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Citation: Muchen, L.; Hamdan, R.;
Ab-Rahim, R. Data-Driven
Evaluation and Optimization of
Agricultural Environmental
Efficiency with Carbon Emission
Constraints. Sustainability 2022,14,
11849. https://doi.org/10.3390/
su141911849
Academic Editor: Teodor Rusu
Received: 26 August 2022
Accepted: 19 September 2022
Published: 20 September 2022
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sustainability
Article
Data-Driven Evaluation and Optimization of Agricultural
Environmental Efficiency with Carbon Emission Constraints
Luo Muchen 1,2, Rosita Hamdan 1, * and Rossazana Ab-Rahim 1
1Faculty of Economics and Business, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia
2School of Management, Suzhou University, Suzhou 234000, China
*Correspondence: hrosita@unimas.my; Tel.: +60-82-584438
Abstract:
To cope with global carbon reduction pressure, improved agricultural production efficiency,
and optimize regional sustainability, we constructed a data-driven evaluation and optimization
method for agricultural environmental efficiency (AEE) under carbon constraints. This study con-
structs a comprehensive input-output AEE evaluation index system, incorporates carbon emissions
from agricultural production processes as undesired outputs, and optimizes their calculation. The
Minimum Distance to Strong Efficient Frontier evaluation model considering undesired output, and
the kernel density estimation, are used to quantitatively evaluate AEE from static and dynamic
perspectives. Tobit regression models are further used to analyze the driving influences of AEE
and propose countermeasures to optimize AEE. The feasibility of the above methodological process
was tested using 2015–2020 data from the Anhui Province, China. Although there is still scope for
optimizing the AEE in Anhui, the overall trend is positive and shows a development trend of “double
peaks”. The levels of education, economic development, agricultural water supply capacity, and rural
management are important factors contributing to AEE differences in Anhui. Data and regression
analysis results contribute to the optimization of AEE and proposes optimization strategies. This
study provides extensions and refinements of the AEE evaluation and optimization, and contributes
to sustainable development of regions.
Keywords:
data-driven; agricultural environmental efficiency; carbon emission; evaluation and
optimization
1. Introduction
Environmental efficiency, also known as eco-efficiency, is a measure of the economic
performance (or resource use performance) and environmental impact of human produc-
tion activities that are assessed in an integrated manner [
1
,
2
]. Optimizing eco-efficiency
usually refers to the process of seeking to maximize output efficiency, while minimizing
environmental impacts [
3
,
4
]. As an important tool for sustainable development research,
environmental efficiency has become the focus of attention in an increasing number of
fields, and related research has shown a multidimensional and diversified trend, e.g., in
industry [
5
,
6
], farming [
7
], urban construction [
8
], tourism [
9
], mineral exploration [
10
],
and energy [11], etc.
It is noteworthy that agriculture serves as the basis of economic development in many
countries [
7
,
12
], and its sustainable development is considered as an important component
of sustainable development in general [
13
,
14
]. Therefore, studies on agricultural environ-
mental efficiency (AEE) have received attention from many scholars worldwide [
15
–
17
].
Currently, there are global challenges in agricultural development due to factors such as
the increasing population [
18
], water scarcity [
19
], and the declining availability of arable
land [
20
,
21
], which have highlighted the urgent need to improve agricultural production
efficiency. Furthermore, the widespread use of fertilizers, pesticides, agricultural machinery,
and other factors in agricultural production, has brought about serious surface pollution
Sustainability 2022,14, 11849. https://doi.org/10.3390/su141911849 https://www.mdpi.com/journal/sustainability
Sustainability 2022,14, 11849 2 of 22
and environmental damage, while promoting agricultural production [
22
,
23
], along with
bringing about increasing carbon emissions [
24
,
25
]. The IPCC assessment report states that
the frequency and intensity of some extreme weather and climate events have increased due
to global warming, and will continue to increase under medium to high emission scenarios.
Agriculture has become an important source of global greenhouse gases, with agriculture,
forestry, and other land uses accounting for 23% of global anthropogenic greenhouse gas
emissions [
26
], while CO
2
accounts for 75% of the composition of greenhouse gases. Among
them, the production, transportation, and use of fertilizers and pesticides in agricultural
production processes, account for a large proportion of carbon emissions [
27
,
28
]. Con-
sidering the huge global pressure to reduce carbon emissions, and the important role of
agriculture in the national economy of most countries, it is a broad ideal to study AEE from
a low-carbon perspective.
Within this context, in order to better cope with the pressure of carbon emissions,
optimize agricultural production processes, and to improve the AEE, the research tasks of
this study aims to construct a data-driven method for AEE evaluation and optimization
under carbon emission constraints. Specifically, an environmental efficiency model from
a low-carbon perspective is used to evaluate the potential for a harmonious relationship
between agricultural production and environmental quality, to analyze the trend of AEE
changes in each region dynamically. Through a review of the existing literature, six key
factors that might influence the difference in AEE are hypothesized, and the mechanisms
of each factor are tested through an empirical model. This study makes theoretical and
practical contributions. Firstly, we extend the research method of AEE evaluation, improve
the accuracy and comprehensiveness of the evaluation results, and also enrich the theo-
retical foundation of the field. Secondly, this study constructs an AEE indicator system
under carbon emission constraints and provides a detailed method for measuring carbon
emissions in agricultural production, and the evaluation results can provide the necessary
scientific basis for improving agricultural production efficiency and limiting agricultural
carbon emissions. Finally, this study constructs a data-driven AEE evaluation and opti-
mization process, which provide the basis to formulate agricultural environmental policies
based on environmental sustainability, production sustainability, and energy sustainability,
and provide the necessary basis for improving agricultural production and environmental
protection.
In Section 2, we briefly review the previous literature on AEE evaluation and opti-
mization. Section 3provides the methodology and data. Section 4evaluates the AEE and
its distribution characteristics, dynamic evolution trends in Anhui Province from 2015 to
2020, and examines the influencing factors of AEE. In Section 5, we discuss the calculation
results and empirical results and summarize the innovations and advantages of this study,
as well as the research contributions. Section 6gives conclusions and provides some policy
recommendations combining the evaluation and empirical results.
2. Literature Review
To date, many research results have been generated on AEE [
7
,
12
–
25
,
27
,
28
], and
scholars have mainly focused on the following three aspects.
2.1. AEE Measurement and Evaluation Methods
The existing methods for measuring and evaluating AEE are relatively diverse. As
the research on AEE assessment continues to deepen, scholars have begun to consider
the agricultural production process as a multi-input and multi-output system, and data
envelopment analysis (DEA) models have become commonly used to measure AEE. The
basic idea of this efficiency measurement method is to compose the input data and output
data of the observed Decision-Making Units (DMUs), (DEA refers to the object of efficiency
measurement as DMU, which can be any department or unit with measurable inputs and
outputs, such as cities, manufacturers, schools, hospitals, project implementation units
(regions), or individuals, such as teachers, students, doctors, etc. DMUs must be comparable
Sustainability 2022,14, 11849 3 of 22
with each other) into a production possibility set, to determine the production efficient
frontier, and judge whether the inputs and outputs of the decision unit are reasonable
by measuring the distance of each DMU from this frontier, i.e., whether it is technically
efficient. The most commonly used models are the Banker, Bardham and Cooper (BBC) [
29
]
and Charnes, Cooper and Rhodes (CCR) models [30].
The CCR model and BCC model are divided into input perspective and output per-
spective, and the evaluation results of both perspectives are consistent. This study briefly
introduces the two models with the output perspective as an example. Specifically, first
assume that there are n DMUs of the same type, mand srepresent the number of input
types and output types of each DMU, respectively, which can be expressed in matrix form
as follows:
Xj=X1j,X2j,··· ,Xmj T
Yj=Y1j,Y2j,··· ,Ysj T
Xj
and
Yj
represent the input and output vectors of the
DMUj
,
j=
1, 2,
···
,
n
.
xmj
is the m
type of input on the
DMUj
, and
xmj >
0;
ysj
is the total of the
s
type output on the
DMUj,
ysj >0; S−and S+are the input and output slack variables, respectively.
