Content uploaded by Abdelhak Lefilef
Author content
All content in this area was uploaded by Abdelhak Lefilef on Jul 08, 2024
Content may be subject to copyright.
Namaa for Economic and Trade Journal
ــــــــــــ
Vol 08, N° 01, June (2024) / (65-76)
65
Green Finance in Germany: Examining the Role of Environmental Tax Revenue,
Renewable Energy, and Exports (2010-2022)
ﺎﻴﻧﺎﳌﺃ ﰲ ﺮﻀﺧﻷﺍ ﻞﻳﻮﻤﺘﻟﺍ :ﺺﺤﻓ ﺔﻴﺌﻴﺒﻟﺍ ﺐﺋﺍﺮﻀﻟﺍ ﺕﺍﺪﺋﺎﻋ ﺭﻭﺩ ، ﺔﻗﺎﻄﻟﺍﺓﺩﺪﺠﺘﳌﺍ، ﺕﺍﺭﺩﺎﺼﻟﺍﻭ)2010-2022(
Abdelhak Lefilef
1,
*
1
Abdelhafid Boussouf University Center, (Algeria), abdelhak.lefilef@centre-univ-mila.dz
Received: 21/02/2024;
Accepted : 28/06/2024;
Published: 30/06/2024;
*Corresponding author,
Abstract:
This study examines the interplay between environmental tax revenue, renewable energy
consumption, and sustainable exports in shaping Germany's green bond market from 2010 to 2022.
Employing Quantile Regression, the research analyzes causal relationships and differential
impacts across varying quantiles of the green bond market. Findings reveal that renewable energy
consumption significantly boosts green bond issuance, while environmental tax revenues present a
diminishing negative effect, suggesting targeted alleviations could enhance synergies.
Furthermore, exports demonstrate decreasing adverse impacts, highlighting resilience. This
research, the first German-specific econometric study in this domain, provides novel insights into
the causal mechanisms of green finance flows. It underscores the complex interdependencies within
the green finance ecosystem and offers valuable perspectives for policy-making in Germany's
transition towards environmental sustainability. The study's methodology and insights contribute
substantially to the academic discourse on green finance, guiding future research and policy
strategies in this field.
Keywords: Green Finance, Renewable Energy, Environmental Tax Revenue, Sustainable Exports,
Germany, Quantile Regression
.
Jel Classification Codes :
Q2
;
Q5
;
C21
Green Finance in Germany: Examining the Role of Environmental Tax Revenue, Renewable
2022)-Energy, and Exports (2010
66
Introduction:
Green finance and sustainability have been growing priorities in Germany over the past decade
(Pegels&Lütkenhorst, 2014). The country has made significant progress through renewable energy
expansion, energy efficiency policies, reduced emissions, and environmentally-focused financial
services (Xue et al., 2023). Germany allocated 13.2% of its stimulus package toward green
investments, the second highest globally, displaying its commitment to sustainability transitions
(Geels, 2013). Despite burgeoning green finance literature, few studies provide an integrated
analysis of major green finance flows in Germany, specifically from 2010-2022 (Taghizadeh-
Hesary& Yoshino, 2020; Sun et al., 2023). This represents a gap in understanding the macro-level
dynamics driving Germany's sustainability transitions across environmental tax revenues,
renewable energy uptake, green innovations, and sustainable exports. Bridging this research gap is
vital to inform policy and support growing green finance ecosystems in Germany amidst
intensifying ecological pressures (Chen et al., 2023; Chireshe, 2020).
This paper addresses the identified research gap by investigating linkages and causal relationships
between Germany's environmental tax revenue collection, renewable power capacity expansion,
development of green innovations, and exports of sustainable goods and services since 2010. The
research questions guiding this study are: 1) How are environmental tax revenues, renewable energy
consumption, and exports of sustainable goods causally interlinked in Germany? And 2) What are
the differential impacts of these green finance flows across varying quantiles of Germany's green
bond market from 2010-2022? The motivations for this research include leveraging rigorously
developed causal and predictive models to inform policymaking regarding green finance
prioritization in Germany. This will be the first German-specific econometric study analyzing
macro-level green finance flows from 2010-2022 using Quantile Regression, providing a novel
perspective on causal mechanisms and nuanced effects across the green bond market distribution.
Key findings indicate that renewable energy consumption substantially positively affects green
bond issuance across distribution quantiles. Environmental tax revenues exhibit diminishing
negative drag, suggesting targeted alleviations could promote synergies. Exports showcase
decreasing adverse impacts, pointing to resilience. The paper will encompass the research
background, questions, data, methodology, results, discussion, and conclusion sections.
