Toward the Construction of a Sustainable Society: Assessing the Temporal Variations and Two-Dimensional Decoupling of Carbon Dioxide Emissions in Anhui Province, China

Abstract

This study comprehensively assessed carbon dioxide emissions over a span of two decades, from 2000 to 2020, with the decomposition and decoupling analyses considering multiple influence factors across both short-term and long-term dimensions. The results revealed great fluctuations in the decoupling analysis index (DAI) for subjected sectors such as natural resource processing, electricity, gas, water, textiles, machinery, and electronics manufacturing. Of note, significantly changed sectoral DAIs were observed in urban traffic and transportation, logistics warehousing, and the postal industry within Anhui Province. In contrast, the DAIs of other sectors and social services exhibited a weak decoupling state in Anhui Province. The industrial sectors responsible for mining and textiles and the energy structure encompassing electricity, gas, and water emerged as the primary contributors to carbon dioxide emissions. Additionally, the efficiency of the socio-economic development (EDE) was identified as the principal driver of carbon dioxide emissions during the observed period, while the energy consumption intensity (ECI) served as the putative crucial inhibiting factor. The two-dimensional decoupling of carbon dioxide emissions attributable to the EDE demonstrated a gradual transition from industrial sectors to buildings and tertiary industries from 2000 to 2020. In the future, the interaction between urban carbon dioxide emissions and the socio-economic landscape should be optimized to foster integrated social sustainable development in Anhui Province.

Full text

Citation: Zhang, K.; Jiang, L.; Liu, W.
Toward the Construction of a
Sustainable Society: Assessing the
Temporal Variations and
Two-Dimensional Decoupling of
Carbon Dioxide Emissions in Anhui
Province, China. Sustainability 2024,
16, 9923. https://doi.org/10.3390/
su16229923
Received: 28 August 2024
Revised: 21 October 2024
Accepted: 12 November 2024
Published: 14 November 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Toward the Construction of a Sustainable Society: Assessing
the Temporal Variations and Two-Dimensional Decoupling of
Carbon Dioxide Emissions in Anhui Province, China
Kerong Zhang 1, * , Liangyu Jiang 2and Wuyi Liu 3,*
1School of Business, Fuyang Normal University, Fuyang 236037, China
2School of International Business and Economics, Nanjing University of Finance and Economics,
Nanjing 210023, China; 1920230014@stu.nufe.edu.cn
3School of Biological Science and Food Engineering, Fuyang Normal University, Fuyang 236037, China
*Correspondence: zkrahfy@fynu.edu.cn (K.Z.); lwuyi@fynu.edu.cn or lwycau@163.com (W.L.)
Abstract: This study comprehensively assessed carbon dioxide emissions over a span of two decades,
from 2000 to 2020, with the decomposition and decoupling analyses considering multiple influence
factors across both short-term and long-term dimensions. The results revealed great fluctuations
in the decoupling analysis index (DAI) for subjected sectors such as natural resource processing,
electricity, gas, water, textiles, machinery, and electronics manufacturing. Of note, significantly
changed sectoral DAIs were observed in urban traffic and transportation, logistics warehousing,
and the postal industry within Anhui Province. In contrast, the DAIs of other sectors and social
services exhibited a weak decoupling state in Anhui Province. The industrial sectors responsible
for mining and textiles and the energy structure encompassing electricity, gas, and water emerged
as the primary contributors to carbon dioxide emissions. Additionally, the efficiency of the socio-
economic development (EDE) was identified as the principal driver of carbon dioxide emissions
during the observed period, while the energy consumption intensity (ECI) served as the putative
crucial inhibiting factor. The two-dimensional decoupling of carbon dioxide emissions attributable to
the EDE demonstrated a gradual transition from industrial sectors to buildings and tertiary industries
from 2000 to 2020. In the future, the interaction between urban carbon dioxide emissions and the
socio-economic landscape should be optimized to foster integrated social sustainable development in
Anhui Province.
Keywords: industrial economy; carbon dioxide emissions; Logarithmic Mean Divisia Index;
attribution analysis; Tapio model; decoupling analysis index
1. Introduction
In response to the warming of the Earth’s atmosphere, governments have implemented
a range of regulatory measures aimed at mitigating carbon dioxide emissions, which pose
significant threats to all living organisms globally [
1
–
3
]. Currently, anthropogenic activi-
ties are the primary contributors to the pressing challenges of global climate change and
environmental degradation, largely stemming from increased carbon dioxide emissions as-
sociated with the consumption of fossil fuels such as coal and oil [
4
–
8
]. Both developed and
developing nations are grappling with the physicochemical and biochemical repercussions
of global warming and environmental decline driven by carbon dioxide emissions [
7
–
14
].
The future challenges regarding human living conditions are expected to be even more
pronounced in developing countries.
China stands as a prominent developing nation characterized by its substantial fossil
energy consumption. This extensive reliance on fossil fuels typically results in a rapid
escalation of carbon dioxide emissions, which, in turn, exert serious impacts on global
climate change and environmental degradation in key cities across the nation [
7
,
13
,
14
].
Sustainability 2024,16, 9923. https://doi.org/10.3390/su16229923 https://www.mdpi.com/journal/sustainability

Sustainability 2024,16, 9923 2 of 25
Consequently, the Chinese government has enacted national “dual carbon” strategies and
corresponding measures aimed at curbing carbon dioxide emissions at their source [
15
,
16
].
On 22 September 2020, and again on 21 September 2021, President Xi Jinping articulated the
national “dual carbon” reduction strategy during the 75th and 76th United Nations General
Assemblies [
17
], targeting carbon neutrality and peak emissions in China. The introduction
of the “dual carbon goal” has intensified the resource and environmental constraints
encountered by industrial development. In this context, it is imperative to decouple
carbon dioxide emissions from socio-economic development, particularly in light of the
sluggish international economic environment and increasingly fierce domestic industrial
competition, to foster a sustainable eco-society [
18
–
20
]. Anhui Province, recognized as one
of the significant manufacturing hubs and a recipient of industrial transfers, traditionally
exhibits high energy consumption levels [
21
]. Addressing the intricate relationship between
industrial socio-economic development and carbon dioxide emissions is crucial for Anhui
Province to meet the national “dual carbon” reduction objectives [20–28].
With the push for national integrated development, Anhui Province has gradually
become part of the expansive eco-society of the Yangtze River Delta (YRD). However, due
to its relatively brief integration period and underdeveloped foundation, Anhui lags behind
other more developed regions within the YRD [
18
,
29
–
31
]. The integrated development
of the YRD has progressively shifted secondary industries to surrounding cities with less
stringent environmental regulations and outdated technology [19–21,24,29–32]. As a vital
receptor area for industrial transfers within the YRD, Anhui Province is also endowed
with rich mineral resources. Particularly, prefecture-level cities such as Huainan, Huaibei,
Ma’anshan, and Tongling are principal resource-based cities, collectively accounting for
one-fourth of Anhui’s total area. This has resulted in traditional high-carbon dioxide-
emitting industries occupying a disproportionately large share within the industrial sector
structure (ISS) of Anhui Province amid regional integration development [21,32].
In recent years, a variety of methods have been employed to decouple the increasing
consumption of resources and energy from different perspectives within socio-economic
development. Currently, three primary types of decoupling analysis indexes (DAIs) are
commonly utilized in research. The first is the widely recognized DAI approach established
by the OECD in 2002 [
33
], which is based on the growth of the emission intensity. The
second is Tapio’s elastic index (TEI), typically used to delineate the potential decoupling of
resources and energy [
34
]. The third is a newly emerged combined DAI approach recently
introduced by scholars [26,35,36].
Since 2005, the concepts of elasticity and the elastic index were integrated into various
models of decoupling analysis, such as the popular TEI model, along with related criteria
for decoupling classification. Subsequently, it was determined that the DAI represents a
long-term or adaptive process necessitating a certain duration and cost for effective “decou-
pling” [
37
]. Building on this understanding, an enhanced TEI model was proposed [
37
–
40
].
The improved TEI model was then employed to examine the environmental pressures from
discharged waters and to decouple industrial water resource consumption from regional
socio-economic development in the Yangtze River Delta (YRD) from 2000 to 2017 [
41
]. The
findings indicated a trend of increasing decoupling strength across the YRD, albeit with
significant regional variations [
30
,
41
–
44
]. During the period from 2007 to 2017, carbon
dioxide emissions from tourism in developing areas along the international “Belt and
Road” initiative surged by approximately 0.84 times [
42
]. Notably, regions with elevated
carbon dioxide emissions were predominantly located in southeastern and northeastern
China [
42
]. It was also observed that the decoupling of carbon dioxide emissions from
regional socio-economic factors is not a short-term phenomenon but rather an adaptive
process requiring a substantial historical timeframe and associated costs [
42
–
44
]. Given that
many decoupling analysis methods fall short in evaluating macrosocial and macroeconomic
driving factors, numerous scholars have employed Structural Decomposition Analysis
(SDA) and Index Decomposition Analysis (IDA) to delve deeper into the potential influ-
encing factors [
45
–
49
]. Compared to SDA and other decomposition methodologies, IDA

