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Citation: Li, J.; An, D.; Shi, Y.; Bai, R.;
Du, S. A Review of the Physical and
Chemical Characteristics and
Energy‑Recovery Potential of
Municipal Solid Waste in China.
Energies 2024,17, 491.
hps://doi.org/10.3390/
en17020491
Academic Editor: Fabio Montagnaro
Received: 6 December 2023
Revised: 10 January 2024
Accepted: 12 January 2024
Published: 19 January 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Swierland.
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distributed under the terms and
conditions of the Creative Commons
Aribution (CC BY) license (hps://
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4.0/).
energies
Review
A Review of the Physical and Chemical Characteristics and
Energy‑Recovery Potential of Municipal Solid Waste in China
Jinsong Li 1, Donghai An 2, Yuetao Shi 1, *, Ruxue Bai 1and Shanlin Du 3
1Laboratory Energy & Power Engineering, Shandong University, Jinan 250012, China;
201934149@mail.sdu.edu.cn (J.L.); 202014436@mail.sdu.edu.cn (R.B.)
2Laboratory Physics & Materials, Changji University, Changji 831100, China; 202134478@mail.sdu.edu.cn
3Laboratory of Thermo‑Fluid Science and Nuclear Engineering, Northeast Electric Power University,
Jilin 132012, China; 1202300074@neepu.edu.cn
*Correspondence: shieddie@sdu.edu.cn; Tel.: +86‑135‑8908‑9752
Abstract: The complexity and strong spatial and temporal characteristics of municipal solid waste
(MSW) have made resource utilization a major challenge in establishing the life‑cycle model of MSW.
Based on the planning of the domestic “dual‑carbon” target and the current status of the structural
transformation of resource utilization, this paper summarizes the physicochemical properties of
MSW in China by component, species, and region. The aim is to identify the physicochemical com‑
ponents of MSW in dierent regions of China, drawing on the research ndings of various scholars.
A total of 159 sets of MSW data were collected, including 90 sets of physical composition and 69 sets
of elemental composition. These data were used to calculate the caloric value of MSW and deter‑
mine the energy‑recovery and power‑generation potentials before and after MSW classication. The
analysis estimates the volume of MSW requiring removal in dierent regions of China in 2021 and as‑
sesses the eectiveness of the energy‑recovery potential (ERP) and power‑generation potential (PGP)
before and after MSW classication in these regions. The aim is to oer insightful guidance and rec‑
ommendations for municipal waste‑treatment strategies tailored to the diverse regions of China.
Keywords: MSW; physicochemical properties; caloric value; energy‑recovery potential; power‑
generation potential
1. Introduction
At present, the world’s energy paern is still dominated by fossil fuels. However,
with the changing international situation and political landscape, the world’s energy price
uctuates unpredictably. Additionally, as global environmental and climate problems
worsen, all countries have started investing in energy transition. Yuan Hu et al. [1] classi‑
ed 144 countries, including China, into four categories based on the per capita GDP and
economic structure data of the world’s countries. They conducted normalization research
on the energy consumption structure of these four categories of countries. The study high‑
lighted that China’s main energy source is coal, followed by oil, while renewable energy
accounts for only 16%. From the above, it can be seen that China urgently needs to carry
out a structural transformation of energy consumption. The “double carbon target” pro‑
posed by China at the 75th United Nations General Assembly in 2020 refers to the targets
of carbon peaking by 2030 and carbon neutrality by 2060. This demonstrates China’s deter‑
mination and implementation of the transformation of its energy‑consumption structure.
In recent years, there has been an increase in the production of MSW due to popula‑
tion growth and urbanization [2] The World Bank predicts that the annual global produc‑
tion of MSW is expected to reach 3.4 billion tons by 2050. This highlights the importance
of managing MSW as a valuable resource and a crucial aspect of transforming the energy‑
consumption structure. China, as a typical developing country, recognizes that implement‑
Energies 2024,17, 491. https://doi.org/10.3390/en17020491 https://www.mdpi.com/journal/energies
Energies 2024,17, 491 2 of 21
ing complete life‑cycle tracking of MSW is an eective measure to enhance the resource
recycling rate and address the growing issue of “garbage surrounding the city” [3].
According to 《China’s National Statistical Yearbook 2022》[4], the domestic waste re‑
moval volume in China in 2021 is projected to reach 248,692,000 tons. The daily capacity
for waste treatment is expected to be 1,057,064 tons, with an incineration treatment rate of
68.01% and a sanitary landll treatment rate of 24.74%. From the above, it can be seen that
the mainstream treatment of MSW in China is incineration and landll. Scholars from var‑
ious countries have conducted extensive research on these two treatment methods. MSWI
primarily involves the combustion of a variety of combustible materials, such as dried food
waste, paper, wood chips, textiles, leather, Ref. [5], and rubber. The chemical composition
of these materials, including C, H, O, N, S, and Cl, Ref. [6], plays a key role in determining
the caloric value of MSW. However, the release of pollutants and greenhouse gases (GHC)
from the incineration of N, S, and Cl cannot be overlooked. Ioan [7], Han [8], Chen [9], and
Liu et al. [10] conducted in‑depth research on energy recovery and pollutant emissions dur‑
ing MSWI using plant‑specic data. In particular, Chen et al. highlighted the relationship
between the physical composition of MSW and its inuence on energy recovery and GHG
emissions. The previous studies mentioned in the instructions focus on improving the ef‑
ciency of waste‑incineration power generation and reducing pollutant emissions based
on the physical and chemical properties of MSW in a specic location. However, there is
a lack of research on the variation in energy‑recovery eciency due to the dierences in
MSW and limited studies on the properties and treatment of MSW in a regional area.
Another common method for treating MSW is landlling. Research in this area fo‑
cuses on the biogas production capacity and pollutant emission level associated with land‑
lling. Liu et al. [11] conducted an analysis of the eect of dierent component contents
and concentrations of various MSW, particularly kitchen waste, on the fermentation pro‑
cess in Xi’an, China. They determined the carbon‑to‑nitrogen ratio of the waste compo‑
nents at the optimal gas production rate. Di et al. [12] analyzed the physical composition
of MSW in the study area and constructed an anaerobic kinetic model to investigate the
relationship between the components of domestic waste, leachate, and methane gas pro‑
duction rate. The treatment measures of the landll can produce clean methane, but the
damage of fermented liquid and residue to the environmental soil cannot be ignored. The
study of Kristin et al. [13] reveals the direct inuence of the pretreatment process and waste
components on the heavy‑metal content of digestate, which points out the direction for the
subsequent fermentation of digestate with low environmental damage. It can be observed
that utilizing MSW directly for fermentation biogas production can lead to signicant en‑
vironmental pollution and impact the gas production rate. Current research by scholars
focuses on the classication of MSW and the utilization of kitchen waste with high wa‑
ter content and easy fermentation for fermentation biogas production. This also provides
ideas for the following research on MSW classication and treatment.
In summary, based on China’s national conditions, this paper will focus on two treat‑
ment methods of MSW: incineration and landll fermentation. The incineration eciency
of MSW is positively correlated with its caloric value, which is closely related to its com‑
position. Similarly, the gas production rate of landll fermentation of MSW is also closely
linked to its composition. As far as China is concerned, there is a signicant potential
for MSW resource recovery. However, there is a lack of research on the compositional
data of MSW in dierent regions of China, and there is insucient research on the dier‑
ences in MSW composition among dierent regions. In this paper, we have conducted a
comprehensive review of the relevant literature published since 2000 using the literature
reading method. Each piece of data pertaining to the physical or chemical composition
of municipal solid waste (MSW) was treated as a distinct data point. In total, we gath‑
ered 159 data points, with 90 related to physical composition and 69 related to chemical
composition. Furthermore, we took care to normalize these data points for the purpose
of our analysis. The physical and chemical compositions of municipal domestic wastes in
various regions of China have been summarized in recent years. Meanwhile, it is known
Energies 2024,17, 491 3 of 21
from the literature [14] that kitchen waste in MSW has a high moisture content and is dif‑
cult to incinerate, but it is easy to ferment to produce biogas [15]. Therefore, this paper
proposes a classication strategy for MSW, specically focusing on the fermentation of
kitchen waste and the incineration of the remaining waste. It analyzes the elemental com‑
positions and caloric values of the classied MSW and evaluates the potential of power
generation from MSW before and after the classication. The objective of this paper is to
characterize the physicochemical composition of MSW in various regions of China, taking
into account China’s unique national conditions. By doing so, we aim to provide valuable
insights for the development of eective MSW treatment methods and strategies tailored to
specic regions.
