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Research article
Towards carbon neutrality & COP29 Baku / Azerbaijan - COP30
Belem / Brazil: Exploring the impacts of economic, environmental,
social, and governance (ECON-ESG) factors on Climate Policy
Uncertainty (CPU) for sustainable development
Cem Is
¸ık
a,b,c,d
, Serdar Ongan
e
, Jiale Yan
f,*
, Hasibul Islam
g
a
Department of Economics, Faculty of Economics and Administrative Sciences Anadolu University, Eskis
¸ehir, Türkiye
b
Baku Eurasian University, Economic Research Center (BAAU-ERC), Baku, Azerbaijan
c
Azerbaijan State University of Economics (UNEC), Clinic of Economics, Baku, Azerbaijan
d
Western Caspian University, Economic Research Center (WCERC), Baku, Azerbaijan
e
Department of Economics, University of South Florida, Tampa, USA
f
College of Letters and Science, University of California Berkeley, CA, 94720, USA
g
Department of Business Administration, Varendra University, Rajshahi, Bangladesh
ARTICLE INFO
Keywords:
Climate policy uncertainty
ECON-ESG
Carbon neutrality
EESG
ABSTRACT
Climate policy is crucial in shaping global responses to environmental challenges and steering
societies towards sustainable and resilient futures. Thus, in research study, we examine the im-
pacts of Economic (ECONF), Environmental (ENVF), Social (SOCF), and Governance (GOVNF)
factors, as well as the combined (ECON-ESG) factors, on Climate Policy Uncertainty (CPU) at the
global level. The new ECON-ESG form of sustainability, dened in this study, refers to the holistic
approach to sustainability by including economic factors (ECON) to traditional ESG factors.
Empirical ndings reveal that while environmental (E) and social (S) factors worsen the CPU,
economic factors (ECON) improve it in the long run. Governance (G) factors have no impact on
the CPU. While a 1 % increase in E and S increases the CPU by 22 % and 27 %, the same per-
centage increase in ECON decreases it by 40 %. These results clearly show that analyses con-
ducted only through the conventional form of ESG may be insufcient and inaccurate in
analyzing the effects on the CPU. Our study’s result should not be considered limited to the CPU
only, and it will be helpful to use this proposed form, ECON-ESG, as a more comprehensive
sustainability concept in sustainability studies since it also incorporates economic factors. This
approach will enable policymakers to look at climate policies through a holistic sustainability lens
with ECON-ESG. These results show that policymakers should adopt a holistic sustainability
approach that includes economic factors when shaping climate policies.
1. Introduction
In environmental policy and climate governance, comprehending the intricacies of climate policy uncertainty (CPU) is important
* Corresponding author.
E-mail addresses: cemisik@anadolu.edu.tr (C. Is¸ ık), serdarongan@usf.edu (S. Ongan), morningyjl@berkeley.edu (J. Yan),
hasibulislamshanto143@gmail.com (H. Islam).
Contents lists available at ScienceDirect
Heliyon
journal homepage: www.cell.com/heliyon
https://doi.org/10.1016/j.heliyon.2025.e41944
Received 4 August 2024; Received in revised form 12 January 2025; Accepted 13 January 2025
Heliyon 11 (2025) e41944
Available online 13 January 2025
2405-8440/© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
( http://creativecommons.org/licenses/by-nc-nd/4.0/ ).
for informed decision-making and promoting sustainability. CPU, stemming from various sources such as regulatory ambiguity, socio-
economic uctuations, and technological advancements, presents signicant hurdles to policymakers, businesses, and society. Thus,
unraveling the drivers and ramications of CPU has emerged as a central focus within scholarly research and policy discussions.
Concurrently, the signicance of sustainability risk, also known as the risk stemming from non-nancial elements or Environmental,
Social, and Governance (ESG) risk, becomes increasingly pronounced when considering its implications for both individual rms and
the broader economy [1]. Ongoing efforts seek to streamline the diverse denitions of ESG risk, including initiatives spearheaded by
the European Banking Authority. The elevation of environmental risk’s importance is evident in the Global Risks Reports, which rank it
as the paramount risk among its counterparts [2]. Notably, climate change emerges as a predominant risk category within this context.
Moreover, studies and reports are progressively elucidating the impact of non-nancial factors on nancial performance [3].
Climate change risk represents a signicant facet of environmental risk within the ESG risk framework. It is one of the foremost
threats among the top ve risks capable of exerting a profound impact [2]. The pervasive inuence of climate change and its rami-
cations permeate every sector of the economy [4]. Notably, the escalating operational expenses for businesses and governmental
initiatives aimed at mitigating its effects are both inuenced by climate change [5]. The maintenance of robust climate policies is
paramount in addressing climate-related concerns. Uncertainty surrounding climate policy can engender broader implications for
climate stability. Achieving net zero carbon emissions of carbon neutrality, COP 29 -Baku and COP 30 Belem is imperative for fostering
global sustainable development.
To address the novelty of this paper, this study pioneers an integrated approach to understanding the global dynamics of CPU
through the lens of ECON-ESG factors. Unlike previous research, which often focuses on CPU’s rm-level or isolated aspects, this paper
broadens the scope by conducting an in-depth country-level analysis. This study employs additional statistical methods, such as
principal component analysis (PCA) and econometric models, to investigate the short run and also long-run effects on ECON-ESG based
factors on CPU from a global perspective. This comprehensive approach not only addresses a critical gap in current literature but also
offers new insights that can help policymakers and businesses develop more effective strategies for achieving climate goals, such as
carbon neutrality and COP 29/30 in Baku/Belem. The integration of ECON-ESG into the discourse on CPU thus represents a unique
contribution to the eld, offering theoretical and practical implications.
This study explores the interrelation among ECON-ESG factors alongside CPU on a global scale. It appears conspicuously inno-
vative, representing novel and sparse. Research in CPU is critically important due to its impact on various economic and societal
sectors. Previous studies have examined various aspects of this phenomenon, including its effect on forecasting uctuations on eco-
friendly, clean, and environmentally sustainable monetary markets and its contribution to encouraging green initiatives. However,
there are still crucial gaps in understanding its broader effects. Persakis [6] provides noteworthy insights by delving into the re-
percussions of CPU on multiple fronts, including ESG performance, rm performance, and CO
2
emission performance, particularly
within Fortune 1000 rms. Such research sheds light on how CPU inuences nancial markets, organizational behavior, and envi-
ronmental outcomes.
Moreover, while existing research has examined the effect of CPU at the rm level, more country-level analyses are needed. ECON-
ESG (a newly developed concept by Is¸ık et al. [7–12] has potential effects on CPU at the individual country and global levels. Un-
derstanding how economic factors intersect with ESG considerations and inuence CPU can provide invaluable insights for policy-
makers, businesses, and stakeholders worldwide. Conducting a study on the CPU is crucial for comprehensively understanding its
multifaceted impacts on the economy, society, and the environment. Addressing these research gaps can inform more effective pol-
icymaking, corporate strategies, and global initiatives to achieve sustainability and resilience in the face of climate change. As a result,
this paper formulates the following research question (RQ).
RQ. How do ECON-ESG factors impact global CPU in the short and long run?
