An Analysis Based on SD Model for Energy-Related CO 2 Mitigation in the Chinese Household Sector

Reducing carbon dioxide (CO2) emissions has become a global consensus in response to global warming and climate change, especially to China, the largest CO2 emitter in the world. Most studies have focused on CO2 emissions from the production sector, however, the household sector plays an important role in the total energy-related CO2 emissions. This study formulates an integrated model based on logarithmic mean Divisia index methodology and a system dynamics model to dynamically simulate household energy consumption and CO2 emissions under different conditions. Results show the following: (1) the integrated model performs well in calculating the contribution of influencing factors on household CO2 emissions and analyzing the options for CO2 emission mitigation; (2) the increase in income is the dominant driving force of household CO2 emissions, and as a result of the improved standard of living in China a sustained increase in household CO2 emissions can be expected; (3) with decreasing energy intensity, CO2 emissions will decrease to 404.26 Mt-CO2 in 2020, which is 9.84% lower than the emissions in 2014; (4) the reduction potential by developing non-fossil energy sources is limited, and raising the rate of urbanization cannot reduce the household CO2 emission under the comprehensive influence of other factors.


Introduction
Carbon dioxide (CO 2 ), which is the prominent greenhouse gas that can result in global warming and climate change, has caused widespread concern in the international community [1].Many scholars have conducted considerable research on CO 2 , such as the measurement of regional CO 2 emission amounts and analyses of its evolution trends [2,3], relationships between population, economy and CO 2 emissions [4], the influencing factors of energy consumption and CO 2 emission [5][6][7], and policies to reduce CO 2 emissions [8,9].
China, the largest CO 2 emitter in the world, is facing intense pressures to cut its CO 2 emissions.During the Copenhagen Climate Change Conference in 2009, the Chinese government committed to reduce its CO 2 emissions per unit of the gross domestic product (GDP) in 2020 by 40% to 45% relative to 2005 levels.After the industry and transportation sectors, the household sector has the most significant influence on the total energy-related CO 2 emissions [10].Reducing household CO 2 emissions has attracted increasing attention, and several studies have quantified household CO 2 emissions for various countries, such as Italy [11], USA [12], UK [13], Ireland [14] and China [15][16][17].

