Energy 294 (2024) 130846
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Energy
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Decarbonization pathways to subregional carbon neutrality in China based
on the top-down multi-regional CGE model: A study of Guangxi
Ling He a, b, Xiaofan Li c, *, Qi Cui d, e, **, Bing Guan f, Meng Li c, Hao Chen c
a
School of Economics, Beijing Wuzi University, Beijing, 101149, China
Institute for Carbon Peak and Neutrality, Beijing Wuzi University, Beijing, 101149, China
c
Beijing Key Lab of Study on Sci-Tech Strategy for Urban Green Development, School of Economics and Resource Management, Beijing Normal University, Beijing,
100875, China
d
School of Economics and Management, China University of Petroleum, Qingdao, 266580, China
e
Shanghai Academic of Global Governance and Area Studies (SAGGAS), Shanghai International Studies University, Shanghai, 201620, China
f
Industrial Economy Institute, China Center for Information Industry Development, Beijing, 100036, China
b
A R T I C L E I N F O
A B S T R A C T
Handling Editor: P Ferreira
Deep decarbonization efforts are necessary in subregions to achieve China’s dual carbon goals. However, existing
studies on subregional decarbonization pathways have neglected the constraints of national decarbonization
schemes, resulting in incompatibility between national and subregional decarbonization pathways. In this study,
we employed a top-down multi-regional computable general equilibrium (CGE) model using Guangxi as an
example to assess the decarbonization pathways of the subregion under two typical national decarbonization
pathways. Under a conservative decarbonization scenario, Guangxi’s carbon emissions will peak at 325 Mt CO2
by 2032, with residual carbon emissions of 111 Mt CO2 by 2060. Under a proactive decarbonization scenario,
Guangxi’s carbon emissions will be significantly reduced, peaking at 294 Mt CO2 by 2030 and retaining residual
carbon emissions of 63 Mt CO2 by 2060. The service, light industry, metallurgy, building materials, and elec­
tricity sectors were the major carbon emitters. Decarbonization plans can potentially transform Guangxi’s energy
structure into a low-carbon system and narrow the supply-demand gap for fossil fuels. However, the supply
demand gap for electric power will increase unless energy efficiency is largely improved. Realizing dual carbon
goals can cause some damage to Guangxi’s macroeconomy and energy-intensive industries.
Keywords:
Decarbonization pathways
Carbon neutrality
Top-down multi-regional CGE model
Subregion
China
1. Introduction
China is the world’s largest carbon emitter, and the carbon emissions
reached 10,648.5 million tons (Mt) in 2021, accounting for 31.7% of the
total global carbon emissions [1]. This share is expected to increase
gradually in the future, owing to population growth and industrial
expansion. At the Paris Climate Summit in 2015, the Chinese govern­
ment committed to implement the Nationally Determined Contributions
(NDCs) for achieving the carbon peak by 2030 and reducing the carbon
emissions per unit of gross domestic product (GDP) by 60%–65% from
its level in 2005 [2]. On September 22, 2020, at the 75th session of the
United Nations General Assembly, President Xi declared China’s
dual-carbon goals of achieving its carbon peak by 2030 and carbon
neutrality by 2060. The share of non-fossil energy consumption will
gradually increase to approximately 20 percent by 2025, 25 percent by
2030, and over 80 percent by 2060 [3]. Against this background, the
central government has extensively intensified its carbon mitigation
policies and developed the “1 + N” policy system [4,5].
The attainment of dual-carbon goals requires both the promotion of
low-carbon energy transformation at the national level and deep
decarbonization at the subregional level. Stimulated by the ambition of
national leaders, subregional governments have also made their own
dual carbon goals and developed action plans to reduce carbon emis­
sions [6,7]. By the end of May 2023, 28 of 31 provinces (including
municipalities and autonomous regions) in mainland China released
official documents announcing dual carbon goals and implementing
carbon reduction policies. Most have committed to attaining carbon
neutrality by 2060, in line with the national plan, through the optimi­
zation of industrial and energy structures, promotion of clean and effi­
cient utilization of fossil fuels, and determination of the development of
* Corresponding author.
** Corresponding author. School of Economics and Management, China University of Petroleum, Qingdao, 266580, China
E-mail addresses: li_xiaofan@mail.bnu.edu.cn (X. Li), cuiqi@upc.edu.cn (Q. Cui).
https://doi.org/10.1016/j.energy.2024.130846
Received 1 June 2023; Received in revised form 2 December 2023; Accepted 27 February 2024
Available online 27 February 2024
0360-5442/© 2024 Elsevier Ltd. All rights reserved.
L. He et al.
Energy 294 (2024) 130846
renewable energy and negative emission technologies. Whether prov­
inces can attain dual-carbon goals remains a difficult question, consid­
ering the uncertainty regarding future economic development,
technological progress, and resource constraints [8,9]. Hence, the
evolving path of carbon emissions and energy structures at the provin­
cial level must be urgently assessed and region-specific mitigation pol­
icies be formulated.
Decarbonization pathways and policies at the national level have
been analyzed previously. Most projected that China’s carbon emissions
will peak at 9.2–11.5 billion tons (Bt) CO2 by 2030 and that the carbon
neutrality goal will be attained by 2060, with 1.4–4.0 Bt of residual
carbon emissions [10–13]. Carbon pricing, terminal electrification,
renewable energy development, and energy efficiency improvements
are efficient policies for reducing carbon emissions [14–17]. Carbon
neutrality cannot be achieved with a single policy, therefore, a combi­
nation of multiple policies is required [18,19]. Moreover, great dispar­
ities were observed in the impacts of different decarbonization schemes
on the economy, with most studies indicating that carbon reduction
policies put pressure on China’s economic growth. Nevertheless, certain
policies, such as the implementation of a carbon tax, could achieve the
dual benefits of carbon reduction and GDP growth [19,20]. Recent
studies have also considered the emission reduction potential and
contribution to achieving carbon neutrality presented by negative
emission technologies (e.g., carbon capture, utilization and storage
[CCUS] technology) [21–23]. As the resource endowments and eco­
nomic development of the subregions differ significantly from those of
the nation overall, the pathways for carbon reduction in each province
may differ. Hence, studies that have analyzed decarbonization pathways
at the national level have not provided sufficient information to
formulate region-specific decarbonization pathways.
The decarbonization pathways, carbon reduction policies, and eco­
nomic impacts of certain subregions in China have been analyzed pre­
viously. The decarbonization pathways to attain dual carbon goals have
been examined in Sichuan, Jilin, Fujian, Shandong, and Gansu, and
these provinces were predicted to reach a carbon peak of 150–925 Mt
CO2 in 2028–2030 and achieve carbon neutrality before 2060, with
residual emissions of 40–283 Mt CO2 [24–28]. However, these studies
have rarely considered the constraints of national decarbonization
pathways regarding dual carbon goals [29–31]. A recent study incor­
porated the constraints of national decarbonization pathways in the
calculation of a carbon budget for Guizhou province aimed at meeting
the 2 ◦ C and 1.5 ◦ C temperature control goals of the Paris Agreement
[25]. The effects and economic costs of carbon reduction policies in the
subregions have also been analyzed [30,32,33]. For example, Zhai et al.
