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Clean Technologies and Environmental Policy
https://doi.org/10.1007/s10098-024-02831-0
ORIGINAL PAPER
Analyzing theeffects ofsolar energy innovations, digitalization,
andeconomic globalization onenvironmental quality intheUnited
States
TomiwaSundayAdebayo1,5,6· MuhammadSaeedMeo2,7,8· BabatundeSundayEweade3· OktayÖzkan4
Received: 30 December 2023 / Accepted: 30 March 2024
© The Author(s) 2024
Abstract
The escalating apprehension regarding climate change mitigation has intensified the quest for energy alternatives that are
low in carbon emissions, economically viable, and consistently available. Within this context, renewable energy sources
emerge as fitting candidates, being recognized for their eco-friendliness and cleanliness. Nonetheless, despite the allure of
transitioning towards cleaner energy, there exists a notable dearth of literature addressing the pivotal role of solar energy
innovations and economic globalization in advancing the agenda of climate change mitigation (SDG-13), thus complicating
the prediction of factors influencing ecological quality. Consequently, this study undertakes the inaugural investigation into
the impact of solar energy innovation on ecological footprint, while also considering the influences of digitalization, eco-
nomic globalization, renewable energy, and natural resources in the USA. To this end, Quantile-on-Quantile Kernel-Based
Regularized Least Squares (QQKRLS) and wavelet quantile regressions (WQR) methodologies are employed, utilizing data
spanning from 2000 to 2020. The analysis reveals that solar energy innovation, along with renewable energy, digitalization,
and economic globalization, exerts a negative impact on ecological footprint, whereas natural resources exhibit a positive
influence. Drawing from these insights, it becomes apparent that a concerted effort from stakeholders and policymakers is
imperative in realizing the objectives of SDG-13 and SDG-7, necessitating a paradigm shifts in the USA’s energy portfolio
away from fossil fuels towards renewables.
* Babatunde Sunday Eweade
eweade.babatunde@gmail.com
Tomiwa Sunday Adebayo
twaikline@gmail.com
Muhammad Saeed Meo
saeedk8khan@gmail.com
Oktay Özkan
oktay.ozkan@gop.edu.tr
1 Faculty ofEconomics andAdministrative
Science, Cyprus International University, Nicosia,
NorthernCyprus,Mersin10, Turkey
2 SunwayBusinessSchool,SunwayUniversity, Malaysia
3 Eastern Mediterranean University, Famagusta,
NorthernCyprus,viaMersin10, Turkey
4 Tokat Gaziosmanpasa University, Tokat, Turkey
5 Adnan Kassar School ofBusiness, Lebanese American
University, Beirut, Lebanon
6 University ofTashkent forApplied Sciences, Str.Gavhar1,
Tashkent100149, Uzbekistan
7 University ofEconomics andHuman Sciences,
Warsaw, Poland
8 Advanced Research Centre, European University ofLefke,
NorthernCyprus,TR-10, Mersin, Turkey
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
T.S.Adebayo et al.
Graphical abstract
Nonlinear Negative effect
Nonlinear Positive effect
Ecological
Footprint
Solar Energy
Innovations
Digitalization
Economic
Globalization
Renewable
Energy
Natural
Resources
Keywords Natural resources· Digitalization· Economic globalization· Solar energy innovations· Ecological footprint·
Renewable energy· KRLS approach
Introduction
Environmental sustainability is of growing global concern,
particularly regarding nations like the USA, given their sub-
stantial ecological footprint and economic sway (Zhang etal.
2024a; Zhu etal. 2023a, b). Recent research has increas-
ingly focused on the intricate relationships among natural
resource use, digitalization, economic globalization, and
advancements in solar energy (Chien etal. 2022; Adebayo
etal. 2024). Recognizing how these elements interact and
impact environmental quality is pivotal for shaping policies
and fostering sustainable development (Eweade etal. 2023a,
b). In December 2023, government leaders and environmen-
tal experts convened at the 28th United Nations Climate
Change Conference (COP28) to agree on measures to com-
bat climate change and limit temperature increases to below
1.5°C. During COP28, the integration of finance, sustain-
ability, and technology objectives was highlighted, empha-
sizing the interconnectedness of these goals. This synergy
is exemplified by digitalization, a concept that blends finan-
cial and environmental technologies to drive sustainable
development forward (Magazzino 2023; Saqib etal. 2023).
At COP28, nations worldwide gathered to enhance existing
goals and commitments. In the USA, efforts focus on dem-
onstrating leadership in addressing the climate crisis and
working with global allies. Collaborating with international
partners, the USA aims to strengthen climate ambition and
achieve meaningful results at COP28. With a primary aim of
advancing the global transition to achieve net-zero emissions
by 2050, the USA is committed to playing a crucial role in
combating the imminent climate crisis, both now and in the
future (Gao etal. 2024; Usman etal. 2024).
Natural resources play a pivotal role in shaping environ-
mental quality, as their extraction, utilization, and manage-
ment practices have profound impacts on ecosystems, air
and water quality, and biodiversity (Razzaq etal. 2022;
Mukherjee 2021). With the USA being a major consumer
and producer of natural resources, the sustainable manage-
ment of these resources is paramount for preserving environ-
mental integrity (Gozgor etal. 2020; Adedoyin etal. 2020;
Alola etal. 2023; Cui etal. 2022). Additionally, the advent
of digitalization has revolutionized various sectors of the
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Analyzing theeffects ofsolar energy innovations, digitalization, andeconomic globalization…
economy, leading to increased efficiency, productivity, and
connectivity (Ahmed etal. 2021; He etal. 2024; Li etal.
2021). However, the proliferation of digital technologies
also raises concerns about their environmental implications,
such as energy consumption, electronic waste generation,
and carbon emissions (Chien etal. 2022). Understanding the
net effects of digitalization on environmental quality is cru-
cial for harnessing its benefits while mitigating its adverse
impacts (Ansari etal. 2021; Pata etal. 2023).
Digitalization has the potential to significantly contrib-
ute to environmental sustainability in the USA by financ-
ing solar panel installations, promoting renewable energy
sources, and monitoring environmental impacts. It facilitates
loans for individuals and companies aiming to reduce their
environmental footprint, while also channeling capital into
eco-conscious businesses. However, transitioning to renew-
able energy requires substantial investments in infrastruc-
ture, research and development, and widespread adoption of
clean energy technologies(Karlilar etal. 2023). Therefore,
integrating financial inclusion with digitalization simplifies
access to funding for renewable energy projects, encouraging
more entities to embrace clean energy solutions. Leveraging
digital platforms, crowdfunding, and peer-to-peer financing
broadens access to capital for renewable energy initiatives,
attracting investment from a wider array of stakeholders.
Economic globalization, characterized by the increas-
ing interconnectedness of economies and the movement
of goods, services, and capital across borders, has both
positive and negative implications for environmental qual-
ity (Adedoyin etal. 2020; Bekun and Ozturk 2024; Pata
etal. 2023). While globalization has facilitated economic
growth and development, it has also led to environmental
degradation through increased resource extraction, pollu-
tion, and carbon emissions(Gupta and Kumar 2023; Bekun
and Ozturk 2024; Eweade etal. 2024). Examining the envi-
ronmental consequences of economic globalization in the
context of the USA is essential for identifying opportunities
for sustainable development. Furthermore, innovations in
solar energy technologies have the potential to revolutionize
the energy landscape and mitigate environmental impacts
associated with fossil fuel combustion. Solar energy offers
a clean, renewable alternative to traditional energy sources,
reducing greenhouse gas emissions, air pollution, and reli-
ance on finite resources (Khan etal. 2020a, b; Razzaq etal.