After an extensive review of prior studies, this research asserts that CPU, from a global perspective, is inuenced by many factors,
collectively known as ECON-ESG factors. This research employs a structured methodology consisting of two phases to enhance the
previous studies and improve comprehension. First of all, this study rigorously explores the evolution of CPU and the imperative for
achieving carbon neutrality and COP 29/30 – Baku/Belem across various dimensions. The study employs additional statistical
methods, such as PCA, to derive comprehensive individual ECON-ESG indices for a global perspective throughout the sample period.
This phase elucidates the intricate patterns and trends characterizing CPU and ECON-ESG performance. The study’s second phase aims
to further investigate the complex interactions between key determinants and CPU from a global perspective from 2001 to 2020.
Specically, this paper seeks to clarify the impact of ECON-ESG factors on CPU globally, both in the short and long term. This phase
employs advanced econometric models and analytical frameworks to identify the relationships and causal mechanisms inuencing
outcomes related to CPU in a global context. By adopting this comprehensive and multi-faceted approach, the study strives to
contribute novel insights into the determinants and dynamics of CPU within the global context.
2. Literature review
2.1. Empirical studies
The relationship between ESG principles and CPU is profound and multifaceted. Industries such as oil and gas, signicant con-
tributors to global emissions, are increasingly adopting ESG practices to confront climate-related issues [13]. This shift is driven by
regulatory pressures and mounting demands from investors and governments for businesses to commit to achieving net-zero emissions
by 2050. ESG management is vital for addressing climate concerns, fostering environmental stewardship, and curbing carbon
C. Is¸ık et al.
Heliyon 11 (2025) e41944
2
footprints [14]. Persakis [6] explores the repercussions of CPU on ESG outcomes, corporate performance, and carbon emissions amid
signicant developments in climate policy. Employing the CPU Index as a metric, the study reveals that heightened CPU positively
inuences ESG performance while exerting adverse impacts on rm performance and CO
2
emission performance.
Interestingly, rms are inclined to bolster their ESG performance amidst investor pessimism, particularly in periods of heightened
uncertainty. Notably, the study underscores that despite improvements in ESG performance, it does not mitigate the impacts of CPU on
rm performance and CO
2
emission. Climate policy, including initiatives such as carbon pricing and emissions regulations, is vital in
advancing carbon neutrality goals by incentivizing emission reductions and promoting the transition to renewable energy sources.
Effective climate policy frameworks provide essential guidance and incentives for industries and governments to work towards
achieving carbon neutrality and reducing the impacts of climate change.
Carbon neutrality means achieving a state of net-zero carbon emissions by balancing the release and removal of carbon compounds
from the atmosphere. This equilibrium is fundamental in mitigating the adverse impacts of climate change [15]. The pursuit of carbon
neutrality has seen a signicant increase in research activity, especially with scholars from China and the U.S. leading the publications
in this eld. Initiatives to achieve carbon neutrality encompass a spectrum of measures, including policy frameworks, technological
advancements, and socio-technical transitions [16]. Various branches of the social sciences contribute signicantly to comprehending
and devising pathways towards carbon neutrality [17]. Realizing carbon neutrality necessitates a dual approach to curtailing emissions
and enhancing carbon sequestration, with digital intelligence technologies assuming a pivotal role in this endeavor [18]. Notably,
carbon neutrality has emerged as a focal point in the automotive sector, with electric and alternative energy vehicles emerging as
promising solutions for attaining carbon-neutral transportation [19]. The advocacy and facilitation of alternative energy sources, such
as through green nance initiatives, play a critical role in propelling the transition towards carbon neutrality.
ESG considerations are intricately intertwined with the pursuit of carbon neutrality. Many studies have explored the connection
between ESG factors and commitments to achieving carbon neutrality. For instance, Xie et al. [20] observed that companies
committing to carbon neutrality may encounter declines in market value, yet robust prior ESG performance and comprehensive carbon
disclosure mechanisms can help alleviate negative market reactions. Similarly, Liu [21] shed light on how the quality of ESG
disclosure, particularly in realms like corporate governance and social responsibility, signicantly inuences nancing costs for
manufacturing enterprises in China.
Recent research highlights the signicant role of ESG performance in promoting sustainability and mitigating CO
2
emissions across
various sectors and regions. Kong et al. [19] highlight green credit as a key factor in halting carbon emission growth by facilitating
green innovation, with its effects varying by region. Zhou et al. [22] demonstrate the positive effect of ESG performance on reducing
corporate emissions in manufacturing, inuenced by factors like digital transformation and competition intensity. Yang and Hei [23]
nd that ESG ratings contribute to carbon reduction, particularly in megacities, while Qian and Liu [24] show that improved ESG
ratings enhance carbon efciency by addressing nancial constraints and promoting innovation. Similarly, Li and Xu [25] assert that
ESG practices signicantly reduce emissions across both state-owned and non-state-owned rms, calling for stronger government
regulations. However, the greenwashing phenomenon is addressed by Treepongkaruna et al. [26] who argue that high ESG ratings do
not always correspond to lower emissions, suggesting a need for deeper scrutiny. Li et al. [27] explore how climate change negatively
affects ESG performance but can be mitigated through resource reallocation and external pressures such as media and advocacy.
Y´
ebenes [28] underscores the rising importance of ESG criteria in nancial reporting, driven by regulations like the European
Corporate Sustainability Reporting Directive (CSRD), which is expected to enhance transparency and investor accountability. Finally,
Kluza et al. [29] explore the relationship between climate policy and the Sustainable Development Goals (SDGs), highlighting the
complex geographic and policy-specic factors that inuence the success of climate initiatives. These ndings collectively underscore
the intricate interplay between ESG practices, regulatory frameworks, and climate policy in driving global sustainability efforts.
Furthermore, Li et al. [30] investigated public perceptions of carbon neutrality. They revealed that environmental, social, and
governance concerns prominently inuence public sentiment, with a majority expressing optimism regarding the prospects of
achieving carbon neutrality. Achieving carbon neutrality is a crucial approach in the worldwide efforts to address climate change. It
entails expediting the transition from reliance on fossil fuels to renewable energy sources and does not contradict economic progress.
Conversely, adopting carbon neutrality can improve the effectiveness and sustainability of traditional industries while promoting
greater societal progress [31]. A concerted effort towards achieving carbon neutrality demands meticulous long-term planning and a
holistic approach encompassing energy, economics, and environmental considerations. Developing comprehensive programming
models has proven indispensable in pursuing carbon neutrality targets, offering quantitative benchmarks and strategic guidance for
transforming energy systems and industrial structures at regional levels. Embracing green economic development emerges as a pre-
requisite for realizing carbon neutrality, necessitating a shift towards a development paradigm characterized by a "low carbon"
threshold. This approach is paramount in advancing China’s green economic agenda [32]. The nancial sector exerts substantial
inuence on CO
2
emissions and thus plays a critical role in reducing emissions. Policymakers are urged to prioritize sustainable
development strategies, including promoting green logistics and innovative nancial mechanisms, to effectively steer towards carbon
neutrality [33]. The pursuit of carbon neutrality extends beyond climate change mitigation, heralding unprecedented societal
transformations marked by sustainable economic growth, conscientious consumer behaviors, and enhanced human well-being. Thus,
achieving carbon neutrality holds the promise of ushering in a new era of sustainable prosperity and environmental stewardship
[34–39].