At this time for DMUj0the CCR model is as follows:
max{α}
s.t. n
∑
j=1
Xjλj+S−=Xj0
n
∑
j=1
Yjλj−S+=αYj0
λj≥0, S−≥=0, S+≥0
where
Xj0
denotes the input vector of
DMUj0
, and
Yj0
denotes the output vector of
DMUj0
;
α
denotes the output expansion ratio, and
λ
denotes the linear combination coefficient of
the DMU.
α∗
denotes the optimal solution of the model. If
α∗=
1,
S−∗ +S+∗=
0, then
the
DMUj0
is DEA effective, i.e., the comprehensive technical efficiency of the DMU under
evaluation is maximised; if
α∗<
1, then is DEA invalid, and the comprehensive technical
efficiency of the DMU is not at the desired level.
The BCC model is based on the CCR model with the introduction of constraints
n
∑
j=1
λj=
1, whose production frontier is variable in terms of payoffs to scale. The mathe-
matical expression of the model is as follows:
max{α}
s.t. n
∑
j=1
Xjλj+S−=Xj0
n
∑
j=1
Yjλj−S+=αYj0
n
∑
j=1
λj=1
λj≥0, S−≥=0, S+≥0
the optimal solution of model BCC is
α∗
,
λ∗
,
S−∗
,
S+∗
, when and only when
α∗=
1, and
S−∗ =S+∗=
0, the evaluated DMU is DEA valid; When
α∗<
1, then the evaluated DMU
is considered to be DEA invalid.
The CCR model and the BCC model are currently widely used in the study of AEE.
For example, Yougbarãet al. used this approach to analyze agricultural performance in
Burkina Faso [
31
], Martin et al. analyzed the production and investment efficiency of food
processing [
32
], and Gurdeep et al. evaluated the energy use efficiency of wheat in north-
west India [
33
]. Slacks-Based Measure (SBM) models have been increasingly used, because
they can better take into account slack improvements. For example, Li et al. evaluated
Sustainability 2022,14, 11849 4 of 22
the AEE of the Jianghuai Ecological Economic Region; what they found was an increasing
trend in the AEE of the region from 2005 to 2019, and lower trends were observed in both
redundancy and deficiency rates of undesired and desirable outputs [
34
]. Li et al. analyzed
the total factor productivity of the agricultural environment in
30 Chinese provinces
from
2001 to 2007, and the evaluation results showed a positive trend. Based on the evaluation
results, the study also proposed countermeasures to optimize the development of agricul-
tural industrialization, etc. [
35
]. This is informative for the application of the SBM model,
as well as for the analysis of the evaluation results.
The DEA–Malmquist index became a common choice to reflect the dynamic changes
in efficiency. For example, Pan et al. used this method to carry out a study on sustainable
agricultural development and efficiency in China [
36
], and Xie et al. measured the total
factor productivity change characteristics of arable land use in China’s major grain produc-
ing regions from 1993–2016 [
37
]. These studies provide research ideas for the study of the
dynamics of efficiency and lay the foundation for studying the drivers of efficiency.
2.2. Development of the Evaluation Index System
The AEE indicator system is a complex system, often accompanied by multiple inputs
and multiple outputs. Martin et al. considered agricultural labor and production materials
as input indicators [
32
]. Yougbarãet al. constructed an evaluation indicator system that
included input indicators such as arable land area and labor input, and output indicators
such as crop output [
31
]. Gu et al. added fertilizer as another input indicator [
38
,
39
].
Theodoros pointed out that the quantity of pesticides used in agricultural production is
crucial, and has a great impact in the evaluation of AEE [
40
]. Guo added agricultural
plastic film (also known as plastic mulch) as an input indicator in the analysis process,
further suggesting that agricultural production requires energy consumption, and the
consumption of agricultural diesel fuel should be included among the input indicators
from the perspective of energy conservation and environmental protection [
41
]. Luo et al.
included farmers’ per capita income as an output indicator in the evaluation index to
better reflect the role of AEE in achieving sustainable agricultural development. Moreover,
because the use of agricultural plastic films can bring about environmental pollution while
improving agricultural yield, it was also added into the evaluation index system [
12
].
Kuang et al. introduced the total power of farm machinery and agricultural irrigated area
among the input indicators, because they are essential elements in agricultural production
nowadays and contribute to greenhouse gas emissions [
42
]. As research progresses, scholars
have reached a realization that some undesired outputs inevitably occur in the agricultural
production process, and ignoring undesired outputs may produce misleading results in
assessing efficiency [
43
,
44
]. Considering that a large quantity of greenhouse gas is emitted
in agricultural production, some scholars have started to include greenhouse gases as
an undesired output in the evaluation index system, among which carbon emissions are
mostly included as undesired outputs [45,46].
2.3. AEE Optimization Measures
To optimize the AEE, scholars in different countries made suggestions based on the
evaluation results reflecting on their own national characteristics. Zainab et al. evaluated
the AEE of South Asian countries and suggested the need for more cooperation among
them in scientific research and agricultural development [
47
]. Dong et al. discussed policies
to support the development of low-carbon agriculture, including subsidizing low-carbon
agricultural technology development and diffusion and eliminating subsidies for high-
carbon production factors [
46
]. Gurdeep et al. proposed that the improvement of AEE
requires the implementation of strong interventions to develop intensive agriculture, which
requires strategic planning of the different resources used in crop production [
33
]. Gai
et al. examined agricultural land use efficiency in China’s grain-producing regions and
proposed optimal strategies to increase agricultural science and technology investment,
strengthen agricultural infrastructure construction, and optimize resource allocation [
48
].
Sustainability 2022,14, 11849 5 of 22
There are many other aspects investigated, such as promoting diversification of agricultural
production [
49
], implementation of biodiversity-friendly farming practices [
50
], optimizing
the use of agricultural surplus resources [51], and pricing [52].
Based on the above literature review, it can be observed that the current academic
research on AEE has achieved rich results and laid a solid theoretical foundation for this
research area, but there are still aspects requiring further expansion regarding research
content and research methods.
First, in the evaluation method of AEE, the efficiency values measured by the CCR
and BCC models are based on the assumption of equal proportional reduction of inputs,
or equal proportional increase of outputs [
29
,
30
], which is contradictory to the actual
economic and production activity conditions. The SBM model can avoid the disadvantages
of equal down-scaling of inputs and up-scaling of outputs, and has the ability to examine
the efficiency values in the case of the existent undesired outputs, and has been used more
often in AEE studies [
41
,
53
,
54
]. The SBM model has its advantages, but the drawbacks
remain obvious [
55
]. The objective function of the SBM model is to minimize the efficiency
value, i.e., to maximize the inefficiency value of inputs and outputs [
56
]. In a practical sense,
the ideal situation would be for all evaluation objects to reach the production front surface at
minimum cost, while the SBM model calculates the opposite, which is the main shortcoming
of the SBM model [
57
]. Simultaneously, most of the research methods used to investigate
the trend of regional AEE changes are Malmquist models, which can evaluate the changes
in efficiency, but cannot visualize the characteristics of efficiency distribution. To remedy
these shortcomings, it is generally necessary to build a better efficiency evaluation model
and research method to achieve more accurate AEE evaluation, and to visually demonstrate
the efficiency distribution characteristics and dynamic evolution of regional AEE.
Second, scholars have established different evaluation metrics due to the different
focus of the study region and the study content [
12
,
31
,
32
,
38
,
39
,
41
,
42
]. Current scholars have
less consideration of undesired outputs when evaluating AEE. Considering the current
carbon emission limits being pursued in many countries globally, agricultural carbon
emissions, as an important overall contributor, should rightly be included in the studies
of AEE [
58
,
59
]. Some scholars have addressed the measurement of carbon emissions in
studies of environmental efficiency [
45
,
46
], but to our knowledge, the current literature
has not been able to comprehensively include all elements of carbon emissions in the
agricultural production process. In general, it is necessary to include carbon emissions
as an undesired output in AEE evaluations, and the calculation of total carbon emissions
requires further improvement. It is worth mentioning that limiting carbon emissions
has become an important task that the whole world should face together. In addition to
agriculture, research in industry [60], manufacturing [61], and other related fields, should
also consider carbon emissions as an important evaluation element in an appropriate
manner, and actively propose optimization and improvement solutions.
