The Influencing Factors and Future Development of Energy Consumption and Carbon Emissions in Urban Households: A Review of China’s Experience

Abstract

Household energy consumption is one of the major drivers of carbon emissions, and an in-depth analysis of its influencing factors, along with forecasting carbon emission trajectories, is crucial for achieving China’s carbon emission targets. This study reviews the research progress on urban household energy-related carbon emissions (HErC) in China since 2000, with a focus on the latest developments in influencing factors. The study categorizes these factors into five major groups: household characteristics, economic attributes, energy consumption features, awareness and norms, and policies and interventions. The findings indicate that income levels, energy efficiency, and household size are the key determinants of urban HErC of China and are commonly used as core assumptions in scenario-based forecasts of emission trends. In addition, although environmental awareness and government services have increasingly garnered attention, their specific effects require further investigation due to the challenges in quantification. A synthesis of existing forecasting studies suggests that, without the implementation of effective measures, HErC will continue to rise, and the peak emission period will be delayed. Enhancing building and energy efficiency, promoting low-carbon consumption and clean energy applications, and implementing multidimensional coordinated policies are considered the most effective pathways for emission reduction.

1. Introduction

With the intensification of global climate change, reducing energy consumption and carbon emissions has become one of the central concerns of global society. Many countries have set stringent emission reduction targets in response to climate change. Japan aims to achieve carbon neutrality by 2050 [1] and has committed to reducing greenhouse gas emissions by 46% by 2021 [2]. The United States has set a target to reduce carbon emissions by 26–28% from 2005 levels by 2025, with a goal of carbon neutrality by 2050. The European Union has set a target to reduce greenhouse gas emissions by at least 55% from 1990 levels by 2030 [3]. In this background, China has also outlined an ambitious goal of reaching carbon emission peak by 2030 and achieving carbon neutrality by 2060 [4].

In global emission reduction efforts, household energy consumption, as a major source of carbon emissions, has garnered increasing attention, particularly in urban areas. In 2021, the global urban population accounted for 56% of the total population, and it is projected to rise to 68% by 2050 [5]. The rapid process of urbanization has driven economic growth while also intensifying energy consumption and carbon emissions, especially in China. China’s urbanization rate is expected to surpass 65% in 2022, and with economic development and rising income levels, urban household energy consumption has been rapidly increasing [6]. Between 2010 and 2020, China’s urban household energy consumption grew at an average annual rate of about 6% [7]. This shift has not only increased energy demand but also led to a significant rise in carbon emissions.

As the world’s largest energy consumer and carbon emitter, China’s urban household carbon emissions play a significant role in the nation’s total emissions. In recent years, despite progress in energy structure adjustments and technological innovation [8], carbon emissions at the household level still face numerous challenges. First, changes in energy consumption patterns due to smaller household sizes and an aging population have the potential to increase per capita carbon emissions [9]. Second, regional development imbalances result in significant variations in energy demand and carbon emission characteristics across different areas [10]. Moreover, environmental awareness and low-carbon behaviors have yet to be fully widespread, and the effectiveness of policy measures is constrained by various factors [11]. Systematically reviewing and analyzing existing research to clarify the multidimensional factors influencing household energy-related carbon emissions (HErC) in China is an urgent issue.

Although existing studies have explored HErC, systematic analysis of urban HErC in China remains insufficient. This study aims to systematically assess the research status and key influencing factors of urban HErC in China through a review of 96 relevant research papers, providing comprehensive references for policymakers, promoting the adoption of low-carbon lifestyles, and ensuring continued carbon emission reductions. Specifically, Section 2 introduces the sources of the literature and the methods of bibliometric analysis and manual review; Section 3 presents the results of the bibliometric analysis; Section 4 and Section 5 discuss the results of the manual review, examining how five major factors affect HErC and how research uses these factors to predict the development of household carbon emissions; finally, Section 6 presents future research directions and conclusions.

The main contributions of this study are as follows: (1) focusing on factors such as household characteristics, economic attributes, energy consumption features, awareness and norms, and policies and interventions, and examining their impact on urban HErC in China; (2) summarizing common models and methods used for household carbon emission forecasting, discussing the strengths and weaknesses of these methods in terms of prediction accuracy and applicability; (3) providing a summary and outlook on the future development and emission reduction pathways for household carbon emissions in China.

2. Materials and Methods

2.1. Bibliometric Analysis and Manual Review

This study conducts a comprehensive analysis of the literature data through two dimensions: bibliometric analysis and manual review. Bibliometric analysis involves the use of statistical and mathematical methods to analyze and evaluate scientific and academic publications and has been widely applied to measure scientific progress in key areas. Through quantitative analysis, it evaluates research output, identifies research hotspots, analyzes collaboration networks, and assesses academic journals. VOSviewer is a widely used bibliometric software known for its ability to identify research hotspots and frontiers and for its excellent performance in constructing and visualizing literature networks, with relatively simple operation [12]. This study uses VOSviewer to systematically review the development of HErC research, identify current hotspots, and generate citation networks and keyword co-occurrence networks, providing a detailed and in-depth view of the structure and dynamic changes in the field.

Manual review is better suited for an in-depth analysis of the literature content. Unlike bibliometric analysis, which focuses on external features, manual review enables a deeper understanding of the relationships, contradictions, gaps, and inconsistencies among various studies. Building upon the foundation provided by bibliometric analysis, a subsequent manual review is conducted to focus on key aspects such as the influencing factors and scenario design for household carbon emissions. This integrated approach leverages the strengths of both methods to deliver a more comprehensive review.

2.2. Literature Sources

The literature sources and search strategy for this study are based on the Science Citation Index Expanded (SCI-E) and the Social Sciences Citation Index (SSCI) in the core collection of the Web of Science (WOS). These two indexes are important and authoritative data sources for bibliometric research.

This study focuses primarily on emissions related to household energy consumption. Considering the urban–rural gap in China, the research is limited to urban households. Common terms used to describe household carbon emissions include “household carbon emissions” [13,14], “household CO₂ emissions” [15], “household carbon footprint” [16], and “household greenhouse gas emissions” [17]. In addition, the terms “indirect”, “embedded”, and “input *”—which represent indirect carbon emissions or common indirect carbon accounting methods—were used to avoid interference from non-energy-related household carbon emissions. A search query was then developed to encompass these terms. In WOS searches, TS stands for the “Topic” field, which refers to searching within the article’s title, abstract, and keywords. The specific search criteria as follows:

TS = (China OR Chinese) AND TS = (urban household) AND TS = (energy consumption OR energy use) AND TS = (carbon OR CO2 OR carbon dioxide OR carbon footprint OR greenhouse gas) NOT TS = (indirect OR embedded OR input *).