2.2. Theoretical background
This study analyzes how climate policy uncertainty (CPU) is sensitive to ECON-ESG factors. Mainstream theoretical models
C. Is¸ık et al.
Heliyon 11 (2025) e41944
3
generally focus on economic growth, increased well-being, and the associated regulation of environmental and social impacts.
However, it is necessary to understand how these main factors interact in the transition to sustainability. Because ESG factors have
recently been accepted as the main pillars of sustainability strategies.
From this perspective, one can ask what the difference is between ECON-ESG studies and the more classic approach of economic
cycle analysis. Classic economic cycle analysis focuses on understanding and managing the economy’s short-term uctuations.
However, ECON-ESG studies broaden the perspective to include ESG factors. These studies aim to promote long-term sustainability and
resilience in economic systems. ECON-ESG represents a more holistic approach to evaluating economic health by integrating non-
economic factors essential for future-proong economies.
The CPU is included in our equation (1) based on practical evidence and conceptual foundations. Previous work by various
scholars, including Persakis [6], has underscored the signicant impact of CPU on ESG outcomes, business performance, and emission
outcomes.
Moreover, theoretical frameworks, as highlighted by van Vuuren et al. [40] and von Stechow et al. [41] emphasize the interplay
between climate policies and broader sustainable development objectives, necessitating consideration of uncertainty in policy
implementation. Thus, integrating CPU into the analysis is crucial in shaping outcomes relevant to climate mitigation efforts and
broader sustainability goals, aligning with empirical observations and theoretical perspectives.
The CPU’s lack of clarity and predictability in government policies and regulations regarding climate change has attracted sig-
nicant research attention due to its complex impacts on society and the environment. Studies have elucidated several key ndings
regarding the consequences of CPU across various domains. One study by Harmsen et al. [42] found a positive correlation between
CPU and corporate innovation investment. This indicates that companies tend to invest more in innovation when confronted with
uncertain climate policies, highlighting how ambiguity in regulatory frameworks can prompt proactive responses from businesses. Zhu
et al. [43] focused on the relationship between climate risk, policy uncertainty, and CO
2
emissions in the USA. Their ndings revealed
that natural disasters, inuenced by climate risk, exacerbate political discord among policymakers and amplify CPU. This highlights
the urgent need for streamlined policymaking and strong regulations to effectively address climate change.
Furthermore, research by Guesmi et al. [44] has highlighted in the substantial impact of CPU on energy-based markets. Extreme
climate events and major policy changes can heighten the causal relationship between policy uncertainty and relevant markets.
Governments are urged to prioritize climate policy implementation in energy transition efforts and work towards reducing uncertainty
to promote stability and sustainability in energy markets. In summary, CPU has wide-ranging implications for corporate innovation,
CO2 emissions, and energy markets. Addressing this uncertainty is crucial for effective climate change mitigation and adaptation
efforts [45]. ECON-ESG factors intertwine to shape the intricate landscape of the CPU. Economic dynamics, such as GDP uctuations
and mitigation strategies’ cost-effectiveness, inuence governmental prioritization and stakeholder perceptions. Concurrently, envi-
ronmental variables, ranging from the intensity of natural disasters to scientic uncertainties, amplify uncertainty by impacting the
urgency and efcacy of policy responses. Social forces, including public sentiment, activism, and societal values, further compound
uncertainty by molding political will and societal support for climate action.
Additionally, governance structures, encompassing institutional stability, leadership changes, and transparency in decision-
making, exert substantial inuence on the uncertainty surrounding climate policies, shaping their development, implementation,
Fig. 1. Theoretical framework.
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Heliyon 11 (2025) e41944
4
and enforcement. This convergence of ECON-ESG factors generates a complex uncertainty, underscoring the multifaceted nature of
climate policy formation and implementation. Fig. 1 illustrates the theoretical framework of the study.
3. Methods
3.1. Model and data
The study investigates the effects of ECON-ESG factors on global consumption per unit (CPU). We utilized secondary data from the
WDI spanning the period 2001 to 2020. This study aims to provide an in-depth analysis of the global impact and interrelationships of
these factors. The analysis is structured around the following equation:
lnCPUit =
α
0+β1ECONFt+β2ENVFt+β3SOCFt+β4GOVNt+et(1)
In the equation.
•lnCPU =natural logarithm of climate policy uncertainty
•ECONF =Economic Factors
•ENVF =Environmental Factors
•SOCF=Social Factors
•GOVNF =Governance Factors
•t =Time
The hypotheses based on the theoretical model are listed as follows.
Hypothesis 1. Economic growth (ECONF) reduces CPU.
Hypothesis 2. Environmental degradation (ENVF) increases CPU.
Hypothesis 3. Social inequalities and low quality of life (SOCF) increase CPU.
Hypothesis 4. Strong governance structures (GOVNF) reduce CPU.
3.2. Selection of variables
The variables selected cover ECON-ESG dimensions, which are crucial in comprehensively understanding CPU. Economic factors
encompass GDP growth, foreign direct investment, and unemployment rates which together indicate economic stability and invest-
ment trends. Environmental indicators such as CO
2
emissions, renewable energy consumption, and forest area percentage capture
environmental sustainability and resource management. Social variables include metrics of education, health, and gender equality,
highlighting social welfare and inclusiveness. Governance related factors, such as public expending for education and central gov-
ernment assertions, help clarify policy frameworks and institutional capacities. This set of variables enables the examination of the
complex inuences on CPU, covering economic, environmental, social, and governance aspects. These variables are presented in
Table A.
Table A
Variables
Variable(s) Denition Measurement Source(s)
CPU Climate Policy Uncertainty Index (100) CPU Index (2024)
ECONF Economic Factors Index (PCA) WDI (2024)
ENVF Environmental Factors Index (PCA) WDI (2024)
SOCF Social Factors Index (PCA) WDI (2024)
GOVNF Governance Factors Index (PCA) WDI (2024)
3.3. Econometrics approach
The analysis begins with a presentation of descriptive statistics, offering a comprehensive summary of all variables, including the
total number of observations, standard deviations, and maximum and minimum values. Year-by-year descriptive statistics are also
provided to offer a concise temporal perspective. To assess the relationships between variables, a correlation matrix is analyzed, and
multicollinearity is evaluated using the Variance Ination Factor (VIF).
To ensure model suitability, the study examines the stationarity of variables through unit root tests, specically the Augmented
Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. If variables are stationary or free from unit roots at their level, Ordinary Least
Squares (OLS) regression is considered appropriate. Conversely, if variables achieve stationarity only at their rst differences, the error
C. Is¸ık et al.
Heliyon 11 (2025) e41944
5
correction model (ECM) is preferred. The Autoregressive Distributed Lag (ARDL) model is employed when variables exhibit a mix of
stationarity at the level and the rst difference.
Fig. 2 shows the econometric methodology adopted in this research.
