Unlocking green potentials for carbon neutrality in OECD countries

ABSTRACT

This study investigates strategies for achieving carbon neutrality within OECD countries, acknowledging their significant contribution to global carbon emissions. Employing advanced econometric techniques, including panel-corrected standard error (PCSE), instrumental variables generalized method of moments (IV-GMM), and quantile regression, it analyzes the impact of green transitions on carbon neutrality from 2000 to 2020. Results reveal that green transitions play a crucial role in achieving carbon neutrality, with varied effects based on different indicators and quantiles. The study emphasizes a multifaceted approach, advocating for the advancement of green policies, political strategies, technological innovations, and financial mechanisms concurrently. By addressing issues such as cross-sectional dependence, heteroscedasticity, endogeneity, and heterogeneity, this study contributes to a deeper understanding of the direct effects of transitions and green indicators on the path toward carbon neutrality in OECD countries. Its findings aim to guide policymakers toward more efficient and tailored sustainability efforts.

KEYWORDS:

• Carbon neutrality
• green transitions
• OECD
• PSCE
• IV-GMM
• quantile regression

1. Introduction

In today’s global landscape, there is a pressing concern over environmental issues, particularly the rapid rise in carbon dioxide emissions. OECD countries, renowned for their advanced economies and substantial energy consumption, stand out as significant contributors to global greenhouse gas emissions. Data from 2019 reveal a stark reality: while the average CO₂ emissions per capita in OECD countries reached 8.3 tons, the rest of the world emitted only 4.4 tons on average (OECD 2023). This glaring disparity underscores the urgent need for action to curb carbon emissions. To address this challenge, the OECD must adopt a multifaceted approach, incorporating strategies such as increasing climate change awareness, fostering technological innovation, and implementing carbon pricing mechanisms to achieve carbon neutrality (Albulescu, Boatca-Barabas, and Diaconescu 2022).

In this context, our study aims to evaluate the effectiveness of green politics, green innovation, and green financing in facilitating the transition toward carbon neutrality within OECD countries. While existing studies have explored various factors influencing environmental degradation, the findings have often been inconclusive. One set of studies (Clulow et al. 2021; Dietz et al. 2015; Dolphin, Pollitt, and Newbery 2020; Garmann 2014; Gokkir and Barkin 2019; Hammar and Jagers 2006; Lamb and Minx 2020; Levi, Flachsland, and Jakob 2020; McCright, Dunlap, and Marquart-Pyatt 2016; Uzar 2020; Wen et al. 2016) has focused on the impact of the green political movement, while another (Yu and Du 2019; Chen and Lee 2020; Lin and Zhu 2019a &b; Zhao et al. 2021; Bilgili et al. 2021; Cheng et al. 2021; Erdogan 2021; Cheng et al. 2021; Hille and Lambernd 2020; Wang and Zhu 2020; Lin and Ma 2021; Ullah et al. 2021; Mongo, Belaïd, and Ramdani 2021) has emphasized the role of green environmental innovation. Additionally, some studies (Gholipour, Arjomandi, and Yam 2022; Khan et al. 2021; Meo and Abd Karim 2022; Wang, Li, and Wang 2021) have highlighted the potential of green finance in enhancing environmental performance.

However, previous research has largely examined these factors in isolation, without fully considering the heterogeneous structures within countries. We argue that addressing environmental degradation requires a comprehensive approach that integrates green politics, green innovation, and green finance. Green politics can establish regulatory frameworks and incentives to embed environmental goals into governance institutions and policymaking. Concurrently, green innovation promotes the development and implementation of eco-friendly technologies and methods, thereby enhancing efficiency and resource preservation. Complementing these efforts, green finance provides the necessary capital and investment mechanisms to scale up sustainable initiatives and facilitate the transition to a low-carbon economy.

By synergistically combining these pillars, societies can not only mitigate environmental degradation but also stimulate economic growth, foster the creation of green jobs, and enhance overall well-being for present and future generations. Our study contributes to this discourse by focusing on the impact of the green transition on achieving carbon neutrality in OECD countries. Methodologically, we employ advanced techniques such as PCSE, IV-GMM, and quantile regression to address various statistical challenges such as cross-sectional dependency, heteroscedasticity, spatial correlation, endogeneity, and heterogeneity effects.

This study offers significant contributions in several key areas. Firstly, it provides valuable insights for policymakers and stakeholders within OECD countries regarding the critical importance of embracing green transitions. By emphasizing the need to align green policies, innovations, and finance, our findings underscore the path toward achieving carbon neutrality. Secondly, unlike prior studies (Aller, Ductor, and Grechyna 2021; Bilgili et al. 2021; Cheng et al. 2021; Dietz et al. 2015; Gholipour, Arjomandi, and Yam 2022; Khan et al. 2021; Lin and Ma 2021; Zhao et al. 2021) that often focused on single green indicators, we argue for the holistic approach of the green transition. By synergistically integrating these pillars, societies can not only address environmental degradation but also stimulate economic growth, foster the creation of green jobs, and enhance overall societal well-being for both current and future generations.

Lastly, our study contributes to the existing literature on green transition and environmental degradation by employing advanced statistical techniques. By utilizing methods such as PCSE, IV-GMM, and quantile regression, we effectively address various statistical challenges that were overlooked in previous studies. This comprehensive approach enables us to account for cross-sectional dependency, heteroscedasticity, spatial correlation, endogeneity, and heterogeneity effects, thereby enhancing the robustness and validity of our findings.

The rest of the paper is organized as follows. Section 2 is the literature. Section 3 studies the methodology, while Section 4 presents the results and discussion. Section 5 offers the conclusion and policy implication.

2. Literature review

Political ideology exerts a profound influence on various facets of our daily lives, including our immediate environment. Extensive research has explored how political beliefs impact environmental performance. For instance, Dietz et al. (2015) revealed that political actions can exacerbate demographic and economic pressures leading to increased CO₂ emissions, yet a leader’s environmental conservation interests may counteract this rise. This highlights the clear connection between politics and environmental outcomes. However, the extent of this impact hinges on ideological divides. Left-leaning political ideologies are typically more inclined toward mitigating environmental degradation and climate change effects (Clulow et al. 2021; Garmann 2014; McCright, Dunlap, and Marquart-Pyatt 2016; Wen et al. 2016). Additionally, citizen ideologies significantly influence environmental performance. Gokkir and Barkin (2019) observed that states with liberal citizen ideologies and a culture of environmental stewardship tend to exhibit lower emissions compared to others.

The role of the political economy in renewable energy adoption is crucial for reducing CO₂ emissions (Uzar 2020). Specifically, enhancing institutional quality can promote the use of renewable energy sources and enhance environmental performance. Additionally, the implementation of carbon pricing policies significantly influences CO₂ emission reductions (Dolphin, Pollitt, and Newbery 2020; Levi, Flachsland, and Jakob 2020). However, resistance to environmental measures, such as CO₂ emissions taxes, may arise among individuals with car access due to political trust issues (Hammar and Jagers 2006). Lamb and Minx (2020) identify key factors for improving environmental performance, including institutional quality, trust levels, climate awareness, and phasing out fossil fuel subsidies. Conversely, Aller, Ductor, and Grechyna (2021) highlight the significant impact of political polarization on environmental performance determination.

