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Greening Growth: The Environmental Implications of Technology Innovation,
Green Finance, and Foreign Direct Investment in Pakistan
Javaid Hussain a, Sadia Anjum b, Muhammad Yousuf c, Fiaz Ahmad d
a Visiting lecturer, University of Layyah, Layyah, Pakistan
b Assistant professor, University of Layyah, Layyah, Pakistan
c PhD Scholar, IUB, visiting lecturer, School of economics, BZU, Multan, Pakistan
d Assistant Regional Director, Allama Iqbal Open University, Regional Campus Dera Ghazi Khan, Pakistan
ARTICLE DETAILS ABSTRACT
History:
Accepted 25 June 2023
Available Online June 2023
This research delves deeply into the intricate interplay among
technology innovation, green finance, foreign direct investment (FDI),
GDP, and their collective impact on the environment. Employing the
Autoregressive Distributed Lag (ARDL) model over the timeframe
spanning 1990 to 2021, the study aims to unveil nuanced insights into
the intricate relationships that shape the environmental landscape. The
study's findings offer an insightful perspective on the connections
between these pivotal variables and their repercussions on
environmental metrics. Specifically, the outcomes reveal a negative
correlation between technology innovation, green finance, and CO2
emissions, as well as ecological footprints. This suggests a noteworthy
linkage between technological advancements and the adoption of
sustainable financial mechanisms with reduced carbon emissions and a
less burdensome ecological footprint. These trends underline their
potential to contribute positively to the well-being of the environment. In
contrast, the study uncovers a positive correlation between FDI, GDP,
and both CO2 emissions and ecological footprints. This observation
underscores the intricate dynamics at play, wherein foreign direct
investment and economic growth appear to exert pressures that escalate
carbon emissions and environmental impact. This intricate relationship
brings into focus the potential trade-offs between advancing economic
development and preserving the environment, necessitating a thoughtful
equilibrium for sustainable progress. The implications of these
revelations hold substantial weight for policymakers and government
officials in Pakistan. By illuminating the nuanced interconnections
among technology innovation, green finance, FDI, and environmental
indicators, this research equips decision-makers with invaluable insights
to formulate effective policies.
© 2023 The authors. Published by SPCRD Global Publishing. This is an
open access article under the Creative Commons Attribution-
NonCommercial 4.0
Keywords:
Technology Innovation, Green
Finance, ARDL, Pakistan
JEL Classification:
O14
DOI: 10.47067/reads.v9i2.490
Corresponding author’s email address: javaidh10@gmail.com
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1. Introduction
In the ever-evolving tapestry of modern society, technology innovation has emerged as an
undeniable catalyst, shaping every facet of our lives, from communication and healthcare to
transportation and entertainment. This transformative power of technology has ushered in unparalleled
convenience and efficiency, revolutionizing our interaction with the world. However, amidst these
transformative triumphs, a growing shadow of concern has emerged – the impact of these innovations
on the environment. As we find ourselves at the pivotal juncture of progress and preservation, it
becomes paramount to unravel the intricate layers of technology's impact on the environment and to
craft strategies that channel its potential toward fostering a sustainable future. The relationship
between technology and the environment is a nuanced interplay of benefits and challenges. On one
hand, technological innovation heralds a wave of solutions brimming with potential to mitigate
pressing environmental concerns (Ali et al., 2022; Faheem et al, 2023). Notably, renewable energy
technologies present alternatives to fossil fuels, tapping into sources like solar, wind, and water power.
This transition not only slashes greenhouse gas emissions but also charts a course toward cleaner
energy ecosystems. Electric vehicles stand as another emblem of progress, poised to metamorphose the
transportation landscape by diminishing air pollution and reducing our carbon footprint (Farooq et al.,
2023; Faheem et al., 2021).
