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

    Summarize the **research methods** used in the study in a way that is informative for policy audiences. Your response should:
    - Describe the overall study design, data sources, tools, and models used
    - Avoid unnecessary technical details or equations
    - Use accessible but precise language

    Write a short paragraph suitable for the “Scientific Research Study Methods” section of a policy brief.

    Scientific Research Paper:
    Nature Climate Change | Volume 15 | February 2025 | 147–152
147
nature climate change
https://doi.org/10.1038/s41558-024-02237-2
Article
Mitigation policies interactions delay the
achievement of carbon neutrality in China

Yu Liu
  1,2,9
, Mingxi Du
  3,9
, Lingyu Yang
  4,5,9, Qi Cui
  6
, Yawen Liu7,
Xinbei Li
  4,5, Nenggao Zhu4,5, Ying Li8, Chen Jiang4,5, Peng Zhou
  6,
Qiuyu Liu3 & Canfei He
  1
The achievement of China’s carbon neutrality is crucial for the 1.5 °C target
of the Paris Agreement and must involve the implementation of various
mitigation policies. However, these efforts are hindered by poor knowledge
of the interactions between policies. Here we use a dynamic computable
general equilibrium model of China (CEEGE model) and create a policy
portfolio area of 1,295 scenarios covering four major mitigation strategies
(carbon pricing, energy efficiency, renewable energy and electrification
of end uses). When the interactions between mitigation policies are
considered, the percentage of scenarios in which the carbon neutrality
target is reached by 2060 decreases by 84%, with the years in which these
scenarios are achieved being delayed by 5–6 years. Only the combinations
with renewable energy and electrification of end uses generate synergetic
effects on both economic and mitigation impacts. Our work can inform the
formulation of more efficient mitigation policy portfolios by emphasizing
policy interactions.
To ensure that the 1.5 °C target of the Paris Agreement is met, effec-
tive climate policies must be implemented at the national level1–4. As
the largest CO2 emitter and the second-largest economy in the world,
China has achieved its goal of a 45% reduction in carbon intensity rela-
tive to 2005 levels in 20204. Demonstrating increased ambition, China
announced the following dual carbon targets in December 2020: peak-
ing CO2 emissions before 2030 and reaching carbon neutrality by 2060.
Various studies have proposed a range of mitigation policies to address
this challenge; these include support for non-fossil energy deployment,
the electrification of end-use sectors, energy efficiency improvement,
negative emission technologies and market-based measures (such as
carbon pricing), as well as other industry-specific policies3,5–7.
To meet its dual carbon targets, China needs to implement these
policies in tandem8. On the basis of the portfolios of mitigation poli-
cies, China is projected to successfully achieve its dual carbon targets,
with cumulative gross domestic product (GDP) losses of ~1.7–5.7%
(refs. 9–11). However, simply aggregating the (seemingly ideal) effects
of multiple policies does not accurately reflect the actual policy out-
come, as the trade-offs and synergies between policies can weaken or
strengthen their effects12,13. Combining and sequencing policy imple-
mentation can largely impact the efficiency, stringency and externality
of mitigation policies14–16. Nevertheless, most studies on achieving
China’s dual carbon targets have focused on the effectiveness and eco-
nomic costs of mitigation policies17–19, while the interactions between
mitigation policies and their impacts on attaining China’s carbon
neutrality have been overlooked.
Recent studies have measured the trade-off and synergetic effects
through comparisons of the results of the simultaneous implementa-
tion of multiple policies with the aggregation of single-implemented
policies12,20. Nevertheless, these studies focused mostly on the
Received: 19 February 2024
Accepted: 11 December 2024
Published online: 31 January 2025
 Check for updates
1College of Urban and Environmental Sciences, Peking University, Beijing, China. 2Institute of Carbon Neutrality, Peking University, Beijing, China.
