themeasurementofgreenfinancedevelopmentindexandits ...and wang (2020) [15] used panel regression...

Research ArticleThe Measurement of Green Finance Development Index and ItsPoverty Reduction Effect Dynamic Panel Analysis Based onImproved Entropy Method

Lili Jiang 1 Hui Wang 2 Aihua Tong 1 Zhifei Hu 1 Hongjun Duan 3

Xiaolei Zhang 2 and Yifeng Wang 4

1Business School Suqian College Suqian 223800 Jiangsu China2Pan-Asia Business School Yunnan Normal University Kunming 650000 China3Jiangsu Vocational College of Finance and Economics Huaian 223001 Jiangsu China4Essence Securities CO LTD Shanghai 200030 China

Correspondence should be addressed to Hui Wang wanghui_0401163com and Yifeng Wang wyf870403163com

Received 7 September 2020 Revised 25 November 2020 Accepted 29 November 2020 Published 16 December 2020

Academic Editor Maria Alessandra Ragusa

Copyright copy 2020 Lili Jiang et al 2is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Finance contributes to poverty alleviation through economic growth and the development of green finance is related to thesustainable development of the world economy and environment Green finance not only helps promote sustainable economicdevelopment but also helps reduce poverty Based on the analysis of related theories about green finance and poverty alleviation thispaper selects 18 indicators from three dimensions of economic development financial development and social environmentaldevelopment and uses the improved entropy method to measure the green finance development index of Chinarsquos 25 provinces andmunicipalities from 2004 to 2017 2e results show that the development level of green finance in Chinarsquos 25 provinces andmunicipalities is quite different On the basis of the above analysis make an empirical analysis of the impact of the green financedevelopment index on poverty alleviation using multiple regression analysis and static panel and dynamic panel estimation methods2e research results show that there is a significant positive correlation between green finance and poverty alleviation the higher thelevel of green finance development the more conducive the poverty alleviation So this paper suggests that poverty can be betteralleviated by improving the level of green finance development financial asset level and economic development level

1 Introduction

2roughout human history peace and prosperity have al-ways been the goals that people have pursued but povertyhas always been a chronic disease that human society cannotget rid of In the ldquoTransforming OurWorld 2030 Agenda forSustainable Developmentrdquo adopted by the United Nations inSeptember 2015 ldquoeliminating all forms of poverty in theworldrdquo became the first goal among the 17 sustainable de-velopment goals Around the world we are still facingenormous development challenges brought about by pov-erty 2e 2019 Global Multidimensional Poverty IndexReport released by the United Nations Development Pro-gram shows that 13 billion people worldwide are in a

ldquomultidimensional poverty staterdquo and there are huge dif-ferences in poverty levels between countries and regionswithin countries 2e report covers 101 countries including31 low-income countries 68 middle-income countries and 2high-income countries

Since 2015 China has paid more attention to povertyalleviation and has adopted diversified poverty reductionmeasures 2020 is the year when Chinarsquos grand goal ofbuilding a moderately prosperous society in an all-roundway is realized and it is also the final year of Chinarsquos fightagainst poverty Chinarsquos poverty alleviation work hasachieved world-renowned achievements not only benefitingthe Chinese people but also benefiting the whole world Ithas made important contributions to the realization of the

HindawiDiscrete Dynamics in Nature and SocietyVolume 2020 Article ID 8851684 13 pageshttpsdoiorg10115520208851684

goals set by the United Nations ldquo2030 Agenda for Sus-tainable Developmentrdquo 2e Chinese style ldquoroad to povertyrdquohas become the focus of world attention

To study poverty we should define it at first Becausepoverty is a complex concept that constantly evolves alongwith the development of economies and societies it is ldquoanelusive conceptrdquo Human understanding of poverty as aconcept has evolved and scholars have used varying defi-nitions of poverty 2e study of poverty by Rowntree andBooth is considered to represent the beginning of the studyof poverty in the social sciences In their study poverty is thelack of materiality in an economic sense Booth defines theldquopoorrdquo as middle-aged people who earns pound1 or less per weekand Rowntree describes poverty as applying to a householdwhose total income cannot cover its most basic survivalactivities Rowntree and Booth emphasize family incomebecause of its necessity to survival From the perspective ofincome another definition of ldquosurvival povertyrdquo is termed asldquoabsolute povertyrdquo In 1948 aWorld Bank report introducedthe concept of ldquoincome povertyrdquo and linked the worldrsquos richand poor to gross national product for the first time withless than US$100 annual income on average representingpoor or underdeveloped status

2e Grameen Bank successfully used microloans to helppeople out of poverty 2is case has attracted great attentionfrom the academic community Many scholars show theirconcerns about the relationship between finance develop-ment and poverty [1ndash5] such as Jalilian (2002) Philip andAsena (2004) Jordan (2005) Sehrawat and Giri (2016) andYılmaz (2017) Financial constraints are proved to be thegreatest difficulty in poverty elimination according to theantipoverty experiences frommany developing countries Inthe process of poverty alleviation through finance we mustalso pay attention to the sustainable development of theeconomy In particular the green finance development toalleviate poverty is more conducive to achieving the 2030sustainable development goals adopted by the UnitedNations

We should consider alleviating poverty effectively notonly by finance development but also by green economydevelopment Althoughmany developing countries have notclearly formulated a green indicator system so far some ofthese countries have adopted ldquogreen growthrdquo as a corestrategy of national development Chinarsquos 12th Five-YearPlan (2011ndash2015) proposed ldquogreen development and re-source conservationrdquo India highlighted sustainable devel-opment in its national 12th Five-Year Plan emphasizing thegoals of a ldquofriendly environment-friendly societyrdquo and ldquofastsustainable and more inclusive growthrdquo which demonstratethe shift of national strategic priorities Many scholars didmany research studies in green development and green fi-nance [6ndash9] such as Csete and Horvath (2012) Edward(2013) Wang and Zhi (2016) and Xiong and Qi (2018)2erefore promoting the development of green finance hasbecome a priority In addition financial institutionsworldwide have been actively developing green finance 2edevelopment history of green finance can be traced back to

the 1970s in the last century As early as 1974 the FederalRepublic of Germany established the worldrsquos first policy-based environmental protection bank named ldquoEco Bankrdquowhich was responsible for providing preferential loans forenvironmental projects that general banks were unwilling toaccept Since 2002 many financial institutions around theworld have adopted the ldquoEquator Principlesrdquo to pay moreattention to the development of green finance

Green finance is a subject worthy of further study Howto measure the development of green finance is the first stepin this area At present there are few articles measuring thegreen finance index (GFI) Existing literatures mainly usealternative indicators to be the proxy of the green financedevelopment index such as Sarah et al (2020) [10] replacedgreen finance with carbon finance Some scholars makespecial calculations on the energy security index SongZhang and Sun (2019) [11] introduced a new aggregatedindicator the China energy security index (CESI) forevaluating how Chinarsquos energy security has changed overyears 2e CESI includes the energy supply dimensioneconomic-technical dimension and environmental di-mension of energy security A small amount of the literatureuses complex network methods to study energy financialmarket issues currently Li et al (2016) [12] used complexnetwork methods to study the global energy investmentstructure and found that most foreign investment andforeign investment relations are still controlled by a fewCountries Xi amp An (2018) [13] used the financial indicatorsof energy stocks to construct a complex network 2e studyfound that the threshold of 07 is a mutation point of thenetwork and as the threshold increases the independence ofthe community increases Some scholars have made relatedresearch on green finance For example Li Yuan andWang(2019) [14] constructed a green finance-ecological envi-ronment composite system coupling and coordinated de-velopment evaluation system and measured the developmentlevel of green finance and the comprehensive level of eco-logical environment in the three major economic circles Leiand Wang (2020) [15] used panel regression methods toanalyze the relationship between environmental pollutiongreen finance and high-quality economic development andused spatial measurement methods to conduct robustnesstests 2e results show that environmental pollution has asignificant inhibitory effect on high-quality economic de-velopment but green finance has the effect of promotinghigh-quality economic development through environmentalimprovement effects 2ere are regional differences in thiseffect which is stronger in the central region and weaker inthe eastern region Zhu (2020) [16] selected 8 indicators fromthe aspects of scale and profit and business operation torepresent green finance and selected 14 indicators frommarine resources marine environment and marine activitiesto represent the marine eco-environment evaluating thecoupling coordination degree of green finance and marineeco-environment based on AHP and grey system theory Liu(2019) [17] used the analytic hierarchy process to compre-hensively evaluate the development status and shortcomings

