Journal of Cleaner Production 435 (2024) 140457
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Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
Carbon emission scenarios of China’s construction industry using a system
dynamics methodology – Based on life cycle thinking
Zhao Zhang a, Qiufeng Gao a, Shuai Shao a, b, Yun Zhang a, *, Yining Bao a, Li Zhao a
a
Key Laboratory of Industrial Ecology and Environmental Engineering (China Ministry of Education), School of Environmental Science and Technology, Dalian
University of Technology, Linggong Road 2, Dalian 116024, China
b
Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, School of Energy and Power Engineering, Dalian University of
Technology, Linggong Road 2, Dalian 116024, Liaoning, China
A R T I C L E I N F O
A B S T R A C T
Handling Editor: Jian Zuo
China is currently one of the world’s major energy consumers and CO2 emitters. To save energy, and reduce
consumption and carbon emissions, China proposed (at the 2020 United Nations General Assembly and Climate
Summit) the introduction of stronger policies and measures for CO2 emissions to peak in 2030 and to achieve
carbon neutrality by 2060. The construction industry is a major contributor to China’s carbon emissions, thus
research on energy saving and carbon reduction in this industry is essential. The construction industry is
characterized by complex upstream and downstream industrial chains, with different energy consumption at
each stage, and a long overall life cycle. We used life-cycle thinking (LCT) to analyze the carbon emissions of the
whole life cycle of the construction industry and built a model by using a system dynamics method, which
analyzes the carbon emission process of the construction industry at different stages. Combined with the plan­
ning and policies implemented by the construction-related departments, we identified the main indicators of
policy regulation and control. We used sensitivity analysis to examine the impacts of factors of regulation and
control. We also adjusted key indicators and set up different scenarios to simulate the carbon emissions based on
the effort to achieve “peak carbon.” The results show that the carbon emissions of the construction industry will
be reduced by 2060, achieving the goals of “peak carbon” and “carbon neutrality.” Although the construction and
operation stages individually can achieve peak carbon by 2030, the whole process of the construction industry
will reach peak carbon by 2045, 2038, or 2036, depending on specific aspects of the scenario considered.
However, the indicators, such as the green building ratio can realize carbon emission reduction at building
operation stage for 4%~6%, but cause greater carbon emission for 5%~7% at construction material production
and transportation stage—a phenomenon called “policy island.” Therefore, the coupling of policies should be a
key concern in policy formulation and implementation.
Keywords:
Construction industry
Carbon emissions
Peak carbon
System dynamics
Life-cycle thinking
Policy coupling
1. Introduction
China is currently the world’s largest energy consumer and CO2
emitter (Ma et al., 2019) and faces severe air pollution as a result (Kong
et al., 2022a). To address this, at the 2020 United Nations General As­
sembly and Climate Summit, China announced that it would introduce
stronger policies and measures to reach peak carbon dioxide emissions
by 2030 and work toward carbon neutrality by 2060. Therefore, policy
research on energy efficiency and carbon reduction is crucial (P. Zhang
et al., 2022a).
Domestic and foreign experts and scholars also have conducted
research on carbon emission reduction in various aspects, such as
(Ghasemi-Mobtaker et al., 2022) predicted greenhouse gas emissions
based on energy input and output through artificial neural networks,
adaptive neuro-fuzzy inference systems, and other methods; (Nabavi-­
Pelesaraei and Damgaard, 2023) jointly adopted life cycle assessment
(LCA) and life cycle costing (LCC) methods to evaluate carbon emissions
from agricultural crop production processes; (Hosseinzadeh-Bandbafha
et al., 2017) applied the data envelopment analysis to calculate and
optimize the energy use efficiency and greenhouse gas emissions of the
farming industry; (Ran et al., 2023) tried to find evidence of Peak Car­
bon in China’s industrial sector by employing machine learning. To
summarize, decarbonization and carbon reduction is now an issue of
great concern to all industries.
* Corresponding author.School of Environmental Science and Technology, Dalian University of Technology, Dalian, China.
E-mail address: zhangyun@dlut.edu.cn (Y. Zhang).
https://doi.org/10.1016/j.jclepro.2023.140457
Received 6 September 2023; Received in revised form 17 December 2023; Accepted 27 December 2023
Available online 1 January 2024
0959-6526/© 2024 Elsevier Ltd. All rights reserved.
Z. Zhang et al.
Journal of Cleaner Production 435 (2024) 140457
following questions.
Many countries, including China, are undergoing rapid urbanization
and industrialization, leading to an increase in the extent of greenhouse
gas emissions, affecting global warming and climate change. (Liu et al.,
2020). The construction industry accounted for 7.1% of GDP in 2020
and is one of the major emitting industries. The total energy consump­
tion of the whole process of national building in 2020 was 2.27 billion
ton, accounting for 45.5% of the total national energy consumption, and
the total carbon emission was 5.08 billion ton, accounting for 50.9% of
the national amount (Research Report of China Building Energy Con­
sumption and Carbon Emissions, 2022). The potential for decarbon­
ization and carbon reduction in China’s construction industry is high.
The construction industry can be divided into 3 main stages (Lu
et al., 2020) (1) construction material production and transportation;
(2) building construction; (3) building operation. In particular, the
production and transportation of building materials involves a large
number of manufacturing and processing industries with high carbon
emissions and energy consumption (Chen et al., 2017), including
cement, reinforcing steel, and flat glass—this stage accounts for more
than 50% of the carbon emissions of the entire building process. The
building construction stage involves the energy input of the construction
equipment used, which involves high fossil energy consumption, which
also generates a certain amount of carbon emissions, but the contribu­
tion of this stage to carbon emissions is relatively small compared to
other stages, usually accounting for only 1%–2%. As the design service
life of a building can be 50 years, the life cycle of the building operation
stage is long. After the building has been put into operation, it will be
accompanied by energy consumption including that based on the daily
life of the residents, centralized heating, etc. This accounts for around
40% of the carbon emissions of the whole process of a building (Wu
et al., 2019).