Finally, to optimize regional AEE and improve the sustainable development of agricul-
ture, it is necessary to further propose scientific and accurate countermeasures based on the
AEE evaluation results [
62
]. The current AEE optimization countermeasures proposed by
scholars are either based on the qualitative analysis of local policies [
33
,
46
,
47
,
63
], or on the
analysis of redundant variables of DEA evaluation results [
31
–
33
,
48
], to propose optimiza-
tion countermeasures to reduce inputs and increase outputs [
64
]. The proposal of scientific
and accurate optimization countermeasures needs to be based on quantitative analysis
and evaluation of the reasons for the differences in AEE. With the profound changes in
agriculture and the natural environment, further research is required to determine the
influencing factors based on the regional AEE evaluation results, and to propose practical
optimization suggestions.
To address the above challenges, fill the existing knowledge gaps, and better opti-
mize AEE for sustainable agricultural development, this study proposes a data-driven
approach to achieve the assessment and optimization of AEE. This study makes several
theoretical and practical contributions. The theoretical contributions are as follows. First,
Sustainability 2022,14, 11849 6 of 22
to compensate for the disadvantages and unrealistic aspects of traditional DEA models, the
study constructs the Minimum Distance to Strong Efficient Frontier (MinDS) evaluation
model to improve the accuracy and scientific approach of evaluation results. Using the
kernel density estimation analysis method, the dynamic evolution trend and distribution
characteristics of AEE are presented visually. The application of multiple methods expands
the research base in this field. Second, we clarify that the evaluation index of AEE should
fully consider carbon emissions in the current economic and social context. To this end,
this study designs a comprehensive content evaluation index system, which includes in-
put variables, output variables, and incorporates carbon emissions as undesired outputs.
Furthermore, the study more accurately and comprehensively calculates the total carbon
emissions in the agricultural production process. Thirdly, we constructed a data-driven
AEE evaluation and optimization process, analyzed the influencing factors that cause
differences in AEE using scientific mathematical methods, and empirically demonstrated
the influence mechanisms of these factors to provide the necessary theoretical basis for
proposing a scientific optimization strategy.
The practical contributions of this study are as follows. First, a systematic AEE
evaluation and optimization process is constructed, which can help governments and
researchers evaluate AEE more objectively. Along with the evaluation and optimization
for AEE, this set of methodological processes can also be used to solve other similar
issues. Second, based on the key factors affecting AEE, an action strategy for optimizing
its use is proposed, which contributes to improving agricultural production efficiency,
reducing environmental pollution, controlling carbon emissions, and promoting sustainable
development of agriculture.
3. Methods and Data
3.1. Methods and Models
3.1.1. MinDS Model
SBM models use the farthest projection point on the efficient frontier [
56
], therefore,
the improvement potential of non-effective DMUs can be overestimated, which ultimately
leads to a lower measured efficiency value than its true value [
55
]. Therefore, it is easy to
devise a method to overcome this drawback by using the nearest point on the efficient front
as the projection point. For this reason, Aparicio et al. devised a calculation method for the
MinDS model, which has gained widespread attention because of the relative simplicity
of the calculation process and its wide applicability [
57
]. This method does not require
the determination of the hyperplane of all fronts, and can be used to limit the reference
markers of the DMUs (decision-making unit) being evaluated to the same hyperplane by
adding constraints. After determining the effective DMUs through the SBM model, the
method proposed by Aparicio requires only one planning formula to solve the MinDS
model, regardless of the number of effective DMUs. This method first requires to find the
set F of effective DMUs through the SBM model, and then use the mixed integer linear
programming method to find the MinDS model efficiency value by using the effective
subset F as its reference set, and the closest point on the strong effective frontier as the
projection point.
It is assumed that there are
A(a=1, 2, ··· ,A)
periods and
N(n=
1, 2,
···
,
N)
DMU,
each DMU has
m
input, denoted as
xi(i=1, 2, ··· ,m)
, and the weights of the inputs
are
vi
;
q1
the desired output, denoted as
yr(r=1, 2, ··· ,q1)
, with weights of
µr
;
q2
the
nondesired outputs, denoted as
zp(p=1, 2, ··· ,q2)
, with a weight of
βp
. The weight of
each
DMU
is
λj(j=1, 2, ··· ,N)
. When
bj=
1,
dj6M
,
λj=
0, then the
DMUj
is not the
reference marker; when
bj=
0,
dj=
0,
λj6M
, then the
DMUj
is the reference benchmark.
In this study, A represents the examination period, and takes the value of 2015–2020; N
represents the number of study cities, and this study contains 16 cities; m represents the
number of input indicators of AEE, and contains eight input indicators, denoted as x
1
–x
8
;
q1
represents the number of desired outputs of AEE, and contains two desired outputs
indicators, denoted as
y1and y2
;
q2
represents the number of undesired output, contains
Sustainability 2022,14, 11849 7 of 22
one undesired output, i.e., carbon emissions, denoted as
z1
. These variables are shown in
Table 1. The MinDS model considering the undesired output is shown in Equation (1):
maxρk=
1
m∑m
i=11−s−
i
xik
1
q1∑q1
r=11+s+
r
yrk +1
q2∑q2
p=1 1+s−
p
zpk !
s.t.∑
j∈F
A
∑
a=1
xij λj+s−
i=xik,i=1, 2, ··· ,m
∑
j∈F
A
∑
a=1
yrj λj−s+
r=yrk,r=1, 2, ··· ,q1
∑
j∈F
A
∑
a=1
ypj λj+s−
p=zpk,p=1, 2, ··· ,q2
s−
i>0, i=1, 2, ··· ,m
s+
r>0, r=1, 2, ··· ,q1
s−
p>0, p=1, 2, ··· ,q2
λj>0, j∈F
−m
∑
i=1
vixij +
q1
∑
r=1
µryrj −
q2
∑
p=1
βpzpj +dj=0
vi>1, i=1, 2, ··· ,m
µr>1, r=1, 2, ··· ,q1
βp>1, p=1, 2, ··· ,q2
dj6Mbj,j∈E
λj6M(1−bi),j∈E
bj∈{0, 1},j∈E
dj>0, j∈E
(1)
where Mis a sufficiently large integer and those DMUs evaluated are optimal only if the
slack variables are all zero. Most importantly, the MinDS model allows the analysis of
undesired output indicators and enables the input-output indicators to achieve the most
efficient desired goal at the lowest cost.
Table 1. AEE input-output indicator system.
Indicator Type Index Variable Unit Reference
Input index
Arable land area x1hm2[31]
Number of employed persons in agriculture
x2
10,000 persons
[31,32]
Amount of chemical fertilizer used x3ton [38,39]
Amount of pesticides used x4ton [40]
Amount of agricultural plastic film used x5ton [12,41]
Total power of agricultural machinery x610,000 kw [42]
Agricultural diesel use x7ton [41]
Agricultural irrigation area x81000 hm2[42]
Desirable Output index Disposable income of rural residents r1Yuan/person [12]
Gross agricultural production r210,000 Yuan [31,32,38]
Undesirable Output index Carbon emissions z1kg [45,46]
3.1.2. Kernel Density Estimation
It is difficult to directly see the distribution characteristics and dynamic evolution
law of AEE by only relying on the efficiency evaluation results of the MinDS model. To
further enrich the research results, the study uses the kernel density estimation method
to analyze the dynamic evolution law of its differences. As a nonparametric estimation
method, kernel density estimation has been widely used to analyze the non-equilibrium of
space. Based on the analysis of the extension, peak, and change position of the continuous
density curve, the overall situation of the regional environmental efficiency distribution can
Sustainability 2022,14, 11849 8 of 22
be explored, and the dynamic changes of regional environmental efficiency can be grasped
more comprehensively. Assuming that the density function of the random variable xis
f(x), the expression is:
f(x) = 1
Nh
N
∑
i=1
Kxi−x
h(2)
In Equation (2),
xi
,
x
denotes the measured AEE values and their mean values for each
region, respectively;
N
,
h
,
K(·)
represent the number of study regions, the bandwidth,
and the kernel function, respectively. Gaussian Kernel, Epanechnikov Kernel, and Quartic
Kernel, are the most commonly used kernel functions, and the Gaussian Kernel is the most
widely used one for estimation:
K(x) = 1
√2πexp [−x2
2](3)
3.1.3. Tobit Regression Model
A comprehensive and accurate evaluation of regional AEE can be achieved by relying
on the MinDS model and kernel density estimation, but the influencing factors of efficiency
cannot be evaluated. To further explore the influencing factors of AEE and lay the foun-
dation for proposing scientific optimization countermeasures, here a multiple regression
model will be established, with the efficiency values measured by the MinDS model as
the explanatory variables and the possible influencing factors as the explanatory variables.