2022). Understanding the adoption, deployment, and effec-
tiveness of solar energy innovations in the USA is critical
for transitioning towards a more sustainable energy future
(Ibrahim etal. 2022; Kabeyi and Olanrewaju 2022; Eweade
etal. 2022).
In light of these considerations, this study aims to com-
prehensively examine the effects of natural resources, digi-
talization, economic globalization, and solar energy innova-
tions on environmental quality in the USA. By analyzing
the interactions between these factors and their cumulative
impacts on environmental indicators such as air and water
quality, biodiversity, and climate change, this research seeks
to inform evidence-based policy interventions and promote
sustainable development practices. Based on the stated
research objective, the following research questions were
raised; (1) Are there significant interactions or synergies
among natural resource utilization, digitalization, economic
globalization, and solar energy innovations that collectively
impact environmental quality in the United States? (2) How
do policy interventions and regulatory frameworks aimed
at promoting sustainable development and environmental
protection interact with the aforementioned factors in shap-
ing environmental quality outcomes in the United States? (3)
What are the regional variations in the relationship between
natural resources, digitalization, economic globalization,
solar energy innovations, and environmental quality across
different states or regions within the United States? Fol-
lowing the objective and the research questions, the con-
tributions of the study provides valuable insights for poli-
cymaking, sustainability, and global environmental efforts.
It sheds light on how these factors interact and influence
environmental outcomes, informing evidence-based policies
and guiding businesses towards sustainability. Understand-
ing their impact contributes to addressing climate change
and promoting sustainable development practices. Addition-
ally, it facilitates international collaborations and agreements
for environmental protection. Overall, this research bridges
academic knowledge with practical policymaking, fostering
sustainability and economic development. To validate the
connections between the variables and provide robust empir-
ical evidence, the study employs a contemporary estimation
technique known as the quantile-on-quantile KRLS method.
This methodological approach offers new perspectives in
analyzing United States data, enabling a deeper understand-
ing of the complex relationships at play. Overall, the study
contributes to the advancement of knowledge in the field of
environmental sustainability by providing insights into the
multifaceted dynamics influencing environmental quality in
the USA and offering practical implications for policymak-
ers and stakeholders striving to promote sustainable devel-
opment practices.
The structure of the paper is as follows: The subsequent
Sect.“Literature review” will provide a literature assess-
ment, concentrating on the correlation between digitaliza-
tion, economic globalization, natural resources, renewable
energy, and solar innovation on the ecological footprint, with
the aim of tackling climate change. This will be followed
by a discussion in Sect.“Data and methodology” focusing
on data and methodology framework used for empirical
research. Sect.“Empirical analysis” will elaborate on the
outcomes of our study, including a comparison with prior
research in the field. Finally, Sect.“Conclusion and policy
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T.S.Adebayo et al.
recommendations” will present our conclusion, highlighting
key findings relevant for scholars and policymakers.
Literature review
Numerous studies have been undertaken to explore the
intricate interplay among solar energy innovations, digitali-
zation, economic globalization, renewable energy, natural
resources, and ecological footprint. This section presents
a comprehensive overview of prior investigations into the
specified variables. Additionally, a meticulous critique of
the existing literature is offered, emphasizing discernible
knowledge gaps.
Solar energy innovations andecological footprint
Solar energy innovation has ushered in a paradigm shift in
environmental sustainability. Its utilization of solar electric-
ity, harnessed from a renewable and abundant resource, has
markedly diminished reliance on fossil fuels, consequently
alleviating greenhouse gas emissions (Ahmadi etal. 2018).
The consequential reduction in air and water pollution
induced by solar energy systems has been expounded upon
by Rabaia etal.(2021), thereby safeguarding ecosystems and
air quality, both of which bear substantial environmental
ramifications. In the discourse surrounding the impact of
solar energy innovations, disparate perspectives emerge.
Advocates of the positive influence of solar energy on envi-
ronmental quality include researchers such as Shahsavari
and Akbari (2018) who, in their examination of developing
nations, asserted that solar energy reduces carbon emissions.
Güney (2022) substantiates these claims by analyzing annual
data from 2005 to 2018 across 35 economies, establishing a
tangible correlation between increased solar energy utiliza-
tion and substantial reductions in carbon emissions. Shah-
savari etal. (2019) observed that each kilowatt-hour of solar
electricity curtails approximately 715g of CO2.
Conversely, a counterargument emerges, underscoring
environmental concerns associated with solar energy, par-
ticularly concerning the manufacturing and disposal of solar
panels. The manufacturing process involves the use of chem-
icals, such as silicon and silver, which may pose hazards if
not handled with due care. Yu etal. (2022) employed QQR
regression in a study spanning from 1991 to 2018, contend-
ing that solar energy has a limited impact on reducing carbon
emissions in France. Zhu etal. (2023a, b) reported similar
findings for Spain and India, indicating that the efficacy of
solar energy in mitigating carbon emissions varies across
countries. These incongruent findings underscore the need
for further investigation to derive generalizable conclusions
regarding the impact of solar energy innovation on carbon
emissions reduction.
Digitalization andecological footprint
The digital transformation of industries has significantly
altered our interactions, consumption, and production meth-
ods, impacting environmental sustainability both positively
and negatively. Digitization, highlighted by Zhang etal.
(2023), improves energy efficiency and minimizes material
consumption through remote monitoring. Digitization brings
forth a dualistic situation. While the shift to digital products
reduces reliance on physical items, lessening environmen-
tal impact, it also leads to increased electronic waste from
frequent device replacements, posing contamination and
resource depletion challenges. The growing energy demands
of digital technologies, particularly data centers and cloud
services, raise environmental concerns with heightened
greenhouse gas emissions, emphasizing the need for sus-
tainable electricity practices.
Zhu etal. (2022) conducted a study in China to scru-
tinize the impact of digitalization on carbon emissions,
revealing substantial reductions attributable to the digital
economy. This reduction is facilitated through the promo-
tion of innovation and the evolution of industrial structures.
Similarly, Ke etal. (2022) found, in a study spanning 77
emerging economies, that digitization exerted a notewor-
thy influence in curtailing carbon emissions. Contrastingly,
recent research by Dong etal. (2022) conducted in China,
utilizing panel data from 2008 to 2018 across 60 countries,
discovered an association between the rise in digitaliza-
tion and an increase in per capita carbon emissions. Wang
etal. (2022) argue that the relationship between digitaliza-
tion and carbon emissions in China can be represented by
an inverted U-shaped curve, a proposition substantiated by
rigorous testing. Furthermore, Yang etal. (2022) conducted
a similar study in China, revealing a curvilinear impact of
digitalization on carbon emissions, following an inverted
U-shaped pattern. The inconclusive nature of these results
necessitates further in-depth exploration to elucidate the
intricate relationship between digitalization and environ-
mental sustainability.
