Access to this full-text is provided by University of California Press.
Content available from Elementa: Science of the Anthropocene
This content is subject to copyright.
RESEARCH ARTICLE
Driving factors of CO
2
emissions in southeast
China: Comparative study of long-term
trends, short-term fluctuations,
and spatial variations
Yimin Huang
1,2,3
, Weisheng Lin
1,2,3,
*, Yuan Wang
1,2,3,4,
*, Huafang Luo
5
, Lin Zhu
6
,
Yanmin He
7
, Feng Wang
8
, Wen-ting Lai
1,2,3
, and Rui Shi
9
This study explores the factors driving CO
2
emissions related to energy use in Fujian Province from 2000
to 2019, with an emphasis on long-term trends, short-term fluctuations, and spatial disparities. Utilizing
annual data on CO
2
emissions and various influencing factors from multiple cities within Fujian Province,
we examine the factors driving long-term changes in CO
2
emissions. To analyze short-term emission
trajectories, we employ a temporal decomposition model, while spatial decomposition techniques are
used to assess the variability in emission drivers across 9 prefecture-level cities over different years.
Our findings reveal an inverted U-shaped relationship between CO
2
emissions and urbanization over the
20-year study period. Furthermore, short-term fluctuations indicate a gradual reduction in the impact of
urbanization on the increase in CO
2
emissions within the industrial, transportation, and household sectors
in Fujian Province. Additionally, economic development, measured as per capita gross domestic product, is
shown to significantly influence CO
2
emissions. Efforts to reduce energy intensity, which refers to the
amount of energy consumed per unit of economic output, in both the industrial and household sectors are
identified as potential strategies for emission reduction. The variability in CO
2
emissions among cities is
primarily attributed to differences in energy intensity and population sizes.These insights are critical for
formulating policies aimed at promoting low-carbon development, reducing carbon emissions, and
enhancing sustainability throughout Fujian Province.
Keywords: Energy-related CO
2
emissions,Energy consumption,The EKC hypothesis,Temporal LMDI model,
Spatial LMDI model
1. Introduction
The surge in industrialization in China and other parts of
the world has precipitated a notable increase in energy
consumption and CO
2
emissions (Wang et al., 2016a;
Wang et al., 2016c). The International Energy Agency
(2019) estimated that global energy-related CO
2
emissions
will reach 33.6 billion tons in 2019, with China contrib-
uting 9.9 billion tons (29.5%), making it the paramount
emitter of CO
2
globally. This has highlighted the impor-
tance of addressing CO
2
emissions within China. In
response, China has committed to attaining a peak in
CO
2
emissions before 2030 and to strive for carbon neu-
trality by 2060, with various provinces targeting specific
emission reduction objectives. For example, Fujian Prov-
ince, situated along China’s southeastern coast, promul-
gated its 13th Five-Year Plan (FYP) aimed at curbing CO
2
emissions and fostering energy conservation (Zheng,
2017). A specific goal was developed to realize
a40%–45%reduction in CO
2
emissions per unit of eco-
nomic output by 2020 relative to 2005 levels, with each
1
Institute of Geography, Fujian Normal University, Fuzhou,
China
2
Key Laboratory of Humid Subtropical Eco-geographical
Processes of Ministry of Education, Fujian Normal University,
Fuzhou, China
3
School of Geographical Sciences, Fujian Normal University,
Fuzhou, China
4
State Key Laboratory of Pollution Control and Resource
Reuse, School of Environment, Nanjing University, Nanjing,
China
5
Fujian Jingwei Digital Technology Co. Ltd, Fuzhou, China
6
Nanjing Institute of Environmental Sciences, Ministry of
Ecology and Environment, Nanjing, China
7
Faculty of Economics, Otemon Gakuin University, Osaka,
Japan
8
School of Business, Nanjing University of Information Science
& Technology, Nanjing, China
9
Department of Environmental Health and Engineering, Johns
Hopkins University, Baltimore, MD, USA
* Corresponding authors:
Emails: weilsnlin@fjnu.edu.cn; y.wang@fjnu.edu.cn;
ywang@nju.edu.cn
Huang,Y, et al. 2024. Driving factors of CO
2
emissions in southeast China: Comparative
study of long-term trends, short-term fluctuations, and spatial variations.
Elem Sci
Anth
, 12: 1. DOI: https://doi.org/10.1525/elementa.2023.00028
city being allocated precise reduction benchmarks based
on this metric. An assertive push toward low-carbon indus-
trial advancement was initiated to encourage the upgrad-
ing of an industrial structure (Central Government of the
People’s Republic of China, 2021). Consequently, carbon
emission discussions have increased at both the national
and regional levels.
In this study, Fujian Province was chosen as the study
object as it is strategically located in a significant eco-
nomic zone along the southeastern coast of China. Fujian
was identified as an inaugural national zone for showcas-
ing best practices for promoting a healthy ecological envi-
ronment. The goal is to build a societal model
characterized by efficient resource use, a robust ecological
environment, and harmonious coexistence between
humans and nature through targeted planning and imple-
mentation. Furthermore, several cities within the prov-
ince, such as Xiamen, have been incorporated into
China’s low-carbon pilot cities initiative (National Devel-
opment and Reform Commission of People’s Republic of
China, 2010). Given these designations, Fujian Province is
poised to exemplify leadership in carbon emission reduc-
tion endeavors. However, the trajectory of rapid industri-
alization and urbanization in the province underscores an
escalating tension between environmental stewardship
and economic advancement. In 2019, the per capita gross
domestic product (GDP) of Fujian Province increased to
107,139 yuan, ranking fifth among all Chinese provinces.
Notably, manufacturing and construction emerged as the
predominant sector, accounting for 48.6%of the eco-
nomic activity (National Bureau of Statistics, 2020). For
example, in the same year, Fujian was positioned 15th
among all provinces in China with regard to the total
energy consumption, and the city experienced an annual
average energy consumption growth rate of approxi-
mately 4.5%, surpassing the national mean of approxi-
mately 3.3%from 2000 to 2019. This trend underscores
the accelerated pace of energy consumption in Fujian
compared to the national average. Alarmingly, in 2019,
fossil fuels constituted 75.1%of the total primary energy
production in the province, significantly eclipsing the pro-
portion of renewable energy sources. This augmented
energy consumption trajectory would produce severe
environmental ramifications, with escalating CO
2
emis-
sions being a primary concern. Without judicious over-
sight, Fujian Province may encounter substantial hurdles
in attaining its peak carbon emissions and carbon neutral-
ity objectives.
There are notable divergences in industrial develop-
ment levels between the distinct subregions of Fujian
Province, which invariably lead to significant disparities
in energy consumption. For example, in 2019, large-
scale industrial enterprises defined as establishments with
an annual revenue surpassing 20 million RMB in Quanz-
hou exhibited a fivefold higher energy consumption than
their counterparts in Xiamen. This pronounced discrep-
ancy in energy consumption between the two cities trans-
lates to significant spatial variances in CO
2
emissions
levels. However, when emission reduction strategies are
designed at a broader regional scale, they often overlook
the unique differences and underlying causes of CO
2
emissionsacrossvarioussubregions.Asaresult,these
broad regional policies may not be as effective as
approaches that are specifically tailored to address the
distinct characteristics of each subregion.
Therefore, it is imperative to rigorously investigate the
driving factors of CO
2
emissions and to understand the
spatial variability of emissions in Fujian Province. This
would greatly assist the government in devising a subse-
quent action plan tailored to the region’s distinct CO
2
emissions landscape for effective emission reductions.
Moreover, an in-depth examination of Fujian Province can
furnish a valuable framework for strategizing research and
formulating CO
2
emission policies in other regions (e.g.,
Guangdong Province and Jiangsu Province) that, while
relatively developed overall, possess internal developmen-
tal heterogeneity.
In light of the aforementioned objective, this study
undertakes a holistic examination of the determinants
that underpin CO
2
emissions, encompassing long-term
trends, short-term fluctuations, and spatial variations.
Understanding long-term trends requires a meticulous
analysis of relationships and impacts over time and are
shaped by variables such as economic evolution, urbani-
zation rates, industrial configurations, and technological
progressions. Conversely, short-term fluctuations, such as
those occurring over 5 years, in carbon emissions are typ-
ically driven by immediate factors such as sudden policy
changes, rapid technological advancements, and short-
lived economic events. It is crucial to acknowledge that
other determinations, including policy shifts and techno-
logical advancements, may be affected by spatial varia-
tions. The study of spatial variations can explain the
factors that lead to spatial differences in carbon emissions,
such as geographical locales, urbanization schema, and
industrial structures. To our knowledge, this study is the
first endeavor to combine these 3 facets to understand the
influence of CO
2
emissions at the urban scale.