(2021) found that with a carbon tax of 100 yuan/ton, Guangdong’s GDP
would fall by less than 0.5%, and carbon emissions would decline by
1.2–1.6 Mt CO2 [34]. Li et al. (2023) found that energy efficiency
improvement, energy mix optimization, and the penetration of renew­
able energy would reduce CO2 emission in Sichuan province by 13.3,
87.8, and 5.4 Mt, respectively, by 2058 [28].
Optimization models, partial equilibrium models, and general equi­
librium models have been used to simulate the pathways of energy and
carbon emissions at subregional levels. The dynamic evolution and
interaction of multiple factors, including atmospheric composition,
climate change, and ecosystems, can be analyzed using optimization
models. However, these models lack the economic mechanisms to
consider the behavioral response-based changes of economic agents to
carbon emission reductions [25,33,35]. Partial equilibrium models,
such as the long-range energy alternatives planning (LEAP) model,
analyze specific sectors in isolation but cannot be used to consider the
transmission and feedback between sectors [30,31,36]. Unlike these
approaches, general equilibrium models, particularly the computable
general equilibrium (CGE) model, can capture comprehensive in­
teractions and feedback among different agents in an economic system.
Provincial CGE models have been used to study the subregional path­
ways to achieve peak carbon emissions without considering the
constraints of national decarbonization pathways [6,29], leading to in­
compatibility between national and subregional decarbonization path­
ways. Bottom-up multi-regional CGE models, such as the enormous
regional model (TERM), require more regional-level data for modeling
and are more difficult to merge into dynamic simulations when there are
a large number of subregions [37]. Comparably, top-down multi-re­
gional CGE models require relatively little data and do not rely on
detailed information on trade flows between provinces. Additionally,
they can consider both national and regional-level decarbonization
pathways simultaneously [38,39].
In this study, we employed a top-down multi-regional CGE model to
explore the decarbonization pathways of a representative subregion,
namely Guangxi Zhuang Autonomous Region (also referred to here as
Guangxi), under the constraints of China’s dual carbon goals. Further­
more, the impacts of different pathways on subregional carbon emis­
sions, energy supply and demand, and sectoral output were assessed to
identify the challenges in carbon reduction at the subregional level and
reveal the trade-offs between carbon reduction and economic growth.
This study contributes to the existing literature in the following ways.
First, it provides a relatively simple approach to explore the decarbon­
ization pathways of a subregion under the constraints of China’s na­
tional decarbonization plan. The top-down multi-regional CGE model
employed in this study considers national and regional-level decarbon­
ization pathways simultaneously while using less data than that
required by bottom-up multi-regional CGE models. Second, this study
reveals the disparity in the difficulties of carbon reduction between the
subregions and the nation. Owing to different resource endowments,
industrial structures, and development stages, the difficulties of carbon
emission reduction in the subregion vary largely between provinces and
countries. Carbon reduction policies identified in national-level studies
may not necessarily apply to these subregions. Therefore, we analyzed
the difficulties in carbon reduction in Guangxi to provide implications
for relevant policymaking. Third, this study reveals the trade-offs be­
tween carbon reduction and economic growth in the decarbonization
pathways in the subregion. Balancing carbon reduction and economic
growth when developing decarbonization pathways is important for a
less-developed province such as Guanxi, and economic costs should be
assessed comprehensively against carbon reduction effects.
The remainder of this paper is organized as follows: the trends of
carbon emissions and dual carbon goals in Guangxi are described in
Section 2; the simulation model, data, and scenarios are introduced in
Section 3; the identified evolving pathways of carbon emissions, energy
structure, macroeconomy, and sectoral output in Guangxi under
different decarbonization schemes are discussed in Section 4; and the
implications and limitations of this study are described in Section 5,
which concludes with several policy implications.
2. Carbon emissions and dual carbon goals in Guangxi
We took Guangxi as an example to illustrate an approach for eval­
uating the decarbonization pathways of the subregions in China.
Guangxi, located in western China, is the gateway of China to the As­
sociation of Southeast Asian Nations (ASEAN) Union and is a lessdeveloped region with a large proportion of low-income residents.
Despite the recent rapid national economic growth, Guangxi’s GDP was
375.3 billion USD, constituting only 2.1% of the national GDP, in 2022.
Meanwhile, the per-capita GDP was 7389 USD, accounting for 60.8% of
the national level. By the end of 2021, 246,200 rural residents were at
the risk of slipping back into poverty. The proportion of minority resi­
dents in this region was 37.6% in 2021, considerably higher than the
national level of 8.9%. With the economic development, the carbon
emissions in Guangxi reached 288.03 Mt CO2 in 2021, accounting for
2.78% of China’s total emissions [40]. Although Guangxi has relatively
abundant hydropower resources, its fossil fuel, wind power, and solar
power endowments are notably insufficient, hindering its trans­
formation into a low-carbon energy system.
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L. He et al.
Energy 294 (2024) 130846
Fig. 1. Trends in Guangxi’s carbon emissions during 2009–2019 (Mt CO2).
Source: CEADS, 2023
As a typical inland region, Guangxi has insufficient energy resources,
a less-developed economy, an energy-intensive industrial structure, and
a large number of low-income residents. Thus, the attainment of dual
carbon goals poses great challenges to Guangxi, unlike that to coastal
and developed provinces, considering the dilemma between economic
growth and carbon emissions reduction. However, the decarbonization
pathways for China’s coastal and developed provinces analyzed previ­
ously provide few implications for carbon reduction policies in lessdeveloped regions [26–28]. Hence, research on Guangxi’s decarbon­
ization pathways and mitigation policies could benefit the
less-developed inland provinces in China by aiding the formulation of
region-specific carbon mitigation policies.
Over the past decade, carbon emissions in Guangxi have increased
rapidly, driven by economic growth and coal-dominated energy struc­
tures. The total carbon emissions increased 1.6-fold from 151.8 Mt CO2
in 2009 to 212.0 Mt CO2 in 2019, although this change exhibited a
complicated trend (Fig. 1A). Total carbon emissions increased rapidly
during 2009–2013, with an annual average growth rate of 11.5%, but
displayed a decreasing trend during 2014–2015 and has been increasing
again since 2016, with an annual average growth rate of 6.4%. Although
coal is the major source of Guangxi’s carbon emissions, the emissions
from crude oil have increased rapidly owing to rising petroleum con­
sumption. Decomposing carbon emission by sector, electricity, metal­
lurgy, building materials, and services were the major carbon emitters,
accounting for 92.3% of Guangxi’s carbon emissions in 2019 (Fig. 1B).
Carbon emissions from the electricity sector increased from 51.8 Mt CO2
in 2009 to 101.4 Mt CO2 in 2019, contributing to approximately half of
Guangxi’s increased carbon emissions in the past decade. In 2019,
metallurgy, building materials, and services accounted for 22.5%,
18.8%, and 8.8%, respectively, of Guangxi’s carbon emissions.
Accompanied by rapidly increasing energy consumption, the supplydemand gap of energy products in Guangxi has widened in recent years
(Fig. 2). Further, the total energy consumption increased from 65.9
million tons of coal equivalent (Mtce) in 2009 to 131.9 Mtce in 2021,
with an annual average growth rate of 5.9%, driven by rapid economic
growth. Although fossil fuels were the major energy source in Guangxi,
the proportion of electricity increased from 25.0% in 2009 to 38.1% in
2021 owing to large-scale investments in electric power facilities and
Fig. 2. Trends in Guangxi’s energy production and consumption during 2009–2021 (Mtce).