The efficiency values measured by the MinDS model considering undesired outputs take
values in the range (0, 1), which are truncated data, when the parameter estimates would
be biased and inconsistent if the model is regressed directly by ordinary least squares
(OLS). To address the case of restricted values of the dependent variable, Tobin in 1958 pro-
posed a regression model for estimating the restricted dependent variable by the maximum
likelihood method, also known as the Tobit model [65], with the following expression:
Yi=α0+
n
∑
i=1
αiXi+εi;i=1, 2, 3 ···n(4)
where
Yi
is the explained variables, i.e., the AEE value;
Xi
is the explanatory variable;
ai
is the parameter to be estimated;
εi
is the random variable; and
n
is the number of
explanatory variables.
3.2. Research Case
In Section 3.1, the research constructs a data-driven AEE evaluation and optimization
method. To further empirically demonstrate the effectiveness of this research process, this
paper takes the Anhui Province in China as an example to show the implementation process
of the above method. Anhui Province is a vitally important part of China’s agricultural
production area and is of strategic importance to the country’s food security. Therefore,
it was adopted as our empirical study area. Anhui Province is located in the east-central
part of China and is the most dynamic component of the Yangtze River Delta. Anhui
Province is about 570 km long from north to south and 450 km wide from east to west, with
a total area of 140,100 km
2
, accounting for approximately 1.45% of China’s land area. By
the end of 2021, Anhui Province had a resident population of 61.13 million people. There
are 16 cities in Anhui Province with a population greater than 1,300,000, namely, Hefei,
Huabei, Bozhou, Suzhou, Bengbu, Fuyang, Huainan, Chuzhou, Lu’an, Ma’anshan, Wuhu,
Xuancheng, Tongling, Chizhou, Anqing, and Huangshan. According to the surveying and
mapping law of the People’s Republic of China, the 16 cities in Anhui Province can be
spatially divided into northern, central, and southern Anhui [
66
]. Their districts are shown
in Figure 1:
Sustainability 2022,14, 11849 9 of 22
Sustainability 2022, 14, x FOR PEER REVIEW 9 of 24
Anhui Province is about 570 km long from north to south and 450 km wide from east to
west, with a total area of 140,100 km2, accounting for approximately 1.45% of China’s land
area. By the end of 2021, Anhui Province had a resident population of 61.13 million people.
There are 16 cities in Anhui Province with a population greater than 1,300,000, namely,
Hefei, Huabei, Bozhou, Suzhou, Bengbu, Fuyang, Huainan, Chuzhou, Lu’an, Ma’anshan,
Wuhu, Xuancheng, Tongling, Chizhou, Anqing, and Huangshan. According to the sur-
veying and mapping law of the People’s Republic of China, the 16 cities in Anhui Province
can be spatially divided into northern, central, and southern Anhui [66]. Their districts are
shown in Figure 1:
Figure 1. Schematic diagram of Anhui region.
3.3. Variable Selection and Data
3.3.1. Variables for Measuring AEE
Based on the existing literature [12,31,32,38–46], we selected 11 indicators to construct
the evaluation system of AEE, as shown in Table 1. The input variables mainly include
two aspects of agricultural production, one is the production base, which includes arable
land area, irrigated agricultural area, and agricultural employees; the other is the produc-
tion process inputs including fertilizer, pesticide, agricultural plastic film, total agricul-
tural machinery power, and agricultural diesel fuel. The desired output is composed of
economic output and food output, which are represented by the disposable income of
rural residents and gross agricultural output value, respectively. In the agricultural pro-
duction process, the generation of desired output is usually accompanied by undesired
output, and the undesired output in this study is the total amount of carbon emissions
generated from engaging in the agricultural production process.
It has been well documented that both the agricultural production base and the agri-
cultural production process contribute directly or indirectly to carbon emissions [67,68].
However, the main sources of carbon emissions considered by current scholars are tillage,
agricultural machinery power, fertilizers, pesticides, agricultural films or mulches, and
irrigation. To our knowledge, less literature considers carbon emissions from the use of
diesel fuel. In this study, based on previous research [42,69–71], a more comprehensive
table of carbon emission factors was constructed based the inclusion of diesel consump-
tion (Table 2). The carbon emission calculation formula is given in Equation (5):
(5)
Figure 1. Schematic diagram of Anhui region.
3.3. Variable Selection and Data
3.3.1. Variables for Measuring AEE
Based on the existing literature [
12
,
31
,
32
,
38
–
46
], we selected 11 indicators to construct
the evaluation system of AEE, as shown in Table 1. The input variables mainly include two
aspects of agricultural production, one is the production base, which includes arable land
area, irrigated agricultural area, and agricultural employees; the other is the production
process inputs including fertilizer, pesticide, agricultural plastic film, total agricultural
machinery power, and agricultural diesel fuel. The desired output is composed of economic
output and food output, which are represented by the disposable income of rural residents
and gross agricultural output value, respectively. In the agricultural production process,
the generation of desired output is usually accompanied by undesired output, and the
undesired output in this study is the total amount of carbon emissions generated from
engaging in the agricultural production process.
It has been well documented that both the agricultural production base and the
agricultural production process contribute directly or indirectly to carbon emissions [
67
,
68
].
However, the main sources of carbon emissions considered by current scholars are tillage,
agricultural machinery power, fertilizers, pesticides, agricultural films or mulches, and
irrigation. To our knowledge, less literature considers carbon emissions from the use of
diesel fuel. In this study, based on previous research [
42
,
69
–
71
], a more comprehensive
table of carbon emission factors was constructed based the inclusion of diesel consumption
(Table 2). The carbon emission calculation formula is given in Equation (5):
E=
n
∑
i=1
Ei =
n
∑
i=1
TiCi (5)
Edenotes total agricultural carbon emissions; Ei denotes carbon emissions from different
agricultural inputs, Ti denotes various carbon sources, and Ci denotes the applicable carbon
emission coefficient (Table 2).
Table 2. Carbon emission coefficients of the main carbon sources of AEE.
Carbon Source Tillage Irrigation Diesel Agricultural
Machinery Fertilizer Pesticide Agricultural
Film
Coefficient 3.126
(kg/hm2)
25
(kg/hm2)
592.7
(kg/ton)
0.18
(kg/kw)
0.8956
(kg/kg)
4.9341
(kg/kg)
5.18
(kg/kg)
3.3.2. Variables Affecting AEE
To provide the necessary scientific basis for the proposed optimal countermeasures,
further scientific analysis of the influencing factors of AEE and their influencing mech-
Sustainability 2022,14, 11849 10 of 22
anisms was needed. In this section, the AEE value measured by the MinDS model was
taken as the dependent variable, and the possible influencing factors from economic, social,
and demographic aspects, were selected as independent variables, and a Tobit regression
analysis model was established to investigate the influence of each factor on AEE and its in-
fluencing mechanism. Through the study of the existing literature, six possible influencing
factors were selected for this paper (Table 3).
Table 3. Description of the variables characterizing the influencing factors.