I-Literature Review:
The landscape of green finance is rapidly evolving, with environmental tax revenues playing an
increasingly significant role. This section embarks on a comprehensive review of the literature,
bridging the gap between theory and practice in the realm of green finance, and highlighting its
intersection with environmental taxation. While green finance refers to the financial initiatives
aimed at supporting environmentally sustainable projects, environmental tax revenue is generated
from taxes levied on activities detrimental to the environment. This literature review systematically
encapsulates the theoretical underpinnings and empirical findings that outline the dynamics of these
two crucial aspects and their interplay in shaping sustainable economic policies.
I-1- Literature Review on Green Finance and Environmental Tax Revenue:
The symbiotic relationship between green finance and environmental tax revenue remains
predominant in contemporary economic and environmental policy debates. Green finance
encompasses a suite of financial instruments and services tailored to fostering environmentally
sustainable initiatives. Conversely, environmental tax revenue stems from impositions on
environmentally deleterious activities. Böhringer et al. (2019) elucidate the concept of 'double
Abdelhak Lefilef
67
dividends' arising from eco-centric tax reforms, positing the potential for recycling these revenues
into the economy via offsets in other fiscal areas, including income tax or VAT.
Labandeira et al. (2019) underscore a lacuna in scholarship concerning third-generation green fiscal
reforms, prompting a call for expanded inquiry beyond the extant foundational expositions on
double dividends. In exploring the nexus between environmental tax evolution and public fiscal
dynamics, Oueslati (2015) propounds an alignment with endogenous growth models, spotlighting
resultant macroeconomic dividends. West & Williams (2004) critically examine the cross-price
elasticity interplay between leisure and environmentally harmful commodities, delineating key
considerations for efficacious environmental tax design. Gago & Labandeira (2014) proffer a
panoramic perspective on energy taxation and overarching green fiscal reformations, distilling
experiences from diverse socio-economic milieus. These cumulative examinations emphasize the
imperatives of an integrated modus operandi aligning economic proliferation with sustainable
ecological guardianship.
I-2- Literature Review on Green Finance and Renewable Energy:
Green finance and renewable energy are cornerstone entities underpinning the global trajectory
toward sustainable development and climate resilience. Their confluence engenders tangible
reverberations on sustainable evolution and environmental preservation. Chen et al. (2023) unravel
a reciprocal loop wherein green finance catalyzes renewable energy innovations, augmenting green
finance demand. Such interactions are seminal for reducing carbon footprints and fostering green
energy breakthroughs.
Utilizing advanced econometric methodologies in the Nigerian milieu, Asemota &Olokoyo (2022)
delineate the intricate interconnections between renewable energy capital, sustainable energy
assimilation, and industrial development. Khan et al. (2022) empirically validate the indissoluble
tethering of green finance evolution to environmental conservation imperatives. Chireshe (2020)
identifies a conspicuous scholarly void concerning the correlation dynamics between financial
frameworks and renewable energy trajectories in the Sub-Saharan African landscape. Both Peng &
Zheng (2021) and Hui et al. (2021) leverage China's contextual backdrop to underscore green
finance's cardinality in driving energy efficacies and steering consumption patterns. Mngumi et al.
(2022) spotlight green finance's paramount role in climate resilience through heightened renewable
infrastructure investments. Meanwhile, Lam & Law (2016) advocate for crowdfunding as an avant-
garde financial channel for propelling green projects. Collectively, the burgeoning scholarship
accentuates the symbiotic alliance between green finance and renewable energy, which is pivotal
for sculpting a sustainable and ecologically robust future.
I-3- Literature Review on Green Finance and Export:
The ascendancy of green finance, vis-à-vis sustainable development, manifests palpable
interjections in multiple sectors, export being paramount among them. Zhang & Liu (2023)
delineate how green finance engenders enhanced export sophistication, with its efficacy modulated
by geo-societal contexts and institutional infrastructures. Concurrently, Wang et al. (2023) elucidate
green finance's role in shaping China's agricultural trade dynamics. Liu et al. (2023) amplify this
discourse by interrogating the impact of green finance on export technology intricacy within China,
emphasizing green finance as a fulcrum for boosting export technology sophistication. Lin et al.
(2022) overlay this narrative with insights from China's power sector, asserting green finance's
transformative influence.
Green Finance in Germany: Examining the Role of Environmental Tax Revenue, Renewable
2022)-Energy, and Exports (2010
68
Globally, Mamola &Herdiansyah (2023) pivot their attention to Indonesia's palm oil export
ecosystem, delineating green finance's potency in reinforcing indigenous value networks.