3.4. Estimation strategy
3.4.1. Augmented Dickey-Fuller (ADF) test
The ADF test was introduced by Dickey and Fuller [46], is a statistical tool for guring out whether a unit root exists in time series
data. This test checks stationarity by analyzing the data levels and their rst differences. It takes into account various models, such as
those that have a constant term alone and those that have a trend in addition to a constant term. equation (2) for the ADF test is as
follows:
d(Yt) =
α
0+βt+YYt-1+d(Yt(-1) +
ε
t(2)
The stationarity of every variable is assessed using the ADF test. Consequently, the following is the mathematical representation for
every construction (Equations (3)–(8)):
Climate Policy Uncertainty
d(CPUt) =
α
0+βt+YCPUt-1+d(CPUt(-1) +
ε
t(3)
Environmental Factor
d(ENVFt) =
α
0+βt+YENVFt-1+d(ENVFt(-1) +
ε
t(4)
Social Factor
d(SCOFt) =
α
0+βt+YSOCFt-1+d(SOCFt(-1) +
ε
t(5)
Governance Factor
d(GOVNFt) =
α
0+βt+YGOVNFt-1+d(GOVNFt(-1) +
ε
t(6)
Economic Factor
d(ECONFt) =
α
0+βt+YECONFt-1+d(ECONFt(-1) +
ε
t(7)
3.4.2. Phillips-Perron (PP) test
The PP unit root test, developed by Phillips and Perron in Ref. [47], is an extension of the Dickey-Fuller test that accounts for serial
correlation and heteroskedasticity in the data. The mathematical formulation of the PP test is as follows:
Fig. 2. Econometrics pathway.
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Heliyon 11 (2025) e41944
6
ΔYt=
α
0+βt+γYt-1+∑
p
i=1
δiΔYt-1+
ε
t(8)
In this equation.
•ΔYt denotes the rst-order difference of the time series variable when the time t
α
and βt represent the coefcients corresponding to
the intercept and trend
•γ represents coefcient of the dependent variable with lag Yt-1
•δ
i
are coefcients of the differences with lag of Yt
•
ε
t represents the error term
3.4.3. ARDL test
The study adopted the ARDL model because of its robustness in handling variables that are stationary at the I(0), I(1), or both. This
adaptability makes the ARDL model suitable for diverse data scenarios, particularly those with mixed stationarity properties.
Furthermore, previous studies [48] have highlighted the ARDL model’s effectiveness, especially in contexts with small sample sizes,
aligning closely with the current study’s framework. In addition, the ARDL model offers a comprehensive analysis by capturing both
long-term relationships and short-term dynamics. The mathematical representation of the ARDL model is as follows (Equation (9)):
ΔlnCPUt=
α
0+∑δ1ΔlnCPUt-1+∑δ2ΔENVFt-1+∑δ3ΔSOCFt-1
+∑δ4ΔGOVNFt-1+∑δ5ΔECONFt-1+φ1lnCPUt-1+φ2ENVFt-1+φ3SOCFt-1
+φ4GOVNFt-1+φ5ECONFt-1+
ε
t(9)
where; δ1δ2δ3δ4 and δ5 show the coefcients for the short run.
φ1φ2φ3φ4 and φ5 show the short run coefcients
ε
t depict the error term.
Within a time series framework, the Error Correction Model (ECM) analyzes short-term dynamics and the process of adjusting
toward long-term equilibrium. The following is the expression for the ECM equation (10):
ΔlnCPUt=
α
0+∑δ1ΔlnCPUt-1+∑φ2ΔENVFt-1+∑
ω
3ΔSOCFt-1
+∑θ4ΔGOVNFt-1+∑Y5ΔECONFt-1+δECMt+ϑt(10)
4. Results
4.1. Descriptive statistics
Table 1 reports the descriptive statistics for ve variables: lnCPU, ENVF, SOCF, GOVNF, and ECONF. Starting with lnCPU, which
represents the natural logarithm of CPU, the mean is approximately 2.01033, indicating the average value of lnCPU in the dataset. The
median value is 1.992, suggesting that half of the observations fall below this value and half above. The standard deviation (SD) of
0.151 signies the extent of variability or dispersion around the mean, with values ranging from 1.814 (minimum) to 2.34 (maximum).
The skewness of 0.649 suggests a slight right-skewed distribution, while the kurtosis of 2.705 indicates the peakedness relative to a
normal distribution. Moving on to ENVF, representing environmental factors, the mean is 2.00e-08 (or 0.00000002), with a median of
−0.171. This indicates that the distribution is negatively skewed, as the mean is closer to the lower end of the range. The standard
deviation of 1.000 shows considerable variability. For SOCF (social factors), the mean is 4.50e-08 (or 0.000000045), with a median of
0.116. The negative skewness (−0.512) implies a distribution where the tail is longer on the left side. GOVNF (governmental factors)
has a mean of 4.00e-08 (or 0.00000004) and a median of 0.109. A very left-skewed distribution is indicated by the skewness of −1.495,
whilst a heavy-tailed distribution with a possibly signicant outlier is indicated by the kurtosis of 5.460. Lastly, ECONF (economic
factors) has a mean of 8.00e-08 (or 0.00000008) and a median of 0.162, with skewness which is close to zero (−0.047) and kurtosis of
1.925, suggesting a distribution similar to a normal distribution but with slightly heavier tails. The Jarque-Bera test indicated that all
Table 1
Descriptive statistics.
Variables Mean Median SD Min Max Skewness Kurtosis Jarque-Bera
lnCPU 2.01033 1.992 0.151 1.814 2.34 0.649 2.705 1.477
ENVF 2.00e-08 −0.171 1.000 −1.341 2.055 0.458 2.233 1.190
SOCF 4.50e-08 0.116 1.000 −1.971 1.279 −0.512 2.172 1.446
GOVNF 4.00e-08 0.109 1.000 −3.063 1.265 −1.495 5.460 12.498***
ECONF 8.00e-08 0.162 1.000 −1.489 1.821 −0.047 1.925 0.970
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Heliyon 11 (2025) e41944
7
variables, except for GOVNF, exhibit a normal distribution. Furthermore, this study offers descriptive data for the chosen variables by
year. Table 2 shows the descriptive data each year.
4.2. Correlation analysis
Table 3 reports a comprehensive correlation analysis among the variables lnCPU, ENVF, SOCF, GOVNF, and ECONF. The corre-
lation between lnCPU and ENVF is −0.3346, suggesting a moderate negative relationship between CPU and environmental factors. In
contrast, lnCPU exhibits a strong positive correlation (0.5315) with SOCF, implying that social factors also tend to increase sub-
stantially as the CPU rises. Interestingly, GOVNF displays a relatively weak negative correlation (−0.3221) with lnCPU, indicating a
slight inverse relationship with governmental factors. Moreover, the correlation matrix reveals a positive relationship between ECONF
and lnCPU (0.0980), suggesting that as economic factors increase, CPU also increases.
4.3. Multicollinearity test
The research evaluated multicollinearity through the Variance Ination Factor. As presented in Table 4, all Variance Ination
Factor values are below 5, indicating that multicollinearity is not a signicant concern in the model. Therefore, based on the Variance
Ination Factor analysis in Table 4, multicollinearity does not pose a problem in the model under investigation.