2 Discrete Dynamics in Nature and Society

of green finance in 17 cities in Shandong Province but she didnot calculate the Shandong Province green finance devel-opment index

Although those approximate indicators can reflect thedevelopment of green finance to a certain extent their ac-curacy is questionable 2is paper tries to measure the GFIby the entropy weight method2e entropymethod has beenwidely used in financial research [18ndash25] for exampleWanget al (2008) Wang et al (2015) Jin and Liu (2017) Gonget al (2019) Dai and Niu (2019) Long et al (2019) Zhangand Wang (2019) and Jiang et al (2019)

Compared with existing research we find that there isvery little literature analyzing the relationship between greenfinance development and poverty alleviation but more re-search studies examine that financial development doesmatter on poverty alleviation Most scholars align them-selves with one or more of the following representativeviews 2e first representative view is that finance devel-opment and poverty alleviation are positively correlated[26ndash28] such as Beck et al (2007) Park (2016) and Rashidet al (2017) 2e second view is that there is a nonpositivecorrelation between finance development and poverty al-leviation [29ndash31] such as Kappel (2010) Perez-Moreno(2011) and Sehrawat and Giri (2016) 2e third represen-tative view is a nonlinear relationship between finance de-velopment and poverty alleviation [32ndash34] such asGreenwood and Jovanovic (1990) Townsend and Ueda(2006) and Zahonogo (2017)2e fourth representative viewis that finance development may be detrimental to povertyalleviation [35] such as Claessens and Perotti (2007) Dif-ferent types of samples and statistical methods used in fi-nance development and poverty alleviation research lead tovaried conclusions 2e paper analyzes the effect of greenfinance on poverty alleviation from an empirical perspectiveand verifies the value of sustainable development for greenfinance indicating that green development and economicgrowth are not incompatible Developing green finance isconducive to eliminating poverty and generating wealth2eresult reveals positive correlations between the GFI and thepoverty alleviation 2e higher GFI is more conducive topoverty alleviation

2is paper has the following contributions Firstly thispaper uses the improved entropy weight method to con-struct the GFI of Chinese provinces and cities It can in-tuitively demonstrate the level of green finance developmentin various parts of China Secondly this paper selectsmultiple basic indicators from the three dimensions ofeconomy finance and environment to build the GFI 2eindicator system of the GFI is relatively comprehensive2irdly this paper systematically studies the relationshipbetween the green finance development and poverty alle-viation Empirical results show that the development ofgreen finance can effectively reduce poverty

2e arrangement of this paper is as follows the first partis the introduction the second part is the theoretical in-troduction of green finance and poverty alleviation the thirdpart is the empirical analysis and the fourth part is theconclusion and policy recommendations

2 Theoretical Analysis of the Impact of GreenFinance Development on Poverty Alleviation

2e research is divided into two parts (1) using the entropyweight method to calculate the GFI and (2) studying theimpact of the green finance development index on povertyalleviation We will conduct theoretical analysis from thoseselected basic indicators through the entropy weight methodand regression analysis

21 Indicators for Measuring GFI 2is paper will use theentropy method to measure the degree of green financedevelopment in 25 provinces and municipalities in ChinaWhen using the entropy method to measure the green fi-nance development index some basic indicators are needed2e goal set by the United Nations ldquo2030 Agenda forSustainable Developmentrdquo is to thoroughly solve the de-velopment problems of the three dimensions of societyeconomy and environment through an integrated approachand to promote human beings to the path of sustainabledevelopment Green finance achieves the sustainable de-velopment goal of the quality of economic growth bypromoting environmental governance In theory ldquogreenfinancerdquo means that the financial sector regards environ-mental protection as a basic policy Potential environmentalimpacts must be considered in investment and financingdecisions and the potential returns risks and costs relatedto environmental conditions must be integrated In dailybusiness we pay attention to the protection of the ecologicalenvironment and the treatment of environmental pollutionin financial business activities and promote the sustainabledevelopment of society through the guidance of socialeconomic resources Potential environmental impactsshould be considered in investment and financing decisionsand potential returns risks and costs related to environ-mental conditions should be integrated into daily businessPay more attention to the protection of the ecological en-vironment in financial business activities and promote thesustainable development of society through the guidance ofsocial economic resources 2is paper believes that greenfinance means that financial institutions incorporate envi-ronmental assessment into the process and pay attention tothe protection of the ecological environment and the de-velopment of green industries in their investment and fi-nancing activities Compared with traditional finance themost prominent feature of green finance is that it emphasizesthe environmental benefits of human society Many scholarssuggest to protect the ecological environment and promotethe development of green finance [36ndash38] such as Pulvir-enti Costa Pavone (2015) Ferrauto Costa Pavone Can-tarella (2013) and Cuspilici Monforte Ragusa (2017) Ittakes environmental protection and effective use of re-sources as one of the criteria for measuring the effectivenessof its activities and guides all economic entities pay attentionto natural ecological balance It emphasizes the coordinateddevelopment of financial activities environmental protec-tion and ecological balance and ultimately achieves

Discrete Dynamics in Nature and Society 3

sustainable economic and social development 2ereforethis paper selects indicators from the three dimensions ofeconomy finance and environment when constructing thegreen finance development index When selecting specificindicators it combines the availability of data and the re-search of previous scholars All the indicators are shown inTable 1

211 5e Dimension of Economics 2is paper argues thateconomic development promotes green finance When theeconomy develops to a certain extent people focus more onsustainable economic development For the dimension ofeconomic development this paper considers three indica-tors GDP per capita income per capita and unemploymentrate 2e higher GDP per capita is the greater local eco-nomic development is and the more strongly green financeis promoted 2erefore GDP per capita is a positive indi-cator of the green finance development index 2e higherincome per capita is the higher overall income is and themore environmental protection is emphasized 2ereforeincome per capita is a positive indicator of green finance2ehigher the unemployment rate the lower the development ofgreen finance 2erefore the unemployment rate is a neg-ative indicator of green finance

212 5e Dimension of Finance We believe that there is apositive correlation between the level of financial develop-ment and the degree of green finance For the dimension offinance this paper considers eight indicators number ofbanks per areas number of bank staff per areas and so on2ere is a positive relationship between all indicators andgreen finance

213 5e Dimension of Environment In this dimension sixindicators are selected to measure three aspects of envi-ronmental development waste discharge energy con-sumption and environmental protection 2e rate of sulfurdioxide the rate of solid waste and the rate of energyconsumption are negative indicators of green finance de-velopment 2e rate of nature reserve and the rate of forestare positive indicators of green finance development

22 5e Entropy Weight Method 2e entropy weightmethod is used to calculate the GFI of various provinces andcities in China 2e advantages and improved models of theentropy weight method will be introduced below

221 Advantages of Entropy Weight Evaluation ModelResearch demonstrates that the entropy weight evaluationmodel has the benefits of high calculation accuracy a wideapplication range and limited susceptibility to subjectivefactors 2e primary reasons for using the entropy weightevaluation model to measure green finance development areas follows