Methodology
In order to reveal the dynamical mechanism of household CO 2 emissions, an integrated model named LMDI-SD model (Figure 1) is built in this study.The construction thinking and operating sequence of LMDI-SD model are as follows: (1) Step 1: Estimation of CO2 Emissions The methodology described in the 2006 Intergovernmental Panel on Climate Change Guidelines for National Greenhouse Gas Inventories [40] indicates that energy-related CO2 emissions in a given year may be estimated as follows: where Ctot represents the total amount of household CO2 emissions, subscripts i represents the energy type, such as coal, petroleum and natural gas, subscripts j represents urban and rural household, Cij represents the amount of CO2 emissions based on energy type i by household sector j, Eij is the energy consumption based on energy type i by household sector j, and Fi is the coefficient of CO2 emissions of the ith energy type.The coefficient of CO2 emissions (Fi) is given by the Energy Research Institute of the National Development and Reform Commission.Here, the coefficient of coal, petroleum, and natural gas are 0.7476, 0.5825, and 0.4435 t•tce −1 , respectively.K is the molecular weight ratio of CO2 to carbon (44/12).
(2) Step 2: Decomposition of CO2 Emissions By investigating the influencing factors of energy-related CO2 emission in household sectors by LMDI methodology proposed by Ang [41], we may preliminarily propose CO2 emission reduction policies.The energy-related CO2 emissions establish the following decomposition model: where Ctot represents the total amount of household CO2 emissions, subscripts i represents the energy type, such as coal, petroleum, natural gas and non-fossil energy, subscripts j represents urban and rural household, Cij represents the amount of CO2 emissions based on energy type i by household sector j, Eij represents the amount of energy consumption based on energy type i by household sector j, Ej represents the total energy consumption of the jth household sector, Ij represents the total disposable income of the jth household sector, Pj represents the population of the jth household sector, and P represents the total population.CF = Cij/Eij represents the CO2 emission factor for energy type i by household sector j, Estr = Eij/Ej represents the proportion of the total energy consumption by household sector j accounted for by the consumption of energy type i, Eint = Ej/Pj represents the energy intensity of household sector j, Ilev = Ij/Pj represents the per capita disposable income of household sector j, and Pstr = Pj/P represents the proportion of the total population accounted for by the population of household sector j.The construction thinking and operating sequence of LMDI-SD model are as follows: (1) Step 1: Estimation of CO 2 Emissions The methodology described in the 2006 Intergovernmental Panel on Climate Change Guidelines for National Greenhouse Gas Inventories [40] indicates that energy-related CO 2 emissions in a given year may be estimated as follows: where C tot represents the total amount of household CO 2 emissions, subscripts i represents the energy type, such as coal, petroleum and natural gas, subscripts j represents urban and rural household, C ij represents the amount of CO 2 emissions based on energy type i by household sector j, E ij is the energy consumption based on energy type i by household sector j, and F i is the coefficient of CO 2 emissions of the ith energy type.The coefficient of CO 2 emissions (F i ) is given by the Energy Research Institute of the National Development and Reform Commission.Here, the coefficient of coal, petroleum, and natural gas are 0.7476, 0.5825, and 0.4435 t•tce −1 , respectively.K is the molecular weight ratio of CO 2 to carbon (44/12). ( Step 2: Decomposition of CO 2 Emissions By investigating the influencing factors of energy-related CO 2 emission in household sectors by LMDI methodology proposed by Ang [41], we may preliminarily propose CO 2 emission reduction policies.The energy-related CO 2 emissions establish the following decomposition model: where C tot represents the total amount of household CO 2 emissions, subscripts i represents the energy type, such as coal, petroleum, natural gas and non-fossil energy, subscripts j represents urban and rural household, C ij represents the amount of CO 2 emissions based on energy type i by household sector j, E ij represents the amount of energy consumption based on energy type i by household sector j, E j represents the total energy consumption of the jth household sector, I j represents the total disposable income of the jth household sector, P j represents the population of the jth household sector, and P represents the total population.CF = C ij /E ij represents the CO 2 emission factor for energy type i by household sector j, E str = E ij /E j represents the proportion of the total energy consumption by household sector j accounted for by the consumption of energy type i, E int = E j /P j represents the energy intensity of household sector j, I lev = I j /P j represents the per capita disposable income of household sector j, and P str = P j /P represents the proportion of the total population accounted for by the population of household sector j.
The changes in energy-related CO 2 emissions from years t − 1 to t can be calculated using the following equation: where subscripts t and t − The CO 2 emission factors for different energy types in this study are constant.Therefore, the contribution of CO 2 emission factors (∆C CF ) on the decomposition is always zero.These factors have changed over time because of the changes in fuel quality, but these changes are extremely minimal, such that they are negligible in the analysis of macro changes in CO 2 emissions [42].Thus, Equation (3) can be rewritten as follows: When additive decomposition is applied, the CO 2 factors for the consumption of energy type i by household sector j can be decomposed as follows: ) where function L(x, y) is the logarithmic average of the two positive numbers x and y, which are defined as: In order to calculate the contribution of each effect on total amount of CO 2 emissions, we form: We building the SD model of household CO 2 emission with the software Vensim PLE (Ventana Systems, Inc., Harvard, MA, USA) according to the main influencing factors of CO 2 emissions.Then we simulate the scenarios by implementing different CO 2 reduction policies, obtaining the options for household CO 2 emission mitigation.

Data Description
Considering the availability of data, this study classifies all fossil energy into three types-coal, petroleum, and natural gas-which constitute 93.35% of the total primary energy consumption in China according to the BP Statistical Review of World Energy [43].Moreover, thermal power and heat are secondary energy sources that have been calculated based on the type of fuel consumed to generate electricity and heat.Thus, the present study considers only hydropower, wind power, solar power, and nuclear power and defines them as non-fossil energy to avoid tautologically calculating the consumption of coal, petroleum, and natural gas in the electricity generation process [10]

Estimation of Household CO2 Emissions
The Figure 3 illustrates that the CO2 emissions from urban and rural households continuously increased from 112.96 Mt-CO2 and 112.89Mt-CO2 in 2000 to 233.5 Mt-CO2 and 214.85 Mt-CO2 in 2014, respectively.The contribution of the total CO2 emissions from urban and rural households are similar to each other, even though the total disposable income of urban households is 3.5 times more than that of rural households.This indicates that the energy intensity of urban households remains lower than that of rural households, and the reduction of the CO2 emissions of rural households is relatively weak.

