Household Carbon Emissions Research from 2005 to 2024: An Analytical Review of Assessment, Influencing Factors, and Mitigation Pathways

Abstract

Rising household carbon emissions (HCEs) substantially increase residential energy consumption. This review evaluates the four principal quantification methods: Emission Coefficient Method (ECM), Input–Output Analysis (IOA), Consumer Lifestyle Approach (CLA), and Life Cycle Assessment (LCA). The methods are compared according to data requirements, uncertainty levels, and scale suitability. The study synthesizes multidimensional determinants—including household income, household size, urbanization, energy intensity and composition, population aging, and household location—and translates these insights into behavior-informed mitigation pathways grounded in behavioral economics principles. Combining compact-city planning, targeted energy-efficiency incentives, and behavior-nudging measures can reduce HCEs without compromising living standards, providing local governments with an actionable roadmap to carbon neutrality.

1. Introduction

The phenomenon of climate change, exacerbated by the proliferation of greenhouse gases (GHGs), represents a formidable environmental challenge of global dimensions [1]. Consequently, the unabated rise in GHG emissions has emerged as a pressing global issue. The Paris Agreement, formulated during the 21st Conference of the Parties (COP21) to the United Nations Framework Convention on Climate Change in 2015, saw over 140 nations pledge to achieve carbon-neutral goals by 2050, thereby aiming to ameliorate the impacts of climate change and establish pathways for emission reductions [2,3,4].

The precise methodologies for achieving these targets remain to be determined. With the rapid growth of households’ demand for energy services, energy consumption in the residential sector has risen sharply [5,6,7,8], including carbon emissions from daily energy use, transportation energy use [9], and non-energy use. Residents play an important role in adjusting the energy structure of the residential sector [10,11] and are among the primary factors influencing energy consumption during building operation [9]. In developed countries, household energy and its associated carbon emissions already exceed industrial energy consumption and its associated carbon emissions. Furthermore, the average standard of living and consumer expenditure among populations in developed countries tend to be superior to those in developing nations, as evidenced by the proportion of household carbon emissions (HCEs) relative to a nation’s total carbon emissions. For example, Residential consumption accounts for 55% of total carbon emissions in Japan [12]. Household travel and residential energy use account for 42% of total U.S. carbon dioxide emissions [13]. In 2010, household carbon emissions in the UK rose to 74% of total emissions, while in Australia this proportion stood at 20% in 2013 [14]. In Singapore, HCE accounts for 25% of total emissions [15]. China is projected to witness a significant increase in HCEs due to its residents’ rapid economic development and rising standard of living and spending power. Estimations indicate that Chinese HCEs account for a substantial share of the country’s total CO₂ emissions, ranging from 30% to 40% [13,16]. The percentage of carbon emissions attributable to Chinese civilian contributors has demonstrated a marked statistical escalation, with the figure rising from 3.80% in 2010 to 4.53% by 2018 [16]. It is thus crucial that significant reductions in HCEs are achieved on a sustainable development path to attain the ambitious targets for emissions reductions. However, an in-depth understanding of the factors that influence HCEs is crucial before the implementation of emission reduction strategies. A comprehensive review of the existing literature on HCEs is required. Therefore, reducing carbon emissions associated with household energy consumption is crucial for achieving carbon neutrality in the residential sector [17].

Research conducted thus far has highlighted the increasing significance of household carbon emissions. Several scholars have systematically analyzed the characteristics and trends of research on household carbon emissions over the past two decades using bibliometric methods. These analyses encompass journal distribution, national and institutional contributions, and keyword analyses. The results offer a comprehensive overview of the field of household carbon emissions research [13,18,19]. By contrast, several other reviews have analyzed the effects of demand-side and supply side policies regarding policy instruments and practical experience. These reviews have focused on household carbon emission reductions and examined the factors influencing policy choices [20].

Table 1. Review of the research on the synthesis of household carbon emissions (HCEs).

Researcher	Year	Main Conclusions
Zhang et al. [20]	2017	This study provides a comprehensive summary of HCE policies worldwide, including carbon taxes, renewable energy quotas, and energy efficiency standards. Flexibility and adaptability in policy implementation are emphasized, particularly in diverse geographical and economic contexts.
Liu et al. [19]	2020	This study analyzes global trends in HCE research and identifies the primary factors influencing this field, including demographic, income, social, technological, and policy factors. Stressing the importance of micro-level studies, such as those at the city and individual levels.
Zeng et al. [13]	2021	It provides a comprehensive overview of the HCEs research field, including research hotspots, leading journals, and country distribution patterns. Keyword analyses reveal trends in the evolution of research themes and provide directions for future research.
Shen et al. [18]	2023	The study provides a detailed analysis of the knowledge base and collaborative networks in HCE research, revealing evolving trends and hotspots. Multidisciplinary and diversified trends in HCE research were demonstrated through keyword co-occurrence and journal overlay graph analyses.

lists their contribution to the area of household carbon emissions (HCEs).

Recognizing the fact that despite the existing body of research, there are gaps that require further exploration. Therefore, this paper aims to contribute to the mitigation of climate change and the advancement of low-carbon societies by identifying key research themes for future investigation. Through a meticulous review of the most relevant literature, this paper systematically compares the differences, advantages, and limitations of HCE quantification methods. The methods under scrutiny include the Emission Coefficient Method, Input–Output Analysis, Consumer Lifestyle Approach, and Life Cycle Assessment. This comprehensive comparison helps identify the applicable scenarios, advantages, and disadvantages of these methods and provides a reference for researchers to select appropriate quantification methodologies. Furthermore, the study provides a comprehensive analysis of the factors influencing HCEs. These factors include household income, household size, urbanization, energy intensity and composition, population aging, and household location. The study also proposes specific mitigation measures. These measures include urban planning, pro-environment policies, low-carbon technologies, and choices regarding residents’ lifestyles and consumption behaviors. This comprehensive analysis enhances the understanding of the factors influencing household carbon emissions. It also provides theoretical support for policy formulation.

