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\begin{document}

\title{Deep learning: To better understand how human activities affect the
value of ecosystem services}



\author[1]{Chang Liu}%
\author[1]{Yi Qi}%
\author[2]{Hong Yao}%
\author[1]{Shan Li}%
\author[1]{Ping Sun}%
\author[1]{Tianhua Ni}%
\affil[1]{Nanjing University}%
\affil[2]{Nantong University}%


\vspace{-1em}



  
  \date{May 27, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The increase in human activities is one of the important factors
affecting the value of ecosystem services. However, understanding of the
driving mechanisms of human activities is limited. We established a deep
learning model to approximate the ecosystem service value (ESV) of
Nanjing City using 23 socioeconomic factors. A multi-view analysis was
then conducted on feasible impact mechanisms using model disassembly.
The results indicated that factors such as the proportion of ecological
waters in the land-use structure and secondary industry output value had
their own independent effects on ESV. Other intrinsically related
factors, for instance, industrial water consumption and industrial
electricity consumption, were likely to be composited together to affect
ESV.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Ecosystem services are the benefits that people obtain from various
ecosystems that can be described and measured (Tamayo et al. 2018;
Costanza et al. 1997). Mendelsohn and Olmstead (2009) described the
value of ecosystem services (ESV) as ``the sum of what all members of
society would be willing to pay'' for ``the economic benefit provided by
environmental products or service'' (Mendelsohn \& Olmstead 2009).
Hence, the estimation of ESV can make a vital contribution to
biodiversity protection and sustainable development (Bill\selectlanguage{ngerman}é et al. 2012).
Assessments of ESV at the national, regional, basin and even single
ecosystem levels can show how these services support our lives and how
people develop natural resources rationally (Wei et al. 2018). The
valuation methods now available are highly developed and can be mainly
divided into behavioral (revealed preference) methods and attitudinal
(stated preference) methods (Mendelsohn and Olmstead 2009). Behavioral
methods attempt to calculate the environmental value of goods indirectly
through market analysis (Braden et al. 2010; Phaneuf et al. 2008;
Harrington \& Portney 1987). Attitudinal methods use subjectively
designed surveys to create a table of ecological value equivalents. Two
common valuation systems include the system created by Costanza in 1997
(Costanza et al. 1997) and the millennium ecosystem assessment framework
(Alcamo 2003).

However, the understanding of ESV is not comprehensive because multiple
types of service interrelate in complex and dynamic ways (Spake et al.
2017). The current research perspectives on ESV consider it to be the
result of a process: ``human-driven factors of ecosystem change
ecosystem process and functions ecosystem services''. ``Human-driven
factors of ecosystem change'' can be interpreted as basic socioeconomic
conditions, including population, GDP, industry structure, and energy
consumption. ``Ecosystem process and functions'' can be represented by
land and land cover change at the geospatial level, which is
traditionally the most important part of the information used to
estimate ESV (Barbier et al. 2011). However, how ESV interacts with
socioeconomic factors remains ambiguous (Meacham et al. 2016), which
leads to difficulties in the application of ESV in ecological
management. In other words, even if a low ESV area is identified, we
still do not know how to promote it efficiently through regional
planning or industry regulation. Studies started to include the
socioeconomic drivers of ESV into consideration for the implementation
of responsive policies. Yang et al. (2019) found that ESV is tightly
correlated with socioeconomic status. Wu et al. (2019) found nonlinear
relations between GDP and ESV and between population density and ESV,
but no more complete causality was explained.

As one kind of machine learning algorithm, deep learning is a multilayer
perceptron neural network (Reichstein et al. 2019). It offers
significant breakthroughs in solving classification and nonlinear
regression problems (Sze et al. 2017). Deep learning can extract the
valid features of data input through complex computational models and
represent them at a higher level of abstraction, eventually achieving
complex self-learning functions through multiple transformations and
combinations (LeCun et al. 2015). Traditional evaluation and analysis
methods are often not sufficiently effective in describing the
continuous and quantitative rules in a complicated ecosystem (Moore et
al. 2017). Deep learning may be an effective tool for dealing with this
problem.