2. Literature review
In contemporary research on the determinants of CO
2
emissions, there are 3 research directions. The first direc-
tion is to delve into the enduring long-term relationships
between CO
2
emissions and various socioeconomic para-
meters including economic growth, population dynamics,
industrial composition, energy consumption, and techno-
logical advancements. Broadly, two fundamental types of
associations have been posited between CO
2
emissions
and socioeconomic factors. The first entails a conjectured,
nearly monotonic linear relationship with economic
growth. The second constitutes an inverted U-shaped, cur-
vilinear relationship: the Environmental Kuznets Curve
(EKC) hypothesis, which was initially advanced by Gross-
man and Krueger (1995). According to this notion, eco-
nomic growth can result in heightened environmental
degradation. Environmental degradation tends to dimin-
ishwitheconomicgrowthonceacertainlevelofeco-
nomic development is achieved, though alternative
relationships, such as exponential associations, may also
exist. As economies progress, several plausible rationales
Art. 12(1) page 2 of 19 Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics
underlie the potential reduction in environmental harm.
Economic growth often accompanies an increase in afflu-
ence, which, in turn, can heighten awareness of environ-
mental challenges and augment the demand for
sustainable solutions. Moreover, advanced stages of eco-
nomic development typically result in the resources to
invest in cleaner technologies and enforce rigorous envi-
ronmental regulations. These advancements can substan-
tially contribute to the mitigation of environmental
damage. This conceptual backdrop underpins our explora-
tion of the interplay between diverse socioeconomic vari-
ables and CO
2
emissions using the theoretical framework
of the EKC.
For example, Dong et al. (2018) scrutinized the
dynamic causal relationship between per capita CO
2
emis-
sions and other environmental and economic variables in
China using the EKC framework. Pata (2018) conducted an
analysis of the long-term, dynamic linkages between CO
2
emissions and socioeconomic factors to assess the validity
of the EKC hypothesis in Turkey from 1971 to 2014. Juan
et al. (2021) presented empirical evidence supporting the
EKC hypothesis within the Association of Brazil, Russia,
India, China, and South Africa (BRICS) economies (exclud-
ing Russia) from 1980 to 2018. The Stochastic Impacts by
Regression on Population, Affluence, and Technology
(STIRPAT) model, an evolution of the IPAT model first pro-
posed by Dietz and Rosa (1997), has been commonly
employed to corroborate the presence of the EKC hypoth-
esis. This model provides a comprehensive theoretical and
analytical framework that encompasses the influence of
economic growth, population dynamics, technological
progress, and urbanization on the environment (Wang
et al., 2016a; Wang et al., 2016b; Wang et al., 2017). For
example, Wang et al. (2017) applied the STIRPAT model to
affirm the existence of the EKC hypothesis in relation to
income/urbanization and CO
2
emissions within the
manufacturing sector in China. Nevertheless, while inqui-
ries into the long-term correlation between socioeco-
nomic variables and CO
2
emissions can shed light on
whether CO
2
emissions decline as socioeconomic develop-
ment reaches a particular stage, they generally fall short of
providing an in-depth analysis of the immediate effects of
socioeconomic shifts or policy implementations on CO
2
emissions.
Hence, the second direction is the study of short-term
influences on CO
2
emissions. Decomposition analysis
techniques, such as structural and index decomposition,
are typically used to study the drivers of CO
2
emissions in
the short term (Dong et al., 2020; Wang et al., 2021).
Structural decomposition analysis is primarily employed
in national-scale research. In contrast, index decomposi-
tion analysis has been nearly universally adopted to ana-
lyze energy savings and CO
2
emission reductions for one
or multiple sectors in a region. The logarithmic mean
Divisia index (LMDI) is one of the most popular index
decomposition analysis methods (Ang, 2005; Cahill and
Gallacho
´ir, 2010) because of its many advantages, includ-
ing the absence of decomposition residuals, the aggrega-
tion of subsector effects to the same value as the total
effects, and the ability to decompose and process datasets
containing zero and negative values (Ang, 2004). The
LMDI model was proposed by Ang et al. (1998), and it has
been extensively used in investigating temporal and spa-
tial variations in energy consumption or CO
2
emissions.
The temporal LMDI model has been applied to examine
the drivers of CO
2
emission changes over time on a global
scale (Xiao et al., 2019), as well as the country level (Gao
et al., 2019; Chen and Lin, 2020; Quan et al., 2020),
regional level (Lin and Du, 2014; Wang et al., 2018; Xue
et al., 2019), city level (Li, et al., 2019a; Cui et al., 2020),
and sector level (Geng et al., 2014; Zhu et al., 2017;
Shigetomi et al., 2018; Quan et al., 2020; Zheng et al.,
2020). Although previous analyses have comprehensively
analyzed the driving factors in short-term change, they did
not consider the spatial differences of the impact factors.
Xiao et al. (2019) used the temporal LMDI model to ana-
lyze the differences and similarities between countries,
provinces (Chen and Yang, 2015; Feng et al., 2020; Li
et al., 2020), and cities (Zhu et al., 2017); however, the
quantitative relationships between drivers could not be
defined.
Therefore, the third perspective considers the spatial
difference of the drivers of CO
2
emissions. Ang et al.
(2015) subsequently expanded the temporal LMDI
methodandproposedaspatialLMDIapproach.This
approach is static and only valid for the year of the
research (Ang et al., 2016). The spatial LMDI has also been
used to study CO
2
emissions, mercury emissions (Li, et al.,
2019b), and water intensities (Yao et al., 2019). Many stud-
ies have applied this approach to study spatial differences
in the influencing factors of CO
2
emissions between
regions. Furthermore, researchers have conducted spatial
decomposition studies of CO
2
emissions at different
scales, such as at the national (Roman-Colladoa and
Morales-Carrion, 2018) and regional scales (Li et al.,
2017; Chen et al., 2019), and for different sectors, such
as the power sector (Liu et al., 2017) and the household
sector (Shi et al., 2019). Decomposition analyses of the
temporal and spatial dimensions of CO
2
emissions are
typically performed at the national and provincial scales.
Moreover, the driving factors of CO
2
emissions are com-
monly categorized into 4 groups: energy structure, eco-
nomic scale, energy intensity, and population effects. In
the context of rapid global urbanization, these have
become essential factors that affect CO
2
emissions.
According to Wang et al. (2016a), for every 1%increase
in the urban population, CO
2
emissions will increase by
0.20%in Southeast Asian countries. Hence, urbanization
should also be included in the study of impact factor
indicators.
The comprehensive identification of affecting factors of
CO
2
emissions is a precondition for developing an action
plan to realize carbon peaking and carbon neutrality
targets. The influencing factors of CO
2
emissions have
been extensively studied in terms of long-term trends,
short-term fluctuations, and spatial variations. However,
very few of these studies have reached broad conclusions
by considering all the aspects together, and this has led to
incomplete research results. In addition, the spatial differ-
ences of driving factors have been studied at the national
Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics Art. 12(1) page 3 of 19
and provincial levels by other researchers. However, the
variability at the city scale has not yet been systematically
analyzed. The study of spatial differences in the driving
factors of CO
2
emissions at the city scale would enable
local units to formulate effective regional emissions reduc-
tion policies. Hence, this topic requires further study.
In this study, we first calculate the CO
2
emissions in 9
sub-provincial-level cities in Fujian Province. Second, we
adopt a sub-provincial-level CO
2
emissions dataset (2000–
2019) covering all sectors to explore the long-term rela-
tionship between CO
2
emissions and urbanization. Third,
we use a temporal LMDI model to study the factors that
affect CO
2
emission changes in the short-term from the
industrial sector, transportation sector, and household sec-
tor at the provincial level. A comparison between the
multiple sectors offers robust proof of the driving factors
and helps discern the difference in the factors across
various sectors. Finally, we apply the spatial LMDI model
to analyze the spatial dynamic of CO
2
emissions of 9
prefecture-level cities from the industrial, transportation,
and household sectors.
3. Methodology and data
3.1. Analysis framework
The aim of this study was to utilize a comprehensive con-
ceptual analytical framework, which is depicted in Fig-
ure 1, to compare the influencing factors of carbon
emissions across different temporal and spatial scales and
various industries in Fujian Province. First, we analyzed
the long-term relationships between population, energy
intensity, economic scale, urbanization rate, and carbon
emissions. Second, we examined the short-term
Figure 1. The research framework.
Art. 12(1) page 4 of 19 Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics
fluctuations and spatial disparities in carbon emissions
within different industries across Fujian Province and its
cities, considering the effects of the energy structure,
energy intensity, industrial composition, economic scale,
urbanization, and population. Furthermore, we assessed
thecarbonemissionreductionpotentialsofdifferent
industries and cities in Fujian Province in order to provide
targeted policy recommendations for facilitating low-
carbon development.
3.2. Model
3.2.1. Calculation of CO
2
emissions
We used the guidelines of the Intergovernmental Panel on
Climate Change (2006) to calculate the CO
2
emissions
from fossil fuel consumption.
Cij ¼Eij EFij;ð1Þ
where irepresents different types of fossil fuels; jrepre-
sents different sectors; C
ij
is the CO
2
emissions from the
combustion of fossil fuel iin sector j;E
ij
is the consump-
tion of fossil fuel iin sector j;andEF
i
represents the
emission factor.
3.2.2. STIRPAT model
The theoretical and analytical framework employed in this
study utilizes the STIRPAT model to examine the EKC
hypothesis, specifically exploring the relationship
between CO
2
emissions and urbanization.
