fig. 3, Indonesia’s fuel consumption for the electricity sector is predominantly reliant on coalfired generation. In 2015, the coal contribution to the power generation fuel mix is 60%, followed by gas (23 %) and High-Speed Diesel (HSD) (15%). Figure 3. Fossil Fuel Consumption in Power Plants (in million BOE)

12

ACCEPTED MANUSCRIPT

We present the energy consumption-related emissions change in the Indonesian household sector from 2000 to 2015 and the associated underlying factors using LMDI in this section. Employing the LMDI model in its additive form presented in the previous section, we decomposed the changes of aggregate household emissions based on the data illustrated in the previous section. Table 2 provides the decomposition results, presenting the variety of CO2 emissions change determinants in the household sector of Indonesia in absolute numbers. Within the study period, economic activity and population growth in the country are the major determinants of emission changes in the sector. The significance of economic activity and population growth in determining emissions change has been investigated in previous studies on Indonesia, notably in Alam et al. (2016), Jafari et al., (2012) and Sa’ad (2011). Therefore, the findings of the combined studies evaluate the kerosene to LPG conversion program, and LMDI confirms the preliminary evaluations carried out in the case of Indonesia. Table 2. Household Emission Change Decomposition (MT CO2) 2000-2004 direct

2005-2009

indirect

direct

2010-2015

indirect

direct

2000-2015

indirect

direct

indirect

Population

1.61

3.02

1.16

2.99

1.15

4.70

3.41

12.30

Income

2.99

5.60

2.68

6.90

3.55

14.51

9.04

32.63

Energy intensity

-3.72

-6.97

-5.91

-15.23

0.88

3.58

-8.54

-30.85

Fuel mix

-1.89

3.24

-6.17

6.82

-2.10

3.06

-12.46

24.91

Carbon intensity

-0.00

-1.22

-0.00

-0.44

0.02

13.65

0.04

6.21

Total

-1.01

3.68

-8.24

1.03

3.49

39.52

-8.52

45.20

Carbon intensity effect We compare the direct emissions from fuel combustion and the indirect emissions from the consumption of electricity. Direct emission factors are calculated by dividing direct CO2 emissions by fuel use, kerosene, LPG, and gas, while indirect emission factors are defined as indirect emissions from the electricity consumed by the household sector. In this context, the impact of emission coefficients on residential CO2 emissions is determined by both the indirect emission coefficients and the direct emission coefficient. Table 2 and Fig. 3 show that the carbon intensity coefficient effect contributed the 13

ACCEPTED MANUSCRIPT

least toward changes in residential CO2 emissions in Indonesia. Furthermore, the results from the 5-year decomposition analysis show that this effect on residential CO2 emissions tends to fluctuate. In fact, during 2000-2009, the CO2 emission coefficient effect on the changes in residential CO2 emissions is found to be negative both for direct and indirect emissions. In 2009, Indonesia had already distributed 24 million LPG packages in almost all of the remaining areas that are considered the most populous in Indonesia, such as in Java, Madura, Bali, and some provinces in Sumatra, Kalimantan, and Sulawesi Island. Over the kerosene-LPG conversion period, the contribution from the energy mix effect was significant. It is shown in Fig. 3 that emissions from LPG and electricity increased sharply, whereas that from kerosene reduced drastically. The conversion program played a role in reducing emissions in the household sector through carbon intensity improvement, but the effect is limited. During the 2010-2015 period, both direct and indirect emissions from carbon intensity are found to be positive. The carbon intensity from the electricity emission coefficient as the indirect emission coefficient could vary depending on the fuel mix used to generate the electricity and their efficiency. In the case of Indonesia, the increments in carbon intensity observed for the period 2010–2015 correspond to the increasing share of coal as fuel in the power plant. Changes in emission factors arose from an increase in the share of coal consumption in electricity generation. During and after the mentioned period, coal consumption increased by more than 100%. Lowering carbon intensity in electricity has a vital role in reducing emissions, not only in the household sector but also in other sectors such as industry and transportation. Fuel mix The fuel mix effect captures the extent to which changes in household emissions are due to changes in the share of energy consumption by fuel type. Excluding emissions from electricity, the aggregate change of household emission change contributed by the energy mix effect varies from -1.9 MT CO2 to -6.2 MT CO2. The largest contribution from 2005 to 2009 accounted for 75% of the overall decrease during the period. Investigating the trend of the energy mix effect requires an analysis of the energy consumption structure, as discussed in the previous section. Changes in fuel mix arose from a shift towards kerosene to LPG in final energy use, especially when related to 14