2. Current Status of MSW Treatment and Data Collection in China
Statistics from the 《China Statistical Yearbook 2022》show that, in 2021, China’s na‑
tional MSW removal volume was 249.682 million tons, and the nonhazardous
waste‑treatment volume was 248.393 million tons, resulting in a high nonhazardous treat‑
ment rate of 99.5%. From the above statistics, it is evident that China has a signicant
amount of domestic waste production (referred to as MSW removal volume), indicating a
substantial potential for resource recovery and energy development. Therefore, this sec‑
tion will analyze the recent situation of MSW removal in China. It will also provide a
brief introduction to the data‑collection methods and approaches used in the following
studies on the physical and chemical properties of MSW. This will help pave the way for
subsequent studies.
This paper provides a count of China’s MSW removal, nonhazardous treatment, land‑
ll treatment, and incineration treatment over the ten‑year period from 2012 to 2021. The
rates of nonhazardous treatment, landll treatment, and incineration are also calculated,
as shown in Figure 1. As can be seen from Figure 1, China’s MSW production has con‑
tinued to rise since 2012, with a small drop in MSW production from 2019 to 2020 due to
Coronavirus disease (COVID‑19). However, the overall increase in MSW production in
the past ten years has been signicant, with a year‑on‑year growth rate of approximately
46.2%. The rate of nonhazardous treatment of MSW has also consistently risen, starting
from an initial rate of 84.8% and reaching 99.5%. This demonstrates the increasing eorts
in reducing and treating China’s municipal solid waste. Municipal solid waste reduction
and resource utilization level saw an overall improvement [16]. The garbage incineration
disposal rate and landll disposal rate show a negative correlation. Over the past ten years,
the MSW incineration disposal rate has consistently shown an upward trend, with the rate
of increase accelerating each year. This trend is closely related to China’s 13th Five‑Year
Plan and 14th Five‑Year Plan, which have policies promoting the incineration of domestic
waste [17]. The number of waste‑incineration plants in China will reach 852 by 2022. It can
be seen that MWSI power generation has become one of the important pillars of China’s
power industry [18]. While, by 2022, the landll rate of MSW in China will still be as high
as 21.0%, so research on landll treatment is also necessary.
To summarize, the MSW treatment initiatives in China mainly involve incineration
for power generation and landll biogas as the two treatment methods [19]. The energy‑
recovery eciency and pollutant emissions of these methods are dependent on the com‑
ponents of MSW. Therefore, data on the MSW components will be collected and studied
in dierent regions of China to identify the physicochemical characteristics of MSW that
align with China’s national conditions. Based on this, corresponding recommendations
for the development of MSW treatment strategies in dierent regions will be provided.
Energies 2024,17, 491 4 of 21
Energies 2024, 17, x FOR PEER REVIEW 4 of 21
Figure 1. Production and disposal of MSW in China.
3. Physical and Chemical Properties of MSW in China
The physicochemical properties of MSW are severely constrained by geographic lo-
cation, weather factors, cultural differences, etc. For instance, shellfish waste makes up a
larger portion of MSW in seaside cities compared to inland areas. As a result, the physical
composition of MSW exhibits a wide range of categories and noticeable differences. Ad-
ditionally, China, with its three major geographic gradients, five climate types, and long
history, experiences high spatial and temporal variations in the physicochemical compo-
sition of MSW.
3.1. Physical Properties of MSW in China
The physical properties of MSW can be summarized as follows: (1) the complexity
and high regionality of the physical composition; and (2) the high moisture content [20].
The following section will focus on these two aspects.
3.1.1. Physical Composition of MSW in China
The physical composition of MSW varies greatly from region to region. A wide vari-
ety of different types of domestic waste are combined to form the domestic waste samples
needed for the study. As a result, the physical composition of MSW is classified differently
in the literature from different sources. This classification facilitates the analysis of the
collected data. To analyze the data, the raw data on the physical composition of MSW
obtained from various sources [21–66] need to be normalized and a reasonable classifica-
tion needs to be developed. In this paper, we refer to the “Sampling and Analytical Meth-
ods f or Dom estic Waste ” (2009) [67] t o clas sify the physi cal comp onents o f domest ic waste.
These components are divided into the following eight categories based on their physical
and chemical properties: kitchen waste, paper, plastic and rubber, textiles, wood and bam-
boo, glass, metal, and others. The “others” category includes mixtures such as gray clay,
ceramic tiles, and other materials that are more difficult to categorize using the aforemen-
tioned criteria.
A total of 90 data points were collected for the physical composition of this paper,
with 6 data points being national data. The data were collected from various locations,
including Jinan, Qingdao, Beijing, Tianjin, and Shanghai, and then summarized and gen-
eralized based on the regions where these cities are located. The data points are
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
16000
18000
20000
22000
24000
26000
China's MSW Removal Volume (t)
Years
China's MSW Removal Volume
Non-hazardous Treatment Rate
Landfill Rate
Incineration Rate
84
86
88
90
92
94
96
98
100
Non-hazardous Treatment Rate (%)
0
10
20
30
40
50
60
70
80
Landfill Rate (%)
20
30
40
50
60
70
80
Incineration Rate (%)
Figure 1. Production and disposal of MSW in China.
3. Physical and Chemical Properties of MSW in China
The physicochemical properties of MSW are severely constrained by geographic lo‑
cation, weather factors, cultural dierences, etc. For instance, shellsh waste makes up a
larger portion of MSW in seaside cities compared to inland areas. As a result, the physical
composition of MSW exhibits a wide range of categories and noticeable dierences. Ad‑
ditionally, China, with its three major geographic gradients, ve climate types, and long
history, experiences high spatial and temporal variations in the physicochemical composi‑
tion of MSW.
3.1. Physical Properties of MSW in China
The physical properties of MSW can be summarized as follows: (1) the complexity
and high regionality of the physical composition; and (2) the high moisture content [20].
The following section will focus on these two aspects.
3.1.1. Physical Composition of MSW in China
The physical composition of MSW varies greatly from region to region. A wide vari‑
ety of dierent types of domestic waste are combined to form the domestic waste samples
needed for the study. As a result, the physical composition of MSW is classied dier‑
ently in the literature from dierent sources. This classication facilitates the analysis of
the collected data. To analyze the data, the raw data on the physical composition of MSW
obtained from various sources [21–66] need to be normalized and a reasonable classi‑
cation needs to be developed. In this paper, we refer to the “Sampling and Analytical
Methods for Domestic Waste” (2009) [67] to classify the physical components of domestic
waste. These components are divided into the following eight categories based on their
physical and chemical properties: kitchen waste, paper, plastic and rubber, textiles, wood
and bamboo, glass, metal, and others. The “others” category includes mixtures such as
gray clay, ceramic tiles, and other materials that are more dicult to categorize using the
aforementioned criteria.
A total of 90 data points were collected for the physical composition of this paper, with
6 data points being national data. The data were collected from various locations, includ‑
ing Jinan, Qingdao, Beijing, Tianjin, and Shanghai, and then summarized and generalized
based on the regions where these cities are located. The data points are distributed across
seven regions in China: EC (East China), NC (North China), NEC (Northeast China), CC
Energies 2024,17, 491 5 of 21
(Central China), SC (South China), SWC (Southwest China), and NWC (Northwest China).