To reduce carbon emissions in the construction industry, China has
implemented a number of policies to formulate phased development
plans based on China’s every 5 years as the starting point of time,
including the “Twelfth Five-Year Plan” for the development of green
buildings and green ecological urban areas, “Building Energy Efficiency
and Green Building Development” 13th Five-Year Plan, The “13th FiveYear Plan for the Development of the Construction Industry,” and the
“14th Five-Year Plan for the Development of Building Energy Efficiency
and Green Buildings.” Plus, other development plans and policies
address carbon reduction and decarbonization measures and targets for
the construction industry, involving green buildings, green building
materials, and the energy structure of the construction industry. This
study will construct a complete system dynamics (SD) model of the
whole life cycle of the construction industry, and focuses on the
1) Will the introduction and implementation of these policy measures
be effective in reducing energy consumption and carbon emissions in
the construction sector?
2) Is the implementation of existing policies able to meet the re­
quirements of China’s construction sector to have peak carbon by
2030 and be carbon-neutral by 2060?
This study summarized the relevant literature (Table 1) and found
that many studies did not go through the whole process of the con­
struction industry in their studies on the carbon emissions of China’s
construction industry, due to the long cyclical nature and the complex
stages of the industry. The effects of policy measures may realize energy
saving and carbon reduction at a certain stage, but cause higher carbon
emissions at other stages, which is not only detrimental to energy saving
and carbon reduction but may also lead to higher energy consumption
for the whole industry (Huang et al., 2020). This study adopts a
comprehensive, integrated, and dynamic analysis to study the whole
process of the construction industry, and the results of the study will
provide a theoretical basis for the realization of China’s goal of peak
carbon by 2030 and carbon neutrality by 2060.
2. Research methodology and data sources
2.1. Life-cycle thinking (LCT)
LCT is a systematic framework that takes a holistic view of the pro­
duction and consumption of a product or service, an integrated analyt­
ical perspective that, as a conceptual rather than a quantitative tool, has
a more macro-level perspective and applies to the assessment of policy
decisions (Huang et al., 2020). It can propose ideas for reducing envi­
ronmental impacts and minimizing resource use at all life-cycle stages
by identifying the potential for improvement of a product or service
(Lazarevic et al., 2012). It can be created by following a basis for
delineating the boundaries of a system based on research needs
(Romanovska et al., 2023). Therefore, given the multi-industry and
multi-stage characteristics of the construction industry, it is suitable to
adopt the whole life-cycle thinking to assess the effectiveness and
comprehensiveness of the carbon emission reduction-related policies.
2.2. System dynamics (SD)
System dynamics is a research methodology for studying the causal
Table 1
Research related to carbon emissions from buildings.
Reference
Site
Methodology
Building Materials
Production and
Transportation
Building
Construction
Building
Operation
Li et al.
(2020a)
Jiang Su
Province
Li et al.
(2020b)
Zhang et al.
(2022b)
Zhang et al.
(2019)
Du et al.
(2019)
China
China
Carbon Emission
Coefficient Method &
Input-Output Model
Generalized Divisia Index
Model
Stock Dynamic Model
China
Life Cycle Assessment
China
System Dynamic Model
Su et al.
(2023)
China
Life Cycle Assessment
✓
Huo et al.
(2022)
China
System Dynamic Model
✓
Scenario Analysis
✓
The drivers are decomposed and different factors are
set to predict the peak carbon emissions
Scenario setting for building stock and construction
patterns
✓
✓
✓
2
Simulating carbon emissions from the construction
sector by 2025, taking into account GDP growth and
policy adjustments
Different settings for the thickness of the insulation
layer, taking into account the carbon emissions
during the operational stage of the building.
Variables such as economic growth, urbanization
rate, and housing space per capita simulate
scenarios by 2060
Z. Zhang et al.
Journal of Cleaner Production 435 (2024) 140457
relationships and dynamics between elements in complex systems (Du
et al., 2018). The method can realize the predetermined goals and satisfy
the predetermined requirements by building an effective model, and
based on the principles of system thinking and feedback control theory,
it can describe the time-varying behavior of complex systems (Zuo et al.,
2017). Modeling includes defining the object of study, specifying the
system boundary, constructing the causal relationship between factors
in the system, and determining the quantitative relationship between
the factors (Kong et al., 2022b). System dynamics has been utilized in
many studies such as time-scale studies of ecological dynamics (Hastings
et al., 2018), modeling of energy consumption and carbon emissions in
the steel industry (Chen et al., 2014), and the coupling of
economy-energy-carbon emissions (Acheampong, 2018). We examine
emissions of the whole life cycle of the construction industry, using
system dynamics to identify relationships between influencing factors.
Table 2
The abbreviations for the variables in Fig. 1.
Abbreviations
Definitions
CEFi
CEp
CEt
CEpt
Carbon Emission Factor of i
Carbon Emission of the building material Production stage
Carbon Emission of the building material Transportation stage
Carbon Emission of the building material Production &
Transportation stage
Carbon Emission of the Construction stage
Carbon Emission of the Operation stage
Carbon Emission of the Building Industry
CEc
CEo
CEb
2.3. Model building process
This study utilized the software AnyLogic University 8.8.7 for the
construction of SD models (Fig. 1) and the abbreviations in (Table 2).