Bıçakcıoğlu-Peynirci &Tanyeri (2020) unravel the antecedents and dividends of ecologically
inclined export stratagems in nascent economies. Lundquist (2022) navigates the terrains of finance
during ecological transitions, focusing on the pivotal role of export credit agencies. These studies
illustrate green finance as an instrumental vector in rejuvenating export sophistication, steering
sustainable agricultural commerce, enhancing export technological granularity, and reinforcing
export-focused industries' resilience.
I-4- The Research Gaps and The Contribution of the Study:
this study addresses critical research gaps in the field of green finance in Germany, particularly
from 2010 to 2022, where previous research has overlooked the integrated analysis of key factors
such as environmental tax revenues, renewable energy, and sustainable exports. It delves into the
less explored macro-level dynamics of Germany's sustainability transition, including green
innovations and renewable energy uptake. By conducting a comprehensive econometric analysis,
the study examines the causal relationships and impacts of these factors on the green bond market.
Utilizing Quantile Regression, a novel approach in this context, it uncovers the varied effects across
different quantiles of the green bond market. This unique perspective is pioneering in the realm of
German green finance and contributes significantly to understanding the complex mechanisms
driving green finance flows. The findings of this research are not only academically relevant but
also provide valuable insights for policymaking, supporting the growth and development of a robust
green finance ecosystem in Germany
II- Data and Methodology:
The objective of this study is to evaluate the impact of environmental tax revenue (ETR),
renewable energy consumption (REC), and exports (Exp) on Green Bond Issuance (GBI) in
Germany for the period from 2010 to 2022. Equation (1) outlines the model employed in our
empirical analysis:
ܩܤܫݐ = ߚ0 + ߚ1ܧܴܶݐ + ߚ2ܴܧܥݐ + ߚ3ܧݔݐ + ߝݐሺ1ሻ
In this equation, β
0
represents the intercept, and ε
t
denotes the white noise term. βk
coefficients indicate the sensitivity of GBI to the explanatory variables in the model. The variables
used in this study are described in detail in Table 1 .
Table n°1:Data Summary
Variables Acronyms Definitions Source
Green Bounds Issuance GBI Green Bouns Issuance in Billion
US$
International
Monetary Fund
(IMF)
Renewable Energy
Consumption REC
Renewable energy consumption
(% of total final energy
consumption)
World Bank
Export EXP Exports of goods and services
(% of GDP) World Bank
Source: By the Researcher
This study utilizes Quantile Regression (QR) to analyze the impact of environmental tax revenue,
renewable energy consumption, and exports on Green Bond Issuance (GBI) across different
quantiles. QR allows examination of the conditional quantiles of the response variable and is
Abdelhak Lefilef
69
especially useful for financial data exhibiting non-normality and skewness (Koenker& Bassett,
1978). Buchinsky (1998) demonstrated QR's applicability for exploring the effects of environmental
regulations on renewable energy. QR addresses cross-sectional correlations and heterogeneities, as
shown in Abakah et al. (2022) and Bashir et al. (2021), using QR to evaluate the influence of
environmental factors on energy use.
Additionally, Fatica & Panzica (2021) and Wang et al. (2019) exhibit QR's capacity to discern
nuanced impacts on GBI like emission cuts and risk premiums. Their findings highlight QR's ability
to capture varied effects across the distribution of the dependent variable. As Hao and Naiman
(2007) demonstrated, QR is robust against outliers and can handle non-normal residuals, making it
well-suited for financial data. QR has examined asymmetric variable relationships across various
economic and financial contexts (Hao & Naiman, 2007).
This QR analysis aims to understand German green bond market dynamics fully. By assessing the
impacts of environmental tax revenue, renewable energy, and exports across GBI quantiles, we
uncover complex relationships to inform policymakers and market participants.
III-Econometric Study Findings
This study examines the effect of environmental tax revenue, renewable energy consumption,
and exports on Green Bond Issuance in Germany between 2010 and 2022. Initial analyses utilize
the Quantile Regression (QR) methodology, including unit root examinations and cointegration
verification. These preliminary assessments validate the QR model's suitability and our econometric
approach's underlying assumptions. This rigorous methodological structure facilitates precisely
capturing the dynamics of green bond issuance across diverse quantiles in the German market. The
initial QR tests confirm appropriate model specification and establish the analytical foundations
prior to assessing the impacts of the key independent variables on the green bond distribution. This
enables a nuanced understanding of how environmental tax income, renewable energy use, and
exports distinctly influence various quantiles of the green bond market.
