Question:
You are given the full text of a scientific research paper.

    Your task is to generate a structured **policy brief** that includes the following components:

    - A concise, descriptive **title** that captures the central theme or policy issue addressed in the paper (do not label it with “Title:”).
    - **Policy Problem** - What societal or strategic challenge is the paper addressing?
    - **Scientific Research Findings** - What are the key results relevant to this issue?
    - **Scientific Research Study Methods** - What methodology or data supports these findings?
    - **Policy Implications** - How might the findings guide or influence policy?

    Write each section in a clear, accessible style suitable for policymakers. Avoid overly technical language, but use essential terms when necessary. Structure the output as a labeled five-part policy brief document.

    Scientific Research Paper:
    Nature Climate Change | Volume 14 | December 2024 | 1261–1267
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nature climate change
https://doi.org/10.1038/s41558-024-02162-4
Article
Assessing the impacts of fertility and
retirement policies on China’s carbon
emissions
Ling Tang
  1, Junai Yang
  2, Jiali Zheng
  3, Xinlu Sun
  4, Lu Cheng5,
Kehan He
  6, Ling Li
  7, Jinkai Li
  8
, Wenjia Cai
  9, Shouyang Wang
  1,2,10,
Paul Drummond
  11 & Zhifu Mi
  4
The gradual adjustment of fertility and retirement policies in China has
social benefits in terms of coping with population aging. However, the
environmental consequences of these policies remain ambiguous. Here we
compile environmentally extended multiregional input–output tables to
estimate household carbon footprints for different population age groups
in China. Subsequently, we estimate the age-sex-specific population under
different fertility policies up to 2060 and assess the impacts of fertility and
retirement policies on household carbon footprints. We find that Chinese
young people have relatively higher household carbon footprints than their
older counterparts due to differences in income by age group. Relaxing
fertility policies and delaying retirement age are associated with an increase
in population (and labour supply) and thus increases in household carbon
footprints, with the majority of these increases from the fertility side.
These results may help policymakers understand interactions among those
measures targeting population aging and climate action.
Mitigating climate change and coping with population aging are
both critical goals for China in achieving sustainable development1,2.
As the world’s largest carbon emitter, China aims to have a carbon
emissions peak before 2030 and achieve carbon neutrality by 20603.
Currently, China is turning towards more sustainable development,
with the deceleration of China’s annual carbon emissions growth from
10% (2000–2010) to 2% (2010–2020)4. However, China remains an
important driver of global carbon emissions due to its large popula-
tion and growing household consumption over the past 20 years. To
better explore the drivers of carbon emissions, the household carbon
footprint (the sum of direct and indirect carbon emissions of house-
hold consumption along the supply chain) has received increasing
attention recently1,5,6. In addition, China is one of the most populous
countries in the world, with a population that is nearing its peak and
aging rapidly7. In 2020, China’s total fertility rate was only 1.3 births
per woman, which is far below the replacement level (2.1) needed for
a stable population8. It is projected that China’s population will peak
at 1.45 billion in 2029 (with a range of 1.42 to 1.48 billion from 2025 to
2035)2,9, after which contraction is expected. At the same time, China
is aging rapidly, with the proportion aged 65 years and above doubling
from 7% in 2000 to 14% in 202010.
China has implemented a national strategy to address popula-
tion aging, including relaxing fertility policies and delaying retire-
ment age. In the 1970s, a one-child policy was introduced to curb
Received: 2 August 2022
Accepted: 18 September 2024
Published online: 11 October 2024
 Check for updates
1School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China. 2Academy of Mathematics and Systems Science,
Chinese Academy of Sciences, Beijing, China. 3The School of Management, Xi’an Jiaotong University, Xi’an, China. 4The Bartlett School of Sustainable
Construction, University College London, London, UK. 5School of Ecology and Environment, Renmin University of China, Beijing, China. 6Institute for
Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong SAR, China. 7International School of Economics and Management, Capital
University of Economics and Business, Beijing, China. 8School of Economics, Beijing Institute of Technology, Beijing, China. 9Department of Earth System
Science, Tsinghua University, Beijing, China. 10School of Entrepreneurship and Management, ShanghaiTech University, Shanghai, China. 11Institute for
Sustainable Resources, University College London, London, UK.
 e-mail: lijinkai@sina.com; z.mi@ucl.ac.uk