3School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, China. 4School of Public Policy and Management, University of Chinese
Academy of Sciences, Beijing, China. 5Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China. 6School of Economics
and Management, China University of Petroleum, Qingdao, China. 7Digital Economy Laboratory, University of International Business and Economics,
Beijing, China. 8Chinese Academy of Personnel Science, Beijing, China. 9These authors contributed equally: Yu Liu, Mingxi Du, Lingyu Yang.
 e-mail: yu.liu@pku.edu.cn; dumingxi28@xjtu.edu.cn; cuiqi@upc.edu.cn

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Successful cases with and without policy
interactions
To examine the effects of policy interactions, we compare the simula-
tion results of mitigation scenarios under two assumptions: an actual
simultaneous-implementation assumption (ASIA), in which the
simulation results are obtained by implementing mitigation policies
simultaneously, and an idealized simple-superposition assumption
(ISSA), in which the simulation results are obtained by aggregating the
results of separately implemented mitigation policies (Supplemen-
tary Section 4.4). The baseline scenario consists of China’s nationally
determined contribution target submitted in 2015, with the goals of
peaking carbon dioxide emissions before 2030 and reducing carbon
dioxide emissions per unit of GDP by 60% from 2005 levels. The energy
and emission development assumptions are designed with reference
to the National Bureau of Statistics of China (NBSC; 2022)28 and the
International Energy Agency (IEA; 2022)29 STEP (stated policies sce-
nario) scenarios. The economic and social development assumptions
mainly refer to the NBSC (2022)30 and the United Nations (2022)31.
A detailed description of the scenario design (including macroeconom-
ics, society, energy, emissions, carbon sequestration and mitigation
strategies) for China from 2020 to 2060 can be found in Methods and
Supplementary Section 3.
Specifically, the results from our simulation indicate that achiev-
ing carbon neutrality in China would be an even greater challenge under
ASIA than what is perceived under ISSA (Fig. 1). While carbon neutral-
ity could be achieved in 23.5% of the scenarios under ISSA, it could be
achieved in only 3.7% of the scenarios under ASIA. This suggests that
because of policy interactions, the percentage of scenarios in which
the carbon neutrality target could be achieved by 2060 decreases
substantially by 84%. Furthermore, the timeline of achieving carbon
neutrality under ASIA is expected to be delayed by ~5–6 years compared
with that under ISSA. For scenarios in which the carbon neutrality target
is successfully achieved, the average year achieving carbon neutrality
is 2054 under ISSA but 2059 under ASIA. These findings highlight the
need for China to carefully consider and optimize its mitigation strate-
gies, as previous research has suggested3,32, particularly in terms of the
potential mitigation effects when multiple policies are implemented
in combination.
Among all the scenarios, the percentages of policy combinations
that lead to the achievement of the carbon neutrality target differ
interactions between mitigation policies in certain industries, includ-
ing transport, construction and agriculture21–24. Since sectors are inter-
linked through the supply chain, the interactions between mitigation
policies in one sector may influence the effects of mitigation policies in
other sectors, which has been overlooked in the literature. Moreover,
the impact of policy interactions on the timeframe of achieving the
carbon neutrality target needs to be further explored25.
Thus, this study aims to address the current insufficient under-
standing of the interactions between mitigation policies in achieving
China’s carbon neutrality target by developing a dynamic computable
general equilibrium model of China (CEEGE model), which follows
the framework of the CHINAGEM model provided by the Centre of
Policy Studies at Victoria University26,27, which is based on China’s
input‒output table in 2017 with detailed energy sectors. To achieve
this goal, we establish a comprehensive policy portfolio area towards
2060 that consists of 1,295 scenarios based on four different types of
aggregated mitigation strategies (carbon pricing (C), energy efficiency
(A), renewable energy (R) and electrification of end uses (E)). Various
grades of policy intensity and portfolios are specified to reduce the
uncertainty of future mitigation policies. For each policy portfolio,
we examine the trade-offs and synergies between mitigation policies
on mitigation effects, the economic costs of mitigation and efficiency
(GDP loss per unit carbon reduction) by comparing the results with or
without the policy interaction assumption.
Our results indicate that the interactions between mitigation poli-
cies make attaining China’s carbon neutrality more challenging than
expected, if this expectation refers to a simple aggregation of the direct
effects of each mitigation policy implemented separately12,20. The per-
centage of scenarios achieving carbon neutrality by 2060 decreases sub-
stantially by 84%, and the attainment of carbon neutrality under these
scenarios is postponed by 5–6 years when the interactions between
mitigation policies are considered. While synergetic effects expand the
potential space of policies with complementary mechanisms, trade-off
effects compress the space of policies with competing mechanisms.