ACCEPTED MANUSCRIPT

cooking. Over the kerosene-LPG conversion period, the contribution from the energy mix effect was significant. It is shown in Fig. 3 that emissions from LPG and electricity increased sharply, whereas that from kerosene reduced drastically. The conversion program played a significant role in reducing emissions in the household sector. In 2009, Indonesia had already distributed 24 million LPG packages in almost all of the remaining areas that are considered the most populous in Indonesia, such as in Java, Madura, Bali, and some provinces in Sumatra, Kalimantan, and Sulawesi Island. On the other hand, considering the indirect effect, the fuel mix composition is found to be positive over the 2000-2015 period. The share of the electricity generated from coal with high emission coefficients increased from 56.18 MBOE (44% from total fuel consumption in electricity power plants) in 2000 to 209.53 (60% from total fuel consumption in electricity power plants) in 2015. Therefore, it can be understood that even though there was a shift in the direct emissions from kerosene to LPG, the indirect emissions from electricity consumption increased. Accordingly, it implies that the increased portion of electricity largely based on coal-fired power plants substituted for other fuels, resulting in the increase of household CO2 emissions. To reduce the emission form fuel mix and carbon intensity, Indonesia could learn from Brazil, where the reduction of emissions from carbon intensity and fuel mix is related to diversification of the energy mix towards cleaner renewable sources (de Freitas and Kaneko, 2011). In China, the energy mix change in the power plant also considered decreasing emissions, which accounts for a 10% reduction of the thermal electricity generation (Zhou et al. (2014)). Moreover, the emission reduction in the country is also related to the changing structure of their production (Mi et al., 2018). Energy Intensity Effect The energy intensity effect contributed to emissions reductions from 2000 to 2015 in both direct and indirect emissions. The energy intensity effect is considered the primary driving factor responsible for slowing down household CO2 emissions. This effect can offset the positive effects increasing emission from other factors. Table 2 shows that approximately -8.54 Mt CO2 from direct emissions and -30.85 from indirect CO2 emissions reduction were linked to the change in energy intensity of Indonesia’s household sector over the 2000-2015 period. 15

ACCEPTED MANUSCRIPT

It is also noticeable that this factor increased during the 2010-2015 period. Instead of being linked to the effectiveness of energy conservation investment, technological enhancements, and energy efficiency conservation policies, the energy intensity effect is related to economic activity. During this decline of economic activity, energy intensity increased and vice-versa. In the case, the increasing energy intensity effect implies the loss of efficiency in the usage of energy. It also captures fragilities in policies designed to promote energy conservation and continuous technological enhancements. Concerning energy efficiency in the household sector, Sukarno et al. (2017) found three aspects with the potential to reduce energy consumption in Indonesia: fine-tuning the power operation and standby mode, the number of appliances in the home and their operating hours. In addition, to achieve electrical energy efficiency improvement in Indonesia, Batih and Sorapipatana (2016) point out several potential household appliances with potential energy savings, such as air conditioning, lighting system, refrigerators, and television. Energy efficiency in Indonesia can also be affected by local culture, in which case the design policy for energy efficiency policy in Indonesia should be specified based on the particular local characteristics (Wijaya and Tezuka, 2013). Income Effect The income effect had the largest effect on the CO2 emissions increase from the household sectors, especially considering the indirect effect from electricity consumption in Indonesia. Table 2 shows that an increase of 32.6 MT CO2e of emissions was related to the change in household income per capita in the country during the 2000-2015 study period. Indonesia is considered the largest low-middle income country in the world. After being hit by the economic crisis in 1998, from 2000 to 2015, Indonesia experienced significant economic growth, as evidenced by their average GDP growth rates of 5%. The rapid growth of energy demand is linked with particular economic expansion development — in this case, higher household income levels. Furthermore, utilization of more electric home appliances is also triggered by improved access to electricity in the household sector. In fact, the electrification rates of Indonesia increased from 57.96% in 2000 to 88.30 % in 2015. Following increasing income, the annual natural capital consumption in Indonesia, including fossil resources, is accelerating over time (Kurniawan and Managi, 2018b). Concerning the linkage between income and emission, 16