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those of developed countries, where older people are estimated to
have higher carbon footprints5,22. The difference in carbon footprint
distribution by age, between developed and developing countries, is
due mainly to the difference in wealth and income across age groups.
In wealthier developed countries, older people tend to be wealthier
than younger people and thus can afford a higher level of consump-
tion and tend to have higher carbon footprints23. In developing
countries (for example, China), young people have higher incomes
than older people (by 57%, according to our individual data), associ-
ated with higher consumption and carbon footprints (by 69% and
77%, respectively).
After examining the expenditure categories in greater detail, it
is evident that the top two contributors to the average carbon foot-
print of all age groups are consumption related to residence and that
related to transport (Fig. 1b and Supplementary Note 1). There are
some meaningful differences across age groups not only absolutely
but also proportionally (Supplementary Fig. 1), representing their
differences in lifestyle choices and life stage24. For residence, the
young people have the highest carbon footprint (1.08 tCO2 per capita
in 2017) and contribute to the largest share of total residence-related
footprint (46%), the majority of which is from renting or purchasing
a house18 and using electronic devices25; the older people have the
highest proportional share of residence-related carbon footprint (41%)
as they might be accustomed to using traditional energy-intensive
devices for heating and cooking (such as Kang and stove)26 and spend
much of their time at home (and thus have large household energy
consumption)27. For transport, the young people’s transport-related
footprint is the highest both absolutely (accounting for 50% of the
total transport-related footprint by all groups) and proportionally
(accounting for 25% of their own total footprint), largely from com-
muting to work28 and from a few big trips each year (for example, from
their workplace to their hometown)29. Moreover, the absolute and pro-
portional per capita carbon footprints related to clothing, goods and
transport have decreased gradually with age; however, health-related
carbon footprint has increased with age, as has education-related
carbon footprint until the individual is in their 30 s to 40 s, after which
it decreases (Fig. 1b). We further explore how unevenly the per capita
carbon footprint is distributed among different age groups using the
Theil index. The higher the index value is, the greater the inequality
in terms of the distribution between age groups. In 2017, the Theil
index for clothing, transport and education-related carbon footprint
was the highest, at 0.06, three times the average value of expenditure
categories (0.02) (Supplementary Table 3).
The preceding patterns also generally hold in all Chinese prov-
inces: young people have a relatively higher per capita carbon footprint
than that of older people (by 1.21 to 2.93 times), and consumption pat-
terns vary over the life course (for example, the young age group has
larger clothing-, goods- and transport-related carbon footprints, and
the middle-aged group generates most of the education-related carbon
footprint). Regarding the Theil index, eastern provinces (for example,
Guangdong and Hainan), central provinces (for example, Anhui and
Hunan) and southwestern provinces (for example, Guangxi and Chong-
qing) have higher values than northwest provinces (for example, Inner
Mongolia and Gansu) (Fig. 1c and Supplementary Table 5).
Between 2012 and 2017, China’s average per capita carbon foot-
print increased by 17%, from 2.00 tCO2 in 2012 to 2.34 tCO2 in 2017. In
particular, young people experienced larger increases (30%) than did
middle-aged (12%) and older (8%) people during this period, meaning
that the difference in carbon footprints across age groups grew (with
an increase in the Theil index from 0.01 to 0.02; Supplementary Tables 3
and 4). At the provincial level, the average per capita carbon footprint
and Theil index increased in most provinces, mainly because of the
growing carbon footprint of the young generation that ranges from a
10% increase (Yunnan) to a 211% increase (Ningxia) from 2012 to 2017
(Supplementary Tables 5 and 6).
population growth and alleviate severe poverty in China2. Following
the introduction of this policy, the fertility rate decreased—the total
fertility rate declined sharply from approximately 5.8 in 1970 to 2.8
in 1979 and was thought to be approximately 1.6 in 2010—resulting
in a rapidly aging population7,11. In October 2015, China’s one-child
policy was replaced with a two-child policy to counter this trend12.
The two-child policy has had a positive effect on the birth rate: more
than 10 million babies were born as a second child in China dur-
ing 2013–2017, and the proportion of newborns who were second
children in new births increased from 30% in 2013 to 50% in 20178.
However, a continuous fall in the number of women of childbearing
age and a gradual decline in the effect of the two-child policy resulted
in a drop in the number of new births during the period 2017–20208.
In May 2021, in an attempt to tackle demographic challenges, China
further relaxed its fertility policy with a three-child policy, allowing
all couples to have up to three children13. In addition, many sup-
portive measures have been implemented to address housing and
educational costs, aiming to ease the financial burden of raising
children14. However, the retirement age in China, 60 years for men
and an average of 52.5 years for women (50 years for women workers
and 55 years for women cadres)2, is among the lowest in the world:
the official retirement age for most developed countries is 65 years
or even higher15,16. According to the Outline of the 14th Five-Year Plan
(2021–2025), China called for the extension of the statutory retire-
ment age in a gradual, flexible and differentiated manner to reduce
the negative impacts of population aging17. Changing fertility and
retirement policies are likely to have great effects on the population
age structure and potentially influence household consumption and
carbon footprints.
Many studies have estimated the effect of population aging on
carbon emissions in China, finding that population aging may reduce18,
increase19,20 or have nonlinear effects on carbon emissions21. However,
no study has assessed the impacts of policies that address population
aging—including fertility (particularly the three-child policy) and
retirement policies—on carbon emissions or household carbon foot-
prints. Thus, we aim to address this gap in the literature. In this Article,
we first investigate age-based household carbon footprints in China
and its provinces by compiling a global multiregional input–output
(MRIO) table and employing a large-scale household survey. We fur-
ther estimate the age distribution of the population in China and its
provinces up to 2060 by using a cohort-component method and then
assess the impacts of fertility and retirement policies on household
carbon footprints.
Age-based household carbon footprint
The total and per capita household carbon footprint varies greatly
across China’s provinces. Eastern provinces (which have large popu-
lations) tend to have higher total carbon footprints (particularly in
Shandong, Guangdong and Jiangsu; Fig. 1a and Supplementary Table 1).
Northwestern provinces (with high carbon intensity) and eastern
provinces (with high household consumption) tend to have higher
per capita carbon footprints. For example, Ningxia (a northwestern
province) had the highest per capita carbon footprint (6.68 tons CO2
(tCO2) in 2017, six times that of Sichuan (a southwestern province)
at 1.05 tCO2 (Fig. 1c). Taking China as a whole, its per capita carbon
footprint is much lower than that of developed countries. Specifically,
the Chinese per capita carbon footprint was 2.34 tCO2 in 2017, approxi-
mately one-sixth of that in the United States (13.37 tCO2) and one-third
of that in Japan (6.29 tCO2) and the United Kingdom (6.03 tCO2), but
similar to that in Mexico (2.31 tCO2) and almost three times that in
India (0.78 tCO2) (Fig. 1b).
In China, carbon footprint is inversely correlated with age. Young
Chinese people (<30 years) have relatively higher household car-
bon footprints than those of middle-aged (30–59 years) and older
(≥60 years) groups. The observed results are quite different from