First according to the relationship between basic indi-cators and green finance the basic indicators are divided

into positive indicators and negative indicators It shows thebasic ideas of positive-negative image duality thus ensuringthe consistency of the research perspective between thetheoretical and empirical research Second the weight valueobtained by the algorithm of the entropy weight evaluationmodel and the corresponding final evaluation value not onlyprovide objective results but also reflect the informationcontained in various indicators for evaluating green financedevelopment 2ird calculation of the green finance de-velopment index includes various evaluation indicators suchas GDP waste discharge and environmental protection2is model integrates these indicators comprehensivelyBesides that the entropy weight evaluation model algorithmand standardized data are more likely to ensure consistentand rational scientific evaluation results Finally the GFIbased on the entropy weight method ensures the compa-rability of green finance development indices betweenprovinces

222 Improved Entropy Weight Method 2e general en-tropy weight method has at least two shortcomings for thestudy of green finance development Firstly the generalentropy weight method will cause a large change in theentropy weight of indicators which will lead to a large errorin the entropy weight coefficient 2e resulting GFI lacksrationality Secondly the GFI calculated by the traditionalentropy weight method can only provide horizontal com-parison among different provinces and it is no help when itcomes to time series data of green finance development2erefore this paper uses an improved entropy weightmethod to calculate the GFI 2e improved entropy weightmethod is defined as follows

(1) If m provinces are selected for measuring their GFIsin order to calculate the GFI we choose n indicatorsSuppose xij stands for the jth indicator of the ithprovince 2e following matrix serves as a basicindicator of green finance development levels

Z zij1113872 1113873mmiddotn

z11 middot middot middot zn1

⋮ ⋱ ⋮

zm1 middot middot middot zmn

⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠

mmiddotn

(1)

(2) Because the units of each indicator are inconsistentwe need to standardize equation (1) by (2)

rij

zij minus min zij

max zij minus min zij

if zij is a positive indicator

max zij minus zij


if zij is a negative indicator

⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(2)

(3) Normalize the matrix as follows


cij

zij1113936

mi1 z

2ij

1113969 if zij is a positive indicator

1zij

1113936mi1 1zij1113872 1113873

21113969 if zij is a negative indicator

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎩

(3)

(4) Modify the normalization matrix as follows

bij rij + 00001 (4)

pij bij

1113936mi1 bij

(5)

(5) 2e entropy Hj and difference coefficient Gj of thejth indicator are obtained as shown in equations (6)and (7)

Hj minus1

ln(m)middot 1113944

m

i1pij lnpij j 1 2 n (6)

Gj 1 minus Hj j 1 2 n (7)

(6) 2e improved entropy weight coefficient is calcu-lated as follows

ωj Gj + 01 middot 1113936

nj1 Gj

1113936nj1 Gj + 01 middot 1113936

nj1 Gj1113872 1113873

1 minus Hj + 01 middot 1113936

nj1 1 minus Hj1113872 1113873

1113936nj1 1 minus Hj + 01 middot 1113936

nj1 1 minus Hj1113872 11138731113872 1113873

(8)

(7) A standardized decision matrix is obtained

V vij1113872 1113873mmiddotn

ω1c11 middot middot middot ωnc1n

⋮ ⋱ ⋮

ω1cm1 middot middot middot ωncmn

⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠ (9)

2e positive ideal solution v+j and negative ideal so-

lution vminusj of the jth index can be expressed as follows

v+j max vij|i 1 2 m1113966 1113967 (10)

vminusj min vij|i 1 2 m1113966 1113967 (11)

Table 1 Relevant indicators of green finance development

Dimension Basic indicator Calculation method Code Attributes

EconomicsGDP per capita GDPpopulation Z1 +

Income per capita Disposable incomepopulation Z2 +Unemployment rate mdash Z3 mdash

Finance

Number of banks per areas Amount of banksareas Z4 +Number of bank staff per areas Amount of bank staffareas Z5 +Number of banks per capita Amount of bankspopulation Z6 +Number of banks per capita Amount of bank staffpopulation Z7 +

Deposits DepositsGDP Z8 +Loans LoanGDP Z9 +

2e density of insurance Premiumpopulation Z10 +2e depth of insurance PremiumGDP Z11 +

Environment

2e rate of wastewater Wastewater(deposit + loan) Z12 mdash2e rate of sulfur dioxide Amount of sulfur dioxide(deposit + loan) Z13 mdash2e rate of solid waste Amount of solid waste(deposit + loan) Z14 mdash

2e rate of energy consumption Amount of energy consumption(deposit + loan) Z15 mdash2e rate of nature reserve Amount of nature reserve(deposit + loan) Z16 +

2e rate of forest Amount of forest(deposit + loan) Z17 +


(8) 2e Euclidean distance of the positive and negativeideal solutions is calculated as s+

i and sminusi respectively

as follows

s+i

1113944

n

j1vij minus v

+j1113872 1113873

2

11139741113972

i 1 2 m (12)

sminusi

1113944

n

j1vij minus v

minusj1113872 1113873

2

11139741113972

i 1 2 m (13)

(9) GFIi is calculated according to the Euclidean dis-tance of a provincersquos and cityrsquos positive and negativeideal solutions

GFIi s

minusi

s+i + s

minusi

i 1 2 m (14)

A higher (lower) GFI value indicates a higher (lower)level of green finance development

23 Effect of the GFI on Poverty Alleviation

231 Influence of GFI on Poverty Alleviation according toLinear Regression We first use a simple multivariate linearregression method to study the effect of the Chinarsquos GFI onpoverty alleviation 2e model is built as follows

Yi α + β1GFIi + β2CPIi + β3LROADi

+ β4LGVi + β5LOPENi + β6LEDUi

+ β7LASSETi + β8LPGDPi + εi

(15)

Here Yi is the explained variable poverty alleviationGFIi is the green finance development index CPIi is theinflation level LROADi is the infrastructure constructionLGVi is the level of government economic interventionLOPENi is the economic openness LEDUiis the educationlevel LASSETi is the financial assets and LPGDPi is theeconomic development level

232 Influence of GFI on Poverty Alleviation according toPanel Regression Static and dynamic panel regressionmodels are employed

Yit αYitminus1 + βZit + ut + δit (16)

where Yitminus1 is the first-order lag of the explained variable andZit is the explanatory variable Poverty alleviation is a dy-namic process because of the continuity and inertia ofpoverty 2erefore dynamic panel models must be used tostudy the dynamic relationship between the GFI and povertyalleviation2is paper introduces a first-order lag term of thegreen finance development index into the model 2e dy-namic panel model is built as follows

LPKit α0LPKitminus1 + α0GFIit + βZit + ut + λit (17)

where LPK represents the poverty level and its explainedvariable In the aforementioned formula the explanatoryvariable contains the first-order lag item of the explainedvariable therefore the dynamic panel estimation methodcan be used for analysis For estimating dynamic panelmodels predominantly the differential GMM and systemGMM are used Compared with the differential GMM thesystem GMM can effectively deal with unobserved hetero-geneity problem It is widely used in parameter estimation ofdynamic panel models 2erefore in this paper we use thesystem GMM to do the estimation

In China medical and educational expenditures com-prise a higher proportion of overall household expenditureSo the Engel coefficient is not unsuitable for Chinarsquos caseLimited by the statistical data availability for all regions thispaper adopts per capita consumption expenditure to rep-resent poverty levels and analyze the ability of green financedevelopment to explain poverty Inflation level infrastruc-ture construction government intervention economic leveleconomic openness education level financial asset devel-opment level and economic development level are selectedas control variables Details are shown in Table 2

3 Empirical Analysis

31 Data In data selecting and processing for establishingthe Chinarsquos GFI relevant indicator data from 2004 through2017 are used 2e data processing is conducted as follows(1) after removing the missing values we select sample datafrom 25 provinces and cities in China for empirical analysis(2) using per capita consumption expenditure as a proxyvariable for poverty alleviation in China and (3) the data arefrom the statistical yearbooks of various provinces as well astheir annual financial report