Due to the variation in the number of domestic and foreign scholars studying domestic
waste in dierent cities, the distribution of data points across the seven regions is uneven.
Therefore, the study of the physical composition of domestic waste in dierent regions
may have corresponding errors. However, the specic distribution of the collected data
points falls within a reasonable range, as shown in Figure 2.
Energies 2024, 17, x FOR PEER REVIEW 5 of 21
distributed across seven regions in China: EC (East China), NC (North China), NEC
(Northeast China), CC (Central China), SC (South China), SWC (Southwest China), and
NWC (Northwest China). Due to the variation in the number of domestic and foreign
scholars studying domestic waste in different cities, the distribution of data points across
the seven regions is uneven. Therefore, the study of the physical composition of domestic
waste in different regions may have corresponding errors. However, the specific distribu-
tion of the collected data points falls within a reasonable range, as shown in Figure 2.
Figure 2. Distribution of data points on the physical composition of domestic waste.
As shown in Figure 2, the data points collected on the physical composition of MSW
are primarily concentrated in the NC and EC regions. These two regions, compared to the
rest of China, exhibit clear seasonality and have a higher economic level. As a result, the
MSW components in these regions are more complex and volatile. Additionally, a larger
number of data points are available, making it easier to determine the representative phys-
ical composition of MSW in these regions. The collected data from different regions were
processed using MATLAB (hps://www.mathworks.com/products/matlab.html, accessed
on 5 December 2023) in combination with box-plot data processing. This involved elimi-
nating outliers and calculating averages. The resulting data represent the modified region
universally, and the specific physical composition of different regions is shown in Table 1
below.
Table 1. Mean values of physical composition of MSW in different regions.
Regions
Component EC NC NEC CC SC SWC NWC Nationwide
Paper, % 8.93 13.73 6.99 6.62 10.15 9.12 7.13 9.76
Plastic, rubber, % 11.75 15.74 10.40 12.09 20.92 13.66 8.95 13.13
Textile, % 2.47 1.83 2.94 2.00 6.40 3.57 2.57 2.65
Wood, % 1.47 3.19 1.36 5.24 4.00 1.77 3.75 2.40
Kitchen waste, % 60.15 58.03 57.73 57.72 51.72 58.72 50.33 58.31
Glass, % 1.80 1.49 4.45 3.07 1.93 1.18 3.28 1.87
Metal, % 0.54 0.50 1.69 0.76 0.63 1.04 1.48 0.70
Other waste, % 10.16 5.97 13.64 16.25 4.24 9.10 22.50 10.44
The table reveals a close connection between the physical composition of MSW in
China and the geographic location and climate of each region. For example, in SC, the
proportion of plastic and rubber in domestic waste is as high as 20.20%, while in NWC,
the proportion of other materials like gray soil, brick, and stone reaches 22.50%. This
demonstrates that the physical composition of domestic waste is significantly influenced
Figure 2. Distribution of data points on the physical composition of domestic waste.
As shown in Figure 2, the data points collected on the physical composition of MSW
are primarily concentrated in the NC and EC regions. These two regions, compared to
the rest of China, exhibit clear seasonality and have a higher economic level. As a result,
the MSW components in these regions are more complex and volatile. Additionally, a
larger number of data points are available, making it easier to determine the representative
physical composition of MSW in these regions. The collected data from dierent regions
were processed using MATLAB (https://www.mathworks.com/products/matlab.html, ac‑
cessed on 5 December 2023) in combination with box‑plot data processing. This involved
eliminating outliers and calculating averages. The resulting data represent the modied
region universally, and the specic physical composition of dierent regions is shown in
Table 1below.
Table 1. Mean values of physical composition of MSW in dierent regions.
Component
Regions EC NC NEC CC SC SWC NWC Nationwide
Paper, % 8.93 13.73 6.99 6.62 10.15 9.12 7.13 9.76
Plastic, rubber, % 11.75 15.74 10.40 12.09 20.92 13.66 8.95 13.13
Textile, % 2.47 1.83 2.94 2.00 6.40 3.57 2.57 2.65
Wood, % 1.47 3.19 1.36 5.24 4.00 1.77 3.75 2.40
Kitchen waste, % 60.15 58.03 57.73 57.72 51.72 58.72 50.33 58.31
Glass, % 1.80 1.49 4.45 3.07 1.93 1.18 3.28 1.87
Metal, % 0.54 0.50 1.69 0.76 0.63 1.04 1.48 0.70
Other waste, % 10.16 5.97 13.64 16.25 4.24 9.10 22.50 10.44
The table reveals a close connection between the physical composition of MSW in
China and the geographic location and climate of each region. For example, in SC, the pro‑
portion of plastic and rubber in domestic waste is as high as 20.20%, while in NWC, the
proportion of other materials like gray soil, brick, and stone reaches 22.50%. This demon‑
strates that the physical composition of domestic waste is signicantly inuenced by the
region’s geography and climate. The data presented in the table align with China’s na‑
tional conditions and regional characteristics, conrming the accuracy of the data collec‑
tion. With the exception of the physical composition of domestic waste in SC and NWC,
which dier more from the summarized data, the remaining data are relatively consistent.
Energies 2024,17, 491 6 of 21
Therefore, it can be concluded that this study provides a certain level of universality in
summarizing the physical compositions of domestic waste in dierent regions and across
the entire country.
The above section provides a summary of the physical compositions of MSW in dif‑
ferent regions and across the entire country of China. The data presented in this paper
appears to be relatively accurate for the physical composition of waste in each region.
However, it should be noted that the data for the physical composition of MSW across
the country may contain some errors due to variations in climate, environment, and other
regional characteristics.
To address this, the paper utilizes box plots to analyze the collected data points. The
aim is to identify the most densely distributed intervals of data points and summarize the
physical‑composition intervals of MSW in China. This information can assist in the subse‑
quent treatment of MSW throughout its life cycle in China and provide valuable insights
for researchers studying MSW in the country.
A box‑and‑line diagram, depicted in Figure 3below, was constructed using the mass
percentage (wet basis) of dierent types of MSW reported in the related literature.
Energies 2024, 17, x FOR PEER REVIEW 6 of 21
by the region’s geography and climate. The data presented in the table align with China’s
national conditions and regional characteristics, confirming the accuracy of the data col-
lection. With the exception of the physical composition of domestic waste in SC and NWC,
which differ more from the summarized data, the remaining data are relatively consistent.
Therefore, it can be concluded that this study provides a certain level of universality in
summarizing the physical compositions of domestic waste in different regions and across
the entire country.
The above section provides a summary of the physical compositions of MSW in dif-
ferent regions and across the entire country of China. The data presented in this paper
appears to be relatively accurate for the physical composition of waste in each region.
However, it should be noted that the data for the physical composition of MSW across the
country may contain some errors due to variations in climate, environment, and other
regional characteristics.
To address this, the paper utilizes box plots to analyze the collected data points. The
aim is to identify the most densely distributed intervals of data points and summarize the
physical-composition intervals of MSW in China. This information can assist in the sub-
sequent treatment of MSW throughout its life cycle in China and provide valuable insights
for researchers studying MSW in the country.
A box-and-line diagram, depicted in Figure 3 below, was constructed using the mass
percentage (wet basis) of different types of MSW reported in the related literature.
Figure 3. Interval diagram of the physical components of domestic waste.