Figure 1 illustrates the process of generating the literature dataset. After determining the search query, the document timespan was set from 1 January 2000 to 30 June 2024. Once the initial search was completed, the document set was filtered by criteria such as document type and language. Specifically, the language was set to English, and only research articles were included, excluding review papers and other publication types.

Subsequently, the titles and abstracts of each article were manually screened to exclude those irrelevant to the review topic or whose research objects did not align with the study focus, resulting in a final set of 96 target articles. The authors, affiliations, countries/regions, publication dates, source journals, titles, abstracts, and keywords of these articles were then exported from the database. These documents were imported into the bibliometric analysis tool for further study.

3. Bibliometric Analysis

3.1. Research Performance

As shown in Figure 2, research on urban HErC in China began to gradually increase from 2009, reaching 18 publications in 2022. Around 2015, a key milestone in this field occurred with the Paris Climate Conference, where the Paris Agreement significantly influenced national energy policies, leading to a rapid rise in related publications. Papers published in 2024 are relatively recent, with the search cutoff in June 2024, showing some delay. Given that the included literature underwent strict manual screening, some articles with similar topics but not meeting the research object were excluded, which explains the absence of a consistent upward trend. However, this does not affect the overall analysis, as the number of articles on this topic from 2000 to 2024 shows a linear growth trend (R² = 0.780). This also indicates that the household sector, as a significant source of carbon emissions, is receiving increasing attention regarding its emission reduction potential and measures.

In terms of disciplinary focus, the research spans 24 distinct fields, as identified by the WOS platform. Studies on HErC are inherently interdisciplinary, reflecting the complexity and multifaceted nature of the topic. As shown in Figure 3, the 98 target articles are associated with 207 occurrences across disciplines, indicating that each study involves an average of 2.15 disciplines. “Environmental Sciences” ranks first, with over half of the articles linked to this field, highlighting significant attention to the environmental impacts of HErC. “Energy Fuels” and “Green Sustainable Science Technology” follow with 34 and 27 occurrences, respectively, emphasizing the importance of sustainable energy practices and green technology innovation in the household sector. Other notable fields include “Environmental Studies” (19), “Engineering Environmental” (16), “Economics” (11), and “Thermodynamics” (9). These findings underscore the value of research on HErC in advancing knowledge across environmental, economic, and sustainability domains.

Over the past two decades, research on urban HErC in China has been published in 38 different journals, as shown in

Table 1. The performance of the journals.

No.	Journal	TP	TC	AC	APY	IF
1	Journal of cleaner production	15	548	36.5	2019.9	9.8
2	Energy	9	225	25.0	2021.1	9.0
3	Sustainability	6	122	20.3	2018.3	3.3
4	Energy policy	5	355	71.0	2019.2	9.3
5	Energy and buildings	5	266	53.2	2014.4	6.6
6	Journal of environmental management	5	108	21.6	2022.6	8.0
7	Applied energy	4	366	91.5	2017.3	10.1
8	Environmental science and pollution research	3	39	13.0	2020.3	-
9	Energy economics	2	134	67.0	2022.5	13.6
10	Atmospheric pollution research	2	80	40.0	2017.5	3.9

Note: TP: Total publications, TC: Total citations, AC: Average citations, APY: Average publish year.

. Based on total publications, total citations, and average citations, the top ten contributing journals are identified, along with their impact factors. In terms of publication volume, “Journal of Cleaner Production” is the leading journal with 15 articles, followed by “Energy” with 9 articles. Both of these journals also rank highly in total citations, with 548 and 225 citations, respectively. Average citations serve as an important measure of academic impact, with “Applied Energy” leading at 91.5, followed by “Energy Policy” and “Energy Economics”. This indicates that papers published in these three journals are widely cited by researchers. Notably, these three journals also have the highest impact factors, reflecting their significant influence and contribution to the field.

3.2. Keyword Co-Occurrence

Recent research on urban HErC has shown multidimensional hotspots and trends. Through keyword contribution analysis in VOSviewer, a keyword co-occurrence network was generated, as shown in Figure 4. The research in this field primarily focuses on energy, socio-economic, and policy factors.

Energy factors are central to the research. High-frequency core keywords such as “carbon emission”, “energy consumption” and “energy efficiency” indicate the critical role of household energy use in carbon emissions. Studies not only address energy consumption but also focus on the potential for carbon emission reductions through improved energy efficiency and the application of renewable energy, highlighting the importance of transitioning to low-carbon energy systems. Socio-economic factors have gained increasing attention in relation to household energy consumption and carbon emissions. Keywords like “income”, “inequality” and “lifestyle” suggest that income levels, social inequality, and household characteristics are significant influencing variables.

Policies are essential external conditions driving household carbon reductions. Keywords such as “economic-growth”, “urbanization” and “climate change” reveal the significant role of climate action, carbon police, and sustainable development in regulating household energy use and carbon emissions. The interaction of these factors provides a multi-level analytical framework for understanding urban HErC.

Research methods have become a significant focus in the literature, highlighting the widely adopted techniques and analytical frameworks in the field. Keywords such as “empirical analysis”, “STIRPAT model”, and “decomposition analysis” frequently appear, indicating their central role in the research. Empirical analysis, as a foundational method, uses statistical models and data to validate the relationship between energy consumption behaviors and carbon emission patterns, providing solid support for theoretical and policy studies. The STIRPAT model, a core tool for examining the drivers of carbon emissions, is particularly effective in analyzing the combined effects of population, economic growth, and technological advancement on carbon emissions. Decomposition analysis is commonly used to break down carbon emissions into their various sources and drivers, offering a clear quantitative basis for evaluating policy impacts and technological progress. The frequent co-occurrence of these methodological keywords in the literature underscores their key role in advancing research in this field and provides important technical references for future studies.

The study also highlights several key trends and emerging directions. Keywords such as “electricity” and “inequality” have appeared more recently, with average publication years in 2021 and 2022, suggesting a shift in research focus from the traditional relationship between energy consumption and carbon emissions to more detailed investigations into household energy usage patterns, social equity, and the application of low-carbon technologies. Specifically, electricity consumption, as a major component of household energy, is increasingly recognized in the context of the low-carbon transition, with a focus on the promotion of smart electricity systems and the household adoption of renewable energy. The term “inequality” highlights the significant impact of socio-economic disparities on household energy use and carbon emissions, particularly in how different income groups allocate energy resources and adopt low-carbon technologies.