In doing so, the following section presents an overview of the background of HCEs, followed by a description of the research methodology, a classification of quantitative methods, a summary of influencing factors, and mitigation measures. Additionally, this paper highlights the limitations of the current studies and suggests directions for future research on HCEs. To orient readers, a visual roadmap summarizing the review structure is provided in Figure 1.

2. Background of HCEs Research

In recent years, there has been a noticeable increase in carbon dioxide emissions around the world. This escalation is temporally aligned with the burgeoning academic interest and research in HCEs. The expanding body of scholarly work in this domain indicates an evolving comprehension regarding the substantial contribution of HCEs to the global carbon footprint, predominantly stemming from the routine activities of households. This paper provides a review of the literature in this field, assessing the quantitative techniques for HCE measurement, the key determinants, and an evaluation of the most efficacious strategies for HCE mitigation. Figure 1 illustrates the research roadmap developed for this purpose.

The terminology employed to describe HCEs predominantly pertains to household carbon emissions [14,21,22,23], household carbon dioxide emissions [24,25], household greenhouse gas emissions [26,27,28], household carbon footprint [29,30,31], etc. HCEs can also be described from different conceptual perspectives, including the following: (1) From an energy demand perspective, greenhouse gas emissions are defined as those caused by the direct and indirect energy demand of residential households [24,32,33]. (2) In terms of final consumption by households, it is defined as direct carbon emissions (DHCEs) resulting from the energy consumed by households and indirect carbon emissions (IHCEs) resulting from the production process of relevant industries that provide them with consumer goods [34]. (3) Originated from the concept of “lifecycle”, which is recognized as the total amount of carbon dioxide emitted by the production and consumption of products and services by households throughout their lifetime [35]. (4) Depending on the generation source, it mainly covers carbon emissions from transportation and housing in residential household life [36]. (5) From an energy consumption perspective, it is defined as releasing greenhouse gases from fossil fuel combustion for domestic purposes within residential households [37].

HCEs comprise both direct and indirect carbon emissions. Direct household carbon emissions (DHCEs) are categorized into two primary types: firstly, HCEs directly attribute to residential energy consumption, including but not limited to heating, cooling, and electricity, and secondly, carbon emissions resulting from the consumption of petroleum products by family members for private transportation purposes, such as the use of automobiles, motorcycles, and agricultural vehicles for commuting. Indirect carbon emissions (IHCEs) are defined as CO₂ emissions resulting from household consumption of goods and services, excluding those related to energy products and services. This includes a wide range of consumption areas such as food, clothes, residence, culture, medical care, household equipment and daily necessities, transportation and communication, education and entertainment, and other goods and services. Figure 2 illustrates the way HCEs are constituted.

DHCEs and IHCEs from residents’ living and consumption significantly impact HCEs. For example, Yu et al. demonstrated that an increase in the number of households and consumer behavior leads to a corresponding increase in IHCEs [38]. Liu et al. determined that the combined proportion of DHCEs and IHCEs from residential consumption accounts for over 40% of total HCEs [21]. However, HCEs exhibit regional variability due to differences in geographical and socio-economic conditions. For example, Guo, Chen et al. suggest that DHCEs are higher than IHCEs in three cities in Xuzhou City, China [39]. Conversely, Tian and Geng et al. found that IHCEs exceed DHCEs in residential sectors of Liaoning Province, China [40]. Additionally, the study by Ma et al. highlighted that residential energy consumption habits and the standard of living, reflected by the ownership of appliances and vehicles, significantly influence DHCEs [41].

Currently, research emphasis on carbon emissions is progressively transitioning from industrial applications to the residential sector [42]. Resident energy-use patterns outweigh building structure in determining emissions; behavioral conservation measures in homes surpass structural efficiency interventions [43]. Mitigating carbon emissions stemming from residential behaviors has emerged as a critical priority. Household energy use and housing-sector carbon emissions are increasingly coupled. Decoupling quality-of-life gains from carbon growth is central to climate-change mitigation. Achieving the 1.5 °C global warming target, as outlined in the Paris Agreement, will require significant reductions in HCEs. T These strategies are essential for aligning with international climate goals and ensuring long-term sustainability [44].

3. Methodology

This study employed a quantitative evaluation. The search strategy incorporated Boolean operators and specific keywords to refine the retrieval of the relevant literature. The selected keywords encompassed combinations such as “household” and “carbon emissions”, “residential” and “carbon emissions”, “household carbon emissions” or “household carbon footprint”, “household” and “carbon emissions reduction”, “household carbon emissions” and “influencing factors”, “household carbon footprint” and “assessment methodologies”, “Carbon emissions” and “lifestyles”, and “carbon emissions” and “mitigation”. Usage Examples: The search string was searched in the Web of Science platform’s SCI database (https://www.webofscience.com/wos/ (accessed on 11 December 2024) and the China National Knowledge Infrastructure (CNKI) academic journal network publishing database (https://www.cnki.net/ (accessed on 11 December 2024). Thus, To ensure the pertinence of the retrieved documents, the search was limited to the literature published within the last 19 years, emphasizing English-language sources, and prioritized the selection of the most relevant and highest-quality publications. Notably, journals such as the Journal of Cleaner Production, Energy Policy, Applied Energy, Resources, Conservation and Recycling, Sustainable Cities and Society, Sustainability, and reports from the Intergovernmental Panel on Climate Change (IPCC) are also included to provide a comprehensive understanding. The method section in Figure 1 presents the relevant process.

This paper presents a systematic analysis based on a qualitative assessment of meticulously selected scholarly articles. Conducted manually by the authors, this process involved a meticulous review of each retrieved article based on their titles, keywords, and abstracts. To ensure timeliness, the time frame spans from 2005 to 2024. Articles were categorized based on their research objectives and findings, and the literature outside the scope of the research was filtered (e.g., carbon emissions in the construction sector or carbon emissions in the agricultural sector). The 286 papers that remained after the initial elimination process were subjected to further analysis. Subsequently, the complete text of each paper was subjected to meticulous examination, excluding an additional 131 papers.

The criteria for identifying and categorizing relevant scholarly articles are as follows:

(1)

The temporal distribution and thematic categorization of publications align with anticipated future trends;

(2)

Literature closely related to this study was preferred;

(3)

Where multiple papers within the same journal address identical subjects, priority was accorded to the most recent publication;

(4)

Research objectives and findings were categorized in terms of quantitative methods, influencing factors, and emission reduction countermeasures.