In this work, deep learning was used to explore the relationships
between ``human drivers of ecosystem change'' and ``ESV'' on a dataset
from Nanjing, China. The city of Nanjing is one of the megacities in the
Yangtze River basin; it has experienced rapid economic development since
the 1970s that is still occurring today (Li et al. 2016) (Figure S1). At
the end of the 20th century, the urbanization of Nanjing entered an
accelerated phase, which led to a rapid increase in population,
unreasonable industrial structure, unbalanced land use, high energy
consumption, and environmental degradation (Yuan et al. 2018; Shi et al.
2019). Over the last two decades, the population has increased from 3 M
to 8.5 M, and its GDP has increased from 338.12 billion CNY in 2008 to
1171.51 billion CNY in 2018. As an ecologically sensitive area, the
changes in its ecological system and services have been continuously
monitored and studied. Taking ESV as a parameter of the ecosystem, a
better understanding of their internal driving mechanism will be
conducive to optimizing local policies and regional planning (Shiferaw
et al. 2019).

\textbf{Methods}

\textbf{Data preprocessing}

The Terrestrial Ecosystem Service Value Distribution Database used in
this study was constructed by Xie using attitudinal methods
(http://www.resdc.cn) (Table S1).

According to China's green development policy (Ji et al. 2017), the
factors related to ecological conditions and sustainability were divided
based on six aspects: resource utilization, environmental governance,
environmental quality, ecological protection, growth quality, and green
life. Due to the spatial scale of this study, natural conditions such as
temperature, perception, and humidity are relatively similar across the
city, so they were not used as model input for differentiation. Taking
the correlation with ESV and anthropogenic controllability into account,
23 indexes from 2015 were chosen and altered from three perspectives
(Table S1) as input for the following ESV deep learning model. The data
resources and spatial revolution of raster data are shown in Table S1.

Multidata fusion on the same scale (Openshaw 1984; Perkins 2017;
Hodgkinson and Andresen 2019) is necessary to make labeled samples meet
the common format and quantity requirements for deep learning model
training. Considering the area of Nanjing and the ecological
significance of ESV, we used a 2*2 km grid for data processing by grid
transformation. A total of 2191 grid units were obtained as samples. The
socioeconomic~data were allocated by key weighting factors (Table S2)
using Eq. 1. Spatial socioeconomic data with a resolution of 2*2 km were
obtained.

\(\text{Af}_{i}=\text{As}_{i}\times\frac{\text{Wf}_{i}}{\text{Ws}_{i}}\)(Eq. 1)

\emph{Af\textsubscript{i}: the values in 2*2 km units of the ith (No.)
allocated indicators. As\textsubscript{i}: the total value of the ith
(No.) allocated indicators in Nanjing City. Wf\textsubscript{i}: the
values in 2*2 km units of the ith (No.) weighting factors.
Ws\textsubscript{i}: the total value of the ith (No.) weighting factors
in Nanjing City.}

Finally, data standardization was conducted as a standard procedure
before training. Standard deviation standardization was used to
eliminate the influence of the magnitudes. The land-use factors were
represented by individual area proportions (from 0\% to 100\%) for each
type, which were then individually standardized.

\textbf{Data modeling}

Because of the complexity of the anthropogenic driving mechanisms, the
relations among the 23 independent variables and ESV cannot be described
by the conventional linear model. We used a feed-forward dense network
as the deep learning model.

The network consists of 8 layers (4 dense layers), including 6 hidden
layers in the network. Each layer has a certain number of neurons and
activation functions (Table 1). Nonlinear activation functions such as
the rectified linear unit (ReLU) were introduced in the
3\textsuperscript{rd} and 5\textsuperscript{th} hidden layers to learn
the nonlinearity. The ReLU function was used to avoid vanishing gradient
problems. Additionally, the dropout rate was set to 0.3 in all dropout
layers to avoid overfitting problems.

We partitioned 70\% of the 2191 units as training samples and 30\% as
testing samples. In the training phase, the optimizer and loss function
were established based on adaptive moment estimation (ADAM) and the mean
square error (MSE). After conventional model optimizations were
performed, the above hyperparameters were determined. The corresponding
model was trained and used in the study.