Sustainability 2024,16, 9923 3 of 25
is more prevalently applied in the decoupling analyses of carbon dioxide emissions due
to its flexibility in data handling [
50
,
51
]. At the same time, the Logarithmic Mean Divisia
Index (LMDI) decomposition approach, characterized by comprehensive decomposition
and zero residual error, has gained widespread acceptance in combined research reports on
carbon dioxide emissions [
52
–
58
]. For instance, the combined index model of Tapio and
the LMDI have frequently been utilized to assess multi-sector decoupling efforts aimed at
reducing carbon dioxide emissions in China [
52
–
55
]. These studies have revealed a shift
in the decoupling state of the carbon dioxide emissions from weak to strong, with the
energy intensity, technological innovation, and the economic structure identified as the
key drivers in emissions reduction [
52
–
55
]. Subsequently, combined index models such as
Tapio+STRIPAT+LMDI and Kaya+LMDI have been employed to compute and estimate the
decoupling relationship between economic growth and carbon dioxide emissions, as well
as to predict the carbon peak timeline in Chengdu, China [
56
,
57
]. Recently, the comprehen-
sive index model of Tapio+Kaya+LMDI was used to evaluate the decoupling relationship
between economic growth and waste emissions in the construction sectors of the European
Union and China [
58
]. The findings indicated that from 1991 to 2022, most E.U. countries’
construction sectors remained in a state of weak or negative decoupling, while China’s
sector primarily exhibited weak decoupling [58].
Nevertheless, due to the shifting inhibitory effects of recent socio-economic structural
factors, natural resources are depleting, and the potential for reducing carbon dioxide
emissions is diminishing in China too. In light of these analyses, this study focused on
Anhui Province, a representative central province, to conduct a decoupling analysis aimed
at providing effective recommendations for the evolution of low-carbon policies in Central
China. By integrating various computation models of the DAI with diverse decomposition
methods, such as SDA and IDA, the decoupling status of carbon dioxide emissions and
relevant influencing factors can be accurately assessed. For example, utilizing extended
approaches of the Kaya identity and LMDI decomposition with energy consumption
data, the size of the consumer population and the level of socio-economic output were
identified as critical factors contributing to the increase in carbon dioxide emissions in
Xinjiang from 1952 to 2010 [
59
]. From the perspectives of energy consumption and carbon
dioxide emissions, social changes and the potential influencing factors were also explored
and evaluated using a two-stage approach of LMDI decomposition in Sichuan Province
from 2000 to 2018 [
60
]. It was found that Jiangsu Province significantly contributed to
emission reductions and exhibited high energy efficiency [
60
]. Later, the novel approach of
attribution analysis (AA) was further integrated to measure and evaluate the contribution
rates of different sectors based on the LMDI decomposition approach [
40
,
61
,
62
]. Through
the AA method, factors such as the energy consumption intensity (ECI) and industrial
sector structure (ISS) were comprehensively assessed from the perspectives of targeted
intensity and carbon dioxide emission volumes [
63
]. It was determined that the ECI and
industry category were the primary factors driving the regional decline in carbon dioxide
emissions and other greenhouse gases [
63
]. By combining the TEI and AA approaches, a
study investigated the decoupling status and potential influencing factors of carbon dioxide
emissions in BRICS countries, revealing a general transition trend from negative to initially
weak and subsequently to strong decoupling [40].
Industries, particularly the manufacturing sector, are typically viewed as the pri-
mary contributors to regional carbon dioxide emissions. Consequently, the estimation
and assessment of carbon dioxide emissions, along with potential pollutants from indus-
trial production and their influencing factors, have consistently been focal points of re-
search [
24
,
27
,
64
–
69
]. With the rise of the tertiary sector, carbon dioxide emissions stemming
from transportation have also garnered increasing scholarly attention [
70
–
73
]. Therefore,
conducting simultaneous analyses across multiple national economic sectors can yield
a more comprehensive understanding of the level and intensity of the carbon dioxide
emissions within each industry, facilitating the exploration of how various production
factors influence industrial emissions [32,74–76].

Sustainability 2024,16, 9923 4 of 25
In addition, distinct regions exhibit unique developmental characteristics, often shaped
by the dominant countries, areas, and representative provinces or big cities [
19
,
77
–
81
]. The
factors influencing carbon dioxide emissions have gradually transitioned from traditional
metrics—such as the carbon productivity (CP), energy consumption intensity (ECI), indus-
trial sector structure (ISS), and energy structure (ES)—to encompass aspects like public
expenditure, the research and development intensity, the energy technology efficiency,
renewable energy innovation, and private debt [
23
,
28
,
66
–
86
]. However, the existing litera-
ture predominantly focuses on the industrial sectors, especially manufacturing, leaving
a notable gap in the research pertaining to other national sectors. When examining in-
dividual industrial sectors, there is a scarcity of in-depth analyses regarding the impact
of various national economic sectors and production changes on carbon dioxide emis-
sions. In terms of the methodologies, the research is largely confined to Logarithmic Mean
Divisia Index (LMDI) analyses, with few diverse combined studies, leading to inconclu-
sive
findings [87–90]
. Future research should prioritize combinatorial analyses, and more
comprehensive studies are needed. At present, the prefecture-level cities within Anhui
Province currently face a myriad of challenges related to energy consumption, including an
unsuitable energy structure, excessive consumption, and a complex composition of energy
use. Accordingly, this study sought to explore and evaluate the regional economies of
resource-based areas by categorizing them into five industrial sectors in Anhui Province,
utilizing the combined analyses of the LMDI decomposition approach [
24
,
38
,
40
,
52
–
96
] and
Tapio decoupling models [37–40,97].
Previous research has emphasized the pressing need for carbon reduction in China, un-
derscoring the significance of decomposition methods in such investigations. In this study,
combined analytical methods based on the widely accepted five national economic sectors
were employed, with the further subdivision of the industrial sectors facilitating a deeper
understanding of the impacts of the various national economic sectors on carbon dioxide
emissions. To comprehensively evaluate the actual effects of governmental strategies and
measures, this study systematically analyzed the relationship between carbon dioxide
emissions and the economic benefits of the five national sectors, considering six influencing
factors. The research is divided into four distinct temporal stages: 2001–2005, 2006–2010,
2011–2015, and 2016–2020, with a detailed analysis of the changing characteristics of the
industrial DAI and the primary influencing factors of the carbon dioxide emissions in each
stage. This study sought to address several pertinent questions: (1) To what extent has the
economic growth in various industrial sectors decoupled carbon dioxide emissions from
energy consumption in Anhui Province? (2) If decoupling effects emerged, what were the
key driving factors? (3) What impact did each industrial sector have on the driving forces
behind the decoupling of carbon dioxide emissions from economic growth? Ultimately,
this research aspired to conduct multi-factor decomposition and decoupling analyses of
regional carbon dioxide emissions, contributing to the construction of a sustainable eco-
society in Anhui Province. Additionally, it aims to provide a valuable reference for research
in similarly industrially dominated regions facing the dual constraints of resources and
environmental challenges, akin to those in Anhui Province.
2. Materials and Methods
2.1. Research Area
The research area encompassed Anhui Province, situated in the eastern part of main-
land China, which serves as a vital component of the broader Yangtze River Delta (YRD)
region. This province occupies a strategic position within the YRD, acting as an important
intermediary zone among the several major domestic economic hubs critical to the national
socio-economic development. The economy and culture of Anhui Province share significant
historical and natural ties with other regions within the YRD. Geographically, this study
encompassed the entirety of Anhui Province, which currently comprises 16 cities (see
Figure 1).

Sustainability 2024,16, 9923 5 of 25
Sustainability 2024, 16, x FOR PEER REVIEW 5 of 27
important intermediary zone among the several major domestic economic hubs critical to
the national socio-economic development. The economy and culture of Anhui Province
share significant historical and natural ties with other regions within the YRD.
Geographically, this study encompassed the entirety of Anhui Province, which currently
comprises 16 cities (see Figure 1).
Figure 1. Map of research area (Anhui Province, AH in abbreviation).
2.2. Research Datasets
Acknowledging the significance and diversity of the industrial levels [98], this study
categorized the overall economic industries of Anhui Province into five principal socio-
economic sectors: construction, industry, transportation, agriculture, and trade. In
alignment with the International Standard Industrial Classification (ISIC), the industrial
sectors were further delineated into mining, textiles, resource processing, ME (machinery
and electronics manufacturing), and EGW (electricity, gas, and water) [98]. The research
datasets were sourced from the Annual China Energy Statistical Yearbooks, accessible via
the China National Knowledge Infrastructure (CNKI) website (URL:
hps://oversea.cnki.net/ (accessed on 2, January 2023)). The types of consumed energy
were subsequently classified into three categories: coal, oil, and electricity. Due to the
absence of industrial output data for Anhui Province from 2017 to 2020, this study utilized
the “Main Business Revenue” figures from the Anhui Statistical Yearbook and employed
the interpolation method to estimate the missing industrial output values. The
interpolation method is usually adopted to address the issue of missing data based on
statistical sampling methods [99]. As interpolation is a widely accepted data imputation
technique adept at addressing gaps in datasets, this study specifically applied linear
interpolation to establish the linear relationships among the available data points, thereby
effectively filling in the gaps [99].
2.3. Research Methods
2.3.1. The Measuring Method of Carbon Dioxide Emissions
Figure 1. Map of research area (Anhui Province, AH in abbreviation).
2.2. Research Datasets
Acknowledging the significance and diversity of the industrial levels [
98
], this study
categorized the overall economic industries of Anhui Province into five principal socio-
economic sectors: construction, industry, transportation, agriculture, and trade. In align-
ment with the International Standard Industrial Classification (ISIC), the industrial sectors
were further delineated into mining, textiles, resource processing, ME (machinery and elec-
tronics manufacturing), and EGW (electricity, gas, and water) [
98
]. The research datasets
were sourced from the Annual China Energy Statistical Yearbooks, accessible via the China
National Knowledge Infrastructure (CNKI) website (URL: https://oversea.cnki.net/ (ac-
cessed on 2 January 2023)). The types of consumed energy were subsequently classified into
three categories: coal, oil, and electricity. Due to the absence of industrial output data for
Anhui Province from 2017 to 2020, this study utilized the “Main Business Revenue” figures
from the Anhui Statistical Yearbook and employed the interpolation method to estimate the
missing industrial output values. The interpolation method is usually adopted to address
the issue of missing data based on statistical sampling methods [
99
]. As interpolation is
a widely accepted data imputation technique adept at addressing gaps in datasets, this
study specifically applied linear interpolation to establish the linear relationships among
the available data points, thereby effectively filling in the gaps [99].
2.3. Research Methods
2.3.1. The Measuring Method of Carbon Dioxide Emissions
The refined model proposed by the IPCC was employed to calculate and measure the
volumes of carbon dioxide emissions in Anhui Province, specified in Equation (1) [79]:
CO2=
n
∑
i=1
Qi×NCVi×EKi×COFi×44/12 (1)
Herein, Q
i
means the consumption amount of the ith kind of energy, and NCV
i
stands
for the average low calorific value of the ith kind of energy. Moreover, EK
i
denotes the