For the last 155 papers, Figure 3 shows the annual publication count of reviewed papers in both SCI and CNKI databases from 2005 to 2024. The pronounced acceleration observed after 2015 aligns with the Paris Agreement and China’s carbon-peaking commitments, underscoring the growing policy relevance of household-level studies.

4. Quantification Methodologies

Employing various assessment methodologies to quantify HCEs can lead to differing results due to the diverse components that constitute HCEs. The methodology can be categorized into Emission Coefficient Method (ECM), Input–Output Analysis (IOA), Consumer Lifestyle Approach (CLA), Life Cycle Assessment (LCA), etc.

Table 2. Comparison of methodologies for quantifying household carbon emissions (HCEs).

	Advantages	Disadvantages	Applications
ECM	Simple operation	Inconsistency of carbon emission factors across countries/regions	DHCEs
IOA	Results are accurate It could be easily combined with other methods	High data requirements Inability to identify emissions from imported and exported products	IHCEs
CLA	Integrates the advantages of ECM and IOA		DHCEs and IHCEs
LCA	Detailed calculation process Results are comprehensive and fine-grained	Heavy workload Time-consuming Poor system integration	IHCEs

summarizes the advantages and disadvantages of the quantification methods involved, as well as the range of their applications.

4.1. Emission Coefficient Method (ECM)

The computation of DHCEs is straightforward and is mainly based on the ECM. ECM, recognized as the IPCC Reference Approach, is principally sourced from The IPCC Greenhouse Gas Inventory Guidelines [45]. This is performed by multiplying the energy consumed by a household by the corresponding carbon-emitting factor. ECM is extensively applied to determine DHCEs from residential households at both the macro and micro levels [46,47]. Jones et al. employed the ECM to analyze and estimate HCEs in 28 U.S. cities; the findings uncovered a significant variance in HCEs, with figures fluctuating between approximately 25 metric tons and over 80 metric tons in the 50 largest metropolitan areas [48]. Meng et al. estimated DHCEs by applying the IPCC carbon emission factor to household energy consumption. Consequently, the DHCEs escalated from 3.76 metric tons in 2014 to 3.94 metric tons in 2018 [49].

In summary, the data source of ECM draws data from historical statistics or household survey data, which is favored for its simplicity, ease of implementation, and widespread adoption, primarily applicable to DHCEs. However, precision is low when estimating IHCEs. It is crucial to acknowledge that variations in carbon emission factors among different countries and regions introduce uncertainties that can diminish the comparability of calculated outcomes. Consequently, choosing the correct coefficients for each region becomes a critical step in the process.

4.2. Input–Output Analysis (IOA)

Input–Output Analysis (IOA), pioneered by American economist Wassily Leontief, is frequently utilized to quantify HCEs. Illustratively employing the IOA framework, Ma et al. executed a comparative examination of how residential consumption impacts HCEs in China and the United States. Their quantitative assessment revealed that while China’s IHCEs from residential consumption are rapidly increasing, those in the United States exhibit stability or a slight decline. This upward trend in China is primarily attributed to the increased demand for housing, with IHCEs escalating from 150 million metric tons in 2002 to 500 million metric tons in 2010, contrasted with the United States, where the figure has been relatively constant at 400 million metric tons [50]. Wang et al. employed the IOA method to examine the correlation between living consumption and HCEs among residents of Shandong Province; the study’s results indicated that urban residents’ HCEs totaled approximately 70.2921 million metric tons, which is three times greater than the emissions of their rural counterparts [51]. Peng et al. applied the IOA to assess the HCEs in both urban and rural settings, revealing that the share of carbon emissions associated with income-based households is markedly higher, ranging from 28% to 35%, compared to that of consumption-based households, which accounts for roughly one-third [52]. Mi et al. utilized the IOA to estimate HCEs across 12 distinct income groups in 30 Chinese regions. Their study determined that the HCEs from households in the highest income bracket, representing the top 5% of earners, constituted 17% of the nation’s total carbon emissions [30].

The IOA evaluates the carbon emissions associated with imported and exported products and services at a macroeconomic level by examining the quantitative interdependencies among various sectors of the economic system. This approach forms the foundation for calculating IHCEs, offering the benefit of yielding more comprehensive and precise assessment outcomes. However, the IOA necessitates a substantial dataset and considerable analytical effort, and it struggles to differentiate clearly between the emissions from imported and exported products that are not associated with services.

4.3. Consumer Lifestyle Approach (CLA)

The CLA, initially introduced by Bin et al., serves as a holistic analytical framework for evaluating consumer activity energy consumption and carbon emissions, frequently employed in the quantification of both DHCEs and IHCEs [24]. This approach integrates the ECM and IOA to assess HCEs comprehensively; the ECM is utilized for the assessment of DHCEs, while the IOA is applied to evaluate IHCEs. For example, Liu et al. employed a combined CLA and IOA to evaluate the IHCEs of urban households from 2002 to 2012. Their analysis elucidated the varying contributions of distinct income groups to overall carbon emissions. The study’s findings indicate a marked escalation in IHCEs, surging from 894.08 million metric tons to 1957.03 million metric tons over the decade [53]. Based on the CLA, Zhang et al. determined that HCEs resulting from residential consumption in the United States accounted for 41% of overall emissions, surpassing the contributions from the industrial sector for the year in question [54]. Shang et al. employed CLA to analyze the impact of household characteristics on HCEs as mediated by consumption patterns [55]. Wu conducted an assessment of IHCEs across 201 urban areas in China spanning the period from 2010 to 2018 using the CLA. The analysis revealed that the implementation of smart city policies led to a 5.2% reduction in IHCEs [56]. Xue applied the CLA to assess the HCEs in the Gujiao mining area in Shanxi Province, China, and found that the annual per capita HCEs were 119,633.36 kg, with 61% attributable to domestic energy use and 39% to HCEs of residential transportation [57]. Wang et al. employed CLA to estimate HCEs from urban residents’ living consumption; households in the unit neighborhoods exhibited the highest DHCEs, with an average of 723.79 kg of carbon per month. In contrast, households in commercial residential neighborhoods displayed the highest IHCEs, with an average of 707.70 kg of carbon per month [58].