Among the analysed policies, only combinations of renewable energy
and electrification of end uses are complementary and show no com-
peting mechanisms compressing the space of policies. In contrast,
combinations of carbon pricing and renewable energy have the greatest
trade-off effect. Thus, the findings highlight the need for careful policy
combination to minimize carbon emissions and economic losses.
2020
2025
2030
2035
2040
2045
2050
2055
–2
0
2
4
6
8
10
12
2060
2055
2050
2045
2040
2035
2030
2025
2020
–2
0
2
4
6
8
10
12
CO2 emissions (Gt)
ASIA
ISSA
2050
2052
2054
2056
2058
2060
2062
Year
ASIA
Year
ISSA
a
b
Fig. 1 | Emission pathways in China from 2020 to 2060. a, CO2 emission under
ASIA and ISSA (GtCO2). The divergent colour from light to dark reflects the
increasing number of policies combined. The black line represents emissions
under the baseline scenario. The white dot represents residual CO2 emissions
allowed (2 GtCO2) to achieve the carbon neutrality target considering the carbon
sequestration capacity of carbon capture and storage (CCS) technology and
forest systems. The sample size is 1,295, including all policy scenarios.
b, Distribution of years for the scenarios in which the carbon neutrality target
is achieved. The width of the density map shows the frequency of the data
distribution and the box plot shows the first quantile, mean value and third
quantile of all the results. The sample size is 48, including policy scenarios
achieving carbon neutrality under ASIA.

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considerably under ASIA and ISSA (Fig. 2). To test the impact of each
policy combination, two mitigation strategies are combined and
maintained at their highest levels, which represent those prioritized,
whereas the other two strategies are randomly combined with varying
intensities. Under ISSA, residual CO2 emissions in 2060 for all mitiga-
tion policy scenarios range from −0.8 to 2.8 GtCO2, with the proportion
of mitigation policy scenarios achieving carbon neutrality ranging from
88% (A5R5CxEx and C5R5AxEx) to 100% (A5E5CxRx and C5E5AxRx). However,
under ASIA, residual CO2 emissions rise to 1.4–3.8 GtCO2, with the
proportion of scenarios in which carbon neutrality is achieved being
reduced to a range of 28% (C5A5RxEx) to 72% (R5E5CxAx).
The ISSA results suggest that the most effective policy combina-
tions for achieving the carbon neutrality target are dominated by the
highest levels of A and E, or C and E, with an average attainment of carbon
neutrality of 2053–2054 (Fig. 2a). However, the most effective policy
combination under ASIA is that of R and E, with an average timeline of
reaching carbon neutrality by 2059. The electrification of end uses is a
key factor for attaining carbon neutrality, as the most potent portfolios
must contain E policies under both AISA and ISSA. Furthermore, in terms
of the timeline for achieving carbon neutrality, all scenarios under ASIA
are projected to achieve the target later than those under ISSA. The aver-
age economic costs under ISSA and ASIA differ considerably (Fig. 2b).
The results reveal that, under ISSA, the average cumulative economic
cost from 2020 to 2060 is ~3.8% of GDP, which is much greater than the
results (~2.9%) of the scenarios under ASIA, with the best economic
performance observed in the combination of the highest level of A
and R. Nevertheless, under ASIA, the best policy combination from the
economic side changes to the highest level of R and E. R policy clearly
plays a crucial role in determining the economic cost.
Trade-offs and synergies between mitigation
policies
Our findings highlight the notable synergetic effect of the policy com-
bination of renewable energy and electrification of end uses (R and E),
which results in a 5–20% increase in carbon reduction and a −5% to −25%
decrease in cumulative GDP cost by 2060 under ASIA compared with
ISSA (Fig. 3). The E policy supports the substitution of electricity for
fossil fuels by encouraging end-use sectors to adjust energy consump-
tion preferences and increase electricity demand; in contrast, the R
policy increases the proportion of green electricity by reducing the
cost of renewable electricity generation in the overall electricity mix.
On the economic cost side, the R policy promotes low-cost renewable
electricity and mitigates the economic cost. Even though the E policy
increases the cost of electricity production due to its expanded scale,
the R policy helps mitigate the economic impact. Thus, when R and E
policies are combined, the policy space of each is expanded to deliver
results that are stronger than those from individual implementation,
highlighting their potential mutual complementarity.