ACCEPTED MANUSCRIPT

Sugiawan and Managi (2016) reveal the short and long run relationship among the parameters. Furthermore, they found a significant role of renewable energy in electricity production on CO2 emission reduction in Indonesia. Population Effect In Indonesia’s household sector, the population effect contributed to increasing CO2 emissions directly (3.41 MTCO2e) and indirectly (12.30 MTCO2e) during the 2000-2015 study period. From our decomposition analysis, the population effect in Indonesia was relatively constant over the study period. From 2000 to 2015, the population grew steadily, with an average annual growth rate of 1.34%. This expanding growth and significant population size triggered rapid energy consumption in the household sectors. Concerning the sustainability issue, over the long term, larger populations are linked with declining inclusive wealth per capita (Kurniawan and Managi, 2018c). Furthermore, the percentage of the population residing in urban areas is estimated to be more than 50% in 2015 (UNDP, 2017). This urbanization trend, along with the already large population size, caused the increase in CO2 emissions in the household sector. Several limitations of the decomposition methodology are linked with the emission change estimation, such as utilization of the current value of energy content, emission factor, and renewable energy emission assumption (de Freitas and Kaneko, 2011). Under ideal conditions, we would consider another type of energy utilized in the Indonesian household sector, such as firewood. Therefore, it is difficult to collect datasets of the traditional biomass, and we did not cover the type of energy source in this study. There are several issues that could be addressed in future research in the sector, including further elaborations, such as consideration of urban and rural household energy consumption. Explicitly considering prices, the urbanization effect and considering panel data of province would enhance our understanding of the driving forces of emissions from the household sector, especially in Indonesia. 6. Conclusions This study intends to examine the characteristics of the Indonesian energy matrix and its evolution in the household sector alongside the impact of kerosene to the LPG 17

ACCEPTED MANUSCRIPT

conversion program to Indonesian emission change in the sector. We also aim to investigate the underlying determinant of emissions change, both the direct and indirect effect, from household energy consumption in Indonesia during 2000-2015. To achieve this objective, we conduct the LMDI decomposition analysis. We compare the direct emissions from fuel combustion (kerosene, LPG, and gas) and the indirect emissions from the electricity consumed by the household sector. From direct emission activity, switching kerosene consumption to LPG program would reduce CO2 emissions by 29% or 8.52 million tons of CO2 from 2000 to 2015. However, from indirect activity, by including electricity consumption in the household sector, CO2 emissions increased by 86% or 45.2 million tons of CO2 during the study period. When comparing 2015 to 2000, the population, income, and carbon intensity effect led to an increase in both direct and indirect energy emissions, while energy intensity reduced them. The fuel mix effect, which reflects the conversion of kerosene to LPG, has contributed to reduction for direct emission change. However, the high share growth of coal in electricity generation led to increasing indirect emissions for the period. Dividing the study into three stages, we found the role of national kerosene in the LPG conversion program. The program played a role in reducing emissions in the household sector through carbon intensity improvement, but the effect is limited. During the 2010-2015 period, both direct and indirect emissions from carbon intensity are found to be positive. The fuel mix effect captures the extent to which changes in household emissions are caused by changes in the share of energy consumption by fuel type. Changes in the fuel mix arose from a shift towards kerosene to LPG in final energy use, especially related to cooking activity. Over the kerosene-LPG conversion period, the contribution from the energy mix effect was significant. Energy intensity effect is considered the primary driving factor responsible for slowing down household CO2 emissions. This effect can offset the positive effects arising from emission from the other factors. It is also noticeable that this factor has increased during the 2010-2015 period. Even though policy recommendation is beyond the scope of the study, our result sheds light on policies to mitigate emissions in the household sector, both directly and indirectly. The growing population, increases in urbanization, and growing household income levels trigger a drastic utilization of electronic home appliances in Indonesia. Given the increasing trend of energy intensity, Indonesia must improve the energy 18