Elements in a system fall into four categories: stocks, flows, variables,
and constants. Stocks reflect the cumulative state of the elements; flows
indicate the rate of input or output of stock levels; variables are used to
establish relationships between elements; and constants are usually
unchanging. Each arrow in the diagram is a causal chain and quantita­
tive relationship from the influencing element to the affected element
(Yang et al., 2021). Detailed variable names, units, and equations of
quantities are shown in (appendix 1). The model was used to simulate
future carbon emissions of the construction industry up to 2060 and the
changes in carbon emissions at various stages by setting up different
scenarios for key factors.
We divide the whole system into four subsystems: government,
socio-demographic-economic, building, and carbon emission. There are
different relationships between the subsystems (Fig. 2), and the
following assumptions are made.
Fig. 2. Relationship between subsystems.
(1) The government subsystem’s policy regulatory instruments are
effective in real-time, as soon as they are issued, regardless of the
lag time for their entry into force;
Fig. 1. Sd modeling of carbon emissions in the construction industry.
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Z. Zhang et al.
Journal of Cleaner Production 435 (2024) 140457
materials (CEpt) are influenced by the size of the new building area
(Tang et al., 2016). This can be measured as the consumption of different
building materials per square meter of floor area for different building
types. The stage of extraction, manufacture, and transportation of con­
struction materials includes six construction materials (cement, concrete
blocks, sand, aggregates, plate glass, and steel), They are characterized
by high usage and energy consumption in the production process, with
high carbon emissions. The carbon emissions from the production and
transportation of building materials were calculated according to the
carbon emission factors for each stage specified in (“Standard for
calculating carbon emissions from buildings,” 2019) (Fig. 3).
Carbon emissions in this stage are divided into two components: (1)
carbon emissions from the production of building materials (CEp); (2)
carbon emissions during the transportation of construction materials
(CEt). The calculations (1) (2) (3) are:
(2) In the socio-demographic-economic subsystem, population
growth and economic development are related as mutually
reinforcing and co-developing (Furuoka, 2013). We exclude fac­
tors such as population aging, movement of people, and special
influences such as ethnicity and religion;
(3) In the building subsystem, building demand is only related to
supply and demand, without considering the investment attri­
butes nor the economic attributes of the building;
(4) In the carbon emission subsystem, the carbon emissions from the
upstream tracing process of mining, production, and energy
consumption involved in the production and transportation of
building materials are considered, but upstream carbon emissions
from energy production are not considered during the operation
and construction phases of the building.
CEpt = CEp + CEt
2.4. Subsystem description
n
∑
2.4.1. Government subsystem
The government subsystem includes the system of policy regulation
and control and influences the changes of the whole system through the
implementation of policies. To make the operation of the system achieve
the expected purpose, the implementation strength of the policy needs
to be adjusted, therefore, this study aims to provide the governmental
decision-making subsystem as the basis for decision-making and the
method of measurement.
CEp =
(1)
Mi Fi
(2)
Mi Di Ti
(3)
i=1
n
∑
CEt =
i=1
where CEp refers to carbon emissions from the production process of
building materials; CEt refers to carbon emissions from the trans­
portation of construction materials; Mi refers to the consumption of the i
construction material; Fi refers to the carbon emission factor of the i
building material; Di denotes the average transportation distance of the i
construction material; Ti indicates the carbon emission factor per unit
weight of transportation distance.
2.4.2. Socio-economic-demographic subsystem
The socio-economic-demographic subsystem, which is the basic
system that hosts the building system, does not produce carbon emis­
sions by itself but affects the building subsystem as represented by
economic indicators (e.g., GDP, GDP of the construction industry, etc.)
and demographic indicators (e.g., number of people in towns and cities,
number of people in total population, etc.). In the economicdemographic subsystem, many experts have studied China’s popula­
tion economic growth. Xuan et al. (2014) used logistic prediction models
to predict China’s population up to 2050, and the study’s models
showed persistent low and steady growth, without a negative natural
population growth rate. Yu and Yang (2014) used a bilinear model for
projections, which suggests that China’s population will peak around
2025, with a slow decline between 2030 and 2040, and a rapid decline
after
2040.
Guo
et
al.
(2019)
used
a
coupled
population-development-environment (PDE) model to make pro­
jections, showing China’s population peak occurs between 2030 and
2035, and the total population will be 1.44–1.49 billion people. Chen
et al. (2023) forecast China’s average annual GDP growth rate for the
next five-year plans and suggest that after 2040, China’s economy will
enter a stage of sustained, stable, low growth. Sun and Lu (2022) used an
economic geography-system dynamics model in combination with the
SWOT-Delphi method (S-D) to analyze China’s economy both quanti­
tatively and non-quantitatively, thus improving the accuracy of the
forecast. This study applies the above findings to refine the
socio-economic-demographic subsystem in the model.
2.4.3.2. Building construction. Carbon emissions during construction
(CEc) are influenced by the construction area and are reflected in the
direct carbon emissions from the energy consumption of machinery and
equipment during construction (Fig. 4).
The calculation (4) is:
n
∑
CEc =
Ei EFi
(4)
i=1
where Ei refers to the consumption of the type i energy source and EFi
refers to the carbon emission factor of the type i energy source.
2.4.3.3. Building Operation. Carbon emissions in operations (CEo) come
from (1) direct carbon emissions from residential energy consumption
and (2) carbon emissions from energy consumption for centralized
heating (Fig. 5).