\textbf{Table 1.} The configuration of the model\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Layer & Operation & Parameter & Parameter\tabularnewline
\midrule
\endhead
Input layer & & 23 & Socioeconomic factors\tabularnewline
1\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}2-hidden & dense & 128 & linear\tabularnewline
& dropout & 0.3 & 0.3\tabularnewline
3\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}4-hidden & dense & 256 & tanh\tabularnewline
& dropout & 0.3 & 0.3\tabularnewline
5\begin{CJK}{UTF8}{gbsn}、\end{CJK}\selectlanguage{english}6-hidden & dense & 16 & ReLU\tabularnewline
& dropout & 0.3 & 0.3\tabularnewline
Output layer & dense & 1 & linear\tabularnewline
\bottomrule
\end{longtable}

\textbf{Model analysis}

We observed how ESV (Y) responded to the change in each influence factor
(X) by sampling continuously in the range of each input X. In the course
of the concrete analysis, other factors were kept as the mean of
samples, and the values of the target factors were changed by adopting
control variables. In addition, the range of target X was regarded as
its definition domain, called the sampling domain. The range of ESV is
called the response domain. The influence intensity of every X factor
can be judged according to the corresponding response domain, and the
influence mode and potential mechanism can be judged according to the
variation trend of the function.

\textbf{Results and discussion}

\textbf{Model performance}

The model was trained for 200 epochs and showed a significant
convergence trend. In terms of precision, the Nash-Sutcliffe efficiency
and root MSE (RMSE)-observation standard deviation ratio (RSR) were used
as indicators to reflect the model performance and reached values of
0.51 and 0.70 respectively, indicating ``satisfactory'' performance.
Spatially, the observed value and predictive value of each sample were
visualized with one set of legends showing similar spatial
characteristics (Figure 1). However, overall, the sum of the predictive
values was lower than the ground truth sum.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} (a) Map of the observed ESV value in Nanjing. (b) Map
of the predictive ESV value in Nanjing.

\textbf{Single-factor response}

23 X factors were divided into three categories, extremely significant
influence intensity (ES), significant influence intensity (S), and
nonsignificant influence intensity (NS), according to the range of the
response domain (Table 2).

\textbf{Table 2.} The influence intensity and influence mode of 23 X
factors\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Index & Original sampling domain & Standardized sampling domain & The
range of response domain & Function shape\tabularnewline
\midrule
\endhead
2\textsuperscript{nd} industry output value (million yuan) & (0, 14000)
& (0, 14) & 0.310 (ES) & monotone decreasing\tabularnewline
Water area (\%) & (0, 100) & (0, 100) & 0.236 (ES) & monotone
increasing\tabularnewline
GDP (million yuan) & (0, 10000) & (0, 15) & 0.181 (S) & monotone
increasing\tabularnewline
Industrial water consumption & (0, 40) & (0, 14) & 0.178 (S) & monotone
decreasing\tabularnewline
Light index & (-100, 600) & (-2, 5) & 0.162 (S) & increasing with
fluctuation\tabularnewline
3\textsuperscript{rd} industry output value (million yuan) & (0, 1750) &
(-1, 5) & 0.154 (S) & monotone decreasing\tabularnewline
Agricultural electricity consumption & (0, 0.35) & (-2, 3) & 0.138 (S) &
monotone decreasing\tabularnewline
Water consumption (kt) & (0, 70) & (0, 20) & 0.138 (S) & inverted
U-shaped\tabularnewline
Tourism output (million yuan) & (0, 80) & (-6, 1) & 0.137 (S) &
increasing with fluctuation\tabularnewline
1\textsuperscript{st} industry output value (million yuan) & (0, 35) &
(-2, 3) & 0.13 (S) & monotone decreasing\tabularnewline
Woodland area (\%) & (0, 100) & (0, 100) & 0.103 (S) & monotone
increasing\tabularnewline
Grassland area (\%) & (0, 100) & (0, 100) & 0.094 (NS) &
U-shaped\tabularnewline
Residential electricity consumption (kw) & (0, 20) & (-1, 5) & 0.089
(NS) & monotone increasing\tabularnewline
NDVI & (0, 4) & (-3, 3) & 0.089 (NS) & U-shaped\tabularnewline
Population (person) & (0, 250000) & (0, 17.5) & 0.088 (NS) &
U-shaped\tabularnewline
Agricultural water consumption (kt) & (0, 2.5) & (-2, 3) & 0.083 (NS) &
U-shaped\tabularnewline
Electrical consumption (kw) & (0, 800) & (0, 20) & 0.079 (NS) & inverted
U-shaped\tabularnewline
Construction industry output (million yuan) & (0, 175) & (-1, 5) & 0.071
(NS) & inverted U-shaped\tabularnewline
Industrial electricity consumption (kw) & (0, 1000) & (0, 14) & 0.067
(NS) & inverted U-shaped\tabularnewline
Residential water consumption (kt) & (0, 20) & (-1, 5) & 0.051 (NS) &
U-shaped\tabularnewline
Construction land area (\%) & (0, 100) & (0, 100) & 0.042 (NS) &
monotone decreasing\tabularnewline
Unused land area (\%) & (0, 100) & (0, 100) & 0.037 (NS) & monotone
increasing\tabularnewline
Cultivated area (\%) & (0, 100) & (0, 100) & 0.017 (NS) & monotone
decreasing\tabularnewline
\bottomrule
\end{longtable}