III-1- Quantile Regression:
III-1-1- Model Quality
Table 2 of the study presents the model quality, quantified through Pseudo R-squared and
Mean Absolute Error (MAE), across different quantiles (q=0.25, q=0.5, q=0.75) for the analysis of
Green Bond Issuance. The Pseudo R-squared values, 0.734 at the 25th Percentile and 0.820 at the
median indicate a strong fit of the model, especially at the median. The Mean Absolute Error, which
measures the average error magnitude in predictions, shows the lowest error at the median (1.4545)
compared to the 25th (2.5910) and 75th percentiles (1.5770), suggesting the model's predictions are
most accurate around the median. This combination of high Pseudo R-squared and low MAE at the
median emphasizes the model's robust predictive capability for Green Bond Issuance in the study.
Table n°2: Model Quality
q=0,25 q=0,5 q=0,75
Pseudo R-squared 0,734 0,820 .
Mean Absolute Error (MAE) 2,5910 1,4545 1,5770
Source: By The Researcher based on SPSS Outcomes
Green Finance in Germany: Examining the Role of Environmental Tax Revenue, Renewable
2022)-Energy, and Exports (2010
70
III-1-2- Parameter Estimation Based on Different Quantiles
a,b
Table 3 in our study presents the parameter estimations for Green Bond Issuance across different
quantiles, demonstrating how the impacts of renewable energy consumption, environmental tax
revenue, and exports on Green Bond Issuance vary across the distribution. At the 25th Percentile,
the model indicates a substantial constant term (272,640), suggesting a high base level of bond
issuance. Renewable energy consumption shows an increasing positive impact across quantiles,
while environmental tax revenue and exports exhibit a decreasing negative impact. These trends
indicate that renewable energy consumption becomes more influential, and the negative impacts of
environmental tax and exports lessen as we move from the lower to the higher quantiles of Green
Bond Issuance.
Table n°3: Parameter Estimation Based on Different Quantiles
a,b
Parameter q=0,25 q=0,5 q=0,75
(Constant) 272,640 83,429 43,879
Renewable energy consumption (% of
total final energy consumption) 3,820 6,377 6,897
Environmental tax revenue (% of GDP) -27,329 -5,817 -2,910
Exports of goods and services (% of
GDP) -5,902 -3,454 -2,874
a. Dependent Variable: Green bond issuance Billion US b. Model: (Constant), Renewable energy consumption (% of
total final energy consumption), Environmental tax revenue (% of GDP), Exports of goods and services (% of GDP)
III-1-3- Quantile Regression Results at the 25
th
Percentile
Table 4, titled "Quantile Regression Results at the 25th Percentile," presents the coefficients and
statistical significance of factors influencing Green Bond Issuance at the 25th Percentile. The
constant term is highly significant (p=0.001) with a coefficient of 272.640, indicating a substantial
baseline level of issuance. Renewable energy consumption shows a significant positive impact
(p=0.003) with a coefficient of 3.820, suggesting its increasing importance at this quantile. In
contrast, environmental tax revenue has a significant negative effect (p=0.022) with a coefficient of
-27.329, highlighting its diminishing impact on green bond issuance at the lower end of the
distribution.
Table n°4: Quantile Regression Results at the 25
th
Percentile
Parameter Coefficient Standard
Error t df Sig.
(Constante) 272,640 35,1928 7,747 4 ,001
Renewable energy
consumption (% of total
final energy
consumption)
3,820 ,6056 6,307 4 ,003
Environmental tax
revenue (% of GDP) -27,329 7,5641 -3,613 4 ,022
Exports of goods and
services (% of GDP) -5,902 ,3353 -17,601 4 ,000
Source: By The Researcher based on SPSS Outcomes
Abdelhak Lefilef
71
III-1-4- Quantile Regression Results at the 50
th
Percentile
Table 5, "Quantile Regression Results at the 50th Percentile," elucidates the influence of various
parameters on Green Bond Issuance at the median level. The constant term (83.429) is not
statistically significant (p=0.725), suggesting a lesser baseline effect at this Percentile. Renewable
energy consumption, while more impactful with a coefficient of 6.377, is not statistically significant
(p=0.169). Similarly, environmental tax revenue and exports of goods and services show non-
significant negative impacts with coefficients of -5.817 and -3.454, respectively (p=0.909 and
p=0.177). These results indicate a nuanced relationship between the examined variables and Green
Bond Issuance at the median of the distribution.
Table n°5: Quantile Regression Results at the 50
th
Percentile
Paramètre Coefficient Standard
Error t df Sig.