ACCEPTED MANUSCRIPT

efficiency of home appliances. To reduce energy consumption and emissions in the sector, Indonesia should continuously enhance energy efficiency standards and labelling systems of home appliance. Furthermore, Indonesia should continually gradually decreasing government subsidies for fossil fuel and electricity. To reduce emissions from fuel mix and carbon intensity, especially from electricity generation, Indonesia should increase diversification of the energy mix towards cleaner renewable sources such as geothermal energy. Having a high share of coal-fired generation is essential for the country to improve efficiency by adopting cutting-edge technology and enhancing the coal-fired power plant.

REFERENCES Achour, H., Belloumi, M., 2016. Decomposing the influencing factors of energy consumption in Tunisian transportation sector using the LMDI method. Transport Policy 52, 64-71. Alam, M.M., Murad, M.W., Noman, A.H.M., Ozturk, I., 2016. Relationships among carbon emissions, economic growth, energy consumption and population growth: Testing Environmental Kuznets Curve hypothesis for Brazil, China, India and Indonesia. Ecological Indicators 70, 466-479. Andadari, R.K., Mulder, P., Rietveld, P., 2014. Energy poverty reduction by fuel switching. Impact evaluation of the LPG conversion program in Indonesia. Energy Policy 66, 436449. Ang, B.W., 2004. Decomposition analysis for policymaking in energy. Energy Policy 32, 11311139. Ang, B.W., 2005. The LMDI approach to decomposition analysis: a practical guide. Energy Policy 33, 867-871. Ang, B.W., 2015. LMDI decomposition approach: A guide for implementation. Energy Policy 86, 233-238. Ang, B.W.a.L., F.L, 2001. A new energy decomposition method: perfect in decomposition and consistent in aggregation. Energy 26, 537-548. Azam, M., Khan, A.Q., Zaman, K., Ahmad, M., 2015. Factors determining energy consumption: Evidence from Indonesia, Malaysia and Thailand. Renewable and Sustainable Energy Reviews 42, 1123-1131. Batih, H., Sorapipatana, C., 2016. Characteristics of urban households‫ ׳‬electrical energy

19

ACCEPTED MANUSCRIPT

consumption in Indonesia and its saving potentials. Renewable and Sustainable Energy Reviews 57, 1160-1173. BPPT, 2014a. Indonesia Energy Outlook 2014: Energy Development in Supporting Fuel Substitution Program. Center for Energy Resources Development Technology, Agency for the Assessment and Application of Technology (BPPT), Jakarta. BPPT, 2014b. Indonesia energy outlook 2014: Energy development in supporting fuel substitution program. Center for Energy Resources Development Technology, Agency for the Assessment and Application of Technology (BPPT), Jakarta. Budya, H., Yasir Arofat, M., 2011. Providing cleaner energy access in Indonesia through the megaproject of kerosene conversion to LPG. Energy Policy 39, 7575-7586. Burke, P.J., Kurniawati, S., 2018. Electricity subsidy reform in Indonesia: Demand-side effects on electricity use. Energy Policy 116, 410-421. Chapman, A., Fujii, H., Managi, S., 2018. Key Drivers for Cooperation toward Sustainable Development and the Management of CO2 Emissions: Comparative Analysis of Six Northeast Asian Countries. Sustainability 10, 244. Chen, J.J., Pitt, M.M., 2017. Sources of change in the demand for energy by Indonesian households: 1980–2002. Energy Economics 61, 147-161. de Freitas, L.C., Kaneko, S., 2011. Decomposition of CO 2 emissions change from energy consumption in Brazil: Challenges and policy implications. Energy Policy 39, 1495-1504. Dutu, R., 2016. Challenges and policies in Indonesia's energy sector. Energy Policy 98, 513519. Fan, J.-L., Zhang, Y.-J., Wang, B., 2017. The impact of urbanization on residential energy consumption in China: An aggregated and disaggregated analysis. Renewable and Sustainable Energy Reviews 75, 220-233. Fankhauser, S., Jotzo, F., 2018. Economic growth and development with low-carbon energy. Wiley Interdisciplinary Reviews: Climate Change 9, e495. Hwang, J.-H., Yoo, S.-H., 2012. Energy consumption, CO2 emissions, and economic growth: evidence from Indonesia. Quality & Quantity 48, 63-73. IPCC, 2006. Guidelines for national greenhouse gas inventories, Japan. Jafari, Y., Othman, J., Nor, A.H.S.M., 2012. Energy consumption, economic growth and environmental pollutants in Indonesia. Journal of Policy Modeling 34, 879-889. Kumar, S., 2016. Assessment of renewables for energy security and carbon mitigation in Southeast Asia: The case of Indonesia and Thailand. Applied Energy 163, 63-70. Kurniawan, R., Managi, S., 2017. Sustainable Development and Performance Measurement: Global Productivity Decomposition. Sustainable Development 25, 639-654. Kurniawan, R., Managi, S., 2018a. Economic Growth and Sustainable Development in