The calculation (5) is:
n
∑
Ei EFi
CEo =
(5)
i=1
where Ei refers to the consumption of the type i energy source and EFi
refers to the carbon emission factor of the type i energy source.
For ease of calculation, the energy consumed in this study is stan­
dardized for multiple energy sources according to the converted stan­
dard coal method in (“General rules for the calculation of the
comprehensive energy consumption” 2020). The initial values in the
model and the parameter values of some variables are based on China’s
overall national development strategy objectives, national statistical
yearbook, and relevant literature. For some variables that change over
time, a table function is used to enter the method. For the regression
fitting relationship between the factors that need to be analyzed.
Regression analysis is performed using SPSS 26.0 software (including
the mathematical derivation of the multivariate curvilinear regression,
2.4.3. Building subsystems
This study divides the whole system by the natural variability in the
socio-economic-demographic subsystem, which affects the critical var­
iable in the building subsystem, i.e., the floor space. Floor space is a key
variable throughout the building subsystem and is inextricably linked to
the consumption of building materials, the energy consumption of
building construction, and the energy consumption during the operation
stage of the building, all of which are sources of energy consumption and
carbon emissions.
2.4.3.1. Construction material production and transportation. Carbon
emissions from the production and transportation stages of building
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Journal of Cleaner Production 435 (2024) 140457
Fig. 3. Sd model in construction materials production and transportation.
Fig. 4. Sd model in construction.
Fig. 5. Sd model in building operational.
one-unit curvilinear regression, multiple curvilinear regression, etc.).
See (Annex 1) for detailed variable names, units, types, and equation
relationships between variables.
2.6. Data sources
The data for this study were obtained from China Statistical Year­
book (2004–2022), China Construction Industry Statistical Yearbook
(2005–2022), China Energy Statistical Yearbook (2004–2022), policy
documents issued by the Ministry of Housing and Urban-Rural Devel­
opment of the People’s Republic of China, and publicly available data
from the National Bureau of Statistics of the People’s Republic of China
for the past 20 years.
2.4.4. Carbon emission subsystem
After the completion of the construction of the above subsystem,
different scenarios are set up to predict and analyze future carbon
emissions. Appropriate adjustments are made, which will provide a
reference for the goal that China will reach the peak of carbon dioxide
emissions in 2030 and will achieve carbon neutrality by 2060.
3. Sensitivity analysis of key policy indicators
2.5. Key factor identification
Sensitivity analysis is used to investigate the impact of the share of
new green buildings, the share of electric energy consumption in the
construction stage, and the share of electric energy in the operation
stage on carbon emissions. The value of one of the factors is adjusted in a
constant step while the other factors are kept constant. The greater the
increase in carbon emissions at the same rate of change, the greater the
impact of the factor on carbon emissions.
To promote carbon emission reduction in the construction industry,
the main regulatory means of the existing policy is to improve building
energy efficiency and adjust the energy structure. Based on the re­
quirements of the national development plan, the industry development
plan, and other relevant policies: “to accelerate the enhancement of
building energy efficiency, accelerate the development of the green
building industry, accelerate the construction of green buildings, and
change the mode of construction (“Circular of the Ministry of Housing
and Urban-Rural Development on the Issuance of the ‘14th Five-Year
Plan’ for the Development of Building Energy Efficiency and Green
Building,” n. d.)" as well as “accelerate the adjustment of the energy
structure, and the scale of the application of renewable energy has been
continuously expanding (“Ministry of Housing and Urban-Rural Devel­
opment on the issuance of the construction industry development ‘13th
Five-Year Plan’ Notice_Departmental Government_China.gov.cn,” n.
d.)." The study has three major policy indicators that have a significant
impact on the carbon emissions of the construction industry: the per­
centage of newly built green buildings, the percentage of electric power
consumption in construction, and the percentage of electric power
consumption in operation.
3.1. Sensitivity analysis of the percentage of new green buildings
A building that meets the Green Building Evaluation Standards
(2019) is rated as a green building. This rating involves a comprehensive
evaluation process with corresponding requirements for building ma­
terials, building energy efficiency, land utilization rate, floor area ratio,
and other indicators. Although green buildings can have good
energy-saving effects during the operation of the building, the produc­
tion of building materials may have a greater contribution to carbon
emissions (Chang et al., 2023). This study takes 10% of new green
buildings as the initial value and increases 5% at each step to analyze the
impact on the carbon emissions of the production and transportation of
building materials, the operation stage of the building, and the whole
process of the construction industry. Through the sensitivity analysis
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Z. Zhang et al.
Journal of Cleaner Production 435 (2024) 140457
Fig. 6. Sensitivity analysis of the percentage of new green buildings.
At present, green buildings account for a relatively small proportion
of the building stock, but the proportion of new green buildings is
gradually increasing. So, with time, the increase in new green buildings
leads to greater carbon emission reduction.
(Fig. 6), it is found that the proportion of green buildings in the pro­
duction and transportation of building materials (Fig. 6a) shows higher
carbon emissions and high total emissions, but in the operation of the
building stage (Fig. 6b), it shows lower carbon emissions and small total
emission reductions.
From this, it seems that the carbon emission reduction potential of
green buildings is negative (Fig. 6c); however, since this model is based
on each year as a time unit, and the design service life of buildings is
usually 50 years if the perspective is switched to a single green building
project, the carbon emissions from building material manufacturing,
construction, and other processes in the early stages will be slightly
larger than that of an ordinary building. Because a green building has a
greater emission reduction potential than ordinary buildings, it may
result in large emission reductions in the whole life cycle process.