The mass percentage of combustible components (paper, plastic, rubber, textiles,
wood, and kitchen waste) in domestic waste is shown in Figure 3. By combining the scaer
plot with the box line graph, we can visualize the distribution of the data. Comparing the
mass percentages, it is evident that the mass share of kitchen waste is significantly higher
than that of the other components. Most of the mass share of kitchen waste falls within
the range of 53.04% to 62.89%. Similarly, the percentage of paper ranges from 7.72% to
12.86%, the mass percentage of plastic and rubber ranges from 9.92% to 16.09%, the mass
19.11
1.50
22.00
3.20
7.30
0
7.40
0
53.04
62.89
58.87
76.00
38.60
5.43
0
2.10
0
37.81
0
Paper Plastic rubber Textile Wood
0
10
20
30
40
2.10
2.55
13.11
10.05
1.00
3.63
1.58
3.96
9.92
16.09
7.72
12.86
Mass percentage（%）
25%~75% Fuel component
Kitchen waste
30
40
50
60
70
80
Glass Metal
-2
0
2
4
6
8
10
12
14
0.68
1.94
0.35
1.10
1.10
2.94
Mass percentage（%）
25%~75% Non−combustible components
1.5IQR
Median
Mean
Other waste
0
10
20
30
40
3.00
6.87
17.22
Physical components of MSW
Figure 3. Interval diagram of the physical components of domestic waste.
The mass percentage of combustible components (paper, plastic, rubber, textiles,
wood, and kitchen waste) in domestic waste is shown in Figure 3. By combining the scaer
plot with the box line graph, we can visualize the distribution of the data. Comparing the
mass percentages, it is evident that the mass share of kitchen waste is signicantly higher
than that of the other components. Most of the mass share of kitchen waste falls within
the range of 53.04% to 62.89%. Similarly, the percentage of paper ranges from 7.72% to
12.86%, the mass percentage of plastic and rubber ranges from 9.92% to 16.09%, the mass
percentage of textiles ranges from 1.58% to 3.96%, and the mass percentage of wood waste
ranges from 1.00% to 3.60%.
Daily life inevitably generates a signicant amount of noncombustible material in do‑
mestic waste. Although this type of waste accounts for a relatively small percentage of the
total domestic waste, it is an important component, with physical and chemical properties
Energies 2024,17, 491 7 of 21
that cannot be ignored. By collecting data, we can determine the mass‑percentage range
of this type of noncombustible household waste, as shown in Figure 3.
From the gure, it can be observed that the mass percentage of discarded glass prod‑
ucts in domestic waste ranges from 1.10% to 2.94%. The proportion of metal waste ranges
from 0.35% to 1.10%, and the mass percentage of other waste ranges from 3.00% to 17.22%.
3.1.2. Moisture Content of MSW
Moisture content is an important physical characteristic of MSW. The level of moisture
content in domestic waste signicantly aects its physical and chemical properties, as well
as its caloric value. Therefore, studying the water content of domestic waste is necessary.
This paper focuses on collecting the physical and chemical composition of domestic
waste, specically the moisture content. The collected moisture data of domestic waste
from dierent regions are summarized and unied to provide insights into the water con‑
tent of domestic waste.
Additionally, the water content of kitchen waste is explored. However, due to the
limited amount of data and its consistently high moisture content across the country, a
multiregional comparison of the water content of kitchen waste is not conducted.
By combining data on the moisture content of seven dierent regions in China and
applying the aforementioned data‑processing methods, outliers can be eliminated, and the
average moisture content of domestic waste in each region can be calculated. It is impor‑
tant to note that variations in the object of study among dierent scholars may introduce
a certain degree of uncertainty in the statistics of water content. To address this, subse‑
quent studies aim to use national data on domestic waste’s moisture content to reduce
uncertainty by utilizing a larger and more uniform dataset. Therefore, the following study
will use national data as the basis and strive to minimize uncertainty. Table 2presents the
moisture content of MSW in dierent regions.
Table 2. Table of average values of the moisture content of MSW in dierent regions.
Regions EC NC NEC CC SC SWC NWC Nationwide
Moisture, % 53.30 52.68 55.07 45.77 52.06 53.43 40.58 51.62
As shown in Table 2, the moisture content in the southern and eastern coastal regions
of China is higher compared to the central and northwestern regions. This can be aributed
to the geographic location and climate. The northwest region of China falls within the arid
and semiarid zone, as well as the alpine zone. These regions are also categorized under
the temperate continental climate and temperate monsoon climate, which have relatively
low water content. On the other hand, EC, NC, and CC exhibit moisture content that is
relatively close to each other. The moisture content of the MSW is 51.62%, which is also
similar to the moisture content of the research location. Therefore, it can be concluded that
the MSW is suitable for the study.
Many scholars have studied the moisture content of domestic waste. This paper col‑
lects data on the moisture content of the domestic waste and kitchen waste separately. The
data is then combined to obtain their respective mass‑percentage range intervals, as shown
in Figure 4.
Energies 2024,17, 491 8 of 21
Energies 2024, 17, x FOR PEER REVIEW 8 of 21
Figure 4. MSW and Kitchen-Waste Moisture Content Map.
From the figure, it is evident that the water content of domestic waste ranges from
47.00% to 56.61%, while the water content of kitchen waste is relatively high, ranging from
63.90% to 69.85%. Combining this with the previous study, it can be concluded that the
water percentage of kitchen waste in domestic waste falls within the range of 26.61% to
44.63%. In other words, the water contained in kitchen waste accounts for 41.64% to
94.97% of the total moisture in domestic waste.
3.2. Chemical Characterization of MSW in China
Domestic waste is used as fuel in incineration boilers, and its constituent elements
need to be analyzed to determine its calorific value and incineration flue-gas products.
This paper collects data on the elemental composition of domestic waste by reviewing the
literature [36,39,49,51,53,68–79]. Box line plots are then used to summarize the mass-per-
centage intervals of the major elemental components.
The data on the physical composition of domestic waste indicates that kitchen waste
accounts for a high percentage, ranging from 53.04% to 62.89%. Therefore, it is also neces-
sary to summarize the elemental components of kitchen waste. This paper will explore
the overall elemental composition of domestic waste and the elemental composition of
kitchen waste separately. Additionally, due to variations in sampling, preparation, dry-
ing, and processing methods used by different scholars, the collected data needs to be
standardized.
3.2.1. Elemental Composition of MSW
The calorific value of domestic waste has a significant impact on its characteristics.
Since this paper aims to use domestic waste as fuel, it is necessary to analyze its chemical
composition. Therefore, this study will investigate the physical composition of domestic
waste using the aforementioned method, focusing on the elemental composition. First, the
chemical composition of domestic waste in different regions will be explored, followed by
an analysis of national characteristics and a comparative study. Additionally, this paper
includes the classification of domestic waste-treatment strategies, such as kitchen-waste
fermentation and incineration power generation for the rest of the waste (mainly dry
waste). Therefore, it is also important to study the chemical composition of kitchen waste.
However, the research on the chemical composition of kitchen waste is limited, and the
64.10
33.16
76.00
58.90
Municipal solid waste Kitchen waste
10
20
30
40
50
60
70
80
90
100
65.65
63.90
69.85
47.00
52.50
56.61
Types of waste
Moisture content
（
%
）
25%~75%
1.5IQR
Median
Mean
Figure 4. MSW and Kitchen‑Waste Moisture Content Map.
From the gure, it is evident that the water content of domestic waste ranges from
47.00% to 56.61%, while the water content of kitchen waste is relatively high, ranging from
63.90% to 69.85%. Combining this with the previous study, it can be concluded that the
water percentage of kitchen waste in domestic waste falls within the range of 26.61% to
44.63%. In other words, the water contained in kitchen waste accounts for 41.64% to 94.97%
of the total moisture in domestic waste.
3.2. Chemical Characterization of MSW in China
Domestic waste is used as fuel in incineration boilers, and its constituent elements
need to be analyzed to determine its caloric value and incineration ue‑gas products. This
paper collects data on the elemental composition of domestic waste by reviewing the litera‑
ture [36,39,49,51,53,68–79]. Box line plots are then used to summarize the mass‑percentage
intervals of the major elemental components.