Influencing Factors Indicator Description Abbreviation Unit
Population Aggregation Level Population density, total regional population/regional area PAL persons/km2
Education Level Regional years of education per capita EL years
Urbanization Level urban population/total population of the region UL %
Economic Development Level Regional gross domestic product/total regional population EDL Yuan/person
Rural Water Supply Capacity Regional rural piped water supply rate RW %
Village Management Level Number of village committees established in region VML —
The proposed rationale for the six influencing factors and the possible influencing
mechanisms are shown in Appendix A.
Combined with the existing studies, we believe that the above factors may have an
impact on AEE. To eliminate the effect of heteroskedasticity, the natural logarithm of PAL,
GPC, and VML was taken. The Tobit model to test the influence of the factors on AEE and
their influence mechanism can be expressed as:
yit =β0+β1LnPALit +β2ELit +β3ULit +β4LnGPCit +β5RWit +β6LnVMLit +εit (6)
where idenotes the ith city of Anhui province, tpresents the year between 2015 and 2020,
yit
stands for the Agricultural environmental efficiency,
βi
is the coefficient, and
εi
is the
stochastic error.
3.3.3. Data Sources and Description
The data of input and output variables of AEE evaluation in Anhui Province and
the data of influencing factors were obtained from Anhui Provincial Statistical Yearbook in
2015–2020
. The descriptive statistics of these variables is presented in Table 4. The data
of Anhui Provincial Statistical Yearbook is collected by the statistical bureaus of each city
according to the uniform caliber and standard. The statistical data is to serve the decision-
making of Chinese government and the research work of think tanks, the source of the data
is credible and reliable.
Table 4. Descriptive statistics of all variables.
Variables Minimum Maximum Mean Standard Deviation
x194,380 1,255,265 557,991.146 341,274.8181
x210.4 227.7 82.421 52.264
x331,942 383,842 196,574.333 108,457.482
x41463 24,097 6061.333 4653.7418
x5423 21,377 6222.635 5902.2107
x680.81 985.49 414.1158 254.85586
x76295 117,410 47,113.021 30,660.0038
x851.23 503.37 281.9784 151.88268
r19001 25,421 14,420.479 3743.3305
r2323,100 3,386,281 1,508,622.396 864,611.3921
z122,273,108 379,431,334 155,396,589.2 99,067,364.67
PAL 135.7295809 828.1649033 488.9979248 210.8723087
EL 8.13 11.29 9.2058 0.67496
UL 0.37 0.82 0.5505 0.10221
EDL 16,121 115,623 49,754.156 23,098.9609
RW 0.5714 0.996 0.842711 0.1046328
VML 276 1764 908.625 418.1613
Data from the Anhui Provincial Statistical Yearbook in 2015–2020.
Sustainability 2022,14, 11849 11 of 22
4. Results
4.1. Evaluation Results of AEE
We entered all the inputs, expected outputs, and undesired outputs of DUM, into the
MaxDEA 9 software. Then, we used the MinDS model and SBM model to calculate the
AEE of 16 cities in the Anhui Province from 2015–2020. The calculation results are depicted
in Table 5.
Table 5. AEE evaluation results of 16 cities in Anhui Province based on MinDS model.
Region 2015 2016 2017 2018 2019 2020 Mean
Anqing 0.737176899 0.756793904 0.773761605 0.752062674 0.755928849 0.7248397 0.750093938
Bengbu 0.58986775 0.593182359 0.579884187 0.590245837 0.656443694 0.564447448 0.595678546
Bozhou 0.846036332 1 0.775948065 0.594890778 0.545084462 0.571754401 0.722285673
Chizhou 0.688164676 0.655149464 0.701873996 0.782966424 0.864379506 1 0.782089011
Chuzhou 0.629923299 0.654182841 0.671354327 0.690153186 0.747582172 0.792030206 0.697537672
Fuyang 0.717930352 0.748742202 0.624964986 0.830173182 0.901116847 1 0.803821262
Hefei 0.693375431 0.765890737 0.841765525 0.901299238 0.95909807 1 0.860238167
Huaibei 0.671202651 0.626439743 0.66893453 0.674083218 0.653283423 0.719512829 0.668909399
Huainan 0.444101309 0.470669371 0.484104041 0.476733982 0.567173138 0.609254471 0.508672719
Huangshan 0.590535175 0.752061508 0.829672831 0.869825605 0.910251368 1 0.825391081
Lu’an 0.711851545 0.75431589 0.766370726 0.755573149 0.857664478 1 0.807629298
Ma’anshan 0.802736687 0.821950202 0.833568245 0.854108458 0.903572054 1 0.869322608
Suzhou 0.494257839 0.531944112 0.566480534 0.585002382 0.599515188 0.62364269 0.566807124
Tongling 0.709830435 0.710674944 0.743167955 0.793129068 0.847332556 1 0.800689160
Wuhu 0.824960947 0.84841145 0.848106028 0.826709851 0.900563881 1 0.874792026
Xuancheng 0.74300186 0.750059964 0.808705234 0.799389514 0.879960012 1 0.830186097
Data from the MaxDEA 9 software analysis.
By comparing the data in Tables 5and 6, it can be observed that the SBM model
amplifies the improvement potential of the evaluated area, and the evaluated efficiency
values are low compared to MinDS. It is also evident from Table 6that there are some
abnormal evaluation results in the SBM model, such as the large differences in Fuyang,
Lu’an, and Ma’anshan in 2019–2020, with a sudden change to DEA effective with lower DEA
efficiency values, while the data of Bozhou in 2016 are highly different from neighboring
years, which are not in line with the normal development law. However, such anomalies
do not appear in the evaluation results of MinDS. This comparison further corroborates the
arguments presented in the previous section. The following analysis and discussion are
based on the evaluation results of the MinDS model.
Table 6. AEE evaluation results of 16 cities in Anhui Province based on SBM model.
Region 2015 2016 2017 2018 2019 2020 Mean
Anqing 0.211286976 0.228906882 0.246741288 0.269749309 0.306464533 0.381906729 0.274175953
Bengbu 0.24141075 0.267606966 0.292685177 0.314075107 0.376805142 0.486095721 0.329779811
Bozhou 0.174289881 1 0.221132607 0.253237559 0.314271172 0.370807515 0.388956456
Chizhou 0.664045838 0.591390071 0.622010083 0.735978405 0.84437789 1 0.742967048
Chuzhou 0.220412641 0.236742452 0.256645139 0.283183645 0.30057585 0.380314396 0.279645687
Fuyang 0.22329406 0.251614849 0.317643687 0.291023199 0.417155269 1 0.416788511
Hefei 0.378154726 0.426298117 0.488221166 0.559882476 0.7532761 1 0.600972098
Huaibei 0.351321302 0.357507986 0.487988244 0.530750256 0.599130013 0.704770799 0.505244767
Huainan 0.200580882 0.214072279 0.233226199 0.265992322 0.27166645 0.3068305 0.248728105
Huangshan 0.579480169 0.61939152 0.662572986 0.748160209 0.858231922 1 0.744639468
Lu’an 0.190844672 0.214850769 0.252822181 0.28495916 0.351544883 1 0.382503611
Ma’anshan 0.476286079 0.497651996 0.522173559 0.531823329 0.605175699 1 0.605518444
Suzhou 0.121798837 0.127122077 0.141491842 0.153150436 0.170726826 0.189308285 0.150599717
Tongling 0.472400464 0.569645725 0.624418532 0.699598717 0.822706041 1 0.698128247
Wuhu 0.523089031 0.587778372 0.622996598 0.677975134 0.826518955 1 0.706393015
Xuancheng 0.434072908 0.529936554 0.613196241 0.658619428 0.797918298 1 0.672290571
Data from the MaxDEA 9 software analysis.
Sustainability 2022,14, 11849 12 of 22
From the average value of AEE in Table 5, it can be observed that the 16 cities in Anhui
Province are still some distance away from the effective AEE in 2015–2020. Wuhu city has
the highest efficiency value, with an average value of 0.87, and three cities had an average
AEE value less than 0.6, namely, Bengbu (0.59), Suzhou (0.56), and Huainan (0.50). The
results of these calculations indicate that the AEE of some cities in Anhui Province is not
high and there is still much space for improvement.