20

ACCEPTED MANUSCRIPT

Indonesia: An Assessment. Bulletin of Indonesian Economic Studies, 1-31. Kurniawan, R., Managi, S., 2018b. Linking Wealth and Productivity of Natural Capital for 140 Countries Between 1990 and 2014. Social Indicators Research. Kurniawan, R., Managi, S., 2018c. Measuring long-term sustainability with shared socioeconomic pathways using an inclusive wealth framework. Sustainable Development. Lin, B., Ahmad, I., 2017. Analysis of energy related carbon dioxide emission and reduction potential in Pakistan. Journal of Cleaner Production 143, 278-287. Liu, Z., Zhao, T., 2015. Contribution of price/expenditure factors of residential energy consumption in China from 1993 to 2011: A decomposition analysis. Energy Conversion and Management 98, 401-410. Ma, M., Yan, R., Du, Y., Ma, X., Cai, W., Xu, P., 2017. A methodology to assess China's building energy savings at the national level: An IPAT–LMDI model approach. Journal of Cleaner Production 143, 784-793. Maes, W.H., Verbist, B., 2012. Increasing the sustainability of household cooking in developing countries: Policy implications. Renewable and Sustainable Energy Reviews 16, 4204-4221. MEMR, 2016. National Inventory of GHG Emissions from Energy Sector. Ministry of Energy and Mineral Resources Indonesia, Jakarta. MEMR, 2017. Handbook of Energy and Economic Statistics of Indonesia 2016. Ministry of Energy and Mineral Resources Indonesia, Jakarta. Mi, Z., Meng, J., Green, F., Coffman, D.M., Guan, D., 2018. China's “exported carbon” peak: patterns, drivers, and implications. Geophysical Research Letters 45, 4309-4318. Mi, Z., Meng, J., Guan, D., Shan, Y., Song, M., Wei, Y.M., Liu, Z., Hubacek, K., 2017. Chinese CO2 emission flows have reversed since the global financial crisis. Nature communications 8, 1712. Mousavi, B., Lopez, N.S.A., Biona, J.B.M., Chiu, A.S.F., Blesl, M., 2017. Driving forces of Iran's CO 2 emissions from energy consumption: An LMDI decomposition approach. Applied Energy 206, 804-814. NEC, 2014. Outlook Energi Indonesia 2014. National Energy Council-Ministry of Energy and Mineral Resources, Jakarta. Nejat, P., Jomehzadeh, F., Taheri, M.M., Gohari, M., Abd. Majid, M.Z., 2015. A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries). Renewable and Sustainable Energy Reviews 43, 843-862. Nie, H.-g., Kemp, R., Xu, J.-h., Vasseur, V., Fan, Y., 2018. Drivers of urban and rural residential energy consumption in China from the perspectives of climate and economic