Note: “Prod.” and “Cons.” are the abbreviation of “Production” and “Consumption”.
Source: Guangxi Statistical Yearbook, 2022
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L. He et al.
Energy 294 (2024) 130846
coal-electricity substitution. The increase in energy production was
more slowly than that of energy consumption during this period; the
production increased rising from 18.2 Mtce in 2009 to 35.0 Mtce in
2021, with an annual average growth rate of 5.6%. Insufficient natural
endowments of fossil fuels have restricted the expansion of conventional
energy production. However, electricity production grew from 15.6
Mtce in 2009 to 32.5 Mtce in 2021 because of the relative abundance of
hydropower resources. The enlarged gaps in energy supply and demand
indicate that Guangxi must import large amounts of energy products
from other regions, exacerbating the risk of deep decarbonization.
Therefore, decarbonization pathways and policies should be carefully
designed.
Following the central government’s dual carbon goal announcement,
Guangxi declared its decarbonization goals and issued a series of official
documents. The Guangxi government committed to achieving peak
carbon emissions by 2030 and carbon neutrality by 2060, keeping pace
with the national goals [41,42]. Several specific targets for energy
structure, energy intensity, CO2 emissions intensity, renewable energy
development, and carbon sequestration were also identified. Regarding
the energy structure, the Guangxi government pledged that the pro­
portion of non-fossil fuel-based energy generation would exceed 54% by
2025, and that the proportion of non-fossil fuel-based energy con­
sumption would reach 30%, 35%, and 80% by 2025, 2030, and 2060,
respectively. Regarding energy and CO2 emission intensities, energy
consumption per unit of GDP in 2025 is expected to decrease by 13%
from the 2020 level, and energy consumption and CO2 emissions per
unit of GDP in 2030 are expected to meet the goals set by the Chinese
central government. Regarding renewable energy development, the
grid-connected installed capacity of renewable power is set to reach 61
million kilowatt and 91 million kilowatt by 2025 and 2035, respectively,
whereas renewable power generation is set to reach 126 billion kWh by
2025. Regarding carbon sequestration, the rate of forest coverage is
expected to be maintained at 62.6%, and the forest stock is expected to
reach 1.05 billion cubic meters by 2030.
In accordance with these objectives, the Guangxi government has
implemented various measures to mitigate CO2 emissions. (1) The
measures for non-fossil energy development and clean utilization of coal
include non-fossil energy substitution, optimization of raw material
processes, improvement of terminal electrification levels, energy con­
servation retrofits, and upgradation of coal-fired power generators [43].
(2) The carbon intensity of energy-intensive industries, especially
chemistry, building materials, construction, and transportation, will be
significantly reduced through energy efficiency improvements, pro­
duction capacity control, and green finance policies [44]. (3) The
development and utilization of renewable energy will be encouraged
through the promotion of advanced clean energy technologies,
improving market-oriented accommodation, establishing electricity
market mechanisms, and expanding investments in the power grid [45,
46]. (4) Several measures will be adopted to increase the carbon sinks of
forests, such as the promotion of large-scale afforestation, imple­
mentation of projects to improve forest quality, and strengthening of
forest resource protection [43]. However, research on the decarbon­
ization pathways and challenges faced by Guangxi to implement these
measures is lacking, thus hindering sufficient guidance for constructing
feasible carbon mitigation plans.
supply and demand, and sectoral output were also simulated. The
CHINAGEM model, developed by the Center of Policy Studies (CoPS) at
Victoria University, has been extensively applied to evaluate the impacts
of various decarbonization policies [14,47]. The CHINAGEM model is
based on the neoclassical economics theory and assumes a fully
competitive market and constant returns to scale of production. It con­
tains six economic agents (production, investment, consumption, gov­
ernment, foreign, and inventory) and three primary factors (labor,
capital, and land) and is solved using the GEMPACK software [48]. The
CHINAGEM model comprises a series of linearized mathematical equa­
tions that describe production activity, investment, household con­
sumption, exports, equilibrium, and dynamic mechanisms. To
summarize this model, we briefly introduce the nested structure of en­
ergy commodities consumed by production sectors in the CHINAGEM
model and the top-down regional disaggregation method in this paper.
The other modules have been described by He et al. (2022b) and Cui
et al. (2020) [47,49].
Nested constant elasticities of substitution (CES) functions were
employed to describe the substitution between the different energy
commodities consumed by each production sector (Fig. 3). Producers
determine their optimal energy inputs according to the principle of cost
minimization. At the top of the nested structure, the production sector
output is produced from various inputs (primary-energy composite, nonenergy intermediate inputs, and other costs) based on the Leontief
function. The primary-energy composite comprised labor, land, and
capital-energy composites, according to the CES function, with a sub­
stitution elasticity of 1.0. Based on the Armington assumption, nonenergy inputs comprised domestic and foreign products. Then, the
capital-energy composite consisted of capital input and energy com­
posite, with a substitution elasticity of 0.5. The energy composite con­
sisted of electricity and non-electricity energy, with a substitution
elasticity of 0.5. The next level comprised coal, oil, and gas, described by
a CES function with a substitution elasticity of 0.16. At the bottom of the
non-electricity products were gas, comprising natural gas and gas suppl;
coal, consisting of crude coal and coke; and oil, composed of crude oil
and petroleum products. The substitution elasticities of these compo­
nents were set to 0.5. The Leontief function was employed for the nested
structure of electricity, assuming a fixed proportion of transmission and
distribution for electricity generation. Electricity generation was then
divided into intermittent and baseload power, with a substitution elas­
ticity of 2.0. Intermittent power included wind, solar, and biomass
power, with a substitution elasticity of 5.0. The baseload power
comprised thermal, hydro, and nuclear power with a substitution elas­
ticity of 5.0. At the bottom of the nested structure was thermal power
generation, comprising coal-fired and gas-fired power, with a substitu­
tion elasticity of 4.0. The values of substitution elasticities specified in
this study were further verified for their feasibility based on the ranges
in the existing literature (see Table B2 in Supplementary Materials).
To assess Guangxi’s decarbonization pathway under the national
pathways to carbon neutrality, we combined the CHINAGEM model
with a top-down regional disaggregation module. The combined model
decomposes carbon emissions and economic activities at the national
level for each province. This disaggregation method, initially proposed
by Dixon et al. (1982) [50], takes the nationwide results as the input and
produces results for each province as the output (Fig. 4). The major
advantage of this method lies in its cost-effectiveness regarding data
demands and calculation resources, because it avoids the need for
detailed information on inter-regional trade flows. The weights of the
economic activities of provinces, such as production, consumption, in­
vestment, and trade, were determined by the difference between the
national and subregional GDP growth rates. As both national and
regional GDP are influenced by carbon reduction policies, the weights
vary among the scenarios. These weights were then employed to
disaggregate national economic activities and carbon emissions in each
province. Production activities, carbon emissions, and energy supply
and demand of the provinces were then calculated.
