Prediction of agricultural carbon emissions in China based on a GA-ELM model

Abstract

Introduction: Strengthening the early warning of greenhouse gas emissions from agriculture is an important way to achieve Goal 13 of the Sustainable Development Goals. Agricultural carbon emissions are an important part of greenhouse gases, and accelerating the development of green and low-carbon agriculture is of great significance for China to achieve high-quality economic development and the goal of “carbon neutrality in peak carbon dioxide emissions”.
Methods: By measuring the total agricultural carbon emissions in China and seven administrative regions from 2000 to 2021, the paper analyzes the influencing factors of agricultural carbon emissions in China by using STIRPAT environmental pressure model, and on this basis, predicts the peak trend of agricultural carbon emissions in China under different development scenarios by using the extreme learning machine model optimized by genetic algorithm.
Results: The results showed that the extreme learning machine model improved by the genetic algorithm can overcome the shortcoming that the extreme learning machine model is easy to fall into the local optimal solution, thus obtaining higher prediction accuracy. At the same time, from 2000 to 2021, the total agricultural carbon emissions in China showed a continuous fluctuation trend, and due to the constraints of the agricultural economic level, agricultural industrial structure, and agricultural human capital, the agricultural carbon emissions showed spatial differentiation. It is worth noting that, in the context of green development, the agricultural carbon emissions of the seven regions in China all have the potential to achieve the “peak carbon dioxide emissions” goal in 2030, with only a slight difference at the peak.
Discussion: The research results of this paper provide evidence for the government to formulate flexible, accurate, reasonable and appropriate agricultural carbon reduction policies, which is helpful to strengthen the exchanges and cooperation of regional agricultural and rural carbon reduction and fixation, and actively and steadily promote China's agriculture to achieve the goal of “peak carbon dioxide emissions carbon neutrality”.

Full text

Prediction of agricultural carbon
emissions in China based on a
GA-ELM model
Xiaoyang Guo
1
,
2
, Jingyi Yang
1
,
2
*, Yang Shen
1
,
2
and
Xiuwu Zhang
1
,
2
*
1
Institute of Quantitative Economics, Huaqiao University, Xiamen, China,
2
School of Statistics, Huaqiao
University, Xiamen, China
Introduction: Strengthening the early warning of greenhouse gas emissions from
agriculture is an important way to achieve Goal 13 of the Sustainable Development
Goals. Agricultural carbon emissions are an important part of greenhouse gases,
and accelerating the development of green and low-carbon agriculture is of great
significance for China to achieve high-quality economic development and the
goal of “carbon neutrality in peak carbon dioxide emissions”.
Methods: By measuring the total agricultural carbon emissions in China and seven
administrative regions from 2000 to 2021, the paper analyzes the influencing
factors of agricultural carbon emissions in China by using STIRPAT environmental
pressure model, and on this basis, predicts the peak trend of agricultural carbon
emissions in China under different development scenarios by using the extreme
learning machine model optimized by genetic algorithm.
Results: The results showed that the extreme learning machine model improved
by the genetic algorithm can overcome the shortcoming that the extreme learning
machine model is easy to fall into the local optimal solution, thus obtaining higher
prediction accuracy. At the same time, from 2000 to 2021, the total agricultural
carbon emissions in China showed a continuous fluctuation trend, and due to the
constraints of the agricultural economic level, agricultural industrial structure, and
agricultural human capital, the agricultural carbon emissions showed spatial
differentiation. It is worth noting that, in the context of green development,
the agricultural carbon emissions of the seven regions in China all have the
potential to achieve the “peak carbon dioxide emissions”goal in 2030, with
only a slight difference at the peak.
Discussion: The research results of this paper provide evidence for the
government to formulate flexible, accurate, reasonable and appropriate
agricultural carbon reduction policies, which is helpful to strengthen the
exchanges and cooperation of regional agricultural and rural carbon reduction
and fixation, and actively and steadily promote China’s agriculture to achieve the
goal of “peak carbon dioxide emissions carbon neutrality”.
KEYWORDS
peak carbon dioxide emissions, agricultural carbon emissions, STIRPAT model, genetic
algorithm, extreme learning machine, scenario prediction
OPEN ACCESS
EDITED BY
Salvador García-Ayllón Veintimilla,
Polytechnic University of Cartagena,
Spain
REVIEWED BY
Jijian Zhang,
Jiangsu University, China
Sudipto Mandal,
University of Burdwan, India
*CORRESPONDENCE
Jingyi Yang,
22013021014@stu.hqu.edu.cn
Xiuwu Zhang,
zxwxz717@hqu.edu.cn
RECEIVED 23 June 2023
ACCEPTED 28 September 2023
PUBLISHED 13 October 2023
CITATION
Guo X, Yang J, Shen Y and Zhang X (2023),
Prediction of agricultural carbon
emissions in China based on a GA-
ELM model.
Front. Energy Res. 11:1245820.
doi: 10.3389/fenrg.2023.1245820
COPYRIGHT
© 2023 Guo, Yang, Shen and Zhang. This
is an open-access article distributed
under the terms of the Creative
Commons Attribution License (CC BY).
The use, distribution or reproduction in
other forums is permitted, provided the
original author(s) and the copyright
owner(s) are credited and that the original
publication in this journal is cited, in
accordance with accepted academic
practice. No use, distribution or
reproduction is permitted which does not
comply with these terms.
Frontiers in Energy Research frontiersin.org01
TYPE Original Research
PUBLISHED 13 October 2023
DOI 10.3389/fenrg.2023.1245820