The ``2\textsuperscript{nd} industry output value'' was an extremely
significant (ES) factor with the highest range in the response domain.
The monotonic decreasing function of the ``2\textsuperscript{nd}industry
output value'' and ESV meant that a more developed 2\textsuperscript{nd}
industry led to a lower ESV (Figure 2a). In addition, there were a
series of X factors with a relatively high range of response domains and
significant influence intensities. Among them, ``GDP'', ``light index''
and ``tourism output'' had a positive impact on ESV, and ``industrial
water consumption'', ``3\textsuperscript{rd}industry output value'',
``agricultural electricity consumption'' and ``1\textsuperscript{st}
industry output value'' had a negative impact on ESV. The function of
the relationship between ``water consumption'' and ESV adopted an
inverted U-shaped curve. The inflection point of ``water consumption''
was approximately 10 kt, while ESV reached a maximum value of 810 k
(Figure S2).

``Water area'' was another extremely significant factor, and the areas
of other types of land were all NS factors except ``woodland area'' (S),
which displayed the lowest range in the response domain compared with
other significant (S) factors. The ``water area'', ``woodland area'' and
``unused land area'' had a positive impact on ESV (Table 2). In
contrast, the ``cultivated area'' and ``construction land area'' had a
negative impact on ESV. Moreover, the relationship between the
``grassland area'' and ESV displayed a U-shaped curve that had an
inflection point at approximately 80\% of the grassland area (Figure 2b
and Figure S2).

Other factors had a relatively low range of response domains, which
meant that their influence was not significant. However, the functions
capturing the relationship between these NS factors and ESV almost all
assumed a U-shaped curve or an inverted U-shaped curve (Figure 2b and
Figure S2). All the analyses suggested that there is a response
relationship between ESV and socioeconomic conditions. Therefore, the
ESV can be improved indirectly by adjusting these factors. However,
understanding how to regulate and control the above factors to achieve a
balance between ESV and economic development still requires further
multifactor coupling research.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} The functions of the relationships between
standardized X factors and ESV

(a) The function of the relationship between nonland-use-type factors
(changed from -3 to 3) and

ESV; (b) The function of the relationship between land-use factors
(changed from 0\% to 100\%) and ESV.

\textbf{Multifactor response}

It is difficult to achieve maximum ESV and adjust all socioeconomic
factors to the optimum intervals because of the complex coupling
influence. We attempted to select several sets of factors with
significant interactions to identify which proportions could regulate
them for the purpose of maximizing ESV. As a result, ``the proportion of
construction'' and ``population'', ``1\textsuperscript{st} industry
output value'' and ``population'', and ``industrial water consumption''
and ``industrial electric consumption'' were chosen to analyze how they
cooperatively influence ESV.

This approach suggested that ESV is most affected by the population
(Figure 3a). The population within 4 km\textsuperscript{2} should be
controlled to be less than 20 k or approximately 120 k. In addition, ESV
dropped sharply with the increase in construction land when the
population was 50 k. In contrast, ESV declined slowly with the increase
in construction land when the population was more than 100 k, which
suggests that a relatively single distribution of construction land will
not place too much pressure on the ecosystem in densely populated
centers. However, controlling construction land still has a positive
influence on ESV. Therefore, the city center can sacrifice
nonconstruction land for infrastructure construction and population
accommodation, and it will be necessary for it to reserve ecological
space.