Beyond this, the realm of technology-driven data collection and analysis opens the door to
refined environmental monitoring and assessment (Ahmed et al., 2023). A symphony of sensor
networks, satellite imagery, and advanced analytics furnishes scientists and decision-makers with real-
time ecological insights, fostering informed judgments and targeted interventions. Here, technology
evolves into a potent instrument for deciphering the intricate dance of ecosystems, tracking shifts in
biodiversity, and orchestrating effective responses to natural calamities (Ullah et al., 2023). Yet, even as
technology surges forward with unprecedented velocity, it carries its own ecological balance sheet. The
manufacturing, utilization, and disposal of electronic devices generate electronic waste (e-waste), an
escalating environmental concern. Mismanagement of e-waste translates into the contamination of soil,
water, and air, while imperiling human health. Moreover, energy-intensive technological processes, like
data centers and cryptocurrency mining, wield a hefty ecological toll through augmented energy
consumption and ensuing carbon emissions. The dawn of the digital era, with its proliferation of
smartphones, computers, and digital devices, casts a latent environmental shadow. Data centers,
bastions of the digital realm, sustaining the deluge of information we both create and consume, demand
prodigious amounts of energy for their operations and cooling systems. These energy requirements,
oftentimes met by non-renewable sources, can exert undue strain on local power grids (Anwar et al.,
2016; Ahmad et al., 2021; Chaudhry et al., 2021; Farooq et al., 2023; Uche et al., 2023).
Green finance, an innovative financing approach that places environmental sustainability at its
core, is swiftly reshaping the financial landscape, bearing significant implications for our planet (Farooq
et al., 2020). With its central goal of directing capital toward initiatives and projects with positive
environmental outcomes, green finance is not only revolutionizing investment strategies and resource
allocation but also playing a pivotal role in addressing urgent environmental challenges. One of the
most substantial effects of green finance lies in its power to propel sustainable development and
alleviate environmental degradation. By channeling funds toward endeavors that promote renewable
energy, energy efficiency, sustainable agriculture, and clean technologies, green finance operates as a
catalyst, diminishing carbon emissions and mitigating the impact of climate change. For instance, the
funding of renewable energy initiatives, such as solar and wind farms, not only diminishes dependency
on fossil fuels but also expedites the transition toward a low-carbon economy. Furthermore, green
finance assumes a critical mantle in fostering innovation. The financial incentives extended to
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businesses and organizations committed to environmentally friendly projects stimulate the
advancement of research and development in clean technologies. This, in turn, gives rise to innovative
solutions for enhancing energy efficiency, curbing waste, and managing resources sustainably. This
innovative drive ripples across industries, nurturing a culture of innovation essential for addressing
intricate environmental challenges.
Beyond its positive influence on environmental outcomes, green finance contributes to economic
resilience and the mitigation of risks (Taneja & Ozen., 2023). As businesses and investors incorporate
environmental considerations into their decision-making processes, they are better poised to anticipate
and adapt to environmental risks. This proactive stance not only shields investments against potential
losses resulting from environmental shocks but also encourages the adoption of sustainable business
practices that can augment long-term profitability. Additionally, green finance champions transparency
and accountability. The integration of environmental criteria into investment decisions demands
heightened disclosure and reporting on environmental performance. This fosters a culture of
transparency within businesses and financial institutions, enabling stakeholders to accurately assess the
environmental ramifications of their investments. Consequently, companies are incentivized to enhance
their environmental practices to attract green investments and showcase their dedication to
sustainability. The reach of green finance transcends a single sector; its impact reverberates throughout
economies, societies, and ecosystems. By mobilizing private capital in support of sustainable initiatives,
green finance complements governmental endeavors to attain environmental targets outlined in
international agreements like the Paris Agreement. It aligns financial objectives with environmental
imperatives, fostering a harmonious synthesis of economic growth and ecological well-being (Triki et
al., 2023; Anwer et al., 2023; Ahmad et al., 2023; Farooq et al., 2023; Chaudhry et al., 2021).