1 Introduction
The climate problem is closely related to all aspects of human
production and life, and the rapid increase in CO
2
concentration in
the atmosphere is the most crucial factor that causes climate change.
Since China’s reform and opening-up, great achievements have been
made in agricultural economic construction. However, behind the
rapid economic growth, resources are increasingly exhausted and
the ecological environment is deteriorating. Facing the dual
constraints of resource shortage and environmental carrying
capacity, the problem of modern ecological environment
governance in agricultural and rural areas has never been
systematically solved. Agriculture, as a natural ecological carbon
sink system, has distinct industrial characteristics under the impact
of climate change (Zhao et al., 2018;Guang et al., 2023). However, at
present, the carbon sink capacity is insufficient to offset the
greenhouse gases generated in its production process, posing a
serious threat to global food security, human health, and
sustainable economic and social development (Federici et al.,
2015). According to the Food and Agriculture Organization
(FAO) of the United Nations, global carbon emissions from
agricultural and food systems increased by 16% between
1990 and 2020, reaching 17 billion tons of carbon dioxide-
equivalent (CO
2
-eq) in 2020, accounting for 30% of global
anthropogenic carbon emissions. As a major agricultural country,
China’s agricultural carbon emissions account for approximately
24% of China’s greenhouse gas emissions, and this proportion is
even increasing (Shi et al., 2023). Indeed, agricultural development
has reached an important period when it is necessary to change the
“environment for growth”development model. Promoting carbon
sequestration and emission reduction in agriculture, significantly
improving the degree of green production, and resource utilization
efficiency have become important aspects for China to achieve
carbon peak and carbon neutrality goals (Song et al., 2022;Wang
et al., 2023b). As the world’s largest emitter of greenhouse gases,
China is an active participant and contributor to the global response
to climate change. In order to achieve the Sustainable Development
Goals (SDGs) and practice the lofty mission of building a
Community of Shared Future for Mankind, in September 2020,
China solemnly announced at the 75th United Nations General
Assembly that it would strive to achieve peak carbon dioxide
emissions by 2030 and achieve carbon neutrality by 2060.
Therefore, focusing on the relevant research of agricultural
carbon emissions is not only conducive to achieving the goal of
“double carbon”and the development of agricultural low-carbon
life, further easing the bottleneck of resource elements and ensuring
agricultural ecological security, but also conducive to thinking about
moving toward food quality and security on the premise of ensuring
food quantity security, further satisfying people’s demand for high-
quality agricultural products and having important practical
significance for preventing and resolving food safety risks and
exploring the agricultural low-carbon path to realize “lucid
waters and lush mountains are invaluable assets.”
From the published literature, the research contents and
perspectives of agricultural carbon emissions are extensive,
mainly focusing on carbon emission measurement (Sun et al.,
2022a;Xu et al., 2023), driving factors (Su et al., 2023), temporal
and spatial evolution (Chen et al., 2019;Liao and Liu, 2020),
decoupling effect (Han et al., 2018;Zhang et al., 2022), and
emission reduction path (Wang et al., 2020a;Cardoza Cedillo
et al., 2023), and there is a lack of in-depth study on the trend
prediction of agricultural carbon emissions. Specifically, the first is
about the calculation of agricultural carbon emissions. Most scholars
use the emission coefficient method (Guo et al., 2022a), agricultural
ecosystem model (Zhao et al., 2019;Sun et al., 2023), methane
emission model of paddy field (Wang et al., 2018), and regional
nitrogen cycle model (Mao et al., 2018) to measure and analyze
agricultural carbon emissions in different regions. Among them, it
has become the mainstream practice to modify agricultural carbon
emission sources and related emission coefficients on the basis of the
emission coefficient method (Liu et al., 2013;Wójcik-Gront and
Gront, 2014;Ghosh, 2018;Gao et al., 2023). The second is the
research on the influencing factors of agricultural carbon emissions.
Many scholars build an LDMI model based on Kaya identity
proposed by Japanese professor Yoichi Kaya or use an
econometric model to study the influence of driving factors on
agricultural carbon emissions. For example, Shan et al. (2021) and Li
et al. (2022) used the spatial Dobbin model and LDMI model,
respectively, and thought that the agricultural industrial structure,
intensive level, urbanization rate, and environmental regulation
were important factors affecting agricultural carbon emissions.
From the perspective of spatial correlation, Xia et al. (2019) used
an ESTDA framework and GWR model to analyze the mechanism
of the agricultural carbon emission rate and the evolution
characteristics of the spatial pattern by factors such as the
agricultural economic development level, farmers’income
structure, planting structure, and cultivated land scale. The third
is about the trend prediction of agricultural carbon emissions.
Scholars mainly rely on traditional forecasting methods to predict
the peak value of agricultural carbon emissions, such as the
environmental Kuznets curve (Pandey and Mishra, 2021;
Ojaghlou et al., 2023), IPAT identity (Du et al., 2012;Yang et al.,
2023), support vector machine model (Gao et al., 2022;Zhao et al.,
2023), low-emission analysis platform (Sun et al., 2022b;Chen et al.,
2023), gray forecasting model (Wang et al., 2023a;Saxena et al.,
2023), and various combination models. On the basis of the extreme
learning machine model (ELM), Wang et al. (2020b) used the whale
algorithm to optimize it and used the WOA-ELM model to predict
China’s carbon emissions, and the prediction results were more
accurate. Jia and Chen (2023) used the improved support vector
regression model optimized by the differential evolution gray wolf
optimizer to predict agricultural carbon emissions in Hebei Province
and compared the prediction results with the single support vector
regression model, which showed that the former had higher
prediction accuracy and faster convergence speed.
To sum up, the existing literature has carried out a lot of useful
research on measuring agricultural carbon emissions in China,
identifying the influencing factors of high carbon emissions and
predicting the peak value of agricultural carbon emissions, which
provides ideas and methods for this paper, but there is still some
room for expansion.
The contribution of this paper is as follows. First, the evaluation
index system constructed by the existing literature when measuring
agricultural carbon emissions only considers the carbon dioxide
released by agricultural films, pesticides, and agricultural machinery
and equipment used in agricultural production and operation,
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Guo et al. 10.3389/fenrg.2023.1245820