Sustainability 2024,16, 9923 6 of 25
carbon content per unit calorific value of the ith kind of fuel, and COF
i
means the carbon
oxidation rate of the ith kind of fuel.
In addition, the carbon dioxide emission coefficient was calculated and estimated with
the method previously reported [100], specified in Equations (2)–(4):
EEVm=
∑
i
(FCi,m×ui)
EGm(2)
Km=EEVm−ECV
EEVm(3)
EFm=
∑
i
(FCi,m×EFco2,i)
ECm(4)
EEV
m
represents the energy equivalent value of electricity in the mth year (kgce/kW
·
h),
while ECV denotes the corresponding energy calorific value of electricity (kgce/kW
·
h).
Similarly, FC
i,m
is the consumption of the ith kind of fuel in electricity production in year
m (kg or m
3
), and ui means the coal conversion coefficient of the ith kind of fuel (kgce/kg
or kgce/m
3
). EG
m
expresses the provincial regional power production in the mth year
(108 kW
·
h), whereas Km stands for the ratio of the provincial regional power production
in the mth year. EF
m
means the carbon dioxide emission index of electricity in the mth
year (tco2/104 kW
·
h), and
EFco2,i
indicates the carbon dioxide emission index of the ith
kind of fuel (kgco2/kg). EC
m
reveals the electricity consumption of the provincial regional
consumer in the mth year (108 kW
·
h), while i indicates the ith kind of fossil fuel consumed
in the electricity production of a certain provincial region in the mth year.
Especially, the specific electric calorific value of China’s energy was 0.1229 (kgce/kW
·
h),
released and published by the official sectors. Table 1shows the indicators for the car-
bon dioxide emission factors of each energy source that were derived from the compiled
guidelines for the provincial greenhouse gas inventories in China [101].
Table 1. Carbon dioxide emission correlation coefficients of energy varieties.
Energy Type EF COF NCV EF
(kg C/Gj) % (kcal/kg or kcal/m3) kgco2/kg or kgco2/m3
Raw Coal 26.4 0.94 5000 1.9027
Cleaned Coal 25.4 0.93 6300 2.2855
Other Washed Coal 25.4 0.93 2497 0.9059
Briquettes 33.6 0.9 4200 1.9498
Coke 29.5 0.93 6800 2.864
Crude Oil 20.1 0.98 10,000 3.024
Gasoline 18.9 0.98 10,300 2.9827
Kerosene 19.6 0.98 10,300 3.0372
Diesel Oil 20.2 0.98 10,200 3.0998
Fuel Oil 21.1 0.98 10,000 3.1744
LPG 17.2 0.98 12,000 3.1052
2.3.2. Computation of Decoupling Analysis Index (DAI)
Decoupling analysis is primarily employed in the realms of resources and environ-
mental studies to assess and illustrate the diminishing correlation between socio-economic
development and carbon dioxide emissions [
25
]. Given that economic fluctuations are
susceptible to external shocks and business cycles in the short term, the decoupling analysis
index (DAI), when expressed as a chain ratio, may not accurately reflect the long-term
developmental trajectory. Additionally, variations in carbon dioxide emissions are influ-
enced by factors such as the energy consumption intensity (ECI), industrial structure shift
(ISS), economic output, and technological advancement. Considering the characteristics of
China’s industrial development phase, particularly within the framework of the five-year

Sustainability 2024,16, 9923 7 of 25
plan, provincial and municipal economic sectors are endowed with more specific devel-
opmental objectives and coherent strategic plans. Thus, measuring the DAI within this
five-year planning context proves to be more applicable [
22
]. In calculating the long-term
DAI, with the year 2000 designated as the base period, this study further examined the
dynamic changes in each short-term DAI. Consequently, this approach facilitated a com-
prehensive assessment of the evolving patterns of industrial carbon dioxide emissions.
The specified estimation formula of the elastic DAI (denoted as the K value) is shown in
Equation (5):
K=CT−C0
C0/GT−G0
G0=CT−C0
GT−G0×G0
C0(5)
Herein, C
T
denotes the total carbon dioxide emissions at the conclusion of the period,
while C
0
signifies the total carbon dioxide emissions at the outset. G
T
represents the final
gross domestic product, and G
0
indicates the initial gross domestic product. The study
period was segmented into four principal intervals: 2001–2005, 2006–2010, 2011–2015, and
2016–2020. The final year of each interval was selected as the reference base period for
calculating the elastic decoupling analysis index (DAI) for each respective period (Table 2).
According to the Tapio decoupling model, the decoupling states were divided into eight
types with the corresponding classification criteria, shown in Table 2.
Table 2. The partition standard of decoupled states.
Type of Decoupling Remark %GDP %C k
Decoupling
Strong decoupling >0 <0 k < 0
Weak decoupling >0 >0 0 < k < 0.8
Recessive decoupling <0 <0 k > 1.2
Coupling Expansive coupling >0 >0 0 < k < 0.8
Recessive coupling <0 <0 0 < k < 0.8
Negative decoupling
Weak negative decoupling <0 <0 0 < k < 0.8
Expansive negative decoupling >0 >0 k > 1.2
Strong negative decoupling <0 >0 k < 0
2.3.3. The Exponential Decomposition of the LMDI Model
The integration of the decoupling analysis method with the Logarithmic Mean Divisia
Index (LMDI) model enhances the examination of the putative drivers behind fluctuations
in carbon dioxide emissions [
23
,
45
–
49
,
65
–
102
]. The results derived from both the additive
and multiplicative decompositions of the LMDI model and indicators were found to be
consistent. Utilizing the LMDI indicators alongside the multiplicative decomposition
technique, this study disaggregated the presumed influencing factors into the carbon
emission efficiency (CEE), energy structure (ES), energy consumption intensity (ECI),
industrial structure shift (ISS), socio-economic development efficiency (EDE), and carbon
productivity (CP).
Next, employing the LMDI multiplicative decomposition method, the alterations
in the carbon dioxide emissions and the contribution of each influencing factor to these
changes were systematically investigated and assessed from the standpoint of change ratios.
The specified formula is shown in Equation (6):
CO2=
I
∑
i=1
J
∑
j=1
Cij
Eij
×Eij
Ej
×Ej
Qj
×Qj
Q×Q
P×P=
I
∑
i=1
J
∑
j=1
EDi j ×ESij ×EIj×ISj×G×P(6)
C
ij
represents the carbon dioxide emissions of the ith-type energy in the jth industry, E
ij
is ith-type energy consumption in the jth industry, and E
j
denotes the energy consumption
in the jth industry. Similarly, Q
j
means the production value of the jth industry, Q is the
value of the total industry production, and P represents the number of total employees in

Sustainability 2024,16, 9923 8 of 25
the industry. ED
ij
and ES
ij
are the values of the carbon dioxide emission efficiency (CEE)
and ES belonging to the ith-type energy in the jth industry, while EI
j
and IS
j
indicate the
values of the ECI and ISS in the jth industry, respectively. Moreover, G stands for the value
of the EDE, and P means the total engaged population.
With the LMDI decomposition approach, the influencing factors of the carbon dioxide
emissions across various national economic sectors and sub-industries in Anhui Province
were calculated, utilizing the conclusion of each period as the reference point. The estima-
tion formulas of the six ED
ij
, ES
ij
, EI
j
, IS
j
, G, and P factors are shown in (7a)–(7f). Referring
to the Sato–Vartia method, we calculated the weight (Wij) of the energy (i) of the industry
(j). The estimation equations are shown in Formulas (7g) and (7h):
DED =exp(
I
∑
i=1
J
∑
j=1
wij ln EDij,t
EDi j,t0
)(7a)
DES =exp(
I
∑
i=1
J
∑
j=1
wij ln ESij,t
ESij,t0
)(7b)
DEI =exp(
I
∑
i=1
J
∑
j=1
wij ln EIj,t
EIj,t0
)(7c)
DIS =exp(
I
∑
i=1
J
∑
j=1
wij ln ISj,t
ISj,t0
)(7d)
DG=exp(
I
∑
i=1
J
∑
j=1
wij ln Gt
Gt0
)(7e)
DP=exp(
I
∑
i=1
J
∑
i=1
wij ln Pt
Pt0
)(7f)
wij =L(Cij,t/Ct,Cij,t0/Ct0)
I
∑
i=1
J
∑
j=1
L(Cij,t/Ct,Cij,t0/Ct0)
(7g)
L(a,b) = a−b
ln a−ln b,a=b(7h)
2.3.4. The Computation of Attribution Analysis (AA)
The AA approach was utilized to quantify the contributions of the terminal sub-
industries or industrial sectors to the levels of carbon dioxide emissions [
38
]. This method
further facilitated the identification of the primary sources or influencing factors of the
carbon dioxide emissions, such as the energy structure (ES) and energy intensity (EI). This
study adopted a five-year cycle, using the conclusion of each period as the reference point
to calculate the single-period attribution of the six influencing factors. Taking the EI as an
example, the specific estimation formula is shown in Equations (8) and (9):
EIt
EIt0
−1=
I
∑
i=1
J
∑
j=1
rij (EIj,t
EIj,t0
−1)(8)
rij =
wij EIj,t0
L(EIj,t,EIj,t0EIt/E It0)
I
∑
i=1
J
∑
j=1
wij EIj,t0
L(EIj,t,EIj,t0EIt/E It0)
(9)