Moreover, with the promotion of green building policy, the stock of
green buildings will also increase, so in the early stage of the model
simulation (2004–2026) the proportion of green buildings will increase
and carbon emissions from the production of building materials will
increase. In the middle stage (2026–2036), the proportion of green
buildings reaches a certain level and the carbon emissions from the
production of building materials in this period will have slow growth. In
the later part of the model simulation (2036–2060), as the proportion of
green buildings reaches a high level and due to the life span of the
buildings, the stock of floor space may decrease, resulting in the overall
carbon emissions reaching a peak and then decreasing. This study does
not take into account the updating of green building evaluation stan­
dards, but it does not rule out the possibility that the energy efficiency
requirements for green buildings will be more demanding in the future.
This would lead to a greater potential for energy savings and emission
reductions in the operational stage of the green building.
3.2. Sensitivity analysis of the percentage of electric energy in the
construction
Energy structure adjustment in the construction stage is also a means
of carbon emission reduction. This study takes 10% of electricity energy
consumption in the construction stage as the initial value and increases
it by 5% each time step. Through the sensitivity analysis (Fig. 7), it can
be seen that the emissions from the proportion of electric power con­
sumption in the construction stage are significant when the proportion is
relatively low; however, when the proportion reaches a certain per­
centage, the emissions decrease (Fig. 7a). Since the carbon emissions of
the construction stage account for a relatively small proportion of the
carbon emissions of the entire construction industry, adjusting the en­
ergy structure of the construction stage does not have a high carbon
emission reduction benefit overall (Fig. 7b).
3.3. Sensitivity analysis of the percentage of electric energy in the
operation
Adjustment of energy structure is an important means of energy
saving and emission reductions, and China is adjusting the energy
structure and industrial structure to this end (Wu et al., 2021). The main
cause of carbon emissions in the operation stage of buildings is caused
by energy consumption. In this study, we take 10% of electricity energy
consumption in the operation stage as the initial value and increase it by
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Z. Zhang et al.
Journal of Cleaner Production 435 (2024) 140457
Fig. 7. Sensitivity analysis of the percentage of electric energy in construction.
5% each time step. Through the sensitivity analysis (Fig. 8), it can be
seen that the change in energy structure affects the peak of carbon
emissions. If electric energy consumption is lower than 20%, the peak of
carbon emission in the construction industry will appear in 2052; when
the proportion is more than 25%, the peak of carbon emission will
appear in 2036. For the increase in the share of electric energy con­
sumption, at lower levels, its emission reduction in the operation stage
and the entire construction industry is large, but after reaching a certain
percentage it will be lower.
Through sensitivity analysis, it is found that the three selected key
policy indicators, namely, the proportion of new green buildings, the
proportion of electric energy consumption in the construction stage, and
the proportion of electric energy consumption in the operation stage,
will have obvious impacts on different stages of the construction in­
dustry as well as on the whole process at different levels, and therefore,
by appropriately adjusting these three indicators, a simulation predic­
tion of the future carbon emissions of the construction industry can be
made.
4.2. RG scenario
The RG scenario is based on China’s “12th Five-Year Plan for the
Development of Green Buildings and Green Eco-city,” “13th Five-Year
Plan for the Development of Building Energy Saving and Green Build­
ings,” “14th Five-Year Plan for the Development of Building Energy
Saving and Green Buildings,” and other important policy documents.
The planning of the three indicators is based on relevant indicators in
the “Statistical Yearbook of China,” the “Statistical Yearbook of Energy,”
and the “Statistical Yearbook of China’s Building Industry,” and
analyzing the growth rate of the three indicators from 2004 to 2022,
which will keep the same growth rate last until 2060.
4.3. CP scenario
To realize China’s requirement of achieving peak carbon by 2030,
the proportion of new green buildings in this scenario increases rapidly,
with 95% of all new buildings complying with the current green building
standards by 2030. Electric energy in the building operation and
building construction stages will become the main source of energy by
2030, thus accelerating the building industry’s goal of achieving peak
carbon.
4. Scenario Analysis of key policy indicators
Four scenarios were set up for each of the three key policy indicators
identified in the study (the percentage of new green buildings, electric
energy consumption in construction, and electric energy consumption):
Business as Usual (BAU), Regular Growth (RG), Carbon Peak (CP), and
Low-Carbon (LC).
4.4. LC scenario
This is a scenario to achieve carbon neutrality requirements by 2060.
Higher low-carbon requirements are proposed, i.e., new buildings will
fully meet the green building requirements after 2030 and, by 2060,
almost all of the buildings will be green. Electric energy in the building
operation and construction stages will replace traditional primary en­
ergy sources, with an increase of 1% every 5 years compared to the
proportion of electric energy consumed in the CP Scenario, thus real­
izing lower carbon emissions.
4.1. BAU scenario
In the BAU scenario, the current strength of policy implementation
remains unchanged, i.e., the proportion of new green buildings, the
share of electricity energy consumption in the building operation stage,
and the building construction stage will not be affected by the policy
over time and will remain consistent with the 2022 level. In this sce­
nario, the proportion of green buildings increases slowly and because
the proportion of the energy mix remains unchanged, energy con­
sumption is dominated by fossil energy.
Fig. 8. Sensitivity analysis of the percentage of electric energy in operation.