In addition, policy-driven research continues to play a central role, with keywords such as “policy” and “driving forces” showing considerable academic influence, with average citation counts of 24.1 and 49.8, respectively. Research in this area not only covers traditional measures such as energy pricing regulation and carbon taxation but also explores household energy optimization strategies within the context of urbanization. As policy goals increasingly emphasize multi-dimensional issues like carbon neutrality and sustainable development, the effectiveness of policy interventions at the household level has become a critical focus of study.

3.3. Co-Citation Analysis

Co-citation refers to the occurrence of two or more documents being cited together in the reference lists of subsequent papers. By analyzing co-citation relationships, it helps uncover knowledge structures, research trends, and academic influence. In this study, co-citation analysis is applied to the 96 articles retrieved, aiming to identify the key literature that has significantly influenced the research on HErC. Based on a threshold of 10 co-citations, 17 documents have had a notable impact on the field, providing essential support for research on HErC.

Table 2. The top 10 articles in co-citation.

No.	Cited References	Co-Citation Times	Citation Times	Mark
	Authors, Year, Source
1	Liu et al., 2011, Journal of cleaner production	19	261	[18]
2	Wei et al., 2007, Energy policy	17	293	[19]
3	Feng et al., 2011, Energy	16	238	[20]
4	Donglan et al., 2010, Energy policy	14	223	[21]
5	Zhao et al., 2012, Energy policy	14	182	[22]
6	J. Li et al., 2019, Ecological Economics	12	188	[14]
7	Y. Li et al., 2015, Journal of cleaner production	12	163	[15]
8	Bin and Dowlatabadi, 2005, Energy Policy	11	497	[23]
9	Fan et al., 2013, Applied energy	11	157	[24]
10	Miao, 2017, Ecological Indicators	11	147	[25]

presents the top 10 most frequently co-cited papers.

These high-frequency co-citation papers employ innovative research methods that have advanced the HErC field’s methodology. Wei et al. [19] and Feng et al. [20] used the Consumer Lifestyle Analysis (CLA) method to explore the relationship between household lifestyles and carbon emissions, pioneering a path for lifestyle-based carbon emission analysis. Liu et al. [18], Y. Li et al. [15], and Donglan et al. [21], among others, applied quantitative tools such as input–output methods and the Logarithmic Mean Divisia Index (LMDI) to examine the impact of household consumption on carbon emissions from various dimensions. These methodological innovations not only illuminate the complex relationship between household consumption and carbon emissions but also provide a reference framework that continues to be utilized by subsequent scholars [26].

These frequently co-cited papers also have strong policy relevance and social impact, offering critical insights for policy analysis. J. Li et al. explored the influence of social awareness and lifestyles on household carbon emissions, suggesting that governments should promote green lifestyles and incentive policies to reduce emissions [14]. Bin and Dowlatabadi studied U.S. energy use and carbon emissions, highlighting the dominant role of consumer demand in carbon emissions and advocating for more comprehensive and integrated carbon policies [23]. Fan et al. proposed that optimizing energy structures and guiding households toward energy-saving behaviors could effectively reduce carbon emission intensity [24]. Miao recommended reducing residential energy consumption and carbon emissions through urban design optimization and technological innovation [25]. Additionally, Donglan et al. suggested that policymakers should develop differentiated policies based on the distinct driving factors in urban and rural areas [21]. These policy-oriented findings and recommendations offer significant value for China’s energy policies, thus being widely cited in the field of HErC.

These papers also demonstrate forward-looking research perspectives and cross-disciplinary impact, anticipating new issues and challenges that may emerge in the future. Y. Li et al. proposed that household carbon emissions are likely to continue increasing as urbanization progresses, offering important research directions for subsequent scholars [15]. Wei et al. and J. Li et al. not only revealed the profound effects of lifestyles and social awareness on carbon emissions but also foresaw that social changes would lead to new patterns of carbon emissions [14,19]. These studies broaden the scope of traditional energy and environmental research by incorporating multiple disciplines, including economics, sociology, and behavioral science. They also indicate that technological advancements alone are insufficient to comprehensively address the challenges, underscoring the necessity for policy interventions targeting consumption behavior, social awareness, and lifestyle.

These highly cited papers not only provide systematic support for the theoretical framework, research methods, and core drivers of household carbon emissions but also contribute to a deeper understanding of the complex relationship between household energy consumption and carbon emissions, offering valuable theoretical insights and empirical analyses.

4. Factors Affecting HErC

As shown in

Table 3. Classification of influencing factors.

Main Category	Subcategory	Description
I. Household characteristics	a. Household structure	Family structure type, family life cycle, etc.
	b. Household size	The number of people in a family
	c. Education level	Education level of household members
II. Economic attributes	a. Income level	Per capita income, household income, etc.
	b. Household assets	Assets (car, house ownership), savings accumulation, etc.
	c. Household expenditures	Energy expenditures, expenditure patterns, etc.
III. Energy consumption features	a. Source and structure	Primary energy demand, fuel mix, energy structure, etc.
	b. Energy use behavior	Energy end-use, energy-saving behaviors etc.
	c. Intensity and efficiency	Energy intensity, efficiency, building energy intensity, etc.
IV. Awareness and norms	a. Environmental ideology	Lifestyle, environmental awareness, etc.
	b. Behavioral attitudes	Behavioral attitudes, subjective norms, etc.
	c. Social norms	Normative motivation and cultural attitudes, etc.
V. Policies and interventions	a. Energy policy	Carbon tax, green policies, fiscal expenditure policies, etc.
	b. Price elasticity	Low-carbon incentives, energy price and subsidies, etc.
	c. Government service	Housing policies, public service satisfaction, etc.

, this study categorizes the influencing factors addressed in 96 target articles into five main categories: household characteristics, economic attributes, energy consumption features, awareness and norms, and policies and interventions, further subdivided into 15 subcategories. Each subcategory reflects the literature’s focus on different dimensions of influencing factors.

The specific details of the factors addressed in each article are provided in Table S1. Based on this information, the study quantifies the frequency of each category and its subcategories within the literature, as illustrated in Figure 5, to assess the significance and attention given to these factors in the field. This quantitative analysis lays the foundation for an in-depth exploration of the driving mechanisms behind household energy behaviors and carbon emissions.