(Constante) 83,429 221,2600 ,377 4 ,725
Renewable energy
consumption (% of total
final energy
consumption)
6,377 3,8074 1,675 4 ,169
Environmental tax
revenue (% of GDP) -5,817 47,5561 -,122 4 ,909
Exports of goods and
services (% of GDP) -3,454 2,1083 -1,638 4 ,177
Source: By The Researcher based on SPSS Outcomes
III-1-5- Quantile Regression Results at the 75
th
Percentile
Table 6, titled "Quantile Regression Results at the 75th Percentile," illustrates the impact of various
parameters on Green Bond Issuance at the higher end of the distribution. The constant term
(43.879) lacks statistical significance (p=0.663), indicating a moderate baseline level of issuance at
this Percentile. Renewable energy consumption demonstrates a significant positive effect (p=0.013)
with
a coefficient of 6.897, underlining its increasing relevance at the upper quartile. Conversely, the
impact of environmental tax revenue on green bond issuance is negligible and statistically
insignificant, with a coefficient of -2.910 (p=0.892), denoting a minimal influence at this level.
Green Finance in Germany: Examining the Role of Environmental Tax Revenue, Renewable
Table n°6:
Quantile Regression Results at the 75
Paramètre
(Constante)
Renewable energy consumption (% of
total final energy consumption)
Environmental tax revenue (% of GDP)
Exports of
goods and services (% of
GDP)
Source: By The Researcher based on SPSS Outcomes
III-1-6-
Parameter Estimates of Quantile Regression Analysis
In Figure 1, we present the parameter estimates obtained from a Quantile Regression
scrutinize the determinants of Green Bond Issuance (GBI) in Germany from 2010
charts illustrate the estimated coefficients for the constant term, renewable energy consumption
(REC), environmental tax revenue (ETR), and expo
denoted by quantiles ranging from 0.3 to 0.7. The constant term reflects a high baseline level of GBI
at the lower quantiles, tapering as we move to higher quantiles, suggesting a non
issuance ac
ross the market. REC showcases a strengthening positive influence on GBI across the
quantiles, highlighting it is escalating significance in green bond markets. In contrast, ETR and Exp
are charted with diminishing negative impacts, illustrating a lessenin
quantile spectrum. The blue-
shaded areas represent the confidence intervals for the QR estimates,
providing a visual gauge of estimate precision. At the same time, the comparative red dashed lines
illustrate the bounds for an
ordinary linear regression, underscoring the enhanced explanatory power
of QR in capturing the effects of REC, ETR, and Exp across the GBI distribution. This nuanced
depiction of the QR methodology affirms its robustness in financial data analysis. It off
insights into the multifaceted drivers of green bond issuance, enabling policymakers and investors to
fine-
tune strategies for leveraging green finance in promoting environmental sustainability.
Figure n°1:
Parameter Estimates of Quantile
Green Finance in Germany: Examining the Role of Environmental Tax Revenue, Renewable
2022)-Energy, and Exports (2010
72
Quantile Regression Results at the 75
th
Percentile
Coefficient Standard Error
43,879 93,4168
Renewable energy consumption (% of
total final energy consumption)
6,897 1,6075
Environmental tax revenue (% of GDP)
-2,910 20,0784
goods and services (% of
-2,874 ,8901
Source: By The Researcher based on SPSS Outcomes
Parameter Estimates of Quantile Regression Analysis
In Figure 1, we present the parameter estimates obtained from a Quantile Regression
scrutinize the determinants of Green Bond Issuance (GBI) in Germany from 2010
charts illustrate the estimated coefficients for the constant term, renewable energy consumption
(REC), environmental tax revenue (ETR), and expo
rts (Exp) across varying market conditions,
denoted by quantiles ranging from 0.3 to 0.7. The constant term reflects a high baseline level of GBI
at the lower quantiles, tapering as we move to higher quantiles, suggesting a non
ross the market. REC showcases a strengthening positive influence on GBI across the
quantiles, highlighting it is escalating significance in green bond markets. In contrast, ETR and Exp
are charted with diminishing negative impacts, illustrating a lessenin
g drag on GBI as we ascend the
shaded areas represent the confidence intervals for the QR estimates,
providing a visual gauge of estimate precision. At the same time, the comparative red dashed lines
ordinary linear regression, underscoring the enhanced explanatory power
of QR in capturing the effects of REC, ETR, and Exp across the GBI distribution. This nuanced
depiction of the QR methodology affirms its robustness in financial data analysis. It off
insights into the multifaceted drivers of green bond issuance, enabling policymakers and investors to
tune strategies for leveraging green finance in promoting environmental sustainability.