Apart from environmental politics, green technology or innovation emerges as a pivotal concern. Yu and Du (2019) assert that technological innovation plays a critical role in reducing CO₂ emissions, particularly in rapidly developing nations. This impact is pronounced in countries characterized by higher income levels, robust technological innovation, and increased CO₂ emissions (Chen and Lee 2020). Notably, renewable energy innovation proves highly effective in addressing climate change and other environmental challenges (Kwakwa 2023b & b; Lin and Zhu 2019a & b). However, Zhao et al. (2021) argue that the relationship between technological innovation and environmental performance may not consistently align. Nevertheless, research and development in energy efficiency innovations significantly contribute to CO₂ emission reduction, particularly in absolute terms (Bilgili et al. 2021). By counterbalancing the benefits of economic growth and amplifying the advantages of renewable energy sources, technological innovation holds promise in reducing CO₂ emissions (Cheng et al. 2021; Erdogan 2021).

Cheng et al. (2021) highlighted the significant yet context-dependent impact of technological innovation on the environment. Investments in research, development, and education drive technological innovation, leading to improved environmental performance. This innovation has notably fueled growth in the renewable energy sector and reduced CO₂ emissions by decreasing energy intensity (Hille and Lambernd 2020). The effects of technological innovation extend beyond national borders, particularly in high-income nations with advanced technology (Chen and Lee 2020). Wang and Zhu (2020) emphasized the substantial role of energy innovation in decreasing CO₂ emissions, although its effectiveness may vary across different regions. However, technological innovation can also present challenges, potentially hindering economic growth and affecting the relationship between economic growth and CO₂ emissions (Cheng et al. 2021). Moreover, the assimilation of foreign innovation may contribute to increased CO₂ emissions, negatively impacting environmental performance (Lin and Ma 2021). Ullah et al. (2021) and Mongo, Belaïd, and Ramdani (2021) identified a short-term positive correlation between technical advancement and CO₂ emissions in Pakistan.

Furthermore, Gholipour, Arjomandi, and Yam (2022) emphasize the critical role of green finance in emerging economies, where population growth is not necessarily accompanied by increased reliance on oil consumption. This underscores the importance of financing green initiatives, particularly in supporting environmentally sustainable properties. Khan et al. (2021) highlight the potential of mitigation funding to finance projects aimed at environmental mitigation and advancing sustainability efforts. Another avenue for enhancing environmental performance is through the issuance of green bonds, directing funds toward energy-related initiatives (Wang, Li, and Wang 2021). However, the relationship between green financing and environmental performance may be influenced by factors within the green finance industry itself (Meo and Abd Karim 2022). Additionally, regional characteristics can further complicate this relationship, as the financial structure in developing economies may inadvertently contribute to increased CO₂ emissions, resulting in distortions.

Numerous studies have explored the determinants of environmental performance and carbon neutrality, often focusing on specific factors such as technological progress, political ideology, and green finance (Aller, Ductor, and Grechyna 2021; Bilgili et al. 2021; Cheng et al. 2021; Dietz et al. 2015; Gholipour, Arjomandi, and Yam 2022; Khan et al. 2021; Lin and Ma 2021; Zhao et al. 2021). However, we argue that these studies may overlook the broader impact of national frameworks and the strategy of individualization on these factors. This research seeks to address this gap by examining how the green transition contributes to achieving carbon neutrality. Specifically, we investigate the role of three key green factors – green politics, green technological innovation, and green finance – in shaping environmental outcomes. By incorporating these elements, we aim to provide a more comprehensive understanding of the pathways to carbon neutrality and sustainability.

3. Theoretical framework

According to economist Michael Porter’s 1991 formulation of the Porter Hypothesis, strict environmental laws can promote innovation, boost competitiveness, and lead to better environmental performance while maintaining economic growth (Lambertini, Pignataro, and Tampieri 2020). The Porter Hypothesis offers a paradigm for understanding how environmental regulations might promote positive effects across the green politics, green technology, and green finance components of green transitions.

Green politics refers to governmental actions such as policies, regulations, and incentives that aim to encourage ecologically friendly practices (Yu and Huang 2020). The Porter Hypothesis proposes that strict environmental regulations, including emissions standards, carbon pricing mechanisms, and renewable energy targets, can stimulate innovation (Lim and Prakash 2023). By regulating emission reductions or requiring the adoption of clean technology, governments create a market demand for green innovation (The UCL Green Innovation Policy Commission 2021). This encourages businesses to invest in research and development (R&D) for eco-friendly products and processes. Furthermore, enterprises that adhere to these requirements obtain an advantage in markets where customers prioritize sustainability (Lestari et al. 2021). This incentivizes companies to create more environmentally friendly products, including energy-efficient appliances, electric automobiles, or eco-friendly packaging. Furthermore, stringent rules often compel businesses to improve efficiency to fulfill compliance standards (Gray 2015). The pursuit of efficiency can result in cost reductions, less resource usage, and fewer emissions.

Regarding green technology indicators, government policies that encourage research and development in clean energy, waste management, and sustainable agriculture offer financial incentives for firms to create new solutions in green technology. More so, environmental regulations drive the market need for green technologies (The UCL Green Innovation Policy Commission 2021). This need motivates enterprises to invest in the development of innovative technology like solar panels, wind turbines, or energy-efficient machinery. Furthermore, innovations in a single sector often end in positive impacts on other businesses (Bakhtiari and Breunig 2018). Advancements in battery technology for electric vehicles can enhance energy storage solutions for renewable power systems.

Green finance pertains to investments in projects, businesses, and technology that have positive environmental effects or promote sustainability. Porter’s hypothesis suggests that enterprises with significant carbon footprints or unsustainable activities may face financial risks due to increasing environmental regulations. Investors and financial organizations may prioritize investments in enterprises shifting toward more environmentally friendly operations. Furthermore, measures like carbon pricing and green bonds offer financial rewards to firms for supporting sustainable initiatives. Green financing methods direct funds toward renewable energy projects, energy-efficient buildings, and sustainable agriculture. Also, using green technologies and sustainable practices can improve a company’s long-term value and resilience to environmental risks (Verdolini et al. 2018). Strong environmental performance by corporations can lead to greater values and reduced borrowing costs in financial markets.

The Porter Hypothesis emphasizes the synergistic interaction between green politics, green technology, and green finance to promote sustainability and innovation. Governments may promote the development of green technologies, encourage investments in sustainable projects, and provide firms with a competitive advantage in the burgeoning green economy by establishing regulations that prioritize environmental responsibility. This not only helps the environment but also boosts economic growth, creates jobs, and enhances long-term resilience against environmental issues.

4. Data and methodology

4.1. Data

The study uses annual panel data on eight variables from 2000 to 2020. The dependent variable is carbon neutrality, measured by CO₂ emissions reductions, while the explanatory variables consist of green transition, measured through principal component analysis (PCA) involving green politics, green technology, and green finance (ECOPOL) (refer to Appendix 1), consumer price index (INF), foreign direct investment (FDI), GDP growth (ENG), GDP per capita (PI), trade openness (TRD), and urban population (URB). Refer to Table 1 for specifics on the measure and the source of the variables.

Table 1. Variable description and expectations.

