Research on Carbon Emissions and Influencing Factors of Residents’ Lives in Hebei Province

Abstract

The standard of living has significantly risen along with ongoing economic progress, but CO₂ emissions have also been rising. The reduction in CO₂ resulting from the daily activities of residents has become a crucial priority for every province. A relevant study on the carbon emissions of Hebei Province residents was conducted for this publication, aiming to provide a theoretical basis for the sustainable development of Hebei Province. The first part of the article calculates the carbon emissions of Hebei Province people from 2005 to 2020 using the emission factor method and the Consumer Lifestyle Approach (CLA). Secondly, the Logarithmic Mean Divisia Index (LMDI) decomposition approach is used to assess the components that influence both direct and indirect carbon emissions. Finally, the scenario analysis approach is employed in conjunction with the LEAP model to establish baseline, low-carbon, and ultra-low-carbon scenarios to predict the trend of residents’ carbon emissions in Hebei Province from 2021 to 2040. The results show that the total carbon emissions of residents in Hebei Province from 2005 to 2020 rose, from 77.45 million tons to 153.35 million tons. Income level, energy consumption intensity, and population scale are factors that contribute to the increase in direct carbon emissions, while consumption tendency factors have a mitigating effect on direct carbon emissions. Economic level, consumption structure, and population scale factors are factors that contribute to the increase in indirect carbon emissions, while energy consumption intensity and energy structure factors have a mitigating effect on indirect carbon emissions. The prediction results show that under the baseline scenario, the cumulative residents’ carbon emissions in Hebei Province will not reach a zenith from 2021 to 2040. However, under the low-carbon situation, the carbon emissions of residents in Hebei Province will peak in 2029, with a peak of 174.69 million tons, whereas under the ultra-low-carbon scenario, it will peak in 2028, with a peak of 173.27 million tons.

1. Introduction

The escalating ecological issues resulting from global warming are increasingly severe [1,2,3], which not only restricts the economic development of various countries but also threatens the survival and development of human beings. Therefore, the Chinese government put forward the goal of “carbon peak, carbon neutrality” [4] in September 2020. As the main consumers of products and services, the carbon emissions generated by residents [5] from daily life have become one of the important sources of global carbon emissions. Therefore, reducing carbon emissions is important to achieve sustainable social development. As a province with a large population and energy consumption, the carbon emissions of residents in Hebei Province should not be underestimated. Therefore, it is of great significance to study the trend of carbon emissions of residents in Hebei Province and its influencing factors, explore the driving factors of the rapid growth of CO₂, and put forward corresponding emission reduction strategies and paths, which are conducive to accelerating the promotion of green and low-carbon lifestyles and implementing China’s “double carbon” goal.

Numerous studies have been conducted on the variables that affect carbon dioxide emissions. Zhang et al. [6] studied the carbon emissions of Chengdu–Chongqing urban agglomeration revealed that the region’s industrial structure, GDP, population, and rate of urbanization all significantly affect carbon emissions. Cai et al. [7] found that the spatial and temporal differences in land carbon emissions in Jiangsu Province are related to economic development, industrial structure, energy intensity, land use, and human activities. Xie et al. [8] explored the influencing factors of carbon emissions from electricity consumption in China, and found that economic development level, industrialization level, population density, and foreign direct investment all had significant effects. Yue et al. [9] studied the carbon emissions of Chinese households from 2010 to 2018 and found that household carbon emissions in different regions and different income groups are not the same. These differences are affected by many factors such as population, technology, and socioeconomic status.

Similarly, the prediction of carbon dioxide has been studied by many scholars [10,11,12,13]. Based on the analysis of carbon emissions from energy consumption, Tan et al. [14] employed the Logarithmic Mean Divisia Index (LMDI) model to quantitatively analyze the effects of production scale, energy efficiency, energy structure, and emission coefficient on carbon increment in China’s metal chemical production enterprises. This paper employs the scenario analysis method, as described by Hou et al. [15], based on a hybrid model of LMDI and TentSSA-ENN, to explore the possibility of Shaanxi Province’s manufacturing industry attaining a carbon peak in the future. Zhang et al. [16] constructed a prediction model for carbon emissions from building operations and predicted its embodied carbon emissions in six scenarios. Peng et al. [17] predicted the carbon emissions of the construction industry by constructing a genetic algorithm–neural network carbon emission prediction model. Chen et al. [18] used neural networks to establish three scenarios to study the carbon emission prediction and carbon peak path of China’s logistics industry. However, at present, the influencing factors and predictions of carbon emissions are mostly concentrated in industry [19,20,21], construction industry [22,23,24,25], manufacturing industry [26,27], etc. There are few studies on the carbon emissions of residents’ lives and the prediction of their trends. Therefore, this study selects the life of residents in Hebei Province as the object to study and predict the trend of carbon emissions.

There is research on the carbon emissions of residents’ lives. Markaki et al. [28] studied the influencing factors of the Greek household carbon footprint from 1995 to 2012. Muhammad et al. [29] researched how the population, economic growth, and urbanization of SAARC countries affected the carbon emissions of their residents. Trotta [30] found that household consumption carbon emissions are affected by many factors, such as family size, income level, technological progress, etc. Nie et al. [31] measured household consumption carbon emissions through questionnaires and analyzed relevant influencing factors. There is a dearth of studies on the prediction of residents’ living carbon emissions, and the majority of publications on the subject analyze influencing variables. As a result, based on the investigation of the factors impacting residents’ living carbon emissions, this study forecasts the trend in carbon emissions.