Parameter Estimates of Quantile
Regression Analysis
Green Finance in Germany: Examining the Role of Environmental Tax Revenue, Renewable
Percentile
t df Sig.
,470 4 ,663
4,291 4 ,013
-,145 4 ,892
-3,229 4 ,032
In Figure 1, we present the parameter estimates obtained from a Quantile Regression
(QR) analysis to
scrutinize the determinants of Green Bond Issuance (GBI) in Germany from 2010
-2022. The radar
charts illustrate the estimated coefficients for the constant term, renewable energy consumption
rts (Exp) across varying market conditions,
denoted by quantiles ranging from 0.3 to 0.7. The constant term reflects a high baseline level of GBI
at the lower quantiles, tapering as we move to higher quantiles, suggesting a non
-uniform baseline
ross the market. REC showcases a strengthening positive influence on GBI across the
quantiles, highlighting it is escalating significance in green bond markets. In contrast, ETR and Exp
g drag on GBI as we ascend the
shaded areas represent the confidence intervals for the QR estimates,
providing a visual gauge of estimate precision. At the same time, the comparative red dashed lines
ordinary linear regression, underscoring the enhanced explanatory power
of QR in capturing the effects of REC, ETR, and Exp across the GBI distribution. This nuanced
depiction of the QR methodology affirms its robustness in financial data analysis. It off
ers critical
insights into the multifaceted drivers of green bond issuance, enabling policymakers and investors to
tune strategies for leveraging green finance in promoting environmental sustainability.
Regression Analysis
Abdelhak Lefilef
73
IV- Discussion:
The findings of our Quantile Regression (QR) analysis elucidate nuanced dynamics between
renewable energy consumption (REC), environmental tax revenue (ETR), exports (Exp), and Green
Bond Issuance (GBI) in Germany from 2010-2022. Aligning with Chen et al. (2023), we discern an
escalating positive impact of REC on GBI across distribution quantiles, affirming green finance's
potency in catalyzing renewable energy uptake. This reciprocal interplay likely stems from REC's
heightening policy prioritization, which swells demand for green bonds financing renewable
ventures.
However, our results diverge from Chireshe (2020) and Asemota &Olokoyo (2022) by revealing a
negative ETR coefficient, potentially attributable to higher energy taxes dampening GBI incentives
in the German context. We concur with Lam & Law (2016) and Mngumi et al. (2022) regarding
crowdfunding and infrastructure financing as complementary policy instruments. The diminishing
ETR effect across quantiles suggests targeted alleviations at the lower end could strengthen green
finance-environmental tax alliances.
The decreasing drag of Exports on GBI contradicts Zhang & Liu (2023), instead aligning with
Lundquist (2022) in showcasing contractions in export financing amidst crises. Economic turmoil
potentially curtails export-linked green bond demand. However, the tapering negative impact
indicates the German export engine's resilience. Policy fine-tuning could leverage export-oriented
green bonds for ecological transitions.
These findings emphasize green finance's complexity, with relationships contingent on geographical
and temporal realities. While corroborating green finance's overarching potential, policy
formulations must account for context-specific nuances highlighted in our quantile regression
analysis.
V- Conclusion:
This study offers seminal perspectives on the German green bond market by employing Quantile
Regression (QR) to evaluate the impacts of renewable energy consumption (REC), environmental
tax revenue (ETR), and exports (Exp) on Green Bond Issuance (GBI) from 2010-2022.
The QR analysis enriches comprehension of the nexus between green finance, environmental
sustainability, and economic growth in Germany. Our findings underscore escalating GBI
sensitivity to REC across distribution quantiles, cementing green finance's role in spurring
renewable transformation. Meanwhile, diminishing negative coefficients for ETR and Exports
suggest that targeted reforms could amplify synergies.
These results advance academic discourse on green finance interlinkages while equipping
policymakers to craft nuanced interventions harnessing Germany's sustainable comparative
advantages. Our methodology and German case study offer blueprints for scholarship exploring
complex green finance ecosystems. Further research could entail comparing cross-country QR
estimates or examining sectoral heterogeneities.
With sustainability imperatives intensifying, insights from our QR approach will assist stakeholders
in leveraging green finance's fullest potential while circumnavigating associated complexities. This
study lays the foundation for more robust modeling of intricate green finance dynamics.