ignoring the use of pesticides and fertilizers and the improper
disposal of agricultural livestock manure, which causes certain
pollution to soil, air, and agricultural products. So, the
calculation results may be underestimated. This paper focuses on
the agricultural ecological function, including agricultural materials,
soil, paddy fields, and livestock breeding into the agricultural carbon
emission accounting system, and measures the dynamic evolution
process and key influencing factors of agricultural carbon emission
levels in different regions of China. Second, when the existing
literature studies predict the trend of agricultural carbon
emissions, there are often phenomena such as complex
calculation, poor stability, and lack of learning and testing
processes, which leads to the problems of fuzzy internal relations
and insufficient generalization ability of a complex nonlinear
agricultural carbon emissions system, which reduces the accuracy
of the predicted values (Bokde et al., 2021). In this paper, a GA-ELM
model is constructed to predict agricultural carbon emissions in
different development scenarios by using the excellent global
optimization ability of the genetic algorithm and the high
generalization of the ELM model so as to provide a quantitative
reference for China to promote agricultural peak carbon dioxide
emissions and carbon neutrality and realize high-quality green
development.
2 Research methods and data sources
2.1 Research methods
2.1.1 Calculation of agricultural carbon
emissions—the emission coefficient method
Agricultural carbon emissions are an important part of global
greenhouse gases (Vermont and Cara, 2010). Compared with other
sectors, the carbon emission sources in the agricultural sector are
more complicated. Considering the consistency of the agricultural
industrial structure, statistical items, and data comparability in
various regions of China, this paper uses the emission coefficient
method to measure agricultural carbon emissions according to the
2006 National Greenhouse Gas Inventory Guidelines issued by the
United Nations Intergovernmental Panel on Climate Change
(IPCC). Referring to the practices of Hou et al. (2023),Shen
et al. (2023), and Tian et al. (2023), this paper divides
agricultural carbon emission sources into four dimensions,
namely, agricultural materials, soil, paddy fields, and livestock
breeding. The specific calculation formula is as follows:
EEiTi·δi
()
.(1)
In Eq. 1,Eis the total agricultural carbon emission, Eiis the
carbon emission of the ith carbon source, Tiis the ith carbon source,
and δiis the carbon emission coefficient of the ith carbon source.
2.1.1.1 Carbon emission of agricultural materials
This paper focuses on the carbon emission in the process of
agricultural land use, that is, the carbon emission caused by the
production activities of human beings using agricultural land.
According to the theory of agricultural production factors, the
input factors of agricultural materials are defined, and five
pollution sources, namely, chemical fertilizer, pesticide,
agricultural film, diesel oil, and irrigation, are selected as the
main sources of agricultural carbon emission, which are
characterized by the amount of chemical fertilizer, pesticide,
agricultural plastic film, agricultural diesel oil, and effective
irrigation area, respectively. In addition, according to the existing
literature (Li et al., 2023;Hu et al., 2023;Ye et al., 2023), the carbon
emission coefficient of each carbon source can be determined, as
shown in Table 1.
2.1.1.2 N
2
O emission from soil
Due to plugging and leveling during planting crops such as rice
and wheat, substances such as nitrogen and phosphorus are driven
by rainfall and topography, which damage the soil surface to a
certain extent and release a lot of greenhouse gases (Gangopadhyay
et al., 2023). Among them, N
2
Oisthemostprominent.Thisgas
has the characteristics of long air retention time and high single-
molecule warming potential andislistedasthethirdlargest
greenhouse gas after carbon dioxide and methane (Qi et al.,
1999). Referring to the practices of Xu et al. (2016),Zhao et al.
(2023),andMirzaei et al. (2022), this paper selected rice, wheat,
soybean,andothercropsasthemainsourcesofN
2
Oemission
from soil and listed their N
2
Oemissioncoefficients, as shown in
Table 2.
2.1.1.3 CH
4
emission from paddy fields
The greenhouse gas CH
4
produced by rice fields is one of the
important sources of agricultural carbon emissions (Hadi et al.,
2010). Considering the regional differences in rice planting area,
water-saving irrigation technology, and hydrothermal conditions in
China, the CH
4
released by rice fields in different regions is different.
In view of this, this paper refers to the practices of Han et al. (2010),
Zhou et al. (2022),Gangopadhyay et al. (2022), and Zhang et al.
(2022), calculating the CH
4
emission coefficient during the rice
growth cycle in various regions. The specific results are shown in
Table 3.
2.1.1.4 Carbon emission from livestock farming
Carbon emission from livestock farming mainly includes CH
4
emission caused by the intestinal fermentation of herbivorous
livestock and CH
4
emission and N
2
O emission caused by manure
storage and management. Referring to the practices of Nunes (2023),
Zhang et al. (2023a), and Akamati et al. (2023), this paper selects
cattle, horses, donkeys, mules, camels, pigs, goats, and sheep as the
important sources of carbon emissions from livestock farming. The
carbon emission coefficients of various livestock are shown in
Table 4.
2.1.2 Analysis on influencing factors of agricultural
carbon emissions—the STIRPAT model
The STIRPAT model, an important tool for environmental
analysis, is derived from the IPAT environmental pressure
equation proposed by Ehrlich et al. and is widely used to analyze
the non-proportional impact of human driving factors on the
environment (Ehrlich and Holdren, 1971). In this paper,
referring to the practices of Guo et al. (2022b),Zhang et al.
(2023b),Peng et al. (2023), and Liu et al. (2023), a nonlinear
STIRPAT model with multiple independent variables is
constructed, and its basic formula is as follows:
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IiaPb
iAc
iTd
iεi.(2)
In Eq. 2,I,P,A,andTrepresent the environmental factors,
population size, economic development level, and technical level,
respectively; arepresents the model coefficient, that is, the
comprehensive action degree of influencing factors; b,c,andd
represent the humanistic driving force index of their respective
influencing factors; that is, every 1% change in P,A,andTcauses I
to change by b%, c%, and d%, respectively; and εrepresents the model
error. For the convenience of calculation, this paper algorithmizes the
aforementioned equation and obtains the following equation:
lnI lna +blnP +clnA +dlnT +ln ε.(3)
The STIRPAT model not only overcomes the limitation of the
assumption that the traditional IPAT environmental pressure
equation “all factors have the same influence degree”but also
expands and improves the model through increase or decrease or
factor decomposition so as to enhance its own analysis and
interpretation ability to better qualitatively or quantitatively analyze
the impact of human indicators on carbon emissions (Chen et al.,
2014). Based on the existing research results, agricultural industrial
structure, agricultural carbon emission intensity, per capita
agricultural GDP, and rural population all have significant effects
on agricultural carbon emissions (Wang et al., 2012;Fang et al., 2019;
Zhu et al., 2019). Therefore, in this paper, the aforementioned four
macro factors are regarded as important factors affecting agricultural
carbon emissions; the environmental pressure is represented by
agricultural carbon emissions, and the STIRPAT model is
extended, which is expressed as
ACEiaAISb
iEIc
iAPd
iRPe
iεi.(4)
Logarithmic transformation of Eq. 4yields the following
expression:
lnACE lna +blnAIS +clnEI +dlnAP +eLnRP +ln ε.(5)
In Eq. 5,ACE stands for the agricultural carbon emissions; AIS,
EI,AP, and RP represent the agricultural industrial structure,
agricultural carbon emission intensity, per capita agricultural
GDP, and rural population, respectively.
2.1.3 Prediction of the agricultural carbon emission
peak—the GA-ELM model
2.1.3.1 Basic principles of ELM
Extreme learning machine is a machine learning method
proposed by Huang et al. (2006) based on a single-hidden layer
feedforward neural network (SLFN), which has good learning ability
and nonlinear approximation ability. This algorithm fills the defect
that the SLFN needs to constantly adjust parameters according to
the loss function, can randomly generate the input layer weight
matrix and threshold of hidden layer nodes, abandons the iterative
adjustment strategy of the gradient descent algorithm, and no longer
falls into the local optimal solution because of the step size setting
problem, thus improving the training speed and prediction accuracy
(Huang et al., 2012;Wu et al., 2023).
Given any N different training samples (xi,y
i),xi
[xi1,x
i2,...,x
in]Trepresents the input vector and yi
[yi1,y
i2,...,y
in]Trepresents the expected output vector. The
number of hidden layer nodes of the ELM network is set as L,
the threshold as bi, and the activation function as g(x). The input
weight between the hidden layer neurons and the input layer
neurons is ωi[ωi1,ωi2,...,ωin ]T, and the weight matrix
between the hidden layer and the output layer is
β[β1,β2,...,βL]T, and its mathematical model is expressed as
follows:
HβT,
Hhx
1
()
T,...,h x
N
()
T

