HCEs refer to greenhouse gas emissions attributable to household activities and final consumption and are commonly divided into two components^[21]. DCEs generally refer to emissions associated with household energy use, including fuels used for cooking, heating, and private transport, as well as emissions from purchased electricity and district heating when these are allocated to household energy demand^[22]. DCEs are commonly estimated using the emission coefficient method, which multiplies the physical consumption of each energy source by its corresponding carbon emission factor^[22].

ICEs, by contrast, refer to emissions embodied in the goods and services consumed by households. These emissions arise across the upstream production and supply chains that support household final demand^[22,23]. ICEs are commonly estimated using environmentally extended input-output analysis, which links household expenditures across consumption categories to the upstream emission intensities of corresponding production sectors^[24]. Depending on data availability and classification systems, household expenditures are typically grouped into broad categories, such as food, clothing, housing, household services, transport, education and recreation, healthcare, and other goods and services^[25]. Input-output sectors are then aggregated and matched to these categories to align household expenditure data with production-side emission data. The resulting estimates may vary with the system boundaries, sectoral aggregation, expenditure classifications, and allocation rules adopted.

Distinguishing between DCEs and ICEs is analytically important because the two components are shaped by different household characteristics and consumption processes. DCEs are closely tied to housing characteristics, home occupancy, thermal comfort requirements, and private mobility, whereas ICEs depend more strongly on consumption structure, expenditure allocation, and the carbon intensity of upstream production systems^[26,27]. Studies focusing solely on household energy use may capture housing- and transport-related effects but overlook changes in service consumption or expenditure composition^[28-30]. Conversely, consumption-based approaches may reveal mechanisms that remain invisible in energy-based analyses^[31,32]. Moreover, the same dimension of population dynamics may affect DCEs and ICEs differently or even in opposite directions. Recognizing these differences in emission sources and accounting scope is therefore essential for interpreting the effects of population dynamics and explaining divergent empirical findings.

Traditional population-environment models, such as IPAT and STIRPAT, identify aggregate population size as a key determinant of environmental impact^[11]. However, the influence of population dynamics on HCEs extends beyond this aggregate scale effect. For the purposes of this review, population dynamics refer to changes in household size and structure, age structure, fertility and childrearing, and the spatial distribution of populations through migration and urbanization. These dimensions reshape household demand through distinct but interrelated pathways.

First, changes in total population do not translate proportionally into changes in the number of households. Declining household size, the increasing prevalence of single-person living, delayed marriage, and household dissolution can increase the number of households even when population growth is slow^[13]. Second, changes in age structure, particularly population ageing, alter consumption patterns, time allocation, mobility, and energy-service needs across the life course^[28,31,32]. Third, fertility and childrearing affect household composition, housing demand, educational expenditure, time allocation, and intergenerational financial planning^[20]. Finally, migration and urbanization redistribute populations across space, thereby affecting housing density, commuting distances, infrastructure access, and local energy systems^[33].

Taken together, population dynamics function as upstream conditioning processes that shape the number, composition, and spatial distribution of households, thereby influencing both the scale and composition of HCEs^[34]. Their effects are neither uniform nor deterministic; rather, they depend on the socioeconomic, institutional, infrastructural, and spatial contexts in which households are embedded.

Population dynamics affect HCEs through several interrelated mechanisms. First, a household-scale mechanism reflects how changes in household size and living arrangements influence resource sharing and per-capita emissions. Larger households can share dwelling space, heating, lighting, appliances, and other energy-using equipment, thereby generating economies of scale and reducing per-capita emissions. By contrast, household fragmentation duplicates housing space and durable goods and may increase both DCEs and ICEs per person^[35]. The strength of this mechanism depends on household size and co-residence patterns, which are themselves shaped by fertility, cultural norms, income, and housing conditions.

Second, a life-course mechanism emphasizes how age structure and life-course transitions reshape consumption behaviour, time use, and energy demand. For example, increased home occupancy among older adults may raise residential energy demand^[36], whereas reduced mobility may lower transport-related emissions. Changes in healthcare needs and discretionary consumption may also affect ICEs. The net effect therefore depends on the relative strength of these countervailing pathways.

Third, a family-formation mechanism highlights how fertility and childrearing alter household composition, time allocation, and consumption structure. Raising children may increase expenditure on food, housing, mobility, healthcare, education, childcare, and other services, each with a different carbon intensity^[12]. At the same time, the financial demands of childrearing may encourage precautionary saving and constrain adults’ discretionary consumption. Fertility and childrearing may therefore affect different components of HCEs in different directions.

Finally, a spatial-contextual mechanism situates these processes within broader geographic and infrastructural environments. Compact housing, shorter commuting distances, and access to public transport may reduce emissions, whereas income growth, motorization, suburban expansion, and consumption upgrading may increase them. Urban form, infrastructure provision, climate conditions, energy mix, and development stage therefore shape how migration and urbanization translate into DCEs and ICEs.