Among the 96 analyzed papers, economic attributes and energy consumption features are the most frequently mentioned categories, appearing 52 and 51 times, respectively. Within these, the subcategories of income level, which appeared 37 times, and energy intensity and efficiency, which appeared 30 times, are the most prevalent, underscoring the central role of economic conditions and energy efficiency in the research. The household characteristics category appears 35 times, with household size being the most prominent, appearing 21 times. The awareness and norms category appears 24 times, with environmental ideology as the primary focus, appearing 14 times. In the policies and interventions category, which appears 21 times, government services stand out with 10 occurrences, highlighting the significance of policy tools in guiding energy consumption and carbon reduction.

4.1. Household Characteristics

With the acceleration of urbanization in China, household sizes have gradually decreased, and the aging population has become increasingly prominent. The number of small and single-person households has been rising, while the educational levels of household members have also seen significant improvements. These changes collectively shape the new characteristics of urban households. Factors such as household size, structure, and education level, as key determinants of energy and resource use efficiency, jointly influence HErC.

Different household structures result in varying HErC [27]. Y. Wang et al. found that in urban areas, childless households and households consisting of spouses plus parents have the lowest and highest impacts on household energy consumption, respectively [28]. Some studies have further validated this by analyzing the age structure of household members. L. Jiang et al. demonstrated that each additional laborer in a household can reduce per capita energy consumption by 43.8 kg of standard coal per year [29]. Similarly, Zhu, P. and Lin showed that the retirement of household members increases household electricity consumption by 20.0–32.1% [30]. These studies indicate that the diversity of urban household structures leads to significant differences in energy consumption patterns and carbon emission levels. Consequently, policymakers need to consider this diversity when developing energy-saving and emission reduction policies.

The impact of household size on urban HErC cannot be overlooked. T. Wang et al. observed that as the number of household members decreases, primarily due to the prevalence of nuclear families, per capita energy consumption increases [6]. In another study, Y. Wang et al. focused solely on residential electricity use and concluded that an increasing proportion of single-person households tends to curb the growth of electricity consumption [31]. Lei et al. provided a more comprehensive perspective by classifying urban households based on size and income [32]. Their findings suggest that a shift toward smaller households may lead middle-income families to adopt carbon-intensive energy lifestyles, whereas high-income households are more likely to embrace energy-efficient and greener consumption patterns. These studies have established the significant impact of changes in household size on HErC. However, divergent views indicate that the underlying mechanisms require further investigation.

The influence of educational attainment on HErC is significant and multifaceted. On one hand, households with higher education levels typically exhibit stronger environmental awareness and are more likely to adopt energy-saving and emission-reduction measures [28]. They are also more receptive to energy transitions and the use of clean energy [33,34]. Q. Li et al. approached the issue from the perspective of carbon capability and found that residents with higher education perform better in terms of carbon knowledge, motivation, behavior, and management capacity [35]. On the other hand, higher education is often associated with higher household income, which may lead to more lenient energy consumption practices and consequently increased HErC. Zhu. P. and Lin confirmed this view in a study of elderly residents [30], and Lyu et al. found a significant positive correlation between educational attainment, household income, and commuting-related carbon emissions, noting that households with higher education levels tend to favor high-carbon private transportation modes [36]. The complex impact of educational attainment on HErC suggests that improving education and disseminating environmental knowledge are crucial for fostering lower-carbon lifestyles and preventing excessive energy use.

4.2. Economic Attributes

Household economic attributes play a crucial role in the HErC. In recent years, China has made significant progress in poverty alleviation, gradually narrowing income disparities, although wealth gaps and regional differences still require attention. Factors such as income level, household assets, and expenditure patterns collectively shape the complex pattern of HErC.

Income level plays a key role in urban HErC [37] Numerous studies have indicated that per capita energy consumption and carbon emissions in urban households tend to increase with rising income [38,39]. Income levels influence carbon emissions by altering household sensitivity to energy consumption. Low-income households are often more concerned with energy costs, while high-income households are more likely to prioritize quality of life and consumer experiences, favoring carbon-intensive products and services [40]. However, higher income levels can also drive households to shift from traditional to cleaner energy sources, which helps reduce carbon emissions [33]. Z. Li et al. found that wealthier households are more willing to increase their use of clean energy, while low-income households, due to habitual use and cost considerations, are less likely to completely abandon low-quality energy sources [41]. Yang, A. and Wang also confirmed this in their study on the energy transition in urban household cooking [42]. The influence of income level on urban HErC is complex and variable and may differ due to factors such as regional climate and urban development [43]. It is essential to consider multiple factors and develop tailored strategies to balance the pursuit of higher living standards with the increase in HErC that accompanies rising income.

Household assets, as an important indicator of economic status, also influence HErC. It is widely recognized that carbon emissions increase with the level of asset ownership [38]. Assets such as savings, real estate [44], automobiles [39], and home appliances [32] contribute to this effect. In particular, household savings play a critical role in mediating the impact of income on energy transition. Heterogeneous savings behaviors significantly affect this relationship, with medium- to high-savings households more inclined to adopt clean energy systems [45]. Previous studies have shown that approximately one-third of future electricity demand growth will arise from an increase in certain large appliances [46]. Therefore, optimizing household asset allocation, promoting energy-efficient appliances, and encouraging green transportation are important strategies for reducing HErC.

Household expenditures are another key factor influencing urban HErC. Increased consumption expenditure is widely regarded as a significant driver of higher HErC [47], even playing a dominant role [48]. However, some studies have found that the impact of consumption expenditure on energy consumption and carbon emissions varies regionally [49], with a more persistent and pronounced effect in economically disadvantaged areas [50]. Zhao et al. attribute this variability to regional differences in reliance on fossil fuels within household expenditure patterns, with cleaner energy practices in some regions mitigating the emissions that would otherwise result from increased expenditure [51]. X. Shi et al. support this view, suggesting that shifts in household expenditure patterns can offset the increased energy demand associated with improved living standards [52]. This suggests that improving quality of life does not necessarily increase carbon emissions [53] and can be effectively controlled by optimizing consumption patterns [54].

4.3. Energy Consumption Features

China’s energy transition has played a significant role in carbon reduction. This transition involves not only the cleaning of energy sources but also the optimization of energy usage behaviors and the improvement of household energy intensity and efficiency. These three aspects, energy sources, energy end-use, and energy usage processes, collectively influence HErC [55].

Energy sources and structure are critical factors affecting urban HErC. Extensive household surveys indicate that, despite notable urban-rural and north-south disparities in China, the energy consumption structures for key activities such as cooking and space heating have been significantly optimized [27]. Particularly, the “coal-to-electricity” initiative has significantly reduced HErC by electrifying activities like transportation, heating, and cooking [56]. The shift from coal to natural gas in kitchens is also a major factor in carbon reduction [42]. However, studies also highlight regional inequities in the transformation of energy structures [57]. In some areas, changes in the primary energy structures for electricity and heating are minimal [48], resulting in less effective emission reductions following the energy transition [34]. This highlights that cleaning energy sources is key to reducing carbon emissions. To achieve greater reductions, both cleaner energy sources and energy structure transformation must progress together.

