2023 International Seminar on Computer Science and Engineering Technology (SCSET)
7KH6DWLVIDFWLRQ,PSURYHPHQW6WUDWHJ\RI*UHHQ'HYHORSPHQW%DVHGRQWKH$+3
The Satisfaction Improvement Strategy of Green Development Based on the AMP­
HQWURS\:HLJKW0HWKRGDQG&$57$OJRULWKP
entropy
Weight Method and CART Algorithm
2023 International Seminar on Computer Science and Engineering Technology (SCSET) | 979-8-3503-0147-2/23/$31.00 ©2023 IEEE | DOI: 10.1109/SCSET58950.2023.00115
6XNH.RQJ
Suke
Kong
6FKRRORI0DQDJHPHQW
School
of Management,
;LDPHQ8QLYHUVLW\7DQ.DK.HH
Xiamen
University Tan Kah Kee
&ROOHJH
College
=KDQJ]KRX&KLQD
Zhangzhou,
China
#TTFRP
920445685@qq.com
<LQJQLQJ6KHQ
Yingning
Shen
6FKRRORI0DQDJHPHQW
School of Management,
;LDPHQ8QLYHUVLW\7DQ.DK.HH
Xiamen
University Tan Kah Kee
&ROOHJH
College
=KDQJ]KRX&KLQD
Zhangzhou,
China
#TTFRP
23908
1 326l @qq.com
<X&KXQJ&KDQJ
Yu-Chung
Chang* 6FKRRORI0DQDJHPHQW
School
of Management,
;LDPHQ8QLYHUVLW\7DQ.DK.HH&ROOHJH
Xiamen
University Tan Kah Kee College
=KDQJ]KRX&KLQD
Zhangzhou,
China
#TTFRP
2933662796@qq.com
$EVWUDFW,QWKLVVWXG\WKH$+3HQWURS\ZHLJKWPHWKRGLVXVHG
Abstract-In
this study, the AHP-entropy weight method is used
DQGGHWHUPLQHWKHGLUHFWLRQIRULPSURYHPHQW7KHUHIRUHWKH
and
determine the direction for improvement. Therefore, the
IRXQGDWLRQ for
IRU achieving
DFKLHYLQJ JUHHQ
GHYHORSPHQW is
LV the
WKH
foundation
green development
FRQVWUXFWLRQRIWKHJUHHQGHYHORSPHQWLQGLFDWRUV\VWHP
construction
of the green development indicator system.
7KLVVWXG\DWWHPSWVWRFRQVWUXFWDPHDVXUHPHQWLQGH[IRU
This
study attempts to construct a measurement index for
XUEDQ green
JUHHQ development
GHYHORSPHQW using
XVLQJ the
WKH AHP-entropy
$+3HQWURS\ weight
ZHLJKW
urban
PHWKRG providing
SURYLGLQJ aD tool
WRRO for
IRU evaluating
HYDOXDWLQJ cities,
FLWLHV verifying
YHULI\LQJ
method,
ZKHWKHU their
WKHLU development
GHYHORSPHQW meets
PHHWV green
JUHHQ development
GHYHORSPHQW
whether
VWDQGDUGVDQGILQGLQJRXWWKHLQIOXHQFLQJIDFWRUVRISHRSOH
standards,
and finding out the influencing factors of people'sV
VDWLVIDFWLRQ with
ZLWK green
JUHHQ development.
GHYHORSPHQW Through
7KURXJK
satisfaction
TXHVWLRQQDLUHV we
ZH also
DOVR collect
FROOHFW personal
SHUVRQDO practices
SUDFWLFHV of
RI green
JUHHQ
questionnaires,
OLIH from
IURP the
WKH public,
SXEOLF assessment
DVVHVVPHQW of
RI the
WKH quality
TXDOLW\ of
RI the
WKH
life
VXUURXQGLQJHQYLURQPHQWYLHZVRQSDUNVJUHHQVSDFHVDQG
surrounding
environment, views on parks, green spaces, and
LQIUDVWUXFWXUH provided
SURYLGHG by
E\ the
WKH municipal
PXQLFLSDO government,
JRYHUQPHQW
infrastructure
SHUFHSWLRQVRIHQWHUSULVHJUHHQSURGXFWLRQSHUIRUPDQFHWKH
perceptions
of enterprise green production performance, the
DFWVRIJRYHUQPHQWVXSHUYLVLRQDQGLPSOHPHQWDWLRQRIJUHHQ
acts
of government supervision and implementation of green
HQYLURQPHQWDOSURWHFWLRQSROLFLHVDQGODZVDQGVDWLVIDFWLRQ
environmental
protection policies and laws, and satisfaction
GDWD on
RQ urban
XUEDQ green
JUHHQ development.
GHYHORSPHQW The
7KH classification
FODVVLILFDWLRQ and
DQG
data
UHJUHVVLRQ tree
WUHH (CART)
&$57 algorithm
DOJRULWKP is
LV used
XVHG to
WR establish
HVWDEOLVK aD
regression
PRGHO to
WR predict
SUHGLFW citizens'
FLWL]HQV satisfaction
VDWLVIDFWLRQ with
ZLWK the
WKH green
JUHHQ
model
GHYHORSPHQW of
RI city
FLW\ government's
JRYHUQPHQW V green
JUHHQ development
GHYHORSPHQW and
DQG
development
SURYLGH strategies
VWUDWHJLHV for
IRU the
WKH city
FLW\ government
JRYHUQPHQW to
WR improve
LPSURYH
provide
FLWL]HQV VDWLVIDFWLRQZLWKJUHHQGHYHORSPHQW
citizens'
satisfaction with green development.
WR weigh
ZHLJK various
YDULRXV indicators
LQGLFDWRUV that
WKDW affect
DIIHFW green
JUHHQ development,
GHYHORSPHQW
to
HVWDEOLVK urban
XUEDQ green
JUHHQ development
GHYHORSPHQW evaluation
HYDOXDWLRQ indicators,
LQGLFDWRUV and
DQG
establish
WKHQ explore
H[SORUH the
WKH important
LPSRUWDQW factors
IDFWRUV that
WKDW DIIHFW
SHRSOH V
then
affect people's
VDWLVIDFWLRQZLWKJUHHQGHYHORSPHQWWKURXJKWKH&ODVVLILFDWLRQ
satisfaction
with green development through the Classification
5HJUHVVLRQ Tree
7UHH algorithm.
DOJRULWKP Finally,
)LQDOO\ aD prediction
SUHGLFWLRQ model
PRGHO is
LV
Regression
HVWDEOLVKHG It
,WZDV
IRXQG that
WKDW the
WKH classification
FODVVLILFDWLRQ and
DQG regression
UHJUHVVLRQ
established.
was found
PRGHO prediction
SUHGLFWLRQ results
UHVXOWV of
RI the
WKH CART
&$57 DOJRULWKP
KDYH high
KLJK
model
algorithm have
DFFXUDF\
7KH city's
FLW\ Vgreen
JUHHQ coverage
FRYHUDJH area
DUHD indicator
LQGLFDWRU is
LV the
WKH most
PRVW
accuracy. The
LPSRUWDQWLQGLFDWRUWKURXJKWKH$+3HQWURS\ZHLJKW
PHWKRG
important
indicator through the AHP-entropy weight method.