Decomposition Analysis
The influencing factors of household CO 2 emissions in China can be decomposed using Equation (3).The results, presented in Table 1, reveal that income level and population scale are the main drivers of CO 2 emissions, energy intensity and energy mix are the inhibitory factors that decreased CO 2 emissions, whereas population structure is a stimulatory factor at the beginning and then an inhibitory factor.4, coal is no longer the major energy type for household CO 2 emissions in China.The proportion of total energy consumption accounted for by coal continuously decreased from 68.37% in 2000 to 32.65% in 2014.By contrast, between 2000 and 2014 that accounted for by petroleum increased from 23.61% to 37.57%, natural gas increased from 4.67% to 20.11%, and non-fossil energy source increased from 3.35% to 9.67%.Therefore, reducing fossil energy consumption and enhancing the applications of non-fossil energy sources are significant ways to mitigate CO 2 emissions.Acceleration of hydroelectric and nuclear power development is mentioned in the "13th Five-year Plan of Electicity Developmen" (from 2016 to 2020).The installed capacity of nuclear power will reach 58 GW by 2020, which increased 2.86 times (relative to 2014).2014 that accounted for by petroleum increased from 23.61% to 37.57%, natural gas increased from 4.67% to 20.11%, and non-fossil energy source increased from 3.35% to 9.67%.Therefore, reducing fossil energy consumption and enhancing the applications of non-fossil energy sources are significant ways to mitigate CO2 emissions.Acceleration of hydroelectric and nuclear power development is mentioned in the "13th Five-year Plan of Electicity Developmen" (from 2016 to 2020).The installed capacity of nuclear power will reach 58 GW by 2020, which increased 2.86 times (relative to 2014).In addition, the energy mix effect on CO 2 emissions from urban and rural decreased to 40.34 Mt-CO 2 and 23 Mt-CO 2 , respectively.This indicates that the energy mix of urban households was more rational than that of rural households, which means that the reduction of CO 2 emissions in the former is more efficient than that in the latter.

Impact Analysis of Energy Intensity
The accumulated changes in household CO 2 emissions from the energy intensity effect from 2000 to 2014 decreased by 75.12 Mt-CO 2 , which accounted for 38.13% of the total changes in CO 2 emissions in absolute value (Figure 5).The energy intensity of urban households decreased from 0.14 tce/10 4 yuan in 2000 to 0.08 tce/10 4 yuan in 2014, whereas the energy intensity of rural households increased from 0.2 tce/10 4 yuan in 2000 to 0.23 tce/10 4 yuan in 2014.Accordingly, the energy intensity effect on CO 2 emissions from urban households decreased by 82.31 Mt-CO 2 , and that from rural households increased by 7.19 Mt-CO 2 .A probable cause is that the series of energy-saving policies, which resulted in a decrease in the total amount of energy consumption, was more smoothly implemented in urban areas than in rural areas.Therefore, if other factors remain unchanged, then a decline in energy intensity reduces CO 2 emissions, and vice versa.In the future, using energy-efficient appliances and new energy vehicles is an efficient approach to reduce CO 2 emissions.
Energies 2016, 9, 1062 7 of 18 In addition, the energy mix effect on CO2 emissions from urban and rural decreased to 40.34 Mt-CO2 and 23 Mt-CO2, respectively.This indicates that the energy mix of urban households was more rational than that of rural households, which means that the reduction of CO2 emissions in the former is more efficient than that in the latter.

Impact Analysis of Energy Intensity
The accumulated changes in household CO2 emissions from the energy intensity effect from 2000 to 2014 decreased by 75.12 Mt-CO2, which accounted for 38.13% of the total changes in CO2 emissions in absolute value (Figure 5).The energy intensity of urban households decreased from 0.14 tce/10 4 yuan in 2000 to 0.08 tce/10 4 yuan in 2014, whereas the energy intensity of rural households increased from 0.2 tce/10 4 yuan in 2000 to 0.23 tce/10 4 yuan in 2014.Accordingly, the energy intensity effect on CO2 emissions from urban households decreased by 82.31 Mt-CO2, and that from rural households increased by 7.19 Mt-CO2.A probable cause is that the series of energy-saving policies, which resulted in a decrease in the total amount of energy consumption, was more smoothly implemented in urban areas than in rural areas.Therefore, if other factors remain unchanged, then a decline in energy intensity reduces CO2 emissions, and vice versa.In the future, using energy-efficient appliances and new energy vehicles is an efficient approach to reduce CO2 emissions.

Impact Analysis of Income Level
Figure 6 shows that the income level effect on household CO2 emissions is positive and has the largest contribution to CO2 emissions during the whole study period.The accumulated changes in CO2 emissions from the income level effect between 2000 and 2014 increased by 325.62 Mt-CO2, accounting for 165.27% of the total changes in CO2 emissions in absolute value.The per capita disposable income of urban and rural households increased by 2.67 and 2.55 times, respectively, from 2000 to 2014.The rapid growth in the demand for home appliances and private car ownership increased the household energy consumption and CO2 emissions to some extent as the income level and standards of living rose.In addition, although the total disposable income of urban households is 3.5 times more than that of rural households in 2014, the income level effect on CO2 emissions from

Impact Analysis of Income Level
Figure 6 shows that the income level effect on household CO 2 emissions is positive and has the largest contribution to CO 2 emissions during the whole study period.The accumulated changes in CO 2 Energies 2016, 9, 1062 8 of 18 emissions from the income level effect between 2000 and 2014 increased by 325.62 Mt-CO 2 , accounting for 165.27% of the total changes in CO 2 emissions in absolute value.The per capita disposable income of urban and rural households increased by 2.67 and 2.55 times, respectively, from 2000 to 2014.The rapid growth in the demand for home appliances and private car ownership increased the household energy consumption and CO 2 emissions to some extent as the income level and standards of living rose.In addition, although the total disposable income of urban households is 3.5 times more than that of rural households in 2014, the income level effect on CO 2 emissions from urban and rural households are similar to each other.This indicates that the energy intensity of urban households remains lower than that of rural households.The government should pay more attention to reducing the energy intensity and CO 2 emissions in rural households.