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3.64–19.24
19.25–101.19
101.20–194.40
194.41–345.57
N
0
500 km
Total carbon footprint
(MtCO2)
<30
30–39 40–49 50–59
≥60
Average
Age group
Province
Region
United
States
Japan United
Kingdom
Russia
EU27
Mexico Brazil
India
0
2
4
6
12
14
16
Others
Household direct
Goods
Transport
Education
Food
Clothing
Residence
China
Health
2.92
2.58
1.97
1.87
1.65
2.34
13.37
6.29
6.03
5.55
5.46
2.31
1.68
0.78
a
b
c
Xinjiang
Beijing
Inner Mongolia
Shanghai
Ningxia
Tianjin
Shanxi
Liaoning
Shandong
Qinghai
Heilongjiang
Hebei
Guangdong
Zhejiang
Jiangsu
Guizhou
Jilin
Shaanxi
Gansu
Hainan
Chongqing
Henan
Anhui
Fujian
Hubei
Hunan
Jiangxi
Guangxi
Yunnan
Tibet
Sichuan
0
2
4
6
8
10
Theil index
<30
30–39
40–49
50–59
≥60
0
0.03
0.06
0.09
0.12
0.15
Average
Theil index
Per capita carbon footprint (tCO2)
Per capita carbon
footprint (tCO2)
Per capita carbon footprint (tCO2)
No data
0.01–1.85
1.86–2.56
2.57–4.44
4.45–6.68
Fig. 1 | Household carbon footprint in 2017. a, Total and per capita household
carbon footprints for 31 of China’s provinces. The cut-out of islands is the South
China Sea Islands. The data for the basemap were derived from the Resource
and Environment Data Cloud Platform (https://www.resdc.cn/Default.aspx).
b, Per capita household carbon footprints for eight expenditure categories for
China’s different age groups and for international comparisons. We calculate
the per capita household carbon footprints of the United States, Japan,
United Kingdom and other countries using data from the EXIOBASE database
(https://www.exiobase.eu/index.php)36. c, Per capita household carbon
footprints and the Theil index for 31 of China’s provinces. Provinces are sorted
according to their average per capita carbon footprint.

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Fertility and retirement policies
To evaluate the impacts of fertility and retirement policies on China’s
household carbon footprints, we first estimate the population of China
and its 31 provinces up to 2060 by age (0–100+) and sex (male and
female) under different fertility policies: previous two-child policy,
the latest three-child policy and the assumed ‘replacement-level’ pol-
icy (with fertility rate reaching the replacement level of 2.1 (ref. 30)).
Then we explore the potential effect of these fertility policies and their
combination with retirement delay policies on the household carbon
footprints (Supplementary Data 1–6). We use retirement age as the
threshold to classify older people and assume that such a retirement
delay policy affects only the population age structure31.
We find that the preceding two kinds of policies will both pose
a challenge to carbon emissions mitigation. As for fertility policies,
they affect the population mainly in terms of size and structure,
and thus affect the carbon footprints. In specific, our results show
that the Chinese population will peak in 2023 (1.41 billion), 2030
(1.41 billion) and 2040 (1.44 billion) under the two-child, three-child
and replacement-level policies, respectively (Fig. 2a). From 2017
to 2060, the total population will decrease from 1.40 billion to 1.15
billion (two-child policy), 1.30 billion (three-child policy) and 1.39
(replacement-level policy), which means the population differences
are 12–20% under different policies (Fig. 2a). The mean population age
of a person will increase from 38 years to 51 years (two-child policy),
47 years (three-child policy) and 45 years (replacement-level policy),
thus, the proportion of older people will increase from 17% to 42%
(two-child policy), 37% (three-child policy) and 35% (replacement-
level policy) (Fig. 2b–e). Due to relaxing fertility policies, there is an
8–12% increase in per capita footprints (the blue, yellow and red solid
curves in Fig. 3 (China)), and the total footprints in China are likely to
be 21–35% higher.
The preceding effects also hold at the provincial level, but the
extent of the impact varies (Supplementary Note 2). The provinces
with a higher Theil index are more sensitive to changes in fertility
2020
2025
2030
2035
2040
2045
2050
2055
2060
0.8
1.0
1.2
1.4
1.6
Two-child policy
Three-child policy
Replacement-level policy
Population (billion)
1.41
1.41
1.44
a
5–9
15–19
25–29
35–39
45–49
55–59
65–69
75–79
85–89
95+
0
0.01
0.02
0.03
0.04
0.05
Age group
Age group
Percentage of population
0.01
0.02 0.03 0.04 0.05
5–9
15–19
25–29
35–39
45–49
55–59
65–69
75–79
85–89
95+
0
0.01
0.02
0.03
0.04
0.05
Percentage of population
0.01
0.02 0.03 0.04 0.05
5–9
15–19
25–29
35–39
45–49
55–59
65–69
75–79
85–89
95+
0.01
0.02
0.03
0.04
0.05
Age group
Age group
Year
5–9
15–19
25–29
35–39
45–49
55–59
65–69
75–79
85–89
95+
0.01
0.02
0.03
0.04
0.05
0
0.01
0.02 0.03 0.04 0.05
b
c
e
d
0
0.01
0.02 0.03 0.04 0.05
Male
Male median
Male mean
Female median
Female mean
Female
Fig. 2 | Population changes and population age structure in China under
different fertility policies. a, Population changes at the national level from 2017
to 2060 under the two-child policy, three-child policy and replacement-level
policy. b, Population pyramid for males and females by age in 2017 (two-child
policy). c–e, Population pyramids for males and females in 2060 under the
two-child policy (c), three-child policy (d) and replacement-level policy (e).