%\ using
XVLQJ the
WKH CART
&$57 algorithm,
DOJRULWKP the
WKH most
PRVW important
LPSRUWDQW factors
IDFWRUV
By
DIIHFWLQJ
FLWL]HQV satisfaction
VDWLVIDFWLRQ with
ZLWK the
WKH green
JUHHQ development
GHYHORSPHQW of
RI
affecting citizens'
;LDPHQ City
&LW\ are
DUH the
WKH actions
DFWLRQV of
RI the
WKH municipal
PXQLFLSDO government.
JRYHUQPHQW
Xiamen
%DVHG on
RQ the
WKH research
UHVHDUFK results,
UHVXOWV DQ
LPSURYHPHQW strategy
VWUDWHJ\ for
IRU
Based
an improvement
JUHHQGHYHORSPHQWVDWLVIDFWLRQLQ;LDPHQLVSURSRVHG
green development satisfaction in Xi amen is proposed.
.H\ZRUGV$+3HQWURS\ZHLJKWPHWKRG&$57DOJRULWKP
Keywords-AHP-entropy
weight method, CART algorithm,
JUHHQGHYHORSPHQWVDWLVIDFWLRQ;LDPHQ
green
development, satisfaction, Xiamen
,1752'8&7,21
I., INTRODUCTION
:LWKWKHSURJUHVVRIWHFKQRORJ\WKHGHYHORSPHQWRIPRGHUQ
With
the progress oftechnology, the development of modern
VRFLHW\
FRQWLQXHV to
WR move
PRYH towards
WRZDUGV industrialization
LQGXVWULDOL]DWLRQ and
DQG
society continues
XUEDQL]DWLRQ leading
OHDGLQJ to
WR an
DQ increase
LQFUHDVH in
LQ industrial
LQGXVWULDO pollution
SROOXWLRQ
urbanization,
DQG waste,
ZDVWH as
DV well
ZHOO as
DV aD signifi
VLJQLILFDQW
FRQVXPSWLRQ of
RI natural
QDWXUDO
and
cant consumption
UHVRXUFHV To
7R achieve
DFKLHYH sustainable
VXVWDLQDEOH development
GHYHORSPHQW of
RI human
KXPDQ
resources.
EHLQJV and
DQG the
WKH natural
QDWXUDO environment,
HQYLURQPHQW the
WKH concept
FRQFHSW of
RI green
JUHHQ
beings
GHYHORSPHQW has
KDV been
EHHQ proposed.
SURSRVHG Green
*UHHQ development
GHYHORSPHQW is
LV
development
GLIIHUHQWIURPWUDGLWLRQDOGHYHORSPHQWPRGHOVLWVFRUHLVWR
different
from traditional development models, its core is to
SURPRWH economic
HFRQRPLF growth,
JURZWK VRFLDO
GHYHORSPHQW and
DQG
promote
social development,
HFRORJLFDO coordination
FRRUGLQDWLRQ with
ZLWK effi
HIILFLHQF\
KDUPRQ\ and
DQG
ecological
ciency, harmony,
VXVWDLQDELOLW\
DV the
WKH JRDO
ZKLFK is
LV an
DQ important
LPSRUWDQW trend
WUHQG of
RI
sustainability as
goal, which
ZRUOGGHYHORSPHQWLQWKHIXWXUH
world
development in the future.
7KH construction
FRQVWUXFWLRQ of
RI the
WKH evaluation
HYDOXDWLRQ index
LQGH[ system
V\VWHP for
IRU
The
JUHHQ development
GHYHORSPHQW has
KDV provided
SURYLGHG aD scientific
VFLHQWLILF defi
GHILQLWLRQ
DQG
green
nition and
UHDOL]DWLRQ path
SDWK for
IRU green
JUHHQ development.
GHYHORSPHQW )RU
JUHHQ
realization
For aD FLW\
city, green
GHYHORSPHQW evaluation
HYDOXDWLRQ indicators
LQGLFDWRUV can
FDQ be
EH used
XVHG to
WR evaluate
HYDOXDWH
development
ZKHWKHUWKHFLW\PHHWVWKHVWDQGDUGVIRUJUHHQGHYHORSPHQW
whether
the city meets the standards for green development
979-8-3503-0147-2/23/$31.00
979-8-3503-0147-2/23/$3
1 .00 ©2023 IEEE
DOI 10.1109/SCSET58950.2023.00115
DOl
0LQJ[LQJ+XDQJ
Mingxing
Huang
6FKRRORI0DQDJHPHQW
School of Management,
;LDPHQ8QLYHUVLW\7DQ.DK.HH
Xiamen
University Tan Kah Kee
&ROOHJH
College
=KDQJ]KRX&KLQD
Zhangzhou,
China
P[DLUOLQHV#FRP
mxairlines@l
63 .com
,,35(/,0,1$5,(6
II.
PRELIMINARIES
7KH$+3HQWURS\ZHLJKWPHWKRGLVXVHGWRDVVLJQZHLJKWWR
The
AHP-entropy weight method is used to assign weight to
HDFK Indicator
,QGLFDWRU affecting
DIIHFWLQJ green
JUHHQ GHYHORSPHQW
LQ this
WKLV paper,
SDSHU
each
development in
DQGWKHQH[SORUHWKHIDFWRUVLQIOXHQFLQJSHRSOH
and
then explore the factors influencing people'sVVDWLVIDFWLRQ
satisfaction
ZLWK green
JUHHQ development
GHYHORSPHQW through
WKURXJK the
WKH classification
FODVVLILFDWLRQ and
DQG
with
UHJUHVVLRQWUHH
&$57 DOJRULWKP,QWKLVVHFWLRQZHPDLQO\
regression
tree (CART)
algorithm. In this section, we mainly
LQWURGXFH the
WKH relevant
UHOHYDQW methods
PHWKRGV and
DQG data
GDWD applications.
DSSOLFDWLRQV
introduce
6XEVHFWLRQ A
$ introduces
LQWURGXFHV the
WKH analytic
DQDO\WLF hierarchy
KLHUDUFK\ process
SURFHVV
Subsection
$+3 method.
PHWKRG Subsection
6XEVHFWLRQ B
% is
LV the
WKH introduction
LQWURGXFWLRQ of
RI the
WKH
(AHP)
HQWURS\ZHLJKWPHWKRG6XEVHFWLRQ&LVWKHLQWURGXFWLRQWR
entropy weight method. Subsection C is the introduction to
WKH&$57DOJRULWKP
the
CART algorithm.
494
Authorized licensed use limited to: Adana Alparslan Turkes Bilim ve Teknoloji Universitesi. Downloaded on October 19,2023 at 18:24:46 UTC from IEEE Xplore. Restrictions apply.