32 5e Result of GFI Before we use the entropy weightmethod to calculate the GFI we need to describe the sta-tistical properties of the sample data Table 3 shows thestatistical characteristics of the 17 indicators in this paper

Table 2 and Model (3) are used to obtain the normalizedmatrix seen in Table 4

2e GFI for each region is shown in Table 5 andFigures 1ndash7

From Table 5 and Figures 1ndash7 the green finance de-velopment is unbalanced not only in different regions butalso in different provinces within a specific region 2echaracteristics of the green finance development in differentregions and provinces of China can be summarized asfollows

Figure 1 indicates the unbalanced green finance devel-opment in north China From 2004 to 2008 the green fi-nance development index of Beijing showed an upwardtrend and then gradually decreased with a small decrementbut it is still the highest in north China and even the wholecountry Tianjinrsquos GFI is relatively high as well Hebeirsquos GFI


Table 2 Measures of major variables

Variablename Variable meaning Construction method

LPK Poverty level Logarithm of per capita consumption expenditureGFI Green financial development level Green financial development indexCPI Inflation level Consumer price levelLROAD Infrastructure construction Logarithm of the number of road mileage

LGV Government intervention ineconomic level Logarithm of government expenditures

LOPRN Economic openness Logarithm of the total import and export volume

LEDU Education level 2e logarithm of the average number of students enrolled in higher educationinstitutions per 100000 people

LASSET Financial asset development level Logarithm of the total assets of financial institutionsLPGDP 2e level of economic development Logarithm of GDP per capitaNotes because of space limitations the statistical characteristics of only some indicators from 2017 are provided

Table 3 Statistical data characteristics

Mean Median Maximum Minimun Std dev SkewnessX1 5974668 4955800 12899400 1875600 2793363 106X2 2700592 2221994 5898800 1601100 1137221 173X3 327 334 421 143 062 minus083

X15 025 026 059 003 014 058X16 139 018 2426 001 483 451X17 006 005 014 001 004 062Notes because of space limitations the statistical characteristics of only some indicators from 2017 are provided

Table 4 Normalized matrix for the green finance development index in 2004

c1 c2 c3 c15 c16 c17Beijing 053 044 052 053 000 003Tianjin 039 024 018 018 000 004Hebei 016 016 017 005 000 006

Shanxi 011 013 018 010 001 022Gansu 008 013 020 008 007 007Qinghai 011 013 017 004 100 027

Table 5 Green finance development levels of each province and city from 2004ndash2017

Region Province 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

North China

Beijing 088 087 091 092 092 090 089 088 089 084 087 089 088 073Tianjin 088 081 079 073 076 074 075 079 079 063 058 065 060 058Hebei 010 010 009 009 008 007 009 006 008 004 003 004 004 005Shanxi 018 022 034 014 023 022 019 035 035 030 018 026 029 027

Northeast ChinaLiaoning 070 052 042 049 068 042 056 063 060 056 056 064 062 057Jilin 038 060 054 038 038 030 044 046 047 029 031 040 048 058

Heilongjiang 025 022 021 009 030 025 024 032 036 026 025 029 027 024

East China

Shanghai 086 091 092 094 091 079 085 089 090 093 093 086 083 082Jiangsu 008 013 009 008 015 009 010 015 020 017 014 023 019 024Zhejiang 044 053 053 048 054 064 068 068 072 061 069 071 066 063Anhui 000 001 001 000 001 001 001 001 001 001 001 002 001 000Fujian 018 022 020 016 024 022 019 024 030 020 019 024 023 020Jiangxi 003 002 003 002 003 002 002 003 004 002 002 003 004 003

Shandong 007 013 012 005 005 004 004 007 009 005 006 003 007 006

South China

Henan 002 003 002 001 002 001 000 001 001 001 000 001 001 004Hubei 001 007 005 004 004 003 002 003 005 002 002 003 002 002Hunan 001 004 003 002 001 001 000 001 001 000 001 001 001 000

Guangdong 040 026 027 023 030 025 023 024 038 019 025 030 023 022


0

02

04

06

08

1

Beijing

Tianjin

Hebei

Shanxi

Liaoning Jilin

Heilong

jiang

Shangh

aiJia

ngsu

Zhejiang

Anh

uiFu

jian

Jiang

xiSh

ando

ngHenan

Hub

eiHun

anGuang

dong

Chon

gqing

Sichuan

Guizhou

Yunn

anSh

aanx

iGansu

Qingh

ai

2004200520062007200820092010

2011201220132014201520162017

Figure 1 GFI of provinces and cities from 2004 through 2017

Table 5 Continued

Region Province 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Southwest China

Chongqing 006 005 004 001 002 002 001 002 002 002 001 002 004 005Sichuan 011 012 011 012 012 011 012 015 019 012 011 013 012 011Guizhou 000 000 000 000 000 000 000 000 000 000 000 001 001 002Yunnan 001 001 001 000 000 000 000 000 000 000 000 000 000 000

Northwest ChinaShaanxi 008 010 009 006 009 005 006 011 011 008 007 009 009 009Gansu 004 004 003 002 004 003 005 006 009 005 004 007 006 008Qinghai 036 031 028 021 027 023 022 029 040 026 025 035 040 035

0

01

02

03

04

05

06

07

08

09

1

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

BeijingTianjin

HebeiShanxi

Figure 2 GFI in north China from 2004ndash2017


is relatively low 2e Shanxirsquos green finance developmentindex is not high in north China

Due to the high forest coverage rate of the threenortheast provinces the overall GFI of northeast China ishigher In the eastern and southern regions in China theGFIs of Shanghai Zhejiang and Guangdong with bettereconomic development are higher while other provincesrsquoGFIs are lower Apart from Qinghai and Sichuan Provincethe GFIs in Northwest and Southwest China are relativelylow

From the above analysis we can find that there is apositive relationship between the degree of green financeand regional economic development 2e GFIs of BeijingShanghai Tianjin Zhejiang and Jiangsu are relatively highwhile the GFIs of Anhui Yunnan and other regions with thelow economic development level are relatively low2e GFIsof Beijing Shanghai Shanxi Qinghai and other regionsshow a stable trend while Jiangsu Sichuan Zhejiang andother regions show an upward trend

0

01

02

03

04

05

06

07

08

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

LiaoningJilinHeilongjiang

Figure 3 GFI in northeast China from 2004ndash2017

0

01

02

03

04

05

06

07

08

09

1

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Shanghai

JiangsuFujian

Jiangxi

Zhejiang

AnhuiShandong

Figure 4 GFI in east China from 2004ndash2017


33 Effects of GFI on Poverty Alleviation 2e multiple re-gression method and static and dynamic panel regressionmethod are all used to analyze the relationship between theGFI and poverty alleviation For the panel regression a panelregression with random effects is established 2e Hausmantest statistic is 1239259 and the corresponding P value is 0We reject the null hypothesis that no systematic differenceexists between the fixed-effect model and the random-effectmodel 2e fixed-effect model is constructed 2e parameterestimation results appear in Table 6

Table 7 shows the estimation results of the multipleregression model and static (fixed-effect) and dynamic(system GMM) panel model

2e results of multiple regression analysis demon-strate that the GFI inflation financial asset level andeconomic development level all significantly positivelyaffect poverty alleviation Infrastructure constructionand openness have significant negative effects on povertyalleviation

2e results of the fixed-effect analysis demonstrate thatthe GFI size of financial assets and level of economic de-velopment all have a significant positive effect on povertyalleviation Government spending and openness have asignificant negative effect on poverty alleviation

System GMM analysis reveals that GFI infrastructureconstruction and financial asset levels have significant

0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

HenanHubei

HunanGuangdong

Figure 5 GFI in south China from 2004ndash2017

0

002

004

006

008

01

012

014

016

018

02

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ChongqingSichuan

GuizhouYunnan

Figure 6 GFI in southwest China from 2004ndash2017


positive effects on poverty alleviation and openness has asignificant negative effect on poverty alleviation