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Journal of Cleaner Production 435 (2024) 140457
5. Results
5.1. Construction material production and transportation
CEpt is expected to peak in 2036 under the BAU scenario, CP sce­
nario, and LC scenario, and in 2038 under the RG scenario (Fig. 9);
under the RG scenario, CP scenario, and LC scenario, their peaks are
higher than that of the BAU scenario. The peak of carbon emissions at
the stage of production and transportation of building materials is lower
than that of the other three scenarios in the BAU scenario due to the
lower proportion of newly built green buildings. In the CP and the LC
scenarios, the peak is reached earlier than in the RG scenario in this
stage and, due to the higher proportion of new green buildings, it results
in a higher peak carbon emission. It is worth noting that in 2036, the CP
and LC scenarios; and in 2041, the RG, CP and LC scenarios, the values of
carbon emissions in this stage of overlap, which means that as the
proportion of newly built green buildings continues to grow, the newly
built buildings each year can eventually meet the green building stan­
dard, and the carbon emissions in this stage stabilize when a certain
proportion of the green building stock is reached. The CEpt in the BAU
scenario are always lower than that of the other three scenarios, so it
seems that the implementation of the policy of the proportion of green
buildings in new construction has negative benefits for carbon emission
reduction in the stage of the production and transportation of building
materials.
Fig. 10. Scenario analysis of carbon emissions in building construction.
necessary as the simulation does not show the peak of carbon emissions
by 2060. Under RG, the peak of carbon emissions in this stage will occur
in 2036, under the CP scenario, the peak will occur in 2025, and under
the LC scenario, the peak will occur in 2022. The analysis reveals that
due to the increase in the proportion of green buildings and the
adjustment of the energy mix, there is carbon emission reduction.
5.2. Building construction
From the analysis of the building construction stage (Fig. 10), the
only indicator that affects the magnitude of carbon emissions at this
stage is the share of electric energy consumption, which will peak in
2036 in the BAU scenario, 2026 in the RG and CP scenarios, and 2025 in
the LC scenario. The continued acceleration of energy structure adjust­
ment can have an important impact on carbon emission reduction.
However, when the share of electricity energy reaches a certain per­
centage, its carbon reduction effect will also produce diminishing mar­
ginal benefits.
5.4. The whole process of carbon emissions in the construction industry
Analyzing the whole process of the construction industry (Fig. 12),
under the BAU scenario, the peak of carbon emissions from the con­
struction industry will occur in 2045; under RG it will in 2038; Under
CP, it will in 2036; under the LC scenario, it is also 2036. Comparing the
BAU scenario with the other three scenarios, the three policy indicators
selected in this study can have a positive effect on carbon emission
reduction, but none of the four scenarios realizes the target of the whole
process of the construction industry to reach the peak of carbon emis­
sions in 2030. The time for the construction industry to reach peak
carbon coincides with the time for the peak carbon emission obtained
from the analysis of the production and transportation stages of building
5.3. Building Operation
From the analysis of the building operation stage (Fig. 11), under the
BAU scenario, energy restructuring and green building requirements are
Fig. 9. Scenario analysis of carbon emissions in construction material production and transportation.
8
Z. Zhang et al.
Journal of Cleaner Production 435 (2024) 140457
Fig. 11. Scenario analysis of building operational carbon emission.
development of China’s economy. Rapid urbanization and industriali­
zation led to a surge in construction demand. By contrast, carbon
emissions from the operation stage of the building were relatively stable
but they did decrease to some degree following the global pandemic of
Covid-19. In the post-pandemic period from 2023 to 2026, according to
the model, there will be a small increase in carbon emissions from the
construction industry, and then a stable or low increase from 2036 to
2036. The model indicates the peak time of carbon emissions from
China’s construction industry for the whole process as 2038, and the
peak time of carbon emissions for each stage as 2038 or 2036. The main
reason is that, according to the model, China’s urbanization rate reaches
72.7% in 2036. The result is consistent with Wu et al. (2017), i.e., that
urbanization rate growth will drives carbon emissions increase at low
levels of urbanization. However, once developing countries reach a
certain level of urbanization, their contribution to carbon emissions
declines (Martínez-Zarzoso and Maruotti, 2011). China’s population
will peak around 2036, and its economy will enter a stage of steady,
low-rate development (Chen et al., 2023; Guo et al., 2019). As a result,
China’s building demand will peak around 2035, and carbon emissions
from the construction sector as a whole will peak as well.
The results of the simulation under BAU are similar to the conclusion
of (You et al., 2023), who used an Logarithmic Mean Divisia Index
(LMDI) decomposition method to analyze the end-use consumption of
energy and carbon emissions from buildings up to 2060, concluding that
the carbon emissions from buildings peak in 2033, with the highest peak
carbon emissions of about 2.52 billion tons. Chang et al. (2023) also
discuss the impact of green buildings, net-zero and ultra-low-energy
buildings, etc., on the carbon emissions of China’s construction in­
dustry, and his simulation predicts that the peak of China’s building
carbon emissions will occur around 2035, with emissions of about 2.7
billion tons. Carbon emissions from China’s construction industry have
been modeled under various other scenarios and methods with most of
the research agreeing that the peak in carbon emissions will occur be­
tween 2035 and 2040. Zhang et al. (2022a) also had similar findings to
the simulation results of this study.