4.4. Unit root test
The study further investigates the unit root using the ADF and PP tests, as selecting the appropriate model hinges on the stationarity
of the variables. Table 5 indicates that the variables lnCPU, ENVF, SOCF, GOVNF, and ECONF are stationary at the rst difference level.
Stationarity is a critical assumption in the time series analysis, as it ensures that the statistical properties of the data remain consistent
over time. Through trend elimination and series stabilization, the rst difference transformation aids in achieving stationarity. As a
result, based on the results of the ADF and Phillips-Perron PP tests, it is concluded that taking the rst difference of these variables
ensures their stationarity. This allows for appropriate modeling and analysis in the research.
4.5. ARDL bound test
For reliable long-term relationship estimation, the ARDL model depends on cointegration between variables. It is veried using the
ARDL bound test. The results of this test, shown in Table 6, demonstrate that the F-statistics are higher than the critical values,
indicating cointegration among the studied variables. The computed coefcients are robust and statistically signicant, according to
the improved F-statistics, indicating a consistent, long-term link between the factors. As a result, the authors’ use of the ARDL model
for their tests, as cointegration supports this method’s validity in capturing the data’s long-term dynamics.
4.6. Autoregressive Distributed Lag test
The analysis of the Autoregressive Distributed Lag model revealed several signicant short-term relationships between ECON-ESG
and CPU, as presented in Table 7. Notably, a negative correlation was observed between environmental, social, and governance factors
Table 2
Descriptive statistics.
Year lnCPU ENVF SOCF GOVNF ECONF
2001 1.971 −0.779 −1.971 0.291 −1.27
2002 1.946 −0.946 −1.852 1.265 −1.469
2003 1.832 −0.62 −1.1 1.028 −1.489
2004 1.814 −0.188 −1.054 −0.419 −1.111
2005 1.82 0.586 −0.856 −0.03 −0.349
2006 1.829 0.765 −0.693 0.059 0.537
2007 2.032 0.821 −0.513 −0.138 1.821
2008 1.987 2.055 −0.219 0.066 0.912
2009 1.996 −0.154 0.084 −1.194 −0.508
2010 2.051 0.665 0.05 −1.262 0.156
2011 2.059 1.8 0.149 0.343 0.771
2012 2.00 0.981 0.304 0.78 0.338
2013 1.89 0.59 0.934 0.154 0.498
2014 1.92 0.191 1.027 −0.824 0.168
2015 1.967 −1.109 1.069 0.937 1.308
2016 2.082 −1.279 1.279 0.57 1.258
2017 2.23 −0.953 1.208 0.667 0.524
2018 2.156 −0.409 0.723 0.758 −1.284
2019 2.283 −0.677 0.767 0.011 0.048
2020 2.34 −1.341 0.663 −3.063 −0.856
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and CPU. Specically, for every 1 % increase in ENVF, there was a corresponding decrease of 4 % in lnCPU (natural logarithm of CPU),
indicating a signicant inverse relationship. Similarly, a 1 % increase in SOCF was associated with a notable 26 % decrease in lnCPU,
highlighting the impactful negative correlation between social factors and CPU. Additionally, GOVNF exhibited a negative short-term
relationship with lnCPU, with a 1 % increase resulting in a 1 % decrease in lnCPU.
Conversely, economic factors displayed a positive short-term relationship with CPU. The analysis revealed that a 1 % increase in
ECONF corresponded to an 11 % increase in lnCPU in the short run. This outcome highlights the impact of ESG practices at the national
level in reducing CPU. It indicates that countries with robust ESG frameworks and practices experience lower uncertainty regarding
climate policies. This suggests that governments and policymakers prioritizing sustainability, social responsibility, and transparent
Table 3
Correlation analysis.
lnCPU ENVF SOCF GOVNF ECONF
lnCPU 1.0000
ENVF −0.3346 1.0000
SOCF 0.5315* −0.1093 1.0000
GOVNF −0.3221 0.0060 −0.1622 1.0000
ECONF 0.0980 0.3892 0.5174* 0.0724 1.0000
Note: * denotes a 5 % level of signicance.
Table 4
Multicollinearity test.
Variable VIF 1/VIF
ECONF 1.98 0.504
ENVF 1.74 0.575
SOCF 1.42 0.704
GOVNF 1.08 0.924
Mean VIF 1.56
Table 5
Unit root test.
Variables I(0) I(1)
ADF PP ADF PP
lnCPU −0.197 −0.197 −3.866*** −3.858**
ENVF −1.759 −1.727 −4.829*** −4.890***
SOCF −2.434 −2.433 −3.518** −3.519**
GOVNF −0.105 −1.506 −3.167** −2.920*
sECONF −2.255 −2.217 −4.301*** −4.406***
Note: ***, **, and * represent the 1 %, 5 %, and 10 % level of signicance, respectively.
Table 6
ARDL bound test.
Model F statistics Lag Signicant I(0) I(1)
lnCPU/(ENVF, SOCF, GOVNF, ECONF) 13.215 2 10 % 2.45 3.52
5 % 2.86 4.01
2.5 % 3.25 4.49
1 % 3.74 5.06
Table 7
Short run results.
Variables Coefcient Std. Error t-statistic Prob.
D(ENVF) −0.049042*** 0.007217 −6.795510 0.002
D(SOCF) −0.263151** 0.025102 −10.48321 0.000
D(GOVNF) −0.012634* 0.005813 −2.173605 0.095
D(ECONF) 0.111524*** 0.009191 12.13465 0.000
CointEq(-1)* 0.611320 0.053178 11.49564 0.000
R
2
97 % Mean Dependent Variable 0.021881
Adjusted R
2
94 % SD dependent Variable 0087467
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governance create a more stable and predictable regulatory environment for climate-related initiatives. By integrating ESG principles
into national policies and regulations, countries can effectively address environmental and social challenges while fostering economic
stability and growth.
Moreover, the observed decrease in CPU for countries with strong ESG practices underscores the importance of holistic approaches
to sustainability and governance in achieving climate resilience and long-term prosperity. It also highlights the growing recognition of
the role of governments in promoting sustainability and addressing climate change on a global scale. Overall, these ndings emphasize
the signicance of ESG integration at the national level as a catalyst for sustainable development and climate action.
The results from the analysis of ARDL model provide information about the intricate dynamics between ESG and economic factors
and their impact on CPU, as outlined comprehensively in Table 8. Delving into the specics, it becomes apparent that environmental
considerations wield a signicant inuence, with even a little 1 % rise in this component leading to a substantial 22 % surge in CPU
over the long term. Similarly, the inuence of social factors on uncertainty is profound, with a mere 1 % increase in social factors
correlating with a considerable 27 % increase in CPU. On the contrary, economic factors show a relationship that is in opposition to,
demonstrating a notable negative correlation with CPU. A minute 1 % shift in economic factors results in a signicant 40 % reduction
in uncertainty regarding climate policy, underscoring the relationship between these variables in shaping the landscape of policy
uncertainty surrounding climate action.