Since the implementation of the integration of Beijing, Tianjin, and Hebei in 2014, the economy of Hebei Province has developed rapidly, and the total amount of carbon dioxide emissions has been ranked second in the country all year round [32]. The results of the seventh census of Hebei Province in 2020 show that the total population of the province had reached 74.61 million. To effectively reduce carbon emissions and achieve sustainable development, this study focuses on the influencing variables and trend analysis of carbon emissions of inhabitants in Hebei Province. The accomplishment of China’s 2030 carbon peak and 2060 carbon neutral aim is very significant from a scientific and practical standpoint.

2. Methods and Data Sources

2.1. Carbon Dioxide Emissions Accounting

Depending on how they consume energy directly and indirectly, residents’ carbon emissions are separated into two categories: direct carbon emissions and indirect carbon emissions. Direct carbon emissions refer to the carbon emissions generated by the direct consumption of coal, oil, heat, electricity, and other energy products in the daily life of residents; indirect carbon emissions cover the carbon emissions generated by residents’ consumption of non-energy products and services in the production process in terms of clothing, food, and use. Consequently, the following formula can be used to determine the total carbon dioxide emissions of residents in Hebei Province:

$$E_t=E_d+E_{ind}$$

(1)

where $E_t$ is the total carbon emissions of residents in Hebei Province (10⁴ t); $E_d$ is the direct carbon emissions (10⁴ t); and $E_{ind}$ is the indirect carbon emissions (10⁴ t).

2.1.1. Direct Carbon Emission Accounting

Referring to the emission factor method proposed by the Intergovernmental Panel on Climate Change (IPCC) [33], the direct carbon emissions of residential energy consumption in Hebei Province are calculated. The calculation formula is as follows:

$$\sum E_d=\sum C_i\times K_i$$

(2)

where $E_d$ is the total amount of direct carbon emissions from residents’ lives; $C_i$ is the consumption of type I energy; and $K_i$ is the carbon emission factor of type I energy. (i = 1, 2, …, 14), respectively, raw coal, other coal washing, briquette, coke, coke oven gas, other gas, gasoline, kerosene, diesel oil, natural gas, liquefied petroleum gas, heat, electricity, other energy, and various energy carbon emission factors (

Table 1. Carbon emission factors of various energy sources.

Energy Category	Carbon Emission Factor	Unit
Raw coal	1.90	tCO2/tce
Other coal washing	0.88	tCO2/tce
Coal products	1.85	tCO2/tce
Coke oven gas	0.86	tCO2/tce
Blast furnace gas	0.97	tCO2/tce
Other gas	0.75	tCO2/tce
Gasoline	2.93	tCO2/tce
Kerosene	3.03	tCO2/tce
Diesel fuel	3.10	tCO2/tce
Liquefied petroleum gas	3.10	tCO2/tce
Natural gas	1.98	tCO2/tce
Other energy sources	2.77	tCO2/tce
Heat	0.11	tCO2/GJ
Electricity	0.5703	tCO2/MWh

).

2.1.2. Indirect Carbon Emission Accounting

According to the “Hebei Statistical Yearbook” per capita income and expenditure indicators, the consumption and expenditure behavior of residents in Hebei Province is divided into eight categories: food, clothing, housing, household equipment supplies, health care, culture, education and entertainment, transportation and communication, and other miscellaneous items. According to the Consumer Lifestyle Approach (CLA) method [34], the calculation formula for indirect carbon emissions from residents’ lives is as follows:

$$E_{ind}={\sum }_{m=1}^8{A}_m^n÷B_m^n\times S_m^n\times {\mathrm{C}\mathrm{O}}_{2\mathrm{k}}$$

(3)

where $E_{ind}$ is the total indirect carbon emissions of residents’ lives; n is the year; m is eight categories of industries, (m = 1, 2, …, 8); $A_m^n$ is the total energy consumption of the related industries included in the m-type consumption items in n years; $B_m^n$ is the output value of the mth corresponding industry in n years; $S_m^n$ is the expenditure of residents on eight categories in m years; and ${\mathrm{C}\mathrm{O}}_{2\mathrm{k}}$ is the carbon emission coefficient of standard coal with a value of 2.77 tCO₂/tce.

2.2. LMDI Decomposition

Decomposition analysis is a mainstream method for the quantitative analysis of the contribution of CO₂ emission factors. The commonly used decomposition methods include exponential decomposition analysis (IDA) and structural decomposition analysis (SDA). Since IDA is based on terminal output data, it is easier to use smaller data samples for analysis. An example of an IDA version is the Logarithmic Mean Divisia Index (LMDI) [35], a time-series-based decomposition technique with few variables. Furthermore, in contrast to alternative decomposition techniques, the LMDI is simple to formulate and capable of efficiently resolving residual issues in the decomposition process and yielding more accurate and persuasive decomposition results. Furthermore, the calculation process is reasonably easy to comprehend. Additive and multiplicative decomposition are the two primary LMDI techniques. This research uses LMDI additive factor decomposition, since it is a simple and intuitive method of additive decomposition.