Economic globalization andecological footprint
The indicators of economic globalization include trade,
foreign direct investment (FDI), portfolio investment, and
regulatory issues such as tariffs, import restrictions, and
levies on international trade (Gygli etal. 2019). The pro-
cess of economic globalization has the potential to enhance
the ecology by capitalizing on the positive effects of inter-
national trade and FDI. The adoption of environmentally
friendly technology and structural modifications are encour-
aged by the technique and composition effects of trade,
resulting in improvements to the environment. Conversely,
the proliferation of opportunities for exporting goods in an
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Analyzing theeffects ofsolar energy innovations, digitalization, andeconomic globalization…
era of unrestricted trade has resulted in increased produc-
tion, thereby negatively impacting the environment due to
the scale effect (Ahmed etal. 2021). Langnel and Amegavi
(2020) employed yearly data spanning from 1971 to 2016
for Ghana and employed the ARDL methodology to exam-
ine the data. Their data confirm that globalization results
in a rise in ecological footprint. Hussain etal. (2021) show
a causal relationship between globalization and ecological
footprints in Thailand by examining yearly data from 1970
to 2018 through the utilization of the NARDL approach.
The findings indicate that an increase in globalization has a
direct correlation with an increase in ecological footprints.
Moreover, Rudolph and Figge (2017) also conducted a
study in which they analyzed 146 nations using the Extreme
Bounds Analysis (EBA) method. They found that economic
globalization had a positive influence on the ecological foot-
print. However, Ansari etal. (2021) have presented diver-
gent results in their latest research conducted in prominent
economies that heavily rely on renewable energy. Their
research indicates that globalization has a tendency to
decrease ecological footprints. In addition, Ahmed etal.
(2021) examined the relationship between economic glo-
balization and ecological footprint in Japan. By employing
the ARDL technique, they discovered significant effects of
economic globalization on the rise of ecological footprints.
Nevertheless, the NARDL technique produces contrasting
outcomes, indicating that both positive and negative shifts in
economic globalization contribute to a decrease in ecologi-
cal footprint. The contrasting results highlight the need for
thorough research in order to develop robust policies.
Renewable energy andecological footprint
Renewable energy, encompassing solar, wind, and hydro-
electric power, gains popularity due to its environmentally
friendly attributes as a substitute for fossil fuels. Character-
ized by a smaller ecological impact, it involves decreased
land usage, absence of air pollutants, sustainable water
resource management, and the promotion of biodiversity,
making it an appealing and sustainable option. Xue etal.
(2021) examined yearly data from 1990 to 2014 for South
Asian countries utilizing the AMG approach. Their findings
suggest that renewable energy has a substantial impact in
reducing the ecological footprint. Sharif etal. (2021) did a
study that examined the top ten economies with the great-
est use of solar energy from 1990 to 2018. By employing
a nonparametric quantile on quantile regression method,
they discovered a significant and positive influence of solar
energy on the state of the ecosystem. Li etal. (023) con-
ducted a study on the economies of 130 nations using panel
threshold regression. Their findings revealed that renewable
energy has a notable impact on minimizing the ecological
footprint. In contrast, Chalendar and Benson (2019) have
recently raised doubts about the effectiveness of solar energy
in mitigating carbon emissions. In their study, Nathaniel
etal. (2020) observed varying results when examining the
relationship between renewable energy and ecological foot-
print in MENA economies. They utilized annual data from
1990 to 2016 and employed the AMG methodology. Their
findings indicated that there was no significant influence on
ecological footprint or environmental quality.
Natural resources andecological footprint
The study of the relationship between economic growth
and the environment involves examining the use, exces-
sive exploitation, and deterioration of natural resources,
as well as issues related to pollution and global warming.
Currently, there is a strong emphasis on climate change at
a global level, with a recognition of the serious risks that
it presents to the well-being of humans and the ability to
maintain sustainable economic growth (Khan etal. 2020a,
b). Consequently, there has been an increased focus in recent
years among academics, scholars, and legislators on the
deterioration of the ecology and the exhaustion of natural
resources. Awosusi etal. (2022a, b) investigated the influ-
ence of natural resources on ecological footprints in BRICS
countries. By analyzing annual data spanning from 1992 to
2018 and utilizing the MMQR approach, they discovered
a substantial detrimental impact of natural resources on
the ecosystem. In a study by Ahmad etal.(2020) covering
22 developing economies, using annual data from 1984 to
2016 and employing the CS-ARDL technique, it was dem-
onstrated that increase in natural resources has a significant
positive impact on the ecological footprints of the economies
under examination. Kongbuamai etal. (2020) conducted a
study on ASEAN economies, analyzing annual data from
1995 to 2016 through the Driscoll–Kraay panel regression
method. Their findings indicate that natural resources have
a substantial impact in reducing ecological footprints. In
a similar vein, Zafar etal.(2019) examined the impact of
natural resources in the USA by analyzing annual data from
1970 to 2015 and employing the ARDL technique. They
discovered a significant reduction in the ecological footprint.
Table1 offers a detailed summary of the reviewed studied.
Gaps intheliterature
Following an extensive review of the literature on envi-
ronmental studies, we identified several knowledge gaps.
(1) We discovered that the link between proposed fac-
tors is less focused on the USA setting, resulting in pio-
neering studies that investigate the effect of solar energy
innovations, and digitalization on ecological sustain-
ability while considering the role of economic globali-
zation, renewable energy, and natural resources. (2) We
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T.S.Adebayo et al.
noted that the majority of the research used traditional
techniques such as ARDL, CCEMG, DCCE, and so on,
therefore this study used a variety of unique econometric
methodologies such as the innovative QQKRLS, WQR,
and QQR approach.
Table 1 Summary of past studies
QQR, CCEMG, ARDL, NARDL, PMG, MG, DCCE, MMQR, and CS-ARDL stand for quantile-on-quantile regression, common correlated
effects mean group, autoregressive distributed lag, asymmetric autoregressive distributed lag, pooled mean group, mean group, dynamic com-
mon correlated effects, quantile regression methods of moments, and cross-sectionally augmented autoregressive distributed lag, respectively
Authors Sample countries Methods Time periods Findings
Nexus between solar energy innovations (SEINO) and ecological footprint (ECOFP)
Sharif etal. (2021) Top ten solar energy-consum-
ing economies
QQR approach 1990–2018 Solar energy reduces the
ECOFP
Kuşkaya etal. (2023)USA Morlet wavelet analysis 1990:1–2022:6 Solar energy reduces CO2
emissions
Güney (2022) 35 economies CCEMG approach 2005–2018 Solar energy reduces CO2
emissions
Yu etal. (2022) Top ten solar energy-consum-
ing economies
QQR approach 1991–2018 Except in France, solar energy
reduces CO2 emissions
Zhu etal. (2023a, b) Top-ten solar energy-con-
sumer countries
QQR approach 1991–2018 Except in Spain and India,
solar energy reduces CO2
emissions
Digitalization (DIGIT) and Ecological Footprint (ECOFP)
Zheng etal. (2023) China's 281 cities Spatial spillover analysis 2016–2019 Inverted U-shaped curve
Zhu etal. (2022) 30 Chinese provinces Fixed-effects model 2009–2019 DIGIT reduces CO2 emissions
Yang etal. (2022) China Panel regression 2006–2019 DIGIT and CO2 emissions
show an inverted U-shaped
curve link
Dong etal. (2022) 60 countries Intermediary effect model 2008–2018 DIGIT reduces CO2 emissions
Nexus between economic globalization (ECGLO) and ecological footprints (ECOFP)
Ahmed etal. (2021) Japan NARDL approach 1971–2016 ECGLO reduces ECOFP
Ansari etal. (2021) Top renewable energy con-
suming economies
PMG approach 1991–2016 ECGLO reduces ECOFP
Langnel and Amegavi (2020) Ghana ARDL approach 1971–2016 ECGLO increases ECOFP
Hussain etal. (2021) Thailand NARDL approach 1970–2018 ECGLO increases ECOFP
Rudolph and Figge (2017) 146 countries The Extreme Bounds Analysis
(EBA)
1981–2009 ECGLO increases ECOFP
Nexus between renewable energy (RENEN) and ecological footprint (ECOFP)
Xue etal. (2021) South Asian nations AMG approach 1990–2014 RENEN reduces ECOFP
Li etal. (2023) 130 countries Panel threshold regression 1992–2019 RENEN reduces ECOFP
Nathaniel etal. (2020) MENA AMG approach 1990–2016 No impact of RENEN on
ECOFP ECGLO
Sahoo and Sethi (2021) Developing countries MG, AMG, and DCCE 1990–2016 RENEN reduces ECOFP
Nexus between natural resources (NATRE) and ecological footprint (ECOFP)
Awosusi etal. (2022a, b) BRICS MMQR approach 1992–2018 NATRE increases ECOFP
Ahmad etal. (2020) 22 emerging economics CS-ARDL approach 1984–2016 NATRE increases ECOFP
Kongbuamai etal. (2020) ASEAN Driscoll-Kraay panel regres-
sion
1995–2016 NATRE decreases ECOFP
Zafar etal. (2019)USA 1970–2015 NATRE decreases ECOFP
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Analyzing theeffects ofsolar energy innovations, digitalization, andeconomic globalization…
Data andmethodology
Data
The present analysis assesses the effects of advancements in
solar energy technology and digitalization on environmental
sustainability, taking into account the influence of economic
globalization, the utilization of renewable energy sources,
and the management of natural resources. The United States
serves as the focal point for this investigation, covering the
period from 2000 to 2020. To mitigate the challenge of lim-
ited observational data, this study adopts the quadratic sum-
up approach, building upon the methodologies outlined in
prior works such as Pata etal. (2022) and Khan etal. (2023),
thereby transforming low-frequency data into a higher fre-
quency. Additionally, a comprehensive overview of the
research procedures is provided in Table2.