Figs. 3 and 4 demonstrate that the model’s stability is validated by the Cumulative Sum (CUSUM) and Cumulative Sum of Squares
(CUSUM squared) analyses.
5. Discussion
The study revealed the relationship between environmental factors and CPU, unveiling short and long-term contrasting dynamics.
A negative correlation was found in the short run, indicating that CPU decreases promptly as environmental factors increase. This
suggests that immediate environmental actions may lead to a more stable regulatory climate for climate policies. In the long run, a
positive correlation becomes evident that prolonged environmental factors may lead to increased uncertainty in climate policy. This
juxtaposition underscores the intricate interplay between environmental conditions and policy uncertainties, emphasizing the
importance of adaptive policymaking strategies to address evolving environmental challenges while maintaining regulatory stability
over time.
Similarly, the study shed light on intriguing ndings regarding social factors and their impact on CPU, uncovering short and long-
term relationships. In the short run, a negative correlation was observed, indicating that as social factors increase, climate policy
uncertainty decreases. This suggests that immediate social considerations may contribute to a more stable regulatory environment for
climate policies. However, a positive correlation emerged in the long run, suggesting that sustained social factors may lead to
heightened uncertainty in climate policy. This dichotomy underscores the complex interaction between social dynamics and policy
uncertainty, emphasizing the need for adaptable policy frameworks to navigate evolving social inuences while maintaining regu-
latory predictability over time.
Moreover, the study found in the short run, a negative relationship exists between governance factors and CPU, suggesting that
immediate changes in governance practices or policies may contribute to reducing uncertainty regarding climate policies. This could
be due to implementing specic regulatory measures or initiatives to clarify climate-related policies, leading to a more stable envi-
ronment for businesses and stakeholders. Nonetheless, no signicant link is found between governance factors and CPU over the long
term suggests that while short-term governance interventions may have an impact, their effects might not endure over time. Long-term
factors such as shifts in political priorities, changes in leadership, or evolving global circumstances could contribute to the diminishing
inuence of governance on CPU in the long run. Furthermore, other factors or external forces may play a more signicant role in
shaping CPU over long periods, overshadowing the impact of governance factors.
Furthermore, the positive effect of economic factors on CPU in both the short and long run suggests a complex relationship between
economic conditions and the formulation or implementation of climate-related policies. In the short run, economic factors may
contribute to uncertainty by inuencing decision-making processes, investment patterns, or policy priorities within governments and
industries. Economic instability, market uctuations, or competing nancial interests could all exacerbate uncertainty regarding the
direction and effectiveness of climate policies. Similarly, economic factors may continue to shape CPU in the long run by inuencing
broader socio-economic trends, technological advancements, or global economic dynamics. Economic considerations such as costs,
competitiveness, and resource availability may pose ongoing challenges to developing and implementing clear and consistent climate
policies, perpetuating uncertainty over time. Additionally, the intricate interplay between economic factors and other determinants of
climate policy, such as societal preferences, political considerations, and environmental concerns, further complicates the relationship
and sustains uncertainty in the climate policy landscape.
Table 8
Long run results.
Variables Coefcient Std. Error t-statistic Prob.
ENVF 0.227298** 0.064973 3.498333 0.0249
SOCF 0.275653*** 0.058944 4.676552 0.0095
GOVNF 0.071904 0.048399 1.485627 0.2116
ECONF −0.407299** 0.101617 −4.008169 0.0160
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6. Conclusion
This study highlights the shortcomings of current ESG approaches by revealing the effects of ESG factors on CPU. The ECON-ESG
model, created by including economic factors in the traditional ESG framework, clearly shows that sustainability policies must be
addressed more comprehensively.
Empirical ndings indicate that environmental and social factors increase CPU, while economic factors reduce this uncertainty.
This situation emphasizes that focusing only on environmental and social factors in sustainability studies is insufcient, and economic
factors should also be considered. The fact that governance factors are ineffective indicates that this dimension needs further
investigation.
As a result, the holistic sustainability approach offered by the ECON-ESG model for policymakers will allow climate policies to be
shaped more effectively. This study’s ndings reveal that climate policies must be re-evaluated, integrating economic, environmental,
and social factors to achieve SDGs.
Additionally, in the long run, economic factors have higher impacts on the CPU than E and S. These results clearly show that
analyses conducted only through the conventional form of ESG may be insufcient and inaccurate in analyzing the effects on the CPU.
Our study’s result should be considered more than just the CPU, and it will be helpful to use this proposed form, ECON-ESG, as a more
comprehensive sustainability concept in sustainability studies since it also incorporates economic factors.
6.1. Limitations and future research direction
While this study has made signicant contributions to the relationship between ECON-ESG factors and CPU on a global scale,
several limitations and avenues for future research are worth considering. First, data availability and quality limitations may have
limited the accuracy and comprehensiveness of the ndings. Additionally, the study’s focus on a global perspective may limit its
applicability to specic regions or contexts and warrant further region-specic analyses to capture regional differences. Methodo-
logical limitations, such as the assumptions underlying the analytical models used, pose potential limitations on the robustness of the
results. Future research directions include longitudinal studies that track CPU over long periods, qualitative research to complement
Fig. 3. CUSUM test.
Fig. 4. CUSUM square test.
C. Is¸ık et al.
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quantitative analyses, and interdisciplinary approaches to enrich our understanding of complex dynamics.
Furthermore, investigating the practical implications of reducing CPU through targeted policy interventions is essential to inform
evidence-based policy-making strategies to promote sustainable development and climate resilience. Additionally, comparisons be-
tween countries with different levels of economic development can provide valuable insights into how CPU differs across developed
and developing economies. Finally, a closer look at the impacts of climate policy changes on social welfare can help policymakers
develop more comprehensive solutions that also consider social justice.
6.2. Policy implication
Policymakers must adopt strong adaptive strategies, prioritizing carbon neutrality while integrating ECON-ESG factors, for a
sustainable and resilient future confronting CPU. The ensuing policy implications emerge from the outcomes of this study.
•Setting Clear Targets for Carbon Neutrality: Policymakers should establish clear and ambitious targets for achieving carbon neutrality
and COP 29/30 – Baku/Belem within dened timelines. By setting specic goals, such as reaching net-zero carbon emissions by
2050, governments can provide clarity and direction to stakeholders, reducing uncertainty surrounding long-term climate policies.
•Incorporating ECON-ESG for Holistic Climate Policy Analysis: When policymakers analyze the effects on CPU, they should do this
not only through the conventional ESG form but also through the ECON-ESG form proposed in this study, in which economic factors
are also added. This will help them look at sustainability holistically and shape their climate policies accordingly.
•Adaptive Policymaking for Environmental Stability: The study highlights the relationship between environmental factors and CPU.
In the short term, immediate environmental actions can reduce uncertainty, suggesting swift measures towards environmental
stability are essential. This implies that policies aimed at achieving carbon neutrality, such as COP 29/30 – Baku/Belem and
mitigating environmental degradation, can contribute to a more stable regulatory climate for climate policies.