2.2.1. Direct Carbon Emission Decomposition

To break down the direct carbon emissions of residents in Hebei Province from 2005 to 2020 by the LMDI, this study chooses the five influencing elements of energy structure, energy consumption intensity, consumption tendency, income level, and population size. The model is then constructed as follows:

$$\begin{gathered} E_d=\sum _i{E}_{di}=\sum _i{\displaystyle \frac{E_{di}}{F_i}}\times {\displaystyle \frac{F_i}{S_i}}\times {\displaystyle \frac{S_i}{G_i}}\times {\displaystyle \frac{G_i}{P}}\times P \\ \hspace{1em}\hspace{1em}\hspace{1em}={\sum }_i{L}_i\times M_i\times N_i\times O_i\times P \end{gathered}$$

(4)

where $i$ is residents’ living energy consumption types; $E_d$ is the total direct carbon emissions of residents’ living consumption; $E_{di}$ is the carbon emissions of the i-th energy source; $F_i$ is the energy consumption corresponding to the third energy source; $S_i$ is the consumption expenditure of residents; $G_i$ is the disposable income of residents; and $P$ is the total population at the end of the year. $L_i=E_{di}/F_i$ is the energy structure factor; $M_i=F_i/S_i$ is the energy consumption intensity factor; $N_i=S_i/G_i$ is the consumption tendency factor; and $O_i=G_i/P$ is the income level factor.

Set $E_0$ as the direct carbon emissions of residents’ lives in the base period, $E_T$ as the direct carbon emissions of residents’ life in the T period, and $∆E_{tot}$ as the change in direct carbon emission of residents’ lives from the base period to the T period. Decomposition allows for its acquisition:

$$∆E_{tot}=E_T-E_0=∆E_L+∆E_M+∆E_N+∆E_O+∆E_P$$

(5)

The decomposition formula of each factor is as follows:

Energy structure factors:

$$∆E_L={\sum }_i{\displaystyle \frac{E_{Ti}-E_{0i}}{ln{E}_{Ti}-ln{E}_{0i}}}\ln \left({\displaystyle \frac{L_{Ti}}{L_{0i}}}\right)$$

(6)

Energy consumption intensity factor:

$$∆E_M={\sum }_i{\displaystyle \frac{E_{Ti}-E_{0i}}{ln{E}_{Ti}-ln{E}_{0i}}}\ln \left({\displaystyle \frac{M_{Ti}}{M_{0i}}}\right)$$

(7)

Consumption tendency factors:

$$∆E_N={\sum }_i{\displaystyle \frac{E_{Ti}-E_{0i}}{ln{E}_{Ti}-ln{E}_{0i}}}\ln \left({\displaystyle \frac{N_{Ti}}{N_{0i}}}\right)$$

(8)

Income level factor:

$$∆E_O={\sum }_i{\displaystyle \frac{E_{Ti}-E_{0i}}{ln{E}_{Ti}-ln{E}_{0i}}}\ln \left({\displaystyle \frac{O_{Ti}}{O_{0i}}}\right)$$

(9)

Population size factor:

$$∆E_P={\sum }_i{\displaystyle \frac{E_{Ti}-E_{0i}}{ln{E}_{Ti}-ln{E}_{0i}}}\ln \left({\displaystyle \frac{P_T}{P_0}}\right)$$

(10)

2.2.2. Indirect Carbon Emission Decomposition

To break down the indirect carbon emissions of Hebei Province people from 2005 to 2020, five influencing factors—energy structure, energy consumption intensity, consumption structure, economic level, and population size—have been chosen. The model is constructed as follows:

$$\begin{gathered} {\mathrm{E}}_{\mathrm{i}\mathrm{n}\mathrm{d}}=\sum _j{E}_{indj}=\sum _j{\displaystyle \frac{E_{indj}}{F_j}}\times {\displaystyle \frac{F_j}{S_j}}\times {\displaystyle \frac{S_j}{W}}\times {\displaystyle \frac{W}{P}}\times P \\ \hspace{1em}\hspace{1em}\hspace{1em}={\sum }_j{Q}_j\times R_j\times W_j\times U\times P \end{gathered}$$

(11)

where j is eight types of expenditure items of indirect carbon emissions from residents’ lives; $E_{ind}$ is the total indirect carbon emissions of residents’ lives; $E_{indj}$ is the carbon emissions of category j consumption items; $F_j$ is the energy consumption corresponding to the category j consumption item; $S_j$ is the expenditure of category j consumption items; $W$ represents the total consumption expenditure of residents; $P$ represents the total population at the end of the year; $Q_j=E_{indj}/F_i$ is the energy structure factor; $R_j=F_j/S_j$ is the energy consumption intensity factor; $W_j=S_j/W$ is the consumption structure factor; and $U=T/P$ is the economic level factor.

The indirect carbon emissions of residents living in the base period are $E_0$, the indirect carbon emissions of residents living in the T period are $E_T$, and the change in indirect carbon emissions of residents living from the base period to the T period is $∆E_{tot}$. The difference decomposition of the equation can be obtained:

$$∆E_{tot}=E_T-E_0=∆E_Q+∆E_R+∆E_W+∆E_U+∆E_P$$

(12)

The decomposition formula of each factor is as follows:

Energy structure factors:

$$∆E_Q={\sum }_j{\displaystyle \frac{E_{Tj}-E_{0j}}{ln{E}_{Tj}-ln{E}_{0j}}}\ln \left({\displaystyle \frac{Q_{Tj}}{Q_{0j}}}\right)$$

(13)

Energy consumption intensity factor:

$$∆E_R={\sum }_j{\displaystyle \frac{E_{Tj}-E_{0j}}{ln{E}_{Tj}-ln{E}_{0j}}}\ln \left({\displaystyle \frac{R_{Tj}}{R_{0j}}}\right)$$

(14)

