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Striving to achieve the Sustainable Development Goals (SDGs), countries are increasingly embracing a sustainable financing mechanism via green bond financing. Green bonds have attracted the attention of the industrial sector and policymakers, however, the impact of green bond financing on environmental and social sustainability has not been yet been confirmed. There is no empirical evidence on how this financial product can contribute to achieving the goals set out in Agenda 2030. In this study, we empirically analyze the impact of green bond financing on environmental and social sustainability by considering the S&P 500 Global Green Bond Index and S&P 500 Environmental and Social Responsibility Index, from 1st October 2010 to 31st July 2020 using a combination of Quantile-on-Quantile Regression and Wavelet Multiscale Decomposition approaches. Our results reveal that green financing mechanisms might have gradual negative transformational impacts on environmental and social responsibility. Furthermore, we attempt to design a policy framework to address the relevant SDG objectives.
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Does Green Financing help to improve the Environmental & Social Responsibility?
Designing SDG framework through Advanced Quantile modelling
Avik Sinha
Centre for Excellence in Sustainable Development
Goa Institute of Management, India.
Email: f11aviks@iimidr.ac.in
Shekhar Mishra
Department of Business Management
C.V. Raman College of Engineering
Bhubaneswar, Odisha, India
Email: shekhar.ximb2019@gmail.com
Arshian Sharif
Othman Yeop Abdullah Graduate School of Business
University Utara Malaysia
Sintok, Kedah, Malaysia
Department of Business Administration
Faculty of Management Sciences
ILMA University, Karachi, Pakistan
Email: arshian.aslam@gmail.com
Larisa Yarovaya
Southampton Business School
University of Southampton
Email: l.yarovaya@soton.ac.uk
(Corresponding author)
Title Page
1
Does Green Financing help to improve the Environmental & Social
Responsibility? Designing SDG framework through Advanced Quantile
modelling.
Abstract
Striving to achieve the Sustainable Development Goals (SDGs), countries are increasingly
embracing a sustainable financing mechanism via green bond financing. Green bonds have
attracted the attention of the industrial sector and policymakers, however, the impact of
green bond financing on environmental and social sustainability has not been yet been
confirmed. There is no empirical evidence on how this financial product can contribute to
achieving the goals set out in Agenda 2030. In this study, we empirically analyze the impact
of green bond financing on environmental and social sustainability by considering the S&P
500 Global Green Bond Index and S&P 500 Environmental and Social Responsibility Index,
from 1st October 2010 to 31st July 2020 using a combination of Quantile-on-Quantile
Regression and Wavelet Multiscale Decomposition approaches. Our results reveal that green
financing mechanisms might have gradual negative transformational impacts on
environmental and social responsibility. Furthermore, we attempt to design a policy
framework to address the relevant SDG objectives.
Keywords: green financing; green bonds; Agenda 2030; environmental and social
responsibility; wavelet; quantile
Revised manuscript (Clean version) Click here to view linked References
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1. Introduction
Growing environmental degradation forces policymakers to focus on imbibing
sustainability in the economic growth agendas. The recent Sustainable Development Goals
(SDG) report, i.e., Agenda 2030, has attributed the global economic growth pattern to be
responsible for the issue of rising climatic disasters across the globe (United Nations, 2019).
The economic growth prevailing across the nations is majorly dependent on the fossil fuel
consumption bringing forth the ecological predicament in the form of climatic shift. In recent
years, the world experienced a rise in the renewable energy solutions, however, these are yet
to reach their full potential.
As an economic growth is catalyzed largely through an industrial growth, it can be
argued that the trajectory of the industrial growth pattern is shaping the trajectory of climatic
shift. Driven by the profit motive, the industrial sector is largely interested in reducing the
operational costs, however an implementation of renewable energy solutions can cause a
short run decline in their profit due to the high implementation costs (Sinha et al., 2020b).
This incessant rise in industrialization is complemented by the financial mobilization within
the nations, thus the prevailing financial mechanism is also adding to the issue of rising
environmental degradation. This might create a predicament on the way of attaining the
objectives of SDG 13, i.e., climate action. Persistence of this mechanism is not only adding to
environmental issues, but also to social issues, such as rising health issues among the
population. Social issues can cause negative impact on economic growth pattern itself, which
might in turn create a predicament on the way of attaining the objectives of SDG 8, i.e., decent
work and economic growth.
In the recent report on SDG financing by Garroway and Carpentier (2019), the
authors have stressed the deficiency of the nations in making progressions towards SDG
financing. This report is based on the Addis Ababa Action Agenda, which focused on how
financing mechanism can be used as a vehicle for ascertaining sustainable economic growth
(United Nations, 2015). However, the recent progress on this front shows that the
developmental agenda has not yet been prioritized, and many nations might not be able fulfill
the 2030 agenda. One of the major obstacles is mainstreaming or realigning the capital
market with the Addis Ababa Action Agenda. The recent report by United Nations Global
Compact (2019) has discussed this fact and has stressed the importance of reorienting the
global capital markets for ascertaining the attainment of the objectives of SDGs by 2030.
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In this pursuit, capital market products and corporate financing mechanisms have
been identified as the major instruments for this realignment. The need for realignment with
SDG objectives called for a product, which can primarily address the issues of climatic shift.
Thus, Green Bond or Climatic Bond started gaining prominence in the global capital market,
while nations started recognizing its potential. In 2009, the World Bank introduced this
product in the global capital market, with an objective of restoring the environmental and
social balance in the global sustainability ecosystem, driven by the growing concern of the
stakeholders in environmental, social, and governance (ESG) disputes (World Bank, 2009).
Following the Addis Ababa Action Agenda, green bonds started attracting attention of the
individual and institutional investors, and in 2016 debtors from China, European Union, and
the United States of America started capturing the green bonds market. Once the report of
United Nations Global Compact (2019) was published, green bonds started gaining
prominence again among the global investors, after experiencing a slump in 2018.
From the perspective of Limits to Growth (Meadows, 1974), introduction of green
bonds carries a significant place in the global sustainability fora. As an unconstrained
economic growth is catalyzed by natural resource consumption continues, then the existing
pool of natural resources will not only start diminishing fast, but also rising demand of natural
resources might create a disbalance in the social strata. Therefore, in order to address both
the environmental and social issues, green bonds might be considered as a viable solution.
This solution might be traced back to the aspects of decarbonization, which is a major policy-
level concern across the globe. However, the ecological and social implications of green bonds
have not been fully understood yet. Although green bonds are identified as a vehicle for
ascertaining sustainable development, there is not enough evidence demonstrating how
exactly this financial product can fulfill these two crucial simultaneous roles at a global scale.
Criticality of the solution might bring forth a policy trade-off in the decarbonization context,
where the stabilization of the policy implications might be stemmed from the social
background of the context. Bringing this trade-off aspect in the decarbonization scenario
might prove to be crucial from the perspective of sustainable development. There lies the
focus of this study.
Following the ongoing sustainable development agenda across the globe, this study
aims to devise a sustainable development framework through analyzing the impact of the
green bonds returns on Environmental and Social Responsibility at a global scale. For
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promoting environmentally sustainable projects, it is necessary that the financing mechanism
should be transparent and well understood by the different groups of stakeholders. This can
be achieved by sharing a project’s progress and outcomes with the public via various online
platforms and the media. This might help to generate a positive environmental externality of
the financing mechanism. Moreover, the tax benefits received from this mechanism might
help in tacking the social issues associated with environmental degradation, while assisting in
the growth in implementation of renewable energy solutions.
This study aims to assess this impact at a global scale, by considering the S&P 500
Environmental and Socially Responsible Index, as the indicator of socio-ecological
performance of firms. Considering the role of industrialization in shaping economic growth
trajectory, we focus on the impact of S&P 500 Global Green Bond Index on S&P 500
Environmental and Social Responsibility Index, and vice versa. Therefore, our empirical
findings inform our approach for designing a comprehensive policy framework to help the
nations in attaining the objectives of certain SDGs. The proposed policy framework mainly
focused on addressing the issues of climatic shift (SDG 13) and ascertaining sustained
economic growth (SDG 8). However, the proposed policy framework also covers SDG 7
(making energy solutions clean and affordable), SDG 9 (promoting innovation), and SDG 16
(institutionalizing the solutions while maintaining social order). This comprehensive policy
approach for attaining SDG objectives by means of green bond is the main policy contribution
of our paper.
Apart from important policy implications, this paper also contributes to the growing
body of Green Finance literature, and specially to the green bond financing literature (e.g.,
Huynh et al., 2020), providing a novel empirical evidence from the advanced quantile
modelling approach. The existing studies are often based on the using the median of the data
and ignoring potentially meaningful information contained towards the tails of the data
distribution. Thus, we select our methodological approach based on the need of analysis of
socio-ecological impacts of green bond financing using the entire data spectrum and employ
an advanced Quantile-on-Quantile (QQR) method devised by Sim and Zhou (2015). This
method can capture the impact of the explanatory variable on the target policy variable
across the spectrum of the data, which is derived through quantile-decomposition. This
methodological approach complements the policy-level contribution of the study and adds to
the existing empirical evidence in Green Finance literature.
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The remainder of the paper is organised as follows. Section 2 provides overview of
the relevant literature, Section 3 explains the applied methodology, Section 4 discusses the
findings, and Section 5 concludes the study with relevant policy implications.
2. Literature Review
One of the earliest studies on the socio-ecological impact assessment of green bonds
was carried out by Zerbib (2019), where the author considered the demand side of the green
bond markets though the analysis of the impact of environmental preferences on the
premium of green bonds. The author placed emphasis on the rising demand of superior
environmental quality as a main driving factor of the green bonds’ demand. These findings
are relevant to the results obtained by Agliardi and Agliardi (2019), who analyzed the supply
side aspect instead. The authors found that the environmental awareness of the shareholders
and the pro-environmental tax benefits by the government can have a positive impact on the
green bonds’ prices. However, the existing trend in this literature strand is largely inclined
towards the supply side aspect, and this falls in line with the theme of the present study.
Considering of the performance of Chinese listed firms, Zhou and Cui (2019) analyzed the
impact of green bonds issuing announcements on the corporate social responsibility (CSR)
activities, which was further reciprocated to the social and environmental activities carried
out by the firms. Reboredo (2018) further reported that the positive environmental
externality exerted by the green bonds trade eases the implementation and diffusion of
renewable energy solutions across a nation.
Although Wang and Zhi (2016), among others, have reviewed the market
mechanisms, through which green bond financing can partake in environmental protection,
they did not suggest any policy directions, which might deem to be suitable for assuring the
developmental sustainability. A notable exception is the study by Clapp et al. (2015) that
conducted a thorough review of the available arguments on the role of green bonds in
building a low-carbon economy. The authors provided a set of suggestions, which are
seemingly significant, given they have been developed in a pre-SDG epoch. More recently, a
shadowy reflection of these policies can be seen in the study carried out by Flammer (2020),
who discussed the importance of green bonds in shaping environmentally responsible firms,
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while giving an indication to utilize them as a public policy tool to address the SDG objectives.
While Flammer (2020) focused on the environmental aspects, Braouezec and Joliet (2019)
analyzed the role of green bonds in shaping socially responsible firms, and how firms’ actions
can be delivered through their CSR activities.
The abovementioned studies demonstrate on the operational and strategic
transitions of the firms towards socio-ecological evolution, and the instrumentalization of
green bonds as a public policy tool that can be utilized by corporations and enforced by policy
makers. In Chinese context, Ng (2018) presented different scenarios for enforcing institutional
legitimacy and policy-level reorganization to assure the sustenance of green bonds. During
the institutionalizing of the green bond’s operationalization, it is necessary to protect the
interest of the investors, while addressing the issue of climate change. This aspect is critically
important to ascertain the demand of green bonds among the investors. The study by
Gianfrate and Peri (2019) has analyzed this in the European green bonds market bringing
together the demand and supply side of green bonds discussing their ecological impacts.
Huynh et al. (2020) further analyzed green bonds from portfolio diversification perspective,
indicating potential safe haven properties of these assets, and explaining why these new
financial instruments are attractive for investors.
Preference towards achieving a high economic growth can create hindrance in way
of implementing green finance solutions, as it was shown by Nguyen et al. (2018) in the case
of Vietnam. Prevailing political instability within the nation has been attributed as the second
major cause behind this hindrance. In the similar context, Urban et al. (2018) have shown the
inclination towards sustainable development drives the growth of green bonds adding to the
findings of Nguyen et al. (2018). However, the role of the policymakers to recognize the
potential of green bonds remains essential in creating the positive socio-ecological spillovers.
Banga (2019) has considered this research problem for the developing nations, as the
issuance of green bonds might be crucial for these nations, keeping their pro-growth objective
in mind.
While the reviewed studies considered the socio-ecological impact of green bonds in
different contexts, there is a lack of comprehensive policy framework for sustainable
development. This is evident that green bonds can be used as a policy instrument for assuring
social and environmental responsibility among the industrial sector, however, this has not
been yet confirmed empirically. Though the green bonds came into existence during the
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Millennium Development Goals (MDG) regime, there role is coming out to be more crucial in
the era of SDGs, and therefore, the void of a comprehensive policy framework needs to be
addressed. There comes the role of the present study.
In this paper, by analyzing the socio-ecological impact of green bonds, we aim to
devise a comprehensive policy framework for attaining the objectives of SDGs, providing an
original contribution to the literature. In methodological terms, the analytical approach
adopted in this study complements the policy-level contribution of the study by considering
the entire data spectrum of the target and explanatory policy variables, and this particular
approach is necessary for understanding the wholesome depiction of the impact. In this view,
the present study addresses the gap in the literature not only through devising a
comprehensive policy framework for attaining SDGs, but also by applying the QQR
methodological approach, which is necessary for designing the policy framework.
3. Data and Methodology
3.1. Data
The present paper utilizes the time series dataset constituting the daily observations
of S&P 500 Global Green Bond Index as a proxy for green financing and S&P 500
Environmental and Social Responsibility Index. These daily observations for the given
variables cover the period from 1st October 2010 to 30th September 2020. The descriptive
statistical features of the variables and the correlation between is reported in Table 1. The
non-normal distribution nature of the data taken under study gets well evident from the
results of Jarque-Bera Test presented in Table 1. This leads to possibility of non- linear linkage
between the variables and the same may be examined by employing Quantile approaches
(e.g. Bekiros et al., 2016; Balcilar et al., 2016; Troster et al., 2018; Sharif et al., 2019) which
can very well deal with the issue of heavy tails. The correlation coefficient between the Global
Green Bond Index and Environmental Social Responsibility Index is observed to be positive
and statistically significant.
<Insert Table 1 here>
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The innovative approach in the present paper lies in its endeavor to explore the effect
of different frequencies of the time series of Global Green Bond Index on Environmental and
Social Responsibility Index. The paper adopts the wavelet framework to examine the linkage
between the variables taken under study. In this regard we utilize wavelets to decompose the
daily time series of Global Green Bond Index into six different frequency components. Figure
1 presents the time series plot raw data of both dependent and independent variables and
different frequency components of Global Green Bond Index. The high frequency in the short
period accompanied by stability in longer periods may be very well demonstrated in Figure 1.
<Insert Figure 1 here>
As the empirical model is based on a bivariate framework, it is quite obvious that the model
will suffer from the endogeneity issue, and this issue might be arising out of omitted variable
bias. In absence of other control variables in the model, it might be possible that the
stochastic error term is correlated with the explanatory variable, which might cause the
endogeneity issue (Ullah et al., 2018, 2020). In keeping with our research objective and to
proceed with the bivariate framework, we have carried out the analysis in the frequency
domain rather than the temporal domain. Drifting away from the temporal domain will nullify
the possibility of the occurrence of any stochastic error, and therefore, the quantile
estimation has been carried out on the data decomposed in the frequency domain by wavelet
multiscale decomposition method.
3.2. Quantile Autoregressive Unit Root Test
We analyze the stationary properties of the time series by employing Quantile Auto-
Regressive (QAR) unit root test proposed by Koenker and Xiao (2004). The QAR model of unit
root test is instrumental in examining the stationarity of a time series data at both conditional
mean and all the quantiles of the conditional distribution. Galvao (2009) further incorporated
covariates and linear trend in the QAR model and consequently generalized it.
Suppose indicates the presence of strict stationarity with the prior information set