Variables	Description	Sign
CO2	CO2 emissions (metric tons per capita)	N/A
ECOPOL	Average of green politics, green finance, and related environmental technologies	+
INF	Consumer price index (2010 = 100)	+
FDI	Foreign direct investment, net inflows (% of GDP)	-/+
ENG	GDP growth (annual %)	-
PI	GDP per capita (constant 2015 US$)	-
TRD	Sum of import and export (% of GDP)	-
URB	Urban population (% of the total population)	+

4.2. Empirical model and estimation techniques

This study examines the impact of the green transition on achieving carbon neutrality and its heterogeneous effects in the OECD countries using the empirical methodology of Zakari and Toplak (2021), Zakari et al. (2021), Dzator, Acheampong, and Dzator (2021), and Adebayo et al. (2021). Therefore, we expressed as:

(1) $\mathit{In}\mathrm{C}{\mathrm{O}}_{2\mathit{i},\mathit{t}}={\beta }_0+{\beta }_1\mathit{InECOPO}{L}_{\mathit{i},\mathit{t}}+{\beta }_1\mathit{InIN}{F}_{\mathit{i},\mathit{t}}+{\beta }_1\mathit{InFD}{I}_{\mathit{i},\mathit{t}}+{\beta }_1\mathit{InEN}{G}_{\mathit{i},\mathit{t}}+{\beta }_1\mathit{InP}{I}_{\mathit{i},\mathit{t}}+{\beta }_1\mathit{InTR}{D}_{\mathit{i},\mathit{t}}+{\beta }_1\mathit{InUR}{B}_{\mathit{i},\mathit{t}}+{\epsilon }_{\mathit{it}}$(1)

CO₂ represents carbon neutrality, ECOPOL symbolizes the green transition, INF denotes the inflation rate, FDI stands for foreign direct investment, ENG signifies economic growth, PI represents economic development, TRD indicates trade openness, URB represents the urban population, and ln represents the natural logarithm. Equation [1] shows that carbon neutrality is influenced by all the variables listed in the model.

This model aligns well with the theoretical framework proposed by Michael Porter’s 1991 formulation of the Porter Hypothesis. According to Porter, stringent environmental regulations (represented by ECOPOL in the model) can stimulate innovation and encourage businesses to invest in eco-friendly technologies and processes, leading to improved environmental performance. Variables such as FDI, ENG, PI, TRD, and URB may also indirectly impact carbon neutrality through their influence on economic activities, technological innovation, and policy implementation, all of which are central to the Porter Hypothesis. Therefore, the model provides a quantitative framework for assessing the relationships between environmental policies, economic factors, and carbon neutrality, in line with Porter’s theoretical proposition.

4.3. Cross-sectional dependence and unit-root test

Some panel datasets encounter cross-sectional dependence, posing challenges to estimation reliability. To address this issue, we employed the cross-sectional dependence test proposed by Pesaran (2021). This test calculates the average of pair-wise correlation coefficients of Ordinary Least Squares (OLS) residuals obtained using standard augmented Dickey and Fuller (1979) for each individual in the dataset.

(2) $\mathit{CD}=\sqrt{\frac{2\mathit{T}}{\mathit{N}\left(\mathit{N}-1\right)}}\left(\sum _{\mathit{i}=1}^{\mathit{N}-1}\sum _{\mathit{j}=\mathit{i}+1}^N{\rho }_{\mathit{ij}}\right)\to \mathit{N}\left(0,1\right)$(2)

The Pesaran’s CIPS unit-root test, unlike earlier unit-root tests, accounts for cross-sectional dependence. Our study rejects the null hypothesis of a unit root when cross-section dependence is present (Chakamera and Alagidede 2018). Here, we outline the unit root hypothesis as follows:

(3) $\mathrm{\Delta }{y}_{\mathit{it}}={\alpha }_i+{\beta }_i{y}_{\mathit{it}-1}+{\gamma }_i{f}_t+{\epsilon }_{\mathit{it}}\mathit{i}=1,2\dots \mathit{N}\mathit{and}\mathit{t}=1,2\dots \mathit{T}$(3)

$H_0={\beta }_i=0\mathit{foralli}\left(\mathit{seriesnotstationary}\right)$

$H_0={\beta }_i<0,\mathit{i}=1,2\dots N_1,{\beta }_i=0\mathit{i}=N_1+1,N_2+2.\mathit{N}.\left(\mathit{seriesstationary}\right)$

CIPS statistics are constructed below:

(4) $\mathit{CIPS}=N^{-1}\sum _{\mathit{i}=1}^N\mathit{CAD}{F}_i$(4)

4.4. The Panel-Corrected Standard Error (PCSE)

The study employed the PCSE approach to assess the impact of green transition on achieving carbon neutrality. This method corrects for panel dependence in estimating standard errors, as highlighted by Maureen and Maxwell (2020). By addressing issues such as panel heteroscedasticity and spatial correlation, the PCSE model provides more accurate estimates than ordinary least squares (OLS), which may yield erroneous standard errors in the presence of such dependencies. In essence, the PCSE model adjusts the variance of OLS to account for these factors, ensuring robust and reliable results in the analysis of the relationship between green transition and carbon neutrality.

(5) $\mathit{Var}\left(\hat{\beta }\right)=(\mathit{X}{ }^{\mathrm{\prime }}\mathit{X}{)}^{-1}\left[\mathit{X}{ }^{\mathrm{\prime }}\mathrm{\Omega }\mathit{X}\right](\mathit{X}{ }^{\mathrm{\prime }}\mathit{X}{)}^{-1}$(5)

Equation (5)(5) $\mathit{Var}\left(\hat{\beta }\right)=(\mathit{X}{ }^{\mathrm{\prime }}\mathit{X}{)}^{-1}\left[\mathit{X}{ }^{\mathrm{\prime }}\mathrm{\Omega }\mathit{X}\right](\mathit{X}{ }^{\mathrm{\prime }}\mathit{X}{)}^{-1}$(5) highlights the presence of heteroscedasticity and spatial correlation, leading to inaccurate standard errors. To address this issue, we introduce a change in the covariance framework:

(6) $\mathrm{\Omega }=\mathrm{\Sigma }\otimes I_T$(6)

The symbol “$\otimes$” represents the Kronecker product. “$\mathrm{\Omega }$” encompasses panel heteroscedasticity, time-invariant cross-sectional dependence, and first-order, common autocorrelation within the model.

Hence,

(7) $\mathit{Var}\left(\hat{\beta }\right)={\left(X^{\prime }\mathit{X}\right)}^{-1}{X}^{\prime }\left(\mathrm{\Sigma }\otimes I_T\right)\mathit{X}{\left(X^{\prime }\mathit{X}\right)}^{-1}$(7)

Hence, the PCSE can be obtained by calculating the square root of the diagonal elements in equation (7)(7) $\mathit{Var}\left(\hat{\beta }\right)={\left(X^{\prime }\mathit{X}\right)}^{-1}{X}^{\prime }\left(\mathrm{\Sigma }\otimes I_T\right)\mathit{X}{\left(X^{\prime }\mathit{X}\right)}^{-1}$(7) .