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policies (in terms of larger changes in per capita carbon footprints;
Supplementary Fig. 2). For example, in Inner Mongolia, which has
the highest Theil index in 2060, changing fertility policies are pro-
jected to increase its average per capita carbon footprint by 18–28%
(Fig. 3, Inner Mongolia). By comparison, in Guizhou, which has the
lowest Theil index in 2060, changing fertility policies are projected
to increase its average per capita carbon footprint by only 4–5%
(Fig. 3, Guizhou).
Fertility policies in combination with retirement delay tend to
further increase the carbon footprint in China. Notably, most of the car-
bon footprint increase comes from relaxing fertility policies (increas-
ing total (per capita) carbon footprint by 21–35% (8–12%) for 2060),
0
0.4
0.8
1.2
1.6
Footprint change
Footprint change
Footprint change
Footprint change
Footprint change
Footprint change
Footprint change
Footprint change
China
Guizhou
Beijing
Heilongjiang
1.0
1.1
1.2
1.3
Impact of policy
Impact of policy
Impact of policy
Impact of policy
Impact of policy
Impact of policy
Impact of policy
Impact of policy
0
0.4
0.8
1.2
1.6
Yunnan
Tianjin
Shanghai
Zhejiang
1.0
1.1
1.2
1.3
Sichuan
Shanxi
Jiangxi
1.0
1.1
1.2
1.3
0
0.4
0.8
1.2
1.6
Qinghai
Liaoning
Chongqing
Gansu
1.0
1.1
1.2
1.3
0
0.4
0.8
1.2
1.6
Ningxia
Xinjiang
Hebei
Hubei
1.0
1.1
1.2
1.3
0
0.4
0.8
1.2
1.6
Shaanxi
Jiangsu
Hunan
1.0
1.1
1.2
1.3
0
0.4
0.8
1.2
1.6
Guangdong
Guangxi
Jilin
1.0
1.1
1.2
1.3
2020
2030
2040
2050
2060
0
0.4
0.8
1.2
1.6
Fujian
Year
Year
Year
Year
2020
2030
2040
2050
2060
Hainan
2020
2030
2040
2050
2060
Shandong
2020
2030
2040
2050
2060
Inner Mongolia
1.0
1.1
1.2
1.3
0
0.4
0.8
1.2
1.6
Tibet
Older people
Middle-aged people
Two-child policy
Three-child policy
Replacement-level policy
Replacement-level and retirement delay policies
Three-child and retirement delay policies
Two-child and retirement delay policies
Young people
Henan
Anhui
Fig. 3 | The impacts of fertility and retirement policies on carbon footprints
in China and its provinces. Change in total household carbon footprint under
the two-child policy compared with the 2017 level (left y axis); change in per
capita household carbon footprint under different policies compared with the
two-child policy (right y axis). Provinces are sorted according to the value of the
Theil index in 2060, from the lowest value of the Theil index, in Guizhou, to the
highest value, in Inner Mongolia.