 . Further 
is assumed to be the conditional
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distribution function of with
. The linear Quantile Regression Model (QRM) forms the
basis of QAR unit root test which may be indicated as follows:
     
   
 (1)
The quantile of 
is indicated by
and indicate the drift term.
The linear trend and the persistence parameter in the QAR model for unit root test is
represented by and respectively. The errors’ inverse conditional distribution for
   quantiles is indicated by
. In this manner we estimate the tenacity
parameters ( for all the quantiles of the Xt conditional distribution. The QAR model as
suggested by Koenker and Xiao (2004) and Galvao (2009) estimate and analyse the t- statistic
for different quantiles  to test the null hypothesis  
3.3. Quantile Cointegration Test
The present paper further employs Quantile Cointegration Test to explore the
systematic effect of varied frequencies of Green Bond Index on the shape, scale and locational
aspect of Environmental and Social Responsibility Index. The Quantile Cointegration Test was
introduced by Xiao (2009) to deal with the endogeneity issue in a standard cointegration
model. Xiao (2009) followed Saikkonen (1991) to disintegrate the cointegration equation
errors into the lead-lag terms along with the pure innovation component. The Quantile
Cointegration Model terms β(τ) as a vector of constants and thus extends the cointegration
model of Engle and Granger (1987). The special case of Quantile Cointegration Model
comprises of:
   
   (2)
and

   
 

 (3)
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We further add regressor’s quadratic term in the model and can be represented as
follows:

    



 

 (4)
Form the abovementioned equation (4) Xiao (2009) estimated the stability test for
cointegrating coefficients. Over all the quantiles, the null hypothesis,   was
examined by Xiao (2009). Further, the researcher introduced a supermum norm of the
absolute value of difference
 
 
as a test statistic. This test statistic forms
the basis for applying test statistic 
across the distribution of quantiles. The
present research follows the idea of Xiao (2009) to estimate 
test statistic’s critical
values by performing 1000 Monte Carlo simulations.
3.4. Wavelet Multiscale Decomposition
The wavelet analysis of any time series combines it’s both time and frequency domain.
In contrast to other conventional econometric methods, the wavelet analysis disintegrates a
time series data to be analyzed into a number of wavelet scales. Wavelets perform the
orthogonal decomposition of a time series data to present it in a non-parametric way
(Ramsey, 1999). The wavelets perform frequency decomposition of the time series data and
at the same preserve its time series properties. According to Gencay et al. (2002) the Wavelet
Transform presents the holistic information pertaining to individual time horizons and
locational aspects of a time series data. This unique property of the wavelets makes it suitable
to analyze a time series data irrespective of it being stationary or non-stationary.
According to Ramsey (2002) the functions of any time series data are represented by
father (ϕ) and mother (ψ) wavelets. The father wavelets represent a signal’s incredibly large-
scale smooth components while integrating to one. The mother wavelets indicate the
deviations occurring in these smooth components and integrate to zero. Father wavelets
generate scaling coefficients whereas the mother wavelets produce differencing coefficients.
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We represent the father wavelets as:
  
   (5)
The mother wavelet can be indicated as follows:
 
   (6)
These parent wavelets form the basic functions defining the coefficients’ sequence. The
derived smooth coefficients from the father wavelets are indicated as follows:
  (7)
We define the detailed coefficients obtained from the mother wavelets as follows:
   With j = 1………. (8)
The form the maximal scale of the former, whereas the detailed coefficients
deduced from the mother wavelets are at the scales from 1 to We define the function f (.)
from the above-mentioned coefficients in the following manner:
  