•Balancing Immediate and Long-Term Environmental Goals: While immediate environmental actions are essential for reducing
uncertainty, the study indicates that prolonged environmental challenges may lead to increased uncertainty in the long run. This
underscores policymakers’ need to balance immediate goals with long-term sustainability strategies. Investing in renewable en-
ergy, transitioning to low-carbon technologies, and implementing nature-based solutions can contribute to both short-term sta-
bility and long-term environmental resilience.
•Integration of Social Considerations: The study also reveals the inuence of social factors on CPU, with immediate social con-
siderations contributing to a stable regulatory environment. This emphasizes the importance of integrating social perspectives into
climate policymaking processes. Engaging diverse stakeholders, fostering public awareness and participation, and addressing social
equity concerns can enhance the effectiveness and legitimacy of climate policies, thereby reducing uncertainty.
•Reinforcement of Governance Structures: While governance factors can reduce CPU in the short term, their effectiveness may wane.
Policymakers should prioritize reinforcing governance structures to remain resilient in changing global conditions. This includes
strengthening institutional capacities, promoting transparency and accountability, and fostering international cooperation to
address cross-border environmental challenges.
•Addressing Economic Challenges: Economic factors present persistent challenges to climate policymaking, contributing to short-
and long-term uncertainty. Policies promoting green investments, incentivizing sustainable business practices, and internalizing
environmental costs can help alleviate economic barriers to climate action. Additionally, fostering innovation and supporting the
transition to a green and blue economy can create new opportunities for economy while advancing environmental sustainability
goals.
In conclusion, achieving carbon neutrality and COP 29/30 – Baku/Belem [49,50] and addressing CPU requires coordinated efforts
across multiple fronts, including environmental protection, social inclusion, governance reform, and economic transformation. By
leveraging the study’s insights and adopting adaptive, integrated policymaking strategies, policymakers can navigate the complexities
of the climate policy landscape and advance toward a more sustainable and resilient future.
CRediT authorship contribution statement
Cem Is¸ ık: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources,
Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Serdar Ongan: Writing – review & editing, Writing –
original draft, Visualization, Validation, Methodology. Jiale Yan: Writing – review & editing, Writing – original draft, Visualization,
Validation, Methodology, Funding acquisition. Hasibul Islam: Writing – review & editing, Writing – original draft, Visualization,
Validation, Software, Methodology, Investigation, Formal analysis, Data curation.
Data availability
The datasets generated and analyzed during the current study are available in the World Bank Indicator.
Ethics approval and consent to participate
Not applicable.
C. Is¸ık et al.
Heliyon 11 (2025) e41944
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Consent for publication
Not applicable.
Funding
Not applicable.
Declaration of competing interest
The authors declare that they have no known competing nancial interests or personal relationships that could have appeared to
inuence the work reported in this paper.
References
[1] N.A.A. Aziz, et al., Critical success factors of sustainability risk management (SRM) practices in Malaysian environmentally sensitive industries, Procedia - Social
and Behavioral Sciences 219 (2016) 4–11, https://doi.org/10.1016/j.sbspro.2016.04.025.
[2] GRR, Global Risk Report 2020, World Economic Forum, 2023, November 15 from, https://www.weforum.org/publications/the-global-risks-report-2020/.
(Accessed 27 March 2024).
[3] Deloitte, Sustainability Risk Management. Powering Performance for Responsible Growth, Deloitte, 2019. Retrieved from, https://www2.deloitte.com/content/
dam/Deloitte/my/Documents/risk/my-risk-sdg12-sustainability-risk-management.pdf.
[4] J. Lawrence, et al., Cascading climate change impacts and implications, Climate Risk Management 29 (2020) 100234, https://doi.org/10.1016/j.
crm.2020.100234.
[5] M. Kalkuhl, L. Wenz, The impact of climate conditions on economic production. Evidence from a global panel of regions, J. Environ. Econ. Manag. 103 (2020)
102360, https://doi.org/10.1016/j.jeem.2020.102360.
[6] A. Persakis, The impact of climate policy uncertainty on ESG performance, carbon emission intensity and rm performance: evidence from Fortune 1000 rms,
Environ. Dev. Sustain. (2023), https://doi.org/10.1007/s10668-023-03634-x.
[7] C. Is¸ ık, et al., A New Pathway to Sustainability: Integrating Economic Dimension (ECON) into ESG Factors as (ECON-ESG) and Aligned with Sustainable
Development Goals (SDGs), J Ekon, 2024, pp. 34–39, https://doi.org/10.58251/ekonomi.1450860.
[8] C. Is
¸ık, et al., Evaluating the effects of ECON-ESG on load capacity factor in G7 countries, J. Environ. Manag. 360 (2024) 121177, https://doi.org/10.1016/j.
jenvman.2024.121177.
[9] C. Is
¸ık, et al., A roadmap for sustainable global supply chain distribution: exploring the interplay of ECON-ESG factors, technological advancement and SDGs on
natural resources, Resour. Pol. 95 (2024) 105114, https://doi.org/10.1016/j.resourpol.2024.105114.
[10] C. Is
¸ık, et al., ECON-ESG factors on energy efciency: fostering sustainable development in ECON-growth-paradox countries, Gondwana Res. 135 (2024)
103–115.
[11] C. Is¸ık, et al., Navigating sustainability: unveiling the interconnected dynamics of ESG factors and SDGs in BRICS-11, Sustain. Dev. (2024) 1–15, https://doi.org/
10.1002/sd.2977.
[12] C. Is¸ ık, et al., Global environmental sustainability: the role of economic, social, governance (ECON-SG) factors, climate policy uncertainty (CPU) and carbon
emissions (CO
2
), Air Quality, Atmosphere and Health, 1-10 (2024), https://doi.org/10.1007/s11869-024-01675-3.
[13] S.I. Perwitasari, et al., Implementation of ESD (education for sustainable development) in climate change learning: a literature review, IJORER : International
Journal of Recent Educational Research 4 (4) (2023) 399–415, https://doi.org/10.46245/ijorer.v4i4.317.
[14] C. Ahn, E. Lee, Open government and open governance: exploring strategies for open data, service, and process in the public sector, Journal of Safety and Crisis
Management 13 (3) (2023). Retrieved from, https://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE11600121.
[15] A.W.L. Chung, W.M. To, A bibliometric study of carbon neutrality: 2001-2022, Trans. Hong Kong Inst. Eng. 30 (2) (2023) 1–11, https://doi.org/10.33430/
v30n2thie-2022-0056.
[16] Y. Zhang, et al., Carbon neutrality policies and technologies: a scientometric analysis of social science disciplines, Front. Environ. Sci. 9 (2021), https://doi.org/
10.3389/fenvs.2021.761736.
[17] B. He, et al., Carbon neutrality: a review, J. Comput. Inf. Sci. Eng. 23 (6) (2023), https://doi.org/10.1115/1.4062545.
[18] Q. Huang, Analysis of global policy and impact on automobile industry under carbon neutrality, E3S Web of Conferences 308 (2021) 01021, https://doi.org/
10.1051/e3sconf/202130801021.
[19] F. Kong, A better understanding of the role of new energy and green nance to help achieve carbon neutrality goals, with special reference to China, Sci. Prog.
105 (1) (2022) 003685042210863, https://doi.org/10.1177/00368504221086361.