Impact Analysis of Population Structure
The accumulated changes in household CO2 emissions from the population structure effect increased by 10.68 Mt-CO2 over the whole study period, accounting for 5.42% of the total changes in household CO2 emissions in absolute value (Figure 7).The population structure effect on CO2 emission from urban households increased by 67.16 Mt-CO2, while that from rural households decreased by 56.48 Mt-CO2 owing to the improvement of urbanization level.The urbanization rate of China gradually increased from 36.22% in 2000 to 54.77% in 2014, and the contribution of population structure effect to changes in household CO2 emissions increased at the beginning and then began decreasing when China's urban population surpassed the rural population in 2011.Thus, the population structure effect plays an increasingly important role in inhibiting CO2 emissions.If other factors remain unchanged, then an increase in urbanization rate reduces CO2 emissions, and vice versa.

Impact Analysis of Population Structure
The accumulated changes in household CO 2 emissions from the population structure effect increased by 10.68 Mt-CO 2 over the whole study period, accounting for 5.42% of the total changes in household CO 2 emissions in absolute value (Figure 7).The population structure effect on CO 2 emission from urban households increased by 67.16 Mt-CO 2 , while that from rural households decreased by 56.48 Mt-CO 2 owing to the improvement of urbanization level.The urbanization rate of China gradually increased from 36.22% in 2000 to 54.77% in 2014, and the contribution of population structure effect to changes in household CO 2 emissions increased at the beginning and then began decreasing when China's urban population surpassed the rural population in 2011.Thus, the population structure effect plays an increasingly important role in inhibiting CO 2 emissions.If other factors remain unchanged, then an increase in urbanization rate reduces CO 2 emissions, and vice versa.

Impact Analysis of Population Structure
The accumulated changes in household CO2 emissions from the population structure effect increased by 10.68 Mt-CO2 over the whole study period, accounting for 5.42% of the total changes in household CO2 emissions in absolute value (Figure 7).The population structure effect on CO2 emission from urban households increased by 67.16 Mt-CO2, while that from rural households decreased by 56.48 Mt-CO2 owing to the improvement of urbanization level.The urbanization rate of China gradually increased from 36.22% in 2000 to 54.77% in 2014, and the contribution of population structure effect to changes in household CO2 emissions increased at the beginning and then began decreasing when China's urban population surpassed the rural population in 2011.Thus, the population structure effect plays an increasingly important role in inhibiting CO2 emissions.If other factors remain unchanged, then an increase in urbanization rate reduces CO2 emissions, and vice versa.

Impact Analysis of Population Scale
Figure 8 reveals that the accumulated changes in CO 2 emissions from the population scale effect increased by 24.75 Mt-CO 2 from 2000 to 2014, contributing 12.56% to the total changes in CO 2 emissions in absolute value.The population of China increased from 12.67 billion persons in 2000 to 13.68 billion persons in 2014, which follows the average annual growth rates of 5.46‰ and is related to the family planning policy.This finding indicates that the expanding population scale of China increases household CO 2 emissions but is minimized by the income level effect.Given that the two-child policy has implemented by the Chinese government, the fertility rate in China is expected to increase and the population expansion effect on increasing CO 2 emissions will gradually be enhanced.

Establishment of the SD Model and Dynamic Simulation
LMDI methodology offers reasonable driving forces of energy-related CO2 emissions from household sectors.The population and disposable income growth make the total energy consumption increase, and then causes the increase of fossil energy consumption and amount of CO2 emissions.The decline in energy intensity would decrease the total energy consumption, and then reduce fossil energy consumption and amount of CO2 emissions.Additionally, the strengthening of non-fossil energy sources implies a decrease of fossil energy consumption and CO2 emission amount.
Considering the development characteristics of household sectors in China, the stock-flow diagram for the SD model of household CO2 emissions is built by Vensim PLE software, which is composed of three level variables, three rate variables, and 31 auxiliary variables (Figure 9).The time step is one year.The simulation period extends from 2000 to 2020, although 2000 to 2014 is used to fix the parameters of the model and 2015 to 2020 corresponds to the forecast period of the model.