λAmax −- Qn
&,
(5)
CI == PD[
Qn −-1 '
$$QDO\WLFKLHUDUFK\SURFHVV
$+3 A. Analytic hierarchy process (AHP)
$+3
AHP LV
is Da V\VWHPDWLF
systematic DQG
and KLHUDUFKLFDO
hierarchical DQDO\VLV
analysis PHWKRG
method, LW
it
GHFRPSRVHV
decomposes WKH
the UHVHDUFK
research REMHFW
object LQWR
into GLIIHUHQW
different IDFWRUV
factors DW
at
GLIIHUHQW
different OHYHOV
levels DQG
and WKHQ
then DFFRUGLQJ
according WR
to VXEMHFWLYH
subjective MXGJPHQW
judgment,
E\SDLUZLVHFRPSDULVRQRIWKHPXWXDOLPSRUWDQFHRIWKHWZR
by pairwise comparison of the mutual importance of the two
IDFWRUVWRREWDLQDMXGJPHQWPDWUL[7KHVWHSVRIXVLQJ$+3
factors to obtain a judgment matrix. The steps of using AHP
WRFDOFXODWHWKHLQGLFDWRUZHLJKWVDUHDVIROORZV
VHH>@DQG
to calculate the indicator weights are as follows (see
[1] and
>@
[2]).
6WHS
Step 1 . &RQVWUXFW
Construct WKH
the MXGJPHQW
judgment PDWUL[
matrix. 6FKRODUV
Scholars DQG
and
H[SHUWV
experts DFFRUGLQJ
according WR
to VXEMHFWLYH
subjective MXGJPHQW
judgment XVH
use WKH
the 1 -9 VFDOH
scale
PHWKRG
MXGJPHQW PDWUL[
method WR
to REWDLQ
obtain Da judgment
matrix $
A LQ
in WKH
the IROORZLQJ
following
IRUP
form.
ª Dau
«D
$
A == ((DaLM ))P×P == « a2 t
ij mxm
« 0
M
«
D
P
¬ am[
/
L
/
L
2
0
/
L
Dat2
Da
22
0
M
Dam
P 2
&5
CR ==
ZKHUH
where 5,
RI LV
is WKH
the DYHUDJH
average UDQGRP
random FRQVLVWHQF\
consistency LQGH[
index RI
of WKH
the
MXGJPHQWPDWUL[7KHFRQVLVWHQF\RIWKHMXGJPHQWPDWUL[LV
judgment matrix. The consistency of the judgment matrix is
DFFHSWDEOHZKHQ
acceptable when &5
CR << 0.1 .
%
B.(QWURS\ZHLJKWPHWKRG
Entropy weight method
7KH
The HQWURS\
entropy ZHLJKW
weight PHWKRG
method LV
is DQ
an REMHFWLYH
objective ZHLJKWLQJ
weighting
PHWKRG
method EDVHG
based RQ
on GDWD
data GLVSHUVLRQ
dispersion GHJUHH
degree. ,W
It XVHV
uses WKH
the
LQIRUPDWLRQHQWURS\RIWKHLQGLFDWRUWRGHWHUPLQHWKHZHLJKW
information entropy of the indicator to determine the weight
RI
of WKH
the LQGLFDWRU
indicator. :KHQ
When WKH
the GDWD
data LV
is PRUH
more GLVSHUVHG
dispersed, WKH
the
HQWURS\YDOXHLVVPDOOHUZKLFKFDQEHFRQVLGHUHGWRFRQWDLQ
entropy value is smaller, which can be considered to contain
PRUH
more LQIRUPDWLRQ
information, VR
so WKH
the ZHLJKW
weight LV
is JUHDWHU
greater. 7KH
The VWHSV
steps RI
of
XVLQJ
using WKH
the HQWURS\
entropy ZHLJKW
weight PHWKRG
method WR
to FDOFXODWH
calculate WKH
the LQGLFDWRU
indicator
ZHLJKWVDUHDVIROORZV
VHH>@DQG>@
weights are as follows (see
[3] and [4]).
6WHS
Step 1. 'DWD
Data VWDQGDUGL]DWLRQ
standardization. 'XH
Due WR
to WKH
the GLIIHUHQW
different
GLPHQVLRQVRIHDFKLQGLFDWRUQHFHVVDU\GDWDWUDQVIRUPDWLRQ
dimensions of each indicator, necessary data transformation
SURFHVVLQJ
processing LV
is UHTXLUHG7KH
required. The FDOFXODWLRQ
calculation IRUPXOD
formula IRU
for SRVLWLYH
positive
DQGQHJDWLYHLQGLFDWRUVLVDVIROORZVUHVSHFWLYHO\
and negative indicators is as follows, respectively.
Dalm
P º
Da2Pm »»
(1)
0
M»
»
DaPP
mm ¼
ZKHUH
where DaLMif == 1/DaMLi UHSUHVHQWV
represents WKH
the FRPSDULVRQ
comparison UHVXOW
result RI
of WKH
the Li­
j
WKIDFWRUZLWKUHVSHFWWRWKHMWKIDFWRU7KHPHDQLQJRIHDFK
th factor with respect to thej-th factor. The meaning of each
VFRUHLVVKRZQLQ7DEOH
score is shown in Table 1 .
7DEOH&RPSDULVRQVFDOHRI$+3
Table 1 Comparison scale o f AHP
ŔFDOH
Scale
0HDQLQJ
Meaning
I
7KHLWKIDFWRUDQGMWKIDFWRUDUHHTXDOO\LPSRUWDQW
The i-th factor andj-th factor are equally important
Ur.LM ==
3
7KHLWKIDFWRULVVOLJKWO\PRUHLPSRUWDQWWKDQWKHMWKIDFWRU
The i-th factor is slightly more important than thej-th factor
5
7KHLWKIDFWRULVPRUHLPSRUWDQWWKDQWKHMWKIDFWRU
The i-th factor is more important than thej-th factor
7
7KHLWKIDFWRULVYHU\LPSRUWDQWFRPSDUHGZLWKWKHMWKIDFWRU
The i-th factor is very important compared with thej-th factor
9
7KHLWKIDFWRULVDEVROXWHO\LPSRUWDQWFRPSDUHGZLWKWKHMWK
The i-th factor is absolutely important compared with thej-th
IDFWRU
factor
PD[
max(x[ )
- [xLMif
M −
ġġġġġġġġġġġġġġġġġġġġġġġġġġġġ (8)
PD[
−
PLQ
[M ) '
max(x[ )
min(x
M
j
WKLQGLFDWRU
YDOXHVRIWKH
values of the j-th
indicator.
6WHS
Step 2. 'HWHUPLQH
Determine WKH
the ZHLJKW
weight RI
of LQGLFDWRU
indicator
FKDUDFWHULVWLFV7KHFDOFXODWLRQIRUPXODLV
characteristics. The calculation formula is
UrLMy
ġġġġġġġġġġġġġġġġġġġġġġġġġġġġġġġġġġġ (9)
pSLM == P
m Ur
ij ¦
LLi=l
= LM '
l}
ZKHUH
where P
m LVWKHQXPEHURIVDPSOHV
is the number of samples.