This quantification methodology incorporates a range of factors, including the external environment of consumers, personal determinants, household attributes, consumer decisions, and behavioral outcomes, to assess consumer energy utilization and associated carbon emissions. Although it faces analogous constraints to IOA, it is particularly challenged by data collection and processing complexities, especially in the calculation of IHCEs. Nonetheless, this approach provides researchers and policymakers with insights into the environmental impact of household consumption, facilitating the development and implementation of strategies aimed at reducing these impacts and advancing toward sustainable development pathways.

4.4. Life Cycle Assessment (LCA)

Life Cycle Assessment (LCA), a predominant methodology in the field of sustainable consumption, is categorized into three distinct approaches: Input–Output Life Cycle Assessment, Process Life Cycle Assessment, and Integrated Life Cycle Assessment. Li et al. used LCA to calculate and compare HCEs from meat consumption in China and showed that beef was the most significant contributor to HCEs at the major stages of the life cycle, reaching 22.059–81.921 kg of carbon emissions per kg of standard meat [59]. Using the PLCA, Ding et al. found that residential consumption constitutes 45.8% of the total global warming potential (GWP) attributed to household consumption activities [60]. Heinonen and Junnila applied an integrated LCA to assess the carbon emissions from the people living and consuming in two Finnish metropolitan areas, and the average HCEs per person per year from consumption activities vary between 10.1 and 14.4 metric tons [61]. Some scholars have also turned to Environmental Input–Output Life Cycle Assessment (EIO-LCA) to quantify HCEs. Long et al. utilized the EIO-LCA to assess HCEs linked to residents’ online purchasing activities. Their study indicated that the mean carbon footprint attributable to online consumption by residents is 0.46 metric tons, which constitutes 12.30% of the overall carbon footprint from consumption [62]. In a subsequent analysis that integrated age-specific consumption patterns with the EIO-LCA framework, Long et al. discovered that the carbon emissions from consumption activities of the elderly population represented 11.65% of the total emissions [63].

As a result, this method, which analyzes energy demand and carbon emissions for each stage of the entire life cycle of consumer products and services, represents the most accurate calculation method currently available. Nonetheless, it demands a substantial volume of highly detailed data, which is a significant undertaking and the most time-consuming aspect of the process. In practice, it is challenging to obtain complete life cycle data.

In brief, ECM rapidly estimates direct emissions and is applicable in data-scarce con-texts. IOA quantifies supply chain indirect emissions and offers a comprehensive perspective when regional input–output tables are available. LCA and CLA are appropriate for high-resolution or high-income settings. CLA comprehensively evaluates consumer-behavior impacts on carbon emissions and informs behavior-oriented mitigation strategies. LCA delivers the most accurate full-life-cycle carbon assessment and is ideal for detailed analyses.

5. Influencing Factors

As the share of HCEs in the total carbon footprint expands, it becomes increasingly crucial to implement effective strategies to reduce or curb the increase in HCEs. To accomplish this goal, it is imperative to scrutinize the determinants of HCEs, which have been identified through a comprehensive review of the relevant literature. These determinants span across demographic, economic, geographic, and social dimensions [64,65,66]. For the scope of this study, these factors are summarized into the following areas: household income, household size, urbanization, energy intensity and composition, population aging, and household location, as shown in

Table 3. Research on factors affecting household carbon emissions (HCEs).

Influencing Factors	Factor Categories	Researcher
Household income	Economics	Serino [67], Sommer [68], Ottelin [69], Druckman [70], Han [71] Chen [72], Sun [66], Cao [73], Zhang [74], Wang [75], Leavay [76] Mach [77], Wang [78], Liang [79], Feng [80], Huang [81]
Household size	Demographic	Zhang [23], Weber [82], Ellsworth [83], Kenny [84], Jack [85] Qu [86], Guo [87], Wan [88], Zhou [89], Xu [90], Yu [64]
Urbanization	Societal	Liu [91], Huang [92], Zhang [93], Dong [94], Bai [95], Lin [96] Zhang [97], Ottelin [98], Yuan [99], Qu [100], Li [101], Gao [102]
Energy intensity and composition	Economics	Liu [21], Zhou [103], Wang [75], Das [104], Ma [105], Li [106], Ma [107], Chang [108], Luo [109], Dou [110], Wang [111]
Population aging	Demographic	Zheng [112], O’Neill [113], Zhang [114], Li [115], Yu [116] Fan [117], Chai [118], Li [119], Chen [120], Yuan [121]
Household location	Geographic	Jones [48], Druckman [70], Qu [122], Jiang [123], Shirely [124] Murray [125], Jiang [126], Li [127], Zhang [128], Zhao [129] Liu [130], Wang [78], Sun [131], Xu [132]
Gender	Demographic	Wang [133], Druckman [134], Büchs [135], Soltani [136], Huang [137]
Education level	Societal	Xue [57], Han [71], Cao [73], Lian [138], Zhang [139]
Car ownership	Economics	Wang [58], Zhang [140]

.

5.1. Household Income

Income is a pivotal factor in research on HCEs and has garnered significant attention. There is a widely accepted consensus within the scientific community that the occurrence of HCEs tends to rise in tandem with an increase in household income levels [67,68,69]. For example, Druckman and Jackson conducted an extensive analysis of energy consumption patterns and the resulting carbon emissions among U.K. residents. Their study revealed a substantial positive correlation between household income levels and the associated carbon footprint [70]. The correlation between household income and the escalation of carbon emissions is particularly pronounced among both urban and rural households in China. This relationship highlights the significant influence of economic status on the environmental footprint of household activities [71,72]. HCEs tend to be lower in rural areas, primarily due to the lower income levels of rural residents compared to their urban counterparts [66,73]. In China, high-income households generate 6.7–3.8 times more IHCEs than low-income households [21]. The role of income as a driver of HCEs diminishes as living standards improve. However, this result is not significant in rural areas, indicating that there may not be a structural shift in the way income growth affects the HCEs for rural residents [74].