These mechanisms are interconnected rather than independent. Population ageing may coincide with widowhood and household fragmentation; fertility and childrearing alter household size and time allocation; and migration may reshape co-residence arrangements and access to infrastructure. Taken together, these mechanisms provide a conceptual bridge between the different dimensions of population dynamics and HCEs.

Figure 1 summarizes this integrated conceptual framework. Population dynamics operate as upstream conditioning processes that generate competing emission-increasing and emission-reducing pathways through household-scale, life-course, family-formation, and spatial-contextual mechanisms. These pathways may affect DCEs and ICEs differently, while their direction and magnitude depend on the socioeconomic, institutional, infrastructural, and spatial contexts in which households are embedded.

Among the different dimensions of population dynamics, household size exhibits one of the clearest and most consistent relationships with HCEs because of household economies of scale^[37]. A well-documented empirical pattern shows that larger households generate higher total emissions but lower emissions per capita^[24,35,38]. This apparent paradox arises because many household goods and energy services can be shared among household members^[13]. Dwelling space, heating, lighting, refrigeration, and major appliances function as partially shared household goods that can serve additional members at relatively low marginal cost^[39]. Consequently, multi-member households distribute fixed energy and housing services across more individuals, thereby reducing per-capita energy use and associated emissions^[40]. Evidence from Ireland, for example, indicates that households with three or more members generate nearly one-third lower per-capita emissions than single-person households^[21].

This scale effect operates through both DCEs and ICEs. For DCEs, per-capita residential energy use generally rises as household size declines because smaller households often occupy dwellings that are not proportionally smaller than those of larger households. They therefore tend to have greater residential floor area and higher heating, cooling, and electricity demand per person^[41]. Evidence from China supports this pattern, showing that a reduction of one household member was associated with a 17%-24% increase in per-capita electricity consumption between 2010 and 2016^[42]. For ICEs, the shared use of housing space, appliances, and other carbon-intensive goods similarly reduces per-capita embodied emissions in multi-member households^[40].

Changes in household formation may further amplify these effects. The growing prevalence of single-person households, delayed marriage, and household dissolution increases the number of dwellings and duplicates demand for housing space, appliances, and household services^[13]. When one household divides into two smaller units, the additional resource requirements may generate emissions beyond those associated with population growth alone^[43]. Household size and structure therefore influence HCEs not merely by changing the number of consumers, but by determining the extent to which housing, energy services, and durable goods can be shared.

Whereas household size captures the scale dimension of population dynamics, age structure reflects its life-course dimension. Age affects physiological needs, economic resources, mobility, time use, and consumption preferences, thereby affecting both DCEs and ICEs^[44]. Unlike the relatively consistent economies-of-scale effect associated with household size, however, the carbon implications of population ageing remain ambiguous because several countervailing mechanisms operate simultaneously.

Income declines following retirement and more conservative spending patterns may reduce household consumption^[45,46], while lower mobility and less frequent participation in travel-intensive activities may lower transport-related emissions^[47-49]. Conversely, longer home occupancy and greater sensitivity to temperature variation may increase heating, cooling, and electricity demand^[44,50]. Older households may also adopt energy-efficient technologies or undertake housing retrofits more slowly^[51], while expenditure shifts toward healthcare, medicine, and home-based services may raise ICEs in these categories^[52-54]. The net effect therefore depends on whether lower mobility and discretionary consumption outweigh increased residential energy use and healthcare demand.

Empirical evidence reflects the balance between these opposing pathways. Macro-level studies report positive, negative, and nonlinear effects, depending on consumption restructuring, economic activity, residential energy demand, and interactions with urbanization^[55-60]. Household-level evidence from China also reveals differences across emission components. Using CFPS (China Family Panel Studies,a nationally representative, annual longitudinal survey of Chinese communities, families, and individuals launched in 2010 by the Institute of Social Science Survey of Peking University) data, Zhang et al. found a negative association between ageing and ICEs^[32], whereas Chen and Hu reported a positive association with DCEs^[28]. Hu et al. identified an inverted U-shaped relationship between household age structure and HCEs^[25]. Using a broader measure covering both DCEs and ICEs across 22 consumption items, Ge et al. found that the negative consumption effect of ageing outweighed its positive composition effect^[61]. Although ageing reduced emissions in most categories, it increased per-capita emissions from heating, medicine, and healthcare. Thus, a negative aggregate relationship may coexist with increases in specific emission sources.