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while delaying retirement policy has far smaller impacts (by only 2–3%
(2–3%)). The fertility policies in combination with retirement delay are
projected to have greater impacts on those provinces with a higher
Theil index, which is similar to the impacts of fertility policy alone (Sup-
plementary Fig. 3). Moreover, when focusing only on the impact of the
retirement delay policy, we found that the impact tends to be greater
in provinces with large discrepancies in per capita carbon footprints
between middle-aged and older people (Supplementary Fig. 4). For
example, in Inner Mongolia, which has the highest discrepancies in per
capita carbon footprints between middle-aged and older people, the
retirement delay policy is projected to increase its average per capita
carbon footprint by approximately 5% (Fig. 3, Inner Mongolia). By
comparison, in Yunnan, which has the lowest discrepancies in per capita
carbon footprints between these two age groups, the retirement delay
policy is projected to increase its average per capita carbon footprint
by less than 0.10% (Fig. 3, Yunnan).
Discussion
Our results show that Chinese young people have relatively higher per
capita household carbon footprints compared with older people. The
big driver behind this headline result might be differences in income,
which leads to differences in household consumption and then car-
bon footprints (Supplementary Note 3); The results differ from those
of existing research on developed countries, which have concluded
that older people tend to have higher per capita carbon footprints
compared with their younger counterparts. Such a distinctive pattern
is due mainly to the difference in income and consumption of China’s
older people from other developed countries23.
Our analysis highlights residence and transport as the two largest
contributors to carbon footprint, and there is variability in them across
age groups. Notably, the assumptions about how these two factors
might evolve as the population ages will have a meaningful impact
on our projection results, and we conducted an uncertainty analysis
assuming that both residence- and transport-related carbon foot-
prints will still be a feature of today’s young group as they age under
different assumptions (follow Chinese forebears’ or Western peers’
patterns; Supplementary Note 4). In particular, the residence-related
carbon footprint from the young group will remain high as they move
into the next age cohort if following Chinese older people’s specific
patterns (for example, spending more time at home27), or will even
grow if following Western peers (with more electronics and devices
plugged in at the residence32). In addition to these proportional dif-
ferences, our overall results for household carbon footprints and
the impacts of fertility and retirement policies do not change much
(Supplementary Figs. 14–16).
As for policies, our result shows that relaxing fertility policies
and delaying retirement age will boost the population (and labour
supply) and then lead to increases in total and per capita household
carbon footprints, most of which come from the fertility side. We do
not interpret the result to imply that such policies should be avoided to
reduce environmental pressure33. Rather, our result provides evidence
of interactions between the policies targeting population aging and
climate change, highlighting the importance of synergizing these two
types of policies. Although fertility and retirement policies may pose a
challenge to China’s carbon emissions mitigation, these policies (par-
ticularly those for retirement delay) can lower the dependency ratio
and thus improve the demographic dividend (Supplementary Note 2).
In addition, we find that the provinces with large discrepancies
in carbon footprints across age groups are more sensitive to changes
in fertility and retirement policies. This result therefore highlights
the potential of emissions mitigation through reducing the discrep-
ancy in carbon footprints across age groups. Although consumption
patterns and lifestyles are different across age groups due to their
various requirements over the life course, the discrepancy in carbon
footprints between age groups can be narrowed by reducing income
and consumption inequality and encouraging greener consumption.
Specifically, we suggest that the greatest potential leverage from life-
style changes will result from the targeting of young people by promot-
ing green consumption (such as adopting public transportation such
as buses, subways and shared bikes as well as purchasing high-quality
and long-lasting goods6).
China’s Nationally Determined Contributions acknowledges
the difficulty of achieving carbon neutrality by 2060. Therefore, it
is worth exploring what might happen if this target is not reached.
According to related research and official plans34,35, we consider a set
of policy scenarios assuming that China achieves a 60%, 70%, 80%, 90%
or even 100% reduction in carbon intensity (Supplementary Note 5).
Our results show that there are fewer emissions in the future with a
more aggressive target, and achieving 60%, 70% and 80% reductions
from 2017 to 2060 (instead of the 90% target) is estimated to nearly
quadruple, triple and double, respectively, the overall emissions
in all scenarios (Supplementary Fig. 17). Furthermore, there is an
interaction between the policy proposals and emissions targets—the
effects of fertility and retirement policies will somewhat aggravate
the difficulty of achieving carbon neutrality by 2060, and such policy
effects will increase if the official target is not reached. Nevertheless,
our major findings regarding structural patterns of carbon footprints
(across age groups) and changing trends (due to different fertility
and retirement policies) do not change very much (Supplementary
Fig. 17). This verifies the accuracy of our estimates, which might rely
little on this official target.
Overall, this study provides evidence of interactions between
climate actions and demographic policies in China. We find that relax-
ing fertility policies and delaying retirement age are associated with
an increase in total and per capita household carbon footprints. Our
results add to the literature on climate change and population, which
has typically evaluated the effect of demographic structure on emis-
sions without considering the independent effect of the population
policy (especially in China) that contributes to bringing about the
change in demographic structure in the first place. Our results also
offer insights for developing countries undergoing economic and
demographic transformation for more sustainable development.
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acknowledgements, peer review information; details of author contri-
butions and competing interests; and statements of data and code avail-
ability are available at https://doi.org/10.1038/s41558-024-02162-4.
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Publisher’s note Springer Nature remains neutral with regard to
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Methods
Household carbon footprint and MRIO analysis
Carbon footprints measure the greenhouse gas emissions generated
in the value chains connected with the products consumed in the form
of final demand1. In this study, we consider CO2 emissions and focus
on household consumption in China. Household carbon footprints
come from household consumption activities, including direct energy
use (direct carbon footprints), for example, during cooking, heating
and driving, and the consumption of goods and services, which are
produced by using energy as intermediate inputs (indirect carbon
footprints).
Indirect household carbon footprints are calculated on the
basis of input–output analysis. Wassily Leontief developed the
theoretical framework of the input–output analysis in the late
1930s37. The fundamental linear equation of the MRIO model can be
expressed as:
⎛
⎜
⎜
⎜
⎜
⎝
x1
x2
⋮
xr
⎞
⎟
⎟
⎟
⎟
⎠
=
⎛
⎜
⎜
⎜
⎜
⎝
A1,1 A1,2 ⋯A1,s
A2,1 A2,2 ⋯A2,s
⋮
⋮
⋱
⋮
Ar,1 Ar,2 ⋯Ar,s
⎞
⎟
⎟
⎟
⎟
⎠
⎛
⎜
⎜
⎜
⎜
⎝
x1
x2
⋮
xr
⎞
⎟
⎟
⎟
⎟
⎠
+
⎛
⎜
⎜
⎜
⎜
⎝
∑s ∑t y1,s
t
∑s ∑t y2,s
t
⋮
∑s ∑t yr,s
t
⎞
⎟
⎟
⎟
⎟
⎠
,
(1)
where xr denotes the total output for each sector in province r; Ar,s is
the technical coefficient matrix, which reflects the input requirement
by sector in province r to produce one unit of output of the sector in
province s; and yr,s
t  is the final demand vector of category t, including
household consumption (t = 1), government consumption (t = 2), capi-
tal investment (t = 3) and exports (t = 4). Equation (1) can also be abbre-
viated as follows:
x = Ax + y,
(2)
where x, A and y are the block matrix or vector in equation (1); then we
can obtain the following:
x = (I −A)
−1y,
(3)
where I is the identity matrix, and (I −A)
−1 is the Leontief inverse matrix.
Indirect carbon footprints are calculated by introducing carbon
intensity (carbon emissions per unit of economic output) by sector:
e = f(I −A)
−1 ̂y,
(4)
where f is the carbon intensity vector, and the carbon emissions used
to produce f are from the China Emission Accounts and Datasets
(CEADs). Sector-specific indirect carbon footprints es
t consumed by
final demand t in province s can be calculated as follows:
es
t = f(I −A)
−1 ̂yr,s
t .
(5)
Notably, there are 45 sectors in the CEADs (regarding emissions
data) and 42 sectors in the Chinese MRIO tables38. Due to data availabil-
ity, we mapped 45 sectors to 42 sectors to calculate household carbon
footprints and then aggregated to eight expenditure categories for
further analyses: food, clothing, residence, goods, transport, educa-
tion, health and others6. Specifically, goods include household facili-
ties and durables; transport contains transport and communications;
education refers to education, culture and entertainment39.
For direct household carbon footprints, the emissions data are
obtained from the CEADs, where energy-related emissions are listed
separately. We allocate the emissions from the energy use of coal and
natural gas to the direct household carbon footprints of residence, and
oil emissions are for the category of transport1,40.
Finally, the total carbon footprint from household consumption
can be combined as follows:
ces
l.t = ∑
r
er,s
l,t + de
s
l,t,
(6)
where ces
l,t and de
s
l,t are the total and direct carbon footprints, respec-
tively, of expenditure categories l in province r for household con-
sumption (t = 1); er,s
l,t  is the indirect household carbon footprint of
expenditure categories l in province r caused by household consump-
tion in province s.
Tracing household carbon footprints to specific age groups
In this section, we trace the household carbon footprints to various
age groups according to their expenditure in terms of consuming
products. The consumption data used in this study are obtained from
a large-scale household survey (China Family Panel Studies (CFPS))41,
and the age-based population data are from the China Provincial Sta-
tistical Yearbooks. Notably, consumption data in a household survey
are usually collected at the household level and need to be allocated to
per capita consumption for further age-based analysis. In this study,
we use the Organisation for Economic Co-operation and Develop-
ment modified equivalence scale to distinguish children from adults
(including head of household and other adults) in calculating per
capita consumption42,43, rather than simply assuming equal weights
on all household members as has typically been done in previous stud-
ies. In particular, the head of household is weighted by 1, each addi-
tional adult aged 14 years and older is weighted by 0.5, and each child
below 14 years is weighted by 0.3. It is worth exploring the sensitivity
of our results to weight choice; thus we also run our model with a per
capita calculation and weight-adjusted calculation (Supplementary
Note 6), and the associated results show that using a different weight
choice would change the structural pattern of carbon footprints
across age groups but would not change the national average or total
(Supplementary Fig. 18).
The introduction of the CFPS dataset allows for the downscaling
of household consumption into age cohorts:
yr,s
l,q = c s
l,q × ps
q ×
yr,s
l
y s
l
,
(7)
where yr,s
l,q represents the household consumption of sector l in province
r caused by age group q in province s; c s
l,q represents the per capita
household consumption, which is calculated by using the Organisation
for Economic Co-operation and Development modified equivalence
scale; ps
q denotes the population, derived from the China Provincial
Statistical Yearbooks; yr,s
l /y s
l  denotes the proportion of household
consumption for each sector that is finally produced in province r and
consumed in province s (obtained from the MRIO model). Thus, the
age-based indirect household carbon footprint can be obtained by the
following:
er,s
l,q = k r,s
l
× yr,s
l,q,
(8)
where er,s
l,q is the indirect household carbon footprint, and k r,s
l  denotes
the indirect carbon emissions per unit of household consumption:
k r,s
l
=
er,s
l
yr,s
l
.
(9)
For households, the direct carbon footprint of province s can be
split into age group q as follows:
de
s
l,q = θ s
l,q × de
s
l ,
(10)