Wang et al. concluded that the rapid development of per capita household income of urban residents has played a crucial part in promoting electricity and heating carbon emissions in DHCEs and inhibiting household gas emissions [75]. Levay et al. argue that some residents with lower household incomes in Belgium have a higher household consumption emission intensity than wealthier households due to high energy expenditures [76]. Mach et al. applied the concept of expenditure elasticities in their assessment to quantify the substantial reliance of Czech residential HCEs on overall expenditure levels. This analytical approach elucidated the pronounced sensitivity of HCEs to changes in total household spending [77]. Considering the heterogeneity between households with different incomes in different provinces, considerable HCE disparities exist between urban and rural households [78,79]. Feng, Hubacek et al. estimated consumption-based HCEs for nine income levels in the U.S. and showed that the highest-income group was about 2.6 times higher than the lowest-income group [80]. Households with higher incomes typically exhibit more giant carbon footprints, a result of elevated consumption levels and distinctive lifestyle preferences. For example, their HCEs for private transport surpass those of low-income households by 141.20% [81]. Income levels significantly influence the environmental footprint of households, particularly in terms of carbon emissions stemming from the lifestyle and consumption behavior of the population. High-income households often exhibit a stronger inclination toward purchasing high-end products and services, which become more expensive with escalating income. As a result, they frequently opt for high-end ingredients, premium vehicles, superior comfort in their homes, and diverse recreational activities. Undoubtedly, such affluent living patterns contribute to a significant share of the total greenhouse gas emissions, particularly carbon dioxide.

5.2. Household Size

Household size is also a factor that influences HCEs in the residential sector, which researchers have paid more attention to; larger households tend to benefit from economies of scale and play an important role [82,83]. For example, Kenny and Gray reported that Irish residents’ HCEs are affected by household size (number of people), with per capita carbon emissions from households comprising three or more individuals being almost 33% lower compared to those from single-person households [84]. Jack and Ivanova found that Danish residents from smaller households exhibited higher HCEs [85]. According to Zhang et al., household size significantly impacts residents’ per capita carbon emissions. This impact is more significant in reducing IHCEs, outweighing the negative impact on households in China [23]. This finding was also corroborated by Qu et al. [86] in their study of HCEs of farmers and herders in the arid and semi-arid regions of Gansu, Qinghai, and Ningxia in China, noting that HCEs varied directly with household size.

Conversely, a negatively correlated relationship was identified between HCEs per capita and household size [86]. Guo et al. proposed that with each additional household member, there is a potential reduction in per capita emissions by 2.8% and that the cost and utilization of goods and services become more economical through sharing [87]. Wan et al. identified that the size of the urban population is a critical factor in the escalation of HCEs emitted by households in urban regions of China [88]. The relationship between household size and per capita carbon emissions among both urban and rural households in China exhibits a significant negative correlation [89]. Regarding energy conservation, it is possible for family members to communally utilize certain large-scale electrical appliances, including centralized heating systems and hot water supply systems. This arrangement enhances energy efficiency compared to the scenario where multiple smaller households independently install and operate similar equipment. However, a subset of researchers posits that household size is a contributing factor to HCEs [64,90]. Extended families often require more spacious residential accommodations to meet their housing needs, leading to a potential increase in energy consumption for purposes such as heating, cooling, lighting, and various household activities. Furthermore, the frequency and scale of social events and festivals may expand, which could result in higher carbon emissions. Therefore, assessing the relationship between household size and HCEs is essential. While an increase in household size can lead to a decrease in per capita carbon emissions, the total carbon emissions may still increase as a consequence of the expanded household size.

5.3. Urbanization

Urbanization is a complex and multidimensional phenomenon with profound and extensive implications [91]. Consequently, numerous studies have utilized macro-level data from provincial or county jurisdictions to evaluate urbanization from demographic, spatial, and economic perspectives [92]. The urbanization discussed in this paper primarily adopts the household consumption perspective, focusing on the transition of lifestyles, consumption levels, and consumption patterns from rural to urban households. The study of HCEs and household energy consumption is inextricably linked to the phenomenon of urbanization. Moreover, household energy consumption is inextricably linked to the phenomenon of urbanization [93,94,95]. Urbanization has significantly altered household lifestyles, contributing to increased HCEs. This change underscores the growing environmental impact associated with urban living standards [96,97]. Ottelin et al. found that per capita emissions from households in Eastern Europe increased due to urbanization. In contrast, per capita HCEs in certain Western European nations showed a downward trajectory [98]. This can be explained by the fact that Western European nations have historically experienced a more extended process of industrialization and urbanization, exhibit advanced economic development and urbanization, and have implemented a more significant number of environmental policies and technological innovations with the objective of reducing carbon emissions. Yuan et al. investigated the impact of urbanization on IHCEs in China between 2002 and 2007, and their findings indicated that urban expansion significantly contributed to the growth of IHCEs [99]. Qu et al. conducted a comparative analysis of HCEs between urban and rural residents in China from 1995 to 2011, revealing a robust positive correlation with the process of urbanization. Urbanization encompasses not merely a shift in demographic status but also a transformation in lifestyle, which in turn influences HCEs [100]. Li et al. observed a general decline in HCEs across China’s urban and rural areas. Despite this trend, significant disparities in HCEs were identified among the four major regions during their distinct phases of rapid urbanization [101]. Rapid urbanization exacerbates environmental stress on HCEs. Gao et al. discovered that population urbanization notably increases HCEs among rural residents, while economic and spatial urbanization are pivotal in mitigating carbon emissions [102].

5.4. Energy Intensity and Composition

In recent decades, rapid urbanization and economic progress have significantly improved living standards, which in turn has led to a transformation in the energy intensity and structure of households [105]. This transformation has prompted a surge in academic interest, as numerous studies have explored the importance of energy intensity and composition in HCEs [104]. Among these studies, Liu et al. found that biomass use could reduce HCEs in China by analyzing the impact of energy structure on household consumption [21]. Building on this, Zhou and Gu suggest that the rapidly escalating IHCEs by Chinese residents are mainly due to increased per capita living expenditures. Meanwhile, reducing energy intensity has significantly contributed to decreasing IHCEs [103]. Further emphasizing the role of economic factors, Ma et al. identified energy intensity and economy expansion as the two main drivers of fluctuations in HCEs [107]. Wang and Ru argue that changes in energy use patterns in China are the main reason for the slowdown in the growth of carbon emissions from residential energy use [141]. Li’s findings are that domestic energy intensiveness is the key constraint on the growth of direct carbon emissions in Guizhou [106].