To better show the change trend of AEE in the three regions of the Anhui Province,
this study divided it into northern, central, and southern Anhui, and plotted the change
trend of AEE values from 2015–2020. There was an overall increasing trend of AEE in the
three regions (Figure 2). Among them, the AEE of southern Anhui increased from 0.72
in 2015 to 1 in 2020, indicating that this region reached almost the best possible efficiency
in 2020, compared to other regions. Moreover, the AEE of northern and central Anhui
also improved to some extent. Comparing the three curves in Figure 2, we can clearly
see that the AEE shows a clear geographical difference, with southern Anhui performing
better than the other regions in terms of carbon emission reduction and sustainability of
agricultural production. We can observe that the overall performance of AEE in Anhui
Province is good and in a stable growth trend, and that the growth rate gradually increased
from 2017–2020 (Figure 2).
Sustainability 2022, 14, x FOR PEER REVIEW 13 of 24
Figure 2. AEE of three regions in Anhui Province, as well as the average of the overall values for
each year. The data used in the figure are from Table 5.
4.2. Distribution Characteristics and Dynamic Evolution of AEE
To further reveal the spatial distribution and evolutionary trends of AEE in Anhui
Province, the non-parametric kernel density estimation of Gaussian normal distribution
was calculated using the Stata 16.0 statistical software for 2015, 2017, 2019, and 2020 (Fig-
ure 3). The overall position of the nuclear density curve shows a trend of shifting to the
right from 2015–2020, indicating that the AEE in Anhui Province has generally been in-
creasing during the examined period (Figure 3), which is consistent with the results of the
previously described analysis.
Figure 3. AEE kernel density estimation in Anhui Province. The data used in the figure are from
Table 5.
4.3. Influencing Factors of AEE
Using the Stata 16.0 software, the Tobit regression method was applied to test the
influence mechanism of the influencing factors hypothesized in Section 3.3.2. The results
0.5
0.6
0.7
0.8
0.9
1
2015 2016 2017 2018 2019 2020
Mean AEE value
Southern Anhui Region Central Anhui Region
Northern Anhui Region Anhui Region
Figure 2.
AEE of three regions in Anhui Province, as well as the average of the overall values for each
year. The data used in the figure are from Table 5.
4.2. Distribution Characteristics and Dynamic Evolution of AEE
To further reveal the spatial distribution and evolutionary trends of AEE in Anhui
Province, the non-parametric kernel density estimation of Gaussian normal distribution
was calculated using the Stata 16.0 statistical software for 2015, 2017, 2019, and 2020
(Figure 3). The overall position of the nuclear density curve shows a trend of shifting to
the right from 2015–2020, indicating that the AEE in Anhui Province has generally been
increasing during the examined period (Figure 3), which is consistent with the results of
the previously described analysis.
4.3. Influencing Factors of AEE
Using the Stata 16.0 software, the Tobit regression method was applied to test the
influence mechanism of the influencing factors hypothesized in Section 3.3.2. The results of
the regression analysis are shown in Table 7. All explanatory variables except PAL and UL
were significant at the 1% and 5% levels, which indicates that EL, EDL, RW, and VML, have
strong explanatory power for the disparity in AEE. Among them, EDL, RW, and VML, had
a significant positive effect and EL a significant negative effect (Table 7).
Sustainability 2022,14, 11849 13 of 22
Sustainability 2022, 14, x FOR PEER REVIEW 13 of 24
Figure 2. AEE of three regions in Anhui Province, as well as the average of the overall values for
each year. The data used in the figure are from Table 5.
4.2. Distribution Characteristics and Dynamic Evolution of AEE
To further reveal the spatial distribution and evolutionary trends of AEE in Anhui
Province, the non-parametric kernel density estimation of Gaussian normal distribution
was calculated using the Stata 16.0 statistical software for 2015, 2017, 2019, and 2020 (Fig-
ure 3). The overall position of the nuclear density curve shows a trend of shifting to the
right from 2015–2020, indicating that the AEE in Anhui Province has generally been in-
creasing during the examined period (Figure 3), which is consistent with the results of the
previously described analysis.
Figure 3. AEE kernel density estimation in Anhui Province. The data used in the figure are from
Table 5.
4.3. Influencing Factors of AEE
Using the Stata 16.0 software, the Tobit regression method was applied to test the
influence mechanism of the influencing factors hypothesized in Section 3.3.2. The results
0.5
0.6
0.7
0.8
0.9
1
2015 2016 2017 2018 2019 2020
Mean AEE value
Southern Anhui Region Central Anhui Region
Northern Anhui Region Anhui Region
Figure 3.
AEE kernel density estimation in Anhui Province. The data used in the figure are from
Table 5.
Table 7. Results of Tobit regression. See Table 3for factor abbreviations.
Independent Variable Coefficient Standard Error T-Value p-Value
PAL −0.0293694 0.0263043 −1.12 0.267
EL −0.089639 0.0394649 −2.27 0.026
UL 0.0043063 0.0028985 1.49 0.141
EDL 0.1790713 0.0622738 2.88 0.005
RW 0.4611818 0.1460967 3.16 0.002
VML 0.0930493 0.0308424 3.02 0.003
5. Discussion
This study constructed a data-driven AEE evaluation and optimization process, and
empirically demonstrated the influencing factors of AEE using scientific quantitative analy-
sis, which lays the foundation for more accurate policy recommendations. The evaluation
results and influencing factors are further analyzed and discussed below.
5.1. Discussion on AEE Evaluation Results
In order to look at each city, Wuhu, Tongling, Ma’anshan, Hefei, Xuancheng, Huang-
shan, Lu’an, and Fuyang, performed better overall, with an average AEE value reaching
0.8 or more during the study period. Wuhu had the highest average AEE (0.87), which
indicates that it has a high efficiency and a reasonable allocation of resources in the process
of agricultural production, while also paying attention to controlling carbon emissions.
Moreover, there were nine cities with an efficiency score of 1.0 in 2020, namely Wuhu,
Ma’anshan, Hefei, Huangshan, Lu’an, Xuancheng, Tongling, Chizhou, and Fuyang. This
indicates that these cities achieved effective AEE in 2020, including the best relative re-
source efficiency, agricultural production efficiency, and reasonable carbon emissions. With
the exception of Anqing, Bozhou, and Bengbu, most of the cities improved their AEE
to different degrees during the study period, which indicates that the utilization of agri-
cultural resources and production technology are improving, that people’s awareness of
environmental protection is gradually strengthening, and that the capacity for sustainable
development is improving. Huangshan showed the largest improvement, with an AEE
Sustainability 2022,14, 11849 14 of 22
increase of 0.40 during the study period, while Chizhou, Lu’an, Fuyang, and Hefei, also
gained significant improvements, with increases >0.25.
From the three regions of Anhui Province, the average efficiency of northern, central
and southern Anhui was 0.64, 0.78, and 0.83, respectively. During the examination period,
all regions showed an increasing trend in AEE values, with northern, central, and southern
Anhui increasing by 8.64%, 26.86% and 37.6%, respectively. This means that the southern
part of Anhui was better than the other regions in implementing measures to reduce carbon
emissions and promote sustainable agricultural development, while the northern part of
Anhui Province has more space for improvement.
Overall, AEE in Anhui Province showed an increasing trend, from 0.68 in 2015 to
0.85 in 2020, with a growth rate of about 24.87% (Figure 2). The growth rate of AEE in
Anhui increased gradually from 0.005 in 2016–2017 to 0.066 in 2019–2020. Firstly, this could
be attributed to the rapid development of the economic level in the province, in which
the average annual GDP growth reached 8.2% from 2015–2020 [
72
]. Secondly, it could be
due to the improvement in agricultural technology, which promotes the full utilization of
production factors in the production process and reduces their redundancy rate, resulting
in significantly increases in the desired output and reduction of undesired outputs [
73
].