3. Methodology
3.1. Top-down multi-regional CGE model
In this study, we developed a top-down multi-regional CGE model by
combining a dynamic national CGE model (CHINAGEM model) and a
top-down regional disaggregation module. By employing this model,
Guangxi’s decarbonization pathways were projected under the con­
straints of China’s national dual carbon goals. The impacts of different
decarbonization pathways on subregional carbon emissions, energy
4
L. He et al.
Energy 294 (2024) 130846
Fig. 3. The nested structure of energy products for production sectors.
The disaggregation method assumes that commodities are catego­
rized as either local or national ones.1 The regional equations in this
method translate national simulation results into regional results, such
as employment and output for each production sector. The framework of
the top-down multi-regional CGE model consists of three parts. (1) The
original CHINAGEM model was used to project the impact of exogenous
shocks under consideration of economic variables. (2) Economy-wide
sectoral activities at the national level were allocated to subregions ac­
cording to the endogenously determined weights. (3) A system of
commodity-balance equations for each region was established and
solved to project the subregional outputs of local commodities. The
relevant equations for the top-down regional disaggregation module are
detailed in the Supplementary Materials.
sources and one electricity transmission and distribution sector based on
the China Electric Power Statistics Yearbook (2018) and the China
Statistical Yearbook (2018). Wind power was further divided into
offshore and onshore wind power. Similarly, the crude oil and gas sector
can be divided into two distinct sectors: crude oil and natural gas.
Natural gas comprises pipeline and liquefied natural gas. In total, 159
production sectors were identified. We derived the Armington elastici­
ties of commodities from the Global Trade Analysis Project V10 data­
base. Other elasticities, such as demand and supply elasticities, were
taken from Mai et al. (2010) [51]. The energy-related CO2 emissions
were calculated by multiplying the fossil-fuel consumption of produc­
tion sectors and households, including coal, natural gas, crude oil, and
petroleum products, with their corresponding CO2 emission factors
released by the IPCC (2006). It is worth noting that the CO2 emissions
during industrial production processes were not included in the ac­
counting framework.
3.2. Data
We used China’s input-output table of 2017 with 149 original pro­
duction sectors to establish the database. The original electricity sector
was split into seven power generation sectors with different power
3.3. Scenario setting
To simulate pathways to carbon neutrality in Guangxi, we estab­
lished a baseline (BL) scenario to reflect economic growth under current
carbon mitigation policies and two policy scenarios to reflect carbon
mitigation policies derived from the nation’s pathways to carbon
neutrality. Four types of mitigation policies were covered: carbon pric­
ing, energy efficiency improvement, renewable energy development,
and terminal electrification, which are the primary mitigation tools in
1
For local commodities, it is assumed that all sales must be in the region of
their production, mainly including services and perishable commodities. Na­
tional commodities, whose production patterns are not affected by regional
demand patterns, mainly include automobiles, capital equipment, clothing, and
other non-perishable and easily transported commodities.
5
L. He et al.
Energy 294 (2024) 130846
Fig. 4. The framework of the top-down multi-regional CGE model.
the existing literature and official documents.
mitigation policies, including carbon pricing, energy efficiency
improvement, renewable energy development, and terminal electrifi­
cation. In accordance with Zhang et al. (2022) [53], carbon prices are
expected to increase to USD250/ton CO2 by 2060. Considering the
inverted U-shaped learning curve of energy-saving technologies, energy
efficiency improvement adds an additional improvement to the energy
efficiency improvement rate of the BL scenario (Column 2 of Table 1).
The cost of renewable energy will persistently decline owing to tech­
nological learning and increased R&D investment in renewable energy
technologies. Following IRENA (2022), the technological progress rates
for onshore wind, offshore wind, solar, and bioenergy generation were
specified. Terminal electrification rates were increased by assorting of
policies, including renewable energy quotas, green certificates, and
technical retrofits for energy-electricity substitution. Compared to that
in the BL scenario, the terminal electrification rate is assumed to in­
crease by an additional ten percentage points under CN scenario by
2060.
(1) BL scenario
For the BL scenario in 2017–2060, we followed the dynamic cali­
bration and forecasting methodology of Mai (2006) [52]. The growth
rates of China’s GDP, population, and labor force, as well as the struc­
ture of the macro-economy for 2018–2060, were derived from the pro­
jection of authoritative institutions. Following the predictions of the
International Monetary Fund (IMF, 2021) and Zhang et al. (2022) [53],
China’s GDP growth rate would gradually decrease and converge with
the global average. The GDP growth rates are projected to be 4.2%,
3.0%, 2.6%, and 2.5% in 2030, 2040, 2050, and 2060, respectively, with
the share of tertiary sectors gradually increasing and the primary, sec­
ondary, and tertiary sectors in 2060 accounting for 4.7%, 32.4%, and
62.9%, respectively. The proportion of private consumption in the GDP
is predicted to rise persistently, while the proportion of government
expenditure and investment is predicted to decline gradually. By 2060,
the proportion of private consumption, investment, government
expenditure, imports, and exports in GDP would reach 66%, 22%, 10%,
32%, and 31%, respectively. The projections of population and labor
force were from World Population Prospects and Centre Détudes Pro­
spectives et d’Informations Internationals (CEPII) data.
With an endogenously determined energy efficiency improvement
rate, China’s energy consumption in the coming four decades was cali­
brated according to the International Energy Agency’s World Energy
Outlook (2021 edition), with reference to Zhang et al. (2022) and He
et al. (2022a) [12,53]. The BL scenario projects that under the current
mitigation policies, China’s primary energy consumption will peak
around 2043, at 6.32 billion tons of coal equivalent (Btce), and fall
slightly to 6.05 Btce in 2060, with the share of non-fossil energy
reaching 42.8% by 2060. China’s carbon emissions from fossil-fuel
combustion will peak at 10.50 Bt CO2 by 2030, then decline to 7.89 Bt
CO2 by 2060.
(3) Proactive decarbonization scenario (CN2)
Compared with Zhang et al. (2022) [53], the pathway established by
Ding (2021) [54] is regarded as a proactive decarbonization scenario, in
which national carbon emissions are reduced to 1.35 Bt CO2 by 2060.
Under this scenario, the expectations for the carbon sequestration ca­
pacity of forests and CCUS technology are low, leading to lower residual
carbon emissions when carbon neutrality is attained. Additionally, a
combination of four mitigation policies was specified to replicate this
pathway. The carbon price is expected to increase to USD400/ton of CO2
by 2060. A higher technological progress rate in energy efficiency and
renewable energy generation than that under the CN scenario is speci­
fied. The electrification rate is the same as that in the CN scenario,
considering the uncertainty of energy-electricity substitution.
4. Simulation results
4.1. Impacts on carbon emissions
(2) Conservative decarbonization scenario (CN)
4.1.1. Carbon emissions pathways
The typical expected pathways of China’s carbon emissions were
projected using the top-down multi-regional CGE model (Fig. 5, Panel
A). Under the BL scenario, China’s carbon emissions are projected to
grow slowly from 9924 Mt CO2 in 2020 to 10,528 Mt CO2 in 2031, and
then decrease at a relatively low rate during 2031–2060, with an
average annual decarbonization rate of 0.93%. This indicates an
We consider the pathway of China’s carbon emissions projected by
Zhang et al. (2022) [53] as a conservative decarbonization scenario, in
which the nation’s carbon emissions will be reduced to 2.37 Bt by 2060.