In the intricate realm of globalization and interconnected economies, Foreign Direct Investment
(FDI) emerges as a formidable agent transcending geographic confines, orchestrating transformative
shifts in worldwide economies and industries (Chaudhry et al., 2020). Amid the fervent pursuit of
economic expansion, technological progress, and elevated employment prospects, FDI emerges as a
conduit through which capital, proficiency, and inventive ideas traverse borders. Nevertheless, amid
this pursuit of economic ascendancy, the environmental implications of FDI have garnered considerable
attention and sparked lively debates. The nexus between FDI and the environment weaves a complex
narrative, encompassing a spectrum of outcomes that span from constructive contributions to pressing
apprehensions. This dynamic interplay prompts us to embark on a comprehensive exploration of the
intricate impact of FDI on the environment, scrutinizing its potential to ignite environmental
sustainability, propel resource depletion, usher in cleaner technological frontiers, and heighten the
levels of pollution. In this expedition, our endeavor is to meticulously unravel the intricate threads that
tether FDI and the dynamics of the environment, paving the way for a nuanced comprehension of how
economic ambitions intersect with the ecological mandates on the global stage.
At the core of FDI's potential constructive influence rests its capability to nurture environmental
sustainability. Foreign investors frequently import advanced technologies, managerial acumen, and
exemplar practices from their home nations (Jan et al., 2023; Tanveer et al., 2023; Chaudhry et al.,
2021). These transmissions of knowledge can potentially galvanize the adoption of ecologically benign
production methods, resource-efficient methodologies, and sustainable business paradigms in the host
nations. Consequently, FDI holds the potential to contribute to the curtailment of carbon emissions, the
containment of pollution, and the advocacy of judicious resource management, thereby aligning
economic growth with the stewardship of the environment. However, the symbiotic bond between FDI
and environmental advancement is not devoid of intricacies. The pursuit of economic advantages, often
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linked with augmented production and heightened consumption, can yield resource-intensive industries
that exert strains on ecosystems, culminating in concerns of resource overexploitation. The
establishment of extensive FDI ventures, such as mining enterprises or intensive agricultural setups,
can act as catalysts for deforestation, habitat degradation, and soil erosion, thereby fundamentally
reshaping local ecosystems (Wang et al., 2023; Tanveer et al., 2023). In this radiance, the ecological
footprint of FDI extends beyond the immediate sphere of economics, fomenting deliberations on the
equilibrium between development and preservation.
Moreover, the advent of FDI also introduces a dual narrative in the domain of technological
advancements. On one hand, multinational corporations investing in host nations might bring forth
advanced technologies that champion energy efficiency, waste abatement, and the adoption of
renewable energy sources. This infusion of innovation has the potential to expedite the transition
toward sustainable practices and cultivate a culture of environmental consciousness. However, the
converse can also hold true, where industries tethered to antiquated or environmentally detrimental
technologies may relocate to regions boasting less stringent environmental regulations. This migration
exacerbates pollution levels, subsequently impairing air and water quality. In steering through this
intricate terrain, governmental bodies and regulatory entities assume pivotal roles. Devising policies
that incentivize environmentally conscious FDI while discouraging practices that run counter to
environmental well-being becomes paramount (Kiani et al., 2023; Chaudhry et al., 2021; Tanveer et al.,
2021; Umar et al., 2021). The establishment of robust benchmarks for environmental standards,
coupled with mechanisms for vigilant monitoring and stringent enforcement, stands as a safeguard
ensuring that FDI adheres to principles of sustainability. Similarly, the fostering of alliances between
foreign investors, local communities, and environmental advocacy groups can give rise to an all-
encompassing strategy that mitigates adverse repercussions and leverages the potential of FDI for
constructive transformation.
The research impact of technology innovation, green finance, GDP and FDI on Environment in
Pakistan" significantly contributes to our understanding in the following ways: The utilization of the
Autoregressive Distributed Lag (ARDL) model for analyzing data spanning from 1990 to 2021 yields
valuable insights into both the enduring relationships and fleeting dynamics of the studied variables.