Green Finance in Germany: Examining the Role of Environmental Tax Revenue, Renewable
2022)-Energy, and Exports (2010
74
References:
Abakah, E., Tiwari, A., Sharma, A., &Mwamtambulo, D. (2022). Extreme connectedness between green
bonds, government bonds, corporate bonds and other asset classes: insights for portfolio investors. Journal of
Risk and Financial Management, 15(10), 477. https://doi.org/10.3390/jrfm15100477
Asemota, F. and Olokoyo, F. (2022). Renewable energy financing and sustainable industrial development in
nigeria. International Journal of Energy Economics and Policy, 12(4), 563-567.
https://doi.org/10.32479/ijeep.13077
Bashir, M., Ma, B., Bashir, M., Rădulescu, M., & Shahzad, U. (2021). Investigating the role of
environmental taxes and regulations for renewable energy consumption: evidence from developed
economies. Economic Research-EkonomskaIstraživanja, 35(1), 1262-1284.
https://doi.org/10.1080/1331677x.2021.1962383
Bıçakcıoğlu-Peynirci, N. and Tanyeri, M. (2020). Stakeholder and resource‐based antecedents and
performance outcomes of green export business strategy: insights from an emerging economy. International
Journal of Emerging Markets, 17(1), 1-46. https://doi.org/10.1108/ijoem-03-2020-0245
Böhringer, C., Garcia-Muros, X., & González-Eguino, M. (2019). Greener and fairer: a progressive
environmental tax reform for spain. Economics of Energy &Amp; Environmental Policy, 8(2).
https://doi.org/10.5547/2160-5890.8.2.cboh
Buchinsky, M. (1998). Recent advances in quantile regression models: a practical guideline for empirical
research. Journal of human resources, 88-126.
Chen, J., Li, L., Yang, D., & Wang, Z. (2023). The dynamic impact of green finance and renewable energy on
sustainable development in china. Frontiers in Environmental Science, 10.
https://doi.org/10.3389/fenvs.2022.1097181
Chireshe, J. (2020). Finance and renewable energy development nexus: evidence from sub-saharanafrica.
International Journal of Energy Economics and Policy, 11(1), 318-325. https://doi.org/10.32479/ijeep.10417
Fatica, S. and Panzica, R. (2021). Green bonds as a tool against climate change?. Business Strategy and the
Environment, 30(5), 2688-2701. https://doi.org/10.1002/bse.2771
Gago, A. and Labandeira, X. (2014). A panorama on energy taxes amd green tax reforms. Revista Hacienda
PúblicaEspañola, 208(1), 145-190. https://doi.org/10.7866/hpe-rpe.14.1.5
Geels, F. (2013). The impact of the financial–economic crisis on sustainability transitions: financial
investment, governance and public discourse. Environmental Innovation and Societal Transitions, 6, 67-95.
https://doi.org/10.1016/j.eist.2012.11.004
Goulder, L. (1994). Environmental taxation and the "double dividend:" a reader's
guide..https://doi.org/10.3386/w4896
Hao, L., & Naiman, D. Q. (2007). Quantile regression (No. 149). Sage.
Hui, W., Jiang, L., Duan, H., Wang, Y., & Jiang, Y. (2021). Can green finance optimize energy structure: a
spatial econometric analysis based on china's traditional and renewable energy
consumption..https://doi.org/10.21203/rs.3.rs-1087594/v1
Kemfert, C., Schäfer, D., & Semmler, W. (2020). Great green transition and finance. Intereconomics, 55(3),
181-186. https://doi.org/10.1007/s10272-020-0896-y
Khan, S., Akbar, A., Nasim, I., Hedvičáková, M., & Bashir, F. (2022). Green finance development and
environmental sustainability: a panel data analysis. Frontiers in Environmental Science, 10.
https://doi.org/10.3389/fenvs.2022.1039705
Abdelhak Lefilef
75
Koenker, R., &Bassett Jr, G. (1978). Regression quantiles. Econometrica: journal of the Econometric
Society, 33-50.
Labandeira, X., Labeaga, J. M., &López-Otero, X. (2019). New green tax reforms: ex-ante assessments for
spain. Sustainability, 11(20), 5640. https://doi.org/10.3390/su11205640
Lam, P. and Law, A. (2016). Crowdfunding for renewable and sustainable energy projects: an exploratory
case study approach. Renewable and Sustainable Energy Reviews, 60, 11-20.
https://doi.org/10.1016/j.rser.2016.01.046
Lin, Z., Liao, X., & Jia, H. (2022). Could green finance facilitate low-carbon transformation of power
generation? some evidence from china. International Journal of Climate Change Strategies and Management,
15(2), 141-158. https://doi.org/10.1108/ijccsm-03-2022-0039
Lin, Z., Liao, X., & Jia, H. (2022). Could green finance facilitate low-carbon transformation of power
generation? some evidence from china. International Journal of Climate Change Strategies and Management,
15(2), 141-158. https://doi.org/10.1108/ijccsm-03-2022-0039
Liu, Z., Zheng, S., Zhang, X., & Long, M. (2023). The impact of green finance on export technology
complexity: evidence from china. Sustainability, 15(3), 2625. https://doi.org/10.3390/su15032625
Lundquist, P. (2022). Export credit agencies delivering finance for the green transition in times of crisis.