Ii¼aPb
iAc
iTd
iei;ð2Þ
where Irepresents the environmental change; P,A, and T
are the population, affluence, and technology, respec-
tively; a,b,c, and ddenote the estimated coefficients of
the explanatory variables; erepresents the random error;
and the subscript idenotes the panel unit.
1
In our analysis, we adopted the STIRPAT model as the
theoretical and analytical framework, drawing inspiration
from York et al. (2003). To expand this model, we intro-
duced a quadratic term for urbanization, thus employing
a multivariate polynomial regression model. This
approach allowed us to examine the possible EKC relation-
ship between urbanization and its environmental impact.
The incorporation of a negative coefficient for the qua-
dratic term suggests a non-linear relationship, indicating
that with increasing urbanization, CO
2
emissions may ini-
tially rise, but will eventually begin to decline. To ensure
the meaningful interpretation of the estimated coeffi-
cients, we transformed all variables in the model into the
natural logarithmic forms. This transformation allowed us
to interpret the coefficients as the sensitivity of the depen-
dent variable to changes in the independent variable and
facilitated a more comprehensive analysis of the influence
of urbanization on carbon emissions.
The improved STIRPAT models described by Equation 3
provided us with a robust framework to analyze and eval-
uate the impact of urbanization on carbon emissions.
ln Cit ¼aiþb1ln Pit þb2ln Ait þb4ln Eiit þb5URit þb6UR2
it þeit;
ð3Þ
where C
it
is the CO
2
emissions from city iin year t;P
denotes the total population; Ais the GDP per capita; Ei
is the energy intensity, measuring the energy consumed
per unit of GDP output; and UR is the urbanization rate,
denoting the proportion of the urban populace relative to
the total populace.
To overcome the limitations of traditional parametric
modeling methods in identifying the distinct form of rela-
tionship between variables (Yatchew, 1998), we integrated
a semi-parametric panel fixed-effects regression methodol-
ogy into our study (Baltagi and Li, 2002). This method offers
increased flexibility compared to purely parametric models
and can mitigate potential issues of dimensionality. Our
aim was to combine parametric and nonparametric meth-
ods to boost the precision and resilience of our models. We
also applied parametric and semi-parametric panel fixed
effects regression models to estimate Equations 3 and 4,
primarily for the model’s urbanization variables. The regres-
sion results were used to determine whether there was an
inverted U-shaped curve relationship between CO
2
emis-
sions and urbanization. The equation is as follows:
ln Cit ¼aiþb1ln Pit þb2ln Ait þb4ln Eiit þfðURit Þþeit :
ð4Þ
The function f() represents the semi-parametric compo-
nent to be estimated. Since the urbanization variable is
entered nonlinearly into the model, the functional form,
f(), in the model is uncertain.
3.2.3. Decomposition model
We then utilized the LMDI method that incorporates the
Kaya identity to conduct a comprehensive decomposition
analysis of CO
2
emissions. This method allowed us to
quantitatively assess the contribution of each factor to the
CO
2
emissions and compare their relative influences. The
Kaya identity provides a useful framework for understand-
ing the drivers of CO
2
emissions. By using this approach,
we were able to assess the relative importance of different
factors in driving CO
2
emissions, thereby gaining valuable
insights into emission dynamics.
(1) Kaya identity
According to the Kaya identity, we decomposed the
driving factors of CO
2
emissions into 7 factors:
C¼X
ijk
Cijk ¼X
ijk
Cijk
Eijk
Eijk
Eij
Eij
Gij
Gij
Gi
Gi
Pui
Pui
Pi
Pi
¼Qi Es Ei IsGUr P
;
ð5Þ
where subscript irepresents 1 of 9 cities in Fujian Province
(i¼1, 2, 3, ... 9); jrefers to the different sectors type
(j¼1, 2, ... 7), where j¼1 corresponds to the primary
1. Panel data refers to the data collected by different
observation units at different time points and describes the
changes of multiple observation units over time. Panel unit
denotes a subset or specific element that constitutes the
panel data, such as the data of one or more observational units
at a particular time point.
Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics Art. 12(1) page 5 of 19
sectors that include farming, forestry, animal husbandry, fish-
ing, and water conservation, j¼2 is the industry sector, j¼3
is the construction sector, j¼4 is the transportation sector, j
¼5 is the wholesale, retail trade, and hotel, and catering
services sector, j¼6 is the household sector, and j¼7is
other sectors; kdenotes the type of fossil fuels (k¼1, 2, ...
17), as detailed in Table 1;C
ijk
is the CO
2
emissions with the
unit of Mt (million tons); E
ijk
is the energy consumption with
the unit of million tons of the coal equivalent (Mtce); E
ij
is the
total energyconsumption in sector jwith the unit of Mtce; G
ij
is the out value of sector jmeasured in million Chinese yuan
(CNY); G
i
is the total GDP measured in million CNY; Ur
i
is the
urban population size measured in million people; P
i
is
the total population size with the unit of million people;
Qi is the CO
2
emissions coefficient; Es is the energy structure,
Ei is the energy intensity, Is is the industrial structure; Gis the
economic development level; Ur is urbanization; and Pis the
population.
(2) Temporal LMDI model
According to the temporal LMDI model (Ang, 2005),
we decomposed the factors that influence CO
2
emission
changes in region ifrom the base year 0 to the target year
tinto 7: the CO
2
emissions coefficient effect (Qi), the fossil
energy structure effect (Es), the energy intensity effect (Ei),
the industrial structure effect (Is), the economic develop-
ment level effect (G), the urbanization effect (Ur), and the
population effect (P), as shown in Equations (A1–A8) in
the supplementary material.
DCt;0
i¼DCt
iDC0
i
¼X
ijk DCt;0
Cijk=Eijk þDCt;0
Eijk=Eij þDCt;0
Eij=Gij
þDCt;0
Gij=GiþDCt;0
Gi=PiþDCt;0
Pi=PþDCt;0
P
¼Qit;0þEst;0þEit;0þIst;0þGt;0þUrt;0þPt;0:
ð6Þ
(3) Spatial LMDI model
Regarding the selection of the spatial exponential
decomposition model, contrasting with the bilateral-
regional and radial-regional models, the multiregional
model uses the mean of all the research units during the
study period as the reference region, thereby avoiding the
complex computational burden of the bilateral-regional
model and the subjectivity of the radial-regional model
when selecting reference objects (Ang et al., 2015; Ang
et al., 2016). For this reason, the multiregional model (Ang
et al., 2016) was adopted in our study. The spatial LMDI
model was applied to investigate spatial differences in the
driving factors of CO
2
emissions and is described as follows:
DCi;u
i¼DCi
iDCu
i
¼X
ijk DCi;u
Cijk=Eijk þDCi;u
Eijk=Eij þDCi;u
Eij=Gij
þDCi;u
Gij=GiþDCi;u
Gi=PiþDCi;u
Pi=PþDCi;u
P
¼Qii;uþEsi;uþEii;uþIsi;uþGi;uþUri;uþPi;u;
ð7Þ
where the benchmark, u, is calculated using the average
from the 9 cities in Fujian Province. The various influenc-
ing factors were calculated using the Equations (A9–A16)
in the supplementary material.
(4) Relative contribution rate
We converted the absolute contribution rate of different
effects to a relative contribution rate using the ratio of each
effect to the sum of its absolute value (Equation 8). This
approach facilitated a comparison and analysis of the time
trend in the contribution of each effect.
IðDCkÞ¼ DCk
X
k
jDCkj;ð8Þ
where I(4C
k
) represents the relative contribution rate of
each effect; and DC
k
represents the value of each effect.
3.3. Data sources
The GDP figures for the various economic sectors and the
population statistics for the 9 cities within Fujian Province
were sourced from the Fujian statistical yearbooks from
2000 through 2019. Economic data were adjusted to con-
stant 2,000 prices for consistency. To calculate the inven-
tory of the energy consumption and carbon dioxide (CO
2
)
emissions for the 9 cities in Fujian Province, we adhered to
the methodology outlined in the work of Shan et al.
(2017). Due to the unavailability of energy balance sheets
Table 1. Types of fossil fuels
Order Energy Type Order Energy Type Order Energy Type
1 Raw coal 7 Other gas 13 Fuel oil
2 Cleaned coal 8 Other coking products 14 LPG
3 Other washed coal 9 Crude oil 15 Refinery gas
4 Briquettes 10 Gasoline 16 Other petroleum products
5 Coke 11 Kerosene 17 Natural gas
6 Coke oven gas 12 Diesel oil
Art. 12(1) page 6 of 19 Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics
within the statistical yearbooks of the 9 cities, we relied
upon the provincial-level energy balance sheets to esti-
mate the energy consumption at the municipal level. For
ease of reference, Table 2 provides a list of the notations
employed in this study.