Sustainability 2024,16, 9923 9 of 25
I
∑
i=1
rij (EIj,t
EIj,t0
−1)(10)
The expression of the variables in Equation (10) represents the contribution rate of the
industry (j) to the change in the EI index, and the variable r
ij
expresses the weight of the
ith-type energy in the jth industry. Through the estimation of Equation (8), the contribution
ratio of the terminal sub-industries or the industrial sectors to the decomposition index
in each period could be quantified. EI
t
means the energy intensity at time (t), while EI
0
denotes the energy intensity during the base period. The ratio EI
t
/EI
0
reflects the relative
change in the energy intensity at time (t) compared to that in the same base period.
3. Results and Discussion
This study comprehensively assessed carbon dioxide emissions over a span of twenty
years, from 2000 to 2020, while exploring the corresponding multiple influencing factors
from both the short-term and long-term perspectives. The findings from the decomposition
and decoupling analyses are presented as follows.
3.1. Decoupling Analysis
The DAI can be effectively integrated with the change rates of carbon dioxide emissions
and production values to categorize the decoupling status [
59
,
81
]. Utilizing the Tapio index
model, the DAIs and their variations were computed and classified into five national
economic sectors and five industrial sectors within Anhui Province, respectively [
34
]. To
more accurately depict the fluctuations in the DAI of each industry, this study substituted
values of the DAI exceeding 1.5 and those below −1.5 with 1.5 and −1.5, respectively.
3.1.1. Analysis of the Long-Term Decoupling Analysis Indexes
With the exception of the traffic and transportation sector, the DAIs of all other in-
dustrial sectors exhibited a downward trend (Figure 2), indicating that the relationship
between their economic benefits and carbon dioxide emissions gradually diminished in
Anhui Province from 2000 to 2019. The long-term DAI for traffic and transportation has
remained in a state of expansive negative decoupling for an extended period. Between 2000
and 2019, the urban carbon dioxide emissions increased at an annual rate of 15%, while
the economic benefits grew at a rate of 10% (Figure 2). Obviously, the annual growth rate
of the economic benefits lagged behind that of the carbon dioxide emissions. Agriculture
experienced expansive negative decoupling twice, in 2001 and 2003. Following significant
decoupling in 2005 and 2006, the DAI stabilized at 0.35 in subsequent years. The DAIs for
trade and construction were characterized by strong decoupling prior to 2012 and 2013,
after which they transitioned into a state of weak decoupling, tending towards stabilization.
The industrial sector exhibited weak decoupling, with the DAI continuing to decline. This
clearly indicates a diminishing relationship between the industrial socio-economic devel-
opment and carbon dioxide emissions. From the perspective of the DAI, the sectors can be
ranked as follows: traffic and transportation > agriculture > industry > trade > construction.
This finding is partially corroborated by previous studies [25,28,65,79,87,102–104].
The DAIs of various sectors underwent continuous changes from 2000 to 2020 in Anhui
Province (Figure 3). With the exception of the mining sector, the DAIs of all other sectors
experienced a persistent decline and began to stabilize, indicating a gradual weakening
of the relationship between the economic benefits and carbon dioxide emissions within
these sectors. The long-term DAI of the mining sector rose from 0.41 in 2000 to 0.75 in
2020, with the annual carbon dioxide emissions and economic benefits growing at rates of
8.4% and 9.7%, respectively. This suggests a close correlation between the growth of the
social economy and carbon dioxide emissions. The sectors of resource processing, EGW,
textiles, and ME exhibited significant fluctuations in their DAIs during the period from
2000 to 2005, alternating between states of differentiated expansive negative decoupling
and coupling. However, post-2005, with the exception of the traffic and transportation,

Sustainability 2024,16, 9923 10 of 25
logistics warehousing, and postal sectors, the DAIs settled into a state of weak decoupling,
with the range of changes gradually stabilizing.
Sustainability 2024, 16, x FOR PEER REVIEW 10 of 27
substituted values of the DAI exceeding 1.5 and those below −1.5 with 1.5 and −1.5,
respectively.
3.1.1. Analysis of the Long-Term Decoupling Analysis Indexes
With the exception of the traffic and transportation sector, the DAIs of all other
industrial sectors exhibited a downward trend (Figure 2), indicating that the relationship
between their economic benefits and carbon dioxide emissions gradually diminished in
Anhui Province from 2000 to 2019. The long-term DAI for traffic and transportation has
remained in a state of expansive negative decoupling for an extended period. Between
2000 and 2019, the urban carbon dioxide emissions increased at an annual rate of 15%,
while the economic benefits grew at a rate of 10% (Figure 2). Obviously, the annual growth
rate of the economic benefits lagged behind that of the carbon dioxide emissions.
Agriculture experienced expansive negative decoupling twice, in 2001 and 2003.
Following significant decoupling in 2005 and 2006, the DAI stabilized at 0.35 in
subsequent years. The DAIs for trade and construction were characterized by strong
decoupling prior to 2012 and 2013, after which they transitioned into a state of weak
decoupling, tending towards stabilization. The industrial sector exhibited weak
decoupling, with the DAI continuing to decline. This clearly indicates a diminishing
relationship between the industrial socio-economic development and carbon dioxide
emissions. From the perspective of the DAI, the sectors can be ranked as follows: traffic
and transportation > agriculture > industry > trade > construction. This finding is partially
corroborated by previous studies [25,28,65,79,87,102–104].
Figure 2. Trends of decoupling index changes for five national economic sectors in Anhui Province
from 2000 to 2019.
The DAIs of various sectors underwent continuous changes from 2000 to 2020 in
Anhui Province (Figure 3). With the exception of the mining sector, the DAIs of all other
sectors experienced a persistent decline and began to stabilize, indicating a gradual
weakening of the relationship between the economic benefits and carbon dioxide
emissions within these sectors. The long-term DAI of the mining sector rose from 0.41 in
2000 to 0.75 in 2020, with the annual carbon dioxide emissions and economic benefits
growing at rates of 8.4% and 9.7%, respectively. This suggests a close correlation between
the growth of the social economy and carbon dioxide emissions. The sectors of resource
processing, EGW, textiles, and ME exhibited significant fluctuations in their DAIs during
the period from 2000 to 2005, alternating between states of differentiated expansive
negative decoupling and coupling. However, post-2005, with the exception of the traffic
Figure 2. Trends of decoupling index changes for five national economic sectors in Anhui Province
from 2000 to 2019.
Sustainability 2024, 16, x FOR PEER REVIEW 11 of 27
and transportation, logistics warehousing, and postal sectors, the DAIs seled into a state
of weak decoupling, with the range of changes gradually stabilizing.
Figure 3. Trends of decoupling index changes for five industrial sectors in Anhui Province from
2000 to 2020.
3.1.2. Analysis of the Short-Term Decoupling Analysis Indexes
During the years 2001–2005, the industrial DAIs predominantly exhibited strong
decoupling, comprising 44%, primarily concentrated in the construction and trade sectors.
Weak decoupling and expansive negative decoupling accounted for 28%, mainly
emerging from the industrial and traffic and transportation sectors. The DAIs of these
sectors were largely characterized by weak decoupling, which represented 56%,
particularly notable in 2002, 2004, and 2005. Concurrently, the expanding negative
decoupling of these sectors accounted for 32%, predominantly observed in 2001 and 2003.
During this period, the industrial sector most closely associated with economic benefits
and carbon dioxide emissions in Anhui Province was traffic and transportation. The DAIs
of numerous industrial sectors were significantly aligned with the statistical trends of
these years. Moreover, they collectively exhibited a tendency towards expansive negative
decoupling in 2001 and 2003 (Figures 4 and 5).
Figure 3. Trends of decoupling index changes for five industrial sectors in Anhui Province from 2000
to 2020.
3.1.2. Analysis of the Short-Term Decoupling Analysis Indexes
During the years 2001–2005, the industrial DAIs predominantly exhibited strong de-
coupling, comprising 44%, primarily concentrated in the construction and trade sectors.
Weak decoupling and expansive negative decoupling accounted for 28%, mainly emerging
from the industrial and traffic and transportation sectors. The DAIs of these sectors were
largely characterized by weak decoupling, which represented 56%, particularly notable in
2002, 2004, and 2005. Concurrently, the expanding negative decoupling of these sectors
accounted for 32%, predominantly observed in 2001 and 2003. During this period, the
industrial sector most closely associated with economic benefits and carbon dioxide emis-
sions in Anhui Province was traffic and transportation. The DAIs of numerous industrial
sectors were significantly aligned with the statistical trends of these years. Moreover, they
collectively exhibited a tendency towards expansive negative decoupling in 2001 and 2003
(Figures 4and 5).