Compared with other studies, they have a relatively more optimistic
expectation on the carbon sequestration capacity of forests and CCUS
technology, leading to greater residual carbon emissions when carbon
neutrality is attained. To replicate this pathway, we established a mix of
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Energy 294 (2024) 130846
Table 1
Annual changing rates of energy efficiency and the generation cost of renewable energy (%).
Energy efficiency
Generation cost of renewable energy
Onshore wind
2020–2030
2030–2040
2040–2050
2050–2060
Offshore wind
Solar
Bioenergy
CN
CN2
CN
CN2
CN
CN2
CN
CN2
CN
CN2
0.150
1.500
0.600
0.060
1.500
3.000
2.000
0.180
− 0.128
− 1.236
− 1.203
− 0.956
− 0.138
− 1.631
− 1.594
− 1.267
− 1.013
− 1.536
− 1.257
− 0.988
− 1.024
− 1.973
− 1.665
− 1.309
− 0.128
− 1.236
− 1.203
− 1.260
− 0.138
− 1.631
− 1.594
− 1.669
− 0.006
− 0.245
− 0.302
− 0.302
− 0.008
− 0.324
− 0.400
− 0.400
inconsistency of the total carbon emissions not following decarbon­
ization pathways with the Paris Agreement or current Chinese dual
carbon goals. Under the CN scenario, China’s carbon emissions would
peak at 10,348 Mt CO2 in 2027 and then decline significantly to 2321 Mt
CO2 by 2060, with an average annual decarbonization rate of 4.43%,
consequent to mitigation policies. Under the CN2 scenario, the national
carbon emissions decrease more radically, declining from 9924 Mt
CO2in 2020 to 1365 Mt CO2 in 2060, with an average annual decar­
bonization rate of 4.84%.
Under the constraints of the national decarbonization schemes, the
pathways of Guangxi’s carbon emissions were also projected (Fig. 5,
Panel B). Under the BL scenario, Guangxi’s carbon emissions will in­
crease continuously over the next two decades and peak at 402 Mt CO2
by 2047, 17 years after the national peak. Thereafter, emissions are
expected to decline slowly, with residual carbon emissions of 371 Mt
CO2 by 2060. Thus, Guangxi, as a less-developed region, is still at a stage
of low emission and limited development, and future economic growth
without the implementation of effective mitigation policies will signif­
icantly increase its carbon emissions. Under the CN scenario, Guangxi’s
carbon emissions will peak at 325 Mt CO2 in 2032 and then decline
rapidly, with residual carbon emissions of 111 Mt CO2 by 2060. Guangxi
is not expected to achieve its goal of peaking carbon emissions before
2030, even under the scenario national peak carbon emissions in 2027.
Alternatively, under the CN2 scenario, Guangxi could attain its carbon
peak goal with stronger mitigation policies, with carbon emissions
peaking at 294 Mt CO2 by 2030 and decreasing to 63 Mt CO2 by 2060.
The results showed that stronger mitigation policies are required to
attain Guangxi’s dual carbon goals, considering its relatively rapid
economic growth and lower energy efficiency.
However, the temporal patterns differed to some extent (Fig. 6). At the
national level, under the BL scenario, carbon emissions from coal and oil
will fall modestly, whereas carbon emissions from natural gas and gas
supply will increase slightly, and coal will continue to contribute to
more than 70% of the emissions by 2060. Under the CN and CN2 sce­
narios, carbon emissions from coal and oil will decline significantly,
affected by mitigation policies, with coal accounting for 66.0% and
63.5% of national carbon emissions by 2060, respectively. In Guangxi,
carbon emissions from oil, natural gas, and gas supply will increase
persistently under the BL scenario, whereas carbon emissions from coal
will increase before the early 2040s and slightly decline thereafter. This
trend in Guangxi’s carbon emissions from coal differs from that of China
because of the country’s relatively high proportion of energy-intensive
industries and low energy efficiency. Coal is projected to account for
61.5% of Guangxi’s carbon emissions by 2060, which is much lower
than that projected for the national emissions. Under the decarbon­
ization scenarios (CN and CN2), carbon emissions from coal and oil in
Guangxi will fall significantly. However, carbon emissions from coal will
increase at the beginning of the decarbonization schemes but will
decline much more rapidly after reaching the peak consequent to the
mitigation policies. Coal will nevertheless remain the major emission
source, accounting for over 60% of Guangxi’s carbon emissions. Thus,
the highly efficient use of coal and substitution of fossil fuels with
renewable energy are vital for deep decarbonization in Guangxi.
4.1.3. Sources of carbon emissions by sector
Decomposing carbon emissions by sector, electricity, services,
chemical industry, and building materials were the major contributors
of China’s carbon emissions (Fig. 7). Under the BL scenario, carbon
emissions from electricity and building materials will decline gradually,
whereas carbon emissions from services and light industry will rise
persistently. By 2060, these sectors will account for 77.6% of China’s
carbon emissions. Under the decarbonization scenarios, carbon
4.1.2. Sources of carbon emissions by energy
By decomposing carbon emissions by energy type, coal was the
major source of carbon emissions at both regional and national levels.
Fig. 5. The carbon emission pathways of China and Guangxi under different scenarios
(Mt CO2).
Source: Top-down multi-regional CGE model
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Energy 294 (2024) 130846
Fig. 6. The sources of China’s and Guangxi’s carbon emissions under different scenarios (Mt CO2).
Source: Top-down multi-regional CGE model
emissions from all sectors will decline continuously. Carbon emissions
from the light industry will fall significantly to 207 and 144 Mt CO2 by
2060, respectively, excluding it from the list of major emitters. Elec­
tricity, services, chemical industry, and building materials will remain
the major sources of carbon emissions, accounting for 70.3% and 65.9%
of China’s carbon emissions in 2060 under the CN and CN2 scenarios,
respectively. Unlike the BL scenario, the carbon emissions of the pro­
duction sectors in 2060 will decline by 27.1%–91.7% and 46.1%–96.1%
under the CN and CN2 scenarios, respectively. Among the sectors,
mining will have the greatest emissions reduction, followed by gas
supply, chemical industry, and light industry.
For Guangxi, electricity, building materials, metallurgy, chemical
industry, and services were the major carbon emitters. Under the BL
scenario, carbon emissions from services will rise constantly; those from
the electricity and chemical industries will increase and then decrease;
and those from metallurgy and building materials will decline gradually.
By 2060, these sectors will account for 82.8% of Guangxi’s carbon
emissions. Under the CN and CN2 scenarios, emissions from the pro­
duction sectors will exhibit a continuously declining trend, although
those from services and light industry will show an upward trend before
2040 and then a downward trend. Carbon emissions from the chemical
industry will fall significantly to 7 and 3 Mt CO2 by 2060 under the
decarbonization scenarios, respectively, and the industry will no more
be a major emitter. Services, light industry, metallurgy, building mate­
rials, and electricity will remain the major emitters, accounting for
80.1% and 77.9% of Guangxi’s carbon emissions in 2060 under the CN
and CN2 scenarios, respectively. Under the decarbonization scenarios,
carbon emissions from the production sectors in 2060 will be reduced by
27.0%–88.1% and 46.0%–96.4%, respectively, unlike those under the
BL scenario. Similar to the national trend, mining shows the greatest
carbon reduction at regional level as well, followed by gas supply,
chemical industry, and light industry. In summary, the nation and
Guangxi face different difficulties in carbon reduction based on the
major sources of carbon emissions.