6WHS
Step 3. &DOFXODWH
Calculate WKH
the LQIRUPDWLRQ
information HQWURS\
entropy YDOXH
value +
H1M RI
of
WKH
the jWKLQGLFDWRU
-th indicator.
Z
L
ωOJL == ____3_
_ (3)
P
� Lm= Z
w
¦
L...J
i=l L
l
6WHS
Step 4. &DOFXODWH
Calculate WKH
the PD[LPDO
maximal HLJHQYDOXH
eigenvalue λAPD[
of WKH
the
max RI
MXGJPHQWPDWUL[LH
judgment matrix. i.e.,
+M −
P $ω
(Aw)L; �
(4)
QnOJ
ωL;
Y
if
6WHS&DOFXODWHWKHZHLJKWFRHIILFLHQWRILQGLFDWRU
Step 3. Calculate the weight coefficient of indicator Li Li=i
=
Ur..LM ==
j
PD[
max(x[LMif ) DQG
and PLQ
min(x[LMif ) DUH
are WKH
the PD[LPXP
maximum DQG
and PLQLPXP
minimum
ZL = P DLDL / DLP (2)
1
[xLM −- PLQ
if min(x[ jM ) ġġġġġġġġġġġġġġġġġġġġġġġġġġġġġ (7)
PD[
max(x[ )
- PLQ
min(x[ M ) '
M −
ZKHUH
WK VDPSOH
where [xLM LV
is WKH
the YDOXH
value RI
of WKH
the jWK
-th LQGLFDWRU
indicator LQ
in ith
sample,
6WHS1RUPDOL]HWKHMXGJPHQW
Step 2. Normalize the judgment PDWUL[
matrix $
A DQGFDOFXODWH
and calculate
WKHYHFWRU
the vector
λ"�ax
=¦
PD[ =
L...J
Y
2 , 4, 6 , 8 DQGDUHWKHLQWHUPHGLDWHYDOXH
2 , 4 , 6 , and 8 are the intermediate value
'
&,
CI (6)
5,
RI '
P
¦ SLM OQ SLM (10)
OQ P L =
6WHS&DOFXODWHWKHHQWURS\ZHLJKWRIWKH
WKLQGLFDWRU
Step 4. Calculate the entropy weight of the j-th
indicator.
7KHIRUPXODIRUFDOFXODWLQJHQWURS\ZHLJKWLVDVIROORZV
The formula for calculating entropy weight is as follows.
·
ωM
6WHS
Step 5. &RQVLVWHQF\
Consistency WHVW
test. :H
We XVH
use WKH
the FRQVLVWHQF\
consistency LQGH[
index
&, DQGWKHFRQVLVWHQF\UDWLR
&5 WRHVWLPDWHWKH
(CI)
and the consistency ratio (CR)
to estimate the OHYHORI
level of
FRQVLVWHQF\RIWKHMXGJPHQWPDWUL[DVIROORZV
consistency ofthe judgment matrix as follows.
− + M
Q
Q − ¦ M = + M
(11)
495
495
Authorized licensed use limited to: Adana Alparslan Turkes Bilim ve Teknoloji Universitesi. Downloaded on October 19,2023 at 18:24:46 UTC from IEEE Xplore. Restrictions apply.
&
C. &$57DOJRULWKP
CAR T algorithm
7KH key
NH\ to
WR decision
GHFLVLRQ tree
WUHH learning
OHDUQLQJ is
LV to
WR VHOHFW
SDUWLWLRQLQJ
The
select partitioning
DWWULEXWHV First,
)LUVW calculate
FDOFXODWH the
WKH information
LQIRUPDWLRQ gain
JDLQ of
RI each
HDFK
attributes.
DWWULEXWHLQWKHWUDLQLQJVDPSOHDQGVHOHFWWKHDWWULEXWHZLWK
attribute
in the training sample, and select the attribute with
WKHKLJKHVWLQIRUPDWLRQJDLQDVWKHURRWQRGH7KHH[WHQGHG
the
highest information gain as the root node. The extended
EUDQFKHV under
XQGHU the
WKH root
URRW node
QRGH will
ZLOO be
EH determined
GHWHUPLQHG based
EDVHG on
RQ
branches
WKHYDOXHRIWKHDWWULEXWHUHSUHVHQWHGE\WKHURRWQRGH7KHQ
the
value of the attribute represented by the root node. Then,
WKHDWWULEXWHVWKDWKDYHEHHQ
VHOHFWHGDVQRGHVDUHUHPRYHG
the
attributes that have been selected
as nodes are removed
IURP the
WKH candidate
FDQGLGDWH attribute
DWWULEXWH set,
VHW and
DQG the
WKH calculation
FDOFXODWLRQ and
DQG
from
VHOHFWLRQRIWKHLQIRUPDWLRQJDLQRIWKHUHPDLQLQJDWWULEXWHV
selection
of the information gain of the remaining attributes
LQ the
WKH candidate
FDQGLGDWH attribute
DWWULEXWH set
VHW are
DUH repeated
UHSHDWHG until
XQWLO the
WKH preset
SUHVHW
in
7KH classification
FODVVLILFDWLRQ and
DQG
PRGHO training
WUDLQLQJ threshold
WKUHVKROG is
LV reached.
UHDFKHGġ The
model
UHJUHVVLRQ tree
WUHH (CART)
&$57 algorithm
DOJRULWKP no
QR longer
ORQJHU selects
VHOHFWV
regression
DWWULEXWHV based
EDVHG on
RQ information
LQIRUPDWLRQ gain,
JDLQ it
LW measures
PHDVXUHV attributes
DWWULEXWHV
attributes
XVLQJDQLQGLFDWRUWKDWUHSUHVHQWVVDPSOHLPSXULW\FDOOHGWKH
using
an indicator that represents sample impurity, called the
*LQL index.
LQGH[7KHORZHUWKH*LQLLQGH[WKH
KLJKHUWKHSXULW\
Gini
The lower the Gini index, the higher
the purity
6 I GDWD
RI the
WKH sample.
VDPSOH If
,I S
6 LV
WUDLQLQJ set
VHW that
WKDW includes
LQFOXGHV lS
of
is aD training
data
VDPSOHV and
DQG S(
6 &
WKH VXEVHW
RI samples
VDPSOHV in
LQ S
6 WKDW
samples,
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is the
subset of
that
EHORQJ
WR
WKH
&
FODVV
L
=
/
P
WKH
SUREDELOLW\
WKDW
belong to the C;L class ( i = 1, 2,L , m ) , the probability that
WKHVDPSOHVLQWKHWUDLQLQJVHWEHORQJWRWKH
VHH
the
samples in the training set belong to the iL FODVVLV
class is (see
>@DQG>@
[5] and [6]).
%DVHG on
RQ the
WKH ex1stmg
H[LVWLQJ literature,
OLWHUDWXUH expert
H[SHUW opinions,
RSLQLRQV and
DQG
Based
WKHRUHWLFDO research,
UHVHDUFK this
WKLV paper
SDSHU constructs
FRQVWUXFWV an
DQ evaluation
HYDOXDWLRQ
theoretical
LQGH[ system
V\VWHP for
IRU urban
XUEDQ green
JUHHQ development.