4. Results and discussion
4.1. Long-term trends
4.1.1. Data and variable
We used an unbalanced panel data
2
comprising data from
2000 to 2019 to examine the presence of an inverted U-
shaped relationship between urbanization and CO
2
emis-
sions in this study. Descriptive statistics for all the primary
variables are presented in Table 3. With the exception of
urbanization, variables were converted into their natural
logarithmic forms to facilitate analysis. Table 2 provides a suc-
cinct overview of the distribution and variability of the vari-
ables under consideration. This summary offers useful
insights into their central tendencies, variability, and range,
thereby enhancing our understanding of the variables’ char-
acteristics and their potential associations with CO
2
emissions.
4.1.2. Urbanization
-
CO
2
emission models
Table 4 shows the outcomes of the urbanization–CO
2
emissions models according to the EKC hypothesis. The
parametric fixed effects regression model, detailed in the
first column, incorporates “city dummies” and “year
dummies” to mitigate city-specific heterogeneity and
temporal variations. The analysis revealed a robust and
positive correlation between CO
2
emissions and the fol-
lowing key variables: population, energy intensity, and
economic growth, each at a 1%significant level. A 1%
uptick in these variables corresponded to increases in
CO
2
emissions of 0.989%, 0.867%, and 0.913%, respec-
tively. Notably, while the urbanization variable and its
quadratic term did not show statistical significance, their
coefficient signs aligned with the EKC hypothesis, suggest-
ing a potential inverted U-shaped relationship between
urbanization and CO
2
emissions.
The second column focuses on the control variables,
and we used a semi-parametric panel fixed effects model.
This approach further substantiated the significant impact
of population, the energy intensity, and the per capita
GDP on CO
2
emissions. Figure 2 shows a nonparametric
fitting that illustrates the nuanced dynamics between
urbanization and CO
2
emissions. The adjusted CO
2
data
in Figure 2 isolate the specific impact of urbanization on
emissions from other factors, clarifying its independent
contribution. This result aligns with an inverse U-shaped
curve, initially rising before plateauing, indicative of the
EKC hypothesis. Furthermore, this trend implies an initial
surge in CO
2
emissions that is concurrent with early
urbanization phases characterized by rapid, unregulated
growth and increased consumption. However, upon reach-
ing a critical threshold of urban development, a decline in
emissions was observed. This was attributed to advance-
ments in industrial processes, technological innovation,
and urban environmental infrastructure, steering eco-
nomic growth toward a sustainable, low-carbon pathway.
Our findings are consistent with the EKC hypothesis
within the urbanization–CO
2
emissions context, as shown
by similar studies in Organization for Economic Co-
operation and Development countries (Wang et al., 2015).
The initial phase of urbanization, marked by unstructured
growth and elevated consumption, contributed to a rise in
CO
2
emissions. Over time, as the urban areas developed and
adoptedmodernindustrialpracticesandenhancedenviron-
mental strategies, a shift toward sustainable, low-carbon
development was observed, underpinning the inverse
U-shaped relationship postulated by the EKC hypothesis.
4.2. Short-term fluctuations
4.2.1. CO
2
emissions in Fujian Province
During the period from 2000 to 2019, which encompasses
China’s 10th to 13th Five-Year Plans (FYPs), significant fluc-
tuations in CO
2
emissions were observed in Fujian Province.
This study provides a detailed analysis of these changes,
correlating them with the respective FYP timelines.
Figure 3 shows the sector-wise distribution of CO
2
emissions in Fujian Province. Notably, there was a substan-
tial increase in total emissions from 124.2 Mt in 2000 to
459.1 Mt in 2019, representing an average annual growth
rate of 7.12%. This growth, however, was not uniform
across the study period. The initial phase (2000–2005)
had a moderate increase in emissions at an average yearly
rate of 6.48%, followed by a rapid surge (10.65%annu-
ally) between 2005 and 2010. The period from 2010 to
2015 marked a deceleration in this trend, with the growth
Table 2. Notations used in this article
Notation Definition Notation Definition
ICEs CO
2
emissions from the
industrial sector
FZ Fuzhou
TCEs CO
2
emissions from the
transportation sector
LY Longyan
HCEs CO
2
emissions from the
household sector
NP Nangpin
Qi Carbon emissions
coefficient effect
ND Ningde
Es Fossil energy structure
effect
PT Putian
Ei Energy intensity effect QZ Quanzhou
Is Industrial structure
effect
SM Sanming
GEconomic development
level effect
XM Xiamen
Ur Urbanization effect ZZ Zhangzhou
PPopulation effect
2. Unbalanced panel data means that the observed values of
individuals and time in the panel data do not completely match,
that is,the data of some individuals at certain time points are
missing.
Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics Art. 12(1) page 7 of 19
rate dropping to 5.21%per annum. The final phase
(2015–2019) was characterized by variable growth, with
an average annual rate of 6.0%.
The observed CO
2
emission dynamics are reflective of
the evolving economic landscape in Fujian Province that is
influenced significantly by energy conservation and emis-
sion reduction initiatives. Despite these efforts, the trajec-
tory of emissions underscores the need for more robust
measures to effectively curtail emissions. The industrial
sector remains the predominant contributor to the
region’s CO
2
emissions, followed by the transportation
sector, which showed minimal fluctuations ranging
between 3.01%and 8.38%of the total emissions from
2000 to 2019. The household sector emerged as the
third-largest emitter, accounting for 5.56%of the total
in 2019. The contributions of other sectors were marginal
and are thus not a primary focus of this study.
Figure 4 presents an analysis of the energy intensity
across the industrial, transportation, and household sec-
tors for the same period. The energy intensity, defined as
the energy consumption to economic output ratio, had
significant sectoral differences. The industrial sector was
characterized by the highest energy intensity, followed by
the transportation sector. Notably, the industrial sector
experienced a marked decrease in the energy intensity,
averaging an annual decline of 5.27%from 2000 to
2019. In contrast, the household sector showed a more
moderate decrease, with an average annual reduction rate
of 3.19%. The transportation sector, however, exhibited an
increase in the energy intensity, with an average annual
growth rate of 3.15%. These variances in the energy inten-
sity trends across sectors are crucial for understanding
their respective impacts on CO
2
emissions and form a key
component of our analysis.
4.2.2. Temporal decomposition analysis of CO
2
emissions
Figure 5 shows the results of the temporal decomposition
analysis focused on discerning the factors that influence
variances in CO
2
emissions from the industrial sector
(ICEs), CO
2
emissions from the transportation sector, and
CO
2
emissions from the household sector (HCEs) from
2000–2019. The cumulative effect of these changes shows
the annual fluctuations in carbon emissions. This analysis,
Table 3. Descriptive statistics of variables
Variable Definition Mean Std. Dev. Min Max
lnCNatural logarithm of CO
2
emissions (kilogram) 23.7 0.9 21.3 25.9
lnPNatural logarithm total population 8.3 0.4 7.6 9.1
lnEi Natural logarithm of energy use (kce) per 1,000 GDP
(constant 2,000 price)
5.2 0.8 3.8 7.1
lnANatural logarithm (ln) of measured GDP per capita in thousands
(at constant 2,000 price)
3.3 0.7 1.9 4.9
UR The share of the urban population in the total population (%) 55.0 11.0 29.0 89.0
UR
2
The squared term of the urbanization rate 0.3 0.1 0.1 0.8
Table 4. Estimation results of the urbanization–CO
2
emission models
a
Variables
Parametric Model Semi-parametric Model
Coefficient (Std. Dev.) t-Statistic [Pvalue] Coefficient (Std. Dev.) t-Statistic [Pvalue]
Constant 7.978
b
(0.378) 21.1 [<0.001]
lnP0.989
b
(0.032) 31.2 [<0.001] 1.050
b
(0.072) 14.7 [<0.001]
lnEi 0.867
b
(0.020) 42.4 [<0.001] 0.843
b
(0.030) 28.5 [<0.001]
lnA0.913
b
(0.034) 27.1 [<0.001] 0.881
b
(0.029) 30.8 [<0.001]
UR 0.326 (0.522) 0.6 [0.550]
UR
2
–0.151 (0.308) –0.5 [0.638]
City dummies Yes Yes
Year dummies No No
Adjusted R
2
0.996 0.985
Obs. 137 114
a
Cluster-robust standard errors are in parentheses.
b
Denotes Pvalue <0.01.
Art. 12(1) page 8 of 19 Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics
grounded in Equations A1–A8 from the supplenemtary
material, culminates in Table 5, which summarizes the
relative impact rates of each factor.
This analysis offers important insights into the factors
that influence CO
2
emissions over time. It enables policy-
makers and researchers to comprehend the dynamics that
affect emissions and devise strategies for effective
mitigation. Significantly, the economic growth (G)ofthe
industrial, transportation, and household sectors contrib-
uted 269 Mt, 17 Mt, and 15.5 Mt to CO
2
emissions, respec-
tively, with corresponding relative contribution rates of
38.9%,47.6%,and47.1%, respectively. These findings
align with previous research (Li et al., 2017; Chang et al.,
2019; Chen et al., 2019; Shi et al., 2019; Xue et al., 2019;
Figure 2. A partial fit of the relationship between CO
2
emissions and urbanization. The curves represent the
semi-parametric components of the fitted values of the urbanization–CO
2
emissions relationship, and the shaded
portion represents the 95%confidence band, where each point represents a panel unit.