Consumption structure factors:

$$∆E_W={\sum }_j{\displaystyle \frac{E_{Tj}-E_{0j}}{ln{E}_{Tj}-ln{E}_{0j}}}\ln \left({\displaystyle \frac{W_{Tj}}{W_{0j}}}\right)$$

(15)

Economic level factor:

$$∆E_U={\sum }_j{\displaystyle \frac{E_{Tj}-E_{0j}}{ln{E}_{Tj}-ln{E}_{0j}}}\ln \left({\displaystyle \frac{U_T}{U_0}}\right)$$

(16)

Population size factor:

$$∆E_P={\sum }_j{\displaystyle \frac{E_{Tj}-E_{0j}}{ln{E}_{Tj}-ln{E}_{0j}}}\ln \left({\displaystyle \frac{P_T}{P_0}}\right)$$

(17)

2.3. LEAP Model

The Long-range Energy Alternative Planning System (LEAP) [36] is a bottom-up comprehensive measurement model of energy and environment, which can be used to predict energy supply and demand, pollutants, and greenhouse gas emissions under different driving factors. The LEAP model can not only conveniently process the input of time series, but also effectively carry out complex scenario analysis. In addition, the LEAP model system has a huge underlying emission factor database and energy system analysis module. These advantages are exactly in line with the needs of residents’ carbon emission prediction. Residents’ lives involve not only energy modules, but also non-energy modules, and the underlying data is numerous, which is more matched with the LEAP model. The LEAP model calculation principal formula is as follows:

$$TC=ED\times AI\times EI$$

(18)

where $ED$ is total energy demand, $AI$ is energy activity level, $EI$ is energy intensity, $TC$ is total greenhouse gas emission, and $EF$ is the greenhouse gas emission factor.

2.4. Data Sources

The period of all the data used in this paper is 2005–2020, and various energy carbon emission factors are derived from Appendix 4 of “China Energy Statistics Yearbook 2022” [37], “Guidelines for Provincial Greenhouse Gas Inventories (Trial)” [38], “General Principles for Comprehensive Energy Consumption Calculation” [39], “Enterprises in Other Industries-Greenhouse Gas Emission Accounting Methods and Reporting Guidelines” [40], “Notice on Doing a Good Job in Greenhouse Gas Emission Report Management of Power Generation Enterprises in 2023–2025” [41]. The GDP, consumption expenditure, and population data of Hebei Province are derived from the “Hebei Statistical Yearbook” [42]. For individual missing data, the SPSS 26 version interpolation method is used to estimate.

2.5. Scene Setting

In this study, a LEAP model of residents’ lives in Hebei Province is established, based on the structural features of the model that are assembled from the bottom up. The scenario analysis approach is used to set the base scenario, low-carbon scenario, and ultra-low-carbon scenario to study the trajectory of carbon emissions of people in Hebei Province, with 2020 serving as the base year and the projection interval being 2021–2040.

The baseline scenario is the presumption that all influencing factors develop following the current model and that Hebei Province retains its current development status over the projected period. The low-carbon scenario refers to the realization of China’s goal of CO₂ peaking before 2030, based on the benchmark scenario. With the implementation of the “14th Five-Year Plan for Energy Conservation and Emission Reduction in Hebei Province”, the residents’ awareness of energy conservation and emission reduction is initially reflected in life. The population, energy consumption, and consumption expenditure of Hebei Province are no longer rigidly increasing, the energy consumption structure is accelerating, coal is reduced and replaced, and the proportion of non-fossil energy consumption such as electricity and heat is increasing. The living sector introduces advanced technology, energy-saving and durable products, and non-energy consumption reduction. The ultra-low-carbon scenario means that on top of the low-carbon scenario, residents have a high degree of energy conservation and emission reduction actions, residents’ lifestyles are more low-carbon and clean, energy-saving products are purchased, and domestic waste is reduced. Clean energy technology has been promoted, and energy structure optimization and low-carbon, energy-saving technology innovation have been improved. The specific settings of each parameter in each scenario are shown in

Table 2. Various energy parameter scenario settings.

Parameter Category	Scenes	2021–2025	2026–2030	2031–2035	2036–2040
Size of population	Baseline scenario	0.26%	0.25%	0.25%	0.24%
	Low-carbon scenario	0.16%	0.15%	0.11%	0.06%
	Ultra-low-carbon scenario	0.10%	0.05%	0.01%	0.01%
Consumer spending	Baseline scenario	6.00%	5.70%	5.40%	5.10%
	Low-carbon scenario	5.50%	5.00%	4.50%	4.00%
	Ultra-low-carbon scenario	5.40%	4.90%	4.40%	3.90%
Gross production	Baseline scenario	6.00%	5.90%	5.50%	5.20%
	Low-carbon scenario	6.00%	5.80%	5.40%	5.10%
	Ultra-low-carbon scenario	5.90%	5.70%	5.30%	5.00%
Coal	Baseline scenario	1.64%	1.30%	1.10%	0.80%
	Low-carbon scenario	−15.00%	−17.00%	−19.00%	−21.00%
	Ultra-low-carbon scenario	−16.00%	−18.00%	−20.00%	−22.00%
Oils	Baseline scenario	1.64%	1.30%	1.10%	0.80%
	Low-carbon scenario	−3.00%	−3.20%	−3.40%	−3.60%
	Ultra-low-carbon scenario	−3.30%	−3.50%	−3.70%	−3.90%
Natural gas	Baseline scenario	1.64%	1.30%	1.10%	0.80%
	Low-carbon scenario	1.86%	−0.05%	−0.10%	−0.20%
	Ultra-low-carbon scenario	1.84%	−0.07%	−0.12%	−0.22%
Electricity	Baseline scenario	1.64%	1.30%	1.10%	0.80%
	Low-carbon scenario	10.00%	12.00%	15.00%	19.00%
	Ultra-low-carbon scenario	9.90%	11.90%	14.90%	18.90%
Heat	Baseline scenario	1.64%	1.30%	1.10%	0.80%
	Low-carbon scenario	3.04%	3.24%	3.30%	3.32%
	Ultra-low-carbon scenario	2.94%	3.14%	3.20%	3.22%

and

Table 3. Various non-energy parameter scenario settings.