The data on the physical composition of domestic waste indicates that kitchen waste
accounts for a high percentage, ranging from 53.04% to 62.89%. Therefore, it is also neces‑
sary to summarize the elemental components of kitchen waste. This paper will explore the
overall elemental composition of domestic waste and the elemental composition of kitchen
waste separately. Additionally, due to variations in sampling, preparation, drying, and
processing methods used by dierent scholars, the collected data needs to
be standardized.
3.2.1. Elemental Composition of MSW
The caloric value of domestic waste has a signicant impact on its characteristics.
Since this paper aims to use domestic waste as fuel, it is necessary to analyze its chemical
composition. Therefore, this study will investigate the physical composition of domestic
waste using the aforementioned method, focusing on the elemental composition. First, the
chemical composition of domestic waste in dierent regions will be explored, followed by
an analysis of national characteristics and a comparative study. Additionally, this paper in‑
cludes the classication of domestic waste‑treatment strategies, such as kitchen‑waste fer‑
mentation and incineration power generation for the rest of the waste (mainly dry waste).
Therefore, it is also important to study the chemical composition of kitchen waste. How‑
ever, the research on the chemical composition of kitchen waste is limited, and the variation
in chemical compositions among dierent regions is minimal. As a result, a comparative
study of dierent regions is not conducted.
Energies 2024,17, 491 9 of 21
Chemical Composition of Domestic Waste in Dierent Regions
This paper collects a total of 69 data points on the chemical composition of domestic
waste. These include two nationwide data points. Throughout the collection process, there
has been more research conducted by scholars, both domestic and international, on the
chemical composition of domestic waste in the southern region of China and the eastern
seaboard region of China. As a result, the collected data are concentrated in these regions.
The specic distribution of the data points is illustrated in Figure 5.
Energies 2024, 17, x FOR PEER REVIEW 9 of 21
variation in chemical compositions among different regions is minimal. As a result, a com-
parative study of different regions is not conducted.
Chemical Composition of Domestic Waste in Different Regions
This paper collects a total of 69 data points on the chemical composition of domestic
waste. These include two nationwide data points. Throughout the collection process, there
has been more research conducted by scholars, both domestic and international, on the
chemical composition of domestic waste in the southern region of China and the eastern
seaboard region of China. As a result, the collected data are concentrated in these regions.
The specific distribution of the data points is illustrated in Figure 5.
Figure 5. Distribution of chemical-composition data points of MSW.
As shown in the figure above, the collected data points for chemical composition are
distributed across seven regions in China. There are more data points scaered in the
southern and eastern coastal provinces. By processing the collected data and eliminating
anomalies, the mean values can be calculated. The mean-value table, shown in Table 3
(moisture refers to the moisture data contained in the collected chemical-composition data
points), can be obtained.
Table 3. Mean values of the chemical composition of MSW in different regions.
Regions
Elements EC NC NEC CC SC SWC NWC Nationwide
C, % 15.99 14.89 16.44 15.06 17.47 15.26 9.63 15.80
H, % 2.24 1.64 2.30 1.99 2.35 2.01 1.47 2.13
O
,
% 8.70 13.07 10.17 9.42 11.00 12.06 6.02 10.70
N, % 0.42 0.40 0.58 0.43 0.43 0.63 0.22 0.46
S, % 0.09 0.12 0.11 2.30 0.09 0.13 0.09 0.09
Cl, % 0.69 0.46 0.26 - 0.76 0.47 - 0.55
Moisture, % 51.87 46.04 49.53 47.32 49.78 52.85 24.95 51.12
Ash
,
% 24.90 22.82 13.86 28.27 20.39 10.91 59.52 23.000
From the table, it can be observed that the main chemical elements in domestic waste
are C, H, O, N, S, and Cl. The elements C and O account for the majority of the mass
percentage, while the remaining four elements account for a relatively small percentage.
The chemical composition of national domestic waste, as shown in the table, is consistent
with that of domestic waste in the EC region, except for a slight difference in the O ele-
ment. The elemental composition, moisture, and ash are relatively consistent.
Figure 5. Distribution of chemical‑composition data points of MSW.
As shown in the gure above, the collected data points for chemical composition are
distributed across seven regions in China. There are more data points scaered in the
southern and eastern coastal provinces. By processing the collected data and eliminating
anomalies, the mean values can be calculated. The mean‑value table, shown in Table 3
(moisture refers to the moisture data contained in the collected chemical‑composition data
points), can be obtained.
Table 3. Mean values of the chemical composition of MSW in dierent regions.
Elements
Regions EC NC NEC CC SC SWC NWC Nationwide
C, % 15.99 14.89 16.44 15.06 17.47 15.26 9.63 15.80
H, % 2.24 1.64 2.30 1.99 2.35 2.01 1.47 2.13
O, % 8.70 13.07 10.17 9.42 11.00 12.06 6.02 10.70
N, % 0.42 0.40 0.58 0.43 0.43 0.63 0.22 0.46
S, % 0.09 0.12 0.11 2.30 0.09 0.13 0.09 0.09
Cl, % 0.69 0.46 0.26 ‑ 0.76 0.47 ‑ 0.55
Moisture, % 51.87 46.04 49.53 47.32 49.78 52.85 24.95 51.12
Ash, % 24.90 22.82 13.86 28.27 20.39 10.91 59.52 23.000
From the table, it can be observed that the main chemical elements in domestic waste
are C, H, O, N, S, and Cl. The elements C and O account for the majority of the mass
percentage, while the remaining four elements account for a relatively small percentage.
The chemical composition of national domestic waste, as shown in the table, is consistent
with that of domestic waste in the EC region, except for a slight dierence in the O element.
The elemental composition, moisture, and ash are relatively consistent.
In conclusion, the chemical composition of national domestic waste is applicable to
the region where the research object of this paper is located and exhibits a certain degree
of universality. The collected data have a certain degree of credibility.
Energies 2024,17, 491 10 of 21
Ranges of Chemical Composition of MSW in China
Same as above, for the research and analysis of the physical composition of MSW in
China, this section also needs to investigate the chemical composition of domestic waste in
the country. All the collected chemical‑composition data are summarized and presented in
a box‑and‑line diagram, as depicted in Figure 6. The gure illustrates the specic intervals
of mass percentages for several dierent chemical elements present in domestic waste. It
is worth noting that the elemental analysis of domestic waste in the literature is primarily
based on a wet basis; hence, the data shown in the gure represent the elemental mass
percentages of MSW (wet basis).
Energies 2024, 17, x FOR PEER REVIEW 10 of 21
In conclusion, the chemical composition of national domestic waste is applicable to
the region where the research object of this paper is located and exhibits a certain degree
of universality. The collected data have a certain degree of credibility.
Ranges of Chemical Composition of MSW in China
Same as above, for the research and analysis of the physical composition of MSW in
China, this section also needs to investigate the chemical composition of domestic waste
in the country. All the collected chemical-composition data are summarized and pre-
sented in a box-and-line diagram, as depicted in Figure 6. The figure illustrates the specific
intervals of mass percentages for several different chemical elements present in domestic
waste. It is worth noting that the elemental analysis of domestic waste in the literature is
primarily based on a wet basis; hence, the data shown in the figure represent the elemental
mass percentages of MSW (wet basis).
Figure 6. Chemical-element composition of MSW.
3.2.2. Elemental Composition of Kitchen Waste
Kitchen waste refers to the leftover food from various sources, including animal and
vegetable products and fruits. It contains a high amount of moisture and organic maer.
Through treatment and processing, it can be utilized for composting, biogas production,
and biofuel preparation, and transformed into a valuable resource. Given its significant
presence in domestic waste, it is essential to analyze its physicochemical properties.
To analyze the properties of kitchen waste, it is necessary to refer to the existing lit-
erature [80–102]. By collecting data from these sources, the mass percentage of six differ-
ent elements in kitchen waste can be obtained, as shown in Figure 7. Since kitchen waste
has a high moisture content, researchers often prioritize drying the samples before analy-
sis. Therefore, the collected data represents the mass percentage of the elemental compo-
nents of dried kitchen waste.