T

gω1·x1+b1
()
/gωL·x1+bL
()
.
.
.
1.
.
.
gω1·xN+b1
()
/gωL·xN+bL
()
⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦.(6)
In Eq. 6,His expressed as a random feature mapping matrix,
that is, the hidden layer output matrix; Trepresents the training
sample output matrix of the extreme learning machine model.
In fact, when the input weight ωiand the threshold biare
randomly generated by arbitrary continuous sampling distribution
probability and N different training samples are given, the hidden
layer output matrix Hbecomes a known quantity. At the same time,
if the activation function g(x)is given, the model training result t is
also a known quantity. At this point, the minimum norm least
squares solution β* can be solved by the Moore–Penrose generalized
inverse. The specific calculation formula is
β*H†T.(7)
2.1.3.2 Model principle of GA-ELM
Because the weight matrix of the input layer and the threshold of
the hidden layer in the ELM algorithm are randomly set, it is impossible
to ensure that the final calculation results reach the ideal accuracy.
Therefore, in order to improve the stability and accuracy of the output
TABLE 1 Carbon emission coefficient of each production factor.
Carbon source Pesticide Chemical fertilizer Diesel Agricultural plastic sheeting Irrigate
Carbon emission coefficient 4.934 kg/kg 0.896 kg/kg 0.592 kg/kg 5.182 kg/kg 266.483 kg/hm
2
TABLE 2 N
2
O emission coefficient from the soil of crop varieties (kg/hm
2
).
Crop varieties Paddy Spring wheat Winter wheat Soybean Corn Vegetables Another dryland
N
2
O carbon emission coefficient 0.24 0.4 2.05 0.77 2.53 4.21 0.95
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results of the ELM algorithm and avoid over-fitting, this paper uses the
genetic algorithm to optimize the weight matrix and threshold of the
ELM model and accelerate the convergence speed of the ELM model.
Specifically, the genetic algorithm, as a heuristic intelligent search
algorithm for the optimal solution, simulates genetic operations such
as chromosome cross-pairing and mutation accordingtothenatural
law of “survival of the fittest,”thus iterating the feasible solution of the
optimization problem and constantly approaching the optimal solution.
Therefore, the genetic algorithm has stronger global optimization
performance and higher general fitness function. The GA-ELM
model abstracts the “input weight ωiand threshold bi"and“training
error”into a “chromosome gene coding sequence of individual
population”and “fitness function,”sets the number of units of the
ELM hidden layer and activation function, then calculates the fitness of
each chromosome, and seeks the best individual fitness. The specific
steps of the GA-ELM algorithm are as follows:
Step 1. The topology structure of the ELM neural network is
determined, the input weights and hidden layer thresholds are
encoded, and the initial solution population Qγis obtained.
Qγωγ
11,ωγ
12,...,ωγ
1j,...,ωγ
ij,b
γ
1,b
γ
2,...,b
γ
j

,i1,2,...,N.(8)
In Eq. 8,Qγrepresents the γ-th individual of the initial
population; ωij and bjare random values, and the range of
values is [-1,1]; and N represents the number of training samples.
Step 2. After decoding, the input weights and hidden layer
thresholds of the neural network are obtained and brought into
the ELM network, and the objective function is set.
Objfun 1
n
n
k1
yik
()
−y*
ik
()
2.(9)
In Eq.9,nrepresents the number of predicted time points; yi(k)
and y*
i(k)represent the real value and predicted value at time k,
respectively.
Step 3. The fitness function, population size, and evolutionary
algebra are determined. Generally speaking, the reciprocal of the
mean square deviation between the actual and expected values of the
ELM algorithm is used to characterize the fitness function, that is,
fitness 1
1
NY′
i−Yi