(9)
When we simplify Equation (5) we get
         (10)
The orthogonal components of the above-mentioned equation are represented as follows:
, (11)
 j = 1,…. (12)
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The multi horizon or multi resolution decomposition of f() is represented as {J, ,… ,D1}.
The th level wavelet detail related with series’ variations at scale λj is estimated by
. At each level, the cumulative sum of alterations is defined by . With the increase in ,
becomes smoother (Gencay et.al., 2002). We further estimate the scaling and wavelet
coefficients by incorporating the Maximal Overlap Discrete Wavelet Transform (MODWT).
Unlike Discrete Wavelet Transform (DWT), MODWT does not suffer from any limitation like
linked with the sample size to an integer multiple of (Percival and Walden, 2000).
Moreover, the detailed and smooth coefficients of MODWT are linked with zero phase filters
which are instrumental in aligning the original time series features with the features of
Multiple Resolution Analysis (MRA). According to Percival (1995) and Percival and Mofjeld
(1997), the variance estimators derived from MODWT are also asymptomatically more
efficient than of DWT derived estimators. Further unlike DWT, MODWT works with average
operator and moving difference which conserves the actual number of observations at each
wavelet decomposition scale.
In the present paper, we incorporate Daubechies Least Asymmetric (LA) filter of length
8 (LA8) wavelet, which according to Gencay et.al. (2002) are considered smoother than HAAR
wavelet filters. Moreover, as compared to HAAR wavelet filters, the LA8 filters provide better
non-correlations across the scales (Cornish et.al. 2006). We decompose the series into
wavelet coefficients D1 to D6. The detail coefficient Dj gives the resolution of data at scale 2j
to 2j+1. The oscillations of periods 0-4, 4-8, 8-16, 16-32, 32-64, 64-128, days are represented
by λ1, λ2, λ3, λ4, λ5, λ6, respectively. The long-term movements are represented by wavelet
smooth S6.
3.5. Quantile on Quantile Regression Approach
The present study intends to characterize the novel features of Quantile-on-Quantile
Regression approach introduced by Sim and Zhou (2015). The paper demonstrates bivariate
linkage between Global Green Bond Index and Global Environmental and Social Responsibility
Index. The Quantile-on-Quantile Regression approach which is inclusive of Quantile
Regression Approach primarily examines the effect quantiles of independent variables on the
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quantiles of dependent variables. The Quantile-on-Quantile Regression approach integrates
the features of quantile regression as well as non-parametric estimation. The conventional
quantile regression approach primarily examines the influence of independent variable on
the varied quantiles of dependent variable. The normal Ordinary Least Squares model
estimates the conventional effect of a single quantile of an independent variable on the
criterion variable. The novel Quantile on Quantile Regression approach combines both these
conventional Quantile Regression and normal Ordinary Least Squares to model the inter-
linkage between quantiles of both dependent and independent variables. The Quantile-on-
Quantile Regression approach which is non parametric in nature can be modelled as follows:
  
(13)
The Green Bond Index and Environmental and Social Responsibility Index at a
particular time t are represented by  and  respectively. indicate th quantile of
the conditional distribution growth of Environmental and Social Responsibility Index. The th
quantile of the conditional distribution growth of Environmental and Social Responsibility
Index is indicated by. We indicate quantile residual term having th quantile with zero value
with
.  represent the unidentified function with no prior information on inter-
relationship between the variables taken under study. The Quantile-on-Quantile Regression
model is flexible enough to explore and examine the extent of dependency between the
variables in their functional form.
Since the bandwidth controls the smoothness of the estimated results its optimal
choice is highly imperative for any non-parametric analysis. In specific terms, larger the
bandwidth, stronger the bias and smaller the bandwidth, more prevalence of variance in the
estimations. Thus, an optimal balance between the bias and the variance in the estimations
can be ensured through effective and efficient choice of bandwidth. The present study follows
Sim and Zhou (2015) while selecting the bandwidth parameter of h = 0.05.
3.6. Granger Causality in Mean and Quantiles Approach
We further extend the present research by examining the Granger Causality in the
quantiles of Green Bond Index and Environmental and Social Responsibility Index. In terms of
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Granger (1969), a particular time series does not Granger Cause another time series,
else earlier has not been instrumental in forecasting. Let us suppose there is a
describing vector        with is the former evidence set
of   . Further the null hypothesis of Granger non-causality
from to is explained as follows:
   for all , (14)
The  is termed as the conditional scattering function of provided

in the ambit of null hypothesis represented in equation 14. We further follow the
work of Troster (2018) in performing the Dt test which identifies the Quantile Auto Regressive
(QAR) framework  for entire    , upon the null hypothesis of non-Granger
causal relationship. The same may be indicated as follows:
     
 (15)
where the values and are calculated by supreme probability
in an identical space of grid of quantiles, and
 is the converse of a traditional ordinary
scattering function. We further rectify the causality sign between the variables, by calculating
the Quantile Auto regressive framework in equation 15 with the lagged variable to another
variable. The equation of QAR (1) model developed from equation 16 may be represented as
follows:
       