Global Policy, 13(4), 530-533. https://doi.org/10.1111/1758-5899.13128
Mamola, R. and Herdiansyah, H. (2023). Green resolution and resilience of palm oil exports in indonesia:
strengthening local value chains. Potravinarstvo Slovak Journal of Food Sciences, 17, 275-288.
https://doi.org/10.5219/1865
Mngumi, F., Sun, S., Shair, F., & Waqas, M. (2022). Does green finance mitigate the effects of climate
variability: role of renewable energy investment and infrastructure. Environmental Science and Pollution
Research, 29(39), 59287-59299. https://doi.org/10.1007/s11356-022-19839-y
Oueslati, W. (2015). Growth and welfare effects of environmental tax reform and public spending policy.
Economic Modelling, 45, 1-13. https://doi.org/10.1016/j.econmod.2014.10.040
Pegels, A. and Lütkenhorst, W. (2014). Is germany׳s energy transition a case of successful green industrial
policy? contrasting wind and solar pv. Energy Policy, 74, 522-534.
https://doi.org/10.1016/j.enpol.2014.06.031
Peng, J. and Zheng, Y. (2021). Does environmental policy promote energy efficiency? evidence from china
in the context of developing green finance. Frontiers in Environmental Science, 9.
https://doi.org/10.3389/fenvs.2021.733349
Ponitka, J. and Boettner, S. (2020). Challenges of future energy landscapes in germany — a nature
conservation perspective. Energy Sustainability and Society, 10(1). https://doi.org/10.1186/s13705-020-
00250-9
Taghizadeh-Hesary, F., Yoshino, N., &Phoumin, H. (2021). Analyzing the characteristics of green bond
markets to facilitate green finance in the post-covid-19 world. Sustainability, 13(10), 5719.
https://doi.org/10.3390/su13105719
Tingbani, I., Salia, S., Hussain, J., & Alhassan, Y. (2023). Environmental tax, sme financing constraint, and
innovation: evidence from oecd countries. Ieee Transactions on Engineering Management, 70(3), 1006-1025.
https://doi.org/10.1109/tem.2021.3110812
Wang, Q., Yaning, Z., Luo, L., & Ji, J. (2019). Research on the factors affecting the risk premium of china's
green bond issuance. Sustainability, 11(22), 6394. https://doi.org/10.3390/su11226394
Wang, Y., Li, H., Zhou, J., Zang, D., & Shen, Q. (2023). The impact of green finance on china's agricultural
trade. Sustainability, 15(9), 7688. https://doi.org/10.3390/su15097688
Green Finance in Germany: Examining the Role of Environmental Tax Revenue, Renewable
2022)-Energy, and Exports (2010
76
Wei, L., Zhang, Y., & Ji-nan, J. (2023). How do environmental taxes affect green process innovation?
evidence from the chinese manufacturing industry. Journal of Manufacturing Technology Management,
34(5), 669-693. https://doi.org/10.1108/jmtm-09-2022-0345
West, S. E. and Williams, R. C. (2004). Estimates from a consumer demand system: implications for the
incidence of environmental taxes. Journal of Environmental Economics and Management, 47(3), 535-558.
https://doi.org/10.1016/j.jeem.2003.11.004
Xiang, L. and Xie, Y. (2020). Research on the development path of chinese commercial banks based on
green finance..https://doi.org/10.2991/aebmr.k.200306.036
Xue, Y., Ma, T., Zhuang, T., & Ma, Y. (2023). The development of green finance under the goal of carbon
neutrality: a review from china's perspective. Frontiers in Earth Science, 11.
https://doi.org/10.3389/feart.2023.1123020
Zhang, K. and Liu, D. (2023). Does green finance promote export sophistication? an analysis of the
moderating effect based on green taxes. Sustainability, 15(10), 8303. https://doi.org/10.3390/su15108303
Zheng, G., Siddik, A., Masukujjaman, M., & Fatema, N. (2021). Factors affecting the sustainability
performance of financial institutions in bangladesh: the role of green finance. Sustainability, 13(18), 10165.
https://doi.org/10.3390/su131810165