Establishment of the SD Model and Dynamic Simulation
LMDI methodology offers reasonable driving forces of energy-related CO2 emissions from household sectors.The population and disposable income growth make the total energy consumption increase, and then causes the increase of fossil energy consumption and amount of CO2 emissions.The decline in energy intensity would decrease the total energy consumption, and then reduce fossil energy consumption and amount of CO2 emissions.Additionally, the strengthening of non-fossil energy sources implies a decrease of fossil energy consumption and CO2 emission amount.
Considering the development characteristics of household sectors in China, the stock-flow diagram for the SD model of household CO2 emissions is built by Vensim PLE software, which is composed of three level variables, three rate variables, and 31 auxiliary variables (Figure 9).The time step is one year.The simulation period extends from 2000 to 2020, although 2000 to 2014 is used to fix the parameters of the model and 2015 to 2020 corresponds to the forecast period of the model.Considering the development characteristics of household sectors in China, the stock-flow diagram for the SD model of household CO 2 emissions is built by Vensim PLE software, which is composed of three level variables, three rate variables, and 31 auxiliary variables (Figure 9).The time step is one year.The simulation period extends from 2000 to 2020, although 2000 to 2014 is used to fix the parameters of the model and 2015 to 2020 corresponds to the forecast period of the model.
The variable types are listed in Table 2.The simultaneous differential equations in the stock-flow diagram are defined based on the actual data for household CO 2 emissions from 2000 to 2014 in China.(1) Only time-related parameter equations, which contain the variables PGR, UPGR, PCDIUH, PCDIRH, EIUH, EIRH, PREGR, PC and PP.PGR showed logarithm trend as observed in the historical data from 2000 to 2014 and can be simulated by Equation (12).Variables UPGR, PCDIRH, PCDIUH, PREGR, EIRH, EIUH and PP exhibited quadratic polynomial trends and can be simulated by Equations ( 13)- (19), respectively.PC showed quartic polynomial trends and can be simulated by Equation (20) The combination of LMDI methodology and SD models provides a scientific basis for designing scenarios of household CO 2 emissions.Differences among the five scenarios are listed in Table 3: (1) Baseline scenario (BS): The growth rate of population, growth rate of urban population, per capita disposable income, energy intensity, energy mix, and growth rate of non-fossil energy will evolve through the smooth trend of the period 2000-2014, which is extrapolated to 2015-2020 using the geometric growth rate method.
(2) Plan scenario (PS): PS is a current policy scenario that is a frame of reference.The content of the "13th Five-year Plan" (from 2016 to 2020) mentioned that the Chinese government will enhance energy-saving and CO 2 emission reduction efforts.In 2020, the total population is assumed to be 14.2 billion persons, which follows an annual growth rate of 6.26‰; the urbanization rate reaches 60%; the population of urban households will increase to 8.52 billion persons at an annual growth rate of 2.17%; per capita disposable income of urban and rural households will be approximately double that in 2010; the energy intensity of urban and rural households will gradually drop to 6.65 and 17.19 Mtce/billion yuan respectively; and the proportion of total energy consumption accounted for by non-fossil energy will be 15%, following an annual growth rate of 6.42%.
(3) Adjustment scenario 1 (AS-1): Scenario AS-1 is an adjustment scenario by improving the urbanization rate.The proportion of population accounted for by urban population will increase from 54.77% in 2014 to 65% in 2020, and the amount of urban population will increase to 9.1 billion persons in 2020, which follows an annual growth rate of 3.29%.The rest of the variables will evolve as in the BS.
(4) Adjustment scenario 2 (AS-2): Scenario AS-2 is an adjustment scenario by increasing the applications of non-fossil energy sources.The proportion of total energy consumption accounted for by non-fossil energy will increase from 9.67% in 2014 to 20% in 2020, which follows an annual growth rate of 12.87%.The rest of the variables will evolve as in scenario AS-1.
(5) Adjustment scenario 3 (AS-3): Scenario AS-3 is an adjustment scenario by reducing the energy intensity.The energy intensity of urban and rural households will gradually drop to 6 and 15.52 Mtce/billion yuan in 2020 respectively.The rest of the variables will evolve as in scenario AS-2.

Model Testing and Validation
The proposed SD model has been simulated, and the result is compared with the historical real data for total energy consumption and CO 2 emission (Table 4).The errors of energy consumption and CO 2 emissions are less than 2%, the simulated results show good conformity with historical trends.In addition, the results show their fitting degree is more than 0.94 and the model meets the simulation requirements.