Figure 3. Energy consumption structure (%)andtotalCO
2
emissions from 7 sectors in Fujian Province
(Million tons: Mt). The colored bars each represent the percentage of energy consumption by different sectors
relative to the total annual consumption. The black line chart with blue dots indicates the trend in carbon emissions
over time.
Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics Art. 12(1) page 9 of 19
Quan et al., 2020), which identified economic develop-
ment level as the primary factor driving increased CO
2
emissions in these sectors in Fujian Province.
Interestingly, the energy intensity effect (Ei) emerged as
the chief mitigating factor for CO
2
emissions, except in the
transportation sector. This decline in the energy intensity
was attributed to technological advancements and
enhanced research and development efficiencies. These
advancements, which are supported by studies such as Lin
and Wang (2021) and Zhang et al. (2020), highlight the
critical role of scientific and technological progress in
curbing CO
2
emissions. The urbanization rate (Ur) was
identified as the second-largest factor increasing CO
2
emissions. It is driven by infrastructure development,
industrial growth, and population expansion. In addition,
population (P) consistently showed a positive effect on
emissions in all sectors and periods.
Gexhibited short-term fluctuations characterized by an
initial increase, subsequent decrease, and a final increase.
Notably, a peak in the relative contribution rate of Gwas
observed during the 11th FYP period (2005–2010) in
Fujian Province, coinciding with significant shifts in its
economic development. The average annual GDP growth
rates in Fujian Province for the 10th (2000–2005), 11th
(2005–2010), and 12th (2010–2015) FYPs were 10.0%,
14.7%, and 11.2%, respectively. However, the highest rel-
ative contribution rate of Gwas recorded during the 13th
FYP (2015–2019), particularly in the industrial and house-
hold sectors, despite the mitigating influence of Ei. While
Ei played a crucial role in restraining the growth of CO
2
emissions from 2000 to 2019, its impact varied across
different sectors and timeframes. For example, in the
industrial sector, the relative contribution rate of Ei
decreased from 33.4%to 13%between the 12th and
13th FYPs. This trend indicates that early technological
advancements had a more pronounced effect on reducing
CO
2
emissions, but the rate of improvement in the energy
intensity slowed during the later stages, highlighting the
need for continued investment in technological innova-
tion for effective CO
2
emission reductions. These findings
illustrate the complexities in managing the energy inten-
sity and emphasize the continuous efforts required to
promote sustainable development and decarbonization
strategies in Fujian Province.
The relative contribution of Is in the industrial and
household sectors in Fujian Province declined from
10.1%and 2.6%during the 10th FYP period to 9.4%
and 17.5%in the 13th FYP, respectively. This trend sug-
gests that industrial restructuring policies implemented
during the 12th and 13th FYPs (Fujian Provincial People’s
Figure 4. Changes in the energy intensities of the industrial, transportation, and household sectors. Figure (A)
illustrates the trend of energy intensity in the industrial sector, depicted by the ratio of the energy consumption (tons
of standard coal: tce) to economic output (Chinese Yuan: CNY) within the industrial sector over time. Figure (B)
represents the trend of energy intensity in the transportation sector, indicated by the ratio of the energy consumption
(tce) to economic output (CNY) within the transportation sector over time. Figure (C) depicts the trend of energy
intensity in the household sector, characterized by the ratio of the energy consumption (tce) to GDP (CNY) within the
household sector over time.
Art. 12(1) page 10 of 19 Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics
Government, 2016) have effectively contained the growth
of CO
2
emissions. These policies consisted of diverse mea-
sures that included the development of advanced
manufacturing, the promotion of modern service indus-
tries, the acceleration of the internet economy, the
establishment of a marine economy, and the enhance-
ment of service sector capabilities. As a result, there was
a shift from energy-intensive industries to more sustain-
able sectors, contributing to the mitigation of the growth
of CO
2
emissions in the region. However, it is notable that
Figure 5. Temporal decomposition of CO
2
emissions in Fujian Province from 2000 to 2019. (A) Industry sector,
(B) transportation sector, and (C) household sector. This figure illustrates the annual carbon emissions per sector, with
gray bars indicating total emissions and adjacent colored bars indicating contributions from various factors. A positive
value in a colored bar signifies an emission increase due to the corresponding factor, while a negative value denotes
a reduction.
Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics Art. 12(1) page 11 of 19
despite these efforts, the absolute growth of CO
2
emis-
sions has not been fully curtailed, as evidenced in Fig-
ure 3.TheriseinCO
2
emissions since 2016 can be
attributed to factors such as economic growth, changes
in energy consumption patterns, and other influences.
While industrial restructuring policies have mitigated the
growth of CO
2
emissions, they have not led to an overall
reduction in emissions. This fact underscores the need for
ongoing enhancements in policy execution to achieve
substantial CO
2
emission reductions in Fujian Province.
In an analysis of the 3 sectors, the relative contribu-
tion rate of Es in the transportation sector surpassed
those of the industrial and household sectors. Figure
6illustrates the energy consumption, energy intensity,
and energy structure in Fujian Province’s transportation
sector from 2000 to 2009. The data show a gradual
increase in the total energy consumption in the trans-
portation sector, followed by a sharp rise from 2016
onward. The energy intensity pattern mirrored this
trend. Additionally, the energy structure evolved from
3 categories of fossil fuel consumption in 2000 to 9
by 2009, indicating a growing complexity in the energy
structure. These observations highlight the critical need
for comprehensive strategies to address the challenges
within the transportation sector. Key areas include
enhancing the energy efficiency, exploring alternative
cleaner energy sources, and prioritizing technological
innovations. Promoting renewable energy utilization
and developing policies to optimize the sector’s energy
structure are imperative. Moreover, the positive impact
of Ei on the transportation CO
2
emissions (TCEs) from
2000 to 2019 emphasizes the importance of improving
the energy efficiency in the transportation sector.
Although progress has been made, there remains sub-
stantial scope for further advancements to ensure a sus-
tainable and eco-friendly transportation system.
In the analysis of the domestic consumption sector, the
output was measured in terms of the GDP without distin-
guishing between industrial structures. Consequently, the
Is values in the household sector were uniformly zero.
4.3. Spatial variations
4.3.1. Spatial characteristics of CO
2
emissions
This study examined the spatial dynamics of CO
2
emis-
sions across 9 prefecture-level cities in Fujian Province
by analyzing data from the years 2000, 2005, 2010,
2015, and 2019. The CO
2
emissions data are detailed in
Table S1. A general upward trend in CO
2
emissions was
observed in most cities, with Longyan being the excep-
tion, where a decline was noted from 2010 to 2015. The
average annual growth rates of CO
2
emissions varied sig-
nificantly across the cities, ranging from 15.09%in
Ningde to 1.98%in Nangping. The spatial disparity in
emissions was pronounced, exemplified by Quanzhou’s
2019 emissions of 178.7 Mt, which starkly contrasted with
Nanping’s 9.36 Mt. This necessitates a thorough analysis
of the spatial variations in CO
2
emissions to inform tar-
geted emissions reduction strategies.
4.3.2. Spatial decomposition analysis of CO
2
emissions
We employed the spatial LMDI model to investigate the
spatial disparities in CO
2
emissions and to assess the
potential for emission reductions in various areas. The
spatial decompositions of the industrial, transportation,
and household CO
2
emissions (ICEs, TCEs, and HCEs) for
the 9 cities were calculated using Equations A9–A16, with
Table 5. Relative contribution rates (%) of each driver to changes in the CO
2
emissions (Mt) of the different
sectors (2000–2019)
Sector Period 4C(Mt) Qi (%)Es(%)Ei(%)Is(%)G(%)Ur(%)P(%)
Industry sector
10
th
FYPs 27.6 0.2 1.0 –37.1 10.1 29.4 17.6 4.6
11
th
FYPs 95.6 0.5 0.8 –25.8 12.3 44.6 12.8 3.2
12
th
FYPs 78.9 –0.4 0.1 –33.4 8.1 38.5 11.1 8.3
13
th
FYPs 74.2 –0.2 0.1 –13.0 –9.4 49.7 18.0 9.6
2000–2019 276.4 0.1 1.2 –30.1 9.4 38.9 14.6 5.9
Transportation sector
10
th
FYPs 5.7 0.7 6.4 43.0 2.6 26.9 16.2 4.2
11
th
FYPs 7.8 0.0 4.4 –13.8 3.7 57.4 16.5 4.2
12
th
FYPs 1.5 0.0 –1.2 –30.6 –14.0 36.1 10.4 7.8
13
th
FYPs 7.9 0.0 0.5 25.8 –17.5 36.2 13.1 7.0
2000–2019 24.2 0.6 14.1 7.2 –6.2 47.6 17.3 7.0
Household sector
10
th
FYPs 5.1 0.3 2.2 17.8 0.0 45.4 27.2 7.1
11
th
FYPs 4.5 0.3 –1.0 –31.5 0.0 49.5 14.2 3.6
12
th
FYPs 3.1 0.0 2.4 –40.6 0.0 37.9 10.9 8.2
13
th
FYPs 6.2 0.0 1.7 –8.2 0.0 58.0 21.0 11.2
2000–2019 18.9 0.4 6.6 –21.3 0.0 47.1 17.6 7.1
Art. 12(1) page 12 of 19 Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics
detailed results shown in Tables S2, S3, and S4 (supplemen-
tary material). The regional average CO
2
emissions were
derived from the arithmetic mean of the emissions from
the 9 cities of Fujian Province.