Methodology
Quantile‑on‑quantile kernel‑based regularized least
squares (QQKRLS)
Several studies suggest that policy shifts, structural changes,
sudden shocks, and political fluctuations contribute to the
emergence of characteristics such as asymmetry, nonlin-
earity, heavy-tailedness, and extreme values in economic
time series (Bouoiyour and Selmi 2017). Amidst an ongo-
ing global energy crisis and a worldwide pandemic, both
of which have significantly disrupted the global landscape,
various studies indicate that macroeconomic indicators
respond to a new, nonlinear rhythm. Considering nonlineari-
ties among the proposed variables, this study explores the
asymmetric impact of SEINO, DIGIT, ECGLO, RENEN,
and NATRE on ECOFP. Two distinct methodologies, the
quantile-on-quantile kernel-based regularized least squares
(QQKRLS) introduced by Adebayo etal. (2024) and Wavelet
quantile regression (WQR) proposed by Adebayo and Özkan
(2024), are employed to analyze this dynamic interaction.
Originally introduced by Hainmueller and Hazlett
(2014) the KRLS approach is a machine learning technique
motivated by its flexibility in regression without the need
for specific assumptions. The algorithm utilizes Gauss-
ian kernels to identify the optimal fitting function, thereby
mitigating bias resulting from incorrect specifications. The
KRLS method assesses the marginal impact of an explana-
tory variable on each individual data point of an endogenous
variable. By leveraging the distribution of these marginal
impacts, it unveils diverse (nonlinear) outcomes. Specifi-
cally, the KRLS method calculates the average of pointwise
marginal impacts to determine the effect size and statisti-
cal significance of the explanatory variable's impact on
the endogenous variable (Hainmueller and Hazlett 2014).
To elaborate further, KRLS can assess the influence of an
exogenous variable X on an endogenous variable Y in the
following manner:
whereas
E
S
[
ℾY
ℾ
Xk]
represents the average or mean pointwise
marginal impact of the exogenous (X) on the endogenous (Y)
variable. Additionally,
n
denotes an individual observation,
and
S
signifies the sample size. It is clear that KRLS
approach places emphasis on the complete distribution of
the dependent variable rather than the independent variable.
Demonstrating the average pointwise marginal impact of the
independent variable X on the dependent variable Y high-
lights the presence of nonlinearity in each data point of the
projected variable. However, the statistical significance is
ascertained by a singular value—the mean or average point-
wise marginal effect. By utilizing the QQKRLS approach,
this study extends beyond the exclusive consideration of the
entire distribution of the endogenous variable in the KRLS
method. Specifically, we integrate the KRLS approach intro-
duced by Hainmueller and Hazlett (2014) with the QQR
approach by Sim and Zhou (2015). This combined QQKRLS
method enables the assessment of statistical significance for
the complete distributions of both exogenous and endoge-
nous variables. Through computing average pointwise mar-
ginal influence across quantile pairs, the approach provides
valuable insights into the impact size and statistical signifi-
cance associated with the influence of exogenous variable
(1)
E
S
ℾY
ℾX
n
=
−2
𝜎2S
n
i
jieXi−Xn2
𝜎2
Xi−Xn
Table 2 Details of the study
data Variable name Symbol Content of the data Data source
Ecological footprint ECOFP Ecological footprint, per person (gha) GFN (2024)
Solar energy innovations SEINO Annual total patents filed for solar energy technologies OWD (2024)
Digitalization DIGIT Individuals using the Internet (% of population) WDI (2024)
Economic globalization ECGLO KOF Economic Globalization Index calculated by
Gygli etal. (2019)
KOF (2024)
Renewable energy RENEN Renewables (% equivalent primary energy) OWD (2024)
Natural resources NATRE Total natural resources rents (% of GDP) WDI (2024)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
T.S.Adebayo et al.
quantiles on endogenous variable quantiles. This methodol-
ogy facilitates the exploration of complex or asymmetric
relationships between the variables, allowing for a detailed
analysis of how quantiles of the exogenous variable X affect
quantiles of the endogenous variable Y.
Here,
E
S
[
ℾQY𝜏
ℾQX
𝜃n]
presents the mean or average pointwise
marginal impact of the
𝜃
th conditional quantile of the exog-
enous variable
X
on the
𝜏
th conditional quantile of the
endogenous variable
Y
. Furthermore
QY𝜏
and
QX𝜃
represent
the
𝜏
th and
𝜃
th conditional quantile series of
Y
and
X
vari-
ables, respectively.
Wavelet quantile regression (WQR)
Next, we employed the recently introduced wavelet quan-
tile regression (WQR) suggested by Adebayo and Özkan
(2024). Following is the conventional quantile regression
(QR) model for two variables.
The expression,
∫
↺(ℸ)
(Y
|
X
)
represents the conditional
quantile of the endogenous (Y) given the exogenous (X)
variable at quantile
ℸ
. Meanwhile, is the intercept
parameter at quantile
ℸ
, while represents the slope
parameter at quantile
ℸ
.
The quantile regression (QR) approach, introduced by
Koenker and Bassett (1978), stands as a statistical method
that extends beyond conventional linear regression practices.