Lastly, the implementation of carbon emission verification and related monitoring work in
Anhui Province from 2016–2020 [
73
,
74
] would have strengthened environmental awareness
and effectively curbed the carbon emissions in the agricultural production process.
5.2. Discussion of the Spatial Distribution and Dynamic Evolution of AEE
Kernel density estimation was carried out to further reveal the spatial distribution
characteristics and dynamic evolution of AEE in Anhui Province. The center of the density
curve of AEE shifted to the right from 2015–2020 (Figure 3), and the AEE in Anhui Province
increased during the examination period, which is consistent with the above findings.
Compared with 2015, the width and height of the AEE density curve in 2020 continued
to decrease, indicating that the variation of AEE in Anhui Province decreased during the
examination period. The number of peaks gradually changed from one to two. The density
curves in 2019–2020 show a pattern of coexistence of main peaks and secondary peaks,
which correspond to cities with higher and lower AEE, respectively. Furthermore, the
density value of the main peak is significantly higher than that of the secondary peak.
These phenomena suggest that the AEE of the Anhui Province is gradually splitting into
two poles. This may be due to the low AEE in the economically less-progressive regions,
where less attention has been paid to environmental protection and agricultural production
efficiency [
75
]. In the more economically developed regions, the AEE is higher due to
reasonable agricultural production methods and appropriate agricultural inputs. Anhui
Province shows a clear situation of relative poverty in the north and relative affluence in
the south, as reflected in GDP values, which will inevitably lead to a polarization of AEE.
5.3. Discussion of the Influencing Factors of AEE
To explore the influencing factors that cause the differences in AEE between cities, and
to lay the foundation for the proposed optimization measures, it is necessary to further
discuss and analyze the regression results of the influencing factors.
The population aggregation levels (PAL) showed a negative but insignificant correla-
tion in the coefficients, and therefore did not have a significant impact on the differences in
AEE during the examined period. This is different from some previous research [
76
,
77
],
but is similar to the findings of Zhang’s study [53].
It is worth noting that the educational level (EL) of the regional population shows
a significant negative correlation with AEE. This indicates that as the education level
of the regional population increases, the AEE decreases, which is opposite to previous
findings
[78–81]
. This result is worthy of attention and can be further explored. In many
countries worldwide, including China, there is often a large developmental gap between
rural and urban areas, and rural areas are relatively poor [
82
–
84
]. Rural residents who have
Sustainability 2022,14, 11849 15 of 22
received more education, especially more knowledgeable young people, often choose to go
to cities in search of better opportunities, which leads to a loss of agricultural talent
[85,86]
.
The higher the level of education, the more serious the problem of rural population deple-
tion, which ultimately results in a high proportion of elderly persons and children in the
population structure of many villages [
87
]. This situation will be detrimental to agricultural
production activities, and to the attention given to environmental protection and reduction
of carbon emissions in agricultural production processes.
The effect of urbanization level (UL) on AEE is positive, and may increase regional
AEE. However, due to the high complexity and multidimensionality of Chinese society,
including the Anhui Province [
88
], this effect is insignificant from the regression results,
which differs from the outcomes of some previous studies [41,89].
The economic development level (EDL) of the region shows a significant positive
correlation with the AEE of the region, which supports the findings of some previous
scholars [
41
,
90
–
92
]. Based on previous research, it is known that regions with higher
levels of economic development have great advantages in terms of production factors
such as input capital, agricultural science and technology, and agricultural management
policies, and thus can better achieve improved agricultural production conditions and
profitability [
92
]. Moreover, economic growth can lead to a significant increase in urban
construction land and a sharp decrease in the extent of agricultural land [
89
], which to
some degree promotes the transformation of agricultural intensification and improves
agricultural production efficiency.
Rural water (RW) supply capacity showed a significant positive correlation with AEE,
which is consistent with previous findings [
79
,
93
,
94
]. This suggests that the regional AEE
can be improved by increasing the rate of piped water supply in rural areas, and the effect
of this improvement is significant. Popova pointed out that the provision of convenient
and uninterrupted water supply to agricultural areas is one of the factors that ensure food
security [
95
]. Considering the limited availability of water resources, the government
should investigate and forecast the demand for water resources in agricultural production
for each region, and then develop management plans to further improve the efficiency of
water use and promote the rational use of water resources [96,97].
As presented in Table 7, the level of village management (VML) showed a significant
positive correlation with regional AEE. This indicates that village committees contribute
significantly to the promotion of regional agricultural production and environmental
sustainability, which supports previous findings [
76
,
98
]. According to Chinese government
policy, village committees are required to manage the land and other property belonging to
the village farmers’ collective, educate villagers to use natural resources wisely, and protect
and improve the ecological environment. Based on the results of the analysis, it is shown
that currently, village committees effectively implement these responsibilities.
5.4. Innovation and Advantages
Sustainable development of agriculture is an important part of sustainable develop-
ment worldwide. To fill the existing knowledge gap, better evaluate and optimize AEE, and
promote the sustainable development of agriculture, this study aimed to construct a data-
driven method for evaluating and optimizing AEE under carbon emission constraints. This
study accomplishes this research objective and, at the same time, proposes the following
three innovations and advantages over the previous literature.
First, considering the obvious disadvantages and unreasonable aspects of the DEA
model commonly used in the previous literature [
31
–
33
,
36
,
41
,
53
,
54
,
99
,
100
], this study
constructs a more accurate and precise MinDS evaluation model, which improves the
accuracy and scientific basis of the evaluation results. This research incorporates the
kernel density estimation analysis method in the evaluation of AEE, which can present
the dynamic evolution trend and distribution characteristics of AEE more intuitively.
Thus, a more accurate and comprehensive AEE evaluation is achieved compared with
previous literature.
Sustainability 2022,14, 11849 16 of 22
Second, regarding the design of evaluation indicators, compared with previous litera-
ture [
12
,
31
,
32
,
38
–
46
], this study constructs a more comprehensive AEE evaluation index
system, which incorporates both basic agricultural production inputs and production pro-
cess inputs in the input indicators. To better reflect the agricultural sustainability attributes
of AEE, the total agricultural output value and per capita income of farmers were taken as
the desired output indicators, and the total carbon emissions in agricultural production
were taken as the undesired output, to fully consider the carbon emissions in the agricul-
tural production process. At the same time, this study makes up for the neglect of carbon
emissions from diesel consumption in the carbon emission calculation, to lay a foundation
for more accurate AEE calculations.
Finally, AEE evaluation was conducted to optimize AEE more accurately. Compared
with previous research [
33
,
46
–
49
,
51
,
52
], this study constructed a more scientific data-driven
AEE optimization process. Based on the results of AEE evaluation and analysis, a scientific
mathematical method is used to empirically demonstrate the influencing factors of AEE
and their influence mechanisms, which provides a more scientific and accurate theoretical
basis for optimization strategy proposals.
5.5. Research Contribution
Currently, the world is facing severe challenges in agricultural production and dealing
with the climate crisis [
18
–
28
]. Studying AEE from a low-carbon perspective is important
for addressing these challenges and promoting global sustainable development. Specif-
ically, this study makes the following three contributions. First, it expands the research
methodology of AEE evaluation, and constructs a combined dynamic and static evaluation
and analysis method. This not only improves the accuracy and comprehensiveness of the
evaluation results, but also enriches the theoretical basis of the field and provide insights
for better evaluation of AEE under environmental constraints. Secondly, the study con-
structs a comprehensive AEE input-output evaluation index system under carbon emission
constraints, and the evaluation results can fully reflect the carbon emission constraints.
The evaluation results can provide the necessary scientific basis for improving agricultural
production efficiency and limiting carbon emissions. Simultaneously, the study introduces
a detailed method of measuring carbon emissions in agricultural production, which can
be used as a reference for other scholars. Finally, this study constructs a data-driven AEE
evaluation and optimization process. Several influencing factors of AEE are empirically
analyzed from social, economic, and demographic perspectives, and action strategies for
optimizing AEE are proposed based on these key factors. This contributes to improving
agricultural production efficiency, reducing environmental pollution, controlling carbon
emissions, and promoting the sustainable development of agriculture.