The figure shows the fluctuation range of elemental composition (dry basis) in
kitchen waste in China. The C element ranges from 46.86% to 43.42%, the H element
ranges from 5.04% to 7.09%, the N element ranges from 2.15% to 3.69%, the O element
ranges from 32.98% to 41.15%, the S element ranges from 0.30% to 0.75%, and the Cl ele-
ment ranges from 0.01% to 0.42%.
3.60
1.05
0.80
0.16 0.22
0
1.16
0.14
12.75
18.37
16.23
25.41
9.30
7.95
13.10
10.43
18.53
5.12
HNSCl
0
2
4
0
0.15
0.09
0.07
0.39
0.45
0.58
1.75
2.09
2.50
Chemical components of MSW
Mass percentage
（
%
）
25%~75% MSW(Wet basis)
1.5IQR
Median
Mean
CO
0
10
20
30
0.69
0.56
0.27
Figure 6. Chemical‑element composition of MSW.
3.2.2. Elemental Composition of Kitchen Waste
Kitchen waste refers to the leftover food from various sources, including animal and
vegetable products and fruits. It contains a high amount of moisture and organic maer.
Through treatment and processing, it can be utilized for composting, biogas production,
and biofuel preparation, and transformed into a valuable resource. Given its signicant
presence in domestic waste, it is essential to analyze its physicochemical properties.
To analyze the properties of kitchen waste, it is necessary to refer to the existing liter‑
ature [80–102]. By collecting data from these sources, the mass percentage of six dierent
elements in kitchen waste can be obtained, as shown in Figure 7. Since kitchen waste has
a high moisture content, researchers often prioritize drying the samples before analysis.
Therefore, the collected data represents the mass percentage of the elemental components
of dried kitchen waste.
The gure shows the uctuation range of elemental composition (dry basis) in kitchen
waste in China. The C element ranges from 46.86% to 43.42%, the H element ranges from
5.04% to 7.09%, the N element ranges from 2.15% to 3.69%, the O element ranges from
32.98% to 41.15%, the S element ranges from 0.30% to 0.75%, and the Cl element ranges
from 0.01% to 0.42%.
Energies 2024,17, 491 11 of 21
Energies 2024, 17, x FOR PEER REVIEW 11 of 21
Figure 7. Elemental composition of kitchen waste (dry basis).
3.2.3. Elemental Composition of Sorted Waste
This paper aims to compare and analyze the energy-recovery potential and power-
generation potential of MSW from different regions, both before and after classification.
To achieve this, it is necessary to study the chemical compositions of the classified waste
from different regions. However, due to the limited data available for kitchen waste and
for the sake of simplicity in the calculations, this paper assumes that the elemental com-
position of kitchen waste is the same across all regions. Additionally, it is assumed that
the remaining domestic waste, excluding kitchen waste, has the same moisture content.
The elemental composition of kitchen waste is provided in Table 4.
Table 4. Mean chemical composition of kitchen waste.
Elements C H N O S Cl Moisture (M)
Kitchen-waste composition
(dry basis), % 43.23 6.26 2.84 36.53 0.37 0.37 66.743
The chemical composition of domestic waste and kitchen waste can be deduced after
classification, using the known chemical composition. This can be done using Equations
(1)–(4). 𝑒=[𝑒−𝑒 ×(1−𝑀)×𝑎]×(1−𝑎) (1)
𝑒=𝑒′×(1− 𝑏
1−𝑎) (2)
𝑀=(𝑀−𝑎×𝑀)/(1−𝑎) (3)
𝐴
=1−𝑀−𝑒 (4)
eo: Elemental composition of the remaining waste after removal of kitchen waste, %;
eo’: Corrected elemental composition of the remaining waste after removal of f kitchen
waste, %;
ew: Elemental composition of domestic waste wet base, %;
ekd: Elemental composition of dry kitchen waste base, %;
8.15
3.17
5.30
1.19 0.82
00.21
0
52.62
33.67
48.5
2
26.5
0
HNSCl
0
4
8
12
0.73
0.44
0.30
3.69
2.65
2.15
7.09
6.88
5.04
Chemical composition of Kitchen waste
Mass percentage(%)
25%~75% Kitchen waste(Dry basis)
1.5IQR
Median
Mean
CO
10
20
30
40
50
60
39.73
43.42
46.86
32.98
41.15
36.42
Figure 7. Elemental composition of kitchen waste (dry basis).
3.2.3. Elemental Composition of Sorted Waste
This paper aims to compare and analyze the energy‑recovery potential and power‑
generation potential of MSW from dierent regions, both before and after classication. To
achieve this, it is necessary to study the chemical compositions of the classied waste from
dierent regions. However, due to the limited data available for kitchen waste and for the
sake of simplicity in the calculations, this paper assumes that the elemental composition of
kitchen waste is the same across all regions. Additionally, it is assumed that the remaining
domestic waste, excluding kitchen waste, has the same moisture content. The elemental
composition of kitchen waste is provided in Table 4.
Table 4. Mean chemical composition of kitchen waste.
Elements C H N O S Cl Moisture (M)
Kitchen‑waste composition
(dry basis), % 43.23 6.26 2.84 36.53 0.37 0.37 66.743
The chemical composition of domestic waste and kitchen waste can be deduced after
classication, using the known chemical composition. This can be done using
Equations (1)–(4).
eo=[ew−ekd ×(1−Mk)×a]×(1−a)(1)
ec=eo×1−b
1−a(2)
Mc=(Mw−a×Mk)/(1−a)(3)
Ac=1−Mc−ec(4)
eo: Elemental composition of the remaining waste after removal of kitchen waste, %;
eo’: Corrected elemental composition of the remaining waste after removal of f kitchen
waste, %;
ew: Elemental composition of domestic waste wet base, %;
ekd: Elemental composition of dry kitchen waste base, %;
Mk: Moisture in kitchen waste, %;
a: Percentage of kitchen waste, %;
Energies 2024,17, 491 12 of 21
ec: Elemental composition of sorted waste (excluding kitchen waste, metals, glass and ash
bricks, etc.), %;
b: Percentage of noncombustible waste such as glass, metal, and ash bricks and blocks, %;
Mc: Sorted waste moisture, %;
Ac: Ash content of sorted waste, %.
The data in the Table 5are calculated by using Equations (1)–(4) along with the nd‑
ings from the previous study. These calculations determine the chemical composition of
the remaining waste after the classication of MSW in various regions of China and the en‑
tire country. The table oers data support for the calculation of the energy‑recovery rate
and power‑generation potential of MSW classied in dierent regions, as discussed later
in the paper.
Table 5. Mean chemical composition of the remaining waste after sorting in dierent regions.
Regions
Elementals CHNO S Cl Moisture (M)
EC 26.87 3.61 ‑ 5.10 0.06 2.46 29.43
NC 19.24 1.28 ‑ 17.71 0.15 1.28 17.41
NEC 36.19 4.88 0.14 14.01 0.15 1.06 26.02
CC 30.45 3.55 ‑ 10.86 10.04 0.00 20.81
SC 24.20 3.07 ‑ 11.37 0.06 1.79 31.60
SWC 22.77 2.63 0.25 16.45 0.21 1.51 33.08
NWC 10.68 1.88 ‑ ‑ 0.13 ‑ ‑
Nationwide 25.86 3.18 ‑ 12.61 0.07 1.83 29.26
4. Calculations of MSW Caloric Value, ERP, and PGP
When using MSW as fuel for incineration power generation, it is necessary to study
its caloric value. Currently, the calculation method for the caloric value of domestic
waste mainly relies on two approaches. The rst approach involves calculating the caloric
value based on the physical composition of the waste, while the second approach involves
calculating the caloric value based on the elemental composition. By combining these two
methods with the aforementioned research on caloric value calculation, a comprehensive
understanding can be gained.