The ARDL model's aptitude for handling variables with mixed orders of integration proves fitting,
offering a robust framework to assess the intricate connections between technology innovation, green
finance, foreign direct investment (FDI), and the environment. (ii) Against the backdrop of the
contemporary global context, this research addresses a pressing concern, shedding light on the pivotal
roles played by technological advancement, sustainable financial mechanisms, and transnational
investments in shaping economic growth and safeguarding environmental integrity. (iii) By centering
on Pakistan, the study acknowledges the distinctive socio-economic and environmental milieu of the
nation. The insights gained provide a tailored perspective capable of guiding policy formulations and
strategic decisions aimed at harmonizing economic development with the preservation of the
environment. (iv) The research underscores the pivotal significance of technology innovation. As a
catalytic force capable of reshaping production processes, consumption patterns, and the utilization of
resources, technology innovation's interactions with green finance and FDI are scrutinized. The
collective influence of these factors on Pakistan's environmental trajectory is deciphered, contributing
to an enhanced understanding of the intricate relationships at play. (v) The inclusion of green finance
as a distinct element augments the research's contributions. Through a meticulous investigation of the
interplay between green financing initiatives and their outcomes within Pakistan, the study casts light
on the efficacy of such financial mechanisms in propelling sustainable practices and aligning economic
growth with the imperative of environmental well-being. (vi) The ramifications of the research extend
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beyond its immediate focus, permeating the broader discourse in the realm of environmental economics
and sustainability. By unraveling the complex web of connections between economic advancement and
environmental welfare, the findings enrich our comprehension of this nuanced interrelationship.
2. Literature Review
Ahmed et al. (2023) investigated the impact on china economic growth of technological
innovation. Study used auto regressive distributive lag model and data was used over forty years
almost. The results of this study were showed that technology innovation has positive impact on
economic growth of selected economy. Ullah et al. (2023) studied that environment is a big problem
nowadays in the world. Study checked the impact of public investment and technology on the
environment of Pakistan. In this study was used (NARDL) asymmetric technique to check the
association among the variables. Data was used over the time period from 1971 to 2021. The finding of
this study were showed that there is the asymmetric relationship among the ICT, public investment,
economic expansion and environment in Pakistan. Uche et al. (2023) examined that in underdeveloped
countries foreign direct investment have a great role. Study found that foreign direct investment have
positive relation with environment. For this panel auto regressive distributive lag model technique was
used in the study. Study defined also that in the BRICS economies carbon emissions are systematic with
the passage of time foreign direct invest, technological innovation and environment also cleaned the
environment. And also found the in the traditional method foreign direct investment, technological
innovation and environment have positive and negative both impacts
Jan et al. (2023) checked the relationship between financial development, trade, economic
growth, foreign direct investment and innovation on environment in Pakistan. This study was used
dynamic auto regressive distributive lag model to check the relationship among the variables. Results of
the study were found that negative effect of urbanization, energy consumption, and economic growth
on carbon emissions. Kiani et al. (2023) explored the technological impact on environment over the
time period from 1991 to 2018 for selected countries. Auto regressive distributive lag model results
were showed that in the long time period technological innovation have positive impact and in the short
time have negative impact on environment. Ali et al. (2022) looked that in Pakistan remittances,
technological innovation, natural resource and economic growth effect on the environment. In this
study urbanization and energy consumption were used as dependent variables and all other were used
as independent variables. The data was used over the time period from 1990 to 2019 and autoregressive
distributive lag model was used to check the association among the variables. ARDL results were
explained remittances have positive relationship with environment. Results also showed that carbon
emissions, economic growth, urbanization and technology have negative impact on environment.
Usman et al. (2022) used autoregressive distributive lag model and data from 1991 to 2020. In
this study nuclear energy, renewable energy, technology innovation environment variables for selected
country Pakistan. Finding of this study showed that in long time period nuclear energy have negative
impact and technological innovation decrease pollution in the long time period. Ullah et al. (2021)
revealed that the role of technology in the economic development has very important role. Study was
used annual data time period from 1990 to 2018. ARDL (auto regressive distributive lag) model was
used to check the relationship between the variables. The results were showed that trade mark has
symmetric impact on environment in long and short time period. Li and Ali (2023) aimed to study that
how green finance decrease the carbon emission in China economy. The study was used Delphi and
fuzzy analytical Hierarchy method to check the correlation among the selected the variables. The results
were showed that political instability have a great crucial role in the green finance in China. The role of
green finance to reduce the carbon emission very important.