According to the “China Building Energy Consumption and Carbon
Emission Research Report” (2020–2022) published by the “China
Building Energy Efficiency Association,” comparative analysis reveals
that the carbon emissions from the production and transportation stages
of building materials in this study are all higher than the carbon emis­
sions from the production process of building materials (Table 3). The
Fig. 12. Scenario analysis of whole process carbon emissions in the construc­
tion industry.
materials, which means these factors are the main contributors to the
whole process of the construction industry’s carbon emissions. The new
green buildings can increase the carbon emissions from the production
and transportation of building materials, i.e., the higher the proportion
of new green buildings, the higher the carbon emissions in this stage, but
after reaching a certain proportion they will decline; and it can reduce
the carbon emissions from the operation of the construction, i.e., the
proportion of green buildings has opposite effects on the carbon emis­
sions from the operation of the construction industry at different stages.
So, if only the proportion of new green buildings is mandated, it will
create a “policy island” problem, which mean “one indicator create
positive effects at one stage but negative at others,” for the whole pro­
cess of carbon emission reduction in the construction industry.
6. Discussion
Under BAU, from 2004 to 2020, China’s carbon emissions from the
construction industry show a rapid increase, mainly due to the rapid
9
Z. Zhang et al.
Journal of Cleaner Production 435 (2024) 140457
possible to meet the peak carbon requirements. However, for the whole
process of the construction industry, under the BAU scenario with no
policy regulation, the peak of carbon emissions from the construction
industry will appear in 2045, much later than the requirement of
reaching the peak in 2030; under the RG scenario where the policy
targets are tightened according to the current trend, the peak of carbon
emissions will appear in 2038, which will not be able to realize the
target of reaching the peak in 2030; and under the CP scenario and LC
scenario where the policy targets are more stringent, the peak of carbon
emissions will also appear in 2038, which will not be able to realize the
target of reaching the peak in 2030. Peak carbon emissions also occur in
2036 after 2030. In other words, under different energy structures and
different green building proportion scenarios, the carbon emissions and
the peak emissions of China’s construction industry will change differ­
ently, but only adjusting the energy structure and increasing the pro­
portion of green buildings are not enough to support the realization of
the requirement that the whole process of carbon emissions of the
construction industry will reach the peak in 2030, which is mainly due
to the although the increase in the proportion of green buildings can
achieve carbon emission reduction in the operation stage of buildings, it
will bring more carbon emissions in the production and transportation
stage of building materials for green buildings.
In summary, this study concludes that if the implementation of
measures focuses only on the adjustment of the energy structure and
energy efficiency of the building, it will not be able to achieve the
desired results, therefore it is vital to expand the scope of the policies
enacted. Then, the production and transportation process of building
materials is the most important stage in the whole process of the con­
struction industry, which generates the most carbon emissions, so policy
intervention and macro-control of this stage is a key initiative to realize
carbon emission reduction in the construction industry. At the same
time, we believe that the formulation of decarbonization and carbon
reduction policies for the construction industry cannot be purely limited
to a certain stage, but must take into account the whole process of the
construction industry, and the coupling of the effects of the policy
should become a key consideration in the formulation of policy mea­
sures. When adjusting the focus of policy implementation, the coupling
effect of new policies and existing policies should be considered to avoid
the problem of “policy islands”, This is a necessary condition for real­
izing the peak of carbon emissions.
Table 3
Model error analysis.
2018
2019
2020
Real Data
Simulation Data
Difference
Real Data
Simulation Data
Difference
Real Data
Simulation Data
Difference
CEc(Mt)
CEpt(Mt)
CEo(Mt)
CEb(Mt)
100
101.9
1.90%
100
106.1
6.10%
100
109.3
9.30%
2720
3408.7
25.32%
2770
3815.3
37.74%
3069.53
4173.2
35.96%
2110
2041
¡3.27%
2130
2040.7
¡4.19%
2160
2086
¡3.43%
4930
5551.6
12.61%
5000
5962.1
19.24%
5329.53
6368.5
19.49%
main reasons are (1) the transportation process was included in this
study whereas it was not included in the report and (2) carbon emissions
from raw material extraction and production, carbon emissions from
transportation of raw materials and energy, but the report not. The
report is based on the process method and input-output method of
calculating the energy consumption in the production process of build­
ing materials, therefore, the difference between the production and
transportation stages of building materials and the report, the total
carbon emissions are also higher than the total carbon emissions given in
the report. In this study, the difference between the carbon emissions in
the operation stage of the building and the data given in the report is
only 3–4%; the maximum error in the construction stage is 9%, and the
error does not exceed 10%.
In the operation stage of building, the main carbon emission pathway
is the direct and implied carbon emissions from energy consumption.
Buildings have a long service life, so once the building is put into
operation, they will have long-term energy consumption. This is true not
only for the new building, but the adjustment of the energy structure of
existing buildings has an important impact on carbon emission reduc­
tion, and increasing the proportion of electricity energy consumption
can avoid the direct carbon emissions from the large-scale use of fossil
energy. For building construction, the emissions at this stage are mainly
based on the changes in the demand for floor space caused by the
changes in the socio-economic-demographic subsystem. Although this
stage produces a small amount of carbon emissions relative to the whole
building industry, it is also necessary to adjust the energy structure—the
adjustment of the energy mix of the building sector is an indispensable
regulatory tool.
CRediT authorship contribution statement
7. Conclusion and recommendation
Zhao Zhang: Conceptualization, Data curation, Formal analysis,
Investigation, Methodology, Software, Visualization, Writing – original
draft, Writing – review & editing. Qiufeng Gao: Investigation, Super­
vision. Shuai Shao: Conceptualization, Supervision, Validation. Yun
Zhang: Funding acquisition, Investigation, Methodology, Project
administration, Software, Supervision, Validation. Yining Bao: Inves­
tigation, Supervision. Li Zhao: Investigation, Supervision.