Sustainability 2024,16, 9923 11 of 25
Sustainability 2024, 16, x FOR PEER REVIEW 12 of 27
Figure 4. Trends of DAIs of five national economic sectors in each period.
From 2006 to 2010, the industrial DAIs were predominantly characterized by weak
decoupling, comprising 56%, primarily concentrated in the agriculture, industry, and
construction sectors. Expansive negative decoupling accounted for 20%. During this
period, the trade and commerce sector experienced remarkable socio-economic growth,
with an annual rate of 14.7%, leading to a shift in the short-term DAI from strong
decoupling to weak decoupling. The DAIs of these sectors were largely defined by weak
decoupling, accounting for 84%, reflecting an increase from the previously observed
period. Although the short-term DAI of traffic and transportation remained in a state of
expansive negative decoupling, its value continued to decline. Overall, this trend
indicated a gradual decrease in the correlation between economic benefits and carbon
dioxide emissions in Anhui Province (Figures 4 and 5).
During the years 2011–2015, the industrial DAIs were predominantly characterized
by expansive negative decoupling, comprising 44%, primarily concentrated in the
construction, traffic and transportation, and trade sectors. Weak decoupling accounted for
36%, mainly concentrated in agriculture and industry. The DAIs of these sectors were
largely defined by weak decoupling, which represented 72%, indicating a slight decline.
Throughout this observed period, both the mining and EGW sectors experienced
expansive negative decoupling on two occasions, with fluctuations in economic benefits
serving as the primary catalyst for these changes (Figures 4 and 5).
From 2016 to 2020, industrial DAIs continued to be predominantly characterized by
expansive negative decoupling, comprising 35%, primarily arising from the construction,
traffic and transportation, and trade sectors. Weak decoupling accounted for 30%, notably
emerging in the observed year of 2019. The DAIs of these sectors were largely defined by
recessive coupling, which constituted 24%, primarily emanating from the mining and
textile sectors. Expansive negative decoupling accounted for 20%, chiefly originating from
the resource processing and EGW sectors (Figures 4 and 5).
Figure 4. Trends of DAIs of five national economic sectors in each period.
Sustainability 2024, 16, x FOR PEER REVIEW 13 of 27
Figure 5. Trends of DAIs of the five industrial sectors in each period.
Through these analyses, it was revealed that expansive negative decoupling
progressively transitioned from the industrial sectors to construction, traffic and
transportation, and trade. Meanwhile, the short-term DAI of the industry underwent
three distinct phases: expansive negative decoupling, weak decoupling, and recessive
coupling (Figures 4 and 5). This indicates that carbon dioxide emissions surged rapidly
alongside the growth of the tertiary industry in Anhui Province. Concurrently, the
industrial sector exhibited sluggish growth, gradually entering a new state of equilibrium.
These findings align with previous research conducted by other scholars [25,79,103,104].
3.1.3. LMDI Decomposition Analysis
Utilizing the LMDI decomposition approach, the ongoing variations in the carbon
dioxide emissions across the national economic sectors in Anhui Province were dissected
into six key factors: the CEE, ES, ECI, ISS, EDE, and CP. Subsequently, we examined and
assessed the potential influence of each factor on the carbon dioxide emissions during the
various observed periods. Accordingly, the Tapio decoupling model was employed to
investigate and evaluate the relationship between the carbon dioxide emissions and socio-
economic development. The analytical results of the specified models are illustrated in
Figures 6 and 7.
Figure 5. Trends of DAIs of the five industrial sectors in each period.
From 2006 to 2010, the industrial DAIs were predominantly characterized by weak
decoupling, comprising 56%, primarily concentrated in the agriculture, industry, and
construction sectors. Expansive negative decoupling accounted for 20%. During this
period, the trade and commerce sector experienced remarkable socio-economic growth,
with an annual rate of 14.7%, leading to a shift in the short-term DAI from strong decoupling
to weak decoupling. The DAIs of these sectors were largely defined by weak decoupling,
accounting for 84%, reflecting an increase from the previously observed period. Although
the short-term DAI of traffic and transportation remained in a state of expansive negative
decoupling, its value continued to decline. Overall, this trend indicated a gradual decrease

Sustainability 2024,16, 9923 12 of 25
in the correlation between economic benefits and carbon dioxide emissions in Anhui
Province (Figures 4and 5).
During the years 2011–2015, the industrial DAIs were predominantly characterized by
expansive negative decoupling, comprising 44%, primarily concentrated in the construction,
traffic and transportation, and trade sectors. Weak decoupling accounted for 36%, mainly
concentrated in agriculture and industry. The DAIs of these sectors were largely defined
by weak decoupling, which represented 72%, indicating a slight decline. Throughout
this observed period, both the mining and EGW sectors experienced expansive negative
decoupling on two occasions, with fluctuations in economic benefits serving as the primary
catalyst for these changes (Figures 4and 5).
From 2016 to 2020, industrial DAIs continued to be predominantly characterized by
expansive negative decoupling, comprising 35%, primarily arising from the construction,
traffic and transportation, and trade sectors. Weak decoupling accounted for 30%, notably
emerging in the observed year of 2019. The DAIs of these sectors were largely defined
by recessive coupling, which constituted 24%, primarily emanating from the mining and
textile sectors. Expansive negative decoupling accounted for 20%, chiefly originating from
the resource processing and EGW sectors (Figures 4and 5).
Through these analyses, it was revealed that expansive negative decoupling progres-
sively transitioned from the industrial sectors to construction, traffic and transportation,
and trade. Meanwhile, the short-term DAI of the industry underwent three distinct phases:
expansive negative decoupling, weak decoupling, and recessive coupling (Figures 4and 5).
This indicates that carbon dioxide emissions surged rapidly alongside the growth of the
tertiary industry in Anhui Province. Concurrently, the industrial sector exhibited sluggish
growth, gradually entering a new state of equilibrium. These findings align with previous
research conducted by other scholars [25,79,103,104].
3.1.3. LMDI Decomposition Analysis
Utilizing the LMDI decomposition approach, the ongoing variations in the carbon
dioxide emissions across the national economic sectors in Anhui Province were dissected
into six key factors: the CEE, ES, ECI, ISS, EDE, and CP. Subsequently, we examined and
assessed the potential influence of each factor on the carbon dioxide emissions during
the various observed periods. Accordingly, the Tapio decoupling model was employed
to investigate and evaluate the relationship between the carbon dioxide emissions and
socio-economic development. The analytical results of the specified models are illustrated
in Figures 6and 7.
During the years 2001–2005, the EDE experienced substantial growth, with annual
rates of 6.6% in the national economic sectors and 17.9% in specific sectors. This underscored
the EDE as the primary driver of carbon dioxide emissions, while the ECI emerged as the
principal inhibitory factor during the observed periods across the relevant economic sectors.
The ECI declined from 0.88 and 1.01 in 2001 to 0.58 and 0.66 in 2005, reflecting average
annual reduction rates of 7.8% and 8.2%, respectively, as its inhibitory effect continued to
strengthen (Figures 6and 7). Conversely, the changes in the ISS, ES, CEE, and CP were
relatively minor, exerting minimal influence on the carbon dioxide emissions.
From 2006 to 2010, the EDE continued to rise rapidly. The EDE for national economic
sectors increased from 1.12 and 1.2 in 2006 to 1.96 and 2.1 in 2010, while socio-economic
development advanced by 11.9% and 11.8%, respectively, establishing them as the main
drivers of carbon emissions during this period. The ECI for the national economic sectors
and sectors fell from 0.96 and 0.89 in 2006 to 0.61 and 0.56 in 2010, with average annual
reduction rates of 8.7% and 8.9%, respectively. The values for the ES, ISS, CP, and CEE
fluctuated around 1, indicating their limited impact on the carbon dioxide emissions
(Figures 6and 7).

Sustainability 2024,16, 9923 13 of 25
Sustainability 2024, 16, x FOR PEER REVIEW 14 of 27
Figure 6. Decomposition trends of the LMDI of the five national economic sectors in each period.
During the years 2001–2005, the EDE experienced substantial growth, with annual
rates of 6.6% in the national economic sectors and 17.9% in specific sectors. This
underscored the EDE as the primary driver of carbon dioxide emissions, while the ECI
emerged as the principal inhibitory factor during the observed periods across the relevant
economic sectors. The ECI declined from 0.88 and 1.01 in 2001 to 0.58 and 0.66 in 2005,
reflecting average annual reduction rates of 7.8% and 8.2%, respectively, as its inhibitory
effect continued to strengthen (Figures 6 and 7). Conversely, the changes in the ISS, ES,
CEE, and CP were relatively minor, exerting minimal influence on the carbon dioxide
emissions.
From 2006 to 2010, the EDE continued to rise rapidly. The EDE for national economic
sectors increased from 1.12 and 1.2 in 2006 to 1.96 and 2.1 in 2010, while socio-economic
development advanced by 11.9% and 11.8%, respectively, establishing them as the main
drivers of carbon emissions during this period. The ECI for the national economic sectors
and sectors fell from 0.96 and 0.89 in 2006 to 0.61 and 0.56 in 2010, with average annual
reduction rates of 8.7% and 8.9%, respectively. The values for the ES, ISS, CP, and CEE
fluctuated around 1, indicating their limited impact on the carbon dioxide emissions
(Figures 6 and 7).
During the years 2011–2015, the EDE remained the predominant driver of industrial
carbon dioxide emissions. However, compared to the preceding “11th Five-Year Plan”
period, the growth rates of the national economic sectors were relatively modest, at only
3.2% and 0.56%. Notably, the annual rates of the ECI registered at −0.57% and 1.8%,
respectively, although its inhibitory effect gradually diminished. The acceleration of
carbon dioxide emission inhibition decelerated, with the average annual decline rate
dropping from 8.7% in the previous period to 0.57%. The values for the CEE and ES
hovered around 1, signifying a negligible impact on the carbon dioxide emissions. In all
sectors, the annual rate for the sectoral architecture (SA) was recorded at −5%, establishing
it as the primary inhibitor of carbon dioxide emissions during the observed period
(Figures 6 and 7).
Figure 6. Decomposition trends of the LMDI of the five national economic sectors in each period.
Sustainability 2024, 16, x FOR PEER REVIEW 15 of 27
Figure 7. LMDI decomposition trends of the five industrial sectors in each period.
During the years 2016–2020, the EDE and ECI continued to serve as the principal
drivers and inhibitors of industrial carbon dioxide emissions, exhibiting annual rates of
6.7% and −5.1%, respectively. The inhibitory influence of the ISS on carbon dioxide
emissions was bolstered, with a yearly rate of −2.4%. Concurrently, the ECI, SA, and EDE
contributed to the rise in carbon dioxide emissions, with annual rates of 2.8%, 2.8%, and
1.6%, respectively. Notably, during this period, the CP emerged as the primary inhibitor
of carbon dioxide emissions, achieving an average annual reduction rate of 4.8%. The
number of industrial employees declined from 3,509,384 in 2016 to 2,741,585 in 2020,
reflecting an average annual decrease of 5.2%. Meanwhile, employment in the mining,
EGW, and textile sectors fell by 22%, 12%, and 6.4%, respectively (Figures 6 and 7),
marking them as the principal sources of employment decline. During this timeframe, the
workforce in the resource processing industry decreased by 2.2% per year, while
employment in the ME industry saw a modest increase of 0.8% annually. It is evident that
the judicious reallocation of industrial employees during this period played a significant
role in mitigating industrial carbon dioxide emissions. These findings align with those of
previously reported studies [22,86,105].
3.2. Aribution Analysis
Due to the fact that the LMDI decomposition approach could not further quantify the
contribution of the terminal industry to each factor in the observed periods, this study
employed aribution analysis to calculate the contribution rate of the terminal industry
for each period using Equations (8) and (9). The specific results pertaining to the main
factors are presented in Tables 3–7. The contribution values of each terminal industry were
aggregated to derive the overall contribution of the national economic sectors to factors
such as the ECI, EDE, and ISS across different periods. Table 3 illustrates that the EDE and
ECI were the primary influencing and inhibitory factors of carbon dioxide emissions
within the national economic sectors, with their respective contributions diminishing over
time. The ECI exhibited a relatively pronounced inhibitory effect on the carbon dioxide
emissions across various sectors during the initial two periods but shifted to a rising trend
during the “13th Five-Year Plan”. These findings are consistent with the majority of the
previously reported estimations [22,24,84–86,93,105].
Figure 7. LMDI decomposition trends of the five industrial sectors in each period.
During the years 2011–2015, the EDE remained the predominant driver of industrial
carbon dioxide emissions. However, compared to the preceding “11th Five-Year Plan”
period, the growth rates of the national economic sectors were relatively modest, at only
3.2% and 0.56%. Notably, the annual rates of the ECI registered at
−
0.57% and 1.8%,
respectively, although its inhibitory effect gradually diminished. The acceleration of carbon
dioxide emission inhibition decelerated, with the average annual decline rate dropping
from 8.7% in the previous period to 0.57%. The values for the CEE and ES hovered around
1, signifying a negligible impact on the carbon dioxide emissions. In all sectors, the annual