Residual CO2 emissions (Gt)
Economic cost (%)
A5R5
CxEx
88%
88%
92% 96% 100% 100%
64% 60%
28%
72%
32%
28%
73%
68%
22%
58%
4%
33% 33% 29%
94%
83% 86%
C5R5
AxEx
R5E5
CxAx
C5A5
RxEx
A5E5
CxRx
C5E5
AxRx
C5E5
AxRx
A5E5
CxRx
C5A5
RxEx
R5E5
CxAx
C5R5
AxEx
A5R5
CxEx
A5R5
CxEx
C5R5
AxEx
R5E5
CxAx
C5A5
RxEx
A5E5
CxRx
C5E5
AxRx
C5E5
AxRx
A5E5
CxRx
C5A5
RxEx
R5E5
CxAx
C5R5
AxEx
A5R5
CxEx
2053
2053
2054
2052
2053
2054
2059
2059
2059
2059
2059
2059
ISSA
ASIA
ISSA
ASIA
a
b
4
3
2
1
0
–1
–1.5
–2.0
–2.5
–3.0
–3.5
–4.0
–4.5
–5.0
Fig. 2 | China’s residual emissions and cumulative economic cost in 2060.
a, Residual emissions of scenarios with two maximum policy intensities under
ISSA and ASIA (GtCO2). The box plot shows the first, second (the median value)
and third quantiles of all the results, where the maximum and minimum values
are denoted with whiskers. The divergent colour from light to dark reflects the
increasing policy intensity of the scenarios. The dashed line represents the
residual CO2 emissions that reach the carbon neutrality target, which is set below
2 GtCO2 considering the carbon sequestration capacity from CCS technology
and forest systems. The column chart represents the percentage of scenarios
that reach the carbon neutrality target by 2060, and the text in the bar chart
represents the average year for policy combinations to achieve the target.
The sample size is 150, including policy scenarios with two maximum policy
intensities. For example, A5R5CxEx indicates the scenarios with the highest A
and R policy levels, which represents those that are prioritized. b, Cumulative
economic cost of mitigation (%) for scenarios achieving the carbon neutrality
target with two maximum policy intensities. The dashed lines represent the
average economic costs under ISSA and ASIA. The column chart represents the
proportion of scenarios with economic costs below the value indicated by the
dashed line. The box plot shows the first, second (the median value) and third
quantiles of all the results, where the maximum and minimum values are denoted
with whiskers. The sample size is 150, including policy scenarios with two
maximum policy intensities.

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By contrast, the carbon pricing and renewable energy (C and R)
policy combination is the only one that has trade-off effects from both
mitigation and economic perspectives. The carbon reduction attains
a further 4–14% drop, whereas the cumulative GDP cost increases by
an extra 2–12% under ASIA compared with that under ISSA by 2060.
From a mitigation standpoint, the mechanism of the C policy is to
increase the cost of fossil energy to encourage the shift to renewable
energy, whereas the mechanism of the R policy is to increase the share
of renewable energy by reducing the cost of renewable energy. These
mechanisms have high similarity and consistency, which leads to each
policy having a reduced policy space when they are implemented
together. The same applies to the economic side, as the benefit of
the R policy on the economic cost is also inhibited by the simultane-
ous implementation of the C policy. In summary, when policies with
competing mechanisms, such as C and R, are implemented together,
trade-offs are likely to arise, as the potential policy space is compressed
by the policies involved.
The other policy combinations have a trade-off effect in terms
of carbon mitigation but a synergetic effect on the economic cost.
Compared with ISSA, C and A, A and R, C and E and A and E under ASIA
decrease additional carbon reduction by an average of −12%, −9%, −4%
and −5%, respectively, by 2060. The corresponding additional GDP
losses are reduced by an average of −4%, −3%, −7% and −8%, respectively.
The most substantial improvement in mitigation efficiency is observed
in the R and E combination, followed by A and E and C and E. The largest
decrease in efficiency is seen in the C and R combination, followed by C
and A and A and R. The same trends are evident when more mitigation
policies are combined (Supplementary Section 5); specifically, the
simultaneous implementation of policies with R and E at high levels
has notable positive impacts on all dimensions.