(16)
4. Empirical Results
4.1. Quantile Auto Regressive Unit Root Test (QAR) Test
The estimates from the Quantile Unit Root Test examining the stationarity of data are
reported in Table 2. The Quantile Unit Root Test presents the presence of persistence and t-
15
statistics estimates for the null hypothesis postulating that H0 = a(τ) = 1 for the grid of 19
quantiles T = {0.05-0.95}. The study avoids the issue pertaining to possible presence of serial
correlation by employing 10 lags of endogenous variables. The estimates from the QAR test
indicate the presence of unit root at a level for the conditional distribution quantiles thus
leading to inference of existence of non-stationarity in the variables’ data. However, at the
first order difference, the data is observed to be stationary as confirmed by the QAR
estimates.
<Insert Table 2 here>
4.2. The Quantile Cointegration Test
The Quantile Cointegration test introduced by Xiao (2009) investigates the possible
presence of cointegration among the variables taken under study. The present study
examines the existence of cointegration between Global Green Bond Index and Global
Environmental and Social Responsibility Index within the grid of 19 quantiles’ (0.05-0.95)
which are equally spaced. Further the given Quantile Cointegration Model employed in the
study uses two leads and lags of (
) as presented in Equation 3. The Table 3 reports
the estimates from the Quantile Cointegration model performed between Global Green Bond
and Environmental and Social Responsibility Index. The estimates from the indicate the
presence of asymmetric long run linkage between the quantiles of the given variables which
are also statistically significant.
<Insert Table 3 here>
4.3. The Quantile-on-Quantile Estimates
The empirical estimates derived from the Quantile-on-Quantile Regression of Global
Green Bond Index and its various decomposed series on Global Environmental and Social
Responsibility Index are presented in Figure 2. The given Quantile on Quantile Regression
analysis illustrated in Figure 2 presents the slope coefficient   estimates. These slope
16
coefficient estimates predict the influence of τth quantile of Global Green Bond Index and its
decomposed series on the θth quantile of Global Environment and Social Responsibility Index
at divergent values of θ and τ.
<Insert Figure 2 here>
In the composite series, we observe a strong positive effect of Global Green Bond
Index (GRBI) on Environmental and Social Responsibility Index (ESRI) in the area adjoining the
lower (0.1-0.4) quantiles of both the indices taken under study. However, as we move further,
this positive linkage between the variables starts weakening and eventually becomes
negative. The weakening of positive effect of Global Green Bond Index on Environmental and
Social Responsibility Index can be observed in the middle quantiles of both variables. In the
area adjoining the higher (0.7-0.9) quantiles of Environmental and Social Responsibility Index
and lower (0.1-0.3) quantiles of Green Bond Index, we observe a weak negative linkage
between the variables. In the rest of the area adjoining the quantiles of the given variables
the effect of Green Bond index is observed to be almost negligible.
These results indicate that in the initial level, the Green Bond Index (GRBI) might have
a positive impact on the Environmental and Social Responsibility Index (ESRI). However, with
the rise in both the indices, it can be seen that GRBI is gradually losing its impact on ESRI. This
phenomenon might be possible because of focus of the firms on the economic output rather
than socio-ecological outcome, which traces back to the classic tradeoff between growth and
development. In absence of the policy level directives for ascertaining sustainable
development through the business operations, it might be possible that firms might use GRBI
as a tax saving mechanism, rather than envisaging it as an instrument for generating socio-
ecological outcome.
Moreover, in absence of the properly defined guidelines for monitoring socio-
environmental impact of projects, it is quite likely that firms will try to maximize profit,
notwithstanding the intended outcome of the financing mechanism. In the context of the
tradeoff between output and outcome, the intended impact of GRBI on ESRI will gradually
start to diminish, as the marginal utility of the firms to create a positive socio-ecological
externality will start to diminish, as focusing on the developmental aspects might increase the
marginal monitoring cost of the firms. Furthermore, in order to save higher taxes through the
GRBI mechanism, firms will rely more on automation in order to demonstrate a perceivable
improvement in the environmental quality, and this initiative might have a detrimental
17
impact on the employment scenario across industries. Therefore, it can be assumed that high
level of GRBI might have a negative impact on ESRI, as higher proneness towards the
betterment of environmental quality by virtue of automation might lead to a disturbance in
the social order, in the form of rising unemployment and consequential income inequality.
Reflection of this argument can be visible in the latter half of the results.
This segment of the results can be compared with case of the Next 11 economies,
where the energy innovation was found to have a detrimental impact on the social order
through rising income inequality (Sinha et al., 2020). In this way, achievement of the
objectives of SDG 9 might enable the nations to achieve the objectives of SDG 13, but will also
make them depart from the objectives of SDG 16. This tradeoff needs to be internalized
through suitable policy interventions.
In order to understand the phenomenon in a more comprehensive manner, we further
decompose the series and analyze the effects of frequency-level decomposed series (D1-D6
and S6) of Global Green Bond Index on Environmental and Social Responsibility Index. When
we examine the influence of decomposed series of Green Bond Index (GRBI.D1) on
Environmental and Social Responsibility Index we find a similar scenario as observed in the
Quantile-on-Quantile estimates performed on the composite indices. Here also the
decomposed series GRBI.D1 is observed to have strong positive effect on ESRI at the area
adjoining lower to middle (0.2-0.5) quantiles of both the dependent and independent
variables. However, at the rest of the combining quantiles of both the variables the linkage
between the decomposed series of Green Bond Index and Environmental and Social
Responsibility Index are negligible. Identical scenario is also observed in the influence of
decomposed series GRBI.D2 on ESRI, where the strong positive effect of the GRBI.D2 is
observed on the ESRI on the lower to middle (0.2-0.6) quantiles of both GRBI.D2 and ESRI.
When we move further, we observe this positive linkage starts weakening in the middle to
higher (0.6-0.9) quantiles of ESRI combined with lower to middle (0.2-0.6) quantiles of
GRBI.D2. In rest of the area adjoining the quantiles of the variables the effect of GRBI.D2 on
ESRI is observed to be extremely insignificant.
We further examine the influence of GRBI.D3 on ESRI under Quantile-on-Quantile
Regression framework. We find the prevalence of strong positive linkages between the
variables across the quantiles of ESRI combined with lower (0.1-0.3) quantiles of GRBI.D3.
Furthermore, the positive effect of GRBI.D3 on ESRI becoming almost non-existent. In the
18
area adjoining the higher (0.7-0.9) quantiles of GRBI.D3 and lower to middle (0.1-0.5)
quantiles of ESRI we find weak to strong negative effect of decomposed series (GRBI.D3) on
ESRI. Similarly, the decomposed series GRBI.D4 is observed to have positive effect on ESRI in
the area adjoining lower to higher (0.1-0.9) quantiles of ESRI and lower (0.3-0.4) quantiles of
GRBI.D4. Further at middle to higher (0.5-0.9) quantiles of GRBI.D4 we observe its negative
effect of varying strength (weak to strong) on ESRI across its quantiles.
When we further decompose the series and analyze the effect of GRBI.D5 and GRBI.D6
on ESRI, we find complete absence of its positive effect on ESRI across the quantiles of both
dependent and independent variables. We observe a strong negative effect of the
decomposed series of Green Bond Index (GRBI.D5 and GRBI.D6) on ESRI at the area
encompassing lower to higher (0.1-0.9) quantiles of ESRI and lower (0.1-0.4) quantiles of
GRBI.D5 and GRBI.D6. As we move further, from middle to higher (0.5-0.9) quantiles of the
independent variable we find this negative effect starts weakening. However, across the
quantiles of ESRI and GRBI.D5 and GRBI.D6, we find a complete prevalence of negative linkage
between the variables. We find a similar result, when we examine the effect of the most
stable component of the decomposed time series of Green Bond Index GRBI.S6 on ESRI. We
find negative influence of decomposed GRBI.S6 of varying strength on ESRI across the
quantiles of dependent and independent variables.
4.4. Robustness tests
At the next stage of our analysis, we investigate all the segments of the results using
the frequency-level wavelet-based QQR analysis. As we move along from the short-run to
medium-run and long-run frequency domains, it can be seen that the positive impact of GRBI
on ESRI is not only gradually diminishing but is gradually turning out to be negative. These
results show that during the initial level of implementation, GRBI is having a short run positive
impact on ESRI. This particular result has been found both across the time and the frequency
domains, and this demonstrates the validity of the findings. Our results demonstrate that
inadequately defined policy directives and profit motive of the firms might bring the
objectives of sustainable development at the crossroads, i.e., policy instrument for assuring
sustainable development might turn out to be a double-edged sword. An indication of such a
19
scenario from rent-seeking perspective of public sector firms has been provided by Sinha et
al. (2019).
The paper further compares the quantile regression parameter estimates with τ-
averaged QQ parameter estimates and thus ascertains the validity of the adopted Quantile
on Quantile approach. The plots presented in Figure 3 illustrate the estimates of the slope
coefficients derived from the Quantile Regression and average of the slope coefficients from
Quantile-on-Quantile Regression. The plots in Figure 3 reveal the trend in slope coefficients
of Quantile-on-Quantile Regression being similar to that of Quantile Regression. However,
while examining the impact of original time series of Global Green Bond Index and its
decomposed time series, i.e., GRBI.D1, GRBI.D2, GRBI.D3 and GRBI.D4 we may observe the
value estimate from Quantile-on-Quantile Regression being nearly similar to that of the
results of Quantile Regression. On the contrary, the trend line for value estimates of QQ and
QR coincide for the effect of GRBI.D5, GRBI.D6 and GRBI.S6 on ESRI. This segment of the
findings shows the robustness of the findings of QQR estimates.
<Insert Figure 3 here>
Finally, we employ the Granger Causality Test in quantiles for Green Bond Index and
its various decomposed series and Environmental and Social Responsibility Index. The
estimates from Granger Causality Test in Quantiles of dependent and independent variables
are reported in Table 4. From Table 4 we can very well witness the existence of unidirectional
Granger Causality from GRBI and it is decomposed to ESRI across all the quantiles. The
outcome from the Granger Causality Test in Quantiles remains similar for all the lags
considered in the present study. The results confirm the significant effect of changes in Green
Bond Index and its decomposed series on Global Environmental and Social Responsibility
Index. However, at certain instances of extreme lower (0.1) or extreme higher (0.7or 0.9)
quantiles we may somewhat observe the presence of bidirectional Granger Causality in
Quantiles between both the variables. The latter section of the findings indicates that the
initiation and higher levels of ESRI call for equivalent levels of penetration of green bonds,
which can be reflected in terms of the low and high returns on GRBI. This direction of causality
might prove to be significant from the policymaking perspective in a context, where the
higher GRBI might have a negative influence on ESRI.
20
<Insert Table 4 here>
5. Policy Implications
5.1 Central policy framework
While a high penetration of green bonds with low attainment of SDG objectives might
have gradual negative transformational impact on environmental and social responsibility, it
can be assumed that the socio-ecological benefits of green bonds have not been
communicated to the industrial players effectively. This issue can be considered as a classic
outcome-output trade off, and one of the major reasons behind prevalence of this trade-off
might be the strategic myopia of the industrial players regarding their potential role in
ascertaining the sustainable development of nations. To address this problem, an appropriate
complementary policy mechanism for the green financing channel should be implemented.
While firms are using green bonds as a mere tax saving mechanism, policymakers need to
ensure that the social outcome of financing mechanism is also fulfilled.
One of the possible solutions, is to create the demand for a positive social outcome,