The spatial decomposition results, shown in Figure 7,
indicated that there was increasing divergence over time
for CO
2
emissions and their driving factors. The primary
contributor to spatial disparity in CO
2
emissions was ICEs.
Key factors that influenced these disparities included the
economic development level (G), urbanization rate (Ur),
energy structure (Es), industrial structure (Is), and energy
intensity (Ei). Of these, Ei and P(population growth) were
significant in all 3 sectors, with Gbeing notably influential
in driving spatial differences in emissions.
In terms of driving increases in CO
2
emissions, popula-
tion growth (P) was particularly impactful in economically
flourishing cities such as Quanzhou, Fuzhou, and Zhangz-
hou. Conversely, Ei emerged as a major restraining factor,
Figure 6. Energy consumption and energy structure of the transportation sector in Fujian Province from
2000 to 2009. Figure (A) shows the energy consumption (million tons of standard coal: Mtce) of the transportation
sector over time, while Figure (B) depicts the energy structure of the transportation sector, with differently colored
bars representing the proportion of various energy types relative to the total energy consumption for the respective
year (%).
Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics Art. 12(1) page 13 of 19
with Xiamen and Fuzhou demonstrating higher energy
efficiencies than other regions. The contributions of Ei
in cities such as Quanzhou and Zhangzhou were primarily
positive, indicating lower energy efficiencies than the
regional average. Addressing these inefficiencies through
advanced technology, energy-efficient practices, and policy
interventions is crucial for enhancing energy utilization
efficiency.
The analysis revealed that the level of economic devel-
opment (G) is a critical determinant of CO
2
emissions,
with higher economic development correlating with
increased emissions. This underscores the importance of
integrating sustainable development approaches in eco-
nomically advanced regions to mitigate environmental
impacts.
Urbanization (Ur) exerted a more pronounced effect on
TCEs and HCEs than ICEs, highlighting its significant role
in the transportation and household sectors. Higher
urbanization levels, which were seen in cities such as
Quanzhou, Fuzhou, and Xiamen, led to increased mobility
and living standards, consequently elevating CO
2
emis-
sions in these sectors.
The industrial structure (Is) had a notable negative
impact on TCEs in cities such as Quanzhou and Zhangz-
hou, suggesting that adjustments in the industrial struc-
ture can effectively curb emissions in these areas. Finally,
the influence of the energy structure (Es) on regional
emissions disparities was relatively minor, and this was
attributed to the minimal variation in energy structures
across the regions.
4.3.3. Performance ranking
In this analysis, we systematically evaluated the impact of
various regional drivers on ICEs and presented them in
descending order of effect (see Table S5 in the supplemen-
tal materials). This ranking not only allows for the assess-
ment of energy efficiencies but also highlights differences
in economic development levels. Specifically, a higher Ei
value correlated with a greater energy efficiency, whereas
a lower Gvalue indicated advanced economic develop-
ment. Significantly, lower rankings in this context sug-
gested a greater potential for emission reductions,
contingent upon the implementation of appropriate
strategies.
The data, as depicted in Figure 5 and Table 5, offer
several critical insights for emission reductions that are of
substantial relevance for policymaking. For example, the
analysis of Quanzhou revealed a consistent decrease in the
ranking of variables, such as 4C, Es, Ei, G, and P, over the
study period. This trend highlighted Quanzhou’s substan-
tial emissions reduction potential, achievable by enhanc-
ing the energy efficiency and industrial restructuring.
Consequently, local authorities should prioritize policies
that boost the energy efficiencies across sectors, promote
Figure 7. Spatial decomposition of factors that influence CO
2
emissions for the 9 prefecture-level cities in
Fujian Province for 2000, 2005, 2010, 2015, and 2019. Figures (A), (B), and (C) represent the decomposition
results of the spatial index of CO
2
emissions (Mt) from the industrial, transportation, and household sector,
respectively. Different colored bars denote the contribution values of various effects, where positive values indicate
a factor’s contribution to carbon emissions surpassing the regional average, while negative values denote
a contribution lower than the regional average.
Art. 12(1) page 14 of 19 Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics
renewable energy adoption, and facilitate a shift to sus-
tainable industrial practices.
In contrast, Sanming’s scenario presents a dichotomy:
despite economic growth, the persistently low Ei ranking
indicates suboptimal improvements in the energy effi-
ciency relative to economic expansion. To redress this
imbalance, Sanming’s administration needs to elevate the
energy efficiency in various sectors, integrate energy con-
servation into building designs, advocate for energy-
efficient industrial technologies, and develop sustainable
transportation infrastructures.
In Xiamen, a contrasting dynamic was observed, where
a lower ranking in Ur coupled with a higher ranking in 4C
indicated an inverse correlation between urbanization and
CO
2
emissions, supporting the EKC hypothesis. Accord-
ingly, Xiamen’s policy efforts should be directed toward
fostering sustainable urbanization. This includes promot-
ing energy-efficient building designs, implementing effec-
tive transportation systems, and integrating renewable
energy into the urban framework.
In summary, our findings elucidated the distinct chal-
lenges and opportunities for managing CO
2
emissions
across various cities in Fujian Province. Governments are
advised to leverage these insights to craft policies and
initiatives. Focus areas should include enhancing energy
efficiencies, encouraging the widespread use of clean and
renewable energy sources, promoting sustainable urban
development, and establishing regulations and incentives
for environmentally friendly practices.
5. Conclusions and policy implications
5.1. Conclusions
This investigation comprehensively examined the deter-
minants of CO
2
emissions in Fujian Province, emphasizing
long-term trends, transient variances, and spatial dispari-
ties. Our findings offer useful guidance for crafting CO
2
reduction strategies and may serve as a pertinent bench-
mark for similarly situated developed regions. By using the
STIRPAT model within an unbalanced panel data frame-
work from 2000 to 2019, we examined the long-term
factors influencing CO
2
emissions in Fujian Province.
Additionally, temporal and spatial LMDI models facilitated
an assessment of the short-term oscillations and regional
differences in emissions across the industrial, transporta-
tion, and residential sectors. We categorized the impact
factors into 7 distinct clusters: CO
2
emission coefficients,
fossil energy structure, energy intensity, industrial config-
uration, income, urbanization, and population dynamics.
Our principal conclusions are as follows.
For long-term trends, population growth, energy inten-
sity, and economic advancement markedly influenced CO
2
emissions, exerting a positive effect. This indicates that
Fujian Province’s current technological and economic
milieu predominantly drives CO
2
emissions upward. Fur-
thermore, our results corroborated the EKC hypothesis in
the context of urbanization and CO
2
emissions, suggesting
that maintaining an intermediate to high urbanization
level may facilitate industrial modernization, ecological
enhancement, and consequent CO
2
mitigation.
In the realm of short-term fluctuations, economic
growth emerged as the most potent factor that aug-
mented CO
2
emissions in the industrial, transportation,
and residential sectors from 2000 to 2019. The level of
economic development in Fujian Province has not yet
reached a threshold that would counteract CO
2
emissions,
highlighting the necessity for sector-specific regulatory
interventions. In contrast, the energy intensity effect
played a pivotal role in reducing CO
2
emission growth
in most cities, with the exception of the transportation
sector. The introduction of diverse policies has advanced
technological capabilities in Fujian Province, effectively
restraining CO
2
emissions. Moreover, urbanization was
identified as a significant contributor to increased CO
2
emissions across all sectors, predominantly due to the
province’s relatively nascent urbanization stage. Urban
expansion, characterized by heightened infrastructural
development and energy demands, has led to escalated
emissions.
Our spatial analysis revealed escalating disparities in
CO
2
emissionsacrosstheseregionsovertime.High-
emission cities, such as Quanzhou, Fuzhou, and Zhangz-
hou, offer substantial scope for emission reductions
compared to their counterparts. Variations in technologi-
cal levels across the regions were instrumental in these
disparities. Hence, targeted initiatives aimed at technolog-
ical advancement in regions that exhibit positive energy
intensity effects could diminish regional emission discre-
pancies and contribute to the province’s overall emission
reduction.
In summary, this study accentuated the critical need for
intensified CO
2
emission reduction efforts in Fujian Prov-
ince. Thoughtfully formulated policies and measures that
consist of technological advancements, judicious urban
development, and sector-specific regulations are impera-
tive for steering the province toward its carbon emission
reduction goals. These salient findings provide a valuable
reference for policymakers and stakeholders, outlining
a roadmap for devising comprehensive low-carbon devel-
opment strategies and advancing sustainability endeavors.