4.5. Instrumental Variables – Generalized Moment of Method (IV-GMM)

According to Adams and Acheampong (2019), the IV-GMM model effectively tackles endogeneity concerns, yielding dependable outcomes. Renowned for its robustness, the IV-GMM model offers superior results, particularly in scenarios involving unexplained heteroscedasticity, when compared to autocorrelation methods. In our study, we utilized the IV-GMM framework to mitigate endogeneity and unexplained heteroscedasticity in our models (1). The IV-GMM approach is characterized by its dynamic endogenous growth specification, which can be represented as follows:

(8) $y_{\mathit{it}}={\alpha }_0{y}_{\mathit{i},\mathit{t}}+{\alpha }_t\left(y_{\mathit{i}.\mathit{t}-1}-{y^T}_{\mathit{i},\mathit{t}-1}\right)+{\beta }^{\prime }{X}_{\mathit{it}}+\mathit{\eta }{ }_i+{\xi }_t+\mathit{\epsilon }{ }_{\mathit{it}}$(8)

Hence, we employ the lagged components of regressors as instrumental variables, and the transformation involves differencing as follows:

(9) $\left(y_{\mathit{it}}-y_{\mathit{i},\mathit{t}-1}\right)-\left(y_{\mathit{it}}-y_{\mathit{i},\mathit{t}-2}\right)={\alpha }_0\left(y_{\mathit{i},\mathit{t}-1}-y_{\mathit{i},\mathit{t}-2}\right)+\mathit{\beta }\left(X_{\mathit{it}}-X_{\mathit{i},\mathit{t}-1}\right)+\left(\mathit{\epsilon }{ }_{\mathit{it}}-{\epsilon }_{\mathit{i},\mathit{t}-1}\right)$(9)

So that:

(10) $\mathrm{\Delta }{y}_{\mathit{it}}=\mathit{\alpha }\mathrm{\Delta }{y}_{\mathit{i},\mathit{t}-1}+\mathrm{\Delta }{X}_{\mathit{it}}\mathit{\beta }{ }^{\mathrm{\prime }}+\mathrm{\Delta }{\mu }_{\mathit{it}}$(10)

4.6. Quantile regression

Quantile regression characterizes the association between the independent variable and the conditional quantile of the dependent variable, thereby generating a regression model applicable across all quantiles. As described by Lin and Xu (2018), quantile regression accounts for the diverse impacts within the model. The formulation of quantile regressions is expressed as follows:

(11) $y_i=\mathit{x}{{\mathit{ }}^{\mathrm{\prime }}}_{\mathit{i}}{\beta }_0+{\mu }_{\mathit{\theta i}},0<\mathit{\theta }<1$(11)

(12) $\mathit{Quan}{t}_{\theta }(y_i|x_i)=x_i{\beta }_0$(12)

In this equation, we denote x as the vector of independent variables, y as the dependent variable, and $\mathit{\mu }$ as the random error term. The conditional quantile distribution of $\mathit{\mu }$ is set to 0. Additionally, $\mathit{Quan}{t}_{\theta }\left(y_i/x_i\right)$ represents the ${\theta }_{\mathit{th}}$ quantile of the dependent variable (y). The estimated coefficient $\hat{\beta }$ of the independent variables on the ${\theta }_{\mathit{th}}$ quantile is determined as the solution to the following equation:

(13) $\mathit{the}\mathit{min}{\mathrm{\Sigma }}_{y_i\ge {\mathit{x}{\mathit{ }}^{\mathrm{\prime }}}_{\mathit{i}}\mathit{\beta }}\mathit{\theta }\left|y_i-{\mathit{x}{\mathit{ }}^{\mathrm{\prime }}}_{\mathit{i}}\mathit{\beta }\right|+{\mathrm{\Sigma }}_{y_i<{\mathit{x}{\mathit{ }}^{\mathrm{\prime }}}_{\mathit{i}}\mathit{\beta }}\left(1-\mathit{\theta }\right)\left|y_i-{\mathit{x}{\mathit{ }}^{\mathrm{\prime }}}_{\mathit{i}}\mathit{\beta }\right|$(13)

5. Results and discussions

5.1. Correlation analysis and descriptive statistics

Table 2 shows the correlation analysis and descriptive statistics. The upper panel shows the correlation between all the variables selected. The coefficients reveal that most variables have a positive correlation with carbon neutrality, indicating that they tend to increase CO₂ emissions. However, it’s noteworthy that green transition (ECOPOL) and green financing show negative correlations with carbon neutrality, suggesting that they may contribute to reducing CO₂ emissions. Expanding on the descriptive statistics, the data range for CO₂ emissions is substantial, ranging from 3.437 to 25.669, highlighting the diversity in carbon emissions levels among the OECD countries. Within the green transition variable (ECOPOL), the components of green politics, green technology, and green finance exhibit significant variability, indicating differences in the extent and nature of environmental policies and initiatives across the OECD countries. The variability in foreign direct investment (FDI), economic development (PI), trade (TRD), inflation rate (INF), and urbanization (UBR) further underscores the heterogeneity of economic and demographic factors within the study area.

Table 2. Correlation analysis and descriptive statistics.

	CO2	ECOPOL	PID	TECH	GF	INF	FDI	ENG	PI	TRD	URB
CO2	1
ECOPOL	0.0244	1
PID	0.1408**	0.4349***	1
TECH	0.1347**	0.4739***	0.1331**	1
GF	0.0429	0.4851***	0.1093**	0.1079*	1
INF	0.0953**	0.0334	0.1599***	0.0166	0.0189	1
FDI	0.2125***	0.047	0.0105	0.0969*	0.0361	0.1549**	1
ENG	0.03	0.1008	0.0638	0.0738	0.222***	0.0478	0.1505***	1
PI	0.5017***	0.0441	0.1425**	0.0144	0.0844*	0.0327	0.1831***	0.0027	1
TRD	0.3661***	0.2096***	0.0189	0.2622***	0.0716	0.0298	0.3714***	0.1204*	0.4176***	1
URB	0.2476***	0.2611***	0.0127	0.2202***	0.2712***	0.0567	0.0345	−0.0263	0.3797***	−0.0535	1
Summary Statistics
OBS	420	420	420	418	420	420	420	420	420	420	420
Mean	7.962	0.015	1.333	17518.117	0.081	102.645	5.838	2.164	36401.433	100.221	77.351
Std. Dev	4.019	0.924	1.042	60507.263	1.822	11.994	13.039	3.526	22395.172	61.746	11.582
Min	3.437	−6.088	0	0	−1.408	65.853	−58.323	−14.839	7738.578	24.39	51.533
Max	25.669	2.605	3	416476	6.116	203.545	86.589	25.176	105454.73	408.362	98.001

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; ln = Natural logarithm; CO2 = carbon emission; ECOPOL = Eco political movement; PID = political ideology; TECH = green innovation; GF = green finance; INF = inflation rate; FDI= foreign direct investment; ENG = economic growth; PI = economic development; TRD = trade.

Source: Author’s Computations.

Moreover, Table 3 shows the results of the variance inflation factor (VIF) and the findings from the variance inflation factor (VIF) analysis assure us of the absence of multicollinearity issues among the variables, as all VIF scores are below the threshold of 10. This suggests that the independent variables in the regression model do not exhibit strong correlations with each other, enhancing the reliability of the regression results.

Table 3. Variance Inflation Factor (VIF).

Variable	VIF	1/VIF
TRD	1.24	0.808077
FDI	1.22	0.820481
ECOPOL	1.14	0.874942
URB	1.08	0.922704
ENG	1.05	0.952061
INF	1.04	0.960341
Mean VIF	1.13

5.2. Cross-sectional dependence and panel unit-root test

In Table 4, column 2 reveals that while most variables exhibit statistical significance at the 1% level, green transition, green politics, and green technology are not statistically significant. This suggests that there is evidence of cross-sectional dependency among carbon neutrality, green financing, inflation rate, foreign direct investment, economic growth, economic development, trade, and urbanization. The presence of cross-sectional dependency implies that the variables are interrelated across different entities or regions, indicating the need to account for this dependency in the analysis. This finding is consistent with the broader context of environmental policies and economic activities, where factors such as technological innovation, investment patterns, and policy implementation can vary significantly across different jurisdictions or sectors.

Table 4. Cross-sectional dependency (CSD) and unit-root tests.