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Taneja and Ozen (2023) studied the association of green finance and energy consumption on
environment in Asian countries. In this study panel (ARDL) technique was used to check the results of
long and short time period. The results of this study were showed that green finance have negative
impact on CO2. In the Asian Countries green finance and renewable energy reduce the carbon emission.
Triki et al. (2023) influenced the performance of green finance and banks activities. In this study for the
improvement of environment factor analysis technique was used. The results founded that banks and
green financing have great impact on environment. Environment reduced with the use of green
financing in the selected economies. Jahangir et al. (2023) studied the Saudi Arabia economies with the
role of green finance and other variables like renewable energy, environmental quality and public
health. The study was used data over the time period from 1980 to 2020. ARDL (auto regressive
distributive lag) model technique was used to c heck the association among the variables. The finding of
the study was revealed that all variables have impact on environment and also decrease the
environments impact.
Du and Wang (2023) investigated the ASEAN countries impacts of clean energy, grean finance,
economic growth, urbanization and foreign direct investment on environment. The study used twenty
years data from 2000 to 2020. The results of ARDL were showed that clean energy and green finance
have positive relationship with environment and also increased environmental quality. Economic
growth and urbanization process increase the pollutions. Dai et al. (2022) identified China economy
and the role of green finance, geopolitical risk, natural resource and environment. The data was used in
this study time period from 1995 to 2020. Quantile auto regressive distributive lag model was used. The
outcomes of the study showed that variables natural resources, agriculture development and
geopolitical risk have positive impact on environment. Wu et al. (2023) explored the impacts population
growth, foreign direct investment, trade openness and industries value added on environment in
Pakistan India, Bangladesh, Bhutan, Sri Lanka and Nepal economies. The study was used data of
Pakistan and all others economies from 1990 to 2021. Panel Autoregressive Distributed Lag model was
used to check the relationship between variables in this study. Study was founded that industry value
added and CO2 have a negative relation and positive impact of Foreign direct investment on
environment. Wang et al. (2023) studied the China economy and check the relationship of economic
growth, environment, foreign direct investment on economic efficiency. Study was used China data
time period from 2009 to 2021. SBM (super-efficient model) was used to check the China economy
impacts of selected variables. Results were showed that in the China economy environment have
different affects at different places or different regions.
3. Methodology
The analysis employs ARDL model to investigate the relationship between the dependent
variable, which is environmental impact, and the independent variables: technological innovation,
green finance, GDP and FDI. The model is specified as follows:
CO2 = β₀ + β₁(TECN) + β₂(GFIN) + β₃(FDI) + β₃(GDP) + ε
Where:
Environment = ENV; Technological Innovation = TECN; Green Finance =GFIN; FDI = Foreign direct
investment; GDP = Gross domestic product
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Variable Measurement & Data Source
Measurement Data Source
CO2 emissions (kt) WDI Environment (CO2)
Renewable energy consumption (% of total
final energy consumption)
WDI Green Finance (GFIN)
Patent applications, residents WDI Technology Innovation (TECN)
Foreign direct investment, net inflows (% of
GDP)
WDI Foreign Direct Investment
(FDI)
GDP (constant 2015 US$) WDI Gross Domestic Product (GDP)
Descriptive statistics encompass a range of summary measures that are employed to depict and
succinctly outline the essential attributes of a dataset. They offer a condensed perspective of the
dataset's central tendencies, diversities, and distribution configurations. These statistics encompass
metrics such as mean, median, mode, standard deviation, variance, minimum, maximum, and
percentiles. In the realm of effective econometric analysis, descriptive statistics form the bedrock. They
play a pivotal role in facilitating comprehension of the data, evaluation of its quality, assessment of
model assumptions, selection of variables, and formulation of hypotheses. Through their initial
portrayal of the data's characteristics, descriptive statistics aptly steer researchers toward suitable
econometric techniques, thereby bolstering the soundness and dependability of subsequent analyses
and conclusions (Farooq et al., 2022; Chaudhry et al., 2022; Faheem et al., 2022).