5.2. Policy implications
To align with China’s objective of peak carbon emissions
by 2030, national and regional strategies have been
deployed. However, our analysis revealed that Fujian Prov-
ince has lagged behind in meeting these benchmarks, and
this was exacerbated by the growing spatial disparities in
CO
2
emissions. This underscores the need for amplified
efforts toward emission reductions. Based on our results,
we recommend specific strategies for a transition toward
low-carbon development.
Most importantly, it is crucial for urban planners and
policymakers to foster an advanced level of urbanization
and guide intelligent urban growth. This entails the con-
struction of energy-efficient buildings, the enhancement
of public infrastructure and transportation systems, and
stricter enforcement of regulations governing the use of
energy-intensive devices.
In addition, leveraging technological innovation can
significantly decelerate the rise in CO
2
emissions. During
Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics Art. 12(1) page 15 of 19
the 14th FYP period, it is imperative for policymakers to
devise practical initiatives for further augmenting energy
efficiency. This includes the development of fuel-efficient
transportation infrastructure (roads, aviation, and mari-
time), energy-saving buildings and lighting, and efficient
cooking technologies. Attention should also be given to
the regional diversity in energy usage, prioritizing cities
with higher energy consumption, such as Quanzhou and
Sanming, for technological advancements.
Finally, it is vital to adopt differentiated and collabora-
tive policies across different regions to reduce emissions
that balance efficiency and equity. In rapidly developing
cities such as Nanping, Ningde, and Longyan, policies
should focus on decreasing the energy intensity, particu-
larly as these areas that are likely to experience increased
energy demands concurrent with economic growth. The
introduction of energy-efficient technologies in these
regions is thus paramount. Additionally, more developed
cities, such as Fuzhou and Xiamen, should offer financial
or technical support to regions with high energy usage or
those still developing. These cities should also lead by
example in increasing the adoption of renewable energy
sources, such as electric vehicles, setting a precedent for
others to follow. Cities with heavy industrial bases, includ-
ing Quanzhou, Zhangzhou, and Sanming, need to priori-
tize industrial modernization and foster the growth of
service and high-tech industries while phasing out
pollution-intensive sectors.
Data accessibility statement
The datasets used in this study were sourced from the
official portals of the Statistics Bureaus across the 9 cities
in Fujian Province. In addition, we used the Fujian Statis-
tical Yearbook and China Energy Statistical Yearbook. The
precise datasets and variables examined are delineated
herein, accompanied by the respective years and tables
from which they were acquired:
The output value of sector and population metrics
across the 9 cities in Fujian Province were extracted from
the Fujian Statistical Yearbooks. Specific data are pre-
sented in Table S16 and Table S17 of the supplementary
materials.
Due to the absence of energy balance sheets within the
city-specific statistical yearbooks, the energy consumption
at the urban scale was inferred from the provincial energy
balance sheet and industrial energy consumption across
the 9 cities in Fujian Province. Energy consumption data
for Fujian Province from 2000 to 2019 were obtained
from the CEADs database (https://www.ceads.net.cn/).
The industrial energy consumption metrics for Fuzhou,
Longyan, Nanping, Ningde, Putian, Quanzhou, Sanming,
Xiamen, and Zhangzhou for the period from 2000 to 2019
were obtained from their respective official statistical por-
tals, specifically in Tables S6, S8, S10, and S12.
The methodology proposed by Shan et al. (2017) was
used to compute the energy consumption and CO
2
emis-
sions inventories for the aforementioned cities in Fujian
Province (http://creativecommons.org/licenses/by/4.0/).
The municipal estimates for the energy consumption and
carbon emissions are provided in the supplementary
material (Tables S6–S15).
All data used in this analysis are publicly accessible via
the respective websites and were amassed in adherence to
the guidelines and policies set forth by each Statistics
Bureau.
Supplemental files
The supplemental files for this article can be found as
follows:
Supplementary Material.docx
Acknowledgments
We acknowledge the use of the Grammarly software
(Grammarly Inc., USA) to assist with the refinement of
grammar and syntax in this manuscript. While Grammarly
helped improve the clarity and readability of the text, all
the scientific content, ideas, and conclusions presented
are entirely the authors’ own.
Funding
This work was supported by the Key Projects of Natural
Science Foundation of Fujian Province in China [Grant
number 2021J02030], the Social Science Foundation of
Fujian Province in China [Grant number FJ2021B042],
and the Research Funds for the Fujian Province Public-
interest Scientific Institution in China [Grant number
2021R1002004].
Competing interests
The authors declare no competing interests.
Author contributions
Methodology, writing—original draft preparation, formal
analysis, and visualization: YH.
Writing—review and editing: WL.
Conceptualization, supervision, and writing—review and
editing: YW.
Writing—original draft preparation, formal analysis, and
visualization: LZ.
Data curation and writing—review and editing: YH.
Data collection and processing: HL.
Investigations and writing—review and editing: FW, WtL.
Writing—review and editing: RS.
Read and approved the final manuscript: All authors.
References
Ang, B,Zhang, F,Choi, K-H. 1998. Factorizing changes in
energy and environmental indicators through
decomposition. Energy 23: 489–495.
Ang, BW. 2004. Decomposition analysis for policymaking
in energy: Which is the preferred method? Energy
Policy 32: 1131–1139.
Ang, BW. 2005. The LMDI approach to decomposition
analysis: A practical guide. Energy Policy 33:
867–871.
Ang, BW,Bin, S,Wang, HH. 2016. A spatial–temporal
decomposition approach to performance assess-
ment in energy and emissions. Energy Economics
60: 112–121.
Art. 12(1) page 16 of 19 Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics
Ang, BW,Xu, XY,Su, B. 2015. Multi-country comparisons
of energy performance: The index decomposition
analysis approach. Energy Economics 47: 68–76.
Baltagi, BH,Li, D. 2002. Series estimation of partially
linear panel data models with fixed effects. Annals
of Economics and Finance 3: 103–116.
Cahill, CJ,Gallacho
´ir, BPO
´.2010. Monitoring energy
efficiency trends in European industry: Which top-
down method should be used? Energy Policy 38:
6910–6918.
Central Government of the People’s Republic of China.
2021. Opinions of the CPC Central Committee and the
State Council on the complete, accurate and compre-
hensive implementation of the new development con-
cept and the completion of carbon peak and carbon
neutralization (in Chinese). Available at https://www.
gov.cn/zhengce/2021-10/24/content_5644613.htm.
Accessed December 12, 2024.
Chang, CP,Dong, MY,Sui, B,Chu, Y. 2019. Driving forces
of global carbon emissions: From time- and spatial-
dynamic perspectives. Economic Modelling 77:
70–80.
Chen, C,Zhao, T,Yuan, R,Kong, YC. 2019. A spatial-
temporal decomposition analysis of China’s carbon
intensity from the economic perspective. Journal of
Cleaner Production 215: 557–569.
Chen, L,Yang, ZF. 2015. A spatio-temporal decomposition
analysis of energy-related CO
2
emission growth in
China. Journal of Cleaner Production 103: 49–60.
Chen, Y,Lin, B. 2020. Decomposition analysis of patent-
ing in renewable energy technologies: From an
extended LMDI approach perspective based on
three Five-Year Plan periods in China. Journal of
Cleaner Production 269: 122402.
Cui, G,Yu, Y,Zhou, L,Zhang, H. 2020. Driving forces for
carbon emissions changes in Beijing and the role of
green power. Science of The Total Environment 728:
138688.
Dietz, T,Rosa, EA. 1997. Effects of population and afflu-
ence on CO
2
emissions. Proceedings of the National
Academy of Sciences 94: 175–179.
Dong, K,Hochman, G,Timilsina, GR. 2020. Do drivers
of CO
2
emission growth alter overtime and by the
stage of economic development? Energy Policy 140:
111420.
Dong, K,Sun, R,Jiang, H,Zeng, X. 2018. CO
2
emissions,
economic growth, and the environmental Kuznets
curve in China: What roles can nuclear energy and
renewable energy play? Journal of Cleaner Produc-
tion 196: 51–63.
Feng, C,Xia, Y-S,Sun, L-X. 2020. Structural and social-
economic determinants of China’s transport low-
carbon development under the background of aging
and industrial migration. Environmental Research
188: 109701.
Fujian Provincial People’s Government. 2016. Outline
of the 13th Five-Year Plan for National Economic
and Social Development of Fujian Province (in Chi-
nese). Available at https://www.fujian.gov.cn/xwdt/
fjyw/201603/t20160304_1683499.htm. Accessed
December 12, 2024.
Gao, Z,Geng, Y,Wu, R,Chen, W,Wu, F,Tian, X. 2019.
Analysis of energy-related CO
2
emissions in China’s
pharmaceutical industry and its driving forces. Jour-
nal of Cleaner Production 223: 94–108.
Geng, Y,Wang, ML,Sarkis, J,Xue, B,Zhang, L,Fujita, T,
Yu, XM,Ren, WX,Zhang, LM,Dong, H. 2014.
Spatial-temporal patterns and driving factors for
industrial wastewater emission in China. Journal of
Cleaner Production 76: 116–124.
Grossman, GM,Krueger, AB. 1995. Economic growth
and the environment. The Quarterly Journal of Eco-
nomics 110: 353–377.