Sustainability 2024,16, 9923 14 of 25
rate for the sectoral architecture (SA) was recorded at
−
5%, establishing it as the primary
inhibitor of carbon dioxide emissions during the observed period (Figures 6and 7).
During the years 2016–2020, the EDE and ECI continued to serve as the principal
drivers and inhibitors of industrial carbon dioxide emissions, exhibiting annual rates
of 6.7% and
−
5.1%, respectively. The inhibitory influence of the ISS on carbon dioxide
emissions was bolstered, with a yearly rate of
−
2.4%. Concurrently, the ECI, SA, and
EDE contributed to the rise in carbon dioxide emissions, with annual rates of 2.8%, 2.8%,
and 1.6%, respectively. Notably, during this period, the CP emerged as the primary
inhibitor of carbon dioxide emissions, achieving an average annual reduction rate of
4.8%. The number of industrial employees declined from 3,509,384 in 2016 to 2,741,585
in 2020, reflecting an average annual decrease of 5.2%. Meanwhile, employment in the
mining, EGW, and textile sectors fell by 22%, 12%, and 6.4%, respectively (Figures 6and 7),
marking them as the principal sources of employment decline. During this timeframe,
the workforce in the resource processing industry decreased by 2.2% per year, while
employment in the ME industry saw a modest increase of 0.8% annually. It is evident that
the judicious reallocation of industrial employees during this period played a significant
role in mitigating industrial carbon dioxide emissions. These findings align with those of
previously reported studies [22,86,105].
3.2. Attribution Analysis
Due to the fact that the LMDI decomposition approach could not further quantify
the contribution of the terminal industry to each factor in the observed periods, this study
employed attribution analysis to calculate the contribution rate of the terminal industry
for each period using Equations (8) and (9). The specific results pertaining to the main
factors are presented in Tables 3–7. The contribution values of each terminal industry were
aggregated to derive the overall contribution of the national economic sectors to factors
such as the ECI, EDE, and ISS across different periods. Table 3illustrates that the EDE
and ECI were the primary influencing and inhibitory factors of carbon dioxide emissions
within the national economic sectors, with their respective contributions diminishing over
time. The ECI exhibited a relatively pronounced inhibitory effect on the carbon dioxide
emissions across various sectors during the initial two periods but shifted to a rising trend
during the “13th Five-Year Plan”. These findings are consistent with the majority of the
previously reported estimations [22,24,84–86,93,105].
In the attribution analysis of the EDE, the industrial sector emerged as the predomi-
nant contributor to changes in the national economic sectors’ EDEs, with contribution rates
of 0.51, 0.83, 0.12, and 0.08 across the four periods, respectively. It was evident that the
industry’s contribution to the EDE was primarily concentrated in the initial two periods.
Conversely, the contributions from construction, transportation, and trade to the EDE exhib-
ited an upward trend, suggesting that the changes in industrial carbon dioxide emissions
driven by the EDE were gradually transitioning from the industrial sector to buildings and
the tertiary industry. This finding aligned with the results of the aforementioned industry
decoupling analysis. The mining, resource processing, and EGW sectors were the main
contributors to the changes in the EDEs within these sectors. Notably, their contribution
rates declined significantly from 0.41, 1.08, and 0.97 during the “10th Five-Year Plan” period
to 0.18, 0.06, and 0.33 in the “13th Five-Year Plan” period, reflecting a substantial reduction
(Tables 3–6). This shift can be attributed to the rapid industrial development experienced in
Anhui Province during the first two periods, which resulted in substantial carbon dioxide
emissions. However, with advancements in technology and the government’s ongoing
promotion of high-quality industrial development, the enhancement of industrial economic
benefits no longer came at the expense of significant carbon dioxide emissions, thereby
gradually diminishing the role of economic benefits in driving industrial carbon dioxide
emissions. This observation was consistent with the changing trend of the long-term
industrial DAI evaluated in Anhui Province (Tables 4and 6).

Sustainability 2024,16, 9923 15 of 25
Table 3. Single-period attribution of Anhui’s national economic sectors and sectors (unit %).
National Economic Sectors Sectors
2001–2005 2006–2010 2011–2015 2016–2019 2001–2005 2006–2010 2011–2015 2016–2020
ED 0.01 0.004 −0.007 −0.011 0.012 −0.001 −0.001 0.002
ES 0.024 0.001 −0.009 −0.011 −0.003 −0.001 −0.002 0.001
EI −0.419 −0.392 −0.147 −0.218 −0.344 −0.441 −0.055 0.133
IS 0.18 0.139 −0.008 −0.085 0.164 −0.099 −0.372 0.052
G 0.533 0.877 0.101 0.219 2.519 1.332 −0.144 0.572
P 0.231 0.297 0.329 0.06 −0.145 0.394 0.571 −0.39
Table 4. Attribution analysis of ECIs and EDEs of Anhui’s national economic sectors (unit %).
2000–2005 2006–2010 2011–2015 2016–2019
EI G EI G EI G EI G
Agriculture −0.004 0.012 −0.003 0.022 −0.003 0.010 −0.001 0.003
Industry −0.399 0.514 −0.388 0.831 −0.182 0.120 −0.132 0.080
Construction −0.033 0.010 −0.002 0.010 0.005 0.005 −0.011 0.019
Traffic and Transportation 0.026 0.004 0.009 0.008 0.034 −0.046 −0.079 0.101
Trade −0.010 −0.007 −0.008 0.006 0.000 0.013 0.005 0.016
Table 5. Attribution analysis of ISSs and CPs of Anhui’s national economic sectors (unit %).
2000–2005 2006–2010 2011–2015 2016–2019
IS P IS P IS P IS P
Agriculture −0.009 −0.004 −0.009 −0.004 −0.004 −0.004 −0.005 0.000
Industry 0.191 0.201 0.179 0.272 0.003 0.253 −0.151 0.051
Construction 0.008 0.011 0.000 0.004 −0.001 0.002 0.009 0.001
Traffic and Transportation −0.003 0.011 −0.031 0.017 −0.011 0.078 0.055 0.007
Trade −0.007 0.012 −0.001 0.008 0.005 0.000 0.007 0.001
Table 6. Attribution analysis of ECIs and EDEs of Anhui’s sectors (unit %).
2000–2005 2006–2010 2011–2015 2016–2020
EI G EI G EI G EI G
Mining −0.039 0.410 −0.071 0.351 0.084 −0.111 0.012 0.183
Textiles −0.006 0.040 −0.019 0.033 −0.016 0.014 0.002 −0.001
Resources −0.165 1.079 −0.239 0.447 −0.134 0.149 0.073 0.064
ME 0.000 0.016 −0.004 0.010 −0.006 0.005 0.005 −0.001
EGW −0.133 0.974 −0.108 0.491 0.018 −0.201 0.041 0.326
In the results of the attribution analysis of the ECI, it was determined that the ECI
served as the primary inhibitor of the reduction in energy-related carbon dioxide emissions
in the national economic sectors of Anhui Province. Simultaneously, the industrial sector
emerged as the principal contributor to the decline in the ECI. The estimated contribution
rates of the industrial sector to the ECI across the four five-year periods were
−
0.40,
−
0.39,
−
0.18, and
−
0.13, respectively, indicating that all the contribution rates were negative
and exhibited a decreasing trend. The resource processing industrial sectors and EGW
were identified as the main contributors to the decline in the ECI, with their contributions
predominantly concentrated in the initial two periods. In the subsequent two periods, their
contributions to the ECI gradually transitioned from a restraining effect to a promoting one
(Tables 4and 6).