however the implementation of this particular policy can be difficult given the profit motive
of the firms. Therefore, policymakers need to implement a rigorous monitoring mechanism
for measuring the social outcomes of the projects, so that firms can create sufficient social
externality. In this way operational costs of the firms might be increased and might have a
negative impact on their revenue streams. To protect interests of the firms, the policymakers
also need to create certain incentivization scheme, so that the firms can be motivated for
achieving the intended social outcome of the green financing mechanism. Moreover,
presence of an incentivization scheme might also bring forth the effectiveness of promotional
activities carried out by policymakers for elucidating the socio-ecological benefits of green
bonds among the industrial players. The expectation of supernormal profit in the form of
economic incentives, penetration of green bonds might rise while in keeping with the
assurance of social benefits communicated and monitored by the policymakers. Issuance of
green bonds will eventually support the rise in the green projects, which might exert positive
environmental externality. In this way, the financing mechanism of the firms can create
21
environmental benefits, and the nation might make a progression towards achieving the
objective of SDG 13.
Effective communication and continuous monitoring by the government might
discourage the firms to go beyond a certain limit in terms of implementing automation, and
thereby, putting a cap on the possibilities of jobless economic growth. If the firms can
maintain a certain capital-labor ratio, following the mandated maximum permissible limit of
job loss, then firms can add to the prevailing level of per capita income of the citizens by
enhancing the scope and scale of job market. In this way, the possible financial innovations
being carried out by the firms will lead to not only the betterment of environmental quality
but also able to sustainable vocational opportunities, which might lead to rise in the per capita
level of income at the industrial level. This policy initiative in terms of creating an incentivized
monitoring framework might help the nation to make progression towards achievement of
the objectives of SDG 8.
5.2. Policy caveats
When the policy frameworks are being laid out, it is also necessary about to mention
about the required caveats and assumptions, in absence of which the policy frameworks
might not produce the intended results. First, the policy makers should enforce strict
environmental regulations for protecting the pool of natural resources, so that the fossil fuel
consumption can be reduced. Second, the policymakers need to ensure an environment of
trust for making the diffusion of technologies across the industry effective. Third, while
moving away from the traditional fossil fuel-based solutions, it is possible that the labors
employed with the traditional fossil fuel-based energy generation sector might losing their
jobs, because of the gradual decline in demand for this form of energy. Therefore, the
policymakers need to take proactive measures for the capacity building of these labors so that
they can be employed in the other industrial sectors. This policy move is extremely necessary
to retain the balance in the social order. Maintenance of these caveats will also help the
nation to tread along the developmental trajectory in the long run.
6. Conclusion
22
Carried out at a global context, the present study explores the possible impact of green
bonds returns (GBRI) on environmental and social responsibility (ESRI) during the period from
October 1, 2010 to September 30, 2020. Using a combination of advance quantile modelling
methods this paper empirically investigates the patterns of connectedness between the GBRI
on ESRI providing useful insights for policy development in this area. The robustness of our
empirical results confirmed by using the wavelet-based quantile modeling and Granger
Causality in quantiles approaches.
We observe not only the transformational impact of green bonds returns on the
environmental and social responsibility, but also uncover in the role of environmental and
social responsibility in initiating and sustaining the green bonds market. The outcome of this
study might be employed to devise a policy framework for accomplishing the SDG objectives,
and this framework can be considered as an example for the countries, which are
characterized by high penetration of green bonds with low attainment to SDG objectives.
Finally, we acknowledge the limitations of this research and understand that this study
can be extended in the future. Our paper has embarked upon a bivariate analytical approach,
which might be restrictive considering the scale of the problem that we targeted.
Consideration of additional contextual aspects, e.g. entrepreneurship development, level of
human development, and geopolitical aspects could help to provide additional policy-level
insights. Our study employs a baseline approach to understand the impact of green financing
mechanism on socio-ecological sustainability, while future studies can be conducted based
on the volatility of returns, and co-movement among the indices.
23
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28
Figures & Tables
Figure 1. Trend plot of Environmental & Social Responsibility Index and Green Bond Index
-.04
-.02
.00
.02
.04
2012 2014 2016 2018
ESRI
-.15
-.10
-.05
.00
.05
.10
2012 2014 2016 2018
GRBI
-.12
-.08
-.04
.00
.04
.08
.12
2012 2014 2016 2018
GRBI_D1
-.04
-.02
.00
.02
.04
2012 2014 2016 2018
GRBI_D2
-.02
-.01
.00
.01
.02
.03
2012 2014 2016 2018
GRBI_D3
-.02
-.01
.00
.01
.02
2012 2014 2016 2018
GRBI_D4
-.015
-.010
-.005
.000
.005
.010
2012 2014 2016 2018
GRBI_D5
-.008
-.006
-.004
-.002
.000
.002
.004
.006
2012 2014 2016 2018
GRBI_D6
-.006
-.004
-.002
.000
.002
.004
2012 2014 2016 2018
GRBI_S6
Table 1. Descriptive Statistics
ESRI
GRBI
0.000045
0.000347
-0.030789
-0.125476
0.025727
0.092403
0.003918
0.010516
-0.350107
-0.984011
9.789043
23.874770
4632.909***
43724.763***
Correlation Matrix
1.000000
-
0.8478***
1.000000
29
Note: *** represents that variables are significant at 1% level of significance. ESRI represents Environmental
& Social Responsibility Index and GRBI denotes Green Bond Index. Source: Authors Estimation
Table 2: Quantile Unit Root test
Quantile
ESRI
GRBI
α(τ)
t-stats
C.V
α(τ)
t-stats
C.V
0.05
0.907
-2.282
-2.332
0.893
-1.577
-2.292
0.10
0.908
-2.111
-2.490
0.894
-2.057
-2.562
0.20
0.915
-0.653
-2.694
0.912
-1.228
-2.683
0.30
0.917
-0.436
-2.699
0.916
-0.884
-2.520
0.40
0.917
-1.144
-2.769
0.917
-0.573
-2.507
0.50
0.917
-1.943
-2.795
0.917
-0.297
-2.528
0.60
0.917
-0.629
-2.765
0.917
0.201
-2.532
0.70
0.917
-0.370
-2.645
0.917
0.181
-2.565
0.80
0.916
-0.453
-2.665
0.920
0.900
-2.625
0.90
0.917
-0.361
-2.348
0.921
0.313
-2.364
0.95
0.920
1.069
-2.124
0.935
1.073
-2.562
Notes: The table shows point estimates and t-statistics values for the 5% significance level.
Source: Author Estimation.
Table 3: Quantile Cointegration Test Results
Model
Coeff.
Supτ | Vn(τ) |
CV1
CV5
CV10
ESRIt vs. GRBIt
β
87436.312
59431.477
50382.416
47215.765
γ
16477.049
9475.991
6114.003
4441.374
Note: This table presents the results of the quantile cointegration test of Xiao (2009) for the
logarithm of the Environmental & Social Responsibility Index (ESRI) and Green Bond Index (GRBI).
We test the stability of the coefficients β and γ in the quantile cointegration model and CV1, CV5,
and CV10 are the critical values of statistical significance at 1%, 5%, and 10%, respectively. We use
1000 Monte Carlo simulations to generate the critical values. We use an equally spaced grid of 19
quantiles, [0.05-0.95], to calculate the test statistic of the quantile cointegration model between
ESRI & GRBI.
30
Figure 2: Quantile on Quantile estimates of slope coefficient
Impact of Green Bond Index on Environmental & Social Responsibility Index
GRBI
GRBI (D1)
GRBI (D2)
GRBI (D3)
GRBI (D4)
GRBI (D5)
31
GRBI (D6)
GRBI (S6)
Note: GRBI represents Green bond index, GRBI-Di: 04, 48, 816, 1632, 3264, 64128days; i : 1,2,..6; GRBI.s6: long-
term movement.
Figure 3. Comparison between QQR and QR estimates
Impact of Green Bond Index on Environmental & Social Responsibility Index
GRBI
GRBI (D1)
32
GRBI (D2)
GRBI (D3)
GRBI (D4)
GRBI (D5)
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
Quantiles of Environmental & Social Responsibility Index θ
QQR
QR
-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
Quantiles of Environmental & Social Responsibility Index θ
QQR
QR
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
Quantiles of Environmental & Social Responsibility Index θ
QQR
QR
-0.07
-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
Quantiles of Environmental & Social Responsibility Index θ
QQR
QR
-0.1
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
Quantiles of Environmental & Social Responsibility Index θ
QQR
QR
-0.2
-0.15
-0.1
-0.05
0
0.05
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
Quantiles of Environmental & Social Responsibility Index θ
QQR
QR
33
GRBI (D6)
GRBI (S6)
Note: The graph shows the estimates of the slope coefficients against the quantiles of Environmental & Social
Responsibility Index in the y-axis and the quantiles of decomposing green bond index (d1, d2, . . . d6 and s6) in the x-axis.
Table 4. Results of Wavelet Based Granger Causality in Quantile approach.
Panel A: ΔGRBI shocks to ΔESRI
Panel B: ΔESRI shocks to ΔGRBI
Time Scale
quantiles
Number of lags
Time Scale
quantiles
Number of lags
1
2
3
1
2
3
Raw Data
[0.10-0.90]
0.000***
0.000***
0.000***
Raw Data
[0.10-0.90]
0.568
0.340
0.249
0.10
0.000***
0.000***
0.000***
0.10
0.947
0.469
0.597
0.20
0.000***
0.000***
0.000***
0.20
0.381
0.587
0.536
0.30
0.000***
0.000***
0.000***
0.30
0.667
0.637
0.263
0.40
0.000***
0.000***
0.000***
0.40
0.669
0.169
0.520
0.50
0.000***
0.000***
0.000***
0.50
0.234
0.235
0.535
0.60
0.000***
0.000***
0.000***
0.60
0.746
0.855
0.731
0.70
0.000***
0.000***
0.000***
0.70
0.359
0.648
0.757
0.80
0.000***
0.000***
0.000***
0.80
0.193
0.841
0.336
0.90
0.000***
0.000***
0.000***
0.90
0.026**
0.036**
0.004**
D1
[0.10-0.90]
0.000***
0.000***
0.000***
D1
[0.10-0.90]
0.280
0.381
0.974
0.10
0.000***
0.000***
0.000***
0.10
0.262
0.148
0.422
0.20
0.000***
0.000***
0.000***
0.20
0.244
0.300
0.362
0.30
0.000***
0.000***
0.000***
0.30
0.176
0.135
0.801
0.40
0.000***
0.000***
0.000***
0.40
0.703
0.264
0.884
0.50
0.000***
0.000***
0.000***
0.50
0.337
0.957
0.505
0.60
0.000***
0.000***
0.000***
0.60
0.048**
0.047**
0.842
0.70
0.000***
0.000***
0.000***
0.70
0.534
0.048**
0.797
0.80
0.000***
0.000***
0.000***
0.80
0.517
0.575
0.201
0.90
0.000***
0.000***
0.000***
0.90
0.036**
0.805
0.247
D2
[0.10-0.90]
0.000***
0.000***
0.000***
D2
[0.10-0.90]
0.185
0.823
0.613
0.10
0.000***
0.000***
0.000***
0.10
0.105
0.945
0.276
0.20
0.000***
0.000***
0.000***
0.20
0.704
0.464
0.746
0.30
0.000***
0.000***
0.000***
0.30
0.792
0.355
0.825
-0.35
-0.3
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
Quantiles of Environmental & Social Responsibility Index θ
QQR
QR
-1
-0.8
-0.6
-0.4
-0.2
0
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
Quantiles of Enviornmental & Social Responsibility Index θ
QQR
QR
34
0.40
0.000***
0.000***
0.000***
0.40
0.441
0.403
0.551
0.50
0.000***
0.000***
0.000***
0.50
0.333
0.692
0.345
0.60
0.000***
0.000***
0.000***
0.60
0.696
0.956
0.549
0.70
0.000***
0.000***
0.000***
0.70
0.952
0.012
0.198
0.80
0.000***
0.000***
0.000***
0.80
0.750
0.800
0.488
0.90
0.000***
0.000***
0.000***
0.90
0.003***
0.008***
0.009***
D3
[0.10-0.90]
0.000***
0.000***
0.000***
D3
[0.10-0.90]
0.364
0.283
0.903
0.10
0.000***
0.000***
0.000***
0.10
0.983
0.838
0.469
0.20
0.000***
0.000***
0.000***
0.20
0.995
0.405
0.911
0.30
0.000***
0.000***
0.000***
0.30
0.483
0.857
0.311
0.40
0.000***
0.000***
0.000***
0.40
0.397
0.404
0.230
0.50
0.000***
0.000***
0.000***
0.50
0.914
0.659
0.523
0.60
0.000***
0.000***
0.000***
0.60
0.369
0.482
0.034**
0.70
0.000***
0.000***
0.000***
0.70
0.562
0.780
0.612
0.80
0.000***
0.000***
0.000***
0.80
0.926
0.143
0.752
0.90
0.000***
0.000***
0.000***
0.90
0.140
0.701
0.980
D4
[0.10-0.90]
0.000***
0.000***
0.000***
D4
[0.10-0.90]
0.124
0.359
0.446
0.10
0.000***
0.000***
0.000***
0.10
0.063*
0.074*
0.675
0.20
0.000***
0.000***
0.000***
0.20
0.465
0.924
0.581
0.30
0.000***
0.000***
0.000***
0.30
0.106
0.979
0.899
0.40
0.000***
0.000***
0.000***
0.40
0.855
0.702
0.694
0.50
0.000***
0.000***
0.000***
0.50
0.440
0.676
0.610
0.60
0.000***
0.000***
0.000***
0.60
0.648
0.310
0.699
0.70
0.000***
0.000***
0.000***
0.70
0.650
0.952
0.138
0.80
0.000***
0.000***
0.000***
0.80
0.923
0.456
0.545
0.90
0.000***
0.000***
0.000***
0.90
0.007***
0.004***
0.001***
D5
[0.10-0.90]
0.000***
0.000***
0.000***
D5
[0.10-0.90]
0.658
0.730
0.599
0.10
0.000***
0.000***
0.000***
0.10
0.561
0.807
0.561
0.20
0.000***
0.000***
0.000***
0.20
0.969
0.331
0.984
0.30
0.000***
0.000***
0.000***
0.30
0.842
0.490
0.214
0.40
0.000***
0.000***
0.000***
0.40
0.701
0.033**
0.582
0.50
0.000***
0.000***
0.000***
0.50
0.751
0.512
0.148
0.60
0.000***
0.000***
0.000***
0.60
0.565
0.821
0.067*
0.70
0.000***
0.000***
0.000***
0.70
0.009***
0.576
0.279
0.80
0.000***
0.000***
0.000***
0.80
0.354
0.544
0.971
0.90
0.000***
0.000***
0.000***
0.90
0.134
0.100
0.146
D6
[0.10-0.90]
0.000***
0.000***
0.000***
D6
[0.10-0.90]
0.168
0.469
0.266
0.10
0.000***
0.000***
0.000***