Nature Climate Change
Article
https://doi.org/10.1038/s41558-024-02162-4
where de
s
l,q denotes the direct carbon footprints from the household
consumption for the products of sector l by age group q in province s;
θ s
l,q is the proportion of household consumption by age group q in all
age groups for sector l in province s:
θ s
l,q =
c s
l,q × ps
q
∑q(c s
l,q × ps
q) .
(11)
Finally, the total household carbon footprint by age group can be
calculated by combining indirect and direct carbon footprints:
ces
l,q = ∑
r
er,s
l,q + de
s
l,q,
(12)
where ces
l,q represents the province- and sector-specific total household
carbon footprints, and the associated per capita carbon footprints can
be further calculated by dividing them by the population.
Theil index
In this study, we use the Theil index to measure how unevenly house-
hold carbon footprints are distributed across age groups44. The Theil
index ranges from zero to one, with a higher value indicating greater
inequality of distribution across age groups. The Theil index is calcu-
lated by the following:
T = ∑
q
dq ln (
dq
wq
) ,
(13)
where dq is the household carbon footprint share of age group q on the
total and wq is a weighting variable (share of population) for age group
q, which can be calculated as follows:
dq =
ceq
∑qceq
,
(14)
ωq =
Pq
∑qPq
.
(15)
Projection
We conduct projection similarly on the basis of the input–output analy-
sis, introducing the year index n to the original form of equation (4):
es,v
l.q,n = f s
l,n(I−−−A)
−1 ̂y s,v
l,q,n,
(16)
where es
l,q,n is the total household carbon footprints for the year n under
the policy scenario v, f s
l,n is the carbon intensity, (((I−−−A)))
−−−1 is the Leontief
inverse matrix, and y s,v
l,n,q is the household consumption.
Carbon intensity. Carbon intensity is modelled following China’s
Long-Term Low-Carbon Development Strategies and Pathways: Com-
prehensive Report35, as the planned annual changing rates listed in
Supplementary Table 12. Using these planned annual changes (as well
as the linear interpolations around them), we can project future carbon
intensities on the basis of the 2017 baseline (Supplementary Note 5).
Household consumption. The household consumption is calculated
by multiplying per capita household consumption with population. We
model per capita household consumption under the assumption that
young people would become much like their forebears in terms of fol-
lowing the income effect of consumption and concomitant emissions:
log( yq,l) = aq,l + bq,l log(mq),
(17)
where y indicates per capita consumption, and m denotes per capita
income, which is projected on the basis of the long-term gross domestic
product (GDP) forecast under the shared socioeconomic pathways
middle-of-the-road scenario45,46. The coefficient b is the income elas-
ticity of consumption (Supplementary Note 3), which is estimated
positive in all cases on the basis of our individual data and thus suggests
that increases in per capita incomes result in greater consumption
(Supplementary Table 8). We project that the level of consumption
from all age groups will become higher along economic development,
while the structure of expenditure otherwise will not greatly change
(Supplementary Fig. 8).
Population. We estimate the age-sex-specific population from 2020
to 2060 using a cohort-component method—a dominant method of
population projection47. The basic demographic balancing equation is
as follows:
P(N ) = P(0) + B(0, N ) −D(0, N ) + G(0, N ),
(18)
where P(N) is the population at a given time, P(0) is the population at
the start of the interval, and B(0, N), D(0, N) and G(0, N) are the num-
ber of live births, number of deaths and net migration, respectively,
during the interval.
The cohort-component method of population projection
extends the preceding demographic balancing equation to estimate
age-sex-specific populations on the basis of a series of related factors,
such as age-specific fertility rates, sex ratio at birth, age-sex-specific
mortality rates and net migration (Supplementary Note 7).
The impacts of fertility and retirement policies
In this study, we explore the potential effects of fertility policy and its
combination with retirement policy on the household carbon footprint.
Specifically, there are three fertility policies: the two-child policy, the
three-child policy and the assumed replacement-level policy. As for
the retirement policy, we assume that the retirement age (as well as
the threshold for older people in this study) is extended linearly from
the Chinese current level (60 years for men and 55 years for women2)
in 2020 to the age prevailing in developed countries (65 years for both
men and women) in 2050, and remains constant afterwards (Supple-
mentary Table 7)31. The fertility and retirement policies would affect
the population and economy, thereby impacting the household con-
sumption and carbon footprint in equation (16).
The impact on population. The fertility and retirement policies affect
the population in terms of size and structure, respectively. In particular,
we project the population under different fertility policies with differ-
ent fertility rates following the cohort-component method. As for the
retirement policy, we use retirement age as the threshold to classify
older people, such that the retirement policy affects the population
age structure31.
The impact on the economy. Fertility and retirement policies would
have impacts on the supply of labour and the potential levels of the
economy48:
ΔGDPs,v
n = ∑
q
∑
g
Δlabourrates,v
q,g,n × ratios,
(19)
where ΔGDP
s,v
n  is the changes in GDP under policy v (where GDP gener-
ally reflects the potential level of economy and can be used as the
proxy for income45,46), Δlabourrate
s,v
q,g,n is the changes in labour force
participation rate for gender g, and ratio
s is the ratio of labour remu-
neration to GDP.
For fertility policies, we project the population with different
fertility rates, then estimate the associated labour force participation
rates by the ratio of working population to total population, and finally
compute the changes of labour force participation rate across policies.