It presents a broader perspective for modelling the condi-
tional quantiles of an endogenous variable concerning an
exogenous variable. In contrast to the conventional ordinary
least squares (OLS) method, which primarily estimates the
average value of the endogenous variable, QR allows for a
detailed exploration of the relationship between an exog-
enous variable and various quantiles of the endogenous
variable. This methodology holds significant importance in
academia, enabling a thorough analysis of the correlation
between different percentiles of the endogenous variable and
its covariates. The QR approach offers several advantages
that contribute to its academic and practical utility. Firstly, it
provides a more comprehensive depiction of the distribution
of the endogenous variable, going beyond the conventional
emphasis on the mean. Additionally, its capacity to handle
outliers and heteroscedasticity distinguishes it as a robust
analytical tool capable of effectively addressing anomalies
in the data. QR enables researchers to analyze and compre-
hend changes in the interactions between a variable across
(2)
E
S
ℾQY𝜏
ℾQX𝜃n
=
−2
𝜎2s
n
i
jieX𝜃i−X𝜃n2
𝜎2
X𝜃i−X𝜃n
(3)
different quantiles of the distribution, offering a comprehen-
sive view of the phenomena under examination (Saeed Meo
and Karim 2022).
Prior empirical investigations (Kuşkaya etal. 2023; Thi
Hong Nham and Thanh Ha 2023; Zhu etal. 2023a) have
demonstrated the variability in associations between vari-
ables across distinct time periods. However, the conven-
tional QR approach neglects the potential for variations in
the impact of the factor variable on the conditional quantiles
of the response variable over different time dimensions, as
previously highlighted in existing research. To overcome
this limitation, we adopted the Wavelet Quantile Regression
(WQR) method following the approach outlined by Adebayo
and Özkan (2024). This was done to investigate the influence
of an exogenous variable X on the conditional quantiles of
an endogenous variable Y across various time intervals. The
procedural steps for implementing the WQR technique are
as follows:
We begin by applying the maximal overlapping discrete
wavelet transform (MODWT) to decompose the exogenous
(
Xt
) and endogenous (
Y
) series, following Percival & Wal-
den (2000) and following a recent study of Adebayo and
Özkan (2024) as below:
Consider
X[i]
as a signal having a duration of
T
, where
T=2J
for an integer J. Additionally, let
c1[i]
represent as
the low-pass filter and
d1[i]
represent the high-pass filter,
both of which are determined by the orthogonal wavelet.
During the initial phase,
X[i]
experiences convolution with
c1[i]
produces the estimate coefficients,
e1[i]
, which having
a length of
N
, and with
d1[i]
produces the detail coefficients
f1[i]
again with a length of
N
. The procedure can be defined
as follows:
Afterwards, we employ an akin method to filter
e1
[
i]
,
using modified filters
c2[i]
and
d2[i]
, which are obtained
from the dyadic up-sampling of
c1
[
i]
and
d1
[
i]
. This iterative
approach involves the repetition of the recursive procedure.
For values of J ranging from 1 to J0 − 1, where J0 ≤ 1, we can
calculate the parameters of the approximation and detailed
components in the following manner:
(4)
e
1[i]=c1[i]*s[i]=
∑
k
c1[i−k]s[k
]
(5)
f1[i]=d1[i]*s[i]=
∑
k
d1[i−k]s[k
]
(6)
e
+1[i]=cj+1[i]∗ej[i]=
∑
k
cj+1[i−k]ej[i
]
(7)
fj+1[i]=dj+1[i]∗ej[i]=
∑
k
dj+1[n−k]dj
[
j
]
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Analyzing theeffects ofsolar energy innovations, digitalization, andeconomic globalization…
Here,
cj+
1
[i]=U(cj[i])
and
dj+
1
[i]=U(dj[i])
, where the
function U represents the up-sampling process, which
involves injecting a zero value between each consecutive
pair of time-series items.
After applying J-level deconstruction on
Yt
and
Xt
and
obtaining the detail coefficients, we proceed to apply QR
on to the pair of wavelet details,
fj[Y]
and
f[X
], for all
levels J. Therefore, we calculate the results of WQR for
each of the levels J. The WQR is formally defined for the
endogenous variable Y and the exogenous variable X, at
a certain decomposition level J, and for a given quantile
ℸ
, in the following manner:
For detail and clear illustration, Fig.1 presents the
study analytical flow.
(8)
Empirical analysis
Preliminary analysis results
This segment of the study assesses the appropriateness of
utilizing QQKRLS in the investigation. It commences with
a scrutiny of descriptive statistics, followed by an exami-
nation of Quantile–quantile plots to gauge normality, BDS
test estimates, and parameter stability test estimates. Table3
displays essential descriptive statistics for the logarithmic
data series originating from the USA. During the sample
period, the averages for the lnECOFP, 1nSEINO, 1nDIGIT,
1nRENEN, and 1nNATRE series were approximately 0.55,
2.00, 1.07, 1.10, 0.45, and − 0.07 respectively. Skewness
analysis suggests left-skewed distributions for the 1nECOFP,
lnSEINO, lnDIGIT, and lnNATRE series, while 1nRENEN
displays a right-skewed distribution. Moreover, Kurtosis
assessments reveal platykurtic distributions for all series
except for 1nDIGIT, which exhibits a leptokurtic distribu-
tion. The results of the Jarque and Bera (1980) normality
Fig. 1 Workflow of the study
1. Preliminary Analysis
Descriptive statistics
QQ plots
BDS test
Parameter stability test
2. Main Analysis
Quantile on quantile
KRLS
Wavelet quantile
regression
3. Robustness Analysis
Quantile on quantile
regression
Table 3 Descriptive statistics
***(prob) < 0.01, **(prob) < 0.05, and * (prob) < 0.1
lnECOFP lnSEINO lnDIGIT lnECGLO lnRENEN lnNATRE
Mean 0.550 1.999 1.065 1.096 0.445 − 0.066
Median 0.541 2.078 1.069 1.098 0.444 − 0.043
Maximum 0.600 2.212 1.128 1.103 0.605 0.182
Minimum 0.477 1.553 0.931 1.086 0.305 − 0.392
Std. Dev 0.031 0.196 0.046 0.006 0.084 0.135
Skewness − 0.020 − 0.656 − 1.003 − 0.620 0.047 − 0.625
Kurtosis 1.920 1.997 4.035 1.895 1.690 2.837
Jarque–Bera 4.089 9.537*** 17.844*** 9.655*** 6.034** 5.555*
(0.129) (0.008) (0.000) (0.008) (0.049) (0.062)
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T.S.Adebayo et al.
test, reported alongside descriptive statistics, indicate that
all series, with the exception of lnECOFP, depart from a
normal distribution.
This research utilizes the quantile-based approach, spe-
cifically QQKRLS. Therefore, it is essential to examine the
normality aspect of the data intended for analysis through
quantile–quantile (Q–Q) plots. As illustrated in Fig.2, the
data for all variables diverges from normality across vari-
ous quantiles.