0.10
0.152
0.055
0.569
0.20
0.000***
0.000***
0.000***
0.20
0.760
0.659
0.686
0.30
0.000***
0.000***
0.000***
0.30
0.665
0.579
0.213
0.40
0.000***
0.000***
0.000***
0.40
0.165
0.610
0.985
0.50
0.000***
0.000***
0.000***
0.50
0.279
0.472
0.661
0.60
0.000***
0.000***
0.000***
0.60
0.814
0.028**
0.231
0.70
0.000***
0.000***
0.000***
0.70
0.208
0.116
0.274
0.80
0.000***
0.000***
0.000***
0.80
0.857
0.327
0.211
0.90
0.000***
0.000***
0.000***
0.90
0.372
0.412
0.745
S6
[0.10-0.90]
0.000***
0.000***
0.000***
S6
[0.10-0.90]
0.952
0.030**
0.356
0.10
0.000***
0.000***
0.000***
0.10
0.068*
0.877
0.534
35
0.20
0.000***
0.000***
0.000***
0.20
0.725
0.371
0.961
0.30
0.000***
0.000***
0.000***
0.30
0.799
0.576
0.386
0.40
0.000***
0.000***
0.000***
0.40
0.818
0.031
0.239
0.50
0.000***
0.000***
0.000***
0.50
0.438
0.365
0.182
0.60
0.000***
0.000***
0.000***
0.60
0.755
0.543
0.057*
0.70
0.000***
0.000***
0.000***
0.70
0.443
0.649
0.641
0.80
0.000***
0.000***
0.000***
0.80
0.080*
0.266
0.398
0.90
0.000***
0.000***
0.000***
0.90
0.001***
0.004***
0.007***
Notes: **, *** represents the significant level of null hypothesis rejected at 5% or 1%. D1-D6 represents the time horizons
with timescales of 0-4, 4-8, 8-16, 16-32, 32-64 and 64-128 days, respectively.
36
Appendix 1: Summary of the literature
Author
Geography
Period
Method
Outcome
Clapp et.al. (2015)
Global Market
Opinion based survey
Management aligning their policies
with the climate risk attribute to
greater confidence in green bond.
Wang and Zhi (2016)
NA
NA
Review study
Green finance can restore
ecological balance
Ng (2018)
Hong Kong
NA
Multiple-case study
Institutional legitimacy for
sustainability influenced by a
national policy and enhanced
through a market-based finance
approach
Nguyen et al. (2018)
Vietnam
NA
Review study
Green bond reduces the
dependence on imported coal for
energy needs
Reboredo (2018)
Global data
2014-2017
Copula
Substantial spill over effect from
corporate and treasury fixed-
income market on green bond
prices. Negligible effect of equity
and energy markets on green bond
prices.
Shahbaz et al. (2018)
France
1955-2016
Bootstrapping Bounds Testing
Approach
Positive impact of FDI and negative
impact of energy research
innovations on carbon emissions.
Urban et al. (2018)
Vietnam
NA
Review study
Green financing is an enabler of
green transformation
Agliardi and Agliardi (2019)
NA
NA
Review study
Shareholders’ awareness and pro-
environment tax benefit enhance
the price of green bond.
Banga (2019)
NA
NA
Review study
Green bond is a potential source of
climate finance for developing
countries
Braouezec and Joliet (2019)
Germany
NA
Real Option Framework
Addition of CSR dimension to
projects with negative environment
37
externalities induces immediate
firm investment in CSR activities.
Gianfret and Peri (2019)
Europe
2013-2017
Propensity Score Matching
Approach
Green Bonds are more financially
convenient than their non-green
contemporaries.
Nasir et.al. (2019)
ASEAN Countries
1982-2014
FMOLS and DOLS approach
Economic growth, FDI and financial
development leads to
environmental degradation.
Zerbib (2019)
Global data
2013-2017
Matching Method and two step
regression procedure
Low impact of investor’s pro-
environmental preference on green
bond prices.
Zhou and Cui (2019)
China
2016-2019
Event Study Approach
Issuance of green bonds positively
influence companies’ financial
performance and CSR activities.
Buhari et al. (2020)
Europe
1995-2014
Panel Quantile Regression
Renewable energy consumption is
more effective on economic growth
as compared to the non-renewable
energies.
Flammer (2020)
The USA
2007-2018
Event Based Study
Observed strong linkage between
companies’ financial and
environmental performance and
the issuance of green bond.
Huynh et al. (2020)
The USA
2017-2020
Copulas and Generalised Forecast
Error Variance Decomposition
Observed potential safe haven
properties of green bond assets.
Karyawati et al. (2020)
Indonesia
1998-2017
Meta-analysis integrating 55
different contexts with correlation
coefficients as the effect size
Various dimensions like country
characteristics, forms of CSR, CSR
and financial performance
measurements define complex
nature of relationship between CSR
practices and financial
performance.
Kovilage (2020)
Sri Lanka
NA
Interpretive Structural Modelling
Technique
Observed strong effect of lean
practices on green practices which
in turn significantly influence
sustainable performance measures.
38
Pham et al. (2020)
Europe
1990-2014
Panel VAR and FMOLS
Observed role of economic factors
in enhancing environmental
degradation. The sociological
factors like population growth and
urbanisation have negative effect in
short run and positive effect in the
long run. The renewable energy
factors are instrumental in reducing
carbon emission levels.
Pham L and Huynh T.L.D (2020)
Global Data
2014-2019
Diebold-Yilmaz Connectedness
framework and Generalised
Forecasting Error Variance
Decomposition
Time varying feedback between
green bond performance and
investor attention.
Shahbaz et.al. (2020a)
United Kingdom
1870-2017
Bootstrapping ARDL Approach
Financial development and energy
consumption enhance but R&D
expenditures helps in reducing
carbon emissions.
Shahbaz et.al. (2020b)
USA
1976-2016
ARDL Bounds Testing Approach
Negative linkage between oil price
and energy consumption as well as
carbon emission. Further
abundance of energy resources and
economic growth leads to rise in
energy consumption and carbon
emission.
Nasir et.al. (2021)
Australia
1980-2014
Cointegration and Causality Tests.
Observed long run positive impact
of financial development, energy
consumption and trade openness
on carbon emissions. Further
observed short run bidirectional
causality between economic
growth, energy consumption,
industrialization and stock market
development with carbon
emissions.
Nguyen et.al. (2021)
G-6 Countries
1978-2014
Carbon emissions are mainly driven
by economic growth, expansion of
39
capital market and trade openness.
Stock market capitalisation and FDI
has weak yet negative effect on
carbon emissions.
... According to the authors (Nawaz et al., 2021), this has increased the pressure on government officials to act quickly in order to accomplish climate goals and achieve long-term economic growth. Concerns about climate change and global warming have fuelled substantial interest in environmentally friendly investments (Taghizadeh-Hesary et al., 2021;Sinha et al., 2021) geared to protect the natural environment and preserve human health (Kocaarslan, 2021). Kocaarslan (2021) posits that, this observed convergence of environmental and financial issues has engendered curiosity in how the two are connected and how they may be resolved simultaneously. ...
... Green financing has emerged as a critical pathway for industrialised countries to achieve long-term progress (Muganyi et al., 2021). Similarly, Sinha et al. (2021) argue that governments are progressively embracing green bond financing to fulfil SDGs. Ng (2018) posits that using revenues from green financing through the Global Financial Centre of China (GFCC) will allow large financial resources from the international capital market to be allocated to sustainable infrastructure development throughout a geographical region. ...
... Social factors underlying the adoption of green bond financing of infrastructure projects While Sinha et al. (2021) argue that the socio-ecological benefits of green bonds have not been effectively communicated to industrial players; high green bond penetration combined with low SDG achievement may have a gradual negative transformational impact on environmental and social responsibility. Furthermore, the statistics suggest that this is a classic outcome-output trade-off, with strategic myopia about industrial participants' potential role in ensuring long-term growth being one of the key causes. ...
Article
Full-text available
Purpose There is a pressing need to increase investments in sustainable infrastructure to promote low carbon economic growth and ensure environmental sustainability. Consequently, this study examines the socio-political factors underlying the adoption of green bond financing of infrastructure projects. Design/methodology/approach Primary data was gathered from experts with advanced experience in, or knowledge of green bonds in the Kumasi Metropolis. To identify respondents with pertinent knowledge that is relevant to the study, purposive and snowball sampling techniques were used. One-sample t -test and relative importance index were used in this study's statistical analysis. Findings ‘Training and experience with sustainable finance’ was seen as the most important social factor underlying the adoption of green bond financing of infrastructure projects by the respondents and ‘Governmental tax-based incentives’ was rated as the leading political factor. Originality/value This pioneering research attempts to ascertain the socio-political factors affecting the adoption of green bond financing of infrastructure projects. Emergent results of analysis and concomitant discussions add knowledge to fill a void in literature on the social and political factors affecting the adoption of green bond financing of infrastructure projects in developing countries.
... Glomsrod and Wei (2018) observe that green bond financing and divestment from fossil fuels can drastically reduce global coal consumption and emissions compared to the business as usual (BAU) scenario by 2030. Sinha et al. (2021) find the impact of green bonds on environmental and social sustainability gradually decreasing and turning negative in higher quantiles, suggesting that economic aspects like tax savings and monitoring costs may take precedence over the socio-ecological externalities at higher penetration levels. They emphasize a policy mechanism to measure and monitor the social impacts of the issuers. ...
... Study the actual utility of green bonds for the asset owner by integrating the social and financial returns Broadstock and Cheng (2019) Framework to model systemic risk between green bonds and other asset classes from time and frequency perspectives Liu et al. (2021) Study of connectedness using lower frequency weekly data to account for the heterogeneity of investors with long-term horizons Saeed et al. (2021) Investigating the safe haven role of green bonds Naeem et al. (2021a) Extending the study of diversification benefits of green bonds to more asset classes and developing markets Huynh et al. (2020) Drivers and barriers in green bond market development Study of macro-level directives and policy that drive green bond issuances Tolliver et al. (2020) Study of barriers to green bond market development and their changing relevance in developing markets Banga (2019) Green bonds and sustainable development Consideration of additional contextual aspects like entrepreneurial development and geopolitical aspects to gain policy-level insights on impact of green financing Sinha et al. (2021) Study the volatility of returns and co-movement among indices of green bonds Study the link between ESG information disclosure and firm-level performance in India and other developing Asian economies Tolliver et al. (2021) Study the ESG impact of specific firms by analysing relevant data like frequency, volume and environmental impact to shed light on the outcomes of green investments Table 3. Author and year ...
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Abstract Purpose The study aims to consolidate knowledge, explore current dynamics, understand knowledge progression, identify primary research streams, present content analysis and provide future research directions for green bonds research. Design/methodology/approach The authors reviewed 150 high-quality Scopus-indexed articles on green bonds in two stages. First, they use bibliometric analysis to understand the field's most relevant articles, authors, institutions and journals. Second, they analysed 49 curated articles to identify and analyse primary research streams and offer research directions. Findings The authors report the most influential articles, authors, journals and clusters based on article co-citation networks. They identify five green bond research streams: issuance, greenium and its drivers, connectedness, drivers and barriers, and sustainable development. Research limitations/implications Using different databases, tools, sample periods or article screening criteria may yield different results. The study's findings are robust to document selection or analytical tools. Practical implications The study helps researchers, practitioners, regulators, policymakers, issuers and investors understand green bond issuance, pricing and connectedness research. Originality/value This unique study sheds light on publication trends, the most influential articles, authors, journals and the conceptual and intellectual structure of the field. It identifies and elaborates primary research streams, succinctly summarizes the most influential articles and offers future research directions. Citation Kedia, N. and Joshipura, M. (2022), "Green bonds for sustainability: current pathways and new avenues", Managerial Finance, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/MF-08-2022-0367