In summary, synergetic effects occur when mitigation policies
with complementary mechanisms are jointly implemented, expanding
their potential policy space. Conversely, trade-off effects occur when
mitigation policies with competing mechanisms are jointly imple-
mented, compressing their policy space. Beyond mitigation efficiency,
mitigation policies also affect household income, consumption and
utility levels through changes in electricity prices (Supplementary
Section 5). A comparison of the results of ISSA and ASIA reveals that the
R and E scenario still has the most notable positive effect on increas-
ing the cumulative household consumption scale and utility levels
by 2060, which is the opposite for the C and R scenario. A detailed
discussion of the household and sectoral results can be found in Sup-
plementary Section 5.
Policy implications for improving mitigation
efficiency
The Chinese government should explore the synergetic effect of
mitigation policies, which could be largely achieved by constructing
large-scale renewable energy generation and gradually expanding the
share of electricity in final energy consumption. The following sugges-
tions can be used when designing the alignment of the implementation
pace and intensity of policies.
Accelerate the development and commercialization of energy
storage technologies
Energy storage addresses the intermittency of renewable energy and
grid load shortages, promoting renewable energy penetration and
distributed grids. Compared with the baseline scenario, energy stor-
age can increase emission reductions at a cost to the economy (Sup-
plementary Section 5). Documents from the Chinese government33,34
outline goals for energy storage technology, with new energy storage
technologies fully market-oriented by 2030. However, China’s finan-
cial and tax support for energy storage is still in the developmental
stage and is mainly advisory, lacking comprehensive and long-term
incentives, in contrast to the situation in developed countries such
as the United States and Germany35. Additionally, the levelized cost
in China among various types of storage facilities ranges from 0.12 to
0.27 US$ kWh−1 (refs. 36,37). Among them, lithium-ion batteries have
relatively low costs, while hydrogen energy storage tends to be more
expensive. Thus, differentiated subsidy policies should be considered
on the basis of the type of technology and the application scenario of
storage energy facilities.
Emphasize the electrification targets for end-use sectors
Although the Chinese government has issued guidelines related to
end-use electrification38, the measures proposed at the sectoral level
are mostly framework oriented and lack detailed policies for effective
implementation (Supplementary Section 2). An overall electrification
target for end-use sectors, such as transport and construction, are
relatively underdeveloped39,40. Future policies should include explicit
targets for end-use sectors to increase electrification. According to our
results and related studies41–43, total electricity consumption needs to
account for no less than 70% of end-use energy consumption to achieve
carbon neutrality by 2060. By sector, manufacturing sectors with a high
level of electrification, such as textiles and equipment manufacturing,
need to maintain an electrification rate of no less than 80% and for the
construction and transport sectors, the share of electricity should
exceed 70% and 50%, respectively.
Specify long-term emission abatement strategies
Among the 94 government documents collected35 and reviewed in this
study, 59 focus on goals for 2025, 25 focus on goals for 2030 and 18
focus on goals for 2035 (Supplementary Sections 2 and 4). Notably, only
two documents44,45 propose long-term development goals for 2060,
representing 4.9% of the total policies proposed after 2020. In contrast,
France, the United Kingdom and the European Union have introduced
four, nine and nine policies, respectively, that target 2050, accounting
–25
–20
–15
–10
–5
0
5
10
15
20
25
–25
–20
–15
–10
–5
0
5
10
15
20
25
Relative change of economic cost (%)
Relative change of carbon reduction (%)
R and E
A and E
C and E
A and R
C and A
C and R
Fig. 3 | Carbon reduction and economic cost of policy combinations under
ASIA compared with ISSA (%). The colour of the dots denotes the intensity of
the first policy in the policy combinations (darker colours represent greater
intensity) and the size of the dots denotes the intensity of the second policy
(larger sizes represent greater intensity). The sample size is 150, including all
dual-policy scenarios that implement two policies together.

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for 14.3%, 18.8% and 23.1% of all policies proposed after 2020, respec-
tively35. Thus, the Chinese government must enhance the planning and
formulation of emission reduction policies from a long-term perspec-
tive. For example, according to the simulation results presented here,
China’s emissions need to decrease by more than 25% and 55% in 2040
and 2050, respectively, compared with current emissions to ensure the
achievement of the carbon neutrality target by 2060.