.44
.48
.52
.56
.60
.64
.44.48.52.56.60.64
QuantilesoflnECOFP
QuantilesofNormal
l
n
ECOFP
1.4
1.6
1.8
2.0
2.2
2.4
2.6
1.41.61.82.02.22.4
QuantilesoflnSEINO
QuantilesofNormal
l
n
SEINO
0.90
0.95
1.00
1.05
1.10
1.15
1.20
0.90 0.95 1.00 1.05 1.10 1.15
QuantilesoflnDIGIT
QuantilesofNormal
l
n
DIGIT
1.080
1.085
1.090
1.095
1.100
1.105
1.110
1.115
1.084 1.088 1.092 1.0961.1001.104
QuantilesoflnECGLO
QuantilesofNormal
lnECGLO
.2
.3
.4
.5
.6
.7
.30.35.40.45.50.55.60.65
Quantiles of ln RENEN
QuantilesofNormal
lnRENEN
-.6
-.4
-.2
.0
.2
.4
-.4-.3-.2-.1.0 .1 .2
QuantilesoflnNATRE
QuantilesofNormal
lnNATRE
Fig. 2 Quantile–quantile plots for normality. The figures depict the
normality status of the quarterly logarithmic (ln) data series for vari-
ous variables. Specifically, ECOFP represents Ecological footprint,
SEINO stands for Solar Energy Innovations, DIGIT refers to Digitali-
zation, RENEN represents Renewable energy, and NATRE signifies
Natural resources. Additionally, ECOFP also denotes Economic glo-
balization
Table 4 BDS test estimates
***(prob) < 0.01
lnECOFP lnSEINO LnDIGIT lnECGLO lnRENEN lnNATRE
Em. D. [2] 39.436*** 32.168*** 19.066*** 30.052*** 42.835*** 18.960***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Em. D. [3] 41.681*** 34.343*** 20.048*** 31.768*** 45.316*** 18.899***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Em. D. [4] 44.806*** 36.818*** 21.415*** 33.820*** 48.832*** 19.415***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Em. D. [5] 49.602*** 40.281*** 23.512*** 36.902*** 54.089*** 20.511***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Em. D. [6] 56.123*** 44.956*** 26.433*** 41.299*** 61.550*** 22.280***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
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Analyzing theeffects ofsolar energy innovations, digitalization, andeconomic globalization…
In the initial analysis, we further explore the linearity
properties of the quarterly logarithmic data. This investiga-
tion involves employing the BDS test, which was initially
introduced by Broock etal. (1996). It is worth noting that
this particular method has been applied in recent research
conducted by Khan etal. (2023). Table4 displays the find-
ings of the BDS test, revealing that all variables' data devi-
ates from the assumption of independent and identically
distributed behavior across all dimensions. This indicates
the presence of nonlinearity within the data for the entire
duration of the study.
We examine the stability traits of the quarterly logarith-
mic data. This examination involves employing three distinct
tests for parameter stability—Max-F, Exp-F, and Ave-F—
which were introduced by Andrews (1993) and Miao etal.,
(2022). We follow existing literature from the studies of Lee
etal. (2023) and Olanipekun etal. (2023). Table5 presents
the outcomes of the Max-F, Exp-F, and Ave-F tests, indi-
cating data instability across all variables. This suggests
non-stability throughout the study period (Hansen 1997).
The preliminary assessment indicates that the data for all
variables demonstrate abnormal distribution across various
quantiles, as well as nonlinear and unstable patterns over
the studied timeframe. Consequently, the QQKRLS method
appears well-suited for the study data due to its ability to
address abnormality, nonlinearity, and instability effectively.
Quantile onquantile kernel‑based regularized least
squares results
Prior to examining the relationship between the proposed
variables, we conducted estimations to understand key data
characteristics such as normality, linearity, and parametric
stability. Our analysis, using QQ plots, indicated non-normal
distributions for the variables. Subsequent BDS tests con-
firmed their nonlinear nature, while parameter stability tests
revealed instability among the parameters. This prompted
the creation of a robust solution to address these estima-
tion challenges comprehensively. Figure3a–e demonstrates
the impact of SEINO, DIGIT, FGLO, ECGLO, RENEN,
and NATRE on environmental quality in the USA using the
QQKRLS method.
Utilizing the QQKRLS method, Fig.3a illustrates the
relationship between SEINO (solar energy innovation) and
ECOFP (ecological footprint). The findings of the study
indicate a weak correlation between SEINO and ECOFP at
lower quantiles. However, a notable negative correlation is
observed between SEINO and ECOFP at higher quantiles,
particularly within the range of 0.10 to 0.90. In relation to
the previous results, this suggests that while there may not
be a strong overall correlation between solar energy inno-
vation and ecological footprint across all levels, there is a
more pronounced negative relationship at higher levels. This
implies that as solar energy innovation increases, there tends
to be a decrease in ecological footprint, particularly within
the middle to upper quantiles. The observed outcomes align
with previous scholarly findings in the field. Several stud-
ies have highlighted the nuanced relationship between solar
energy innovation and ecological footprint, emphasizing
varying degrees of correlation across different quantiles. For
instance, research by Awosusi etal., (2022) demonstrated
similar weak correlations at lower quantiles but identified
stronger negative correlations at higher quantiles, consistent
with our findings. Additionally, the work of Yi etal. (2023)
corroborated the notion of a more pronounced negative
relationship between solar energy innovation and ecologi-
cal footprint within specific quantile ranges. Therefore, our
results resonate with and reinforce these existing scholarly
insights.
The result illustrated in Fig.3b suggests that digitaliza-
tion (DIGIT) has a significant negative impact on ecological
footprint (ECOFP), especially when considering the upper
quantiles of both variables. This implies that as digitalization
increases, there is a notable reduction in ecological footprint,
particularly among instances where both digitalization and
ecological footprint are relatively high. This finding indi-
cates the potential of digitalization to contribute to envi-
ronmental sustainability by reducing ecological footprints,
particularly in more digitally advanced contexts. The results
presented in Fig.3b are consistent with earlier research
conducted by Karlilar etal. (2023), Qing etal. (2024), and
Zhang etal. (2024b). These studies have also observed a
Table 5 Parameter stability tests
estimates
***(prob) < 0.01
lnECOFP LnSEINO LnDIGIT lnECGLO lnRENEN lnNATRE
Max-F 363.905*** 347.723*** 131.236*** 468.362*** 322.045*** 166.234***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Exp-F 177.986*** 170.444*** 62.957*** 230.105*** 157.352*** 79.223***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Ave-F 142.453*** 125.704*** 76.546*** 136.708*** 161.916*** 57.196***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
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T.S.Adebayo et al.
(a) QQKRLS between SEINO and ECOFP (b) QQKRLS between DIGIT and ECOFP
(c) QQKRLS between ECGLO and ECOFP (d) QQKRLS between RENEN and ECOFP
(e) QQKRLS between NATRE and ECOFP
Fig. 3 Quantile on quantile KRLS estimates. Note:The average pointwise marginal effect coefficients are represented by colour bars, wherein
positive and negative coefficients are represented by green and red colors, respectively **(prob) < 0.05 and *(prob) < 0.1
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Analyzing theeffects ofsolar energy innovations, digitalization, andeconomic globalization…
negative relationship between digitalization and ecological
footprint, supporting the notion that advancements in digital
technologies can lead to reductions in environmental impact,
as evidenced by lower ecological footprints. These results
suggest that embracing digitalization can lead to environ-
mental sustainability by reducing ecological footprints,
offering opportunities for innovation and economic growth.
Leveraging digital solutions could stimulate job creation and
attract investment, while also highlighting the importance
of policy frameworks that incentivize sustainable practices.
Overall, digitalization presents an opportunity for the USA
to achieve economic growth while advancing environmental
goals.
Figure3c illustrates the relationship between economic
globalization (ECGLO) and ecological footprint (ECOFP).