Result of Scenarios
Different simulations based on the abovementioned scenario settings can be obtained by adjusting the parameters in the proposed SD model.The simulated household energy consumption and CO 2 emissions are shown in Figures 8 and 9 from 2015 to 2020: (1) Figure 10 shows that China's household energy consumption may continuously increase from 221.47 Mtce in 2014 to 248.16 Mtce in 2020 (a 12.33% increase) if new policies for CO 2 emission reduction are not implemented after 2014 under BS.PS presents the largest increase in energy consumption among five scenarios because the growth rate of population was higher than in the other four scenarios.The household energy consumption in 2020 will be 259.16Mtce in PS, which is more than 4.43% of the value in BS.However, the trend of energy consumption for AS-1 and AS-2 are close to that for BS.The household energy consumption in 2020 will be 249.65 Mtce both in AS-1 and AS-2, which are 1.849 Mtce more than that in BS.The energy consumptions in AS-3 will reach 231.49Mtce in 2020, which is less than 6.72% of the value in BS after taking further enhanced energy-saving measures to reduce energy intensity.Household energy consumption maintains an increasing trend in all five scenarios in the simulated period.The increase in income and growth of population lead to a rapid growth in the demand for home appliances and private car ownership, which increased the household energy consumption.
(2) Figure 11 shows that household CO2 emissions in BS may increase from 444.03 Mt-CO2 in 2014 to 466.8 Mt-CO2 in 2020 (a 5.13% increase).Similar to the household energy consumption, the PS presents the largest increase in CO2 emissions among the five scenarios.The CO2 emissions in 2020 will be 475.65 Mt-CO2 in PS, which is more than 1.9% of the value in BS.The household CO2 emissions in 2020 in AS-1 is 2.8 Mt-CO2 more than that in BS, which reveals that simply raising the rate of urbanization cannot reduce CO2 emissions.The household energy consumptions increase in AS-2 and AS-3; the CO2 emissions present a trend of decrease in AS-2 and AS-3 after promoting the proportion of energy accounted for by non-fossil energy and reducing energy intensity.The household CO2 emissions in 2020 will be 435.98Mt-CO2 and 404.26Mt-CO2 in AS-2 and AS-3 respectively, which are less than 6.6% and 13.4% of the value in BS.Thus, the household CO2 emissions under "13th Five-year Plan" will maintain an increasing trend, which contradicts the target of CO2 emission reduction.However, the household CO2 emissions may be inhibited by reducing energy intensity and developing non-fossil energy, such as improving the infrastructure of natural gas supply and the incentives to buy fuel-efficient vehicles and energy-efficient electronic products, as well as promoting solar power utilization [10].

Conclusions
(1) An integrated model based on LMDI methodology and SD model is formulated in this study.The errors of the main variables are less than 2%, which indicates that the integrated model performs well in calculating the contribution of the different influencing factors on household CO2 emissions and analyzing the options for CO2 emission mitigation.Household energy consumption maintains an increasing trend in all five scenarios in the simulated period.The increase in income and growth of population lead to a rapid growth in the demand for home appliances and private car ownership, which increased the household energy consumption.
(2) Figure 11 shows that household CO 2 emissions in BS may increase from 444.03 Mt-CO 2 in 2014 to 466.8 Mt-CO 2 in 2020 (a 5.13% increase).Similar to the household energy consumption, the PS presents the largest increase in CO 2 emissions among the five scenarios.The CO 2 emissions in 2020 will be 475.65 Mt-CO 2 in PS, which is more than 1.9% of the value in BS.The household CO 2 emissions in 2020 in AS-1 is 2.8 Mt-CO 2 more than that in BS, which reveals that simply raising the rate of urbanization cannot reduce CO 2 emissions.The household energy consumptions increase in AS-2 and AS-3; the CO 2 emissions present a trend of decrease in AS-2 and AS-3 after promoting the proportion of energy accounted for by non-fossil energy and reducing energy intensity.The household CO 2 emissions in 2020 will be 435.98Mt-CO 2 and 404.26Mt-CO 2 in AS-2 and AS-3 respectively, which are less than 6.6% and 13.4% of the value in BS.Household energy consumption maintains an increasing trend in all five scenarios in the simulated period.The increase in income and growth of population lead to a rapid growth in the demand for home appliances and private car ownership, which increased the household energy consumption.
(2) Figure 11 shows that household CO2 emissions in BS may increase from 444.03 Mt-CO2 in 2014 to 466.8 Mt-CO2 in 2020 (a 5.13% increase).Similar to the household energy consumption, the PS presents the largest increase in CO2 emissions among the five scenarios.The CO2 emissions in 2020 will be 475.65 Mt-CO2 in PS, which is more than 1.9% of the value in BS.The household CO2 emissions in 2020 in AS-1 is 2.8 Mt-CO2 more than that in BS, which reveals that simply raising the rate of urbanization cannot reduce CO2 emissions.The household energy consumptions increase in AS-2 and AS-3; the CO2 emissions present a trend of decrease in AS-2 and AS-3 after promoting the proportion of energy accounted for by non-fossil energy and reducing energy intensity.The household CO2 emissions in 2020 will be 435.98Mt-CO2 and 404.26Mt-CO2 in AS-2 and AS-3 respectively, which are less than 6.6% and 13.4% of the value in BS.Thus, the household CO2 emissions under "13th Five-year Plan" will maintain an increasing trend, which contradicts the target of CO2 emission reduction.However, the household CO2 emissions may be inhibited by reducing energy intensity and developing non-fossil energy, such as improving the infrastructure of natural gas supply and the incentives to buy fuel-efficient vehicles and energy-efficient electronic products, as well as promoting solar power utilization [10].