Sustainability 2024,16, 9923 16 of 25
Table 7. Attribution analysis of SAs and CPs of Anhui’s economic sectors (unit %).
2000–2005 2006–2010 2011–2015 2016–2020
IS P IS P IS P IS P
Mining 0.040 0.010 −0.017 0.032 −0.174 0.133 −0.057 −0.172
Textiles −0.011 −0.003 0.001 0.018 0.006 0.009 −0.006 −0.006
Resources 0.020 −0.077 −0.045 0.265 −0.039 0.081 0.044 −0.038
ME 0.001 0.001 0.002 0.012 0.002 0.004 0.001 0.000
EGW 0.114 −0.077 −0.039 0.066 −0.166 0.344 0.070 −0.174
Through the analysis of the economic benefits and energy consumption across the
sectors during the “13th Five-Year Plan” period, it was revealed that the economic benefits
experienced a decline to varying degrees, with the reduction in economic benefits surpass-
ing that of energy consumption. This disparity resulted in an increase in the ECI, leading to
a period characterized by recessive coupling in the DAI. Examining the trends in the ECI, it
became apparent that economic benefits significantly outweighed energy consumption in
the nascent stages of industrial development. During this period, the continuous enhance-
ment of industrial technology and energy efficiency led to a substantial decline in the ECI.
However, as industrial development progressed in Anhui Province, the economic benefits
generated by the industry began to diminish, while the advancements in energy efficiency
driven by technological progress gradually approached a bottleneck. Consequently, the
inhibitory effect of the ECI on carbon dioxide emissions was continuously diminished.
Given that the technology and energy efficiency remained relatively stable, the ECI became
increasingly determined by the economic benefits of the industry. Thus, fostering steady
growth in industrial economic benefits could more effectively harness the carbon dioxide
emission reduction potential of the ECI (Tables 4and 6).
The results of the attribution analysis of the ISS revealed that its impact on carbon
dioxide emissions could be delineated into two distinct stages. In the initial observed
period, the ISS functioned to promote carbon dioxide emissions, whereas in the subsequent
observed period, it served to inhibit them. The industrial sector emerged as the principal
contributor to the fluctuations in the ISS, with the mining and EGW sectors being the
key drivers of the change in the SA. For instance, the industrial sector’s share escalated
from 0.38 in 2000 to 0.56 in 2010, contributing a cumulative rate of 0.37 to the changes
in the ISS within Anhui Province. However, following this period, as the ISS underwent
continuous adjustments, the proportion of the industrial sector began to decline. During the
“13th Five-Year Plan” period, its contribution to the ISS turned negative. The shares of the
mining and EGW sectors fell from 0.07 and 0.08 in 2010 to 0.03 and 0.05 in 2015, reflecting
significant decreases in Anhui Province. Concurrently, their cumulative contribution to
the changes in the ISS was
−
0.34, marking them as the primary inhibitors of industrial
carbon dioxide emissions during the “12th Five-Year Plan” period. During the “13th Five-
Year Plan” period, the proportion of the ME sector experienced an annual growth rate of
2.3%; however, its contribution rate to the changes in the SA was merely 0.005. From this
analysis, it is evident that to enhance the inhibitory effect of ISS adjustments on carbon
dioxide emissions, it is imperative to continuously reduce the proportion of industry while
promoting the SA to decrease the shares of mining and EGW, alongside increasing the
proportion of ME (Tables 5and 7).
The attribution analysis of the CP indicated that it played a relatively modest role in
promoting industrial carbon dioxide emissions, as evidenced by the LMDI decomposition
results. The industrial sector was the primary contributor to changes in the CP, with
contributions of 0.2, 0.27, and 0.25 in the initial three periods, which subsequently declined
to 0.05 during the “13th Five-Year Plan” period. The annual growth rates of industrial
employment in each period were 4%, 6%, 5%, and 2%, respectively, demonstrating a
consistent correlation between the growth rate of the industrial CP and its contribution
rate. From 2000 to 2019, the annual growth rate of trade employment was 4.9%, surpassing

Sustainability 2024,16, 9923 17 of 25
the 4.3% rate of the industrial sector; however, its contribution rate remained significantly
lower than that of industry. The resource processing industry, EGW, and mining emerged
as the principal contributors to changes in the CP across the sectors. During the period from
2000 to 2020, the annual rates of the employed CP in mining, textiles, resource processing,
ME, and EGW were
−
2.8%, 1.9%, 1.9%, 7%, and 0.5%, respectively. The corresponding
contribution rates to the sectoral CP were 0, 0.02, 0.23, 0.02, and 0.16, respectively, indicating
that the changes in the CP within EGW, mining, and resource processing had a more
significant impact on the sectoral CP, while the employment CP of textiles and ME exerted
a lesser influence. Based on this analysis (Tables 5and 7), three strategic missions could be
identified to effectively reduce industrial carbon dioxide emissions. These missions involve
controlling the number of industrial employees, particularly by decreasing the workforce in
mining, EGW, and resource processing, while simultaneously directing personnel towards
the ME industry and tertiary sector. These measures are crucial for enhancing the role of
CP in mitigating carbon dioxide emissions.
The attribution analysis of the ES revealed that it exerted a minimal impact on the
carbon dioxide emissions of the national economic sectors in Anhui Province. From the
“10th Five-Year Plan” to the “13th Five-Year Plan” period, the contribution of the ES to
carbon dioxide emissions gradually diminished, eventually turning negative. Notably,
the industrial sector emerged as the primary contributor to changes in the ES, with the
contributions from the construction industry as well as traffic and transportation also
increasing, albeit their contribution rates eventually shifted to negative values.
The adjustment of the ES had a limited inhibitory effect on the carbon dioxide emis-
sions across the sectors, with resource processing identified as the main contributor. This
analysis indicates that the ES had little influence on the industrial carbon dioxide emis-
sions, a situation closely tied to Anhui Province’s reliance on coal and oil as predominant
energy sources.
While different industries depend on various specific energy sources, industrial sectors
primarily consume coal and oil resources. Among these, mining, resource processing and
EGW were the chief consumers of coal and oil, whereas the ME and textile sectors gradually
reduced their dependence on these resources, increasingly relying on electricity instead.
In the traffic and transportation sector, oil was the primary energy source, while
electricity dominated in trade. Thus, it becomes evident that further reducing the coal
consumption in resource processing, textiles, and ME, along with enhancing the energy
utilization efficiency in mining and EGW, would foster the development of the sectoral ES
towards a reduction in carbon dioxide emissions. These findings align well with those of
previously reported studies [61,93,96].
3.3. Confirmatory Analysis
In order to avoid the potential biases in research results caused by the use of a single
research method, this study further employed traditional regression econometric models
to validate the aforementioned analysis results, thereby enhancing the credibility of the
research findings. This study adopted the logarithm of energy consumption from different
types of industries and industrial enterprises as the dependent variable, while the carbon
emission rate (ED), energy structure (ES), energy intensity (EI), industrial structure (IS),
economic efficiency (G), and population (P) were employed as the independent variables to
construct a two-way fixed-effects panel regression model, with the corresponding results
and data shown in Tables 8and 9.
Table 8, column (1), shows that the ES, EI, IS, and P all had significant impacts on
the energy consumption of the national economy sectors. In Column (2), the logarithm of
energy consumption in the dependent variable is replaced with the logarithm of carbon
dioxide emissions, and the results indicate that the significance of the variables remained
unchanged. Column (3) shortened the sample time period, analyzing data from 2010 to
2019, and it was found that the significance of most variables remained unchanged, while
the significance of the carbon emission rate increased. In Table 8, the results of the two-way

Sustainability 2024,16, 9923 18 of 25
fixed-effects panel regression model indirectly reflect that different factors had varying
impacts on the carbon emissions in the national economy sectors over the different time
periods, confirming the rationality of the above segmented analyses conducted according
to the five-year plan.
Table 8. Regression results of the carbon emission influencing factors in Anhui’s economic sectors.
Variable (1) (2) (3)
Energy Consumption
Carbon Emissions
Energy Consumption
ED −0.062 0.364 −1.288 ***
(0.527) (0.530) (0.356)
ES −1.108 *** −1.113 *** 0.075
(0.397) (0.399) (0.412)
EI 1.029 *** 1.027 *** 1.100 ***
(0.113) (0.113) (0.071)
IS 5.638 *** 5.613 *** 5.529 ***
(0.819) (0.817) (0.712)
G 0.020 0.020 0.093 ***
(0.049) (0.049) (0.032)
P−0.220 * −0.217 * −0.110
(0.112) (0.112) (0.081)
_cons 4.808 *** 4.646 *** 6.743 ***
Year
Sector
(1.313)
Yes
Yes
(1.317)
Yes
Yes
(1.080)
Yes
Yes
Adj.R2 0.957 0.959 0.977
Note: * and *** indicate significance at the levels of 10% and 1%, respectively.
Table 9. Regression results of the carbon emission influencing factors in Anhui’s industrial sectors.
Variable (1) (2) (3)
Energy Consumption
Carbon Emissions
Energy Consumption
ED −10.274 *** −9.869 *** −10.042 ***
(0.675) (0.669) (0.539)
ES 5.411 *** 5.414 *** 4.343 ***
(0.323) (0.324) (0.270)
EI 0.349 *** 0.350 *** 0.442 ***
(0.054) (0.054) (0.067)
IS 5.924 *** 5.934 *** 5.713 ***
(0.499) (0.499) (1.489)
G 0.006 *** 0.006 *** 0.005
(0.002) (0.002) (0.004)
P 0.244 * 0.244 * 0.014
(0.127) (0.127) (0.397)
_cons 32.159 *** 32.055 *** 37.490 ***
Year
Industry
(2.447)
Yes
Yes
(2.432)
Yes
Yes
(5.366)
Yes
Yes
Adj.R2 0.942 0.942 0.981
Note: * and *** indicate significance at the levels of 10% and 1%, respectively.
Similarly, the relevant results of the panel regression model in the five industrial
sectors are shown in Table 9, with the analyzing process and results being similar to those
of the analyses above. In Table 9, through the analysis of the panel regression model, it
was verified that the impact of the aforementioned factors on the carbon emissions was
significant using a two-way fixed-effects panel regression model. Then, it was further veri-
fied to ensure the robustness of the regression modeling results by adopting the substitute
variables and adjusting the sample time.