		Level	1^st difference
Variable	Pesaran -CSD	Pesaran-CIPS
CO2	6.613***	−2.141*	−3.410***
ECOPOL	0.145	−1.690	−2.664***
PID	−0.513	−0.833	−2.589***
TECH	−2.275	−0.291	−2.365**
GF	5.085***	−0.909	−2.803***
INF	51.800***	−0.936	−2.147*
FDI	5.693***	−2.995***	−4.706***
ENG	44.331***	−2.599***	−4.016***
PI	17.390***	−1.049	−2.422**
TRD	9.447***	−1.232	−2.427**
UBR	0.015	−0.420	−1.609 *

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; ln = Natural logarithm; CO2 = carbon emission; ECOPOL = Eco political movement; PID = political ideology; TECH = green innovation; GF = green finance; INF = inflation rate; FDI = foreign direct investment; ENG = economic growth; PI = economic development; TRD = trade.

Source: Author’s Computations.

Moving to columns 3 and 4, the results of the Pesaran-CIPS unit roots test indicate that only carbon neutrality, foreign direct investment, and economic growth coefficients are statistically significant at levels. Notably, all variables become statistically significant when first differenced, indicating stationarity after differencing.

5.3. PCSE analysis for the green transition

The results from the PCSE regression analysis in Table 5 provide valuable insights into the relationship between the green transition and carbon neutrality, particularly in the context of OECD countries. In column 1, the negative and statistically significant coefficient of the green transition (−0.282*) suggests that policies and initiatives aimed at fostering environmental sustainability, represented by the green transition, are indeed associated with a reduction in CO₂ emissions. This finding supports the Porter hypothesis and Li, Elheddad, and Doytch (2021), which suggests that stringent environmental regulations and incentives for green innovation can lead to improved environmental performance.

Table 5. PCSE results for green revolution (Dep Var: InCO2).

	(1)	(2)	(3)
Variables	CO2	CO2	CO2
InCO2_lag1		0.844***
		(0.0324)
InECOPOL	−0.282*	−0.182***
	(0.154)	(0.0319)
InECOPOL_lag1			−0.338**
			(0.157)
InINF	−0.0305***	−0.00847***
	(0.00544)	(0.00283)
InINF_lag1			−0.0204**
			(0.00913)
InFDI	0.0160	0.000957
	(0.0151)	(0.00334)
InFDI_lag1			0.0171
			(0.0148)
InENG	−0.0153	0.0326**
	(0.0357)	(0.0136)
InENG_lag1			−0.00718
			(0.0380)
InPI	6.43e-05***	1.43e-05***
	(5.52e-06)	(3.97e-06)
InPI_lag1			5.44e-05***
			(5.06e-06)
InTRD	0.0144***	0.00349***
	(0.00252)	(0.00107)
InTRD_lag1			0.0137***
			(0.00233)
InURB	0.0380***	0.00251
	(0.00309)	(0.00202)
InURB_lag1			0.0364***
			(0.00290)
Constant	4.318***	0.946***	3.771***
	(0.593)	(0.296)	(0.960)
Year-effect	Yes	Yes	Yes
Country-effect	Yes	Yes	Yes
Observations	420	419	419
R-squared	0.309	0.839	0.247
Number of ID	30	30	30

Note: ***p < 0.01, **p < 0.05, *p < 0.1; ln = Natural logarithm; CO2 = carbon emission; ECOPOL = Eco political movement; PID = political ideology; TECH = green innovation; GF = green finance; INF = inflation rate; FDI = foreign direct investment; ENG = economic growth; PI = economic development; TRD = trade.

Source: Author’s Computations.

Moreover, the economic interpretation that a 1% increase in the green transition reduces CO₂ emissions by 0.3% underscores the significance of proactive environmental policies in mitigating climate change. This is consistent with the intuitive notion that green politics, through regulatory frameworks and incentives, fosters an environment conducive to green innovation, while green finance provides the necessary capital for these innovations to flourish and scale up.

In column 2, the analysis delves deeper into the dynamics of carbon neutrality and the green transition by examining the impact of lagged variables. The positive and statistically significant coefficient of the lag of carbon neutrality (0.844***) indicates that previous years’ carbon neutrality is associated with an increase in CO₂ emissions. This counterintuitive result suggests that carbon neutrality efforts in the past year did not effectively reduce CO₂ emissions in OECD countries, highlighting potential challenges in achieving sustained reductions in greenhouse gas emissions. Conversely, the negative and statistically significant coefficient of the green transition (−0.182***) suggests that ongoing efforts to promote environmental sustainability through the green transition are indeed helping to maintain carbon neutrality. This finding underscores the importance of continuous investment in green policies and technologies to sustain progress toward climate goals.

Moving to column 3, the analysis focuses on the impact of the lag of the green transition on maintaining carbon neutrality. The negative and statistically significant coefficient of the lag of green transition (−0.338**) indicates that past efforts in promoting environmental sustainability, represented by the lag of green transition, are associated with achieving carbon neutrality. This reinforces the notion that long-term investments in green initiatives contribute to the overall goal of reducing CO₂ emissions and achieving environmental sustainability. Regarding control variables, our findings are consistent with intuition. For instance, inflation is negatively related to carbon neutrality, while economic growth, economic development, trade, and urbanization are positively related to carbon neutrality.

5.4. PCSE analysis for indicators of the green transition

Table 6 sheds light on the relationship between green transition indicators and the achievement of carbon neutrality. Column 1 focuses on the impact of green politics (PID) on carbon neutrality. The negative and statistically significant coefficient indicates that green politics plays a crucial role in improving environmental quality and reducing CO₂ emissions. This is consistent with Porter’s Hypothesis and Adom, Kwakwa, and Amankwaa (2018), suggesting that governmental actions and policies aimed at encouraging ecologically friendly practices can stimulate innovation and ultimately lead to better environmental performance. The assertion that green politics are associated with conservation and environmental movements supports the idea that regulatory measures and incentives promoting sustainability can drive positive environmental outcomes (Mulvaney 2011).

Table 6. PCSE results for indicators of the green revolution (Dep Var: InCO2).

	(1)	(2)	(3)
Variables	CO2	CO2	CO2
InPID	−0.0556*
	(0.0296)
InINF	0.0348***	0.0407***	0.0349***
	(0.00271)	(0.00286)	(0.00267)
InFDI	0.000865	0.000580	0.000948
	(0.00584)	(0.00551)	(0.00584)
InENG	0.0353*	0.0364**	0.0353*
	(0.0181)	(0.0167)	(0.0182)
InPI	5.88e-05***	5.80e-05***	5.84e-05***
	(2.23e-05)	(2.07e-05)	(2.21e-05)
InTRD	−0.0321***	−0.0307***	−0.0323***
	(0.00492)	(0.00455)	(0.00494)
InURB	−0.0340	−0.0739***	−0.0332
	(0.0274)	(0.0285)	(0.0271)
InTECH		−2.1105***
		(3.66e-06)
InGF			−0.0148*
			(0.0202)
Constant	15.60***	19.15***	15.41***
	(2.955)	(2.947)	(2.972)
Year-effect	Yes	Yes	Yes
Country-effect	Yes	Yes	Yes
Observations	420	418	420
R-squared	0.971	0.973	0.971
Number of ID	30	30	30

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; ln = Natural logarithm; CO2 = carbon emission; ECOPOL = Eco political movement; PID = political ideology; TECH = green innovation; GF = green finance; INF = inflation rate; FDI = foreign direct investment; ENG = economic growth; PI = economic development; TRD = trade.

Source: Author’s Computations.