In addition, we calculated the carbon emission intensity of the pro­
duction sectors in China and Guangxi under different scenarios (Ap­
pendix C in Supplementary Materials). The results show that the carbon
emission intensities at the sectoral level will change over time, and the
initial levels and temporal changes differ between China and Guangxi.
Most sectors have relatively higher carbon emission intensities in
Fig. 7. Carbon emissions of China and Guangxi from production sectors under different scenarios (Mt CO2).
Note: 159 original sectors in the model are aggregated into 14 sectors; the concordance of sectoral aggregation can be seen in the Supplementary Materials.
Source: Top-down multi-regional CGE model
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Energy 294 (2024) 130846
Guangxi than in China. Carbon reduction policies under the decarbon­
ization scenarios will significantly reduce the carbon emission in­
tensities of the production sectors for both the nation and Guangxi.
will be lower than those in 2030, accompanied by the phase-out of coalfired power facilities; however, other fossil fuels and electric power will
have a widened supply-demand gap. Unlike in the BL scenario, the
supply-demand gap of fossil fuels will be narrowed under the CN sce­
nario because of the energy efficiency improvement. However, the
supply-demand of electric power will increase to 77.3 billion kWh in
2030 and 964.4 billion kWh in 2060, as terminal electrification and
large-scale utilization of renewable energy stimulate the consumption of
electric power significantly. Under the CN2 scenario, the supply-demand
gap of fossil fuels will be further reduced; the supply-demand gap of
electric power is also predicted to narrowed down, owing to improved
energy efficiency. However, the supply-demand balance of electric
power will remain at 668.3 billion kWh. Therefore, decarbonization
schemes could improve the supply-demand balance for fossil fuels in
Guangxi; however, the supply-demand balance of electric power must be
treated in a timely and effectively manner. Large-scale investments in
innovative renewable energy sources such as off-shore wind power, tidal
power, and hydrogen, and substantial improvements in energy effi­
ciency are required.
4.2. Impacts on energy supply and demand
Decarbonization schemes will significantly reduce the primary en­
ergy consumption in Guangxi (Fig. 8). Under the BL scenario, primary
energy consumption in Guangxi is projected to grow rapidly from 80.7
Mtce in 2020 to 133.3 Mtce in 2040, with an average annual growth rate
of 2.54%, owing to rapid economic growth and industrialization. The
growth of primary energy consumption will gradually slow during
2040–2050, with an average annual growth rate of 0.95%, and decline
to 144.7 Mtce by 2060. Under the CN scenario, primary energy con­
sumption will also increase continuously during 2020–2060 and rise to
142.8 Mtce by 2060. However, the average annual growth rate is ex­
pected to be lower than that under the BL scenario owing to improved
energy efficiency and carbon pricing. Under the CN2 scenario, the pri­
mary energy consumption in Guangxi will decrease to 114.9 Mtce by
2060, which is much lower than that under the CN scenario. Improved
energy efficiency and high carbon pricing will significantly reduce car­
bon emissions in Guangxi.
On examining the primary energy consumption by energy type, these
decarbonization plans were found capable to promote energy structure
transformation into a low-carbon and clean one. Under the BL scenario,
the proportion of coal in Guangxi’s primary energy consumption will
decline gradually but will still account for 35.8% of the total energy
consumption in 2060. Additionally, the target of surpassing 80% nonfossil energy consumption by 2060, as proposed by the central govern­
ment, will not be fulfilled. Guangxi’s energy structure will remain
inconsistent with the national decarbonization target unless stronger
decarbonization policies are adopted. Unlike in the BL scenario, the
proportion of coal in Guangxi’s primary energy consumption was much
lower under the CN and CN2 scenarios. The proportion of coal con­
sumption is expected to decline to 11.5% and 7.8% by 2060, whereas the
share of non-fossil fuel is expected to increase to 81.5% and 87.5%,
respectively. Moreover, under the CN and CN2 scenarios, the electrifi­
cation rate will increase from 20% in 2020 to 66% and 73% in 2060,
respectively, which is considerably higher than those under the BL
scenario (Fig. 9A). The rate of renewable energy deployment will also
increase from 12% in 2020 to 73% and 77% in 2060 under CN and CN2
scenarios (Fig. 9B). In comparison, the rate of renewable energy
deployment is predicted to be only 38% by 2060 under the BL scenario.
Decarbonization schemes narrow the supply-demand gap of fossil
fuels, but the impacts on the supply-demand gap of electric power are
complicated (Table 2). Under the BL scenario, Guangxi depends highly
on imported fossil fuels. For example, in 2030, coal production is pro­
jected to be 4.4 Mt, but coal consumption will rise to 82.2 Mt. The
supply-demand gap of coal will reach 77.8 Mt, with a self-sufficiency
rate of only 5.35%. The supply-demand gaps of coal and coke in 2060
4.3. Impacts on economy
Decarbonization schemes will cause some damage to the economic
growth rates of China and Guangxi, but the economic losses are
affordable (Fig. 10). Under the BL scenario, China’s GDP growth rate is
expected to gradually decrease from 5.73% in 2023 to 2.49% in 2060,
with an average annual growth rate of 2.22%. Unlike in the BL scenario,
China’s annual average GDP growth rate will decline by 0.02–0.11
percentage points under the decarbonization scenarios. For Guangxi, the
GDP growth rate will decrease from 5.32% in 2023 to 2.47% in 2060
under the BL scenario, with an average annual growth rate of 2.05%.
Under the CN scenario, Guangxi’s annual average GDP growth rate will
decline by 0.05 percentage points relative to that in the BL scenario.
Guangxi’s annual average GDP growth rate will decline further under
CN2 scenario, falling by another 0.05 percentage points.
The relative differences in the GDP of China and Guangxi across the
scenarios also differed (Fig. 11). Under the BL scenario, China’s GDP will
gradually increase from 17,763 billion USD in 2023 to 59,296 billion
USD in 2060, with an annual average growth rate of 3.37%. Unlike in
the BL scenario, China’s average GDP in 2023–2060 will decline by
500–589 billion USD under the decarbonization scenarios. China’s GDP
in 2060 will reach 57,900 and 57,778 billion USD under CN and CN2
scenarios, respectively. For Guangxi, the GDP will increase from 388
billion USD in 2023 to 1471 billion USD in 2060 under the BL scenario,
with an average annual growth rate of 3.71%. Under the CN scenario,
Guangxi’s average GDP in 2023–2060 will decline by 10 billion USD
relative to that under the BL scenario. Guangxi’s GDP will reach 1445
billion USD by 2060, which is 27 billion USD lower than that under the
BL scenario. Guangxi’s average GDP will decline further under the CN2
scenario, falling by another 12 billion USD. Guangxi’s GDP will reach
1420 billion USD in 2060, which is 51 billion USD lower than that under
the BL scenario. Decarbonization schemes would have smaller negative
impact on the GDP growth rate and GDP at the Guangxi regional level
than at the national level. Nevertheless, the economic losses caused by
decarbonization schemes are relatively minor, considering the future
GDP.