Parameter Category	Scenes	2021–2025	2026–2030	2031–2035	2036–2040
Food	Baseline scenario	−2.44%	−1.94%	−1.44%	−0.94%
	Low-carbon scenario	−2.54%	−2.04%	−1.54%	−1.04%
	Ultra-low-carbon scenario	−2.64%	−2.14%	−1.64%	−1.14%
Clothing	Baseline scenario	−8.50%	−6.50%	−4.50%	−2.50%
	Low-carbon scenario	−8.55%	−6.55%	−4.55%	−2.55%
	Ultra-low-carbon scenario	−8.60%	−6.60%	−4.60%	−2.60%
Household Equipment Supplies	Baseline scenario	−2.62%	−2.12%	−1.62%	−1.12%
	Low-carbon scenario	−2.72%	−2.22%	−1.72%	−1.22%
	Ultra-low-carbon scenario	−2.82%	−2.32%	−1.82%	−1.32%
Cultural, Educational, and Entertainment	Baseline scenario	2.31%	1.81%	1.31%	0.81%
	Low-carbon scenario	2.11%	1.61%	1.11%	0.61%
	Ultra-low-carbon scenario	1.96%	1.46%	0.96%	0.46%
Health care	Baseline scenario	−1.04%	−0.84%	−0.64%	−0.44%
	Low-carbon scenario	−1.07%	−0.87%	−0.67%	−0.47%
	Ultra-low-carbon scenario	−1.10%	−0.90%	−0.70%	−0.50%
Living	Baseline scenario	1.54%	−0.11%	−0.41%	−0.71%
	Low-carbon scenario	1.34%	−0.31%	−0.61%	−0.91%
	Ultra-low-carbon scenario	1.29%	−0.36%	−0.66%	−0.96%
Traffic communication	Baseline scenario	2.15%	0.45%	−0.55%	−0.85%
	Low-carbon scenario	2.00%	0.30%	−0.70%	−1.00%
	Ultra-low-carbon scenario	1.90%	0.20%	−0.80%	−1.10%
Other miscellaneous items	Baseline scenario	10.61%	6.31%	2.65%	0.16%
	Low-carbon scenario	8.61%	5.31%	1.85%	0.15%
	Ultra-low-carbon scenario	7.61%	4.81%	1.65%	0.14%

.

3. Results and Analysis

3.1. Analysis of Changes in Carbon Emissions

Figure 1 illustrates the contribution ratio of different energy sources to the carbon emissions of Hebei Province residents between 2005 and 2020. It shows a significant decrease in the carbon emissions from raw coal, while natural gas, electricity, and heat all have gradually rising carbon emissions. Other coal washing and kerosene have not been used since 2012 and 2014, respectively. This demonstrates that the residents of Hebei Province are gradually altering the composition of their direct energy use, and it also demonstrates the astounding impact of coal on gas and coal electricity. However, the total carbon emissions of raw coal and coal products have not been reduced, indicating that the use of coal with high carbon emissions by residents cannot be completely replaced, and the transformation of the energy use structure requires a gradual process. Although the energy consumption in Hebei Province is gradually becoming clean, the transformation of the energy structure needs time. The energy structure of Hebei Province is still dominated by traditional high-carbon energy, and the carbon dioxide emissions generated by combustion are high, so the carbon emissions of residents’ lives are still increasing. Furthermore, as a result of people’s unwavering desire for a better existence, efforts to improve quality of life have gradually increased, and people no longer use energy sparingly. Numerous fossil fuel-powered cars, excessive packing material usage, frequent long-distance travel, and other habits that prioritize personal comfort significantly increase the rate at which CO₂ emissions from residents’ lives are released into the atmosphere.

Figure 2 shows the proportion of carbon emissions generated by residents living in eight categories of expenditure. From the figure, it can be seen that the structure of carbon emissions generated by various types of expenditure of Hebei residents living in 2005–2020 is constantly changing. “Housing”, “transportation and communication”, “food”, “culture, education and entertainment”, and “health care” accounted for a relatively large proportion. Among them, the proportion of “living” changed the most obviously and the proportion increased from 23.66% in 2005 to 42.12% in 2020. The proportion of “traffic communication” category is relatively stable, accounting for around 18% from 2005 to 2020; the proportion of “food” category decreased year by year from 2005 to 2017, and the proportion tended to be stable from 2018 to 2020, accounting for around 18%; the proportion of “culture, education and entertainment” category decreased most significantly, from 19% in 2005 to 7.93% in 2020; the proportion of “health care” was stable, and the proportion decreased by 2.48% from 2005 to 2020. The proportion of “clothing”, “household equipment supplies”, and “other miscellaneous items” was relatively small, and the total proportion of the three categories decreased from 7.02% to 6.25% in 2005–2020.