Conclusions
(1) An integrated model based on LMDI methodology and SD model is formulated in this study.The errors of the main variables are less than 2%, which indicates that the integrated model performs well in calculating the contribution of the different influencing factors on household CO2 emissions and analyzing the options for CO2 emission mitigation.Thus, the household CO 2 emissions under "13th Five-year Plan" will maintain an increasing trend, which contradicts the target of CO 2 emission reduction.However, the household CO 2 emissions may be inhibited by reducing energy intensity and developing non-fossil energy, such as improving the infrastructure of natural gas supply and the incentives to buy fuel-efficient vehicles and energy-efficient electronic products, as well as promoting solar power utilization [10].

Conclusions
(1) An integrated model based on LMDI methodology and SD model is formulated in this study.The errors of the main variables are less than 2%, which indicates that the integrated model performs well in calculating the contribution of the different influencing factors on household CO 2 emissions and analyzing the options for CO 2 emission mitigation.
(2) The simulations indicate that in the case of "13th Five-year Plan", household CO 2 emissions in China will maintain an increasing trend, and reach 475.65 Mt-CO 2 in 2020, which is more than 6.09% of the value in 2014.By decreasing energy intensity, such as by improving the infrastructure of natural gas supply and incentives to buy fuel-efficient vehicles and energy-efficient electronic products, CO 2 emissions will decrease to 404.26 Mt-CO 2 in 2020, which is 9.84% lower than the emissions in 2014.
(3) The consideration of household energy mix, which prioritizes coal, has changed significantly, the reduction potential by developing non-fossil energy sources is limited.The simulation shows that the proportion of total energy consumption accounted for by non-fossil energy increases from 9.67% in 2014 to 20% in 2020, but the total CO 2 emissions amount only decreases by 2.67% from 2014 to 2020.
(4) Although the urbanization improvement makes household CO 2 emissions decrease, raising the rate of urbanization cannot reduce the household CO 2 emissions under the comprehensive influence of other factors.On the contrary, when the proportion of population accounted for by urban population increases to 65% in 2020, the total CO 2 emissions amount increased by 4.74% from 2014 to 2020.
This study builds an integrated model to reveals the options for reducing household CO 2 emissions in China.In our future research, we would further improve the model and expand its application scope based on the present study, providing a more specific basis for policy-makers to develop emission-reduction policies.

4. 1 .
Estimation of Household CO 2 Emissions The resultant household CO 2 emissions in China over the period 2000-2014 based on Equation (1) are presented in Figure 2. The aggregate CO 2 emissions increased from 225.84 Mt-CO 2 in 2000 to 448.36 Mt-CO 2 in 2014 as a result of an annual growth rate of 5.02%.The CO 2 emissions based on coal increased from 172.45 Mt-CO 2 in 2000 to 198.23 Mt-CO 2 in 2014, which indicated a relatively stable amount.However, the CO 2 emissions based on petroleum and natural gas rapidly increased.The result shows that by 2014 the CO 2 emissions based on petroleum and natural gas increased 3.83 and 10.36 times (relative to 2000) respectively owing to the proportion of energy consumption, which accounted respectively for the petroleum and natural gas increase of 1.59 and 4.3 times in 2000.
resultant household CO2 emissions in China over the period 2000-2014 based on Equation (1) are presented in Figure 2. The aggregate CO2 emissions increased from 225.84 Mt-CO2 in 2000 to 448.36 Mt-CO2 in 2014 as a result of an annual growth rate of 5.02%.The CO2 emissions based on coal increased from 172.45 Mt-CO2 in 2000 to 198.23 Mt-CO2 in 2014, which indicated a relatively stable amount.However, the CO2 emissions based on petroleum and natural gas rapidly increased.The result shows that by 2014 the CO2 emissions based on petroleum and natural gas increased 3.83 and 10.36 times (relative to 2000) respectively owing to the proportion of energy consumption, which accounted respectively for the petroleum and natural gas increase of 1.59 and 4.3 times in 2000.