GHYHORSPHQW The
7KH indicator
LQGLFDWRU
index
V\VWHP
LV divided
GLYLGHG into
LQWR three
WKUHH levels:
OHYHOV the
WKH system
V\VWHP dimension,
GLPHQVLRQ
system is
WKHSULPDU\LQGLFDWRUVOD\HUDQGWKHVHFRQGOHYHOLQGLFDWRUV
the
primary indicators layer, and the second-level indicators
OD\HU with
ZLWK aD total
WRWDO of
RI 3
LQGLFDWRUV including
LQFOXGLQJ 2
SRVLWLYH
layer,
1 indicators,
1 positive
LQGLFDWRUVDQGQHJDWLYHLQGLFDWRUV7KHVSHFLILFLQGLFDWRU
indicators
and 10 negative indicators. The specific indicator
V\VWHPLVVKRZQLQ7DEOH7KHHYDOXDWLRQLQGLFDWRUV\VWHP
system
is shown in Table 2. The evaluation indicator system
IRU urban
XUEDQ green
JUHHQ development
GHYHORSPHQW is
LV divided
GLYLGHG into
LQWR three
WKUHH
for
GLPHQVLRQV
SROLF\
LPSOHPHQWDWLRQ
HIILFLHQF\
implementation
dimensions:
policy
effi
ciency,
HQYLURQPHQWDO carrying
FDUU\LQJ capacity,
FDSDFLW\ and
DQG green
JUHHQ technology
WHFKQRORJ\
environmental
LQYHVWPHQW capacity.
FDSDFLW\ 3ROLF\
LPSOHPHQWDWLRQ efficiency
HIILFLHQF\ is
LV
investment
Policy implementation
VXEGLYLGHG into
LQWR two
WZR indicators:
LQGLFDWRUV greening
JUHHQLQJ effi
HIILFLHQF\
DQG
subdivided
ciency and
HFRQRPLFHIILFLHQF\7KHHQYLURQPHQWDOFDUU\LQJFDSDFLW\LV
economic
efficiency. The environmental carrying capacity is
VXEGLYLGHG into
LQWR two
WZR indicators:
LQGLFDWRUV VRFLDO
SUHVVXUH and
DQG
subdivided
social pressure
HQYLURQPHQWDO pressure.
SUHVVXUH Green
*UHHQ technology
WHFKQRORJ\ investment
LQYHVWPHQW
environmental
FDSDFLW\LVVXEGLYLGHGLQWRWZRLQGLFDWRUVILVFDOH[SHQGLWXUH
capacity
is subdivided into two indicators: fiscal expenditure
DQG pollution
SROOXWLRQtreatment.
WUHDWPHQW Establish
(VWDEOLVK an
DQ urban
XUEDQ green
JUHHQ indicator
LQGLFDWRU
and
V\VWHP by
E\ collecting
FROOHFWLQJ data
GDWD on
RQ various
YDULRXV indicators
LQGLFDWRUV in
LQ Xiamen,
;LDPHQ
system
&KLQDIURPWR
China,
from 2003 to 2020.
CJL I
I S6 C&
P;SL = -S6- (12)
I 1 '
7DEOH
Table
2. 7KHXUEDQJUHHQGHYHORSPHQWLQGLFDWRUV
The urban green development indicators
'LPHQVLRQ
Dimension
3ROLF\
Policy
LPSOHPHQWD
implementa
WLRQ
tion
HIILFLHQF\
effi
ciency
DQGWKHLQIRUPDWLRQHQWURS\RIWUDLQLQJVHW
and
the information entropy of training set S6 LVGHILQHGDV
is defined as
m
P
+ 6 =−L
ORJ2 P;
SL · (13)
H(S)
¦ P;SL log
3ULPDU\
Primary
LQGLFDWRUV
indicators
*UHHQLQJ
Greening
HIILFLHQF\
effi
ciency
(FRQRPLF
Economic
HIILFLHQF\
efficiency
L =
i=l
6 ,L
/ , Sv
6Y E\
DWWULEXWH A
$ , the
WKH
6HW S
6 FDQ
EH divided
GLYLGHG into
LQWR S1
Set
can be
by attribute
LQIRUPDWLRQ entropy
HQWURS\ RI
WKH OHDI
QRGH for
IRU classification
FODVVLILFDWLRQ
information
of the
leaf node
LQIRUPDWLRQLV
information
is
Y
6M
M =
6
+ $ 6 = −¦
(QYLURQPH
Environme
QWDO
ntal
FDUU\LQJ
carrying
FDSDFLW\
capacity
+ 6 (14)
DQGWKHLQIRUPDWLRQJDLQLV
and
the information gain is
6RFLDO
Social
SUHVVXUH
pressure
HQYLURQPH
environme
QWDO
ntal
SUHVVXUH
pressure
*DLQ A
$ I S)
6 = H(S)
+ 6 − H(A
+ $ I S).
6 (15)
Gain(
7KHGHILQLWLRQRIWKH*LQLLQGH[LV
The
definition ofthe Gini index is
m
P
*LQL 6 = 1 −L
Gini(S)
¦ PS;L , (16)
L =
i=l
*UHHQ
Green
WHFKQRORJ\
technology
LQYHVWPHQW
investment
FDSDFLW\
capacity
DQG the
WKH Gini
*LQL index
LQGH[ of
RI the
WKH leaf
OHDI node
QRGH for
IRU classification
FODVVLILFDWLRQ
and
LQIRUPDWLRQLV
information
is
*LQL $ I S)
6 =�
*LQL 6)
17) Gini(A
¦ Is I Gini(S
M ( Y
v
M =
)LQDQFLDO
Financial
H[SHQGLWXUH
expenditure
lSI
6M
6
3ROOXWLRQ
Pollution
WUHDWPHQW
treatment
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,,, DATA
III.
SOURCES AND RESEARCH METHODS
$Establishment
(VWDEOLVKPHQWRIXUEDQJUHHQGHYHORSPHQWLQGLFDWRUV
A.