21

ACCEPTED MANUSCRIPT

effects. Journal of Cleaner Production 172, 2954-2963. Nie, H., Kemp, R., 2014. Index decomposition analysis of residential energy consumption in China: 2002–2010. Applied Energy 121, 10-19. Pablo-Romero, M.d.P., Pozo-Barajas, R., Yñiguez, R., 2017. Global changes in residential energy consumption. Energy Policy 101, 342-352. Permadi, D.A., Sofyan, A., Kim Oanh, N.T., 2017. Assessment of emissions of greenhouse gases and air pollutants in Indonesia and impacts of national policy for elimination of kerosene use in cooking. Atmospheric Environment 154, 82-94. Pertamina, 2012. Kerosene to LP Gas conversion program in Indonesia: A case study of domestic energy, France. Sa’ad, S., 2011. Underlying energy demand trends in South Korean and Indonesian aggregate whole economy and residential sectors. Energy Policy 39, 40-46. Shahbaz, M., Hye, Q.M.A., Tiwari, A.K., Leitão, N.C., 2013. Economic growth, energy consumption, financial development, international trade and CO2 emissions in Indonesia. Renewable and Sustainable Energy Reviews 25, 109-121. Shao, S., Yang, L., Gan, C., Cao, J., Geng, Y., Guan, D., 2016. Using an extended LMDI model to explore techno-economic drivers of energy-related industrial CO 2 emission changes: A case study for Shanghai (China). Renewable and Sustainable Energy Reviews 55, 516536. Soldo, B., 2012. Forecasting natural gas consumption. Applied Energy 92, 26-37. Sugiawan, Y., Managi, S., 2016. The environmental Kuznets curve in Indonesia: Exploring the potential of renewable energy. Energy Policy 98, 187-198. Sukarno, I., Matsumoto, H., Susanti, L., 2017. Household lifestyle effect on residential electrical energy consumption in Indonesia: On-site measurement methods. Urban Climate 20, 20-32. Sumabat, A.K., Lopez, N.S., Yu, K.D., Hao, H., Li, R., Geng, Y., Chiu, A.S.F., 2016. Decomposition analysis of Philippine CO 2 emissions from fuel combustion and electricity generation. Applied Energy 164, 795-804. Tanoto, Y., Praptiningsih, M., 2013. Factors Decomposition of Indonesia’s Household Electricity Consumption. Engineering Journal 17, 19-28. Torrie, R.D., Stone, C., Layzell, D.B., 2016. Understanding energy systems change in Canada: 1. Decomposition of total energy intensity. Energy Economics 56, 101-106. UNDP, 2017. World population prospects: The 2017 Revision, New York. Wang, M., Feng, C., 2017. Decomposing the change in energy consumption in China's nonferrous metal industry: An empirical analysis based on the LMDI method. Renewable and Sustainable Energy Reviews.

22

ACCEPTED MANUSCRIPT

Wijaya, M.E., Tezuka, T., 2013. A comparative study of households' electricity consumption characteristics in Indonesia: A techno-socioeconomic analysis. Energy for Sustainable Development 17, 596-604. World-Bank, 2013. Indonesia: Toward universal access to clean cooking. The World Bank Group, Washington DC. World-Bank, 2017. World Bank data World-Bank. Xu, X.Y., Ang, B.W., 2014. Analysing residential energy consumption using index decomposition analysis. Applied Energy 113, 342-351. Yeo, Y., Shim, D., Lee, J.-D., Altmann, J., 2015. Driving Forces of CO2 Emissions in Emerging Countries: LMDI Decomposition Analysis on China and India’s Residential Sector. Sustainability 7, 16108-16129. Zhao, X., Zhang, X., Li, N., Shao, S., Geng, Y., 2017. Decoupling economic growth from carbon dioxide emissions in China: A sectoral factor decomposition analysis. Journal of Cleaner Production 142, 3500-3516. Zhou, G., Chung, W., Zhang, Y., 2014. Carbon dioxide emissions and energy efficiency analysis of China's regional thermal electricity generation. Journal of Cleaner Production 83, 173-184. Zhou, K., Yang, S., 2016. Understanding household energy consumption behavior: The contribution of energy big data analytics. Renewable and Sustainable Energy Reviews 56, 810-819.

23

ACCEPTED MANUSCRIPT

Highlights

 

Population and income stimulate direct and indirect household energy emissions Household's energy intensity effect reduces CO2 emission in the sector



The kerosene conversion program lowers direct household's fuel combustion

 

emissions Energy mix effect significantly reduces direct household's fuel combustion emissions The impact of kerosene conversion program on carbon intensity is less perceptible