Alterations in the lifestyles and consumption patterns of the residents have an impact on the proportion of different kinds of carbon emissions. The “living” category has a significant influence on the indirect carbon emissions of residents’ lives since it has a larger share than the other eight categories of expenditures. The shifting proportion of consumption carbon emissions across the eight industry groups reflects the ongoing shift in residents’ lives, and its effect on carbon emissions varies, indicating that lifestyle variations also have varying effects on carbon emissions. For residents to cut carbon emissions, they should consume less meat, save water, and adopt a more ecologically friendly form of transportation. The low-carbon lifestyle and consumption patterns of the populace are encouraged by social media promotion, education, and polic"y recommendations. By enhancing building energy conservation requirements, utilizing efficient building materials, and supporting smart home technologies, government agencies, and the construction sector can lower carbon emissions from residents’ daily lives. The government, society, and people working together can encourage citizens to live less carbon-exhausted lives.

Figure 3 illustrates the carbon emissions associated with the daily lives of the residents of Hebei Province from 2005 to 2020. The total carbon emissions of these lives continue to rise, with a large increase in direct carbon emissions and a small growth in indirect carbon emissions; the indirect carbon emissions exceed the direct carbon emissions from 2005 to 2007, the direct carbon emissions exceed the indirect carbon emissions from 2008 to 2020, and the difference between the two values is increases. In Hebei Province, the overall amount of direct carbon emissions from people’s daily activities is rising. It increased 2.81 times, from 37,303,900 tons in 2005 to 104,907,300 tons in 2020.

Compared with direct and indirect carbon emissions, the emission reduction tasks in the field of residents’ lives in Hebei Province should focus on the use of direct energy. Government departments should increase investment in research and development of clean energy. Residents should gradually shift their energy use structure to clean energy based on reducing energy use, such as the use of solar power and hydropower. For the use of indirect energy, government departments should adopt guidance to change residents’ demand for life services and guide them to purchase more energy-efficient products.

3.2. LMDI Analysis of Decomposition Results

From the additive decomposition results of the LMDI model, it can be seen that $∆E_L$ represents the contribution of energy structure factors, that is, carbon emission factors, to the change in direct carbon emissions from residents’ lives. Because the carbon emission factor is a constant, $∆E_L$ is always 0 in the decomposition process, so it is not discussed.

Figure 4 shows that between 2005 and 2020, the direct carbon emissions of residents in Hebei Province were influenced by a combination of energy consumption intensity, income level, and population size. This resulted in the generation of 194.53 million tons of carbon emissions, which significantly aided in the rise in carbon emissions. The aspect of income level has the most visible effect. The total amount of carbon emissions rose by 125.06 million tons over the course of the study. This demonstrates that Hebei Province’s economic development is doing very well and that the province’s citizens are becoming increasingly well off. The propensity toward consumption inhibits the rise in direct carbon emissions in the lives of the residents, resulting in an overall 8.765 million ton reduction in carbon dioxide emissions. Over the course of the study, the percentages of residents’ cumulative direct carbon emissions that were attributable to their income level, energy consumption intensity, population size, and consumption propensity were 67.32%, 33.83%, 3.57%, and 4.72%, respectively.

Figure 5 demonstrates how, between 2005 and 2020, the indirect carbon emissions of residents’ living consumption in Hebei Province accumulated to a total of 63.0601 million tons under the combined action of consumption structure factors, economic level factors, and population size factors. This increase in carbon emissions has been facilitated. Among them, the most obvious promotion effect is from the economic level. During the study period, the factor values were all positive, and the cumulative promotion of carbon emissions was the largest, which was 56.09 million tons. With the rapid development of Hebei’s economy, the disposable expenditure of residents has increased, which has a significant effect on indirect carbon emissions. The total amount of carbon dioxide emissions that were reduced between 2005 and 2020 as a result of the combined effects of energy structure and energy consumption intensity was 54.76 million tons. The cumulative indirect carbon emissions of inhabitants in Hebei Province throughout the study period were determined by the following factors: economic level, consumption structure, population size, energy structure, and energy consumption intensity. These contributions were 675.55%, 43.10%, 40.73%, −38.02%, and −621.35%, respectively. There were significant fluctuations in the rate at which economic level elements contributed to energy structure factors, energy consumption intensity factors, and consumption structure factors. During the corresponding study period, the population size demonstrated a lower contribution value and contribution rate. After 2010, the contribution value tended to remain stable year over year, suggesting that the concept of energy conservation and emission reduction is permeating residents’ daily lives.

According to the results of the LMDI analysis, the population size factor has an increasing effect on the direct carbon emissions and indirect carbon emissions of residents in Hebei Province. However, the population growth rate in Hebei Province has decreased, and even negative growth may occur, which is bound to lead to an aging population. Previous research has demonstrated an inverse U-shaped link between population structure and carbon emissions from residents [43]; that is, changes in the influence of population size may result from a greater aging of the population [44].

3.3. Carbon Emission Trend Analysis

According to the latest data available, the total carbon emissions of residents in Hebei Province in 2021 was 160.95 million tons, which is fairly close to the estimated amount and consistent with the trend when considering the scenario setting (Figure 6).

Under the baseline scenario, it is set according to the historical development trend. The carbon emissions of residents in Hebei Province are increasing year by year, and the growth trend has not slowed down, and it has not reached its peak between 2021 and 2040. This result indicates that the carbon emissions from the lifestyle choices and past economic development trends of the residents living in Hebei Province will only go up. Thus, building on current policies, the government must continue to enact ever-stronger energy conservation and emission reduction measures. The local population has to take the lead in preserving the environment, cutting carbon emissions, and altering their own rough long-term energy consumption.