6. Summary and Policy Suggestions
Currently, agricultural production is facing serious challenges due to the increasing
global population, water shortages, and shrinking arable land. Simultaneously, global sus-
tainable development is affected by environmental pollution and severe carbon emissions
in agricultural production. Therefore, we propose the research objective of constructing a
data-driven approach to evaluate and optimize AEE under carbon emission constraints.
We accomplished this research task, specifically, as follows. Firstly, we constructed a
more comprehensive input-output evaluation index system, including carbon emissions
as undesired outputs, and more accurately measure the total carbon emissions of regional
agriculture. Secondly, we applied the MinDS model to evaluate the AEE more accurately,
and analyzed the regional distribution characteristics and evolution of AEE by using the
kernel density estimation method. Finally, we constructed a data-driven process, validated
the hypothesized six AEE influencing factors and analyzed their influence mechanisms.
The validation results showed that PAL and UL were inconsistent with the hypothesis, and
EL, EDL, RW, and VML, were consistent with the hypothesis, and had strong explanatory
Sustainability 2022,14, 11849 17 of 22
power for the disparity in AEE. Based on the influencing factors and influence mechanisms,
we proposed countermeasures to optimize AEE.
6.1. Policy Suggestions
To further optimize the AEE, the following five countermeasures are proposed based
on the efficiency evaluation results and the study of influencing factors: (1) for areas where
the AEE is not effective, there is a general problem of too much input and too much
undesired output. It is necessary to further increase the investment in agricultural research,
research and development of low-energy, and high-efficiency machinery and equipment,
improve resource utilization, and reduce undesired output emissions; (2) the evaluation
results of AEE show obvious regional differences. This is influenced by the level of regional
economic development. AEE can be improved by strengthening inter-regional cooperation
and exchange, by actively introducing advanced low-carbon agricultural technologies, and
by learning and implementing ecological agricultural experience from developed regions;
(3) actively increase collective economic income, raise the income level of rural residents,
and reduce the loss of young talents. Implement a flexible and effective talent policy to fully
attract young talents, entrepreneurs, and technicians, who have already left the country
to return to rural areas, and encourage them to contribute to agricultural production. In
addition to encouragement, young people also want to feel financially incentivized. This
can be done by offering scholarship programs to students who study agriculture and
environmental professions and return to their home regions to participate in agricultural
development. This could be very rewarding; (4) strengthen the construction of agricultural
infrastructure conditions and networks, with special attention to improving rural water
supply capacity and the practicality of water use in the agricultural production process. In
addition, considering the possibility of future climate extremes, it is necessary to construct
dynamic forecasting approaches and to take some necessary government interventions
when facing years of extreme high or low rainfall; and (5) to give full recognition to the
crucial role of village committees in the development of agro-ecology. We should actively
guide farmers to fully understand the advantages of ecological agriculture and change their
traditional farming approaches. Establishing appropriate incentives and compensation
mechanisms to motivate farmers to develop green agriculture and gradually develop a
low-carbon lifestyle will benefit multiple role-players.
6.2. Research Limitations
There are three limitations in this study: (1) the results of AEE evaluation may vary
considerably between different crops in agricultural production, and the study did not
take this into account; (2) the spatial interactions of AEE were not analyzed; and (3) many
other factors may also affect AEE, and although this study verified different influencing
factors, from social, economic, and population perspectives, other factors can still be further
considered to improve future models for evaluating AEE.
6.3. Research Outlook
First, the study can be further refined to analyze the AEE of different crops and
their proportional representation regionally, which can contribute to the improvement of
production efficiency and reduction of carbon emissions for different crops. Secondly, the
spatial correlation of AEE and its drivers can be further explored by combining spatial
autocorrelation models and geographic detectors. Finally, more diverse influencing factors
can be verified in the context of regional or national agricultural development to lay the
theoretical foundation for optimizing AEE and proposing sustainable development policies.
Sustainability 2022,14, 11849 18 of 22
Author Contributions:
Conceptualization, R.H. and R.A.-R.; methodology, L.M.; software, L.M.;
validation, L.M., R.H. and R.A.-R.; formal analysis, L.M., R.H. and R.A.-R.; investigation, L.M.;
resources, L.M.; data curation, L.M., R.H. and R.A.-R.; writing—original draft preparation, L.M.,
R.H. and R.A.-R.; writing—review and editing, L.M., R.H. and R.A.-R.; visualization, L.M., R.H. and
R.A.-R.; supervision, R.H. and R.A.-R.; project administration, R.H. and R.A.-R.; funding acquisition,
L.M., R.H. and R.A.-R. All authors have read and agreed to the published version of the manuscript.
Funding:
Anhui Province Philosophy and Social Science Planning Project Research results, Project
Approval No.: AHSKQ2020D79.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
All data generated or analyzed during this study are included in
this article.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Population Aggregation Level (PAL)
—Scholars have reached different conclusions
regarding the mechanism of population aggregation level on regional environmental
efficiency when analyzing different geographical regions. Some scholars believe that popu-
lation aggregation will promote the rise of regional consumption demand for agricultural
products and accelerate the consumption of environmental resources [
76
]. There are also
research results that prove that as the population increases, it raises the social demand for
agricultural intensification, which in turn may lead to an increase in agricultural production
efficiency [
77
]. Some scholars have also found through empirical studies that the level of
population concentration has no significant effect on environmental efficiency [
53
]. This
study will provide further empirical evidence, and measure the level of rural population
agglomeration in terms of rural population density.
Education Level (EL)
—Many scholars believe that as the regional education level
increases, it can improve people’s awareness of environmental protection, and, thereby, their
knowledge and research ability. More educated people pay closer attention to protecting
the environment and conserving resources when engaging in agricultural production or
agriculture-related work, which can improve AEE [
78
–
81
]. In this paper, we chose the
average years of education of the regional population as a proxy variable for the regional
education level.
Urbanization Level (UL)
—In the regional promotion of urbanization, it has been
argued that the shift of agricultural population with the expansion of urban areas may
lead to changes in agro-ecological efficiency [
41
]. Moreover, this will lead to a significant
increase in the land used for urban construction, with a concurrent reduction in space
for agricultural production [
89
]. Therefore, the urbanization level may be an influential
factor in AEE. The study uses the urban population as a percentage of the total regional
population to represent its urbanization level.
Economic Development Level (EDL)
—The level of economic development is an
important indicator of the comprehensive strength of a region, and the direction of influence
on AEE is often the result of a contest between positive and negative external influences.
According to the theory of environmental Kuznets curve, the level of economic development
may be closely related to the ecological integrity of the agricultural environment and the
resulting anthropogenic impact [
91
]. It has been observed that an increase in the level of
the regional economy can enable people to afford high-quality factors in the process of
agricultural production and related activities, thus increasing production efficiency and
reducing emissions of pollution sources [
90
,
92
]. Gross domestic product (GDP) per capita
(CNY/person) is one of the important indicators reflecting the level of regional economic
development [101], and was therefore included in this study.
Rural Water Supply Capacity (RW)
—In the agricultural production process, the sup-
ply of water resources is indispensable. With the development of agriculture and the
Sustainability 2022,14, 11849 19 of 22
growth of the rural population, the demand for water is increasing [
102
]. According to
previous studies, rural water supply capacity often has a great impact on agricultural
productivity, with lower water supply requiring more resources to sustain agricultural
productivity [
79
], while improved accessibility to water resources can enhance productivity
and contribute to environmental protection [
93
,
94
]. Therefore, this study used rural piped
water penetration rate as a proxy for rural water supply capacity.
Village Management Level (VML)
—A higher village management level is conducive
to providing more policy support and resource preferences for agricultural production, as
well as involving more villagers and the public to actively participate in rural ecological
and environmental protection [
76
,
98
], which is generally beneficial to the AEE. Village
committees are an important network for managing rural areas, and the establishment of
more village committees means more detailed management of villages and a higher level
of agricultural management. Therefore, our study used the number of regional village
committees to indicate the level of village management organization.
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