Sustainability 2024,16, 9923 19 of 25
4. Conclusions
The development strategies of national urban integration and regional integration
have presented significant opportunities for Anhui Province, enabling it to attract indus-
trial transfers from surrounding developed provinces. Given the natural constraints of
resources and the environment, the focus of this study was to explore how to achieve
low-carbon development in resource-based regions. This research employed the Tapio
decoupling model to examine the relationship between the carbon dioxide emissions and
economic benefits across five national economic sectors and five industrial sectors in Anhui
Province. Utilizing the LMDI multiplicative decomposition model, this study investigated
the primary driving and inhibitory factors influencing the carbon dioxide emissions over
five-year intervals, considering six key factors: the CEE, ES, ECI, ISS, EDE, and CP. Fur-
thermore, through attribution analysis, the contribution values of these six influencing
factors to various industries across different periods were determined. The corresponding
summarized conclusions were drawn as follows.
In Anhui Province, there were notable sectoral decoupling adjustment indices (DAIs)
observed in urban traffic and transportation, logistics warehousing, and the postal industry.
In contrast, the DAIs for other sectors and social services remained in a state of weak
decoupling. The industrial sectors associated with mining, textiles, and EGW within the
energy system (ES) were identified as the primary contributors to carbon dioxide emissions.
In addition to the traffic and transportation sector, the DAIs of the remaining four national
economic sectors also exhibited weak decoupling, with a continuing decline evident in
Anhui Province. This trend suggests a gradual weakening of the relationship between
industrial economic benefits and carbon dioxide emissions.
The DAIs experienced a decline following a period of negative decoupling during
the “Tenth Five-Year Plan”, yet they remained in a weak decoupling state for an extended
duration. Through LMDI decomposition analysis, it was determined that the energy de-
mand efficiency (EDE) was the principal influencing factor on the carbon dioxide emissions
across the national economic sectors, with its driving effect diminishing over time. The EDE
emerged as the primary driver of carbon dioxide emissions throughout the observed period,
while the energy consumption intensity (ECI) acted as the principal potential inhibitor.
In the estimation and evaluation, the ECI was confirmed as the key inhibitory factor for
industrial carbon dioxide emissions, exerting a substantial inhibitory effect on the decline
of emissions in the national economic sectors during the early stages. However, its in-
hibitory influence weakened in the later stages, at times even contributing to an increase in
emissions. The industrial structure shift (ISS) initially promoted carbon dioxide emissions
in the first two periods but subsequently inhibited emissions in the latter two periods.
The attribution analysis revealed that the industrial sector was the foremost contributor
to carbon dioxide emissions, with mining, EGW, and resource processing identified as the
primary sources of emissions within the sectors.
5. Policy Implications and Suggestions
This study comprehensively assessed twenty years of carbon dioxide emissions uti-
lizing official energy statistics from 2000 to 2020, and it also examined the corresponding
influencing factors from both the short-term and long-term perspectives. Employing Tapio’s
index model, the research calculated and categorized the decoupling adjustment indices
(DAIs) and their variations across five national economic sectors and five industrial sectors
in Anhui Province. The results from the decomposition and decoupling analyses revealed
substantial fluctuations in the DAIs for natural resource processing, electricity, gas, water,
textiles, and machinery and electronics manufacturing. Notably, significant sectoral DAIs
were observed in urban traffic and transportation, logistics warehousing, and the postal
industry within Anhui Province. In contrast, the DAIs for other sectors and social services
remained in a state of weak decoupling. In the near future, it will be imperative to optimize
the interaction between urban carbon dioxide emissions and the socio-economic landscape
to foster integrated sustainable development.

Sustainability 2024,16, 9923 20 of 25
First and foremost, it is essential to augment the sectoral share of the tertiary indus-
try while diminishing the sectoral representation of mining and energy generation and
water (EGW). A substantial industrial sector proportion hampers efforts to reduce car-
bon dioxide emissions; thus, decreasing its share will facilitate a shift in the industrial
structure (ISS) towards a more favorable trajectory for emissions reduction. Elevating the
sectoral contributions of the machinery and electronics (ME) industry, alongside the tertiary
sector, particularly in traffic and transportation, will enhance the efficacy of ISS adjust-
ments in curbing industrial carbon dioxide emissions, ultimately fostering the continuous
optimization of Anhui’s ISS.
Secondly, there is an urgent need to optimize the distribution of industrial person-
nel and encourage the migration of the workforce towards low-carbon industries. The
industrial sector significantly influences demographic changes, with mining, EGW, and
resource processing being the primary contributors to sectoral carbon footprints. Dur-
ing the “13th Five-Year Plan” period, the inhibitory effect on carbon dioxide emissions
was largely attributed to a decline in the carbon footprint of the mining sector and EGW.
Directing employment towards the tertiary sector and ME, while enhancing automation
in high-carbon dioxide-emitting industries, can more effectively harness the potential of
carbon footprints in mitigating emissions.
Lastly, it is imperative to reduce the reliance on coal within resource processing, the
ME industry, and the tertiary sector while simultaneously improving the energy utiliza-
tion efficiency in mining and EGW. Given the unique characteristics of mining and EGW,
altering their energy structures, which are predominantly coal-based, poses significant chal-
lenges, resulting in minimal impact on industrial carbon dioxide emissions. Strategically
optimizing the energy structure of resource processing, the ME industry, and other sectors
can more effectively contribute to the reduction in carbon dioxide emissions.
Furthermore, it is imperative to persistently cultivate new strategic industries to
guarantee the sustained enhancement and sustainable development of industrial sectors.
The inhibitory effects of the Economic Complexity Index (ECI) on carbon dioxide emissions,
coupled with a decline in industrial benefits, suggest that regional industrial development
in Anhui Province has reached a bottleneck. In light of the current challenges, including the
lack of breakthroughs in novel low-carbon technologies and the diminishing advantages
of traditional industries, the government must continuously recalibrate the industrial
structure shift (ISS) and foster the emergence of industries characterized by lower carbon
dioxide emissions and greater economic returns.
Additionally, the government should progressively elevate the proportion of high-tech
industries within the resource processing and machinery and electronics (ME) sectors. This
approach will not only optimize the industrial configuration and reduce the prevalence of
traditional industries but also ensure the economic development efficiency (EDE) of the
sector, thereby enabling the ECI to more effectively contribute to emissions reduction.
In comparison to the existing research reports, this study presents two significant
theoretical contributions. Firstly, while previous studies predominantly concentrated on
national development levels or economically advanced cities, they largely overlooked
provincial development levels. Anhui Province, as an integral part of the modern Yangtze
River Delta, serves as a quintessential representative of central provinces in Central China.
Thus, evaluating the decoupling status of the carbon dioxide emissions in Anhui Province
constitutes a meaningful endeavor. Given that Anhui is a vital component of the Yangtze
River Delta and a pivotal region for the industrial transition from the developed coastal
zones, this study may offer a promising exploration of how resource-rich yet economically
disadvantaged regions can achieve low-carbon development amidst the dual challenges
of resource limitations and environmental concerns during extensive regional integration.
The insights gleaned from this study are invaluable for similar regions that primarily
depend on industrial sectors. Secondly, fostering provincial low-carbon development can
enhance and expand the industrial transfer responsibilities within the Yangtze River Delta.
Consequently, this will further facilitate industrial upgrading in Jiangsu Province and

Sustainability 2024,16, 9923 21 of 25
Zhejiang Province, bolstered by the strengthened provincial markets of the entire Yangtze
River Delta.
6. Limitations and Future Research
Through full analyses and discussions, several potential limitations were identified in
this study that warrant further investigation. Firstly, although the research broadens its
scope to encompass five major sectors in Anhui Province—namely, agriculture, forestry,
animal husbandry, fisheries, and industry—it neglects to consider other subdivided sectors,
such as construction, transportation, storage, postal services, as well as wholesale, retail,
accommodation, and catering. Consequently, this study does not examine the provincial
decoupling performance of economic growth and carbon dioxide emissions across the
16 prefecture-level cities in Anhui Province, primarily due to spatial constraints. Secondly,
while the study employs a combined Logarithmic Mean Divisia Index (LMDI) model to
assess the impacts of various factors on the provincial carbon dioxide emissions, it only
integrates technological advancements and social policies within the LMDI variations and
lacks a comprehensive elaboration due to length limitations. Furthermore, the model con-
struction does not adequately account for technological changes and production efficiencies.
These aspects should be explored in greater detail in subsequent investigations, and future
research will endeavor to address these gaps.
Author Contributions: Conceptualization, K.Z., L.J. and W.L.; methodology, L.J. and K.Z.; software,
L.J.; validation, L.J., K.Z. and W.L.; formal analysis, L.J., K.Z. and W.L.; investigation, L.J. and K.Z.;
resources, L.J. and K.Z.; data curation, L.J. and K.Z.; writing—original draft preparation, K.Z. and
W.L.; writing—review and editing, L.J., K.Z. and W.L.; visualization, W.L.; supervision, K.Z. and W.L.;
project administration, K.Z. and W.L.; funding acquisition, K.Z. and W.L. All authors have read and
agreed to the published version of the manuscript.
Funding: This research was supported by the projects of the Anhui Provincial Educational Commis-
sion Foundation of China (grant numbers 2023AH040060 and gxgnfx2021005), the Anhui Provincial
Projects of College Student Innovation and Entrepreneurship Training Program (No. S02310371008),
and the Biological and Medical Sciences of Applied Summit Nurturing Disciplines in Anhui Province
(Anhui Provincial Education Secretary Department [2023]13).
Informed Consent Statement: Not applicable.
Data Availability Statement: The literature sources of the data that came from the literature and
official released statistics, the data generated in management practices, and all the remaining data are
indicated in the study.
Acknowledgments: The authors thank the referees for their constructive comments. All individuals
included have consented to the acknowledgement.
Conflicts of Interest: The authors declare no conflicts of interest.
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