In addition to a global perspective, carbon mitigation efforts
depend not only on the policies adopted but also on the policies
achieved46. To achieve the climate target in a just, orderly and equi-
table manner, rather than blindly pursuing more stringent carbon
reduction policies, which could lead to escalating economic losses,
developing countries should focus more on optimizing the combina-
tion and sequence of carbon reduction policies16,47. This approach not
only enhances the effectiveness of carbon reduction but also minimizes
the economic cost of carbon reduction efforts. Additionally, since
policy interactions can have implications beyond national boundaries
under the need to advance national and global climate policy48, policy
interactions must also be considered for international cooperation
among countries to reduce carbon emissions.
In summary, this study assesses the effectiveness of various miti-
gation strategies to achieve China’s carbon neutrality target with the
CEEGE model. Model robustness is tested through uncertainty analysis
across five dimensions: baseline scenarios, mitigation policies, model
parameters, energy storage technology and carbon sequestration
capacity (Methods and Supplementary Section 6). Additionally, two
points can be further explored. First, this study focuses on mature
and foreseeable renewable energy and storage technologies, exclud-
ing emerging technologies such as green hydrogen, because of the
uncertain changing trend in production costs49,50 and data limita-
tions for modelling. Second, although 1,295 scenarios with varying
policy intensities are created here, policy combinations in grades of
policy intensities at different timelines have not been considered,
which could be an important direction for future research to analyse
policy sequencing.
Online content
Any methods, additional references, Nature Portfolio reporting sum-
maries, source data, extended data, supplementary information,
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/s41558-024-02237-2.
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Nature Climate Change
Article
https://doi.org/10.1038/s41558-024-02237-2
Methods
We use a dynamic computable equilibrium model of China (CEEGE
model) and establish a policy range containing 1,295 scenarios that
cover four mitigation strategies with five intensity grades and their
portfolios. It can depict the cost-neutrality mechanisms of mitigation
policies, capture dynamic sectoral changes through the upstream–
downstream industrial chain and adjust both quantity and price at
equilibrium simultaneously. Three indicators are used to measure
the trade-offs and synergies between policy couples in terms of
their carbon-mitigating effects, economic costs of mitigation and
carbon-mitigating efficiency. The research framework is presented
in Supplementary Section 1.
CEEGE model
The CEEGE model is a dynamic computable equilibrium model of
China, following the framework of the CHINAGEM model provided
by the Centre of Policy Studies at Victoria University26,27. The CEEGE
model is based on the neoclassical economics theory and assumes
that the market is fully competitive and that the returns to scale of
production are constant. It contains six economic agents (production,
investment, consumption, government, foreign and inventory) and
three primary factors (labour, capital and land) and is solved with the
GEMPACK software51. In this model, a system of linearized equations is
established to describe the behaviours of agents in response to price
changes and determines the equilibrium price and quantity by equat-
ing the demand and supply for all goods and factors. The model can be
used to capture the direct and indirect effects of exogenous changes in
the economy and assess the impact mechanism across the economy.
Hence, it provides a valuable tool for various policy-oriented studies
related to carbon mitigation52,53. The modules of production, house-
hold consumption, governmental consumption, investment, export
demand, carbon accounting and pricing, the dynamic module, the
equilibrium mechanism, macro-economic closure and energy storage
are presented in Supplementary Section 4.
Scenario design
We establish a baseline scenario for the 2017–2060 period and 1,295
mitigation policy scenarios considering different intensities and port-
folios of four major mitigation strategies (Supplementary Section 3).
The baseline scenario reflects future economic growth, energy con-
sumption and carbon emissions under the current mitigation poli-
cies. The historical economic growth and energy consumption data
for 2017–2021 are calibrated according to the NBSC28,30 and British
Petroleum Statistical Review54. For 2022 to 2060, the projections of
GDP growth, population, labour and industrial structure refer mainly
to existing studies31,55,56. The energy consumption towards 2050 is
calibrated by endogenizing energy efficiency improvement rates
according to the projection of the IEA29 and extrapolated to the year
2060. In 2060, the annual GDP and population growth rate will decline
to 2.5% and −0.6%, respectively; energy consumption will increase to
6,054 Mtce and carbon emissions will decrease to 7.9 GtCO2.