The findings reveal a weak correlation between ECGLO and
ECOFP at lower quantiles, while a robust negative correla-
tion is evident at higher quantiles, specifically within the
range of 0.65 to 0.95. These results suggest potential benefits
for the US economy. As economic globalization increases,
there may be opportunities for expanded trade, investment,
and access to global markets, which can stimulate economic
growth and enhance competitiveness. Additionally, the
negative correlation with ecological footprint implies that
greater economic globalization may lead to more efficient
resource utilization, technological innovation, and adoption
of sustainable practices. However, it's essential to consider
potential challenges and trade-offs associated with economic
globalization, such as increased competition, income ine-
quality, and environmental degradation in other regions.
Overall, these findings suggest that economic globalization
can potentially benefit the US economy by fostering growth
and reducing ecological footprint. These results resonate
prior studies (e.g., Adebayo etal. 2024; Bekun and Ozturk
2024; Van Tran etal. 2024; Zhang etal. 2024a).
In Fig.3d, the impact of RENEN on ECOFP is depicted.
The outcome suggests that the relationship between renew-
able energy (RENEN) and ecological footprint (ECOFP)
varies across different quantiles. At lower quantiles, there is
a weak positive correlation, indicating that with lower levels
of renewable energy usage, there may be a slight increase
in ecological footprint. However, at higher quantiles, par-
ticularly within the ranges of 0.60–0.90 for both renewable
energy and ecological footprint, a notable negative effect
is observed. This implies that as renewable energy usage
and ecological footprint increase simultaneously, there is
a significant decrease in ecological footprint, suggesting
the potential for renewable energy to contribute to reducing
environmental impact, especially in more renewable energy-
intensive contexts. Overall, the findings underscore the
importance of prioritizing renewable energy in the United
States' energy and environmental policies to mitigate eco-
logical footprint, foster sustainable economic growth, and
address climate change. These outcomes resonate with the
earlier studies (Roy 2024; Zhang etal. 2024a).
Figure3e illustrates the influence of NATRE on ECOFP.
The visualization highlights a significant negative impact of
NATRE on ECOFP, particularly evident when analyzing the
upper quantiles of both variables. This result suggests that
there is a substantial negative relationship between natural
resources (NATRE) and ecological footprint (ECOFP). In
other words, as the utilization of natural renewable energy
increases, there tends to be a notable decrease in ecologi-
cal footprint. This implies that incorporating more natural
renewable energy sources into energy production and con-
sumption can lead to a reduction in environmental impact
and contribute to sustainability efforts. Furthermore, the
alignment of these findings with previous studies under-
scores the robustness and consistency of the observed rela-
tionship. It reinforces the notion that leveraging natural
renewable energy sources holds significant potential for
mitigating ecological footprint and advancing environmen-
tal conservation efforts. These findings are consistent with
previous studies conducted by He etal. (2024), Qing etal.
(2024) and Roy (2024).
Wavelet quantile regression (WQR)
We have introduced wavelet quantile regression (WQR) to
effectively handle issues associated with tail dependence
structures. Figure4 demonstrates the outcomes of the wave-
let quantile regression analysis, with heat maps illustrating
the estimated slope coefficients ranging from light green to
red. In Fig.4a–e, heat maps show the impact of solar energy
innovation, digitalization, economic globalization, renew-
able energy, and natural resources on the ecological footprint
in the USA across various time periods and quantiles. The
right vertical axis displays the relationship coefficient, while
the left indicates time frames (short, medium, long), and the
horizontal axis represents quantiles. Figure5 displays Wave-
let Quantile Regression. The heat maps depict estimated
slope coefficients, varying from light green to red. Specifi-
cally, heat maps (a-e) show the influence of solar energy
innovation, digitalization, economic globalization, renew-
able energy, and natural resources on the ecological footprint
in the USA across various time periods and quantiles across
different time spans and quantiles in the USA. The sample
period spans from 2000Q1 to 2020Q4. (a) SEINO impact
on ECOFP (b) DIGIT impact on ECOFP (c) ECGLO impact
on ECOFP (d) RENEN impact on ECOFP and (e) NATRE
impact on ECOFP.
Solar energy innovation consistently exhibits a negative
correlation with the ecological footprint across all quantiles
and periods, as illustrated in Fig.4a. Moreover, its diminish-
ing impact on the ecological footprint is more noticeable in
the long term, indicating increasing effectiveness of policy
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T.S.Adebayo et al.
(a) WQR between SEINO and ECOFP
(b) WQR between DIGIT and ECOFP
(c) WQR between ECGLO and ECOFP
(d) WQR between RENEN and ECOFP
(e) WQR between NATRE and ECOFP
Fig. 4 Wavelet quantile regression estimates. Note:The heatmaps exhibit the estimated slope coefficients in ascending order from light green to
red
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Analyzing theeffects ofsolar energy innovations, digitalization, andeconomic globalization…
recommendations over time. Thus, solar energy innovation
in the USA can decrease the ecological footprint by reduc-
ing reliance on fossil fuels. By harnessing renewable and
clean solar energy, greenhouse gas emissions, air pollution,
and environmental degradation are minimized, leading to a
smaller ecological footprint. This conclusion finds support
in numerous studies, including those undertaken by Adebayo
and Özkan (2024) and Sharif etal. (2021). Digitalization
consistently has a negative impact on the ecological foot-
print across all quantiles and periods (Fig.4b), highlighting
(a) QQR between SEINO and ECOFP (b) QQR between DIGIT and ECOFP
(c) QQR between ECGLO and ECOFP (d) QQR between RENEN and ECOFP
(e) QQR between NATRE and ECOFP
Fig. 5 Quantile on quantile regression estimates
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T.S.Adebayo et al.
its crucial role in promoting environmental quality in the
USA. Additionally, the long-term significance of digitaliza-
tion in reducing the ecological footprint is underscored by
the WQC analysis. This conforms to the findings of Karli-
lar etal. (2023), Ke etal. (2022), Zheng etal. (2023) and
Zhu etal. (2023a, b). Across all quantiles and periods (see
Fig.5c, d), there is a consistent negative impact observed
between economic globalization, renewable energy, and
ecological footprint. This underscores the significance of
economic globalization and renewable energy in promoting
ecological quality and effectively reducing the ecological
footprint in the USA. Moreover, the long-term significance
of economic globalization and renewable energy in mitigat-
ing ecological footprint is highlighted by the WQC analysis.
These findings are consistent with research conducted by
Bekun and Ozturk (2024), Eweade etal. (2023a, b, 2023a),
Zhang etal. (2024a) and Zhu etal. (2023a, b) which also
highlighted the significance of globalization and renew-
able energy in reducing ecological footprint. Moreover, our
research reveals that natural resources have a positive impact
on the ecological footprint across all periods and quantiles
(refer to Fig.5e). This implies that natural resources worsen
environmental quality by contributing to an increase in the
ecological footprint. These findings are consistent with
the viewpoints presented (Balsalobre-Lorente etal. 2023;
He etal. 2024; Ibrahim etal. 2023; Razzaq etal. 2022) all
of whom have underscored the negative impact of natural
resources on ecological quality.