4 Conclusions and Policy Recommendations

2is paper selects 17 basic indicators and uses the improvedentropy weight method to measure the GFI of differentprovinces and cities in China2e relationship between the GFIand poverty alleviation is studied by using the dynamic panelregression method 2e results show that there are obvious

differences in the GFI among provinces and cities in China2eGFIs of Beijing and Shanghai are relatively high Multiple re-gression and static panel and dynamic panel estimationmethods all reveal a significant positive relationship between theGFI and poverty alleviation indicating that developing greenfinance can effectively reduce poverty 2e sustainable devel-opment of green finance can enable to support environmentalconservation demonstrate the optimization of economic andenvironmental benefits and realize the development of thegreen economy all of which ultimately help to alleviate poverty

0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ShanxiGansuQinghai

Figure 7 GFI in northwest China from 2004ndash2017

Table 6 Hausman test results

Test summary Chi-sq Statistic Chi-sq df ProbCross section random 1239259 8 00000

Table 7 Regression analysis of the impact of green financial development on poverty alleviation

Variable Multiple regression analysis Fixed effect System GMMLPK (minus1) minus04143lowastlowastlowast (minus47012)GFI 01274lowastlowast (30157) 02127lowast (24668) 06370lowast (16604)CPI 00135lowastlowastlowast (84430) 00023 (09541) 00081 (13223)LROAD minus02127lowastlowastlowast (minus66589) 00027 (00450) 02304lowast (18444)LGV 00230 (06828) minus01373lowastlowastlowast (minus45649) minus01381 (minus10336)LOPEN minus01682lowastlowastlowast (minus90565) minus01603lowastlowastlowast (minus36618) minus04001lowastlowastlowast (minus29249)LEDU minus00459 (minus09926) minus01130 (minus13100) 02371 (07523)LASSET 06461lowastlowastlowast (136912) 04365lowastlowastlowast (54059) 09961lowastlowast (24700)LPGDP 03081lowastlowastlowast (106054) 07648lowastlowastlowast (52025) 04174 (07346)CONSTANT TERM minus03215 (minus08474) minus19514lowast (minus18837)N 350 350 325R2 08815 09429F 1637293AR (1) 04741AR (2) 01292Hansen test 01333t statistics in parentheses lowastplt 01lowastlowastplt 005 and lowastlowastlowastplt 001


Financial assets and economic development both show positivecorrelations with poverty alleviation 2us greater financialassets and superior economic development are more conduciveto poverty alleviation Openness negatively affects poverty al-leviation2is may result from that China has increased its levelof openness and promoted economic growth on an ongoingbasis but it has also accelerated capital outflows which are notbeneficial for alleviating poverty 2e coefficient of governmentspending is minus01373 indicating that increasing governmentspending causes poverty 2e reason for this relationship maybe that increasing government spending exerts a crowding-outeffect that inhibits private sector investment activities to acertain extent causing a decline in private consumption andinvestment In addition such a decline is not conducive topoverty alleviation Based on the research conclusions thispaper advances the following policy recommendations

(1) 2e empirical analysis shows that there is a signif-icant positive correlation between the developmentlevel of green finance and poverty alleviation2erefore we should improve the green financedevelopment index of each region and improve thelevel of green finance development Each provinceshould pay attention to the innovation of green fi-nancial development Financial institutions shouldfurther develop green credit green financial bondsand other financial innovation businesses Allprovinces and cities should develop green energygreen environmental protection industry and en-ergy conservation and environmental protectionindustry Green financial industry has the charac-teristics of long industrial chain high degree ofrelevance and strong absorption capacity which hasa comprehensive pulling effect on economic devel-opment and can effectively alleviate poverty

(2) Financial assets should be increased Empiricalanalysis indicates there is a positive correlation be-tween financial assets and poverty alleviation 2egreater financial assets are the greater poverty al-leviation is 2erefore various measures should beadopted to increase the financial assets of financialinstitutions continually deepen financial systemreform improve the industrial financing situationand increase the income of residents In addition toimproving and strengthening their supervision fi-nancial institutions should eliminate rural financialrepression further loose access requirements focuson improving the professionalism of financial in-stitutions expand the scale of credit funds andimprove bond issuance and loan financing for thereal economy industry In conclusion the quality offinancial enterprisesrsquo service to the real economyshould be improved and the problems such as fi-nancial difficulties should be eliminated Povertyalleviation requires active adjustment to the creditstructure of financial institutions suitable financialproducts and services rational reduction of corpo-rate financing costs enhancement of regional busi-ness environments support of real economic

development and efficient integration of finance andindustry

(3) 2e level of economic development should be im-proved in all regions Empirical analysis demon-strates a positive correlation between economicdevelopment and poverty alleviation 2erefore theeconomic development of various regions should bepromoted in a variety of ways To reduce poverty allregions should combine their own charactersidentify their most advantageous resources fullyexploit their comparative advantages vigorouslydevelop their advantageous industries and improvetheir economic development 2is helps to facilitatepoverty elimination more effectively

(4) Increasing investment in infrastructure From theabove empirical analysis we can see that infra-structure construction has a positive role in pro-moting poverty alleviation 2e development ofinfrastructure plays an indispensable role in acountryrsquos economic development Infrastructureconstruction is the driving force of economic de-velopment which plays a huge role in promotingnational economic growth Increasing infrastructureinvestment can effectively improve the workingenvironment in economic activities and reducetransaction costs so it can improve peoplersquos livingstandards and alleviate poverty

Data Availability

All data used to support the findings of the study areavailable from the corresponding author upon request

Conflicts of Interest

2e authors declare that they have no conflicts of interest

Acknowledgments

2is paper was supported by 2020 Suqian Science andTechnology Planning Project (Social Development) ldquoRe-search on Science and Technology Finance Boosting theDevelopment of Science and Technology SMEs in Suqianunder the Background of the Epidemicrdquo 2is paper wassupported by the Jiangsu Planning Office of Philosophy andSocial Sciences in 2017 (no 2017ZDIXM168) and YunnanPlanningOffice of Philosophy and Social Sciences in 2019 (noHX2019082760) 2is paper was supported by the JiangsuBlue Project

References

[1] H Jalilian and C Kirkpatrick ldquoFinancial development andpoverty reduction in developing countriesrdquo InternationalJournal of Finance amp Economics vol 7 no 2 pp 97ndash1082002

[2] P Arestis and A Caner ldquoFinancial liberalization and povertychannels of influencerdquo in Proceedings of the EconomicsWorking Paper Nairobi Kenya December 2004


[3] J Shan ldquoDoes financial development ldquoleadrdquo economicgrowth a vector auto-regression appraisalrdquo Applied Eco-nomics vol 37 no 12 pp 1353ndash1367 2005

[4] M Sehrawat and A K Giri ldquo2e impact of financial de-velopment on economic growthrdquo International Journal ofEmerging Markets vol 11 no 4 pp 569ndash583 2016

[5] Y Bayar ldquoFinancial development and poverty reduction inemerging market economiesrdquo Panoeconomicus vol 64 p 142017

[6] M Csete and L Horvath ldquoSustainability and green devel-opment in urban policies and strategiesrdquo Applied Ecology andEnvironmental Research vol 10 no 2 pp 185ndash194 2012

[7] B Edward ldquo2e policy challenges for the green economy andsustainable economic developmentrdquo Comparative Economicamp Social Systems vol 35 no 3 pp 233ndash245 2013

[8] Y Wang and Q Zhi ldquo2e role of green finance in envi-ronmental protection two aspects of market mechanism andpoliciesrdquo Energy Procedia vol 104 pp 311ndash316 2016

[9] L Xiong and S Qi ldquoFinancial development and carbon emis-sions in Chinese provinces a spatial panel data analysisrdquo 5eSingapore Economic Review vol 63 no 2 pp 447ndash464 2018