Figure 3
Figure 3 illustrates that the CO 2 emissions from urban and rural households continuously increased from 112.96 Mt-CO 2 and 112.89Mt-CO 2 in 2000 to 233.5 Mt-CO 2 and 214.85 Mt-CO 2 in 2014, respectively.The contribution of the total CO 2 emissions from urban and rural households are similar to each other, even though the total disposable income of urban households is 3.5 times more than that

Figure 4 .
Figure 4. Accumulated changes in energy mix effect on household CO2 emissions and energy mix in China (2000-2014).Figure 4. Accumulated changes in energy mix effect on household CO 2 emissions and energy mix in China (2000-2014).

Figure 4 .
Figure 4. Accumulated changes in energy mix effect on household CO2 emissions and energy mix in China (2000-2014).Figure 4. Accumulated changes in energy mix effect on household CO 2 emissions and energy mix in China (2000-2014).

Figure 5 .
Figure 5. Accumulated changes in energy intensity effect on household CO2 emissions and energy intensity in China (2000-2014).

Figure 5 .
Figure 5. Accumulated changes in energy intensity effect on household CO 2 emissions and energy intensity in China (2000-2014).

Figure 7 .
Figure 7. Accumulated changes in population structure effect on household CO2 emissions and population structure in China (2000-2014).4.2.5.Impact Analysis of Population ScaleFigure8reveals that the accumulated changes in CO2 emissions from the population scale effect increased by 24.75 Mt-CO2 from 2000 to 2014, contributing 12.56% to the total changes in CO2 emissions in absolute value.The population of China increased from 12.67 billion persons in 2000 to 13.68 billion persons in 2014, which follows the average annual growth rates of 5.46‰ and is related to the family planning policy.This finding indicates that the expanding population scale of China increases household CO2 emissions but is minimized by the income level effect.Given that the twochild policy has implemented by the Chinese government, the fertility rate in China is expected to increase and the population expansion effect on increasing CO2 emissions will gradually be enhanced.

Figure 6 .
Figure 6.Accumulated changes in income level effect on household CO 2 emissions and income level in China (2000-2014).

Figure 6 .
Figure 6.Accumulated changes in income level effect on household CO2 emissions and income level in China (2000-2014).

Figure 7 .
Figure 7. Accumulated changes in population structure effect on household CO2 emissions and population structure in China (2000-2014).

4. 2 . 5 .
Figure 8 reveals that the accumulated changes in CO2 emissions from the population scale effect increased by 24.75 Mt-CO2 from 2000 to 2014, contributing 12.56% to the total changes in CO2 emissions in absolute value.The population of China increased from 12.67 billion persons in 2000 to 13.68 billion persons in 2014, which follows the average annual growth rates of 5.46‰ and is related to the family planning policy.This finding indicates that the expanding population scale of China

Figure 7 .
Figure 7. Accumulated changes in population structure effect on household CO 2 emissions and population structure in China (2000-2014).

Figure 8 .
Figure 8. Accumulated changes in population scale effect on household CO2 emissions and population in China (2000-2014).

Figure 9 .
Figure 9. Stock-flow diagram for the SD model of household CO2 emissions in China.

Figure 8 .
Figure 8. Accumulated changes in population scale effect on household CO 2 emissions and population in China (2000-2014).

Figure 9 .
Figure 9. Stock-flow diagram for the SD model of household CO2 emissions in China.The variable types are listed in Table 2.The simultaneous differential equations in the stock-flow diagram are defined based on the actual data for household CO2 emissions from 2000 to 2014 in China.

Figure 9 .
Figure 9. Stock-flow diagram for the SD model of household CO 2 emissions in China.
1 denote the values for years t and t − 1 respectively; ∆C tot denotes the changes in household CO 2 emissions from years t − 1 to t; C t and C t−1 denote the total CO 2 emissions in years t and t − 1 respectively; and ∆C CF , ∆C Estr , ∆C Eint , ∆C Ilev , ∆C Pstr , and ∆C P refer to the contribution of CO 2 emission factors, energy mix, energy intensity, income level, population structure, and population scale, respectively.
. The data on energy consumption used in this study mainly come from the China Energy Statistical Yearbooks 2001-2015 [44-58], while the CO 2 emission factors of each energy type are given by the Energy Research Institute of the National Development and Reform Commission of China.The data on population and income come from the China Statistical Yearbooks 2001-2015 [59-73].Calorific value, population, disposable income, energy consumption, and CO 2 emission data are calculated in billion person, yuan at constant prices in 2005, million tons of coal equivalent (Mtce), and million tons (Mt-CO 2 ), respectively.

Table 1 .
Complete decomposition of changes in the household CO 2 emissions of China.(Mt-CO 2 ).Impact Analysis of Energy Mix Figure 4 reveals that the accumulated changes in household CO 2 emissions from the energy mix effect decreased to nearly 63.34 Mt-CO 2 from 2000 to 2014, accounting for 32.15% of the total changes in CO 2 emissions in absolute value.As shown in Figure

Table 4 .
Simulated data versus historical data.