As a significant determinant of household energy end-use, energy use behavior is a major factor driving inequities in HErC. Research indicates that these patterns are closely related to household type [27], largely due to differences in traditional culture and climate adaptation among households [58]. In addition, household energy use behavior marked seasonal variations. T. Wang et al. segmented urban household energy consumption in Chinese provinces into five end uses and demonstrated through inter-provincial comparisons that climatic, economic, and cultural differences significantly influence household energy use behavior [6]. X. Wang et al. quantitatively demonstrated that behavioral adjustments can reduce household cooling electricity consumption by 35% while maintaining current service levels [59]. Mi et al. argued that actual demand and economic status drive differences in household energy consumption behaviors by influencing psychological motivations [60]. Correspondingly, enhancing residents’ awareness of climate change and energy conservation can prompt them to adopt voluntary energy-saving practices, thereby optimizing household energy use [24].

Enhancing household energy intensity and efficiency, meaning reducing carbon emissions per unit of energy consumed, is central to household carbon reduction strategies [47]. J. Jiang et al. found that reductions in energy intensity contributed to up to 89% of the decrease in urban residential carbon emission intensity [61]. Energy efficiency gains can be achieved through both technological innovations and behavioral changes [24,62,63]. However, the rebound effect may limit these emission reductions [64]. L. Wang et al. noted that the actual reduction achieved through enhanced household energy intensity and efficiency often falls short of expectations because cost savings are redirected toward activities that lead to additional energy consumption and emissions [65]. Moreover, the effectiveness of energy intensity reductions varies across regions and time. Reductions in energy intensity have been shown to mitigate residential carbon emissions in eastern regions [66], while in other areas, such reductions may even promote carbon emissions in some years [67]. Although enhancing household energy intensity and efficiency plays a key role in reducing carbon emissions, its full potential remains constrained by factors such as the rebound effect.

4.4. Awareness and Norms

Awareness and norms, as key factors influencing HErC, play a particularly crucial role. Environmental ideology, behavioral attitudes, and social norms are interconnected and interact, profoundly affecting household energy consumption and carbon emissions at various levels.

Environmental ideology is an intrinsic driver of HErC and plays a crucial role in shaping household lifestyles [68]. It directly impacts HErC by affecting households’ subjective intentions [69]. W. Sun et al. found that urban residents who identify as environmentalists consume fewer resources [70], suggesting that households with stronger environmental awareness are more likely to adopt low-carbon lifestyles, reducing unnecessary energy consumption and carbon footprint [71]. Environmental ideology also influences household consumption choices, indirectly affecting carbon emissions [72]. Households with strong environmental awareness prioritize green energy products and services, such as high-performance housing and energy-efficient appliances [62,73]. While the positive impact of environmental awareness on HErC is widely acknowledged, studies indicate that its effectiveness in promoting household energy conservation remains limited [74]. A quantitative model developed by Q. Li et al. [35] also shows significant differences in environmental awareness levels among urban residents, indicating that overall awareness still needs to be improved. Research suggests that behavioral interventions, such as social campaigns, personalized energy feedback, and default green options, can significantly enhance the effectiveness of environmental awareness in driving behavioral change [75,76].The households provided with real-time energy consumption feedback via smart meters are more likely to adjust their usage patterns to reduce waste [77]. Similarly, default enrollment in green energy programs has been shown to increase participation rates without requiring active decision-making by consumers [78]. These approaches help bridge the gap between environmental awareness and tangible energy-saving behaviors.

Positive behavioral attitudes can significantly drive the adoption of energy-saving and emission-reduction measures [38]. Establishing subjective norms to guide household intentions and behaviors, along with modulating perceived behavioral control [79], can effectively enhance emission reduction potential [59]. Moreover, behavioral attitudes are a core driver of policy effectiveness and play a crucial role in policy implementation [69,80]. It is important to note that these attitudes vary significantly among households with different economic statuses [38] and energy consumption levels [60].

To reinforce positive behavioral attitudes, education, and outreach, economic and policy incentives are essential. Financial incentives such as subsidies for energy-efficient appliances, tax deductions for renewable energy installations, and time-of-use electricity pricing have been proven effective in motivating households to adopt energy-efficient behaviors [81]. These mechanisms reduce financial barriers associated with clean energy adoption, making sustainable choices more economically viable for consumers. Additionally, reward-based programs that provide discounts or rebates for reduced energy consumption can further encourage households to shift toward more sustainable energy behaviors [82].

Social norms indirectly regulate and guide household energy consumption by shaping the social environment and promoting low-carbon values. As value-oriented factors, social norms influence residents’ beliefs and habits, thereby altering HErC [35]. In addition, as channels for information dissemination, social norms enhance awareness of high-efficiency appliances, encouraging their adoption and effectively reducing HErC [53]. Moreover, social norms exert a group effect, as individuals tend to conform to the behaviors of those around them, which further motivates energy-saving and emission-reduction actions [65,83]. Overall, awareness and norms significantly influence household energy consumption behavior. However, their effectiveness can be further enhanced through targeted behavioral interventions and incentive structures. By integrating economic incentives, real-time feedback mechanisms, and community-driven strategies, policymakers can strengthen the impact of awareness and norms, ultimately leading to more effective household carbon reduction strategies.

4.5. Policies and Interventions

China has implemented a range of energy policies and government interventions. These measures include macro-level policies such as China’s Intended Nationally Determined Contributions (INDC) and the appliance Energy Efficiency Index (EEI), as well as electricity pricing mechanisms such as time-of-use pricing and tiered pricing. Several studies have evaluated the effectiveness of these policies and interventions.