[10] H Sarah J Aled A Annela and P Jan ldquoClosing the greenfinance gap-A systems perspectiverdquo Environmental Innova-tion and Societal Transitions vol 34 no 3 pp 26ndash60 2020

[11] Y Song M Zhang and R Sun ldquoUsing a new aggregatedindicator to evaluate Chinarsquos energy securityrdquo Energy Policyvol 132 pp 167ndash174 2019

[12] H Li H An F Wei et al ldquoGlobal energy investmentstructure from the energy stock market perspective based on aheterogeneous complex network modelrdquo Applied Energyvol 194 pp 648ndash657 2016

[13] X Xi and H An ldquoResearch on energy stock market associatednetwork structure based on financial indicatorsrdquo Physica AStatistical Mechanics amp its Applications vol 490 pp 1309ndash1323 2018

[14] H Li Y Yuan and N Wang ldquoEvaluation of the coordinateddevelopment of regional green finance and ecological envi-ronment couplingrdquo Statistics and Decision vol 8 pp 161ndash1642019

[15] H Lei and X Wang ldquoEnvironmental pollution green financeand high-quality economic developmentrdquo Statistics and De-cision vol 15 pp 18ndash22 2020

[16] F Zhu ldquoEvaluating the coupling coordination degree of greenfinance and marine eco-environment based on AHP and greysystem theoryrdquo Journal of Coastal Research vol 110 no sp1pp 277ndash281 2020

[17] Y Liu ldquoComprehensive evaluation of the development ofgreen finance in Shandong Provincerdquo Financial DevelopmentResearch vol 7 pp 32ndash39 2019

[18] Y N Wang and Y R Su ldquoEvaluation system to the sus-tainable development of human resource based on entropymethodrdquo in Proceedings of the IEEE International Conferenceon Automation amp Logistics Qingdao IEEE Qingdao ChinaSeptember 2008

[19] Q Wang X Yuan J Zhang et al ldquoAssessment of the sus-tainable development capacity with the entropy weight co-efficient methodrdquo Sustainability vol 7 no 10pp 13542ndash13563 2015

[20] L Jin ldquoEvaluation of green building energy-saving technologybased on entropy weight methodrdquo Applied Mechanics andMaterials vol 865 pp 301ndash305 2017

[21] Q Gong M Chen X Zhao and Z Ji ldquoSustainable urbandevelopment system measurement based on dissipativestructure theory the grey entropy method and coupling

theory a case study in Chengdu Chinardquo Sustainabilityvol 11 2019

[22] S Dai and D Niu ldquoComprehensive evaluation of the sus-tainable development of power grid enterprises based on themodel of fuzzy group ideal point method and combinationweighting method with improved group order relationmethod and entropy weight methodrdquo Sustainability vol 9no 10 pp 1900ndash1910 2019

[23] Y Long Y Yang X Lei Y Tian and Y Li ldquoIntegratedassessment method of emergency plan for sudden waterpollution accidents based on improved TOPSIS shannonentropy and a coordinated development degree modelrdquoSustainability vol 11 no 2 p 510 2019

[24] B Zhang and Y Wang ldquo2e effect of green finance on energysustainable development a case study in Chinardquo EmergingMarkets Finance and Trade 2019

[25] L Jiang A Tong Z Hu and Y Wang ldquo2e impact of theinclusive financial development index on farmer entrepre-neurshiprdquo PLoS One vol 14 no 5 Article ID e0216466 2019

[26] T Beck A Demirguccedil-Kunt and R Levine ldquoFinance in-equality and the poorrdquo Journal of Economic Growth vol 12no 1 pp 27ndash49 2007

[27] C Y Park and R V Mercado ldquoDoes financial inclusionreduce poverty and income inequality in developing Asiardquo inFinancial Inclusion in Asia Palgrave Macmillan UK LondonUK 2016

[28] A Rashid and M Intartaglia ldquoFinancial development-does itlessen povertyrdquo Journal of Economic Studies vol 44 no 1pp 69ndash86 2017

[29] V Kappel ldquo2e effects of financial development on incomeinequality and povertyrdquo in Proceedings of the German De-velopment Economics Conference Hannover Germany July2010

[30] S Perez-Moreno ldquoFinancial development and poverty indeveloping countries a causal analysisrdquo Empirical Economicsvol 41 no 1 pp 57ndash80 2011

[31] M Sehrawat and A K Giri ldquoFinancial development andpoverty reduction panel data analysis of South AsianCountriesrdquo International Journal of Social Economics vol 43no 4 pp 400ndash416 2016

[32] J Greenwood and B Jovanovic ldquoFinancial developmentgrowth and the distribution of incomerdquo Journal of PoliticalEconomy vol 98 no 5 Part 1 pp 1076ndash1107 1990

[33] T K Ueda ldquoFinancial deepening inequality and growth amodel-based quantitative evaluationrdquo 5e Review of Eco-nomic Studies vol 73 no 1 pp 251ndash280 2006

[34] P Zahonogo ldquoFinancial development and poverty in devel-oping countries evidence from Sub-Saharan Africardquo Inter-national Journal of Economics and Finance vol 9 no 1 p 2112017

[35] S Claessens and E Perotti ldquoFinance and inequality channelsand evidencerdquo Journal of Comparative Economics vol 35no 4 pp 748ndash773 2007

[36] S Pulvirenti R M S Costa and P Pavone ldquoFrancescoCupani the ldquoscientific networkrdquo of his time and the making ofthe Linnaean ldquosystemrdquo rdquo Acta Botanica Gallica vol 162 no 3pp 215ndash223 2015

[37] G Ferrauto R M S Costa P Pavone and G L CantarellaldquoHuman impact assessment on the sicilian agroecosystemsthrough the evaluation of melliferous areasrdquo Annals of Bot-any vol 3 pp 237ndash244 2013

[38] A Cuspilici P Monforte and M A Ragusa ldquoStudy ofSaharan dust influence on PM 10measures in Sicily from 2013to 2015rdquo Ecological Indicators vol 76 pp 297ndash303 2017


goals set by the United Nations ldquo2030 Agenda for Sus-tainable Developmentrdquo 2e Chinese style ldquoroad to povertyrdquohas become the focus of world attention

To study poverty we should define it at first Becausepoverty is a complex concept that constantly evolves alongwith the development of economies and societies it is ldquoanelusive conceptrdquo Human understanding of poverty as aconcept has evolved and scholars have used varying defi-nitions of poverty 2e study of poverty by Rowntree andBooth is considered to represent the beginning of the studyof poverty in the social sciences In their study poverty is thelack of materiality in an economic sense Booth defines theldquopoorrdquo as middle-aged people who earns pound1 or less per weekand Rowntree describes poverty as applying to a householdwhose total income cannot cover its most basic survivalactivities Rowntree and Booth emphasize family incomebecause of its necessity to survival From the perspective ofincome another definition of ldquosurvival povertyrdquo is termed asldquoabsolute povertyrdquo In 1948 aWorld Bank report introducedthe concept of ldquoincome povertyrdquo and linked the worldrsquos richand poor to gross national product for the first time withless than US$100 annual income on average representingpoor or underdeveloped status

2e Grameen Bank successfully used microloans to helppeople out of poverty 2is case has attracted great attentionfrom the academic community Many scholars show theirconcerns about the relationship between finance develop-ment and poverty [1ndash5] such as Jalilian (2002) Philip andAsena (2004) Jordan (2005) Sehrawat and Giri (2016) andYılmaz (2017) Financial constraints are proved to be thegreatest difficulty in poverty elimination according to theantipoverty experiences frommany developing countries Inthe process of poverty alleviation through finance we mustalso pay attention to the sustainable development of theeconomy In particular the green finance development toalleviate poverty is more conducive to achieving the 2030sustainable development goals adopted by the UnitedNations