The surface of ``1\textsuperscript{st} industry output value'' and
``population'' showed that there were two scenarios that achieve higher
ESV (Figure 3b). One scenario was when the population was nearly 0 and
the output of the 1\textsuperscript{st} industry was more than 15
million CNY. Under this scenario, the object region had a small
population but played an important role in agricultural production. The
other scenario was when the population was approximately 150 k and the
1\textsuperscript{st} industry output value was less than 10 million
CNY.

Regarding energy consumption, ``industrial water consumption'' was
presumably the limiting factor compared with ``industrial electricity
consumption'' (Figure 3c). Industrial water consumption should be kept
below 15 kt if one hopes to achieve a high ESV. When industrial
electricity consumption was between 400 and 600 kw, ESV rapidly
decreased with increasing industrial water consumption. This finding
suggested that water consumption needs to be restricted and reserved in
industrial areas.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3} . The surface of ESV and three sets of coupling
factors

(a) The coupling influence of ``the proportion of construction'' and
``population''; (b) The 3D surface of ESV, ``the proportion of
construction'', and ``Population''; (c) The coupling influence of
``1\textsuperscript{st} industry output value'' and ``population''; (d)
The coupling influence of ``industrial water consumption'' and
``industrial electric consumption''.

\textbf{Urban cognizable synergistic features}

As the activation function in the output layer is linear, it is possible
to extract cognitive and comprehensible features from the penultimate
layer (Yu \& Seltzer 2011). Nine features with bottom-up information
were interpreted and understood. Considering the range of Y and the
weight of the features, significant features that contained more
information about the corresponding factors were selected. The features
were named according to X, which had a great influence on them, except
feature 7, which contained information on almost all the factors and
features 9 and 15, which contained little information on any factors.
Therefore, 6 cognizable synergistic features integrated from multiple
factors were extracted, including the urban expansion factor (0.0133),
land use-industrial structure-energy consumption structure (0.0146),
land use-energy consumption structure, agricultural development
(0.0146), city-scale factor (0.0151) and tourism exploitation potential
(0.0138) (Table 3). Among these factors, the city-scale factor was the
most significant urban cognizable feature with the highest weight
(0.0151), and it contained information about GDP, population and tourism
output. This finding demonstrated that the deep learning model may
predict ESV by quantifying regional characteristics, including land-use
structure, energy consumption structure, industrial structure, and city
scale. Therefore, it is possible to adjust the urban macro
characteristics to maintain or even improve the regional ESV.