Energy policies play a key role in reducing HErC. The China INDC, an important new initiative, has attracted widespread attention [84]. As a central component of the INDC, the Carbon Inclusion Policy is considered effective in curbing HErC by enhancing urban innovation capacity [85] and boosting green consumption awareness and green supply capabilities [73]. R. Xing et al. found that the INDC also promotes reductions in HErC by encouraging improvements in building energy efficiency [86]. Similarly, appliance EEI [87] and Minimum Energy Performance Standards [88] have significantly improved appliance efficiency and reduced electricity consumption. Moreover, China’s electrification policies have driven an unprecedented transformation in household energy structures and represent a key measure for reducing carbon emissions [56]. However, despite the confirmed carbon reduction effects of these policies, their implementation faces notable challenges. The effectiveness of policy interventions is often constrained by regional disparities in economic development, technological readiness, and public acceptance [6]. For example, while carbon tax policies effectively reduce energy consumption by raising prices, they may disproportionately impact low-income households, leading to affordability concerns and potential social resistance. Additionally, the effectiveness of energy efficiency standards largely depends on the enforcement capacity of local governments, which varies significantly across regions. In underdeveloped areas, inadequate infrastructure and limited financial incentives may hinder compliance with stringent efficiency regulations [89].

The government commonly uses price mechanisms to adjust energy consumption loads. Tiered electricity pricing and time-of-use pricing policies, which implement prices based on consumption levels or time of use, have successfully incentivized residents to reduce electricity consumption [80,90]. Carbon tax policies, by raising energy prices, reduce energy consumption and thus lower HErC [91]. However, price mechanisms also raise concerns about fairness and equity, as low-income households often face higher economic pressure. J. Wang points out that more government intervention is needed to address the challenges low-savings households face in accessing clean energy [45]. In addition, increased government spending on environmental protection and energy infrastructure can contribute to household carbon reduction [92]. Liu, G. argues that by optimizing natural gas pricing and strengthening natural gas supply coordination, the government has facilitated the transformation of household energy structures [93].

Therefore, policy design and implementation should be tailored to local conditions. While urban households may benefit from cleaner energy sources, rural areas often rely more on traditional biomass and coal, making the transition to clean energy slower and more challenging. The transition to clean energy in rural areas is further hindered by lower consumer acceptance, limited financial incentives, and weaker infrastructure support [94]. Thus, it is necessary to develop targeted strategies, such as increased government subsidies for rural clean energy adoption and targeted infrastructure investments, to bridge this gap.

The effectiveness of regional energy policies also depends on local energy infrastructure, economic structure, and governmental capacity. Coastal regions, where renewable energy penetration is higher, are more likely to benefit from strict carbon reduction policies [95]. In contrast, coal-dependent provinces may face challenges in policy implementation due to limited alternative energy sources and economic reliance on carbon-intensive industries [96]. Strengthening localized policy frameworks and flexible regulatory mechanisms is essential to ensure equitable and effective carbon reduction measures. Policy interventions often face varying levels of public acceptance across different socioeconomic groups and regions. Rural residents are more dependent on traditional fuels due to income, cultural and psychological factors, and over-intervention may increase the financial burden and trigger resistance [97]. Therefore, the development of region-specific incentives and enhanced public communication are essential to increase policy compliance and reduce negative impacts. Governments should also provide more environmental education and publicity to raise public awareness of green consumption and the energy transition in order to promote a shift in sustainable consumption habits [98].

5. Prediction of Household Carbon Emission in China

With the gradual advancement of the “dual carbon” goals, the pathways to peak carbon emissions and achieve carbon neutrality have become key topics in both academia and practice. Against this backdrop, accurately assessing and predicting the dynamic changes in household carbon emissions using scientific methods and multidimensional data has become a key challenge. Pathway predictions typically explore multiple potential trends by constructing different development scenarios, providing a more comprehensive depiction of carbon emission trajectories. Using citation analysis, Table 4 summarizes a selection of articles with high citation counts on household carbon emission forecasting and scenario design in China over the past five years. The table lists the prediction models and study area.

In terms of study regions, eight articles examined the dynamics of household carbon emissions nationwide, with Yu et al. [99] and Huo et al. [100] focusing specifically on urban areas. An et al. [101] and Zhao et al. [102] explored provincial-level carbon emission disparities. Additionally, three articles focused on individual provinces or megacities, including Fujian Province [103], Wuhan City [104], and Beijing City [105]. Compared to other studies, Huo et al. and Su et al. further disaggregated household energy consumption into specific end uses and forecasted carbon emissions for each end use [100,106].

Table 4. Literature on household carbon emission prediction.

No.	Article	Study Area	Prediction Method	Mark
1	Lin and Li, 2024	Fujian province	Kaya-LMDI-SD-MC	[103]
2	Chen et al., 2024	Whole country	Kaya identity	[107]
3	An et al., 2024	Provinces	XGBoost-TPE	[101]
4	Cui and Pan, 2024	Beijing City	Ridge Regression	[105]
5	Bei et al., 2024	Wuhan City	LEAP model	[104]
6	Su et al., 2023	Whole country	Multiple machine learning	[106]
7	X. Zhang et al., 2023	Whole country	Linear extrapolation	[108]
8	Y. Zhang et al., 2023	Whole country	Multiple regression	[109]
9	Yu et al., 2023	Whole country	IPCC and citation data	[99]
10	Zhao et al., 2022	Provinces	STIRPAT model	[102]
11	Huo et al., 2021	Whole country	SD-LEAP	[100]
12	Liu et al., 2021	Whole country	IPCC and citation data	[110]
13	Xia et al., 2019	Whole country	Multiple regression	[111]

Table 4. Literature on household carbon emission prediction.

No.	Article	Study Area	Prediction Method	Mark
1	Lin and Li, 2024	Fujian province	Kaya-LMDI-SD-MC	[103]
2	Chen et al., 2024	Whole country	Kaya identity	[107]
3	An et al., 2024	Provinces	XGBoost-TPE	[101]
4	Cui and Pan, 2024	Beijing City	Ridge Regression	[105]
5	Bei et al., 2024	Wuhan City	LEAP model	[104]
6	Su et al., 2023	Whole country	Multiple machine learning	[106]
7	X. Zhang et al., 2023	Whole country	Linear extrapolation	[108]
8	Y. Zhang et al., 2023	Whole country	Multiple regression	[109]
9	Yu et al., 2023	Whole country	IPCC and citation data	[99]
10	Zhao et al., 2022	Provinces	STIRPAT model	[102]
11	Huo et al., 2021	Whole country	SD-LEAP	[100]
12	Liu et al., 2021	Whole country	IPCC and citation data	[110]
13	Xia et al., 2019	Whole country	Multiple regression	[111]

5.1. Prediction Model

In recent years, data-driven approaches have gained significant attention in household carbon emission forecasting, focusing on leveraging machine learning or statistical regression to uncover relationships between high-dimensional features and carbon emissions from complex, multi-source data [111]. Representative methods include algorithms such as XGBoost, random forests, and support vector machines [106], as well as traditional statistical models like multiple regression [109] and ridge regression [105]. These models often demonstrate high predictive accuracy and can provide interpretability through tools like SHAP values or regression coefficients [101]. However, the complexity of household carbon emissions, involving variables such as demographic structure, behavioral patterns, and policy interventions, presents challenges in model interpretability and adaptability to temporal variations, as shown in

Table 5. Comparison of prediction models.