Decarbonization schemes will also cause some damage to the output
value of the sectors in Guangxi, especially in energy-intensive sectors
(Table 3). Compared with those under the BL scenario, the output value
of gas supply will suffer the largest decline (79.49%) in 2060, followed
by the mining (13.29%), chemical industry (10.13%), building materials
(5.17%), and metallurgy (4.00%) under the CN scenario. Because these
sectors are highly energy-intensive, increased fossil fuel prices caused by
rising carbon prices will raise the production costs of these sectors and
consequently reduce their production. Negative impacts on the output
value of agriculture (0.18%), construction (1.12%), and light industry
Fig. 8. Primary energy consumption in Guangxi (Mtce).
Source: Top-down multi-regional CGE model
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Energy 294 (2024) 130846
Fig. 9. The electrification and renewable energy deployment rates in Guangxi (%).
Source: Top-down multi-regional CGE model
emission reduction efforts in one region are offset by increased carbon
emissions in other regions. Therefore, regional level coordination of
their emission-reduction actions and collective mitigation of carbon
leakage risk is crucial. However, inter-regional trade provides oppor­
tunities for collaborative carbon emission reduction by optimizing trade
structures and facilitating technology transfers. Hence, when formu­
lating emission reduction policies, Guangxi should consider the influ­
ence of interregional trade. For example, Guangxi can strengthen
cooperation with subregions in eastern China by integrating advanced
clean technologies and management practices and prompting local in­
dustrial upgrading and transformation. Furthermore, subregions in
western China should strengthen their cooperation in utilizing carbonfree energy such as wind power, photovoltaics, and solar heat, by pro­
moting “Electricity Transmission to Guangxi” by constructing ultra-high
voltage direct current transmission channels in the western region. Thus,
Guangxi can fully utilize the abundant carbon-free energy sources in the
western region. In future studies, the modeling of intercollaborative
carbon reduction actions must be considered.
China has established a “1 + N” policy system for carbon peak and
carbon neutrality, covering implementation plans for carbon reduction
in industries such as energy, steel, petrochemical and chemical engi­
neering, as well as transportation. Referring to national emission
reduction practices, the subregional government has successively
formulated dual carbon implementation plans and mitigation policies.
However, because resource endowments, industrial structures, and
development stages in subregions vary largely from the national level,
carbon mitigation policies adopted at the national level cannot be
applied directly to subregions. Subregions should enforce regionspecific mitigation policies according to their characteristics, which
would shape their decarbonization pathways. For example, subregions
with abundant renewable energy (such as Guangxi) should accelerate
improvements in technological innovation capacity, encourage energy
substitution, and improve the efficiency of renewable energy utilization.
Subregions abundant in fossil energy resources should adjust their in­
dustrial structures and reduce scattered coal use by phasing out outdated
industrial capacity, promoting clean fuels in the residential sector, and
upgrading industrial boilers. For energy-resource-poor subregions,
improving energy efficiency, establishing a carbon trading market or
pricing mechanism, and developing clean energy and low-carbon tech­
nologies can effectively promote carbon emission reduction and sus­
tainable development. We used a relatively simple method to explore
the decarbonization pathways of subregions under the constraints of
China’s decarbonization schemes, by considering carbon reduction
policies at the national level. In future studies, we plan to incorporate
more differentiated regional mitigation policies into our model.
Table 2
The supply and demand of energy products in Guangxi.
2030
Production
Coal
Crude oil
Natural gas
Petroleum product
Coke
Electric power
Consumption
Coal
Crude oil
Natural gas
Petroleum product
Coke
Electric power
Supply-demand gap
Coal
Crude oil
Natural gas
Petroleum product
Coke
Electric power
2060
BL
CN
CN2
BL
CN
CN2
4.4
0.6
0.0
15.2
7.4
245.5
4.3
0.5
0.0
14.9
7.3
246.6
3.9
0.5
0.0
13.7
6.9
229.3
3.0
0.4
0.0
11.8
5.0
502.2
0.9
0.2
0.0
5.0
2.3
676.7
0.5
0.1
0.0
3.7
1.6
523.5
82.2
21.0
0.3
14.4
18.9
322.3
80.4
20.5
0.3
14.0
18.6
323.9
71.9
17.8
0.3
12.9
17.4
301.3
74.6
23.4
0.5
18.2
14.4
1308.5
21.2
8.5
0.1
6.7
6.4
1641.1
10.9
4.6
0.1
4.4
3.6
1191.8
77.8
20.5
0.3
− 0.9
11.4
76.7
76.1
19.9
0.3
− 0.9
11.3
77.3
68.0
17.3
0.3
− 0.8
10.5
72.0
71.6
23.0
0.5
6.5
9.3
806.4
20.3
8.4
0.1
1.7
4.1
964.4
10.4
4.5
0.1
0.8
2.0
668.3
*The unit of fossil fuels is presented in million tons. The unit of electric power is
billion kWh.
Source: Top-down multi-regional CGE model
(1.79%) were predicted to be minor. Meanwhile, the output value of
electricity will experience the largest increase by 2060 (35.81%)
because of the rising terminal electrification rate and large-scale
deployment of renewable energy, followed by the electronic and elec­
trical industries (0.33%). Similarly, the most energy-intensive sectors
were predicted to be the most negatively affected ones under the CN2
scenario, with only electricity benefiting from the decarbonization
schemes.
5. Discussions and conclusions
5.1. Discussions
We adopted a top-down multi-regional model that avoids the need
for detailed information on inter-regional trade flows to explore the
decarbonization pathways of Guangxi. Thus, the model could not
explore the impact of trade flows and cooperation among different re­
gions. Interregional trade potentially results in carbon leakage, where
10
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Energy 294 (2024) 130846
Fig. 10. The GDP growth rate of China and Guangxi under different scenarios (%).
Source: Top-down multi-regional CGE model
Fig. 11. The GDP change of China and Guangxi under different scenarios (billion USD).
Source: Top-down multi-regional CGE model
Table 3
The output values of production sectors in Guangxi under different scenarios.
Agricultural
Mining
Light industry
Chemical industry
Building materials
Metallurgy
Machinery manufacturing
Electronic and electrical
Other manufacture
Electricity
Gas supply
Water supply
Construction
Services
2020
2030
BL
BL
CN
CN2
2060
BL
CN
CN2
(billion USD)
(billion USD)
(%)
(%)
(billion USD)
(%)
(%)
67.4
9.5
102.0
39.9
27.3
63.2
42.6
31.4
5.8
16.4
0.8
0.5
112.6
240.7
104.9
14.1
186.7
60.1
38.0
113.8
97.0
69.2
8.5
23.7
1.1
1.1
152.4
420.9
0.00
− 0.48
− 0.04
− 0.50
− 0.12
− 0.15
− 0.07
− 0.01
− 0.11
0.59
− 8.73
− 0.03
− 0.02
− 0.05
− 0.38
− 3.02
− 0.51
− 2.51
− 0.63
− 0.94
− 0.48
− 1.00
− 0.96
− 5.23
− 21.96
− 0.62
− 0.02
− 0.64
174.0
19.2
508.7
87.9
40.1
182.2
235.5
209.8
18.9
40.8
1.3
7.2
147.6
1577.5
− 0.18
− 13.29
− 1.79
− 10.13
− 5.17
− 4.00
− 2.68
0.33
− 3.07
35.81
− 79.49
− 1.65
− 1.12
− 1.87
− 0.71
− 17.62
− 2.31
− 33.19
− 30.08
− 8.26
− 3.31
− 2.11
− 4.54
10.33
− 88.55
− 3.22
− 1.95
− 2.63
Note: % Change refers to the change in the output value under the decarbonization scenarios relative to the value of the baseline scenario.