2

1
2
. (10)
Step 4. The optimal fitness function fitnessbest is locally solved.
This article sets the initial value of evolutionary algebra and the
number of individuals in the population to 0, gradually solving the
TABLE 3 CH
4
emission coefficient of paddy fields in different growth periods
(g/m
2
).
Region Early rice Late rice Mid-season rice
Beijing 0 0 13.23
Tianjin 0 0 11.34
Hebei 0 0 15.33
Shanxi 0 0 6.62
Neimenggu 0 0 8.93
Liaoning 0 0 9.24
Jilin 0 0 5.57
Heilongjiang 0 0 8.31
Shanghai 12.41 27.5 53.87
Jiangsu 16.07 27.6 53.55
Zhejiang 14.37 34.5 57.96
Anhui 16.75 27.6 51.24
Fujian 7.74 52.6 43.47
Jiangxi 15.47 45.8 65.42
Shandong 0 0 21.00
Henan 0 0 17.85
Hubei 17.51 39.0 58.17
Hunan 14.71 34.1 56.28
Guangdong 15.05 51.6 57.02
Guangxi 12.41 49.1 47.78
Hainan 13.43 49.4 52.29
Chongqing 6.55 18.5 25.73
Sichuan 6.55 18.5 25.73
Guizhou 5.10 21.0 22.05
Yunnan 2.38 7.6 7.25
Xizang 0 0 6.83
Shanxi 0 0 12.51
Gansu 0 0 6.83
Qinghai 0 0 0
Ningxia 0 0 7.35
Xinjiang 0 0 10.50
TABLE 4 Carbon emission coefficient corresponding to the main livestock
breeds (kg/head year).
Carbon source Intestinal fermentation Fecal
discharge
CH
4
CH
4
N
2
O
Cattle 54.33 7.00 1.24
Horse 18.00 1.64 1.39
Donkey 10.00 0.90 1.39
Mule 10.00 0.90 1.39
Camel 46.00 1.92 1.39
Pig 1.00 4.00 0.53
Goat 5.00 0.17 0.33
Sheep 5.00 0.15 0.33
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fitness function of individuals until traversing all individuals to
obtain the value of fitnessbest.
Step 5. The optimal fitness function fitnessbest′is globally solved.
This article adopts the roulette wheel method to select cross-
populations through crossover and mutation and records the
optimal solution for each round of population until the end of
the operation. At this point, the obtained fitnessbest′is the optimal
fitness function, and its corresponding parameters can serve as the
optimal input weight and hidden layer threshold of the ELM
network, achieving the optimization of the ELM model, that is,
obtaining the GA-ELM model.
In order to evaluate the prediction accuracy of the GA-ELM
model, RMSE,MAPE,MAE, and correlation coefficient R2are
selected as evaluation indexes. Among them, RMSE,MAPE, and
MAE are used to indicate the deviation between the predicted
results and the actual observed values, and R2indicates the
degree of correlation between the predicted results and the actual
observed values. The specific calculation formula is as follows:
RMSE 
1
n
n
i1
xi−x′
i

2
,(11)
MAPE 1
n
n
i1
x′
i−xi
xi
× 100%,(12)
MAE 1
n
n
i1
x′
i−xi
,(13)
R2
nn
i1
x′
ipxi−n
i1
x′
in
i1
xi

2
nn
i1
x′2
i−n
i1
x′
i

2

nn
i1
x2
i−n
i1
xi

2

. (14)
2.2 Data sources
Following the principle of data availability and consistency of
statistical caliber, this paper selects the panel data of 31 provinces in
the Chinese mainland from 2000 to 2021 as statistical samples. The
original data of all variables mainly come from the China Statistical
Yearbook, China Rural Statistical Yearbook, China Rural
Management Statistical Yearbook, China Population and
Employment Statistical Yearbook, China Rural Poverty Detection
Report, China Environmental Statistical Yearbook, China Energy
Statistical Yearbook, and statistical bureaus of various provinces and
cities. All the economic variables involved in monetary
measurement in this paper are smoothed based on 2000.
2.3 Accuracy verification of GA-ELM
In this paper, agricultural industrial structure, agricultural
carbon emission intensity, per capita agricultural GDP, and
rural population are taken as the input data of the model.
Considering that the magnitude difference between the
influencing factors may affect the prediction effect, this paper
standardizes the data before fitting the model, and the calculation
formulaisasfollows:
yymax −ymin