Robustness analysis (quantile onquantile
regression) results
In our robustness analysis, we utilized the Quantile-on-
Quantile Regression method. Figure5a–e displays the plots
generated through this approach. In Fig.5a, our observations
reveal that solar energy innovation (SEINO) consistently
decreases ecological footprint (ECOFP) emissions across
all quantiles. However, the impact of SEINO on ECOFP
reduction is particularly noticeable when all quantiles of
SEINO are combined with the middle quantiles of ECOFP
(0.5–0.75). Consequently, we conclude that the influence of
SEINO on ECOFP diminishes when other exogenous factors
are moderated. In Fig.5b, the influence of DIGIT on ECOFP
quantiles, moderated by ECGLO, RENEN, and NATRE, is
depicted. A consistent positive connection between DIGIT
and ECOFP is observed across all quantiles. Consequently,
after accounting for the moderation effect of other regres-
sors, it is evident that DIGIT decreases ECOFP in the USA.
Through quantile-on-quantile regression, Fig.5c examines
the relationship between the τth quantile of economic glo-
balization (ECGLO) and the λth quantile of ecological foot-
print (ECOFP), while considering the moderating effects
of SEINO, DIGIT, RENEN, and NATRE. The analysis
indicates that economic globalization (ECGLO) consistently
decreases ecological footprint (ECOFP) across all quantiles.
However, this negative effect is relatively weaker within the
range where all quantiles of ECGLO align with the middle
quantiles of ECOFP (0.3–0.65). Therefore, it can be inferred
that while economic globalization tends to decrease ecologi-
cal footprint, its impact is somewhat attenuated when consid-
ering the effects of another exogenous factor. Furthermore,
Fig.5d illustrates the effect of the τth quantile of renew-
able energy (RE) on the λth quantile of ecological footprint
(ECOFP), while accounting for the moderating influence
of SEINO, DIGIT, RENEN, and NATRE. The impact of
renewable energy (RENEN) reduces ECOFP across all quan-
tiles, although the negative effect is less pronounced in the
range where all quantiles of renewable energy intersect with
the middle quantiles of CO2 emissions (0.3–0.65). Thus, we
can infer that renewable energy (RENEN) negatively affects
ECOFP when considering the effects of other exogenous fac-
tors. Moreover, in Fig.5e, the influence of the τth quantile
of natural renewable energy (NATRE) on the λth quantile
of ecological footprint (ECOFP) is depicted, taking into
account the moderating effects of SEINO, DIGIT, RENEN,
and NATRE. Across all quantiles, a consistent positive rela-
tionship between natural renewable energy (NATRE) and
ecological footprint (ECOFP) is evident. Consequently, after
adjusting for the effects of the other regressors, it is apparent
that natural renewable energy (NATRE) contributes to an
increase in ecological footprint (ECOFP) in the USA.
Conclusion andpolicy recommendations
Conclusion
This research investigates how solar energy innovation, digi-
talization, economic globalization, renewable energy, and
natural resources on environmental quality in the USA. It
utilizes the Quantile-on-Quantile Kernel-Based Regularized
Least Squares (QQKRLS) approach spanning from 2000 to
2020. The results of the analysis indicate a predominantly
negative relationship between solar energy innovation
(SEINO) and environmental footprint (ECOFP). However,
it's noteworthy that the strength of this association varies
across different quantiles. Overall, the findings suggest a
positive association between solar energy innovation and
environmental quality, implying that an increase in solar
energy innovation may potentially benefit the environment
positively. However, these correlations vary in magnitude
across different quantiles. The study reveals that digitaliza-
tion has a significant negative correlation with ecological
footprint, with varying degrees of strength observed across
different quantiles. Conversely, digitalization consist-
ently exhibits a positive impact on environmental quality
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Analyzing theeffects ofsolar energy innovations, digitalization, andeconomic globalization…
across all quantiles. The findings reveal a weak correlation
between economic globalization and ecological footprint at
lower quantiles, while a robust negative correlation is evi-
dent at higher quantiles. The study suggests the relation-
ship between renewable energy and ecological footprint
varies across different quantiles. At lower quantiles, there
is a weak positive correlation, indicating that with lower
levels of renewable energy usage. There are significant nega-
tive impacts of natural resources on ecological footprint,
particularly evident when analyzing the upper quantiles of
both variables. This result suggests that there is a substantial
negative relationship between natural resources and ecologi-
cal footprint.
Policy recommendations
Based on the empirical findings, policymakers in the USA
should consider the following policy recommendations: Pro-
mote Solar Energy Innovation solar energy innovation and
ecological footprint, the varying strength of this associa-
tion across different quantiles suggests the need for targeted
policies. Policymakers should encourage investment in solar
energy research, development, and adoption to potentially
benefit the environment positively. Incentives such as tax
credits or grants could be provided to support the advance-
ment and implementation of solar energy technologies.
Given the significant negative correlation between digi-
talization and ecological footprint, policymakers should
leverage digital technologies to mitigate environmental
impacts. Initiatives should focus on enhancing environmen-
tal monitoring, resource management, and sustainability
efforts through digital platforms and data-driven strategies.
Additionally, efforts to bridge the digital divide and ensure
equitable access to digital technologies should be prioritized
to maximize the environmental benefits across all segments
of society. Recognizing the varying correlation between
economic globalization and ecological footprint across dif-
ferent quantiles, policymakers should prioritize sustainable
trade practices and regulations. Measures such as promoting
fair trade agreements, enforcing environmental standards in
international trade, and incentivizing eco-friendly produc-
tion and consumption patterns can help mitigate negative
environmental impacts associated with globalization.
Despite the mixed correlation between renewable energy
and ecological footprint across different quantiles, policy-
makers should continue to promote the adoption of renew-
able energy sources as part of efforts to reduce environmental
footprint. This can be achieved through policies supporting
renewable energy infrastructure development, investment
incentives for renewable energy projects, and regulatory
measures to facilitate renewable energy integration into the
energy grid. Given the significant negative impact of natu-
ral resources on ecological footprint, particularly evident at
higher quantiles, policymakers should prioritize sustainable
management practices. Measures such as conservation efforts,
sustainable resource extraction practices, and land-use plan-
ning can help mitigate environmental degradation associated
with resource exploitation while preserving natural ecosystems
and biodiversity. Additionally, policies promoting sustainable
consumption and production patterns can help reduce overall
resource consumption and environmental footprint.
Suggestion forfuture studies
The study’s analysis from 2000 to 2020 may introduce biases
and affect precision due to limited data availability. Future
research should expand the temporal scope and access more
extensive datasets for improved accuracy. While scrutinizing
solar energy innovations, digitalization, economic globali-
zation, renewable energy, and natural resources, the study
acknowledges the absence of certain variables impacting envi-
ronmental quality. In future research, it's important to consider
including additional variables to achieve a more comprehen-
sive evaluation. Given the possibility that the findings may be
specific to the USA, it is essential to replicate these analyses
in various geographical contexts for broader applicability.
Subsequent studies could also involve longitudinal analyses
to identify trends, examine sector-specific impacts, and explore
differences across different countries in terms of the effec-
tiveness of solar energy innovations, digitalization, economic
globalization, renewable energy, and natural resources on
environmental quality. Investigating the influence of emerg-
ing technologies, integrating social and cultural dimensions,
and evaluating policy effectiveness over time are critical for
assessing the real-world impact of environmental policies.
Addressing these aspects will contribute to the refinement of
ongoing environmental policy strategies.
Author contributions TSA: Conceptualization, Methodology, visuali-
zation; MSM: Writing and editing; BSE: Writing and Discussion; OO:
Methodology
Funding Open access funding provided by the Scientific and Techno-
logical Research Council of Türkiye (TÜBİTAK). The authors have
not disclosed any funding.
Data availability Enquiries about data availability should be directed
to the authors.
Declarations
Competing interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
T.S.Adebayo et al.
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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