Moving to Column 2, the coefficient of green technology highlights its significant impact on maintaining carbon neutrality. The negative coefficient indicates that advancements in green technology contribute to reducing CO₂ emissions, supporting the notion that innovation in clean energy, waste management, and sustainable agriculture plays a crucial role in mitigating environmental impact. This finding resonates with Porter’s Hypothesis and Abrantes et al. (2021), which suggests that strict environmental regulations can drive market demand for green technologies and incentivize businesses to invest in eco-friendly solutions. However, it is inconsistency with previous study (Alataş 2021) underscores the need for further research to explore the nuanced relationships between environmental technologies and specific emission sources.

In Column 3, the coefficient of green finance suggests that financial mechanisms promoting sustainability are associated with achieving carbon neutrality. This implies that investments in renewable energy projects and green technologies financed through green finance products can contribute to reducing CO₂ emissions. This finding contradicts the study of Meo and Abd Karim (2022) but supports the overarching idea that financial incentives and mechanisms can play a pivotal role in driving environmental sustainability. Regarding the control variable, our findings are consistent with our expectations. For instance, the coefficient of inflation and economic development are positive, suggesting that the inflation rate and economic development worsen carbon neutrality. However, the trade coefficient is negative and significant in most panels, suggesting that trade openness is associated with achieving carbon neutrality.

Overall, these findings underscore the interconnectedness of green politics, green technology, and green finance in promoting sustainability and achieving carbon neutrality in the study area. They provide empirical evidence in support of the theoretical framework, highlighting the importance of governmental policies, technological innovation, and financial investments in driving positive environmental outcomes and fostering economic growth while mitigating environmental degradation.

5.5. IV-GMM analysis for green transition

The IV-GMM technique addresses the challenge of endogeneity and cross-sectional dependence inherent in panel datasets. This method allows for correcting biases that may arise due to simultaneous relationships between variables and dependencies across different entities in the dataset. The results presented in Table 7 provide valuable insights into the relationship between green transition policies, carbon neutrality, and other economic factors. In Column 1, the negative and statistically significant coefficient of the green transition (−0.407***) reaffirms the hypothesis that stringent environmental regulations, represented by the green transition, are associated with achieving carbon neutrality. This finding is consistent with the Porter hypothesis, indicating that policies aimed at promoting sustainability can indeed lead to improved environmental performance.

Table 7. IV-GMM results for green revolution (Dep Var: InCO2).

	(1)	(2)	(3)
Variables	CO2	CO2	CO2
InECOPOL	−0.407***	−0.109***
	(0.0786)	(0.0385)
InECOPOL_lag1			−0.571***
			(0.143)
InCO2_lag1		0.953***
		(0.00894)
InINF	−0.0202**	−0.000546
	(0.00854)	(0.00201)
InINF_lag1			−0.0148
			(0.0102)
InFDI	0.0231***	0.000582
	(0.00883)	(0.00264)
InFDI_lag1			0.0178
			(0.0121)
InENG	−0.0502	0.0431***
	(0.0359)	(0.00859)
InENG_lag1			−0.0319
			(0.0409)
InPI	7.51e-05***	2.07e-06
	(5.66e-06)	(1.91e-06)
InPI_lag1			6.62e-05***
			(5.87e-06)
InTRD	0.0116***	−0.000350
	(0.00189)	(0.000525)
InTRD_lag1			0.0145***
			(0.00267)
InURB	0.0157**	−0.00106
	(0.00631)	(0.00367)
InURB_lag1			0.0101
			(0.00807)
Constant	4.683***	0.242	4.548***
	(0.929)	(0.282)	(1.197)
Observations	418	417	417
R-squared	0.300	0.827	0.237

Column 2 delves deeper into the dynamics of achieving carbon neutrality by examining the lag of carbon neutrality and its relationship with the green transition. The positive and significant coefficient of the lag of carbon neutrality (0.953***) suggests that delays in achieving carbon neutrality led to worsened carbon balance. However, the negative and significant coefficient of the green transition variable (−0.109***) indicates that environmental policies aimed at facilitating the green transition can mitigate these negative effects. This highlights the importance of timely and effective implementation of green policies to address environmental challenges.

Furthermore, Column 3 explores the impact of lagged green transitions on achieving carbon neutrality. The negative and significant coefficient of the lag of green transitions (−0.571***) suggests that previous efforts in promoting green transitions are associated with improved carbon neutrality outcomes. This underscores the cumulative nature of environmental policies and the long-term benefits of sustained efforts toward sustainability. Overall, the findings from Table 7 reinforce the conclusions drawn from the main results in Table 5, indicating that the relationships observed are robust and not significantly affected by endogeneity and cross-sectional dependence issues.

5.6. IV-GMM analysis for indicators of the green transition

Table 8 shows a positive and statistically significant coefficient of green politics (0.415***) indicating that governmental actions, policies, and regulations aimed at promoting ecologically friendly practices are associated with worsened carbon neutrality. This result suggests that despite efforts to implement green policies, there may be unintended consequences or inefficiencies in their implementation that lead to increased carbon emissions. In contrast, the negative and statistically significant coefficient of green technology (−8.24e-06***) suggests that investments and advancements in green technology play a crucial role in maintaining carbon neutrality. This finding underscores the importance of technological innovation in mitigating carbon emissions and achieving environmental sustainability goals.

Table 8. IV-GMM analysis for indicators of the green revolution (Dep Var: InCO2).

	(1)	(2)	(3)
Variables	CO2	CO2	CO2
InPID	0.415***
	(0.116)
InINF	−0.0149*	−0.0207**	−0.0351***
	(0.00882)	(0.00852)	(0.0100)
InFDI	0.0251***	0.0196**	−0.0113
	(0.00949)	(0.00841)	(0.0107)
InENG	−0.0223	0.00104	0.0582
	(0.0337)	(0.0359)	(0.0398)
InPI	6.42e-05***	7.37e-05***	7.40e-05***
	(6.38e-06)	(4.91e-06)	(4.92e-06)
InTRD	0.0110***	0.0140***	0.0110***
	(0.00190)	(0.00176)	(0.00192)
InURB	0.0352***	0.0180***	0.0575***
	(0.00606)	(0.00443)	(0.00676)
InTECH		−8.24e-06***
		(5.41e-07)
InGF			−0.430***
			(0.0679)
Constant	2.381**	4.140***	2.964***
	(1.022)	(0.912)	(1.078)
Observations	420	418	420
R-squared	0.300	0.328	0.269

Note: ***p < 0.01, **p < 0.05, *p < 0.1; ln = Natural logarithm; CO2 = carbon emission; ECOPOL = Eco political movement; PID = political ideology; TECH = green innovation; GF = green finance; INF = inflation rate; FDI = foreign direct investment; ENG = economic growth; PI = economic development; TRD = trade.

Source: Author’s Computations.

Similarly, the negative and statistically significant coefficient of green finance (−0.430***) highlights the importance of financial incentives and investments in promoting carbon neutrality. This result suggests that financial mechanisms and instruments that support sustainable projects and practices contribute to the achievement of carbon neutrality. Overall, these findings are consistent with the main results presented in Table 6, which indicate that there are no endogeneity or cross-sectional issues in the model. By examining the specific effects of green politics, green technology, and green finance on carbon neutrality, the study provides valuable insights into the mechanisms through which environmental policies and practices influence environmental outcomes.