To establish mitigation policy scenarios, we extensively review the
existing mitigation policies (Supplementary Section 2) and summarize
the four most important mitigation strategies, including energy effi-
ciency improvement, renewable energy development, electrification
of end uses and carbon pricing. Energy efficiency policy (A) increases
the energy efficiency improvement rate of industries and residents;
the economic cost of energy efficiency policy is burdened by industries
and residents according to their consumption of energy products.
Renewable energy policy (R) refers to the decrease in generation and
accommodation costs of solar power, onshore wind power, offshore
wind power and bioenergy power due to technological progress; the
development of renewable energy sectors will crowd out investment
in fossil energy sectors, so the declining economic costs in the renew-
able energy sector will be accompanied by rising costs in the fossil
energy sector. The electrification of end-uses policy (E) prompts
industries and residents to replace fossil fuels with electricity in their
terminal energy consumption while keeping total terminal energy
consumption unchanged. The carbon pricing policy (C) is modelled
as a tax on carbon emissions emitted by industries and residents and
the carbon tax revenue is recycled to residents via lump-sum transfer.
Then, on the basis of a systematic literature review of the above-
mentioned policies, we specify five grades for the intensity of each
policy (Supplementary Section 3). The first and fifth grades correspond
to the lowest and highest intensities of each policy. Given five grades for
the four mitigation strategies, 20 single-policy scenarios are obtained.
Combining these mitigation policies with different grades, we obtain
150 dual-policy scenarios which implement two policies together,
500 triple-policy scenarios and 625 quad-policy scenarios. Finally,
1,295 mitigation policy scenarios are established to provide a relatively
more comprehensive consideration of the uncertainty in the intensities
and portfolios of future mitigation policies.
Uncertainty discussions
This paper discusses the uncertainty of the simulation results from five
aspects: the baseline scenario, mitigation policies, model parameters,
energy storage technology and carbon sequestration capacity. First,
China’s carbon dioxide emission pathway may present varying predic-
tions under the baseline scenario (Supplementary Section 3) and the
strictness of achieving the mitigation target directly impacts emission
reduction and economic costs. Thus, we set two scenarios for acceler-
ating and delaying the emission reduction progress in the baseline to
test this uncertainty. Second, different intensities of mitigation policies
may lead to a nonlinear changing trend or turning point for reduced
emissions and economic losses. To test this uncertainty, we created
1,295 scenarios by combining different policies with different intensi-
ties on the basis of an extensive literature review (Supplementary Sec-
tion 3.2). We also developed two additional policies with more stringent
intensity (grade 6) and weaker intensity (grade 0). Third, the simulation
results for achieving the carbon neutrality target may be affected by the
substitution elasticity between different energy products in the model.
To address this uncertainty, the substitution elasticity between energy
products was increased or reduced by 25% to test the robustness of the
results. Fourth, energy storage technology can additionally support
the growth of high proportions of renewable energy and increase the
economic cost. In this paper, we set two scenarios with high-cost and
low-cost storage energy development to test the robustness of the
simulation results. Finally, enhancing the carbon sequestration capac-
ity is crucial for achieving carbon neutrality goals, as the percentage
of scenarios in which the carbon neutrality target is achieved by 2060
will decrease substantially from 11.1% to 0.23%, with a decrease in the
carbon sequestration capacity from 2.5 GtCO2 to 1.5 GtCO2 (Supple-
mentary Fig. 22). In this study, we assumed that the capacity is limited
to a maximum of 2 GtCO2 from CCS technology and forest systems by
2060. On the basis of the five categories of uncertainty analysis above,
we find that the trade-offs and synergies between mitigation policy
pairs remain robust: the simultaneous implementation of policies with
R and E at high levels has notable positive impacts on all dimensions,
and the policy combination with C and R has notable negative impacts
on all dimensions. See Supplementary Section 6 for details about the
uncertainty analysis.
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
All data supporting the conclusions of this work are provided in Sup-
plementary Section 7 and are available via Zenodo at https://doi.org/
10.5281/zenodo.14325940 (ref. 57).

Nature Climate Change
Article
https://doi.org/10.1038/s41558-024-02237-2
Code availability
The code of the dynamic computable equilibrium model of China
(CEEGE model) is available via Zenodo at https://doi.org/10.5281/
zenodo.14325940 (ref. 57).