Differences in accounting boundaries, consumption classifications, and research contexts help explain these divergent results. Studies focusing primarily on residential energy are more likely to identify emission increases associated with longer home occupancy, whereas broader consumption-based studies may find reductions when lower transport and discretionary consumption dominate. Broad expenditure categories may also conceal offsetting changes across individual items^[61]. Using urban household data from four Chinese regions, Yu et al. found that older adults slowed the growth of both DCEs and ICEs, although the effects varied across regions and periods^[62]. Cross-country variation is also important: younger adults have higher carbon footprints than older adults in China, whereas some developed-country studies report the opposite pattern^[63]. This contrast may reflect differences in the distribution of income, accumulated wealth, and purchasing power across age groups^[64], although observed age differences may also capture cohort-specific lifestyles rather than ageing itself^[65]. Population ageing therefore has no uniform effect on HCEs; estimates vary with emission boundaries, ageing measures, data levels, model specifications, control variables, and regional conditions.

Whereas population ageing reflects later-life transitions, fertility and childrearing represent family-formation processes that reshape household composition, consumption, and time allocation. Fertility decline contributes to population ageing and household transformation by leading to smaller families and a growing prevalence of childless households, and it often coincides with delayed parenthood^[13,66]. These changes may weaken household economies of scale, but they may also reduce overall demand for child-related goods and services. The carbon implications of low fertility at the population level should therefore be distinguished from those of raising children within existing households.

Childrearing constitutes an important life-course transition that alters household consumption priorities, mobility, housing demand, and daily routines^[20]. The additional needs and time pressures associated with raising children may increase reliance on convenience-oriented goods and services^[67]. DCEs may rise through greater residential energy use and transport demand, while ICEs may increase through additional expenditure on food, housing, appliances, education, childcare, and other child-related services. Existing studies generally find that households with children generate higher HCEs than childless households, although the magnitude and composition of these differences vary with the number and ages of children, household income, and living context^[68-70].

However, the financial demands of childrearing may partly offset these emission increases. Raising children entails substantial current and anticipated financial commitments, which may constrain adults’ discretionary consumption and encourage precautionary saving, particularly in response to future education and housing costs^[14,15,71]. Reduced spending on leisure, travel, and other nonessential goods may lower emissions in some consumption categories. Panel evidence also suggests that a higher proportion of children within a household may be associated with lower ICEs when financial constraints reduce overall consumption^[20].

The effects of fertility and childrearing are therefore unlikely to be uniform. Their net impact depends on whether additional child-related demand outweighs reductions in other forms of household consumption. Results may also vary with children’s ages, household income, housing conditions, urban context, and the availability and carbon intensity of childcare and education services. Compared with household size and population ageing, however, empirical evidence on these family-formation processes remains relatively limited, particularly evidence based on longitudinal data and causal research designs.

Beyond household formation and age composition, migration and urbanization reshape HCEs by relocating households across different spatial, infrastructural, and energy contexts. Population redistribution changes not only where households live but also the built environments, transport networks, public services, and energy systems that shape everyday consumption^[72]. Its carbon implications therefore depend not simply on population movement, but on differences between origin and destination areas.

Urbanization may generate both emission-reducing and emission-increasing effects. In many high-income economies, compact urban development can lower per-capita emissions through smaller dwellings, shorter travel distances, and greater access to public transport^[73,74]. By contrast, low-density expansion, larger residential floor areas, and car-dependent mobility may increase residential and transport-related DCEs. In rapidly developing economies such as China, urbanization has often coincided with income growth, housing expansion, lifestyle changes, and greater reliance on commercial energy, contributing to increases in both DCEs and ICEs^[75-79]. Urbanization is therefore not inherently carbon-increasing or carbon-reducing; its effects depend on development stage, urban form, infrastructure provision, and associated changes in household consumption.

Migration adds household-restructuring and spatial-relocation effects. The migration of young adults for education and employment may create single-person households in destination cities while reducing household size or altering co-residence arrangements in origin communities. Later-life migration may similarly change housing occupancy, access to services, and residential energy needs^[80]. Internal migration also redistributes emissions geographically. Evidence from China suggests that interprovincial migration shifts residential emissions toward economically developed coastal destinations as rural-to-urban migrants adopt different housing, mobility, and consumption patterns^[81]. Migration may limit increases in per-capita residential energy use in some settings while increasing ICEs through income growth and consumption upgrading in destination regions^[79,82,83]. Its net effect therefore depends on how migration changes household structure and on the relative carbon intensity of consumption in origin and destination areas.

These apparently divergent results reflect differences in spatial context and research design. Studies emphasizing density, dwelling size, and public transport are more likely to identify emission-reducing effects, whereas those focusing on income growth, motorization, and consumption upgrading tend to find increases. Results may also vary according to whether migration is measured by migrant status, population flows, or urban population shares, and whether emissions are attributed to the location of residence, production, or final consumption. Migration, urbanization, and urban form should therefore be distinguished analytically: migration refers to population movement, urbanization describes broader changes in settlement patterns and the urban share of the population, and urban form conditions how both processes translate into DCEs and ICEs.