x−xmin
()
xmax −xmin
+ymin. (15)
In Eq. 15,yis the normalized data; ymin and ymax represent the
parameters; xrepresents the data to be processed; and xmin and xmax
represent two extreme data of the same influencing factor.
In order to test the fitting accuracy of the GA-ELM model, this
paper selects historical data from 2000 to 2015 as the training set,
historical data from 2016 to 2021 as the test set, and the fitting results
of the test set as the model fitting result standard to verify the fitting
accuracy of the model. At the same time, this paper also compares
the fitting results of the GA-ELM model with ELM, BP, and GWO-
SVM models. The specific calculation results are shown in Table 5.It
is not difficult to see from Table 5 that the fitting effect of the GA-
ELM model is the best. In different provinces (cities and
autonomous regions) and different historical data, the GA-ELM
model can achieve higher fitting accuracy.
3 Research results and analysis
3.1 Calculation and analysis of agricultural
carbon emissions in China
3.1.1 Analysis on time series characteristics of total
agricultural carbon emissions
According to the aforementioned agricultural carbon emission
calculation method, the total agricultural carbon emission of China
from 2000 to 2021 is calculated, and the agricultural carbon emission
intensity is obtained by combining the agricultural GDP of China.
Figure 1 and Table 6 show the calculation results.
It is not difficult to see from Figure 1 that the total agricultural
carbon emission in China in 2021 was 23,755.354 ten thousand tons,
an increase of only 0.35% compared with 2000. During the research
period, China’s total agricultural carbon emissions showed a
continuous fluctuation trend, and due to external factors, such as
natural disasters and policies to support agriculture and benefit
farmers, the time series changes in China’s total agricultural carbon
emissions can be roughly divided into three stages.
The first stage was from 2000 to 2006, and the total amount of
agricultural carbon emissions showed a significant inverted “V”-
shaped fluctuation trend. Since 2000, a series of policies to support
and benefit agriculture have been introduced, such as the
cancellation of agricultural taxes and the establishment of special
subsidies for grain cultivation, which have accelerated the process of
agricultural marketization and modernization. This has led to a
significant increase in agricultural material input, rice planting scale,
and livestock breeding quantity, leading to a rapid increase in
TABLE 5 Result comparison of the model error index.
Error index RMSE MAPE MAE R
2
GA-ELM 6.37 0.01 3.39 0.92
ELM 129.18 0.28 98.15 0.77
BP 41.06 0.11 28.54 0.83
GWO-SVR 12.89 0.05 9.76 0.81
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agricultural carbon emissions and reaching its peak in 2005.
However, in 2005, natural disasters occurred frequently, mainly
floods and droughts, which severely impacted China’s crop and
livestock production, causing a sharp decrease in the total
agricultural carbon emissions.
The second stage is from 2007 to 2015, and the total agricultural
carbon emissions show a steady upward trend. In 2015, the total
agricultural carbon emissions in China increased by 8.19%
compared with 2007, with an average annual growth rate close to
1%. At this stage, the central government has continuously increased
investment in agriculture, rural areas, and farmers, comprehensively
implemented various support policies for grain, oilseeds, pigs, and
dairy cows, and established a long-term mechanism of “promoting
agriculture through industry and bringing rural areas through
cities,”which has become the main reason for the increasing
agricultural carbon emissions in China. Tian and Chen (2021)
also pointed out that the utilization of agricultural energy, the
continuous increase of agricultural material input, and the
immature alternative technology of chemical fertilizers are the
main reasons for the increase in carbon emissions at this stage.
The third stage is from 2016 to 2021, and the total agricultural
carbon emissions show a continuous downward trend with slight
fluctuations. In 2016, the State Council issued the National
Agricultural Modernization Plan (2016–2020), requiring all
agricultural departments to always focus on the goal of “one
control, two reductions, and three basics”and fight hard for
agricultural non-point source pollution control. At the same
time, the former Ministry of Agriculture carried out a series of
zero-growth actions on the use of chemical fertilizers and pesticides,
gradually strengthened the environmental regulation of agriculture,
and improved the pollution prevention and control ability in
agricultural production, thus reducing carbon emissions and
non-point source pollution. China’s economy has officially
entered a “new normal”which focuses on optimizing the
economic structure and is driven by factors, investment, and
innovation. Therefore, the total agricultural carbon emissions in
China reached the lowest value in 2019, which is consistent with the
research by Huang and Yang (2022), mainly due to the strong
promotion of a national strategy, the continuous improvement in
the utilization efficiency of energy and agricultural materials, and the
sharp reduction in the number of large livestock. At the same time,
the proposal of “deepening the structural reform of agricultural
supply side”indicates that the development direction of China’s
agriculture is to focus on improving the quality of agricultural
supply on the basis of ensuring national food security and
promote the transformation of agriculture and rural areas from
excessive dependence on resource consumption to pursuing green
ecological sustainable development (Ding et al., 2021). In addition,
in 2020, in the face of the major impact of the COVID-19 epidemic,
China’s strict epidemic prevention and control measures have
restricted many agricultural social activities, and agricultural
products have become slow-moving in regions and stages, which,
in turn, has a short-term inhibitory effect on China’s agricultural
carbon emissions.
3.1.2 Regional comparative analysis of total
agricultural carbon emissions
With the acceleration of urbanization, the carbon emissions
between neighboring provinces are spatially dependent. In view of
this, referring to the research results of Physical Geography of
China and Chen and Jiang (2018), this paper divides the
geographical area of China into seven regions, North China,
East China, South China, Central China, Southwest China,
Northwest China, and Northeast China, calculates the total
agricultural carbon emissions in each region (see Figure 2), and
analyzes the spatial pattern characteristics of agricultural carbon
emissions in China in combination with national development
planning and local policies related to agriculture. Figure 3 and
FIGURE 1
Time series changes in agricultural carbon emissions in China.
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Table 7 show the latitude and longitude range of each region and
the description of the provinces included.
As shown in Figure 2, in 2021, the total agricultural carbon
emissions in East China, Central China, and Southwest China were
in the forefront, all higher than the national average agricultural
carbon emissions, accounting for 61.35% of the total national carbon
emissions. Among them, the total agricultural carbon emissions in
East China reached 78.0844 million tons, which was 4.26 times that
in Northwest China. The reason is that East China has an important
agricultural production base in China, and the cultivated land area
accounts for 21.77% of the total cultivated land area in China. With
the improvement of agricultural modernization, pollution sources
such as solid pollutants, chemical fertilizer pollution, and livestock
and poultry are increasing, which causes continuous pollution to
soil, air, and agricultural products, resulting in a high level of
agricultural carbon emissions in East China. From the time series
trend of agricultural carbon emissions in various regions, it can be
seen that the total agricultural carbon emissions in Southwest China,
South China, and Central China have all decreased to varying
degrees, among which South China has the largest decrease,
reaching 48.26%. Generally speaking, the total agricultural carbon