Source: Top-down multi-regional CGE model.
11
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Energy 294 (2024) 130846
As a less-developed region in China, Guangxi must balance carbon
reduction and economic growth to achieve its dual carbon goals. This
study shows that the attainment of the dual carbon goals will cause some
damage to Guangxi’s macroeconomy and the outputs of energyintensive sectors, posing challenges to future economic growth. How­
ever, the economic development of Guangxi lags behind that of most
provinces in China, compounded by serious problems of poverty, in­
come inequality, and a large proportion of ethnic minority residents.
Thus, the attainment of the dual carbon goals is more challenging for
Guangxi, and Guangxi’s government must coordinate economic devel­
opment and CO2 reduction to diminish economic losses and improve
income distribution. Economic development patterns should be trans­
formed by accelerating the upgradation of the industrial structure and
exploring emerging industries, and a more equitable income distribution
system should be established to make economic growth inclusive.
This study has several limitations. First, we used only two typical
decarbonization pathways for the nation. These may not fully consider
the uncertainty surrounding national decarbonization pathways,
potentially leading to an inaccurate assessment of Guangxi’s decar­
bonization pathways. Therefore, national decarbonization pathways
that combine different carbon reduction policies should be developed.
However, this study illustrates an easily applicable method for analyzing
the decarbonization pathways of subregions under the constraints of
national decarbonization schemes. Second, we only considered carbon
emissions from the combustion of fossil fuels in this subregion. The nonCO2 greenhouse gas emissions and carbon emissions from industrial
production processes, such as cement production, were not considered.
Future studies should incorporate more mitigation policies, including
green finance, economic transitions, and policies supporting disruptive
technologies (e.g., hydrogen energy storage and CCUS) when estab­
lishing decarbonization pathways.
decarbonization will promote the transformation of Guangxi’s energy
structure into a low-carbon one. Under the decarbonization scenarios,
the proportions of non-fossil energy are projected to rise to 81.5%–
87.5% by 2060, and the rate of end-use electrification and renewable
energy deployment rates will increase to 66%–73% and 73%–77%,
respectively. Decarbonization schemes narrow the supply-demand gap
for fossil fuels, but have different impacts on the supply-demand gap for
electric power. The supply-demand gap of electric power will be
enlarged in the CN scenario and, interestingly, will be narrowed in the
CN2 scenario owing to the improved energy efficiency, with a gap of
668.3 billion kWh in 2060. (4) The attainment of dual carbon goals will
damage the macro-economy and the output of high-energy-consuming
industries. Guangxi’s annual GDP growth rate will decrease by an
average of 0.05 and 0.1 percentage points per year under the decar­
bonization scenarios, relative to those in the BL scenario; nevertheless,
the economic losses are affordable. The sectors that may be most
negatively affected include the chemical industry, building materials,
metallurgy, and construction, whereas the services industry will benefit
the most.
Based on these findings, the following policy implications are pro­
posed. First, stronger mitigation policies are essential to attain
Guangxi’s dual carbon goals. Mitigation policies, including carbon
pricing, energy efficiency improvement, renewable energy subsidies,
and terminal electrification, should be implemented comprehensively
and vigorously to promote the transition towards a low-carbon econ­
omy. Second, establishing a novel energy system in Guangxi through
high-efficiency utilization of coal, substitution of fossil fuels with
renewable energy, and large-scale investment in unconventional
renewable energy is crucial. More attention should be paid to the
widened supply-demand gap in the electric power sector. Third, estab­
lishing different carbon reduction strategies in different sectors is crit­
ical. The service sector is less energy-intensive and should be
incentivized further. However, stricter policies should be adopted in
these energy-intensive sectors, stimulating them to adapt to energyefficient technologies and processes, or even phase out their opera­
tions. Fourth, seeking effective strategies to reduce economic losses and
alleviate the unemployment associated with carbon reduction is neces­
sary. The government should help identify and utilize new economic
opportunities by adopting targeted social policies to compensate for
damaged sectors, including locally oriented business creation,
strengthening public services, labor retention, and infrastructure
development. Finally, Guangxi should also transform its economic
development pattern and upgrade its industrial structure.
5.2. Conclusions
The attainment of China’s dual carbon goals necessitates deep
decarbonization efforts in subregions, which requires research on sub­
regional decarbonization pathways. Pathways of carbon emission and
relevant carbon mitigation policies have been analyzed previously using
different methods. However, they have mostly ignored the constraints of
the nation’s decarbonization schemes, leading to an incompatibility
between national and subregional decarbonization pathways. We
introduce a relatively simple method to develop decarbonization path­
ways in subregions, taking Guangxi as an example. A top-down multiregional CGE model is employed with endogenized-determined subre­
gional proportions of sectoral activities, investment, and consumption.
Two typical scenarios for the nation’s decarbonization pathways were
established by combining four major mitigation policies: energy effi­
ciency improvement, carbon pricing, renewable energy deployment,
and terminal electrification. The changing trends in Guangxi’s carbon
emissions, energy supply and demand, and sectoral output were exam­
ined under these decarbonization pathways.
The following were the major findings from the study. (1) Under the
BL scenario, Guangxi’s carbon emissions will peak at 402 Mt CO2 by
2047, and then decline slowly to 371 Mt CO2 in 2060. Under the CN
scenario, Guangxi’s carbon emissions will peak at 325 Mt CO2 in 2032
and then decline rapidly, with residual carbon emissions of 111 Mt CO2
in 2060. Under the CN2 scenario, Guangxi’s carbon emissions are
reduced more significantly, peaking at 294 Mt CO2 in 2030 and leaving
residual carbon emissions at 63 Mt CO2 by 2060. (2) Regarding the
difficulties in carbon reductions, the burning of coal is the major source
of carbon emissions for both China and Guangxi. Sector wise, electricity,
services, chemical industry, and building materials are the major sources
of China’s carbon emissions. Comparably, the services, light industry,
metallurgy, building materials, and electricity sectors are the major
sources of Guangxi’s carbon emissions, accounting for over 80% of its
carbon emissions by 2060 under the decarbonization scenarios. (3) Deep
Fundings
This research was funded by Beijing Natural Science Foundation
(9222016), National Natural Science Foundation of China (72373163,
72222020, 72342005), the Youth Scientific Research Fund Project of
Beijing Wuzi University (2023XJQN01), and National Social Science
Foundation of China (21BJY013).
CRediT authorship contribution statement
Ling He: Data curation, Methodology, Writing – original draft.
Xiaofan Li: Formal analysis, Supervision, Writing – original draft. Qi
Cui: Conceptualization, Software, Writing – review & editing. Bing
Guan: Methodology, Writing – review & editing. Meng Li: Writing –
review & editing. Hao Chen: Writing – review & editing.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
12
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org/10.1016/j.energy.2024.130846.
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