Nature Climate Change
Article
https://doi.org/10.1038/s41558-024-02162-4
For retirement policies, the change in labour force participation
rate is the difference between the counterfactual labour force partici-
pation curve (under the new retirement policy) and the actual labour
force participation curve (under the current retirement policy). The
actual curve is estimated by the ratio of working population to total
population, using the data derived from 2020 Chinese Census. For the
counterfactual curve, the ordinary least square method is employed
to quantify the impact of the new retirement age on labour force par-
ticipation rate48:
labourrates
q,g = β0 + β1D(ages
q,g) + β2ages
q,g + εs
q,g,
(20)
where labourrate
s
q,g represents the labour force participation rate, ages
q,g
denotes the lower bound for the corresponding age group (for exam-
ple, age = 50 for the 50–54 age group), and the dummy variable D(ages
q,g)
indicates whether the corresponding age group reaches the retirement
age (D = 1) or not (D = 0). The coefficient β1 measures the extent to which
the new retirement age affects the labour force participation rate. In
building the counterfactual labour force curve, the labour force par-
ticipation rate changes with age, that is, following the trend of actual
labour force participation curve below the current retirement age,
dropping with the magnitude of β1 at the current retirement age, and
returning to and holding the actual trend at and above, respectively,
the delated new retirement age.
The causality of policy to carbon footprint. Supplementary Fig. 6
illustrates a causal chain from policy to carbon footprint. In specific,
the fertility and retirement policies affect the population (in terms of
size and structure, via equation (18)) and the supply of labour (in terms
of labour force participation rate, via equation (20)), then affect the
economy (in terms of GDP and household income, via equation (19))
and per capita consumption (via equation (17)), and result in changes
in household carbon footprints (via equation (16)).
Data description
In this study, the China MRIO tables for 2012 and 2017 are compiled
using a gravity model based on the single regional input–output tables
for Chinese provinces; the detailed information can be found in our
previous work38,49. The carbon emissions inventory can be sourced from
the CEADs, the household consumption expenditure data between age
groups are obtained from the CFPS dataset41, and the age-sex-specific
demographic data in China and its 31 provinces are based on the 2020
Chinese Census. Moreover, the data in the MRIO tables and the house-
hold expenditure data in the CFPS are all calculated on the basis of
2012 prices50.
The CFPS, developed by Peking University, is a nearly nationwide
and comprehensive social survey and aims to serve research needs
regarding various current social phenomena in China41. The main vari-
ables used in this study are as follows: (1) the size of the household (one,
two, three or more persons), (2) the number of children (<14 years) and
adults (≥14 years), (3) the geographic classification of the household
(31 provinces), (4) the expenditures of eight expenditure categories
(food, clothing, residence, goods, transport, education, health and
others) of the household, (5) household income, (6) the head of the
household (the person who is in charge of the household) and (7) the
ages of all household members.
Uncertainties and limitations
There are several uncertainties and limitations in the calculations of
this study. First, the economic data (such as those on national accounts
and interregional trade) and carbon emissions inventories are the
main uncertainties of this study. Previous research reported that the
uncertainty of consumption-based carbon accounts at the national
level is in the range of 5–15% and 2–16%51. Moreover, the MRIO analysis
has also been validated by our previous calculations1,38. Second, the
input–output analysis enables us to estimate the carbon footprints
for the ‘average’ products, and it is often criticized for using too many
sector aggregations52. Due to data availability, we have to use only 42
categories, of which ‘transport’ is one (with no distinction between
private, public and so on) and so is ‘food’ (with no detailed information
on diet shift), and if provincial input–output tables for more than 42
sectors are available for China, we will improve our method by using
more expenditure categories to avoid the bias due to using aggregate
analyses. Third, due to data availability, we assume that the footprint
intensities (carbon emissions per unit of consumption expenditure,
in equation (8)) on a category are the same across the age groups,
which introduces uncertainty; capturing the differences in footprint
intensity between age groups would improve our projection. Fourth,
we consider only CO2 emissions, and including other greenhouse gas
emissions is an important issue for the future research if related data
for Chinese provinces are available. Fifth, most shared socioeconomic
pathways data are provided in GDP terms (which are net of trade), and
long-term scenarios of consumption can be substantially impacted by
changes in trade terms; thus, improving the projections (particularly
for future income and consumption using appropriate proxies) is an
important direction to improve our work45. Sixth, we keep interme-
diate technology constant in the input–output matrix due to data
availability, and capturing the structural change in the economy over
a long time horizon is an important direction to improve our research53.
Seventh, the assumption for future carbon intensity has impacts on
results (Supplementary Note 5), and introducing defensible long-term
plans (if available for China) is an important direction to improve our
method54. Finally, there is tremendous uncertainty about both the
future of fertility and retirement in China (Supplementary Note 8), and
much work is needed to adequately set out such uncertainty (as well as
the linkages to emissions) for future research.
Data availability
All the source data used in this study are publicly available and open
access. The carbon emissions inventory in China is sourced from the
CEADs (http://www.ceads.net/), the household survey is sourced
from CPFS (https://opendata.pku.edu.cn/dataverse/CFPS), and the
age-sex-specific demographic data in China are sourced from the
2020 Chinese Census (https://www.stats.gov.cn/sj/pcsj/rkpc/7rp/
indexch.htm).
Code availability
The codes developed for the analyses and to generate results are avail-
able from the corresponding author on reasonable request.
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Acknowledgements
This work was supported by grants from the National Natural
Science Foundation of China (71971007 to L.T., 72374144 to
L.L. and 71988101 to S.W.), the Beijing Natural Science Foundation
(JQ21033 to L.T.), the National Social Science Fund of China Key
Research Project (23VRC063 to L.T.), the Postdoctoral Fellowship
Program of CPSF (GZC20241864 to J.Y.) and China Scholarship
Council (201906020094 to J.Y.).
Author contributions
L.T., J.L. and Z.M. designed the study. J.Y., X.S. and L.C. collected
data and performed calculations. L.T., J.Y., J.Z. and L.L. prepared
the manuscript. All authors (L.T., J.Y., J.Z., X.S., L.C., K.H., L.L., J.L.,
W.C., S.W., P.D. and Z.M.) participated in performing the analysis and
contributed to writing the manuscript. L.T. and Z.M. coordinated and
supervised the project.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary
material available at https://doi.org/10.1038/s41558-024-02162-4.
Correspondence and requests for materials should be addressed to
Jinkai Li or Zhifu Mi.
Peer review information Nature Climate Change thanks Jared Starr
and the other, anonymous, reviewer(s) for their contribution to the
peer review of this work.
Reprints and permissions information is available at
www.nature.com/reprints.