4.1. Calculation of Physical‑Composition Caloric Value
Since the physical components of MSW are greatly inuenced by climate, geography,
dietary habits, and other factors, the physical composition of domestic waste uctuates
signicantly. Therefore, Li et al. [46] conducted research on MSW in certain Chinese cities
to propose a formula for calculating the caloric value of MSW applicable to Chinese cities.
This formula, shown in Equation (5), considers the physical grouping of MSW based on
dry basis components. However, the above‑mentioned studies were conducted based on
wet‑based components, so the collected data needs to be processed. It is assumed that the
moisture content of all the waste components is the same.
Q=41.1 ×Rud+22.9 ×Kd+20.7 ×Pad−4.5 ×Wm(5)
Q: Caloric value, kJ/kg;
Rud: Components of plastic–rubber MSW on a dry basis, %;
K: Percentage of kitchen waste on a dry basis, %;
Pad: Components of paper household waste on a dry basis, %;
Wm: Moisture content of MSW, %;
Using Equation (5) in conjunction with the physical components of MSW summa‑
rized above, the ratio of dierent physical components within the interval is divided into
10 intervals. By performing calculations in MATLAB, we can obtain 1,000,000 sets of data
that allow us to determine the minimum and maximum values of the caloric value of
domestic waste’s physical components, which are 3148.171 kJ/kg and 5208.922 kJ/kg, re‑
Energies 2024,17, 491 13 of 21
spectively [103]. These results fall within a reasonable range, conrming the validity of
the interval summarization of the physical components.
4.2. Calculation of Caloric Value of Chemical‑Element Composition
Currently, there are numerous studies that utilize elemental analysis to calculate the
caloric value of MSW. The most commonly used formulas for caloric valuecalculation in
these studies are the Dulong formula, Steuer formula, and Scheurer–Kestner formula [77],
as shown in Equations (6)–(8). By combining the elemental composition intervals men‑
tioned above and applying these three formulas, an approximate interval for the caloric
value of MSW can be obtained.
QD=[81 ×C+342.5 ×(H−O/8)+225 ×S−6×(9×H+Wm)] ×4.19 (6)
QS=81 ×C−3
8×O+57 ×3
8×O+345 ×H−O
16
+25 ×S−6×(Wm+9×H)×4.19 (7)
QSK =[81 ×(C−3/4 ×O)+57 ×3/4 ×O+345.2 ×H+22.5 ×S
−6×(Wm+9×H)] ×4.19 (8)
QD,QS, and QSK are the caloric values, calculated by the above three equations, respec‑
tively, kJ/kg;
C,H,O, and Sare the percentage of elemental mass in MSW, %.
Figure 6above presents the data on the chemical composition of domestic waste.
The three equations mentioned above can be combined to calculate the caloric value
intervals of domestic waste as follows: (2672.5~6587.7) kJ/kg, (3363.3~7093.1) kJ/kg, and
(4052.9~7511.7) kJ/kg. All of these intervals fall within reasonable ranges, conrming the
reasonableness of the elemental composition interval summary. Table 6provides a sum‑
mary of the specic distribution intervals of the chemical elemental composition of do‑
mestic waste and its caloric value. The caloric value of domestic MSW in China is rela‑
tively low compared to the EU and other countries, with a range of 7–15 MJ/kg [104]. This
suggests that there is a need for improvement in China’s technology for the classication,
collection, and ecient utilization of MSW.
Table 6. Elemental composition and caloric value range of domestic waste.
Elemental range, % C H O N
12.75~18.37 1.75~2.50 7.95~13.10 0.39~0.59
Elemental range, % S Cl Ash Moisture
0.07~0.15 0.27~0.69 16.01~30.00 46.96~56.65
Caloric value, kJ/kg 2674.50~7511.70
4.3. Analysis of ERP and PGP in Dierent Regions
The treatment of domestic waste is the most crucial aspect of LCA for MSW. Imple‑
menting ecient treatment initiatives can greatly enhance the resource utilization of MSW.
Therefore, this paper uses ERP and PGP as the evaluation criteria. The study above fo‑
cuses on the physicochemical composition of MSW in various regions of China. It identi‑
es the uctuation intervals and mean values of the MSW’s physicochemical composition
in dierent regions. The following section will investigate the energy recovery and uti‑
lization rate, as well as the power‑generation potential of MSW in these regions based on
the aforementioned study. Additionally, it will compare the characteristics of changes
in the energy‑recovery and utilization rate and power‑generation potential of MSW be‑
fore and after classication in dierent regions. This analysis will provide valuable in‑
sights for implementing eective treatment measures for MSW in dierent regions. The
empirical formulas in Sections 4.1 and 4.2 are used to verify the accuracy of the collected
data. Additionally, a study by [77] demonstrates that the calculation of the caloric
Energies 2024,17, 491 14 of 21
value using Dulong’s elemental formula is relatively more accurate. Therefore, in the
following section, we will utilize Dulong’s formula to calculate the caloric value of the
MSW. This calculation will be crucial for determining the energy‑recovery rate and power‑
generation potential.
4.3.1. Analysis of the ERP and PGP of Unclassied Waste
Currently, the classication and transportation of MSW in China (except for some big
cities) is still in the initial stage [105]. As a result, all MSW in China is currently transported
to waste‑incineration power plants and then reclassied to remove noncombustible mate‑
rials. In this paper, the total amount of MSW removed from dierent regions of China in
2021 is used as the basis for calculation. The chemical composition of MSW in dierent
regions is considered to calculate its caloric value, which is then used to determine the
energy‑recovery rate.
The ERP and PGP of incineration are calculated as follows:
ERPI=QD×M×107/(24 ×365)(9)
PGPI=0.25 ×41.67 ×QD×M×105(10)
ERP =ERPI(11)
PGP =PGPI(12)
ERPI: MSW incineration energy‑recovery potential (kWh);
QD: The calculation of the caloric value of MSW is determined in accordance with
Section 3using Equation (6) (kJ/kg);
M: Annual clearing volume of MSW (kt); calculation of the mass in the required unit hours
therefore requires division by the factors 365 and 24;
PGPI: Net electricity‑generation potential from incineration (kW); the conversion eciency
is 0.25 [106].
Using the formula mentioned above, along with the chemical‑element composition
summarized in the previous section on China’s MSW, the energy‑recovery potential of
China’s MSW in 2020 can be calculated to be 1273 billion kWh. This value has an error of
4% compared to the statistical data of 1326 billion kWh in 2020 from the China Business
Intelligence website “https://www.askci.com/ (accessed on 10 December 2023)” [107]. The
accuracy of the MSW component data mentioned above has been veried side by side.
Based on the research conducted on the chemical compositions of MSW in dierent regions,
combined with the waste removal volume of those regions in 2021, the values of ERP and
PGP were calculated for various regions in China. The results, shown in Table 7, indicate
that the SC region has the highest potential for energy recovery from MSW, while the NWC
region has the lowest potential. The coastal region shows relatively high values of both
ERP and GRP. This conclusion aligns with the regional dierences and national conditions
of China.
Table 7. ERP and GRP calculation form for unsegregated garbage.