Under the low-carbon scenario, the peak time of residents’ carbon emissions in Hebei Province is 2029, with a peak of 174.69 million tons, achieving the goal of peaking carbon emissions by 2030. This demonstrates the proactive steps the government has taken to reduce emissions and save energy to consistently encourage citizens to adopt more eco-friendly and low-carbon lifestyles. Carbon inclusion has been effectively promoted, and the cleanliness of residents’ living energy has been improved.

Under the ultra-low-carbon scenario, the peak time of residents’ living carbon emissions is 2028, with a peak of 173.27 million tons, one year earlier than the low-carbon scenario, and the evolution trend of carbon emissions from rising to falling is fast. It can be seen that residents are highly involved in the carbon GSP, saving energy, reducing emissions from daily drips, and forcing low-carbon technologies at the production end to constantly bring forth the new through the old. The province has well optimized the energy structure and high carbon emissions of fossil energy consumption. The degree of substitution is high, and the degree of power cleanliness is continuously improved, prompting the carbon emissions of residents in Hebei Province to peak earlier than 2030.

3.4. Uncertainty Analysis

There exist numerous explanations for the uncertainty that results in the discrepancy between the estimated outcomes and the actual values. The error transfer formula introduced in the “Provincial Greenhouse Gas Inventories Compilation Guide” is used to combine the uncertainty, including addition, subtraction, multiplication, and division error transfer formulas.

When an estimate is the sum or difference of n estimates, the uncertainty of the estimate is calculated as follows:

$$U_c={\displaystyle \frac{\sqrt{{(U_{s1}·{\mu }_{s1})}^2+{(U_{s2}·{\mu }_{s2})}^2+\cdots +{(U_{sn}·{\mu }_{sn})}^2}}{|{\mu }_{s1}+{\mu }_{s2}+\cdots +{\mu }_{sn}|}}={\displaystyle \frac{\sqrt{\sum _{n=1}^N{(U_{sn}·{\mu }_{sn})}^2}}{\left|\sum _{n=1}^N{\mu }_{sn}\right|}}$$

(19)

where $U_c$ is uncertainty in the sum or difference of n estimates (%), ${U_{s1}\dots U}_{sn}$ is the uncertainty of n summed and subtracted estimates (%), and ${\mu }_{s1}\dots {\mu }_{sn}$ is the estimates of n sums and subtractions.

When an estimate is the product of n estimates, the uncertainty of the estimate is calculated as follows:

$$U_c=\sqrt{U_{s1}^2+U_{s2}^2+\cdots +U_{sn}^2}=\sqrt{{\sum }_{n=1}^N{U}_{sn}^2}$$

(20)

where $U_c$ is uncertainty in the product of n estimates (%, ${U_{s1}\dots U}_{sn}$ is uncertainty of n multiplied estimates (%) and ${\mu }_{s1}\dots {\mu }_{sn}$ is the estimates of n sums and subtractions.

Due to the complexity of the kinds of energy consumption that inhabitants encounter daily, a large amount of data is available, and the precise data needed for policy planning is unclear. Considering the uncertainty of statistical data, the uncertainty of emission factors, and the uncertainty of parameter settings, there are also some uncertainties in the accounting and forecasting process of residents’ carbon emissions, which leads to a certain deviation between the estimated results and the actual situation. As a result, to enhance the research techniques and increase their precision, the uncertainty elements are examined. The following points comprise the majority of this study’s uncertainty:

(1)

The uncertainty of statistical data

Since the data selected in this study are obtained from the “Hebei Statistical Yearbook” and the statistical bulletin of national economic and social development in Hebei Province, the uncertainty of the statistical data of energy activity level is selected as 3% because of its well-developed statistical system (

Table 4. Uncertainty in energy activity data.

Departments	Compile Good Statistical Systems		Compile Poor Statistical Systems
	Investigation	Extrapolation	Investigation	Extrapolation
Main activities’ electricity and heat production	Below 1%	3–5%	1–2%	5–10%
Business, institutions, residents’ burning	3–5%	5–10%	10–15%	15–25%
Industrial combustion (energy-intensive industry)	2–3%	3–5%	2–3%	5–10%
Industrial combustion (other)	3–5%	5–10%	10–15%	15–20%
Biomass in small sources	10–30%	20–40%	30–60%	60–100%

).

(2)

Uncertainty of carbon emission factors

Because the calculation of the carbon emission factor is based on the parameters of low calorific value, carbon content per unit calorific value, and the carbon oxidation rate of various energy sources, the values of these parameters published in different statistical data are uncertain, and the parameters of different regions and different times are also uncertain, which will cause the calculation results of carbon emission factor to be uncertain, resulting in differences between the estimated results and the real data. According to the principle of uncertainty, it is quantified by sector, industry, and variety. The principle of quantification is shown in

Table 5. Uncertainties in low-level heat production, carbon content per unit calorific value, and carbon oxidation rates for individual energy sources.