[20] X. Xie, et al., Does carbon neutrality commitment enhance rm value? J. Chin. Econ. Bus. Stud. 21 (1) (2023) 49–83, https://doi.org/10.1080/
14765284.2022.2161171.
[21] H. Liu, An analysis on the impact of ESG system disclosure quality on corporate nancing costs under the carbon peaking and carbon neutrality goals, BCP
Business & Management 38 (2023) 570–578, https://doi.org/10.54691/bcpbm.v38i.3741.
[22] Y. Zhou, W. Huo, L. Bo, X. Chen, Impact and mechanism analysis of ESG ratings on the efciency of green technology innovation, Finance Res. Lett. 58 (2023)
104591.
[23] W. Yang, Y. Hei, Research on the impact of enterprise ESG ratings on carbon emissions from a spatial perspective, Sustainability 16 (9) (2024) 3826.
[24] Y. Qian, Y. Liu, Improve carbon emission efciency: what role does the ESG initiatives play? J. Environ. Manag. 367 (2024) 122016.
[25] J. Li, G. Lian, A. Xu, How do ESG affect the spillover of green innovation among peer rms? Mechanism discussion and performance study, J. Bus. Res. 158
(2023) 113648.
[26] S. Treepongkaruna, H.H. Au Yong, S. Thomsen, K. Kyaw, Greenwashing, carbon emission, and ESG, Bus Strat. Environ. 33 (8) (2024) 8526–8539, https://doi.
org/10.1002/bse.3929.
[27] C. Li, W. Tang, F. Liang, Z. Wang, The impact of climate change on corporate ESG performance: the role of resource misallocation in enterprises, J. Clean. Prod.
445 (2024) 141263.
[28] M. Oliver Y´
ebenes, Climate change, ESG criteria and recent regulation: challenges and opportunities, Eurasian Economic Review 14 (1) (2024) 87–120.
[29] K. Kluza, M. Ziolo, M. Postula, Climate policy development and implementation from the Sustainable Development Goals perspective. Evidence from the
European Union countries, Energy Strategy Rev. 52 (2024) 101321.
[30] R.Y.M. Li, et al., A study on public perceptions of carbon neutrality in China: has the idea of ESG been encompassed? Front. Environ. Sci. 10 (2023) https://doi.
org/10.3389/fenvs.2022.949959.
[31] M. Liu, et al., Long-term energy-environment-economic programming under carbon neutrality target: a study on China’s regional energy transition pathways
and CO 2 mitigation strategies, Energy Sources B Energy Econ. Plann. 18 (1) (2023), https://doi.org/10.1080/15567249.2023.2229321.
[32] Z. Zhao, D. Yu, Study on Green Economic Development Path under the Carbon Neutrality Target, vol. 38, BCP Business & Management, 2023, pp. 3449–3455,
https://doi.org/10.54691/bcpbm.v38i.4327.
C. Is¸ık et al.
Heliyon 11 (2025) e41944
13
[33] S.C. Obiora, et al., Is carbon neutrality attainable with nancial sector expansion in various economies? An intrinsic analysis of economic activity on CO
2
emissions, Sustainability 15 (9) (2023) 7364, https://doi.org/10.3390/su15097364.
[34] C. Is
¸ık, et al., Is economic growth in East Asia pacic and South Asia ESG factors based and aligned growth? Sustain. Dev. (2024) 1–14, https://doi.org/
10.1002/sd.2910.
[35] C. Is
¸ık, et al., The sustainable development goals: theory and a holistic evidence from the USA, Gondwana Res. (2024), https://doi.org/10.1016/j.
gr.2024.04.014.
[36] C. Is¸ ık, et al., Renewable energy, climate policy uncertainty, industrial production, domestic exports/re-exports, and CO2 emissions in the USA: a SVAR
approach, Gondwana Res. (2024), https://doi.org/10.1016/j.gr.2023.08.019.
[37] C. Is¸ ık, et al., Exploring how economic growth, renewable energy, internet usage, and mineral rents inuence CO2 emissions: a panel quantile regression
analysis for 27 OECD countries, Resour. Pol. 92 (2024) 105025, https://doi.org/10.1016/j.resourpol.2024.105025.
[38] C. Is¸ ık, et al., The volatility of global energy uncertainty: renewable alternatives, Energy 131250 (2024), https://doi.org/10.1016/j.energy.2024.131250.
[39] C. Is¸ık, et al., Sustainable Development Goals (SDGs): the nexus of ntech and water productivity in 11 BRICS countries, J. Environ. Manag. 372 (2024) 123405,
https://doi.org/10.1016/j.jenvman.2024.123405.
[40] D.P. van Vuuren, et al., Pathways to achieve a set of ambitious global sustainability objectives by 2050: explorations using the IMAGE integrated assessment
model, Technol. Forecast. Soc. Change 98 (2015) 303–323, https://doi.org/10.1016/j.techfore.2015.03.005.
[41] C. von Stechow, et al., Integrating global climate change mitigation goals with other sustainability objectives: a synthesis, Annu. Rev. Environ. Resour. 40 (1)
(2015) 363–394, https://doi.org/10.1146/annurev-environ-021113-095626.
[42] M. Harmsen, et al., Uncertainty in non-CO
2
greenhouse gas mitigation contributes to ambiguity in global climate policy feasibility, Nat. Commun. 14 (1) (2023),
https://doi.org/10.1038/s41467-023-38577-4.
[43] N. Zhu, et al., The response of corporate innovation to climate policy uncertainty: evidence from China, J. Environ. Assess. Pol. Manag. 25 (2) (2023), https://
doi.org/10.1142/s1464333223500084.
[44] K. Guesmi, et al., The relationship between climate risk, climate policy uncertainty, and CO
2
emissions: empirical evidence from the US, J. Econ. Behav. Organ.
212 (2023) 610–628, https://doi.org/10.1016/j.jebo.2023.06.015.
[45] X. Ren, et al., Impact of climate policy uncertainty on traditional energy and green markets: evidence from time-varying granger tests, Renew. Sustain. Energy
Rev. 173 (2023) 113058, https://doi.org/10.1016/j.rser.2022.113058.
[46] D.A. Dickey, W.A. Fuller, Likelihood ratio statistics for autoregressive time series with a unit root, Econometrica: J. Econom. Soc. (1981) 1057–1072.
[47] P.C.B. Phillips, P. Perron, Testing for a unit root in time series regression, Biometrika 75 (2) (1988) 335, https://doi.org/10.2307/2336182.
[48] F. Chien, et al., Co-movement of energy prices and stock market return: environmental wavelet nexus of COVID-19 pandemic from the USA, Europe, and China,
Environ. Sci. Pollut. Control Ser. 28 (25) (2021) 32359–32373, https://doi.org/10.1007/s11356-021-12938-2.
[49] COP 29, in: The Climate Change Conference in Baku Are, 2024. https://unfccc.int/cop29.
[50] COP 30, https://www.gov.br/secom/en/latest-news/2024/11/one-year-before-cop30-belem-transforms-itself-to-host-the-climate-summit.
C. Is¸ık et al.
Heliyon 11 (2025) e41944
14