emissions among the seven administrative regions in China fluctuate
continuously, with spatial differences, and the level of agricultural
carbon emissions in most regions is related to the scale of
agricultural production and operation, the amount of cultivated
land, the precise control of fertilizers, water, and drugs, etc.,
indicating that the agricultural production mode with high
consumption and high emissions in China has not changed
fundamentally, and solving the problems of small-scale, extensive,
and decentralized agricultural production will still be the focus of
China’s agricultural “emission reduction and carbon fixation”in the
future.
3.2 Prediction results and analysis of the
agricultural carbon emission peak in various
regions of China
3.2.1 Scenario setting of the agricultural carbon
emission trend
In order to ensure that the predicted agricultural carbon
emissions of the seven administrative geographical divisions in
China can meet the actual situation of China’sagricultural
economic and social development, this paper, based on the
TABLE 6 Changes in the total agricultural carbon emissions and intensity in China from 2000 to 2021.
Year Total agricultural carbon emissions
(ten thousand tons)
Chain growth
rate (%)
Agricultural carbon emission intensity
(ton/100 million Yuan)
Chain growth
rate (%)
2000 23,673.542 2,415.660
2001 23,737.178 0.269 2,195.713 −1.226
2002 24,111.254 1.576 2,009.287 −1.153
2003 24,285.678 0.723 1,763.603 −1.715
2004 25,525.925 5.107 1,567.920 −1.573
2005 26,189.523 2.600 1,377.548 −1.759
2006 24,123.286 −7.890 1,068.229 −3.518
2007 24,123.925 0.003 891.130 −2.599
2008 24,408.909 1.181 758.939 −2.364
2009 24,878.984 1.926 710.857 −0.987
2010 25,045.361 0.669 589.051 −2.862
2011 25,219.345 0.695 512.926 −2.169
2012 25,472.231 1.003 469.085 −1.432
2013 25,678.373 0.809 416.201 −1.945
2014 25,949.122 1.054 387.346 −1.191
2015 26,099.477 0.579 359.275 −1.262
2016 25,761.398 −1.295 332.284 −1.327
2017 24,826.065 −3.631 289.387 −2.381
2018 24,266.036 −2.256 264.199 −1.607
2019 23,129.420 −4.684 234.801 −2.115
2020 23,957.453 3.580 236.637 0.143
2021 23,755.354 −0.844 208.794 −2.290
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historical development law and regional development planning,
sets different change rates for four influencing factors, namely,
agricultural industrial structure, agricultural carbon emission
intensity, per capita agricultural GDP, and rural population,
and uses the average annual change rate of agricultural carbon
emissions to predict. In addition, referring to the practices of Pan
et al. (2021) and Wang et al. (2023a), this paper sets the benchmark
scenario and green scenario of agricultural carbon emission
prediction, respectively, which are used to compare the
development trend of agricultural carbon emission between
regions, so as to explore the possible paths and development
potential of agricultural low-carbon development in various
regions.
3.2.1.1 Benchmark scenario
The change rate of influencing factors under this scenario is set
according to the “14th Five-Year Plan”of China and the agricultural
sustainable development plan (2015–2030) so as to ensure that all
influencing factors reach the minimum indicators in the policy
document.
FIGURE 2
Changing trend of agricultural carbon emissions in the seven administrative regions of China.
FIGURE 3
Location map of each area.
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3.2.1.2 Green scenario
This scenario emphasizes that the agricultural economic
development of all provinces (municipalities and autonomous
regions) in China is no longer a “GDP-only”theory but should
change the quantitative growth mode that agriculture depends on
factor input by introducing modern agricultural machinery and
equipment, accelerating agricultural strategic scientific and
technological innovation, and strengthening environmental
regulation, so as to greatly improve the degree of economic
greening and resource utilization efficiency and make resources,
production, consumption, and other factors adapt to each other.
Therefore, the change rate setting of influencing factors in this
scenario will be slightly lower than the benchmark value. The set
values of the change rate of influencing factors in different scenarios
are shown in Table 8.
3.2.2 Prediction and analysis of the agricultural
carbon emission peak
In this paper, through the aforementioned GA-ELM model, the
agricultural carbon emission trend of China and the seven
administrative geographical regions from 2022 to 2050 is
predicted according to the set values of the change rate of carbon
emission influencing factors in different scenarios given in the table,
as shown in Figure 4.
According to the comparative analysis between the baseline
scenario and the green scenario, it can be seen that China is expected
to achieve the goal of “peak carbon dioxide emissions”in 2030, with
the only difference being that the total level of agricultural carbon
emissions in China is higher than that in the green scenario. It can be
seen that establishing a clean, low-carbon, efficient, and safe modern
agricultural production and consumption system, accelerating the
formation of green production methods and lifestyles, and
promoting the beautiful vision of “peak carbon dioxide emissions
and carbon neutrality”will become the main tone for the high-
quality development of agricultural economy and society in China in
the future. From the perspective of sub-regions, the change trend of
agricultural carbon emissions in the seven administrative
geographical regions is similar to that in China, but the only
difference is that the time taken to realize agricultural peak
carbon dioxide emissions in different regions is slightly different
and the level of peak carbon dioxide emissions is different. For
example, the agricultural peak carbon dioxide emission time in
South China, Central China, Northwest China, and Southwest
China is earlier than 2030, and even Northwest China has the
potential to achieve the peak carbon dioxide emission goal in 2024.
However, the peak time of agricultural carbon emissions in
Northeast China is not optimistic, and the peak target will be
achieved in 2045. The possible reason behind this lies in the fact
that Northeast China, as an important agricultural base in China,
has concentrated its agricultural industry from the outside to the
inside. In addition, fertile soil and abundant irrigation water sources
contribute to the popularization and promotion of new agricultural
machinery equipment and technology, which leads to a high level of
agricultural carbon emission and affects the realization of the goal of
“peak carbon dioxide emissions, carbon neutrality.”It can be seen
that under the condition of considering agricultural economic
development and agricultural emission reduction, we should
increase the research and development and application of new
agricultural production technologies as a breakthrough to curb
agricultural carbon emission and cooperate with various
agricultural carbon emission reduction policies and measures to
achieve sustainable development of agriculture with high quality and
low carbon.
4 Research conclusion and policy
recommendations
4.1 Research conclusion
By constructing the evaluation index system of the agricultural
carbon emission level in provinces, the agricultural carbon emission
level of 31 provinces in China from 2000 to 2021 was evaluated by
the emission coefficient method, and the peak trend of agricultural
carbon emission in seven administrative geographical regions in
different development situations in China was predicted and
analyzed by using the GA-ELM combined model. This paper
obtained several results. First, the change trend of the agricultural
TABLE 7 Regional latitude and longitude range and provinces included in each region.
Region Included provinces Latitude and longitude
range
North China Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia Autonomous Region 97°12′E-126°04′E, 34°34′N-53°23′N
East China Shanghai, Zhejiang, Jiangsu, Anhui, Jiangxi, Fujian, Shandong, Taiwan 113°34′W-125°46′W, 20°45′N-
43°26′N
Central China Hunan, Hubei, Henan 108°21′W-116°39′W, 25°29′N-
36°22′N
South China Hainan, Guangdong, Macao Special Administrative Region, Hong Kong Special Administrative Region, Guangxi
Zhuang Autonomous Region
104°29′W-117°20W, 18°10′N-