Regions
Parameters MSW Removal in Dierent Regions ERP PGP
kt GWh GW
EC 43,898 264,897.70 2759.57
NC 45,310 183,349.06 1910.04
NEC 20,208 122,436.12 1275.48
CC 43,344 240,400.32 2504.37
SC 50,427 320,017.95 3333.79
SWC 29,476 139,028.14 1448.33
NWC 16,029 61,324.23 638.85
Nationwide 248,692 1,272,724.55 13,258.61
Energies 2024,17, 491 15 of 21
4.3.2. Analysis of Energy‑Recovery Rate and Power‑Generation Potential of
Waste Separation
China’s abundant population leads to the signicant issue of kitchen waste as part of
MSW. It is characterized by high moisture content and low caloric value, which would
result in a signicant waste of energy if directly incinerated. However, these characteristics
are advantageous for fermentation and biogas production. Therefore, the classication
strategy proposed in this paper involves fermenting kitchen waste to produce biogas, while
incinerating the remaining waste. The aim is to compare the energy‑recovery potential
and net power‑generation potential of MSW before and after classication. The energy‑
recovery potential and net power‑generation potential of MSW landll fermentation and
the total ERP and PGP after categorization are calculated as follows:
ERPL=NCV ×M×η×Pf×104/0.042 (13)
PGPL=0.3 ×ERPL/24 (14)
ERP =ERPI×(1−Pf+ERPL(15)
PGP =PGPI×(1−Pf+PGPL(16)
ERPL: Energy‑recovery potential of MSW fermentation for biogas production (kWh) [106];
NCV: Net caloric value, MSW biomass process normally takes the value
0.218 (kW·m−3) [108];
η: The gas production rate for 1 t of kitchen waste is usually taken as 115.73 (m3) [109];
Pf: Percentage of kitchen waste in dierent regions (%);
PGPL: Net power‑generation potential from fermentation (kW); the conversion eciency
is 0.3 [106].
The calculation of ERP and PGP after categorization utilizes the chemical element
composition data of various regions from the previous study, along with the percentage of
kitchen waste and MSW removal volume data from dierent regions in 2021. These input
data are used to calculate the ERP and PGP data after categorization for dierent regions in
China, as presented in Table 8. Furthermore, a comparison and an analysis are conducted
with the unclassied regions.
Table 8. ERP and GRP calculation tables for sorted waste.
Regions
Parameters MSW Removal in Dierent Regions ERP PGP
kt GWh GW
EC 43,898 384,077.53 4331.42
NC 45,310 239,209.81 2820.86
NEC 20,208 211,079.75 2344.86
CC 43,344 410,664.17 4591.03
SC 50,427 396,976.57 4461.79
SWC 29,476 196,264.97 2261.09
NWC 16,029 96,669.42 1107.97
Nationwide 248,692 1,841,267.39 20,896.42
As shown in Tables 7and 8, the ERP and PGP are relatively high in the CC, SC, and
EC regions of China, which is in positive feedback with factors such as economic level,
geographic location, and environment. The ERP of sorted treatment for MSW is generally
higher than that of unsegregated treatment. However, the net PGP is lower in regions with
a higher percentage of kitchen waste. This can be aributed to the ineciency of fermen‑
tation for biogas production and the immaturity of biogas power‑generation technology
in China.
A comparison of Tables 7and 8reveals a signicant increase in the ERP and PGP of
MSW following its classication. Nationally, the ERP and PGP of MSW after classica‑
tion increased by 44.67% and 57.61%, respectively, compared to unclassied waste. The
increase in ERP and PGP following the classication of MSW in NEC, CC, and NWC is
Energies 2024,17, 491 16 of 21
substantial. Upon analyzing the composition of MSW in these three regions, it is evident
that they have a higher proportion of other waste types, leading to a signicant increase
in the caloric value of MSW after sorting. Additionally, the water content of MSW in CC
and NWC is lower, while the water content of kitchen waste is higher, making fermenta‑
tion easier after sorting. As a result, the increase in ERP and PGP is more pronounced in
these regions compared to others.
The PGP calculated in this study represents the net PGP. Equations (10) and (14) were
computed without taking into account the conversion coecients of 0.25 and 0.3 and then
divided by the amount of garbage removed to yield power‑generation values before and
after classication of 213.25 kWh/t and 308.51 kWh/t, respectively. This result is supported
by the power‑generation potential of incoming garbage, which is approximately 300 kWh/t,
as outlined in the domestic waste‑incineration treatment engineering technology. How‑
ever, when compared to the power‑generation potential of 460–476 kWh/t in European
countries [7], the power‑generation potential of waste in our country is lower. Several
factors may explain this disparity. (1) Our calculations assume that all garbage is used
for power generation, which may result in a relatively conservative estimate of the power‑
generation capacity of incoming garbage. (2) When compared to the NCV of MSW in Euro‑
pean countries, such as the UK where the average caloric value of MSW is 10.6 MJ/t [110],
the range of values calculated in this study uctuates between 2 and 7 MJ/t, indicating a
relatively lower caloric value in our waste, leading to a lower power‑generation poten‑
tial. (3) European countries have relatively mature classication technology, as supported
by the technology of classication. This technology results in a relatively higher propor‑
tion of combustible materials in the physical composition of MSW. For instance, in the
UK, combustibles account for as much as 46.9% (excluding 24.0% of kitchen waste) [110],
supporting the conclusion of the larger caloric value mentioned earlier.
Above all, it is undeniable that the ERP and PGP of separated treatment is signicantly
higher than that of unsegregated treatment, especially in regions with a large amount
of MSW removal. The successful implementation of energy recovery and the reuse of
MSW will directly impact the transformation of China’s energy‑consumption structure in
the future.
5. Conclusions
The research in this paper is based on the collection, summarization, and analysis
of data on the physicochemical composition of MSW in China published in recent years.
By summarizing a large amount of data, the most suitable embodiment of MSW data for
China’s national conditions is identied. The above work leads to the following
four conclusions.
(1) With the development of society and advancements in incineration and power‑
generation technology, the predominant treatment method for MSW in China has shifted
from landll to incineration and power generation. By 2021, the proportion of MSWs being
treated through incineration and power generation has reached a high of 72.55%. (2) This
paper adopts the data‑processing method of box plot to summarize the universal physical‑
composition interval and chemical composition interval of MSW in China. Two methods
were used to calculate the caloric value based on physical composition and elemental
composition. The obtained uctuation interval of the caloric value of the waste was in the
range of 2672.5–7511.7 kJ/kg. The results fell within a reasonable range, which veried the
accuracy of the data. (3) This paper oers a comprehensive review of the physicochemical
composition of MSW in dierent regions of China. It provides references for the disposal
of MSW to the specic characteristics of each region, such as geographic location and cli‑
mate. (4) The volume of MSW removed from various regions of China in 2021, along with
the chemical composition and percentage of kitchen waste in each region, as well as the
moisture content, were used as input data. The ERP and PGP of MSW in dierent regions
of China were calculated before and after classication. The results indicated that the ERP
Energies 2024,17, 491 17 of 21
of MSW in the country increased by 44.67% after classication. This nding provides a
valuable reference for determining the next MSW treatment method in China.
Author Contributions: J.L. and Y.S. carried out the conception and design of the research, J.L., R.B.
and S.D. participated in the acquisition of the data. J.L. carried out the analysis and interpretation of
data. Y.S. and D.A. participated in obtaining funding. J.L. drafted the manuscript and D.A. and Y.S.
participated in the revision of the manuscript for important intellectual content. All authors have
read and agreed to the published version of the manuscript.
Funding: This work was supported by the Shandong Provincial Natural Science Foundation
(ZR2022ME008) and the Natural Science Foundation of China (Grant No. 52306250).
Data Availability Statement: The preceding studies on MSW in China have been limited to the
physical and chemical characteristics of a certain place. In this paper, we collect data from journals
in recent years and study and analyze the physical and chemical properties of MSW in China and
dierent regions. Then, the ERP and PGP of MSW before and after classication are compared and
analyzed to provide some guidance for MSW treatment measures in dierent regions of China.
Conicts of Interest: The authors declare that they have no known competing nancial interests or
personal relationships that could have appeared to inuence the work reported in this paper. Yuetao
Shi reports nancial support was provided by Shandong Provincial Natural Science Foundation.
Abbreviations
MSW municipal solid waste
MSWI municipal solid waste incineration
ERP energy‑recovery potential
PGP power‑generation potential
EC East China
NC North China
NEC Northeast China
CC Central China
SC South China
SWC Southwest China
NWC Northwest China
GHG Greenhouse Gas
LCA Life‑Cycle Assessment
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