We should consider alleviating poverty effectively notonly by finance development but also by green economydevelopment Althoughmany developing countries have notclearly formulated a green indicator system so far some ofthese countries have adopted ldquogreen growthrdquo as a corestrategy of national development Chinarsquos 12th Five-YearPlan (2011ndash2015) proposed ldquogreen development and re-source conservationrdquo India highlighted sustainable devel-opment in its national 12th Five-Year Plan emphasizing thegoals of a ldquofriendly environment-friendly societyrdquo and ldquofastsustainable and more inclusive growthrdquo which demonstratethe shift of national strategic priorities Many scholars didmany research studies in green development and green fi-nance [6ndash9] such as Csete and Horvath (2012) Edward(2013) Wang and Zhi (2016) and Xiong and Qi (2018)2erefore promoting the development of green finance hasbecome a priority In addition financial institutionsworldwide have been actively developing green finance 2edevelopment history of green finance can be traced back to

the 1970s in the last century As early as 1974 the FederalRepublic of Germany established the worldrsquos first policy-based environmental protection bank named ldquoEco Bankrdquowhich was responsible for providing preferential loans forenvironmental projects that general banks were unwilling toaccept Since 2002 many financial institutions around theworld have adopted the ldquoEquator Principlesrdquo to pay moreattention to the development of green finance

Green finance is a subject worthy of further study Howto measure the development of green finance is the first stepin this area At present there are few articles measuring thegreen finance index (GFI) Existing literatures mainly usealternative indicators to be the proxy of the green financedevelopment index such as Sarah et al (2020) [10] replacedgreen finance with carbon finance Some scholars makespecial calculations on the energy security index SongZhang and Sun (2019) [11] introduced a new aggregatedindicator the China energy security index (CESI) forevaluating how Chinarsquos energy security has changed overyears 2e CESI includes the energy supply dimensioneconomic-technical dimension and environmental di-mension of energy security A small amount of the literatureuses complex network methods to study energy financialmarket issues currently Li et al (2016) [12] used complexnetwork methods to study the global energy investmentstructure and found that most foreign investment andforeign investment relations are still controlled by a fewCountries Xi amp An (2018) [13] used the financial indicatorsof energy stocks to construct a complex network 2e studyfound that the threshold of 07 is a mutation point of thenetwork and as the threshold increases the independence ofthe community increases Some scholars have made relatedresearch on green finance For example Li Yuan andWang(2019) [14] constructed a green finance-ecological envi-ronment composite system coupling and coordinated de-velopment evaluation system and measured the developmentlevel of green finance and the comprehensive level of eco-logical environment in the three major economic circles Leiand Wang (2020) [15] used panel regression methods toanalyze the relationship between environmental pollutiongreen finance and high-quality economic development andused spatial measurement methods to conduct robustnesstests 2e results show that environmental pollution has asignificant inhibitory effect on high-quality economic de-velopment but green finance has the effect of promotinghigh-quality economic development through environmentalimprovement effects 2ere are regional differences in thiseffect which is stronger in the central region and weaker inthe eastern region Zhu (2020) [16] selected 8 indicators fromthe aspects of scale and profit and business operation torepresent green finance and selected 14 indicators frommarine resources marine environment and marine activitiesto represent the marine eco-environment evaluating thecoupling coordination degree of green finance and marineeco-environment based on AHP and grey system theory Liu(2019) [17] used the analytic hierarchy process to compre-hensively evaluate the development status and shortcomings























⋮ ⋱ ⋮


⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠

mmiddotn

(1)


rij

zij minus min zij



max zij minus zij



⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(2)



cij

zij1113936

mi1 z

2ij


1zij

1113936mi1 1zij1113872 1113873


⎧⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎩

(3)


bij rij + 00001 (4)

pij bij

1113936mi1 bij

(5)


Hj minus1

ln(m)middot 1113944

m





nj1 Gj

1113936nj1 Gj + 01 middot 1113936

nj1 Gj1113872 1113873


nj1 1 minus Hj1113872 1113873


nj1 1 minus Hj1113872 11138731113872 1113873

(8)




⋮ ⋱ ⋮


⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠ (9)



v+j max vij|i 1 2 m1113966 1113967 (10)






Finance




Environment







as follows

s+i

1113944

n

j1vij minus v

+j1113872 1113873

2

11139741113972

i 1 2 m (12)

sminusi

1113944

n

j1vij minus v

minusj1113872 1113873

2

11139741113972

i 1 2 m (13)


GFIi s

minusi

s+i + s

minusi

i 1 2 m (14)







(15)






























Region Province 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

North China



Heilongjiang 025 022 021 009 030 025 024 032 036 026 025 029 027 024

East China


Shandong 007 013 012 005 005 004 004 007 009 005 006 003 007 006

South China


Guangdong 040 026 027 023 030 025 023 024 038 019 025 030 023 022


0

02

04

06

08

1

Beijing

Tianjin

Hebei

Shanxi

Liaoning Jilin

Heilong

jiang

Shangh

aiJia

ngsu

Zhejiang

Anh

uiFu

jian

Jiang

xiSh

ando

ngHenan

Hub

eiHun

anGuang

dong

Chon

gqing

Sichuan

Guizhou

Yunn

anSh

aanx

iGansu

Qingh

ai

2004200520062007200820092010

2011201220132014201520162017


Table 5 Continued

Region Province 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Southwest China



0

01

02

03

04

05

06

07

08

09

1

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

BeijingTianjin

HebeiShanxi






0

01

02

03

04

05

06

07

08

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017



0

01

02

03

04

05

06

07

08

09

1

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Shanghai

JiangsuFujian

Jiangxi

Zhejiang

AnhuiShandong








0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

HenanHubei

HunanGuangdong


0

002

004

006

008

01

012

014

016

018

02

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ChongqingSichuan

GuizhouYunnan







0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ShanxiGansuQinghai













Data Availability




Acknowledgments


References































































⋮ ⋱ ⋮


⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠

mmiddotn

(1)


rij

zij minus min zij



max zij minus zij



⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(2)



cij

zij1113936

mi1 z

2ij


1zij

1113936mi1 1zij1113872 1113873


⎧⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎩

(3)


bij rij + 00001 (4)

pij bij

1113936mi1 bij

(5)


Hj minus1

ln(m)middot 1113944

m





nj1 Gj

1113936nj1 Gj + 01 middot 1113936

nj1 Gj1113872 1113873


nj1 1 minus Hj1113872 1113873


nj1 1 minus Hj1113872 11138731113872 1113873

(8)




⋮ ⋱ ⋮


⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠ (9)



v+j max vij|i 1 2 m1113966 1113967 (10)






Finance




Environment







as follows

s+i

1113944

n

j1vij minus v

+j1113872 1113873

2

11139741113972

i 1 2 m (12)

sminusi

1113944

n

j1vij minus v

minusj1113872 1113873

2

11139741113972

i 1 2 m (13)


GFIi s

minusi

s+i + s

minusi

i 1 2 m (14)







(15)






























Region Province 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

North China



Heilongjiang 025 022 021 009 030 025 024 032 036 026 025 029 027 024

East China


Shandong 007 013 012 005 005 004 004 007 009 005 006 003 007 006

South China


Guangdong 040 026 027 023 030 025 023 024 038 019 025 030 023 022


0

02

04

06

08

1

Beijing

Tianjin

Hebei

Shanxi

Liaoning Jilin

Heilong

jiang

Shangh

aiJia

ngsu

Zhejiang

Anh

uiFu

jian

Jiang

xiSh

ando

ngHenan

Hub

eiHun

anGuang

dong

Chon

gqing

Sichuan

Guizhou

Yunn

anSh

aanx

iGansu

Qingh

ai

2004200520062007200820092010

2011201220132014201520162017


Table 5 Continued

Region Province 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Southwest China