5.7. Quantile analysis for green consolidation

Our model may be subjected to the heterogeneity effect; therefore, we applied quantile regression, which accounts for heterogeneity and is reported in Table 9. At lower quantiles, the coefficient of green transition is positive and statistically significant. This suggests that initially, the implementation of green transition policies may not effectively promote carbon neutrality. This could be attributed to the fact that individuals with lower income levels may lack the financial resources to invest in green facilities and technologies, despite the presence of supportive policies. This finding underscores the importance of considering income disparities and affordability issues when designing and implementing green transition policies, particularly in ensuring equitable access to sustainable technologies and practices. These results contrast with findings from previous studies, such as Luo, Lu, and Long (2020), which emphasized the role of endogenous innovation in reducing CO₂ emissions.

Table 9. Qreg results for green revolution (Dep Var: InCO2).

	(.05)	(.15)	(.25)	(.35)	(.45)	(.55)	(.65)	(.75)	(.85)	(.95)
Variables	CO2	CO2	CO2	CO2	CO2	CO2	CO2	CO2	CO2	CO2
InECOPOL	0.0463***	0.0756**	0.116***	0.125**	0.102	0.0486	0.0570	0.0381	−0.0805**	−0.0860**
	(0.0151)	(0.0346)	(0.0383)	(0.0584)	(0.0726)	(0.0735)	(0.0486)	(0.0405)	(0.0368)	(0.0368)
InINF	0.0270***	0.0295***	0.0332***	0.0308***	0.0271***	0.0235***	0.0208***	0.0252***	0.0304***	0.0271***
	(0.00154)	(0.00352)	(0.00390)	(0.00594)	(0.00739)	(0.00748)	(0.00495)	(0.00413)	(0.00375)	(0.00374)
InFDI	−0.00261**	−0.00534**	0.00219	0.00188	0.00300	0.00169	0.000724	−0.000269	0.00226	−0.000121
	(0.00103)	(0.00236)	(0.00261)	(0.00399)	(0.00495)	(0.00501)	(0.00332)	(0.00277)	(0.00251)	(0.00251)
InENG	0.0119**	0.0224*	0.0193	0.0198	0.0127	0.0144	0.0302*	0.0249*	0.0241**	0.0344***
	(0.00499)	(0.0114)	(0.0127)	(0.0193)	(0.0240)	(0.0243)	(0.0161)	(0.0134)	(0.0122)	(0.0121)
InPI	3.13e-05***	2.84e-05*	3.72e-05**	4.43e-05*	8.23e-05***	0.000121***	0.000134***	0.000160***	0.000143***	0.000107***
	(6.44e-06)	(1.47e-05)	(1.63e-05)	(2.49e-05)	(3.09e-05)	(3.13e-05)	(2.07e-05)	(1.73e-05)	(1.57e-05)	(1.56e-05)
InTRD	0.00244**	0.000265	−0.00429	−0.00995**	−0.00846	−0.0107*	−0.00918**	−0.00924***	−0.00745***	0.00220
	(0.00116)	(0.00265)	(0.00293)	(0.00447)	(0.00556)	(0.00562)	(0.00372)	(0.00310)	(0.00282)	(0.00281)
InURB	−0.0369**	−0.0805**	−0.0596	−0.0154	0.0475	0.0865	0.120***	0.118***	0.0322	−0.0343
	(0.0143)	(0.0328)	(0.0363)	(0.0554)	(0.0689)	(0.0697)	(0.0461)	(0.0385)	(0.0349)	(0.0349)
Constant	15.55***	19.19***	16.96***	13.58***	7.144	2.805	−0.296	−1.624	6.158*	14.03***
	(1.311)	(3.004)	(3.323)	(5.067)	(6.299)	(6.374)	(4.220)	(3.517)	(3.193)	(3.189)
Observations	420	420	420	420	420	420	420	420	420	420

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; ln = Natural logarithm; CO2 = carbon emission; ECOPOL = Eco political movement; PID = political ideology; TECH = green innovation; GF = green finance; INF = inflation rate; FDI = foreign direct investment; ENG = economic growth; PI = economic development; TRD = trade.

Source: Author’s Computations.

However, at higher quantiles, the coefficient of green transition turns negative and statistically significant. This indicates that as income levels rise, the implementation of green transition policies becomes associated with promoting carbon neutrality. This aligns with the expectation that green transitions, encompassing green politics, green innovation, and green finance, may take time to yield significant environmental benefits, especially as higher income levels facilitate greater investment capacity in eco-friendly solutions. This finding supports findings from studies such as Li et al. (2021), which highlighted the role of green innovation in reducing CO₂ emissions. These results underscore the importance of continued investment in green innovation and the gradual adoption of sustainable practices as income levels increase, ultimately leading to positive environmental outcomes.

In Table 10, we investigated the relationship between various factors and carbon neutrality, building upon the Porter Hypothesis. Our analysis incorporated factors such as green transition policies, economic indicators, and other relevant variables to understand their impact on carbon neutrality. We also explored the impact of the lag of carbon neutrality and found that its effect on carbon neutrality plans varies. Initially, the lag of carbon neutrality appears to exacerbate carbon neutrality goals. However, as time progressed, we observed a shift, with the lag of carbon neutrality ultimately promoting carbon neutrality efforts.

Table 10. Qreg results for the lag of InCO2 and indicators of the green revolution (Dep Var: InCO2).

	(.05)	(.15)	(.25)	(.35)	(.45)	(.55)	(.65)	(.75)	(.85)	(.95)
Variable	CO2	CO2	CO2	CO2	CO2	CO2	CO2	CO2	CO2	CO2
InCO2_lag1	0.0217***	0.0410*	0.196***	0.367***	0.387***	0.353***	0.298***	0.179***	0.0497**	−0.0438***
	(0.00459)	(0.0213)	(0.0223)	(0.0237)	(0.0254)	(0.0271)	(0.0240)	(0.0250)	(0.0201)	(0.0167)
InECOPOL	0.0496***	0.0680	0.0631	0.0341	0.0101	−0.0194	−0.00706	−0.00987	0.0655	0.0246
	(0.00983)	(0.0456)	(0.0478)	(0.0507)	(0.0543)	(0.0580)	(0.0514)	(0.0536)	(0.0430)	(0.0358)
InINF	0.0238***	0.0295***	0.0271***	0.0206***	0.0187***	0.0177***	0.0195***	0.0237***	0.0244***	0.0278***
	(0.000995)	(0.00461)	(0.00483)	(0.00513)	(0.00550)	(0.00587)	(0.00520)	(0.00543)	(0.00436)	(0.00362)
InFDI	−0.00105	−0.00454	−0.00186	−0.00108	−3.55e-05	0.00105	−0.000181	0.00169	0.00130	−0.000471
	(0.000668)	(0.00309)	(0.00324)	(0.00345)	(0.00369)	(0.00394)	(0.00349)	(0.00364)	(0.00292)	(0.00243)
InENG	0.00732**	0.0191	0.0205	0.0213	0.0231	0.0293	0.0254	0.0229	0.0263*	0.00839
	(0.00323)	(0.0150)	(0.0157)	(0.0167)	(0.0178)	(0.0190)	(0.0169)	(0.0176)	(0.0141)	(0.0118)
InPI	3.02e-05***	3.46e-05*	2.64e-05	9.31e-06	9.41e-06	5.03e-05**	5.09e-05**	9.99e-05***	0.000118***	0.000112***
	(4.21e-06)	(1.95e-05)	(2.05e-05)	(2.17e-05)	(2.33e-05)	(2.48e-05)	(2.20e-05)	(2.30e-05)	(1.85e-05)	(1.53e-05)
InTRD	0.00327***	−0.00216	−0.00586	−0.00723*	−0.0105**	−0.0104**	−0.0128***	−0.0147***	−0.00742**	−0.00520*
	(0.000748)	(0.00347)	(0.00363)	(0.00386)	(0.00413)	(0.00441)	(0.00391)	(0.00408)	(0.00327)	(0.00272)
InURB	−0.0206**	−0.0674	−0.0563	−0.0103	−0.00584	0.0154	0.0171	0.0242	−0.00206	0.0682**
	(0.00930)	(0.0431)	(0.0452)	(0.0480)	(0.0514)	(0.0548)	(0.0486)	(0.0507)	(0.0407)	(0.0338)
Constant	13.96***	16.98***	14.39***	9.413**	9.341**	6.643	7.908*	6.834	10.26***	6.395**
	(0.847)	(3.926)	(4.117)	(4.372)	(4.681)	(4.995)	(4.427)	(4.623)	(3.709)	(3.084)
Observations	419	419	419	419	419	419	419	419	419	419