Category	Advantages	Disadvantages	Predictive Accuracy
Machine Learning	Handles high-dimensional, non-linear data; fits complex relationships.	Requires large datasets; lacks interpretability; prone to overfitting.	High accuracy with sufficient data; good for medium/short-term predictions.
Traditional Statistical Models	Interpretable; robust with small datasets.	Difficulty with non-linear relationships; relies on assumptions.	Accurate for simple/linear relationships; limited in complex contexts.
Top-Down Approach	Captures macro-level drivers like population and economy.	Fails to capture household-level behaviors.	Good for trend estimation; weak for specific details.
Bottom-Up Approach	Provides detailed insights into household energy use.	Needs high-quality data; hard to generalize.	Accurate with sufficient data; limited scalability.
Coupled Models	Combines strengths of different models; addresses both macro and micro perspectives.	Complex structure; high costs; dependent on assumptions.	Improves accuracy by integrating models; sensitive to data quality.

. As a result, data-driven models require sufficiently large and high-quality training datasets, along with thorough hyperparameter tuning and feature selection, to accurately predict carbon emissions under high-uncertainty scenarios.

Compared to data-driven methods, “top-down” and “bottom-up” approaches focus on capturing the operational mechanisms of the household system or actual energy activities, respectively, from macro or micro perspectives. The former typically decomposes or regresses using macro indicators and overall economic structures, such as the Kaya identity [107] or the STIRPAT model [102]. These models are effective in quantifying the combined impacts of factors like population, wealth, and technology on carbon emissions and providing an overall assessment of carbon emission changes across regions or long time spans [99]. However, their limitation lies in the difficulty of capturing internal heterogeneity, particularly behavioral differences at the household level, which may introduce errors [110].

In contrast, the “bottom-up” approach emphasizes a detailed depiction of specific energy use activities and technological pathways. The LEAP model can disaggregate household energy consumption for heating, cooking, travel, and other activities [112] and integrates with system dynamics models to quantify dynamic feedback [100]. This approach is closer to actual energy use but requires high-quality bottom-level data and is sensitive to the quality of scenario assumptions in large-scale regional predictions. To address the limitations of individual methods, several studies have attempted multi-model coupling, such as the Kaya-LMDI-SD-MC integrated framework [103], which not only identifies key drivers through decomposition but also better handles complex dynamic evolution and uncertainty through system dynamics and the Monte Carlo method, integrating both macro and micro perspectives.

5.2. Scenario Design and Factor Selection

Scenario design typically starts with baseline development trends and integrates policy planning and findings from related studies to construct multiple variant scenarios with different assumptions. Collecting quantitative or qualitative data for key indicators is critical to ensure the scientific validity and reliability of the models.

The baseline scenario serves as the foundation for setting other scenarios. Bei et al. set a baseline and low-carbon scenario to reflect Wuhan’s efforts in accelerating energy conservation and carbon reduction [104]. Huo et al. constructed conventional, low-carbon, and high-carbon scenarios to simulate the impacts of different economic development paths and energy usage trends on carbon emissions [100]. Liu et al. defined four scenarios, building on the baseline scenario while considering changes in consumption patterns and different assumptions about energy intensity [110].

Population change is also a key focus in scenario design. Yu et al. set five scenarios to comprehensively analyze the complex impacts of changes in population and consumption structures on carbon emissions [99]. Xia et al. compared scenarios of constant fertility rates and the two-child policy to explore the potential impact of population changes on carbon emissions [111]. An et al. proposed as many as 27 scenarios, covering various factors such as different carbon peak times, technological advancement paths, and cumulative carbon budgets, offering a rich set of hypothetical pathways for carbon emission forecasting [101]. Overall, a well-designed scenario can skillfully integrate multiple dimensions such as technology, policy, population, and consumption behavior, reflecting various future possibilities and providing support for HErC forecasting.

Regardless of the model or scenario used, the selection of factors is the foundation of reliable predictions. Some factors are directly applied in carbon emission accounting. Household characteristics such as household size and labor status are commonly included to capture behavioral and demand diversity [107]. Economic attributes such as income levels and consumption expenditures show significant driving effects in most studies and are frequently incorporated into prediction models [108]. Energy consumption features, including energy structure, energy intensity, and specific household energy use behaviors, have the most direct impact on carbon emissions and are typically core variables in model calculations [104]. These core variables have received significant attention, with some studies refining them to an hourly scale for predicting dynamic carbon emission changes [113].

Although awareness and norms, as well as policies and interventions, have been considered in some studies [100], they are often overlooked or inadequately addressed in many models. This is primarily due to difficulties in data collection and the complexity of quantifying these factors, which often results in their simplification during model construction. Currently, these factors are generally not directly used in carbon emission calculations and are considered background factors. However, they still play an important role in scenario design. Carbon accounting factors and background factors are closely interconnected, forming a complex network that influences carbon emissions. Their interactions highlight the multiple uncertainties in household carbon emission forecasting. Future research should not only continue to address traditional factors such as energy consumption and population size but also focus on quantifying background factors.

5.3. Carbon Emission Development and Reduction Approaches

Extensive studies indicate that, in the absence of systematic and stringent interventions, household carbon emissions tend to continue increasing, with peak emission times delayed. Under a high-carbon scenario, An et al. project that per capita household carbon emissions in various provinces and cities will rise to 6.4 tons by 2060 [101], while Bei et al. predict that Wuhan’s emissions peak may be delayed until 2040 [104]. Similarly, Cui and Pan report that Beijing’s household carbon emissions in 2035 will be 25% higher than in 2020 [105]. Considering aging populations and different scenario assumptions, Chen et al. find that peak HErC in some regions may be further delayed [107]. Zhao et al. also observe continued emission growth in certain provinces even after the national peak [102].

Additionally, Lin and Li’s case study on Fujian Province [103] and Liu et al.’s exploration of four consumption patterns for 2025 [110] both suggest that even with some mitigation measures, household energy demand will remain difficult to curb in the short term. While Su et al. [106] and Xia et al. [111] observe significant potential for reducing household carbon emissions under low-carbon scenarios, the actual outcomes remain heavily dependent on policy and technological investments. Without stricter and more systematic mitigation efforts, the continued rise in household carbon emissions and delays in peak emissions will be difficult to reverse in the short term, both temporally and spatially.