    Policy Brief:

Answer:
**Relaxing fertility policies and delaying retirement age increase China’s carbon emissions**

**Policy Problem**The world is witnessing a demographic shift towards ageing societies, posing a serious challenge to sustainable development and climate actions. In response, China has progressively relaxed its fertility policy (that is, shifting from the one-child policy to the two-child policy, and now to the three-child policy) and has proposed a gradual increase in the statutory retirement age (currently 60 for men and 55 for women, compared with 65 for both men and women in developed countries). These policy changes are expected to impact population size, age structure and the labour market, potentially influencing household consumption and carbon footprint. However, such impacts lack quantitative assessment. Capturing the potential impacts of relaxing fertility policies and delaying retirement age on household carbon footprints is critical for policymakers to coordinate demographic policies with those for climate change mitigation.

**Scientific Research Findings**We find that younger people in China tend to have higher household carbon footprints due to greater income and consumption, a pattern that contrasts with developed countries where older people typically have higher carbon footprints. Relaxing fertility policies and delaying retirement age are projected to increase household carbon footprints in China, primarily through boosting the population and labour. These impacts of the policy changes seem to be greater in regions where disparities in income and consumption among different age groups are larger. These findings do not imply that such policies targeting population ageing should be avoided to alleviate environmental pressure. Instead, they provide evidence of the interactions between policies targeting population ageing and climate change, and highlight the importance of synergizing policies with different targets.

**Scientific Research Study Methods**We developed an integrated framework of climate, population and economy models to assess the impacts of fertility and retirement policies on household carbon footprints. We estimate household carbon footprints in different age groups by combining the environmentally extended multiregional input–output model with detailed household survey data. Fertility and retirement policies affect household carbon footprints via two channels. First, they affect the population in terms of size and structure, which is assessed by using the cohort-component method. Second, they also affect the supply of the labour force and subsequently the economy, which is estimated by analysing changes in labour force participation rates and household consumption patterns across age groups.

**Policy Implications**• Assessing the climate impact is crucial when developing fertility and retirement policies.
• Promoting greener consumption and sustainable lifestyles, particularly among young people, is an effective measure to reduce household carbon footprints in China.
• Reducing income and consumption disparities between age groups could alleviate the impacts of fertility and retirement policies on household carbon footprints.
• Delaying retirement can considerably lower dependency ratios and enhance demographic dividends, although it may increase household carbon footprints.
• Policymakers need to consider the synergy between policies addressing population ageing and those addressing climate change.