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; ln = Natural logarithm; CO2 = carbon emission; ECOPOL = Eco political movement; PID = political ideology; TECH = green innovation; GF = green finance; INF = inflation rate; FDI = foreign direct investment; ENG = economic growth; PI = economic development; TRD = trade.

Source: Author’s Computations.

Table 11 similarly investigate into the impact of the green transition on carbon neutrality revealed a heterogeneous relationship. Initially, an increase in green transition policies seems to have a negative effect on carbon neutrality, potentially due to adjustment costs or transitional challenges. However, as time elapses, we observed a mitigation of this negative impact, suggesting that the green transition becomes increasingly effective in promoting carbon neutrality over time.

Table 11. Qreg results for the lag of independent variables (Dep Var: InCO2).

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
Variables	CO2	CO2	CO2	CO2	CO2	CO2	CO2	CO2	CO2	CO2
InECOPOL_lag1	0.0484***	0.111***	0.0679*	0.0749	0.0696	0.0358	−0.0412	−0.0371	−0.0589***	−0.0136***
	(0.0147)	(0.0367)	(0.0372)	(0.0579)	(0.0679)	(0.0775)	(0.0514)	(0.0462)	(0.0389)	(0.0280)
InINF_lag1	0.0294***	0.0285***	0.0304***	0.0309***	0.0283***	0.0282***	0.0277***	0.0287***	0.0256***	0.0267***
	(0.00174)	(0.00433)	(0.00439)	(0.00683)	(0.00800)	(0.00913)	(0.00606)	(0.00544)	(0.00458)	(0.00330)
InFDI_lag1	−0.000126	−0.00210	−0.00254	0.000674	−0.000280	−0.00125	−0.00424	−0.00208	−0.00485*	0.000779
	(0.00106)	(0.00265)	(0.00269)	(0.00418)	(0.00490)	(0.00559)	(0.00371)	(0.00333)	(0.00281)	(0.00202)
InENG_lag1	0.0279***	0.0402***	0.0433***	0.0423**	0.0362*	0.0306	0.0464***	0.0430***	0.0346***	0.0668***
	(0.00456)	(0.0114)	(0.0115)	(0.0179)	(0.0210)	(0.0240)	(0.0159)	(0.0143)	(0.0120)	(0.00866)
InPI_lag1	−1.55e-06	−1.69e-06	−8.66e-06**	−8.22e-06	−7.44e-06	−4.45e-06	−1.06e-06	−1.62e-06	−2.28e-05***	−1.00e-05***
	(1.46e-06)	(3.63e-06)	(3.68e-06)	(5.73e-06)	(6.71e-06)	(7.66e-06)	(5.08e-06)	(4.56e-06)	(3.84e-06)	(2.76e-06)
InTRD_lag1	0.000154	−0.000427	−0.000233	−0.000930	−0.00174	−0.00218	−0.00300*	−0.00361**	0.00133	−0.00120
	(0.000514)	(0.00128)	(0.00130)	(0.00202)	(0.00237)	(0.00271)	(0.00180)	(0.00161)	(0.00136)	(0.000976)
InURB_lag1	0.00128	0.00227	−0.00125	−0.00446	0.00253	0.00395	−0.00289	0.00227	0.00332	0.000311
	(0.00254)	(0.00634)	(0.00643)	(0.0100)	(0.0117)	(0.0134)	(0.00889)	(0.00798)	(0.00672)	(0.00483)
Constant	12.94***	13.04***	14.04***	14.47***	14.41***	14.90***	15.35***	15.26***	16.90***	16.46***
	(0.296)	(0.739)	(0.749)	(1.166)	(1.366)	(1.560)	(1.035)	(0.929)	(0.783)	(0.563)
Observations	419	419	419	419	419	419	419	419	419	419

Note: *** p < 0.01, ** p < 0.05, * p < 0.1; ln = Natural logarithm; CO2 = carbon emission; ECOPOL = Eco political movement; PID = political ideology; TECH = green innovation; GF = green finance; INF = inflation rate; FDI = foreign direct investment; ENG = economic growth; PI = economic development; TRD = trade.

Source: Author’s Computations.

6. Conclusion and policy implication

The study focuses on the intersection of economic activities, environmental degradation, and the transition toward sustainability, particularly in OECD countries. It recognizes the urgent need to address environmental degradation caused by economic and social activities and seeks to understand the role of green transition initiatives in achieving carbon neutrality. The empirical analysis employs panel data techniques such as PCSE and IV-GMM to address methodological challenges and ensure robust estimations. Quantile regression is also utilized to account for heterogeneity in the model, acknowledging that different countries may experience varying levels of success in transitioning toward sustainability.

The empirical findings highlight the relationship between green transition policies, green politics, and carbon neutrality. There is evidence of a positive correlation between green transition initiatives and carbon neutrality, underscoring the importance of policy interventions in promoting sustainability goals. Governments in OECD countries play a significant role in facilitating this connection through the implementation of green policies, fostering innovation, and allocating financial resources. However, the study also reveals a detrimental correlation between green ideology/politics and carbon neutrality, suggesting that mere rhetoric or ideology without effective implementation strategies may not lead to tangible environmental improvements. It emphasizes the need for concrete actions and policies to accompany environmental aspirations.

Policy recommendations derived from the findings include the importance of integrating green transition initiatives with specific legislation and implementation tactics, maintaining investments in green innovation and technology, strengthening regulatory frameworks to ensure compliance with environmental standards, and enhancing public awareness and participation in sustainability efforts through educational campaigns and community engagement. Furthermore, recognizing the global nature of environmental challenges, the study emphasizes the necessity of international cooperation and collaboration among governments to exchange best practices, technologies, and resources to achieve common environmental objectives.

Overall, the study contributes to the understanding of how green transition initiatives can drive progress toward carbon neutrality and offers practical policy recommendations to guide decision-makers in promoting sustainability within OECD countries and beyond.

Disclosure statement

No potential conflict of interest was reported by the author.

Appendix 1:

Principal compound analysis (PCA)

Table

	Principal components/correlation
Component	Eigenvalue	Difference	Proportion	Cumulative
Comp 1	1.170	0.207	0.390	0.390
Comp 2	0.963	0.095	0.321	0.711
Comp 3	0.867	.	0.289	1.000
	Principal components (eigenvectors)
Variables	Comp 1	Comp 2	Comp 3	Unexplained
Green politics	0.547	−0.665	0.509	0
Green technology	−0.662	0.029	0.749	0
Green finance	0.513	0.746	0.424	0