Similarly, upgrading the energy consumption structure and the population’s consumption tendencies will suppress the growth of HCEs [108]. In the urban and rural comparative analysis, energy intensity most significantly inhibits DHCEs in rural areas. A significant difference exists between the energy consumption composition in urban and rural areas, favorably affecting HCEs. Urban dwellers prefer to use cleaner energy, whereas rural dwellers often resort to cheaper, lower-quality, and more polluting energy to save on living expenses. For example, rural residents consume more coal energy compared to urban residents, who consume more electricity and natural gas [109,110]. In China, energy intensity has a significant beneficial effect on residential HCEs, and energy composition has a significant negative effect [111]. Meanwhile, accumulating residents’ savings also affects HCEs through their potential impact on household energy consumption structure [75].

5.5. Population Aging

Many countries worldwide are experiencing population aging, a key factor affecting HCEs [112,113]. On the one hand, population aging negatively impacts HCEs by reducing labor force participation [114] and altering consumption patterns [115]. The lifestyles and consumption habits of older residents differ significantly from those of younger ones. For instance, changes in energy consumption utility bills for water, electricity, and food lead to alterations in HCEs [116]. China is experiencing a moderate level of population aging. For example, Fan et al. have shown that aging in the urban population correlates with increased urban HCEs. This increase is primarily due to rising income levels and expanded consumption demand among residents. However, in the urban sample, when urban population aging is below 0.083, a 1% increase in urban population aging corresponds to a 12.281% increase in urban HCEs. Conversely, when it exceeds the threshold, the increase is 10.902% [117]. Additionally, Chai et al. found that due to a frugal lifestyle and lower expected income, an increase of 10% in the proportion of individuals over 60 in the household population leads to a roughly 0.60% decrease in carbon emissions. Overall, an aging population significantly impacts HCEs negatively [118].

On the other hand, an aging society may boost demand for energy-intensive services like healthcare, potentially increasing HCEs [119]. Compared to younger residents, older individuals tend to consume more energy, particularly for heating. For every one standard deviation increase in the proportion of individuals over 60 in a household, HCEs per capita increase by 2% of a standard deviation [120]. In rural areas, household energy consumption exceeds that of urban households. Consequently, population aging has significantly contributed to HCEs in both urban and rural areas, disproportionately affecting rural energy consumption. For every 1% increase in the proportion of the population aged 65 and older, rural households’ energy consumption increases by about 14% more than urban households [121].

5.6. Household Location

Household location is a critical factor influencing HCEs. According to Druckman and Jackson, household location significantly drives energy use and HCEs in the U.K. [70]; similarly, Qu et al. found that geographic characteristics, including location, altitude, and climate type, modify HCEs [122]. Jones et al. highlight that HCEs are typically lower in urban cores (around 40 tons) and higher in outlying suburbs (around 50 tons). This difference is because urban cores have a lower carbon footprint due to higher population density, whereas suburbs have a higher footprint due to larger dwellings and increased vehicle use [48]. For example, Jiang et al. suggested that HCEs are higher in northern Japan compared to other regions, primarily due to seasonal differences. The north relies heavily on kerosene for heating during winter [123]. In the northern region, direct emissions are significant, likely due to the colder climate, especially in winter, which increases heating demand and is influenced by specific consumption habits and lifestyles. Households in this region often rely on fuels like paraffin for heating, contributing to higher direct emissions. In contrast, the southern region, with its mild climate and lower heating demand, has relatively low carbon emissions. HCEs in the U.S. Virgin Islands are about 35% lower per capita than the U.S. average [124]. Considering regional variations in energy-saving characteristics and customs, it is crucial to account for the context when implementing energy-saving measures. For example, consumers are more likely to purchase Energy Star-qualified products in California, New York, and New England [125]. The energy consumption for household cooking in Guangzhou, influenced by its traditional Cantonese soup culture, results in higher IHCEs compared to other regions [126]. Some scholars have introduced metrics such as Heating Degree Day (HDD), Cooling Degree Day (CDD), and average January and July temperatures to explore their effects on HCEs. For instance, Zhang et al. concluded that winter Heating Degree Days significantly affect per capita HCEs through electricity, gas, and centralized heating energy consumption in various Chinese cities [128]. Li et al. investigated the effectiveness of average January and July ambient temperatures on per capita HCEs in five provinces in northwestern China. The study found that average January temperatures significantly reduced per capita HCEs, whereas July temperatures had a statistically insignificant impact [127]. Zhao et al. incorporated HDD and CDD into their assessment of factors controlling carbon intensity in the Central Plains Economic Zone, finding both had positive effects [129].

China features a two-tier urban–rural economic structure, where differences in lifestyles and consumption capacities between urban and rural residents lead to significant heterogeneity in HCEs. Liu and Zhang propose that disparities between rural and urban areas exacerbate HCE inequality. They claim that rural regions have higher HCEs than urban areas, primarily due to large expenditure differences among rural households [130]. HCEs are higher in both urban and rural areas [78]. Sun et al. found that rural HCEs in China comprise about one-third of urban HCEs [131]. Xu, Guan et al. found that rural HCEs declined from 2002 to 2020 due to urbanization in China, while urban HCEs remained relatively stable [132].

In summary, factors such as household income, size, urbanization, energy intensity and composition, population aging, and location are key drivers of residential HCEs. Additionally, factors like gender [133,134,135,136,137,142], education level [57,71,73,138,139], and car ownership [58,140] also affect HCEs.

Due to the complexity and diversity of consumer behaviors and lifestyles, influenced by various factors, scholars have adopted diverse research methods from multiple perspectives. The interaction and balance of these factors determine their combined impact on HCEs, potentially promoting or inhibiting their growth. Therefore, policymakers and researchers should consider these factors comprehensively and implement multifaceted measures to effectively control and reduce HCEs.