... In the Sustainable Development Goals report authored by Garroway and Carpentier, it is proposed that the key to helping countries achieve such goals is to improve deficiencies in financing (Sinha et al. 2021). The United Nations Global Compact has also pointed out that to accelerate countries' efforts to address climate change and achieve sustainable development, the most important consideration is to reposition the global capital market-a process that requires adjustment of corporate financing mechanisms. ...
... The United Nations Global Compact has also pointed out that to accelerate countries' efforts to address climate change and achieve sustainable development, the most important consideration is to reposition the global capital market-a process that requires adjustment of corporate financing mechanisms. As part of this pursuit, stakeholders have begun to pay extensive attention to corporate carbon emissions, investment trends have shifted to favor green and low-carbon investments, and new financial instruments such as green bonds and ESG (environmental, social, and governance) funds have emerged one after another, further narrowing the financing channels open to the high-carbon energy industry and forcing enterprises to consider limiting their carbon emissions (Sinha et al. 2021). ...
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The signing of the Paris Agreement has raised concerns about global carbon emissions, which have detrimental consequences in terms of climate change. At the same time, the financing process for listed companies has begun to incorporate investigations into these firms’ carbon emissions. But the current impact of financing costs on firms’ carbon emissions has not been accurately assessed. There are large differences in endowments in different regions of China, and factors flow frequently among regions. To date, no empirical evidence has emerged to show the spatial effects of financing costs on carbon emissions. This study uses the STIRPAT model and a panel lag regression model for empirical testing. The results show that increasing financing costs will increase the burden imposed by carbon reduction efforts in various regions. Although this trend has obvious spillovers to surrounding areas, the location of the enterprise bears a more negative burden of externalities. Further analysis shows that reducing the financing costs of enterprises in economically developed regions can reduce both their carbon emissions and the damage to economic growth. These research conclusions can help policymakers shape carbon reduction activities through reducing corporate financing costs on the basis of regional development differences.
... Previous studies have focused on the effects of CSR on firm performance [3,[17][18][19][20][21][22][23][24][25][26], environmental performance [4,[27][28][29][30][31], and sustainability performance [28,[32][33][34][35][36]. However, little attention has been paid to investigating the impact of CSR on EP [4] in the context of banking institutions, and the results of the existing studies have been largely inconclusive [4,16,21]. ...
... According to Sinha et al. [31], GF mechanisms may demonstrate a progressive detrimental influence on environmental and social responsibility. Besides, GF and CSR are forms of corporate accountability to stakeholders (the public, shareholders, investors, customers, and other groups) that assist organizations in the achievement of financial and sustainable successes while avoiding legitimacy gaps or social and environmental conflicts [34]. ...
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This study aims to examine the impact of Corporate Social Responsibility (CSR) and Green Finance (GI) on the Environmental Performance (EP) of banking institutions in emerging markets like Bangladesh. The study also examines the role of green innovation (GI) as a mediator in the existent relationship between CSR, GF and EP. Data were obtained from 357 bankers of commercial banks in Bangladesh through the aid of structured questionnaires. A structural equation modeling approach was employed in the investigation of the obtained primary data, and results revealed that CSR had a significant positive impact on GI and EP, while GI strongly enhances EP. Besides, the findings revealed that GF had a significant positive influence on GI and EP. Furthermore, the research data indicated that GI fully mediates the link between CSR and EP, and GF and EP significantly. The study highlights the importance of CSR dimensions (social, economic and environmental), GF and GI in the attainment of EP, as well as the urgent need to incorporate sustainability into banking strategies to help achieve the country’s long-term economic development. As a result, major policy implications were further addressed.
... This is because environmental efficiency reveals the impact of human actions on the environment in terms of GDP, carbon emissions, capital, labour, and energy (Gozgor et al., 2020;Bahizire et al., 2022aBahizire et al., , 2022b; and it is thought to be a more inclusive substitute for environmental sustainability. The paper provides policymakers/governments with a more comprehensive understanding of the nexus between infrastructure, industrialization, and innovation, as well as environmental efficiency in countries, transitioning from agriculture, adding to the large body of literature offering policy recommendation to achieve the Sustainable Development Goals (Sinha et al., 2021). Third, to the best of the author's knowledge, this is the first study to look at the impact of infrastructure, industrialization, and innovation on environmental efficiency in 19 emerging African countries, which has never been done before. ...
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The study investigates the impact of infrastructure, industrialization, and innovation in improving environmental efficiency in Africa toward addressing the pressing needs for environmental sustainability in the region. The study employed both Data Envelopment Analysis (DEA) and Driscoll & Kraay methods to data collected for 19 African countries from 2000 to 2019. The results show a negative and significant relationship between infrastructure, industrialization, and innovation and the environmental efficiency in selected countries. Furthermore, our findings indicate that growth and energy demand have both positive and negative effects on these relationships. This paper has important policy implications, and we conclude that policies aiming at the development of both infrastructure and industry should consider the use of green technology to ensure sustainable development and environmental protection.
... Sustainable development goals, or "sustainable development objectives," are balanced advancements in the three pillars of sustainability: social, economic, and environmental. The study by (Sinha et al., 2021) has shed more light on 17 sustainability objectives in all, which may be classified into five categories: Social (people), economic (prosperity), environmental (planet), peace and institutions (peace), and development partnerships (Partnership). These SDGs take into account the environment, society and good governance. ...
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Businesses must do more than safeguard their financial interests to survive in today’s market. Sustainability, or long-term viability, requires consideration of society, the environment, and the global community. However, community-based businesses cannot use some of the current components of the sustainable business framework because they were created for large corporations. Both quantitative and qualitative approaches are used in this study. First, quantitative methods were used to develop a conceptual model for the organization’s current needs using the PPT, the TOE, and an Expectation Confirmation Theory (ECT). A simple random sampling method was used to gather the data, with a sample size of 14 villages. Using a five-point Likert scale, the researcher gathered data from 2,584 households and collected 627 valid responses. After that, descriptive statistics were used to describe the data (frequency distributions, percentages, averages, medians, and standard deviations), and PLS-SEM was used to investigate the interactions between variables and launch the conceptual model using partial least squares (PLS) path modeling. First and foremost, qualitative through Interactive Qualitative Analysis (IQA). There are two ways to create a congested SID and an uncluttered SID: the Affinity Relationship Table (ART), the mapping of the Inter-Relationship Diagram (IRD), and the system influence diagram. According to the study, sustainable community water supply businesses are the primary driver. The conceptual framework presented in this paper is consistent with the results of the combination of quantitative and qualitative methods and the current constraints placed on community water supply businesses to thrive.
... The main reason for this is that most developing nations, including the NICs, focus more on boosting their real growth while paying less attention to the environment. This further substantiates the fact that most emerging nations are drifting away from their SDGs Sinha et al., 2021). Therefore, there is a need for these nations to reorganise/re-strategise their policies regarding growth which is not sustainable. ...
This paper considers newly industrialised countries (NICs) as examples to evaluate the interrelationship between non-renewable energy (oil, coal and gas), renewable energy (hydro and geothermal) and ecological footprint, using panel data from 1990 to 2018. The findings from both the common correlated effects mean group (CCEMG) and augmented mean group (AMG) estimators reveal that economic growth intensifies ecological footprint. Furthermore, non-renewable energy (coal, oil and gas) amplifies the deterioration of the environment, while renewable energy (hydro and geothermal) does not enhance the environment. In addition, the causality provides credibility to the findings generated from the AMG and CCEMG long-run estimators. The results of this study are significant for policymakers in the NICs in terms of achieving the sustainable development goals (SDGs).
... in 2015 to US$ 255.9 billion in 2021 ( Figure 1). Top contributors for mobile payments in the area include, among others, Alipay and WeChat Pay in China, MobiCash in Bangladesh and Paytm in India (Li et al., 2018;Sinha et al., 2019;Liao and Yang, 2020). Countries in Asia and the Pacific dominated the global uptake of e-wallets and mobile POS payments. ...
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The restrictions that have been implemented due to the COVID-19 pandemic have highlighted the growing importance of digital financing. While traditional banking services have been limited by social distancing, reduced work hours, and lockdowns, digital financial services can deal effectively with those restriction measures while facilitating governments to channel relief and stimulus funds to micro, small and medium-sized enterprises (MSMEs). This paper analyzes, by using the bibliometric review approach along with the VOSviewer, a data visualization software, 629 Scopus journal articles relevant to the key components of digital financing for SMEs under the pandemic. Based on the review, it identifies the most crucial policy areas for digital financing. The paper presents policy implications on how digital financial services can support MSMEs in dealing with COVID's challenges. JEL classification codes: G21, G23, G28, G32.
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Based on the relationship between industrial agglomeration, green finance development, and carbon emissions, some relevant theoretical hypotheses are proposed, and this paper employs the combination of spatial Durbin model and panel threshold model to empirically test data from 30 provincial regions in China from 2006 to 2019. The results show that the agglomeration of high energy-consuming industries has an inverse U-curve relationship with carbon emission intensity, and the development of green finance will inhibit the growth of carbon emission intensity. There are significant spatial characteristics of high energy-consuming industrial agglomeration, green financial development, and carbon emissions. And the intensity of local carbon emissions will be influenced by the agglomeration of high energy-consuming industrial agglomeration and green financial development in local and neighboring areas. Moreover, green financial development plays a moderating role in the relationship between high energy-consuming industrial agglomeration and carbon emissions, and the role of high energy-consuming industrial agglomeration and green financial development on carbon emissions has a threshold effect due to the mismatch between the two developments. Under different levels of green financial development, the influence of high energy-consuming industrial agglomeration on carbon emissions varies widely, and green financial development helps to suppress the negative impact of high energy-consuming industrial agglomeration on carbon emissions. Accordingly, we argue that inter-regional joint prevention and control mechanism should be established for pollution control. And China should build more effective high energy-consuming industrial clusters to make them play an active role in reducing emissions. At the same time, China should accelerate the construction of green finance, strengthen the disclosure and transparency of green financial information, and establish a joint mechanism for the development of inter-regional green finance, so that it can contribute to regional industrial transformation and pollution control.