ofurban green development indicators
6HFRQGDU\LQGLFDWRUV
Secondary
indicators
*UHHQLQJFRYHUDJHDUHDRIWKHFLW\
KHFWDUHV
Greening
coverage area of the city(h
ectares)
7RWDOEXVSDVVHQJHUWUDIILF
SHUVRQWLPHV
Total
bus passenger traffic (10000
person times)
5HVLGHQWLDODQGGRPHVWLFZDWHUFRQVXPSWLRQ
Residential
and domestic water consumption
WRQV
(10000
tons)
7RWDOIRUHVWU\RXWSXWYDOXH
50%
Total
forestry output value (10000
RMB) )LVKHU\RXWSXWYDOXH
Fishery
output value (150%
0000 RMB)
3HUFDSLWDGLVSRVDEOHLQFRPH
50% Per
capita disposable income (RMB)
5HJLRQDO*'3
Regional
GDP
SULPDU\LQGXVWU\*'3
primary
industry GDP
WKHVHFRQGDU\LQGXVWU\*'3
the
secondary industry GDP
WKHWHUWLDU\LQGXVWU\*'3
tertiary industry GDP
the
3HUFDSLWD*'3
50%SHUVRQ Per
capita GDP (RMB/person)
(QJHOFRHIILFLHQW
Engel
coefficient %
3HUPDQHQWUHVLGHQWSRSXODWLRQ
Permanent
resident population (1SHRSOH
0000 people) 1DWXUDOQHWLQFUHDVHLQSRSXODWLRQ
SHUVRQ Natural
net increase in population (person)
1XPEHURIVWXGHQWVRQFDPSXV
SHUVRQ Number
of students on campus (person
$YHUDJHOLIHH[SHFWDQF\
\HDUV Averag
e life expectancy (years)
(QHUJ\FRQVXPSWLRQSHUXQLW*'3
WRQRI
Energy
consumption per unit GDP (ton
of
VWDQGDUGFRDO50%
standard
coal/ ! 0000 RMB)
3RZHUFRQVXPSWLRQSHUXQLW*'3
N:K
Power
consumption per unit GDP (k:Wh/10000
\XDQ yuan)
,QGXVWULDOZDVWHZDWHUGLVFKDUJH
Industrial
wastewater discharge (1WRQV
0000 tons)
7RWDOLQGXVWULDOZDVWHJDVHPLVVLRQV
PLOOLRQ
Total
industrial waste gas emissions (100
million
FXELFPHWHUV
cubic
meters) 7KHFRPSUHKHQVLYHXWLOL]DWLRQUDWHRILQGXVWULDO
The
comprehensive utilization rate of industrial
VROLGZDVWH
solid
waste (%)
,QGXVWULDOVXOSKXUGLR[LGHHPLVVLRQV
Industrial sulphur dioxide emissions (1WRQV
0000 tons)
*HQHUDWLRQRIWKHJHQHUDOLQGXVWULDOVROLGZDVWH
Generation
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(1WRQV
0000 tons)
7RWDOLQYHVWPHQWLQHQYLURQPHQWDOSROOXWLRQ
Total
investment in environmental pollution
FRQWURO(10000
50%
control
RMB)
*RYHUQPHQWILQDQFLDOH[SHQGLWXUHKHDOWKFDUH
Government
financial expenditure:health care
50%
(10000
RMB)
*RYHUQPHQWILQDQFLDOH[SHQGLWXUHHQHUJ\
Government
financial expenditure: energy
FRQVHUYDWLRQDQGHQYLURQPHQWDOSURWHFWLRQ
conservation
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Government
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UHVRXUFHVPDULQHPHWHRURORJ\HWF
50%
resources,
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+DUPOHVVWUHDWPHQWUDWHRIGRPHVWLFZDVWH
Harmless
treatment rate of domestic waste (%)
$QQXDOVHZDJHGLVFKDUJH
Annual
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WRQV
Annual
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tons)
6HZDJHWUHDWPHQWUDWH
Sewage
treatment rate (%)
496
Authorized licensed use limited to: Adana Alparslan Turkes Bilim ve Teknoloji Universitesi. Downloaded on October 19,2023 at 18:24:46 UTC from IEEE Xplore. Restrictions apply.
,9
IV. (03,5,&$/5(68/76$1'$1$/<6,6
EMPIRICAL RESULTS AND ANALYSIS
,Q
In WKLV
this SDSHU
paper, WKH
the $+3
AHP PHWKRG
method DQG
and HQWURS\
entropy ZHLJKW
weight
PHWKRGDUHXVHGWRDVVLJQZHLJKWVWRHDFKIDFWRU7KH$+3
method are used to assign weights to each factor. The AHP
PHWKRGEHORQJVWRWKHVXEMHFWLYHHYDOXDWLRQ
method belongs to the subjective evaluation PHWKRG
method, ZKLFK
which
KDVVWURQJH[SODQDWRU\SRZHU&RPELQHGZLWKWKHREMHFWLYH
has strong explanatory power. Combined with the objective
ZHLJKW
weight HYDOXDWLRQ
evaluation PHWKRG
method, LH
i.e., WKH
the HQWURS\
entropy ZHLJKW
weight PHWKRG
method,
ZHLJKWHYDOXDWLRQWKDWLVERWKREMHFWLYHDQGFRQVLVWHQW
weight evaluation that is both objective and consistent ZLWK
with
FRPPRQ
MXGJPHQW FDQ
common VHQVH
sense judgment
can EH
be REWDLQHG
obtained. $IWHU
After REWDLQLQJ
obtaining
VXEMHFWLYH
subjective ZHLJKWV
weights DQG
and REMHFWLYH
objective ZHLJKWV
weights WKURXJK
through WKH
the $+3
AHP
PHWKRG
method DQG
and HQWURS\
entropy ZHLJKW
weight PHWKRG
method, ZH
we REWDLQ
obtain WKH
the
FRPSUHKHQVLYH
comprehensive ZHLJKWV
weights RI
of XUEDQ
urban JUHHQ
green GHYHORSPHQW
development
LQGLFDWRUV
indicators E\
by XVLQJ
using WKH
the OLQHDU
linear FRPELQDWLRQ
combination PHWKRG
method. 7KH
The
$+3HQWURS\
AHP-entropy ZHLJKWV
weights :
W ZLOO
will EH
be REWDLQHG
obtained E\
by FRPELQLQJ
combining
ZLWK
as
and WKH
the HQWURS\
entropy ZHLJKWV
weights :
W(E DV
with WKH
the $+3
AHP ZHLJKWV
weights :
W$A DQG
IROORZV
follows.
$
A.$+3HQWURS\ZHLJKWVRIXUEDQJUHHQGHYHORSPHQW
AHP-entropy weights ofurban green development
LQGLFDWRUV
indicators
7KH
The MXGJPHQW
judgment PDWUL[
matrix KDV
has FRPSOHWH
complete FRQVLVWHQF\
consistency &5
CR == 0 , VR
so
WKH$+3PHWKRGFDQEHXVHGIRUDQDO\VLV7KURXJKWKH$+3
the AHP method can be used for analysis. Through the AHP
PHWKRGDQGWKHHQWURS\ZHLJKWPHWKRGZHREWDLQWKH$+3
method and the entropy weight method, we obtain the AHP
ZHLJKWVDQGWKHHQWURS\ZHLJKWVUHVSHFWLYHO\%\WKHOLQHDU
weights and the entropy weights, respectively. By the linear
FRPELQDWLRQ
WKH
combination RI
of IRUPXOD
formula (18),
the $+3HQWURS\
AHP-entropy ZHLJKWV
weights RI
of
XUEDQJUHHQGHYHORSPHQWFDQEHREWDLQHGDVVKRZQLQ7DEOH
urban green development can be obtained, as shown in Table
5.