26°23′N
Southwest
China
Yunnan, Guizhou, Sichuan, Chongqing, Tibet Autonomous Region 78°42′W-110°11W, 21°13′N-36°53′N
Northwest
China
Qinghai, Gansu, Shanxi, Xinjiang Uygur Autonomous Region, Ningxia Hui Autonomous Region 73°40′W-107°39W, 31°41′N-49°10′N
Northeast China Liaoning, Heilongjiang, Jilin 118°53′W-135°5W, 38°43′N-53°33′N
Frontiers in Energy Research frontiersin.org10
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carbon emission level in China can be roughly divided into three
stages, which experienced the development state of “V" fluctuation
→steady rise →continuous decline. At the same time, due to the
constraints of obstacles, such as the level of agricultural economic
development and the amount of cultivated land, there are spatial
differences in agricultural carbon emissions between regions.
Second, in the context of green development, the agricultural
carbon emissions of seven regions in China all have the potential
to achieve the “peak carbon dioxide emissions”goal in 2030, with
only a slight difference at the peak. Third, the extreme learning
machine model improved by the genetic algorithm can overcome
the shortcoming that the ELM model is easy to fall into local optimal
solution, thus obtaining higher prediction accuracy. This paper is a
useful exploration of carbon sequestration in China’s agriculture in
the background of peak carbon dioxide emissions and carbon
neutrality. The conclusions are helpful for government
departments to formulate flexible, accurate, reasonable, and
appropriate agricultural carbon sequestration policies, strengthen
exchanges and cooperation in regional agriculture and rural areas,
and realize pollution reduction and carbon reduction and greening
so as to actively and steadily promote China’s agriculture to achieve
the goal of “carbon neutrality in peak carbon dioxide emissions.”
4.2 Policy advice
In order to better realize the goal of “double carbon”in
agriculture and rural areas and give play to the role of
agricultural carbon reduction in promoting carbon neutrality in
all areas of China, combined with the conclusion of the article, the
following policy suggestions are put forward.
First, the design of special policies for agricultural carbon
emission reduction is optimized, the accuracy of environmental
governance is improved, and the total amount and growth rate of
regional agricultural carbon emissions are effectively slowed down.
The government should further promote the transformation of
agricultural carbon emission reduction policies from relying on
administrative means to comprehensively considering legal, fiscal,
taxation, technology, and necessary administrative measures,
improve the long-term incentive mechanism for agricultural low-
carbon development, and avoid inefficient emission reduction that
hinders the normal production and operation activities of
enterprises or farmers and inhibits their production enthusiasm
on the grounds of strengthening agricultural pollution control. At
the same time, we promote the substitution of renewable energy,
deduct the carbon emissions from agricultural production and life,
such as crop straws, livestock manure, and other substances that can
produce renewable energy such as bio-natural gas, bio-liquid fuel,
and combustion power generation, deduct the carbon emissions
from agricultural materials such as pesticides and fertilizers, and
shift the focus of emission reduction from reducing the total amount
of agricultural carbon emissions to optimizing functional emission
reduction, so as to help China better achieve the goal of “peak carbon
dioxide emissions, carbon neutrality.”
Second, exchanges and cooperation in emission reduction and
carbon sequestration in agriculture and rural areas in various
regions are strengthened, and advanced agricultural technologies
and the sharing of experience in green and low-carbon agricultural
development are promoted. For areas with less total agricultural
carbon emissions and less pressure to reduce emissions, we should
fill in the shortcomings of green and low-carbon science and
technology in agriculture and rural areas as soon as possible,
speed up the transformation and demonstration application of
achievements, form a standardized green and low-carbon
development model in agriculture and rural areas, and give full
play to the technology spillover effect, so as to drive the surrounding
areas with heavy agricultural carbon emission reduction tasks to
achieve agricultural technological progress and industrial structure
transformation and upgrading, promote the consensus of green
development in planting, animal husbandry, aquatic products,
agricultural machinery, and other fields, and form a joint force to
achieve agricultural high-yield and low-carbon synergy.
Third, agriculture should be guided to participate in carbon sink
trading, and the transformation of “agricultural carbon ticket”into
“farmers’money”is realized. Taking the “carbon label of agricultural
products”as an example, its labeling form enables greenhouse gases
TABLE 8 Setting value of the change rate of influencing factors.
Developmental
pattern
Time Agricultural industrial
structure
Agricultural carbon
emission intensity
Per capita
agricultural GDP
Rural
population
Benchmark scenario 2022–2025 −1.50 7.00 −3.00 0.05
2025–2030 −1.00 5.50 −2.60 0.03
2030–2035 −0.70 4.50 −2.30 0.02
2035–2040 −0.50 3.70 −2.10 0.01
2040–2050 −0.30 3.00 −2.00 0.00
Green scenario 2022–2025 −1.90 5.50 −3.50 0.03
2025–2030 −1.50 4.50 −3.10 0.02
2030–2035 −1.00 3.70 −2.80 0.01
2035–2040 −0.70 3.00 −2.60 0.00
2040–2050 −0.50 2.50 −2.50 0.00
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emitted by agricultural products in the whole life cycle of
production, transportation, and disposal to be displayed in data,
which directly reflects the carbon emission per unit of agricultural
products, which can not only force the research and development
and utilization of energy-saving and emission reduction
technologies and deepen the development of intensive and
efficient green agricultural products but also realize the
simultaneous reduction of pollution and carbon, the
transformation of traditional old kinetic energy, and the
cultivation of green and low-carbon new kinetic energy.
4.3 Research limitation
Although this paper provides some enlightenment for the
government’s decision making and research in the field of carbon
emission reduction and green economic growth, it still has some
limitations. First, due to the availability of data, this paper uses
provincial data to explore the fluctuation trend of the agricultural
carbon emission level in China from 2000 to 2021. Future research
can explore the spatial spillover effect and decoupling effect of
agricultural carbon emissions in China by adjusting research
FIGURE 4
Peak trend of agricultural carbon emissions in China and various regions under different scenarios.
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Guo et al. 10.3389/fenrg.2023.1245820

methods and perspectives. Second, from the research area, as an
important grain production base in China, the major grain
producing areas played an important role in ensuring national
food security. Therefore, the main grain-producing areas can be
included in the research framework in the future so as to draw
extensive and profound conclusions.
Data availability statement
The raw data supporting the conclusion of this article will be
made available by the authors, without undue reservation.
Author contributions
Conceptualization: XZ; methodology: XG, YS, and JY; software:
XG, YS, and JY; formal analysis: XG, YS, and JY; investigation: XZ
and JY; resources: XG and YS; writing—original draft: XG;
writing—review and editing: XG; supervision: XG; and project
administration: XZ. All authors contributed to the article and
approved the submitted version.
Funding
This work was financially supported by the Natural Science
Foundation of Fujian Province (Grant Number 2022J01320) and the
Fundamental Research Funds for the Central Universities in
Huaqiao University.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated organizations,
or those of the publisher, the editors, and the reviewers. Any product
that may be evaluated in this article, or claim that may be made by its
manufacturer, is not guaranteed or endorsed by the publisher.
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