The correlation matrix holds a crucial significance within econometric analysis as it serves to
illuminate the interconnections between variables and assists in the process of selecting and defining
variables for modeling (Chaudhry et al., 2022; Faheem et al., 2022). It establishes a fundamental basis
for generating hypotheses, conducting diagnostic assessments, testing assumptions, and exploring the
dataset. Through the revelation of the inherent structure of the data, correlation matrices contribute to
fortifying the accuracy and dependability of econometric analyses (Faheem et al., 2021).
Stationarity pertains to a characteristic of time series data wherein its statistical attributes
remain consistent over time. Essentially, it implies that the data's mean, variance, and autocorrelation
remain unaltered as time progresses. In the realm of econometric analysis, stationarity holds immense
importance as numerous statistical techniques and models rely on or are optimized for stationary data.
The Augmented Dickey-Fuller (ADF) test and the Phillips-Perron (PP) test are instrumental tools
employed to ascertain whether a given time series dataset exhibits stationarity or not (Farooq et al.,
2022; Tanveer et al., 2022; Faheem et al., 2022).
Cointegration reveals meaningful, enduring relationships among non-stationary time series
variables (Tanveer et al., 2022). The F-Bound test plays a crucial role in this context by enabling the
detection of cointegration. This, in turn, assists in selecting appropriate models, preventing inaccurate
regressions, and enhancing the precision of econometric analyses. As a result, it becomes a vital tool for
researchers and analysts working in this field.
The Autoregressive Distributed Lag (ARDL) estimation technique is a statistical method utilized
in econometrics to explore enduring connections between variables within a time series framework. It
proves especially effective in scenarios where both the dependent and independent variables display
varying integration orders, indicating non-stationary behavior. The ARDL approach entails constructing
autoregressive models for both the dependent variable and lagged independent variables. This strategy
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facilitates the examination of immediate short-term dynamics as well as lasting equilibrium
relationships among the variables. The ARDL estimation method is favored in particular situations due
to its array of benefits: (i) ARDL adeptly manages cases in which variables possess distinct integration
orders (such as one variable being of order 1 and another of order 0). This adaptability renders it
suitable for a diverse array of economic scenarios. (ii) ARDL tackles endogeneity concerns by
incorporating lagged values of independent variables. This step assists in reducing potential bias in
estimations. (iii) ARDL demonstrates solid performance even when dealing with relatively modest
sample sizes. This proves advantageous when confronted with restricted data availability. (iv)ARDL
estimation empowers researchers to analyze extended relationships between variables. This proves
essential for comprehending the interactions of economic variables over prolonged periods. (v)ARDL
allows for model selection based on information criteria, aiding in the identification of appropriate lag
lengths and variables for inclusion in analyses. (vi) ARDL furnishes a framework for dynamic multiplier
analysis, permitting researchers to scrutinize the effects of shocks and variable alterations over time.
(vii) ARDL estimation maintains robustness against various types of heteroscedasticities and
autocorrelation, common characteristics of economic time series data (Chaudhry et al., 2021; Farooq et
al., 2021; Faheem et al., 2022). The following table shows the descriptive statistics and correlation
matrix that description of variables and association of variables.
Descriptive Statistics & Correlation Matrix
CO2 TECN GFIN FDI GDP
Mean 124443.8 116.6516 49.31987 1.040954 1.99E+11
Median 121608.7 91.00000 47.96000 0.735837 1.88E+11
Maximum 198738.8 387.2000 58.09000 3.668323 3.41E+11
Minimum 59026.00 16.00000 42.10000 0.375528 9.95E+10
Std. Dev. 42045.09 104.2243 4.063479 0.825176 7.45E+10
Skewness 0.159102 1.150201 0.420858 2.160517 0.428144
Kurtosis 1.831746 3.351020 2.501282 6.617068 1.910253
CO2 1 0.89 -0.93 0.05 0.98
TECN 1 -0.71 -0.05 0.94
GFIN 1 -0.23 -0.86
FDI 1 -0.048
GDP 1
The results of unit roots shows that all variables are integrated at first order of integration that leads to
apply ARDL estimation.