7DEOH7KH$+3HQWURS\ZHLJKWVRIXUEDQJUHHQGHYHORSPHQWLQGLFDWRUV
Table 5 . The AHP-entropy weights of urban green development indicators
,QGLFDWRUV
Indicators
*UHHQLQJ
Greening FRYHUDJH
coverage DUHD
area RI
of WKH
the FLW\
city
KHFWDUHV (hectares)
7RWDOEXVSDVVHQJHUWUDIILF
Total bus passenger traffic (1
0000
SHUVRQWLPHV
person times)
5HVLGHQWLDODQGGRPHVWLFZDWHU
Residential and domestic water
FRQVXPSWLRQ
WRQV
consumption (1
0000 tons)
7RWDOIRUHVWU\RXWSXWYDOXH
Total forestry output value (10000
50%
RMB)
)LVKHU\RXWSXWYDOXH
50%
Fishery output value (1 0000 RMB)
3HUFDSLWDGLVSRVDEOHLQFRPH
50% Per capita disposable income (RMB
5HJLRQDO*'3
Regional GDP
SULPDU\LQGXVWU\*'3
primary industry GDP
WKHVHFRQGDU\LQGXVWU\*'3
the secondary industry GDP
WKHWHUWLDU\LQGXVWU\*'3
the tertiary industry GDP
3HUFDSLWD*'3
50%SHUVRQ Per capita GDP (RMB/person)
(QJHOFRHIILFLHQW
Engel coefficient (%)
3HUPDQHQWUHVLGHQWSRSXODWLRQ
Permanent resident population
SHRSOH
(10000 people)
1DWXUDOQHWLQFUHDVHLQSRSXODWLRQ
Natural net increase in population
SHUVRQ (person)
1XPEHURIVWXGHQWVRQFDPSXV
Number of students on campus
SHUVRQ (person)
$YHUDJHOLIHH[SHFWDQF\
\HDUV Average life expectancy (years)
(QHUJ\FRQVXPSWLRQSHUXQLW*'3
Energy consumption per unit GDP
WRQRIVWDQGDUGFRDO50%
(ton
of standard coaVlOOOO RMB)
3RZHUFRQVXPSWLRQSHUXQLW*'3
Power consumption per unit GDP
N:K\XDQ
(k:Wh/10000
yuan)
,QGXVWULDOZDVWHZDWHUGLVFKDUJH
Industrial wastewater discharge
WRQV
(1
0000 tons)
7RWDOLQGXVWULDOZDVWHJDVHPLVVLRQV
Total industrial waste gas emissions
PLOOLRQFXELFPHWHUV
(100
million cubic meters)
7KHFRPSUHKHQVLYHXWLOL]DWLRQUDWH
The comprehensive utilization rate
RILQGXVWULDOVROLGZDVWH
of industrial solid waste (%)
,QGXVWULDOVXOSKXUGLR[LGHHPLVVLRQV
Industrial sulphur
dioxide emissions
.
WRQV
(1
0000 tons)
*HQHUDWLRQRIWKHJHQHUDOLQGXVWULDO
Generation ofthe general industrial
VROLGZDVWH
WRQV
solid waste (10000
tons)
7RWDOLQYHVWPHQWLQHQYLURQPHQWDO
Total investment in environmental
SROOXWLRQFRQWURO
50%
pollution control (1 0000 RMB)
*RYHUQPHQWILQDQFLDOH[SHQGLWXUH
Government financial expenditure:
KHDOWKFDUH
50%
health care (10000
RMBl
*RYHUQPHQWILQDQFLDOH[SHQGLWXUH
Government financial expenditure:
HQHUJ\FRQVHUYDWLRQDQG
energy conservation and
HQYLURQPHQWDOSURWHFWLRQ
environmental protection (1
0000
50%
RMB)
*RYHUQPHQWILQDQFLDOH[SHQGLWXUH
Government financial expenditure:
QDWXUDOUHVRXUFHVPDULQH
natural resources, marine
PHWHRURORJ\HWF
50%
meteorology, etc. (1
0000 RMB)
+DUPOHVVWUHDWPHQWUDWHRIGRPHVWLF
Harmless treatment rate of domestic
ZDVWH
waste (%)
$QQXDOVHZDJHGLVFKDUJH
Annual sewage discharge (1
0000
WRQV
tons)
$QQXDOVHZDJHWUHDWPHQWFDSDFLW\
Arumal sewage treatment capacity
WRQV
(10000
tons)
6HZDJHWUHDWPHQWUDWH
Sewage treatment rate (%)
:
:(E (18)
W == 0.45:
W$A ++ 0.55W
%
B.&ODVVLILFDWLRQDQGSUHGLFWLRQRIWKH&$57$OJRULWKP
Classification andprediction ofthe CART Algorithm
7KURXJKDTXHVWLRQQDLUHVXUYH\WKLVDUWLFOHFROOHFWVGDWDRQ
Through a questionnaire survey, this article collects data on
WKH
the SHUVRQDO
personal SUDFWLFHV
practices RI
of JUHHQ
green OLIH
life IURP
from WKH
the SXEOLF
public,
DVVHVVPHQW
assessment RI
of WKH
the TXDOLW\
quality RI
of WKH
the VXUURXQGLQJ
surrounding HQYLURQPHQW
environment,
YLHZVRQSDUNVJUHHQVSDFHVDQGLQIUDVWUXFWXUHSURYLGHGE\
views on parks, green spaces, and infrastructure provided by
WKH
the PXQLFLSDO
municipal JRYHUQPHQW
government, SHUFHSWLRQV
perceptions RI
of HQWHUSULVH
enterprise JUHHQ
green
SURGXFWLRQSHUIRUPDQFHWKHDFWVRIJRYHUQPHQWVXSHUYLVLRQ
production performance, the acts of government supervision
DQG
and LPSOHPHQWDWLRQ
implementation RI
of JUHHQ
green HQYLURQPHQWDO
environmental SURWHFWLRQ
protection
SROLFLHV
policies DQG
and ODZV
laws, DQG
and VDWLVIDFWLRQ
satisfaction GDWD
data RQ
on XUEDQ
urban JUHHQ
green
GHYHORSPHQW
development. $
A WRWDO
total RI
of 235 YDOLG
valid TXHVWLRQQDLUHV
questionnaires ZHUH
were
FROOHFWHG
collected, DQG
and WKH
the UHOLDELOLW\
reliability RI
of WKH
the TXHVWLRQQDLUH
questionnaire ZDV
was
DQDO\VHGXVLQJ&URQEDFKĮDVVKRZQLQ7DEOH.02DQG
analysed using Cronbach a, as shown in Table 3 . KMO and
%DUWOHWW¶V
Bartlett's WHVW
test DUH
are XVHG
used WR
to DQDO\VH
analyse WKH
the YDOLGLW\
validity RI
of WKH
the
TXHVWLRQQDLUHDVVKRZQLQ7DEOH
questionnaire, as shown in Table 4.