6. Mitigation Measures

Residential HCEs significantly contribute to GHGs. With global targets to reduce carbon emissions actively being implemented, adopting low-carbon and green practices is essential for sustainable development. This requires synergy among the government, enterprises, and the public, encouraging all societal parties to practice low-carbon living and commit to the Paris Agreement goals. Therefore, the following measures are recommended to reduce carbon dioxide emissions at political, technological, and residential levels.

6.1. Political Level

6.1.1. Urban Planning

Numerous studies have highlighted the pivotal role of urban forms in influencing HCEs. Scientific city planning, combined with strategic investments in infrastructure like public transport, clean energy, and energy-efficient buildings, along with promoting green travel modes, can effectively reduce reliance on private vehicles. This can reduce external costs associated with low-carbon lifestyles, ultimately contributing to a decline in carbon emissions from transport [143]. Yang et al. posit that constructing urban green infrastructure, such as green roofs and parks, enhances the city’s ecological services, mitigates the urban heat island effect and increases the city’s carbon sink capacity [144].

Furthermore, urban planning policies aim to reduce carbon emissions and achieve environmentally sustainable development. For instance, promoting compact and dense city policies can effectively reduce carbon emissions in urban areas [145]. In China, low-carbon pilot policies could mitigate 7.6% of HCEs annually [146]. However, Cheng and Wang argue that while the new urbanization pilot policy has reduced HCEs disparities between pilot and standard cities and promoted HCEs decline, China’s HCEs have not yet consistently decreased. There is a need to strengthen HCE reduction policies, such as enhancing city-specific carbon mitigation targets and establishing a dynamic monitoring and early warning system [147]. Building smart cities indirectly impacts carbon emissions by reducing energy consumption compared to traditional urban development models [148].

6.1.2. Pro-Environment Policies

To address how consumer behavior and lifestyle choices affect HCEs, governments should use a variety of policy tools, which can be categorized into two main types: incentives and mandatory policies. Encourage green consumption by employing behavioral economics strategies, such as carbon footprint labeling, to raise consumer awareness of the impact of products [149,150]. Next, designing economic incentives like carbon taxes and subsidies [151], feed-in tariffs (FITs) [152], and the carbon generalized system of preferences (CGSP) [153]. For price-sensitive consumers, discounts or rebates on energy-efficient products can be more effective than purely environmental appeals. Additionally, it is crucial to prioritize households at the lower end of the economic spectrum. This can be achieved by providing cleaner energy options and subsidies [139]. Considering consumers’ potential time inconsistency, policies like long-term energy efficiency loans or installment plans can help lower the initial cost of low-carbon products, aiding consumers in overcoming short-term temptations and achieving long-term low-carbon goals [154].

Mandatory emission reduction policies, such as carbon caps, compel energy systems and consumers to cut emissions by setting specific limits [152] based on loss aversion principles, which makes the costs of not adopting low-carbon behaviors, like conserving electricity and reducing transport emissions, more apparent. This can motivate residents to adopt more environmentally friendly energy practices.

Besides incentives and mandatory policies, governments could set low-carbon options as the default to leverage the default effect, reducing consumers’ cognitive burden and providing clear, accessible information on energy consumption and carbon emissions. This could assist consumers in making more environmentally conscious purchasing decisions.

It is reasonable to conclude that the government should actively guide and effectively control political measures. To reduce household consumption, the government must take a more active role in macro-control. Furthermore, enhancing policies and regulations to reduce household consumption is essential. Conversely, the government should reinforce its role by providing policy guidance and support, serving as a societal model to encourage and advocate for green consumption and a low-carbon lifestyle.

Therefore, it is crucial for the government to take a proactive role in guiding and regulating the reduction in household consumption emissions. The development of urban greening infrastructure should balance ecological benefits with economic costs and social benefits. It is vital to tailor policy instruments like carbon taxes and subsidies to local economic conditions and residents’ financial capabilities. This approach ensures that policies are equitable and effective. Furthermore, policymakers should establish region-specific emission reduction targets and strategies, considering the developmental stages and resource endowments. By doing so, the government can lead by example, promoting and facilitating green consumption and a low-carbon lifestyle among residents.

6.2. Technological Level

Implementation at the technical level plays an essential role in mitigating HCEs and promoting green and low-carbon lifestyles. Green innovation is an effective method of reducing the consumption of fossil fuels. This is achieved through the development and promotion of clean energy technologies, such as solar and wind power, which subsequently result in a reduction in HCEs. Residential solar photovoltaic (PV) systems provide economic and environmental benefits tied to the local energy mix and effectively reduce HCEs [155]. There is an inverted U-shaped relationship between smart home technology adoption in China and electricity-related HCEs [156]. This suggests that while initial adoption of smart home technologies may increase emissions, their ability to provide real-time energy insights can significantly influence resident behavior, ultimately reducing HCEs. The application of artificial intelligence (AI) and the Internet of Things (IoT) to integrate green technologies into infrastructure development [157] while also facilitating smart homes to reduce HCEs demonstrates the capacity of digital technologies to advance environmentally sustainable lifestyles. Electric vehicles (EVs), a significant technological advancement in reducing transport-related HCEs, also present challenges [158]. The designation of EVs as a new energy source hinges on the sustainability of the electricity used to charge them. Additionally, Elham et al. demonstrated that using solar PV to charge EVs makes residents heavily reliant on their living arrangements. This is because solar PV is not a reliable energy source, forcing residents to depend on other energy sources for their vehicles [159].

These technologies reflect the increasing public concern for environmental protection and the shift in consumer behavior toward a green, low-carbon lifestyle. Nevertheless, improved energy efficiency from advanced technologies may lead to a rebound effect on carbon emissions, where improvements in household energy efficiency lead to increased consumption, thereby reducing actual energy and emission savings below projected levels

6.3. Lifestyle and Consumer Level

Achieving carbon neutrality involves not only government actions and technology but also reducing HCEs through lifestyle and consumption changes. To illustrate, Abdulaziz et al. devised a low-carbon living index that is based on the consumer lifestyle of residents in their homes. Studies show that residents effectively conserve energy, reduce waste, and increase resource reuse [65]. Examples include turning off unused lights and fans and reusing paper and envelopes.