4. Results & Discussion
Table: Unit Root Test
CO2 TECN GFIN FDI GDP
ADF I(0) -0.731 3.504 -2.149 -2.012 2.677
I(1) -4.619*** -4.444*** -4.254*** -3.613** -3.682***
PP I(0) -0.739 3.345 -2.133 -2.055 2.63
I(1) -4.623*** -6.481*** -4.279*** -3.473** -3.764***
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Table: F-Bound Test
F-Stat:6.61
1% 2.5% 5% 10%
I(0) 3.74 3.25 2.86 2.45
I(1) 5.06 4.49 4.01 3.52
The following table shows association of the variables in short and long run. The results report
TECN and GFIN are negatively associated with CO2 emission while other variables FDI & GDP are
positively associated with CO2 in both short and long run. The lower part of the table shows diagnostic
test that reports our model is free from all problems.
Table: ARDL Results
Long Run Coefficient [t-ratio]
TECN -0.87*** [-3.59]
GFIN -0.02** [-2.53]
FDI 0.34* [1.98]
GDP 0.59*** [8.03]
C 2.65 [1.09]
Short Run
D(CO2(-1)) 0.56*** [4.51]
D(TECN) -0.03* [-1.82]
D(GFIN) -0.78** [-2.46]
D(FDI) 0.68*** [3.50]
D(GDP) 0.07* [2.35]
ECM(-1) -0.81*** [-3.60]
Diagnostic Tests
R2 0.99
D.W 1.98
J.B 0.27(0.87)
Hetero 0.94(0.53)
LM 1.95(0.18)
Ramsey RESET 1.52(0.26)
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-15
-10
-5
0
5
10
15
2002 2004 2006 2008 2012 2014 2016 2018 2020
CUSUM 5% Significance
-0.4
0.0
0.4
0.8
1.2
1.6
2002 2004 2006 2008 2012 2014 2016 2018 2020
CUSUM of Squares 5% Significance
5. Conclusion & Policy Suggestions
To conclude, this study, which investigates the impact of technology innovation, green finance,
and foreign direct investment (FDI) on the environment, utilizing the ARDL model for the period
spanning from 1990 to 2021, has yielded insightful revelations about the intricate relationships among
these variables and their collective ramifications. The findings have illuminated both positive and
negative associations, thereby presenting a comprehensive panorama of the intricate interplay between
economic advancement and the imperative of environmental preservation. The study's revelation of the
inverse correlation between technology innovation and green finance with CO2 emissions and
ecological footprints accentuates the potential of innovation and sustainable financial mechanisms to
make positive contributions to environmental well-being. This accentuates the significance of
cultivating an innovation-driven culture and embracing green financial practices as effective means to
mitigate carbon emissions and alleviate ecological burdens. On the contrary, the observed positive links
between FDI, GDP, and CO2 emissions, as well as ecological footprints, underline the necessity for
circumspect approaches to economic growth. While FDI and economic development are pivotal drivers,
they concurrently possess the capacity to escalate environmental strains. This underscores the
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paramount importance for policymakers to meticulously balance aspirations for economic expansion
with commitments to environmental preservation.
The implications of the study's findings bear substantial weight for policymakers and
government authorities in Pakistan, as they navigate the intricate terrain of technology innovation,
green finance, and FDI within the context of environmental sustainability: (i) Encouraging and
supporting technology innovation can lead to reduced carbon emissions and a diminished ecological
footprint. Policymakers can stimulate research and development in clean technologies, thereby
cultivating an environment conducive to eco-friendly innovation. (ii) The study emphasizes the value of
green finance in mitigating carbon emissions and ecological impact. Policymakers can formulate
policies that further incentivize investments in sustainable projects and encourage the adoption of
green financial practices. (iii) While FDI and economic growth are indispensable, it's imperative to
acknowledge their potential environmental consequences. Policymakers can introduce measures that
ensure FDI aligns with sustainable practices, thereby accounting for both economic and ecological
aspects. (iv) Given the diverse associations uncovered, policymakers can implement robust monitoring
and reporting mechanisms for CO2 emissions, ecological footprints, FDI, and GDP. This data-driven
approach can facilitate timely interventions and informed policy adjustments.
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