Energy Type	The Uncertainty of Average Low Heat	The Uncertainty of Unit Carbon Content Carbon	The Uncertainty of Oxidation Rate	The Uncertainty of the Carbon Emission Coefficient
Raw coal	1%	3.1%	2%	3.82%
Other coal washing	5%	7.3%	6%	10.69%
Briquette coal	5%	5.0%	6%	9.27%
Coke oven gas	1%	4.6%	1%	4.81%
Blast furnace gas	4%	1.5%	1%	4.39%
Other gas	1%	1.5%	1%	2.06%
Gasoline	1%	1.5%	2%	2.69%
Kerosene	1%	2.0%	2%	3.00%
Diesel fuel	1%	1.5%	2%	2.69%
Liquefied petroleum gas	3%	1%	2%	3.38%
Natural gas	1%	3.6%	1%	3.87%

. Sources: “Provincial Greenhouse Gas Inventories Guidelines” and “2006 IPCC National Greenhouse Gas Inventories Guidelines”.

Due to the large amount of data obtained in this study, the data from 2020 are selected for the calculation of uncertainty. In 2020, there were 11,298,500 tons of raw coal, 7,735,300 tons of briquette, 13,200 tons of other gas, 9,120,000 tons of gasoline, 1,351,600 tons of liquefied petroleum gas, and 1,617,400 tons of natural gas. According to the Formula (5), the comprehensive uncertainty is 2.8%. The combined uncertainties in other years such as 2005, 2010, and 2015 were 3.4%, 3.2%, and 2.7%, respectively.

(3)

Uncertainty of parameter setting

In the process of the trend analysis of residents’ living carbon emissions by the LEAP model, it is necessary to set the parameters of relevant economic development degree, per capita consumption, energy consumption structure, and population in each scenario year. However, due to the time-varying and complexity of these factors, although a large number of national and Hebei’s economic, population, and other related policies, “13th Five-Year”- and “14th Five-Year”-related plans, and other scholars’ related research results are referred to in the process of parameter setting, due to the lack of definite emission reduction data of various factors in various departments, they need to be set according to policy planning. There are some uncertainties in this process and the possibility of prediction errors because there are not concrete data on emission reduction for every factor in every sector. Nevertheless, when the results are compared to the measured values from the base year and to relevant research findings from other academics [45], it becomes clear that the values are within a reasonable range, so the uncertainty errors can be accepted.

3.5. Limitation

In this study, only five factors were selected in the study of the influencing factors of residents’ living carbon emissions. Other factors related to the reduction in residents’ living carbon emissions, such as education, policy incentives, and consumer behavior changes, were not analyzed. Therefore, in future work, the factors will be more refined and further discussed.

4. Conclusions and Suggestion

This study takes the carbon emissions of residents in Hebei Province as the research object. Firstly, it calculates the carbon emissions of residents’ lives, then constructs an LMDI model for factor analysis, and finally constructs a LEAP model and scenario analysis method to predict the trend analysis of residents’ carbon emissions in Hebei Province.

The findings indicate that between 2005 and 2020, the carbon emissions of Hebei Province residents increased. When looking at trends in change, direct carbon emissions increased the most, whereas indirect carbon emissions have been steadily declining since 2012. When it comes to contribution, direct carbon emissions make up a larger portion than indirect carbon emissions. Thus, the use of direct energy by inhabitants and expanding the use of clean energy should be the main priorities of Hebei Province’s residential sector’s emission reduction assignment.

The results show that in the analysis of the influencing factors of residents’ carbon emissions, energy consumption intensity, income level, and population size are the factors that promote the increase in direct carbon emissions, and consumption tendency is the factor that inhibits direct carbon emissions. Consumption structure, economic level, and population size are the factors that promote the increase in indirect carbon emissions, while energy structure and energy consumption intensity are the factors that inhibit indirect carbon emissions. Therefore, the carbon emission reduction of residents in Hebei Province should focus on energy consumption intensity and consumption structure. According to a report [46], shared electric bicycles cut 54.50 gCO₂ per kilometer and shared bicycles reduce 48.70 gCO₂ per kilometer after weighted calculations. This makes clear how residents may reduce carbon emissions in their daily lives by using electric and shared bicycles, and it offers a useful model for the advancement of environmentally friendly transportation. Residents ought to modify their consumption patterns and endeavor to embrace a low-emission way of living.

The scenario prediction results of the residents’ living carbon emissions in Hebei Province show that the residents’ living carbon emissions in Hebei Province are on the rise under the baseline scenario, and there is no peak; under the low-carbon scenario, they will peak in 2029, with a peak of 174.69 million tons; in the carbon-inclusive scenario, they will peak in 2028, with a peak of 173.27 million tons.

Based on the previous research on the carbon emissions of residents’ lives in Hebei Province, combined with the “14th Five-Year Plan” and green development documents of Hebei Province, the following emission reduction strategies are proposed for the development of residents’ lives in Hebei Province. Carbon emission standards should be improved and policy support strengthened. Government departments should lead the healthy development of residents’ life emission reduction and improve residents’ autonomy in emission reduction. A low-carbon life concept should be established, a low-carbon atmosphere should be created, and residents’ lives should be driven to reduce emissions. An evaluation system should be constructed that can be more participated in by individual residents and improve residents’ sense of responsibility and honor for emission reduction. The implementation of clean energy substitution should be increased, the energy consumption structure should be improved, and residents should be encouraged to develop the habit of using clean energy. More low-carbon technology services and product equipment from the production side should be promoted, and indirect carbon emissions from residents should be reduced at the source.

In addition, the object of this study is not to distinguish urban and rural residents in Hebei Province as a whole. In future research, it is necessary to be more refined to distinguish urban and rural areas, which can better reveal the differences in carbon emissions caused by individual differences between urban and rural residents, low-carbon technology differences, etc., to more effectively achieve the carbon emission reduction targets of Hebei Province.