Based on the life cycle thinking combined with the system dynamics
model, this study analyzes the whole process of China’s construction
industry and the changes in carbon emissions at each stage from 2004 to
2060 and analyzes the impacts of the changes in the indicators on the
system by adjusting the policy indicators for the energy structure and
the proportion of green buildings at each stage.
It is found that, through analysis for different stages, the production
and transportation of building materials is always the main contributor
to carbon emissions, and although the peak of carbon emissions has
changed after policy adjustment, it is not able to meet the requirements
of peak by 2030; the construction stage of the building: it can meet the
requirements of peak carbon through the adjustment of the energy
structure; the operation stage of the building, with the gradual increase
of the proportion of green buildings and the optimization of the energy
structure, can also meet the requirements of peak carbon. With the
gradual increase in the proportion of green buildings and the optimi­
zation of energy structure in the building operation stage, it is also
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
Data will be made available on request.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2023.140457.
10
Z. Zhang et al.
Journal of Cleaner Production 435 (2024) 140457
Appendix 1. Main variable and mathematical equation of the SD model
Variable
Mathematical Equation
Variable Type
GDP
Population
Building_area
Green_building_area
d (GDP)/dt = GDP_growth
d (Population)/dt = Population_growth
d (Building_area)/dt = New_construction_area - Demolition_construction_area
d (Green_building_area)/dt = New_green_buildings_area
Stockpile
Stockpile
Stockpile
Stockpile
GDP_growth
Population_growth
New_construction_area
Flux
Flux
Flux
Demolition_construction_area
New_green_buildings_area
GDP × GDP_growth_rate
Population × Population_growth_rate
24.697 × Urban_population +14.641 × Construction_GDP − 7.132 × Capita_GDP − 21.893 × Capita_income
− 1127161.943
0.1 × New_construction_area
New_construction_area × New_green_buildings_ration
GDP_growth_rate
Table Function Inputs
Population_growth_rate
Table Function Inputs
Capita_GDP
GDP/Population
Capita_income
0.438 × Capita_GDP − 293.054
Urbanization_rate
Table Function Inputs
Urban_population
Population × Urbanization_rate
Construction_GDP
Construction_GDP_share × GDP
Construction_GDP_share
Table Function Inputs
New_green_buildings_ration
Table Function Inputs
Concrete_consumption
0.2154 × New_construction_area
Steel_consumption
0.0557 × New_construction_area
Sand_consumption
0.9003 × New_construction_area
Stone_consumption
0.5351 × New_construction_area
Concrete_block_consumption
0.1404 × New_construction_area
Glass_consumption
0.0019 × New_construction_area
CEp
CEpt
Sand_consumption × CEF_ Sand + Stone_consumption × CEF_ Stone + Concrete_consumption × CEF_ Concrete
+ Steel_consumption × CEF_ Steel + Concrete_block_consumption × CEF_ concrete_block + Glass_consumption
× CEF_ Glass
(Sand_consumption + Stone_consumption + Concrete_consumption + Steel_consumption +
Concrete_block_consumption + Glass_consumption) × Transportation_distance × CEF_transportation
CEt + CEp
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
CEpt_per_unit_area
CEpt/New_construction_area
Green_building_emission_potential
1.31 × CEpt_per_unit_area
CEpt_of_green_building
Green_building_emission_potential × New_green_buildings_area
CEpt_of_normal_building
CEpt_per_unit_area × New_construction_area - New_green_buildings_area
Total_construction_area
Demolition_construction_area + New_construction_area
Energy_consumption_in_construction
Electricity_energy_consumption_in_construction/Percentage_of_electric_in_construction_stage
Percentage_of_electric_in_construction_stage
Table Function Inputs
Electricity_energy_consumption_in_construction
CEc
0.003 × Total_construction_area − 3.691E-9 × Total_construction_area 2 +2.278E-15 × Total_construction_area
3
-96.517
CEF_standardcoal × Energy_consumption_in_construction
CEc_per_unit_area
CEc/Total_construction_area
Centralized_heating_area
0.182 × Building_area - 7.3296E-09 × Building_area 2 + 2789.293
Centralized_city_heating
0.226 × Urban_population - 0.001 × Centralized_heating_area - 5198.317
Residential_electricity_consumption
0.104 × Urban_population + 2.899E-11 × Urban_population
CEt
3
- 3529.008
Flux
Flux
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
(continued on next page)
11
Z. Zhang et al.
Journal of Cleaner Production 435 (2024) 140457
(continued )
Variable
Mathematical Equation
Variable Type
Residential_energy_consumption
Residential_electricity_consumption/Percentage_of_electric_in_operational_stage
Percentage_of_electric_in_operational_stage
Table Function Inputs
CEo_of_normal_building
(Residential_energy_consumption + Centralized_city_heating) × CEF_standardcoal
CEo_per_unit_area
CEo/Building_area
Green_building_emission_reduction_potential
CEo_per_unit_area × 0.15
CEb
CEo + CEpt + CEc
CE_per_unit_area
CEb/Building_area
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
Dynamic
Variable
CEF_cement
0.735
CEF_steel
2.05
CEF_sand
0.00251
CEF_stone
0.00218
CEF_cement_block
0.336
CEF_glass
1.13
Transportation_distance
100
CEF_standardcoal
2.34
CEF_transportation
0.000129
Parameter
Variable
Parameter
Variable
Parameter
Variable
Parameter
Variable
Parameter
Variable
Parameter
Variable
Parameter
Variable
Parameter
Variable
Parameter
Variable
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