0

01

02

03

04

05

06

07

08

09

1

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

BeijingTianjin

HebeiShanxi






0

01

02

03

04

05

06

07

08

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017



0

01

02

03

04

05

06

07

08

09

1

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Shanghai

JiangsuFujian

Jiangxi

Zhejiang

AnhuiShandong








0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

HenanHubei

HunanGuangdong


0

002

004

006

008

01

012

014

016

018

02

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ChongqingSichuan

GuizhouYunnan







0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ShanxiGansuQinghai













Data Availability




Acknowledgments


References










































cij

zij1113936

mi1 z

2ij


1zij

1113936mi1 1zij1113872 1113873


⎧⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎩

(3)


bij rij + 00001 (4)

pij bij

1113936mi1 bij

(5)


Hj minus1

ln(m)middot 1113944

m





nj1 Gj

1113936nj1 Gj + 01 middot 1113936

nj1 Gj1113872 1113873


nj1 1 minus Hj1113872 1113873


nj1 1 minus Hj1113872 11138731113872 1113873

(8)




⋮ ⋱ ⋮


⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠ (9)



v+j max vij|i 1 2 m1113966 1113967 (10)






Finance




Environment







as follows

s+i

1113944

n

j1vij minus v

+j1113872 1113873

2

11139741113972

i 1 2 m (12)

sminusi

1113944

n

j1vij minus v

minusj1113872 1113873

2

11139741113972

i 1 2 m (13)


GFIi s

minusi

s+i + s

minusi

i 1 2 m (14)







(15)






























Region Province 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

North China



Heilongjiang 025 022 021 009 030 025 024 032 036 026 025 029 027 024

East China


Shandong 007 013 012 005 005 004 004 007 009 005 006 003 007 006

South China


Guangdong 040 026 027 023 030 025 023 024 038 019 025 030 023 022


0

02

04

06

08

1

Beijing

Tianjin

Hebei

Shanxi

Liaoning Jilin

Heilong

jiang

Shangh

aiJia

ngsu

Zhejiang

Anh

uiFu

jian

Jiang

xiSh

ando

ngHenan

Hub

eiHun

anGuang

dong

Chon

gqing

Sichuan

Guizhou

Yunn

anSh

aanx

iGansu

Qingh

ai

2004200520062007200820092010

2011201220132014201520162017


Table 5 Continued

Region Province 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Southwest China



0

01

02

03

04

05

06

07

08

09

1

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

BeijingTianjin

HebeiShanxi






0

01

02

03

04

05

06

07

08

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017



0

01

02

03

04

05

06

07

08

09

1

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Shanghai

JiangsuFujian

Jiangxi

Zhejiang

AnhuiShandong








0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

HenanHubei

HunanGuangdong


0

002

004

006

008

01

012

014

016

018

02

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ChongqingSichuan

GuizhouYunnan







0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ShanxiGansuQinghai













Data Availability




Acknowledgments


References












































as follows

s+i

1113944

n

j1vij minus v

+j1113872 1113873

2

11139741113972

i 1 2 m (12)

sminusi

1113944

n

j1vij minus v

minusj1113872 1113873

2

11139741113972

i 1 2 m (13)


GFIi s

minusi

s+i + s

minusi

i 1 2 m (14)







(15)






























Region Province 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

North China



Heilongjiang 025 022 021 009 030 025 024 032 036 026 025 029 027 024

East China


Shandong 007 013 012 005 005 004 004 007 009 005 006 003 007 006

South China


Guangdong 040 026 027 023 030 025 023 024 038 019 025 030 023 022


0

02

04

06

08

1

Beijing

Tianjin

Hebei

Shanxi

Liaoning Jilin

Heilong

jiang

Shangh

aiJia

ngsu

Zhejiang

Anh

uiFu

jian

Jiang

xiSh

ando

ngHenan

Hub

eiHun

anGuang

dong

Chon

gqing

Sichuan

Guizhou

Yunn

anSh

aanx

iGansu

Qingh

ai

2004200520062007200820092010

2011201220132014201520162017


Table 5 Continued

Region Province 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Southwest China



0

01

02

03

04

05

06

07

08

09

1

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

BeijingTianjin

HebeiShanxi






0

01

02

03

04

05

06

07

08

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017



0

01

02

03

04

05

06

07

08

09

1

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Shanghai

JiangsuFujian

Jiangxi

Zhejiang

AnhuiShandong








0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

HenanHubei

HunanGuangdong


0

002

004

006

008

01

012

014

016

018

02

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ChongqingSichuan

GuizhouYunnan







0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ShanxiGansuQinghai













Data Availability




Acknowledgments


References
























































Region Province 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

North China



Heilongjiang 025 022 021 009 030 025 024 032 036 026 025 029 027 024

East China


Shandong 007 013 012 005 005 004 004 007 009 005 006 003 007 006

South China


Guangdong 040 026 027 023 030 025 023 024 038 019 025 030 023 022


0

02

04

06

08

1

Beijing

Tianjin

Hebei

Shanxi

Liaoning Jilin

Heilong

jiang

Shangh

aiJia

ngsu

Zhejiang

Anh

uiFu

jian

Jiang

xiSh

ando

ngHenan

Hub

eiHun

anGuang

dong

Chon

gqing

Sichuan

Guizhou

Yunn

anSh

aanx

iGansu

Qingh

ai

2004200520062007200820092010

2011201220132014201520162017


Table 5 Continued

Region Province 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Southwest China



0

01

02

03

04

05

06

07

08

09

1

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

BeijingTianjin

HebeiShanxi






0

01

02

03

04

05

06

07

08

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017



0

01

02

03

04

05

06

07

08

09

1

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Shanghai

JiangsuFujian

Jiangxi

Zhejiang

AnhuiShandong








0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

HenanHubei

HunanGuangdong


0

002

004

006

008

01

012

014

016

018

02

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ChongqingSichuan

GuizhouYunnan







0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ShanxiGansuQinghai













Data Availability




Acknowledgments


References










































0

02

04

06

08

1

Beijing

Tianjin

Hebei

Shanxi

Liaoning Jilin

Heilong

jiang

Shangh

aiJia

ngsu

Zhejiang

Anh

uiFu

jian

Jiang

xiSh

ando

ngHenan

Hub

eiHun

anGuang

dong

Chon

gqing

Sichuan

Guizhou

Yunn

anSh

aanx

iGansu

Qingh

ai

2004200520062007200820092010

2011201220132014201520162017


Table 5 Continued

Region Province 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Southwest China



0

01

02

03

04

05

06

07

08

09

1

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

BeijingTianjin

HebeiShanxi






0

01

02

03

04

05

06

07

08

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017



0

01

02

03

04

05

06

07

08

09

1

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Shanghai

JiangsuFujian

Jiangxi

Zhejiang

AnhuiShandong








0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

HenanHubei

HunanGuangdong


0

002

004

006

008

01

012

014

016

018

02

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ChongqingSichuan

GuizhouYunnan







0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ShanxiGansuQinghai













Data Availability




Acknowledgments


References













































0

01

02

03

04

05

06

07

08

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017



0

01

02

03

04

05

06

07

08

09

1

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Shanghai

JiangsuFujian

Jiangxi

Zhejiang

AnhuiShandong








0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

HenanHubei

HunanGuangdong


0

002

004

006

008

01

012

014

016

018

02

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ChongqingSichuan

GuizhouYunnan







0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ShanxiGansuQinghai













Data Availability




Acknowledgments


References















































0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

HenanHubei

HunanGuangdong


0

002

004

006

008

01

012

014

016

018

02

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ChongqingSichuan

GuizhouYunnan







0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ShanxiGansuQinghai













Data Availability




Acknowledgments


References














































0

005

01

015

02

025

03

035

04

045

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

ShanxiGansuQinghai













Data Availability




Acknowledgments


References
















































Data Availability




Acknowledgments


References










































themeasurementofgreenfinancedevelopmentindexandits ...and wang (2020) [15] used panel regression...

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