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This research explores the asymmetric green finance-environmental quality nexus in the top 10 nations that support green finance. Green bonds and ecological footprint are used as proxies for green finance and environmental quality, respectively. Past studies employed panel data approaches, yielding typical results regarding the relationship between green finance and the environment, even though many countries did not establish such a correlation on their own. This study, on the contrary, adopts a unique Quantile-on-Quantile technique, which allows researchers to independently investigate time-series dependence in each economy by providing global but country-specific information on the link between the variables. According to assessments, green financing improves environmental quality in 8 out of 10 selected economies at particular quantiles of data distribution. However, two countries yielded mixed results. Moreover, the results reveal that the asymmetry between our variables varies by nation, stressing the importance of authorities' paying close attention while adopting green finance and ecological sustainability policies. This study could help identify priorities and gaps that must be addressed to achieve the sustainable development goals (SDGs).
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Purpose The key objectives of this study were to investigate the interactions among the lean, green management practices and organizational sustainable performance measures and explore the possibility of simultaneous implementation of these concepts for improving the organizational sustainable performance. Design/methodology/approach Using the interpretive structural modeling (ISM) technique, the interactions among the lean, green practices and organizational sustainable performance measures were established. A focus group which consisted of purposively selected 15 experts was utilized in the primary data collection. Findings In Sri Lankan context, water and material consumption reduction, energy efficiency, water pollution and greenhouse gas reduction were identified as the dominant green practices, while pull production, lot size reduction, continuous improvement, preventive maintenance, employee involvement and cycle time reduction were the dominant lean practices. Inventory level, profitability, quality, cost, employee satisfaction, customer satisfaction, lead time, resources consumption (material, water, energy) and waste generation were determined as the dominant sustainable performance measures. The resulting ISM-based structural model which consisted of eight levels concluded that firstly lean practices influence the green practices and afterward green practices affect the sustainable performance measures. Research limitations/implications The aim of this study was to develop a hypothetical structural model to explain the interactions among the lean, green management practices and organizational sustainable performance measures. But this hypothetical model was not empirically tested in the current study. So further study is required to empirically test the proposed model. Practical implications Currently organizations who practice for sustainable performance engages, lean and green practices separately without understanding on which practices are stared when and how. So, through the findings of this study, organization who desire to improve the sustainable performance are recommended to begin the journey with lean practices and subsequently move in to green and handle both lean and green initiatives through one functional unit. Originality/value The existing literature does not possess a model for explaining the lean–green synergy and organizational sustainable performance and this study successfully fills this gap. Also the study proposes for the practitioners, when and how the lean and green practices should be initiated and implemented for rising the sustainable performance of an organization.
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Rising concern regarding traditional non-renewable energy consumption has led policymakers to explore the potential of economical renewable energy sources. In this regard, biomass energy has received considerable attention because previous studies have found mixed results regarding the effect of biomass energy on environmental quality. Together with modern technology, biomass energy may significantly influence environmental quality. This study investigates the impact of biomass energy consumption, education, and technological innovation on environmental quality by controlling for the role of economic growth and financial development in the function of environmental quality. Second-generation econometric methods were used to solve the issues of heterogeneity and cross-sectional dependence in the study variables. The Westerlund and Edgerton (2008) cointegration technique confirmed the existence of a long-run equilibrium among the variables in the presence of structural breaks. The panel quantile regression results indicate that biomass energy use and technological innovation reduce environmental quality. Similarly, economic growth increases carbon emissions in the environment. Education and financial development contribute to reduce carbon emissions.
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We examine the explanatory and forecasting power of economic growth, financial development, trade openness and FDI for CO2 emissions in major developed economies within the context of the debate on curbing CO2 emissions Post-Paris Agreement (COP21). Using data from G-6 countries from 1978 to 2014 and employing a set of empirical approaches, we find weak evidence of the Environmental Kuznets Curve, while economic growth, capital market expansion, and trade openness are found to be major drivers of carbon emissions. Carbon emissions are also weakly and negatively affected by stock market capitalization and FDI. Moreover, the forecasting performance is quite good, particularly by augmenting the model with energy consumption and oil prices. With respect to climate commitments, our empirical findings reveal important policy implications.
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This paper is a pioneering endeavour to investigate the determinants of environmental degradation in Australia through a comprehensive framework of EKC and STIRPAT. Specifically, the impacts of multiple factors of socio-economic development including economic growth, trade openness, industrialization, energy consumption on CO2 emissions are analysed. Furthermore, the influences of financial development through different dimensions (financial efficiency, access and depth) in two subsectors (financial markets and institutions) and other proxies of financial development are focused over the period 1980–2014. Empirical results show short as well as long-run differences in the association among the variables. Short-term bidirectional causality prevails between economic growth, energy consumption, industrialization, and stock market development with carbon dioxide (CO2) emissions. However, there is no significant evidence found on EKC. This is due to the long-run positive impact of financial development, energy consumption, and trade openness on CO2 emissions. Interestingly, the industrialization process is found to does not affect CO2 emissions. Empirical findings provide insight into why the quality of the Australian environment is truncated with frequent and widespread bushfires and suggest policymakers to have selective and strict environmental-friendly strategies to fulfil a sustainable development goal.
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Since the inception of Sustainable Development Goals (SDGs), the Asia Pacific countries are facing difficulties in attaining the SDG objectives, as maintaining the environmental quality has been a challenge for them. In this study, we have revisited the technology policies of these countries, and in doing so, we have tried to address the problem of environmental degradation, while addressing the issues of sustainable economic growth, clean and affordable energy, and quality education. In this pursuit, we have designed two indices for environmental degradation and technological advancement, and then analyzed the association between them following the Environmental Kuznets Curve (EKC) hypothesis. Following IPAT framework, and by using quantile approach, over a period of 1990-2017, we have found that the turnaround points of EKCs rise with the rise in quantiles, i.e. quantiles with low pollutions are having turnaround points within sample range, whereas quantiles with high pollutions are having turnaround points outside sample range. Using Rolling Window Heterogeneous Panel Causality test, unidirectional causality has been found running from technological advancement to environmental degradation. Following the results obtained from the analysis, we have tried to address the objectives of SDG 13, SDG 4, SDG 8, SDG 9, SDG 7, and SDG 10.
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The 4th industrial revolution and global decarbonisation are frequently referred to as two interrelated megatrends. Particularly, where the 4th industrial revolution is expected to fundamentally change the economy, society, and financial systems, it may also create opportunities for a zero-carbon future. Therefore, in the context of UK's legally binding commitment to achieve a net-zero emissions target by 2050, we analyse the role of economic growth, R&D expenditures, financial development, and energy consumption in causing carbon dioxide (CO2) emissions. Employing the bootstrapping bounds testing approach to examine short- and long-run relationships, our analysis is based on historical data from 1870 to 2017. The results suggest the existence of cointegration between CO2 emissions and its determinants. Financial development and energy consumption lead to environmental degradation, but R&D expenditures help to reduce CO2 emissions. The estimated environmental effects of economic growth support the EKC hypothesis. While a U-shaped relationship is found between financial development and CO2 emissions, the nexus between R&D expenditures and CO2 emissions is analogues to the EKC. In the context of the efforts to tackle climate change, our findings suggest policy prescriptions by using financial development and R&D expenditures as the key tools to meet the emissions target.
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Despite the ongoing research on energy innovation and economic growth, little is known on how degree of energy innovation impacts income inequality within a nation. To address this research gap, we have developed a bivariate model to analyze how distribution of energy innovation affects the income distribution in a certain country. Using the Fisher Ideal Index, we have calculated energy efficiency as an indicator of energy innovation. Quantile-on-Quantile regression has been applied to capture the impact on energy innovation across different income quantiles in Next 11 (N11) countries. Results show that energy innovation can have different outcomes, across the member countries of N11 group, namely a) equitable and positive impact, (b) negative impact, and (c) inequitable impact in terms of distribution of income. We have inferred important policy implications, which might lead to sustainable development strategies in N11 countries. This study is one of the first to establish the direct link between energy innovation and income inequality across different quantiles within a nation. Further, we successfully demonstrate the application of advanced quantile methods in inferring Sustainable Development Goal (SDG) focused policy implications.
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In the context of the 4th industrial revolution, artificial intelligence (AI) and environmental challenges, this study investigates the role of AI, robotics stocks and green bonds in portfolio diversification. Using daily data from 2017 to 2020, we employ tail dependence as copulas and the Generalized Forecast Error Variance Decomposition to examine the volatility connectedness. Our results suggest that, first, portfolios consisting of these assets exhibit heavy-tail dependence which implies that in the times of economic turbulence, there will be a high probability of large joint losses. Second, volatility transmission is higher in the short term, implying that short-term shocks can cause higher volatility in the assets, but in the long run, volatility transmission decreases. Third, Bitcoin and gold are vital assets for hedging, though the Bitcoin is also affected by its past volatility, a feature it shares with green bonds and NASDAQ AI. During economic downturns, gold may act as a safe haven, as its shock transmission to NASDAQ AI is just around 1.41%. Lastly, the total volatility transmission of all financial assets is considerably high, suggesting that the portfolio has an inherent self-transmitting risk which requires careful diversification. The NASDAQ AI and general equity indexes are not good hedging instruments for each other.