Intergovernmental Panel on Climate Change.2006.
Guidelines for National Greenhouse Gas Inventories.
Hayama, Japan: The Institute for Global Environ-
mental Strategies.
International Energy Agency. 2019. Global energy &
CO
2
status report 2019: The latest trends in energy
and emissions in 2018. Available at https://www.iea.
org/reports/global-energy-co2-status-report-2019.
Accessed March 2019.
Juan, L,Shen, Y,Li, X,Hasnaoui, A. 2021. BRICS carbon
neutrality target: Measuring the impact of electricity
production from renewable energy sources and
globalization. Journal of Environmental Management
298: 113460.
Li, FY,Cai, BF,Ye , Z-Y,Wang, Z,Zhang, W,Zhou, P,Chen,
J. 2019a. Changing patterns and determinants of
transportation carbon emissions in Chinese cities.
Energy 174: 562–575.
Li, H,Zhao, Y,Qiao, X,Liu, Y,Cao, Y,Li, Y,Wang, S,
Zhang, Z,Zhang, Y,Weng, Y. 2017. Identifying the
driving forces of national and regional CO
2
emis-
sions in China: Based on temporal and spatial
decomposition analysis models. Energy Economics
68: 522–538.
Li, JS,Song, XH,Guo, YQ,Yang, Q,Feng, KS. 2019b. The
determinants of China’s national and regional
energy-related mercury emission changes. Journal
of Environmental Management 246: 505–513.
Li, N,Wei, C,Zhang, H,Cai, C,Song, M,Miao, J. 2020.
Drivers of the national and regional crop
production-derived greenhouse gas emissions in
China. Journal of Cleaner Production 257: 120503.
Lin, B,Du, K. 2014. Decomposing energy intensity
change: A combination of index decomposition
analysis and production-theoretical decomposition
analysis. Applied Energy 129: 158–165.
Lin, B,Wang, M. 2021. What drives energy intensity fall in
China? Evidence from a meta-frontier approach.
Applied Energy 281: 116034.
Liu, N,Ma, ZJ,Kang, JD. 2017. A regional analysis of
carbon intensities of electricity generation in China.
Energy Economics 67: 268–277.
National Bureau of Statistics. 2020. China Statistical
Yearbook 2020 (in Chinese). Available at https://
www.stats.gov.cn/sj/ndsj/2020/indexch.htm.
Accessed December 12, 2024.
Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics Art. 12(1) page 17 of 19
National Development and Reform Commission of
People’s Republic of China. 2010. National Devel-
opment and Reform Commission Notice on the
pilot work of low-carbon provinces and cities (in
Chinese). Available at https://www.ndrc.gov.cn/
xxgk/zcfb/tz/201008/t20100810_964674.html.
Accessed December 12, 2024.
Pata, UK. 2018. The influence of coal and noncarbohy-
drate energy consumption on CO
2
emissions: Revi-
siting the environmental Kuznets curve hypothesis
for Turkey. Energy 160: 1115–1123.
Quan, C,Cheng, X,Yu, S,Ye, X. 2020. Analysis on the
influencing factors of carbon emission in China’s
logistics industry based on LMDI method. Science
of The Total Environment 734: 138473.
Roman-Colladoa, R,Morales-Carrion, AV. 2018.
Towards a sustainable growth in Latin America: A
multiregional spatial decomposition analysis of the
driving forces behind CO
2
emissions changes.
Energy Policy 115: 273–280.
Shan,Y,Guan, D,Liu, J,Mi, Z,Liu, Z,Liu, J,Schroeder, H,
Cai, B,Chen,Y,Shao, S,Zhang, Q. 2017. Methodology
and applications of city level CO
2
emission accounts in
China. Journal of Cleaner Production 161: 1215–1225.
Shi, YY,Han, BT,Han, L,Wei, ZX. 2019. Uncovering the
national and regional household carbon emissions
in China using temporal and spatial decomposition
analysis models. Journal of Cleaner Production 232:
966–979.
Shigetomi, Y,Matsumoto, K,Ogawa, Y,Shiraki, H,
Yamamoto, Y,Ochi, Y,Ehara, T. 2018. Driving
forces underlying sub-national carbon dioxide emis-
sions within the household sector and implications
for the Paris Agreement targets in Japan. Applied
Energy 228: 2321–2332.
Wang, J,Jiang, Q,Dong, X,Dong, K. 2021. Decoupling
and decomposition analysis of investments and CO
2
emissions in information and communication tech-
nology sector. Applied Energy 302: 117618.
Wang, JF,He, ST,Qiu, Y,Liu, N,Li, YJ,Dong, ZF. 2018.
Investigating driving forces of aggregate carbon
intensity of electricity generation in China. Energy
Policy 113: 249–257.
Wang, Y,Chen, L,Kubota, J. 2016a. The relationship
between urbanization, energy use and carbon emis-
sions: Evidence from a panel of Association of
Southeast Asian Nations (ASEAN) countries. Journal
of Cleaner Production 112: 1368–1374.
Wang, Y,Han, R,Kubota, J. 2016b. Is there an environ-
mental Kuznets curve for SO
2
emissions? A semi-
parametric panel data analysis for China. Renewable
and Sustainable Energy Reviews 54: 1182–1188.
Wang, Y,Li, L,Kubota, J,Han, R,Zhu, X,Lu, G. 2016c.
Does urbanization lead to more carbon emission?
Evidence from a panel of BRICS countries. Applied
Energy 168: 375–380.
Wang, Y,Zhang, C,Lu, A,Li, L,He, Y,ToJo, J,Zhu, X.
2017. A disaggregated analysis of the environmental
Kuznets curve for industrial CO
2
emissions in China.
Applied Energy 190: 172–180.
Wang, Y,Zhang, X,Kubota, J,Zhu, X,Lu, G. 2015. A
semi-parametric panel data analysis on the
urbanization-carbon emissions nexus for OECD
countries. Renewable and Sustainable Energy
Reviews 48: 704–709.
Xiao, H,Sun, K-J,Bi, H-M,Xue, J-J. 2019. Changes in
carbon intensity globally and in countries: Attribu-
tion and decomposition analysis. Applied Energy
235: 1492–1504.
Xue, YL,Jia, ZM,Zeng, WH. 2019. Provincial-level useful
work and influencing factors analysis in China from
1985 to 2016: Based on logarithmic mean divisia
index decomposition and affinity propagation clus-
tering. Journal of Cleaner Production 212: 223–239.
Yao, LQ,Xu, JR,Zhang, LN,Pang, QH,Zhang, CJ. 2019.
Temporal-spatial decomposition computing of
regional water intensity for Yangtze River Economic
Zone in China based on LMDI model. Sustainable
Computing-Informatics & Systems 21: 119–128.
Yatchew, A. 1998. Nonparametric regression techniques
in economics. Journal of Economic Literature 36:
669–721.
York, R,Rosa, EA,Dietz, T. 2003.STIRPAT,IPATand
ImPACT: Analytic tools for unpacking the driving
forces of environmental impacts. Ecological Econom-
ics 46: 351–365.
Zhang, C,Su, B,Zhou, K,Sun, Y. 2020. A multi-
dimensional analysis on microeconomic factors of
China’s industrial energy intensity (2000–2017).
Energy Policy 147: 111836.
Zheng, M. 2017. Notice of the People’s Government of
Fujian Province on Printing and Distributing the
Work Plan for the Control of Greenhouse Gas Emis-
sions in the “Thirteenth Five-Year Plan” of Fujian
Province (in Chinese). Fujian Provincial People’s
Government.
Zheng, X,Wang, R,Du, Q. 2020. How does industrial
restructuring influence carbon emissions: City-level
evidence from China. Journal of Environmental Man-
agement 276: 111093.
Zhu, XH,Zou, JW,Feng, C. 2017. Analysis of industrial
energy-related CO
2
emissions and the reduction
potential of cities in the Yangtze River Delta region.
Journal of Cleaner Production 168: 791–802.
Art. 12(1) page 18 of 19 Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics
How to cite this article: Huang, Y, Lin, W, Wang,Y, Luo, H, Zhu, L, He,Y, Wang, F, Lai, W-t, Shi, R. 2024. Driving factors of CO
2
emissions in southeast China: Comparative study of long-term trends, short-term fluctuations, and spatial variations.
Ele-
menta: Science of the Anthropocene
12(1). DOI: https://doi.org/10.1525/elementa.2023.00028
Domain Editor-in-Chief: Detlev Helmig, Boulder AIR LLC, Boulder, CO, USA
Associate Editor: Lori Bruhwiler, Global Monitoring Division, National Oceanic & Atmospheric Administration Earth System
Research Laboratory, Boulder, CO, USA
Knowledge Domain: Atmospheric Science
Published: December 26, 2024 Accepted: October 14, 2024 Submitted: February 22, 2023
Copyright: ©2024 The Author(s). This is an open-access article distributed under the terms of the Creative Commons
Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.
Elem Sci Anth
is a peer-reviewed open access
journal published by University of California Press.
Huang et al: CO
2
emissions in southeast China: Long and short-term and spatial dynamics Art. 12(1) page 19 of 19