\textbf{Table 3.} The extraction and analysis of urban cognizable
features\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Number & Weight & Factors contained & The weighted range of Y & The name
of urban cognizable features\tabularnewline
\midrule
\endhead
Feature 1 & 0.0133 & Light index & 0.000508 & Urban expansion
factor\tabularnewline
& & Cultivated area & 0.000768 &\tabularnewline
& & Residential electricity consumption & 0.000365 &\tabularnewline
Feature 3 & 0.0146 & Light index & 0.00083 & Land use-industrial
structure-energy consumption structure factor\tabularnewline
& & Cultivated area & 0.000902 &\tabularnewline
& & Woodland area & 0.00108 &\tabularnewline
& & Grassland area & 0.000862 &\tabularnewline
& & Water area & 0.001034 &\tabularnewline
& & Unused land area & 0.000916 &\tabularnewline
& & 2\textsuperscript{nd} industry output value & 0.000484
&\tabularnewline
& & 3\textsuperscript{rd} industry output value & 0.000488
&\tabularnewline
& & Agricultural electricity consumption & 0.000358 &\tabularnewline
& & Industrial water consumption & 0.000469 &\tabularnewline
& & Residential water consumption & 0.000543 &\tabularnewline
Feature 6 & 0.0146 & Woodland area & 0.00106 & Dynamic land use-energy
consumption structure factor\tabularnewline
& & Water area & 0.001037 &\tabularnewline
& & Construction land area & 0.000803 &\tabularnewline
& & Unused land area & 0.001005 &\tabularnewline
& & Construction industry output value & 0.000309 &\tabularnewline
& & Electrical consumption & 0.000628 &\tabularnewline
& & Industrial electricity consumption & 0.000926 &\tabularnewline
& & Residential electricity consumption & 0.000424 &\tabularnewline
& & Water consumption & 0.000347 &\tabularnewline
& & Agricultural water consumption & 0.000369 &\tabularnewline
Feature 8 & 0.0143 & Light index & 0.000645 & Agricultural development
factor\tabularnewline
& & Cultivated area & 0.000905 &\tabularnewline
& & Unused land area & 0.000978 &\tabularnewline
& & 1\textsuperscript{st} industry output value & 0.000547
&\tabularnewline
& & Agricultural electricity consumption & 0.00036 &\tabularnewline
& & Residential electricity consumption & 0.000439 &\tabularnewline
& & Water consumption & 0.000379 &\tabularnewline
Feature 10 & 0.0151 & GDP & 0.000964 & City scale factor\tabularnewline
& & Population & 0.001185 &\tabularnewline
Feature 13 & 0.0138 & Population & 0.000885 & Tourism exploitation
potential factor\tabularnewline
& & NDVI & 0.000846 &\tabularnewline
& & Agricultural water consumption & 0.000819 &\tabularnewline
& & Tourism output value & 0.000632 &\tabularnewline
& & Construction land area & 0.000376 &\tabularnewline
\bottomrule
\end{longtable}

The extraction of urban cognizable synergistic features could be
regarded as a form of dimensionality reduction in 23 X factors.
Therefore, we compared urban cognizable features with the results of a
principal component analysis, which is a widely used technique in
machine learning (Monedero et al. 2019). There were 6 principal
components, which all contained no more than 2 factors (Table S3).
However, we could not give definite practical meaning to the principal
components, which meant that the extraction of urban cognizable features
has unique advantages as a new dimensionality reduction method.

\textbf{Conclusion}

The study proposed deep learning as a new, more effective approach to
understanding the patterns, dynamics, and driving factors of ESV that
are crucial for coping with sustainability challenges. The findings of
the model analysis suggested that underlying social and economic
conditions presumably influence regional ecological functions through
ESV.

Regarding Nanjing City, although the outputs of the
1\textsuperscript{st}, 2\textsuperscript{nd} and
3\textsuperscript{rd}industries all showed a decreasing trend in ESV,
the ``2\textsuperscript{nd} industry output value'' had the highest
influence intensity, indicating the urgency and necessity of controlling
its proportion. We propose that economic development, urbanization, and
tourism should be further accelerated and enhanced in Nanjing, because
``GDP'', ``light index'', ``tourism output'' and ``residential
electricity consumption'' all have positive influences on ESV. In
addition, there should be singleness in the urban function, which means
that city space needs to be separated to serve different functions. The
extraction of high-level urban cognizable factors related to ESV in the
penultimate layer may be a new dimensionality reduction method, and the
analysis suggested that the city scale of Nanjing can truly affect the
ESV. As a result, it is possible for decision-makers to provide policy
guidance and adjust urban features to realize the coordinated
development of the regional economy and ecological functions. For
instance, the most suitable city scale can be found that is within the
regional ecological carrying capacity.

In this work, the relationship between human socioeconomic development
and ESV on the urban scale is at the heart of our research. We built a
deep learning model based on the limited socioeconomic factors (X) to
cognize it and obtained interesting and meaningful results. Furthermore,
our point of view is that there are likely to be obvious differences in
the driving mechanisms under diverse regional and scale contexts.
Therefore, an important direction for further research is the
investigation of more influence patterns and mechanisms on diverse
spatial scales and levels of socioeconomic development affecting the
change in regional ESV.

\textbf{Acknowledge}

This work was supported by the Environmental Protection Research Project
of Jiangsu Province(No. 2018008)and the Environmental Science and
Technology Project of Nanjing(No.201904). The authors thank Prof.
Jiangang Xu for valuable comments and discussion.

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Indic}\textbf{95} : 917-929.\selectlanguage{english}
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