Meanwhile, Huang et al. found that diet, personal care, and transport contribute most to HCEs [31]. Incorporating food as a daily necessity, along with reducing meat intake and buying biodegradable packaged food, can potentially reduce diet-related HCEs. From a behavioral economics perspective, the benefits of a long hot bath after a busy day are immediate and noticeable. However, the resource consumption and carbon emissions from this high-carbon behavior are easily overlooked. This behavior reduces people’s awareness and actions regarding climate change. Implementing eco-friendly travel options like public transport, shared bicycles, and vehicles can effectively reduce HCEs, enhance urban environment quality, alleviate congestion, and improve urban life quality.

The growth of the digital economy has had a significant impact on residents’ online shopping habits, which in turn have an effect on HCEs. It is recommended that residents, especially those in middle- and high-income households, control impulse spending online and ensure purchases are necessary and environmentally friendly. Residents should also purchase more non-physical products or services online to reduce unnecessary carbon emissions while traveling [62].

Changes in lifestyle and consumption behavior are essential for residents to achieve sustained reductions in HCEs. In order to encourage low-carbon consumption patterns and lifestyles, raising consumer awareness is necessary. This can be achieved by implementing educational and awareness campaigns that inform the public about reducing energy consumption. Furthermore, success stories can be shared through community and media channels, leveraging social norms and peer effects to encourage the adoption of these behaviors. Alternatively, encouraging residents to identify as environmentalists can create a synergistic effect, leading to public commitments to reduce carbon footprints through sustainable lifestyles. This could serve to increase the incentive for residents to consciously adopt low-carbon behaviors.

In summary, rational urban planning, pro-environmental policies, low-carbon technologies, and lifestyle choices are essential to reducing HCEs. Advocating for a low-carbon lifestyle does not require residents to downgrade their consumption. Instead, it refers to an environmentally friendly green life that enhances the quality of life. It focuses on building a cleaner, more efficient, and sustainable environment.

7. Limitations and Further Research Directions

7.1. Limitations of Research Methodology

The review is restricted to English- and Chinese-language publications published between 2005 and 2024. This focus may omit pre-2005 studies, non-English and non-Chinese publications, and research absent from mainstream databases. Language barriers and restricted full-text access (e.g., paywalls, limited gray-literature availability) further constrain comprehensiveness and reliability.

7.2. Geographical Bias in the Literature and Spatial Scaling of HCEs Needs Improvement

Scarcity of household-level HCE studies from Africa, Europe and the Middle East in mainstream databases skews the synthesis toward Chinese evidence. This imbalance constrains the generalizability of quantitative trends and highlights the need for primary data collection in under-represented regions.

Further refinement of the spatial scale of HCEs is needed, with a focus on specific county and city levels, key cities with significant differences, and down to street and community scales to better understand the spatial variations in HCEs.

7.3. Research on HCEs in the Context of Local Conditions

The heterogeneity in demographics, urbanization, energy intensity and composition, and household location across different countries and regions results in varied carbon emission characteristics and trends. It is essential to study further how residents’ lifestyles affect HCEs in each region, considering local conditions. This includes rationally allocating emissions mitigation targets and formulating measures to reduce emissions based on actual HCEs in each region. Equity-sensitive mitigation bundles should prioritize distributional impacts—particularly among low-income and aging households—via targeted subsidies and behavioral nudges.

7.4. Introduction of Other Quantitative Methods for Effective Integration

Each quantitative method is designed for specific study populations and spatial scales. Table 2 indicates that future quantitative methods can integrate cross-disciplinary approaches tailored to HCE characteristics. Integrating these approaches with established quantitative methods enhances HCE assessment accuracy and measurement precision, facilitates the identification of emission-reduction potentials, and supports the implementation of targeted carbon-reduction strategies.

Concurrently, city-level IHCEs protocols should standardize the integration of household surveys with local emission factors to balance cross-city comparability and regional heterogeneity. High-resolution smart-meter data (electricity, gas and water) should be assimilated into IOA frameworks to capture real-time behavioral variations and mitigate aggregation bias.

7.5. Further Analysis of Factors Influencing HCEs

As indicated in Table 3, existing influencing factors on HCEs mainly focus on demographic, economic, and social factors, and research on influencing factors on HCEs should continue to be gradually refined. Future research should focus on emerging abatement technologies and social innovations, including smart grids, the sharing economy, and green building. Pay special attention to the fairness and affordability of green building technologies across different social and economic groups and prevent the “green premium” from intensifying carbon emission inequalities. These innovations could provide novel avenues for reducing HCEs.

8. Conclusions

Rising living standards have markedly increased HCEs, underscoring the residential sector’s substantial mitigation potential. This review synthesizes the literature on HCE quantification methods, determinants, and mitigation measures, highlights existing gaps, and outlines future research directions to inform scholars and guide priority setting. Key findings are summarized below.

ECM rapidly quantifies direct emissions; IOA captures supply chain impacts; and CLA and LCA yield behavioral and life-cycle insights but demand richer data. However, they require extensive activity data collection and life cycle inventory compilation, which are often challenging to acquire and complex to compute. In practice, the choice of methodology often hinges on data accessibility and research objectives.

Rising income, declining household size, rapid urbanization, regional energy intensity and composition, population aging and location interact non-linearly, necessitating context-specific mitigation.

Compact-city planning, targeted subsidies, mandates and rapid adoption of green technologies (e.g., rooftop PV, smart homes, EVs). In terms of lifestyle, inspiring residents to embrace low-carbon, eco-friendly consumption fosters a cleaner, more efficient, and sustainable environment, elevates the quality of life, and eases the shift toward a green, low-carbon existence.

Finally, future research should build upon current knowledge to deepen our understanding of HCEs, tailoring studies to local conditions and regional characteristics. To move from diagnosis to action, we propose three immediate research priorities: city-level IHCEs measurement protocols; integrating smart-meter data with IOA; and equity-sensitive mitigation bundles. This will enable us to more effectively address the challenges of climate change, gradually achieve residential decarbonization, and collaboratively forge a sustainable, low-carbon future.