References
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Equip. Model 1, 815–876 (2013).
53.	 Anderson, K. & Wittwer, G. in Policy Analysis and Modeling of the
Global Economy (ed. Maskus, K.) 249–278 (World Scientific, 2021).
54.	 Statistical Review of World Energy (British Petroleum, 2021);
www.bp.com/en/global/corporate/energy-economics/statistical-
review-of-world-energy.html
55.	 Zhang, X. et al. Energy–economic transformation pathways
and policies for carbon neutrality targets. Manag. World 38,
35–66 (2022).
56.	 Real GDP Long-Term Forecast (Indicator) (OECD, 2023); www.
oecd.org/en/data/indicators/real-gdp-long-term-forecast.html
57.	 Liu, Y. et al. Code scripts for ‘Mitigation policies interactions
delay the achievement of carbon neutrality in China’. Zenodo
https://doi.org/10.5281/zenodo.14325940 (2024).
Acknowledgements
Yu Liu was supported by the National Natural Science Foundation of
China (grant nos. 72125010, 72243011 and 71974186), The Fundamental
Research Funds for the Central Universities, Peking University and
High-performance Computing Platform of Peking University. M.D. is
supported by the National Natural Science Foundation of China
(grant no. 72304222) and Young Talent Program of Xi’an Jiaotong
University (grant no. GG6J007). Q.C. was supported by National Natural
Science Foundation of China (grant no. 72373163) and Beijing Natural
Science Foundation (grant no. 9222016), Fundamental Research
Funds for the Central Universities from China Petroleum University
(grant no. 23CX06039A) and Taishan Scholar Program of Shandong
Province. Yawen Liu was supported by the China Postdoctoral Science
Foundation (grant no. 2023M730590) and the Fundamental Research
Fund for the Central Universities in UIBE (grant no. 22QD30).
Author contributions
Yu Liu, M.D. and L.Y. conceived the research. Yu Liu, M.D., L.Y. and Q.C.
designed the research. Q.C., L.Y., Yu Liu, M.D. and Yawen Liu collected
the data and conducted the model simulations with inputs from X.L.,
N.Z., Y. Li and C.J. M.D., Q.C., L.Y. and Yu Liu interpreted the results and
wrote the paper with inputs from Yawen Liu, X.L., N.Z., Y. Li, C.J., P.Z.,
Q.L. and C.H. All authors discussed the results and contributed to
revising the paper.
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/s41558-024-02237-2.
Correspondence and requests for materials should be addressed to
Yu Liu, Mingxi Du or Qi Cui.
Peer review information Nature Climate Change thanks Sheng Zhou
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.

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Economic data and emission data are based on the China's input-output table of the year 2017 and China Energy Statistical Yearbook(2022).
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Study description
Here we use a dynamic computable general equilibrium model, create a policy portfolio area of 1,295 scenarios covering four major
mitigation strategies, and examined trade-offs and synergies between mitigation policies on mitigation effects, and economic costs
by comparing the results between the ASIA and ISSA assumptions.
Research sample
159 production sectors and 157 products based on the China Input-output table  and the China Electric Power Yearbook are involved
in this study.
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For this study, the ‘electricity’ sector in the original input-output table is split into nine new sectors with different power sources,
including eight generation sectors and one sector of ‘power transmission and distribution’. The original ‘crude oil and gas sector’ is
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Data collection
Economic data and emission data are based on the China's input-output table of the year 2017 and China Energy Statistical
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Answer: We established a comprehensive policy portfolio area toward 2060, consisting of 1,295 scenarios based on 4 different types of aggregated mitigation strategy (carbon pricing, energy efficiency, renewable energy and electrification of end uses). Meanwhile, we developed a dynamic computable general equilibrium model of China (CEEGE model) to assess the impacts on emissions reductions, economic costs of mitigation and mitigation efficiency under different policy combinations. All scenarios were simulated and compared under two different assumptions: an actual simultaneous-implementation assumption (ASIA), in which the simulation results are obtained by implementing mitigation policies simultaneously; and an idealized simple-superposition assumption (ISSA), in which the simulation results are obtained by aggregating the results of separately implemented mitigation policies. By comparing the emissions reduction effects of all scenarios under the two assumptions, we specifically demonstrate the importance of policy interactions for achieving carbon neutrality.