7DEOH
Table 3.5HOLDELOLW\DQDO\VLV
Reliability analysis
)DFHWV
Facets
3HUVRQDOSUDFWLFH
Personal practice
(QYLURQPHQWDOTXDOLW\
Environmental quality
,QIUDVWUXFWXUH
Infrastructure
(QWHUSULVHSHUIRUPDQFH
Enterprise performance
*RYHUQPHQWDFWLRQ
Government action
(YDOXDWLRQ
Evaluation
&URQEDFK¶VĮ
Cronbach's a
0.773
0.854
0.841
0.880
0.900
0.873
2YHUDOOTXHVWLRQQDLUH
Overall questionnaire
0.903
7DEOH9DOLGLW\DQDO\VLV
Table 4. Validity analysis
.DLVHU0H\HU2ONLQ
Kaiser-Meyer-Olkin
%DUWOHWW
Bartlett'sVWHVW
test
$SSUR[LPDWH
Approximate χ
%2 WHVW
test
)UHHGRP
Freedom
6LJQLILFDQFH
Significance
1RWH
S
Note: *:S
p<0.05 , **:p<0.01
0.9 12
3595.850
253
0.000**
)URP
From 7DEOHV
Tables 3 DQG
and 4, LW
it FDQ
can EH
be VHHQ
seen WKDW
that WKH
the
TXHVWLRQQDLUH
questionnaire KDV
has KLJK
high UHOLDELOLW\
reliability DQG
and HIILFLHQF\
efficiency. 7KHUHIRUH
Therefore,
ZH
we ZLOO
will XVH
use WKH
the &$57
CART DOJRULWKP
algorithm WR
to DQDO\VH
analyse WKH
the SXEOLF
public'sV
SHUVRQDOSUDFWLFHRI
personal practice of JUHHQOLIHDVVHVVPHQWRIWKHTXDOLW\RI
green life, assessment of the quality of
WKHVXUURXQGLQJHQYLURQPHQWYLHZVRQSDUNVJUHHQVSDFHV
the surrounding environment, views on parks, green spaces,
DQG
and LQIUDVWUXFWXUH
infrastructure SURYLGHG
provided E\
by WKH
the PXQLFLSDO
municipal JRYHUQPHQW
government,
YLHZV
views RQ
on WKH
the SHUIRUPDQFH
performance RI
of JUHHQ
green SURGXFWLRQ
production E\
by
HQWHUSULVHV
enterprises, JRYHUQPHQW
government VXSHUYLVLRQ
supervision DQG
and HQIRUFHPHQW
enforcement RI
of
JUHHQ
green HQYLURQPHQWDO
environmental SURWHFWLRQ
protection SROLFLHV
policies DQG
and ODZV
laws, DQG
and
VDWLVIDFWLRQ
satisfaction GDWD
data RQ
on XUEDQ
urban JUHHQ
green GHYHORSPHQW
development, LGHQWLI\
identify WKH
the
LPSRUWDQW
important IDFWRUV
factors WKDW
that DIIHFW
affect JUHHQ
green GHYHORSPHQW
development VDWLVIDFWLRQ
satisfaction
DQGHVWDEOLVKDSUHGLFWLRQPRGHO
and establish a prediction model.
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weights
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weights
0.09380
0.02760
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weights
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0.04690
0.02810
0.03656
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classifi
cation is shown in Figure 1. From Table 8, it can be
VHHQ that
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WKH most
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LPSRUWDQW factors
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satisfaction
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WKH actions
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WKH municipal
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RI supervising
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industrial
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environmental
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the
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algorithm.
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Tables
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classification
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time
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Table
6. The CART model parameters
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Figure
1. &$57DOJRULWKPFODVVLILFDWLRQUHVXOWV
CART algorithm classification results.
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Minimum
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The
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VKRZQ in
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and
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shown
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Figure
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RI the
WKH CART
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high.
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PRGHOKDVH[WUHPHO\KLJKH[SODQDWRU\SRZHU
model
has extremely high explanatory power.
7DEOH
Table
9. (YDOXDWLRQUHVXOWVRI&$57UHJUHVVLRQPRGHO
Evaluation results of CART regression model
7DEOH7KHPRGHOHYDOXDWLRQUHVXOWV
Table
7. The model evaluation res nits
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indicators
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Accuracy
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Table
8. The importance ranking
)HDWXUHV
Features
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Government
action
(QWHUSULVHSHUIRUPDQFH
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performance
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Environmental
quality
3HUVRQDOSUDFWLFH
Personal practice
,PSRUWDQFH
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0.664
0.141
0.079
0.069
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V.
,Q this
WKLV study,
VWXG\ the
WKH AHP-entropy
$+3HQWURS\ weight
ZHLJKW method
PHWKRG is
LV used
XVHG to
WR
In
ZHLJK various
YDULRXV indicators
LQGLFDWRUV that
WKDW affect
DIIHFW green
JUHHQ development,
GHYHORSPHQW
weigh
HVWDEOLVK urban
XUEDQ green
JUHHQ development
GHYHORSPHQW evaluation
HYDOXDWLRQ indicators,
LQGLFDWRUV
establish
DQG then
WKHQ explore
H[SORUH the
WKH important
LPSRUWDQW factors
IDFWRUV that
WKDW affect
DIIHFW people's
SHRSOH V
and
VDWLVIDFWLRQ with
ZLWK green
JUHHQ development
GHYHORSPHQW through
WKURXJK the
WKH
satisfaction
7KH importance
LPSRUWDQFH ranking
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DQG proportion
SURSRUWLRQ of
RI each
HDFK data
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The
IHDWXUHDUHVKRZQLQ7DEOHDQGWKHRXWSXWUHVXOWRI&$57
feature
are shown in Table 8, and the output result of CART
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improvement VWUDWHJ\
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satisfaction in Xiamen is proposed.
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important indicator through the AHP-entropy weight method,
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and
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ACKNOWLEDGMENTS
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This ZRUN
work ZDV
was SDUWLDOO\
partially ILQDQFLDOO\
financially VXSSRUWHG
supported E\
by WKH
the 2022,QQRYDWLRQDQG(QWUHSUHQHXUVKLS7UDLQLQJ3URJUDPIRU
2023 Innovation and Entrepreneurship Training Program for
&ROOHJH
College 6WXGHQWV
Students IXQG
fund, ;LDPHQ
Xiamen 8QLYHUVLW\
University 7DQ
Tan .DK
Kah .HH
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College. Project No. 1 89: Big Data Analysis and Intelligent
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and *DPH
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Corresponding author: Yu-Chung Chang.
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Weight
&RHIILFLHQWVWRZDUGV6LWWLQJ&RPIRUW%DVHGRQ$+3WR
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in
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Singh, P. S. Bilga, K. Jatin, J. Singh, S.
[4] R. Kumar, S. 6LQJK36%LOJD.-DWLQ-6LQJK6
6LQJK0
Singh, M. /6FXWDUXDQG
L. Scutaru, and &,3UXQFX
C. I. Pruncu, 5HYHDOLQJ
Revealing WKH
the
%HQHILWV
Benefits RI
of (QWURS\
Entropy :HLJKWV
Weights 0HWKRG
Method IRU
for 0XOWL
Multi-
499
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