To address this challenge, a multi-level and integrated emission reduction strategy is essential. This includes policy and technological interventions as well as optimizing household behavior and socioeconomic structures. Numerous studies suggest that improving building energy efficiency, such as through retrofitting, setting higher energy standards for new buildings, promoting distributed heating systems, and adopting energy-efficient appliances, can significantly reduce household carbon emissions [100,104]. However, the feasibility of such measures varies by region. For instance, while urban areas with well-developed infrastructure can rapidly implement energy efficiency retrofits, rural and lower-income regions may face financial and technical constraints, requiring additional government subsidies and technological assistance. Additionally, stringent energy efficiency standards for new buildings may be more effective in high-income regions, whereas in developing areas, incentives for incremental retrofitting and cost-sharing models could enhance policy adoption.

Optimizing the energy mix, reducing energy intensity, increasing the share of clean energy, and advancing electrification and renewable technologies also yield significant mitigation effects [103]. Nevertheless, the transition to clean energy is uneven across regions. Coastal and economically developed provinces have made substantial progress in renewable energy integration, whereas many inland regions still rely heavily on coal-based energy sources [114]. This underscores the need for region-specific energy transition pathways, balancing energy security, affordability, and decarbonization goals.

Encouraging low-carbon consumption and lifestyles, such as through enhanced environmental education, promoting low-carbon diets, and supporting green transportation, has been shown to effectively reduce household carbon footprints [106,110]. Furthermore, to address complex factors such as declining household sizes, population aging, and regional development disparities, existing research advocates for multidimensional policy coordination. This includes differentiated fertility and housing policies, promoting the sharing economy, and supporting multigenerational households [105,109]. However, regional economic structures and cultural preferences must be considered in implementation. For example, policies promoting multigenerational households may be more effective in regions where extended family living arrangements are culturally prevalent, while in metropolitan areas, co-housing or shared resource models may be more suitable [115].

In summary, while improving building and energy efficiency, promoting low-carbon consumption, and expanding clean energy applications are key pathways for emission reduction, their success depends on overcoming implementation challenges and ensuring regional adaptability. Policymakers should develop context-specific strategies that consider financial feasibility, infrastructure readiness, and socioeconomic diversity to maximize policy effectiveness.

6. Discussion and Conclusions

6.1. Discussion and Future Directions

Existing studies primarily explore the impact of individual factors on household energy consumption (HErC), often analyzing them in isolation. However, these factors are interconnected, and single-factor analysis tends to overlook interactive effects. For example, household size influences energy consumption but also intertwines with education level, income, and assets, forming a multi-layered impact mechanism. Households with higher education levels may exhibit stronger environmental awareness, making them more inclined to adopt low-carbon energy sources. However, higher income and consumption capacity can simultaneously drive demand for high-carbon products, creating a dual effect that adds complexity and uncertainty to energy consumption behavior.

Additionally, income growth not only increases energy consumption directly but also indirectly by altering household consumption habits and lifestyles. These effects are further shaped by household size, cultural background, and policy environments. The lack of consideration for cross-factor interactions in existing studies introduces significant uncertainties in predicting household energy consumption. Future research should adopt a more integrated and dynamic analytical framework to capture these interdependencies, providing policymakers with scientifically grounded insights for addressing complex energy transitions.

Despite progress in household carbon emission forecasting, existing models still face critical limitations. First, due to data constraints, certain dynamic factors that are difficult to quantify, such as individual lifestyle choices and cultural influences on energy consumption, have not been fully explored. Future research should incorporate more detailed micro-level or high-frequency data to improve model accuracy. Second, while existing predictive models capture economic and technological trends, they still face challenges in addressing uncertainties related to policy interventions and behavioral changes. Lastly, the impact of cultural, behavioral, and psychological factors on household energy consumption warrants further investigation. Integrating perspectives from psychology, sociology, and behavioral economics will facilitate a more comprehensive understanding of household energy consumption dynamics and provide more precise scientific evidence for policy formulation.

6.2. Conclusions

This review systematically examines studies on urban household energy consumption and carbon emissions in China from 2000 to 2024. A bibliometric analysis of 96 selected papers reveals a steady increase in academic interest driven by global climate actions. The research in this field is interdisciplinary, spanning environmental science, energy, and economics, reflecting the complexity of addressing household carbon emissions.

This study systematically identifies key influencing factors and their interactions, deepening the understanding of HErC within a multidisciplinary framework. Additionally, the comparative analysis of predictive methods provides methodological references for future research, particularly in the application of machine learning techniques and scenario-based modeling in carbon emissions studies. These findings offer valuable insights for policymakers in designing more targeted and effective carbon reduction policies. The identified influencing factors can serve as a foundation for developing incentive mechanisms that promote low-carbon consumption behaviors. Furthermore, understanding the advantages and limitations of different predictive methods can assist policymakers in selecting appropriate models for policy evaluation and scenario planning. This study also emphasizes the critical role of behavioral and social factors in climate policies and highlights their importance in enhancing the long-term effectiveness of policy measures.

In terms of influencing factors, household characteristics such as household size and education level are identified as major drivers of HErC. Economic attributes, particularly income and household assets, affect energy use and carbon emissions. Energy efficiency and policy interventions are also widely recognized as effective emission reduction measures. Additionally, the interaction of environmental ideology, behavior attitudes, and social norms emphasizes the importance of cultivating a sustainable culture alongside technological and policy advancements. These key influencing factors have all been applied in HErC forecasting.

Various predictive methods, including traditional statistical approaches, the Kaya identity, and the LEAP model, have been widely applied, each with its own advantages. The rise of data-driven machine learning algorithms, in particular, has significantly improved the accuracy of predictions. Scenario analysis is widely used to construct multiple future possibilities, exploring different carbon emission trajectories. In terms of forecasting results, it is generally believed that in the absence of strict intervention measures, household carbon emissions will continue to rise, and the peak will be delayed. Although there is significant potential for carbon reduction in households, its actual effectiveness remains highly dependent on the implementation of policies and technologies.

To achieve China’s carbon goals, it is urgent to adopt systematic and multi-level mitigation strategies. Improving building and energy efficiency, promoting low-carbon consumption and clean energy applications, and implementing multidimensional coordinated policies to address the diverse needs of regions and populations are considered the most effective and feasible pathways for emission reductions. Future research should focus on optimizing predictive models with more granular data, refining factor selection, quantitatively assessing the impacts of policy interventions, and fostering interdisciplinary collaboration to develop comprehensive solutions.