The Interplay Between Green Finance, Policy Uncertainty and Carbon Market Volatility: A Time Frequency Approach

Abstract

Climate change and the transition to sustainable development have heightened the global focus on carbon markets and green finance as critical tools for reducing greenhouse gas emissions. Understanding the factors driving carbon market volatility has become increasingly important as countries strive to meet climate goals. In this connection, our study investigates the interplay between green finance and carbon market volatility in China. For this purpose, we use monthly data from January 2015 to April 2023. The findings reveal that policy uncertainty significantly influences carbon market volatility, with a positive short-term relationship indicating that heightened policy uncertainty drives carbon market volatility upward due to increased market volatility. Conversely, issuing green finance-related certificates dampens carbon market volatility, suggesting that enhanced green finance reduces the demand for carbon allowances. This study underscores the critical role of stable economic policies and robust green finance initiatives in mitigating carbon market volatility, providing valuable insights for policymakers aiming to foster resilient and sustainable carbon markets.

1. Introduction

The growing impetus in recent years to tackle climate change has spotlighted sustainable development and reducing carbon emissions. Economic policy uncertainty, or policy uncertainty (PU), essentially means the unpredictability of government actions and policies, which impact investment decisions and significantly affect market dynamics. As Baker et al. [1] noted, PU encompasses fiscal, monetary, and regulation policy uncertainty that can make it optimal for a firm to delay or even forego certain investments in green practices. Real Options Theory explains this behavior; hence, firms wait for better signals before investing in such capital-intensive green technologies. This hesitation is more significant in volatile environments where uncertain policies crowd out investments that reduce carbon emissions.

On the contrary, green finance has been termed the key instrument for financing environmentally viable projects. The bond signal is not only a way of financing but also a commitment to sustainability, per stakeholder theory, that requires organizations to consider various stakeholders’ interests in financial decisions. These elements, therefore, are in an intimate connection with the dynamics of carbon pricing, regulated under the supply and demand framework. The relationship between PU, green finance, and carbon pricing can be understood from the perspective of behavioral economics by underlining that policy and market conditions affect the perception and psychological biases of actions taken by market participants.

In the background of rising global temperatures and alarming abnormalities in weather conditions, the need to limit carbon emissions and ensure sustainable development has gained widespread global consensus [2]. In conjunction with achieving a carbon-free future by this century’s end, states around the globe are organizing all kinds of farsighted mechanisms and policies [3]. Indeed, the most surprising factor is that carbon emission trading, generally called CET, emerges as the most efficient market-related instrument to lead a few states and regions to a green and sustainable future.

On the matter of carbon emissions, China has become one of the largest global carbon dioxide emitters [4]. However, the commitment by China to combat climate change has reached unprecedented heights [5]. A good example of such commitment was when, in 2013, carbon emissions trading pilots were established within several regions of China. The crowning achievement came on 16 July 2021, when the national carbon emissions trading market was launched in China, making the nation accomplish the feat of having the globe’s most significant greenhouse gas emissions in the market. However, this budding financial frontier raises fundamental issues concerning institutional development, market functioning, regulatory arrangements, and the constant threat of price volatility [6]. Studying such instability samples and identifying carbon market volatility drivers provides an overview of what goes into the market price formation process. It is supposed to make us better equipped to exploit the environmental and economic benefits of more efficient trading in carbon and bring us closer to a greener and more prosperous future.

The following is how the market mechanism for CET works convincingly: The government sets a ceiling on the firms to limit the aggregate carbon emissions from all firms with emission control. If those firms do not have enough carbon emission quotas, they can cover their quota deficit in terms of their emission reduction commitment by procuring additional quotas. Surprisingly, if the per unit price of carbon exceeds the bordering price of emission reduction, then the firms are capable of using innovative techniques, for instance, clean technologies, hence enhancing energy effectiveness [7,8]. This extraordinary combination of the mechanism that the carbon market imposes and the mechanism for adopting innovative green technology reduces the carbon footprint and fosters a corporate landscape of sustainable, low-carbon development.

Large-scale enterprise innovation of green technologies is very dependent on the integration of financial resources. Green finance thus becomes one of the most important means for enterprise financing in this context [9]. Green finance provides an excellent mechanism whereby the risk reduction related to the market price can play a key role in the financial markets and the global economy. Despite being comparable to government and conventional corporate bonds, green finance has a distinctive feature: it channels the proceeds from green projects through these bonds. These adaptation projects relate to climate change mitigation, natural resource preservation, pollution control, and biodiversity conservation [10]. Therefore, through green finance, organizations need the capital to support green projects to lead us toward a more resilient and sustainable future.

Integrating carbon trading and the green finance market occupies a central place within the Chinese green financial system under the exact shared mechanisms of regulation and policies [11,12,13]. With climate change topping their minds, green finance usually hedges savvy investors against uncertainties in the carbon trading landscape. Therefore, this should be a fascinating correlation that invites further investigation. A few scholars have recently explored the relationship between carbon market volatility and green finance. As a global priority, there is an urgent need for in-depth research into how the cost of green finance will influence carbon market volatility in a manner that will lead to a greener future.

Regarding important global situations, such as the 2020 COVID-19 pandemic and the 2008 financial crisis, which had profound consequences and brought about seismic changes to the economic landscape, there has always been a need to develop economic policies. Responding to changing international dynamics, political and economic upsets, anti-globalization sentiments, a return to trade protectionism, and a surge of economic policy uncertainty (PU) characterizes the world stage. Against this background, more and more academicians are interested in understanding how economic policy uncertainty impacts economic conditions [14]. Hence, this growing interest testifies to the pressing necessity to dig deeper into the complex interplay of blurred economic policies. The functioning and health of the carbon market as a vital component of the monetary system depend on the state’s economic policies [15].

Accordingly, the carbon market exhibits higher sensitivity regarding economic policy uncertainty (PU) conditions [16]. Even though researchers have observed various interrelationships, no consensus has been clearly found. For instance, several studies, including Adams et al. [17], find that economic policy ambiguity favorably affects carbon emissions, while others, such as Adedoyin and Zakari [18], draw various conclusions and report that economic policy ambiguity lowers carbon emissions. Conversely, deep insights into the influence of monetary policy ambiguity on carbon market volatility must be brought forth.

The influence of geopolitics on carbon market volatility is immense since it sets the tone for world energy markets. The conflict between Russia and Ukraine and the sanctions levied against energy exports have raised the price of fossil fuels and lifted the cost of carbon emission allowances. Events like these introduce a volatile aspect: industries will shift to carbon-intensive fuels during energy shortages. In addition to that, volatility is caused by tensions between global powers like the US and China, which affect trade and commitments to climate policy. Geopolitical uncertainty often translates into delays or shifts in climate agreements such as the Paris Agreement, resulting in wild fluctuations in carbon markets because businesses struggle to make good guesses about the changing regulatory environment. Moreover, geopolitical competition accelerates the transition to renewable energy sources, as many countries, such as those in Europe, seek energy independence from politically unstable regions. Although this may lead to convergence in carbon market volatility in the long run, volatility still exists in the short term.

This paper primarily explores the dynamic linkages of policy uncertainty (PU), green finance, and carbon market volatility in China. A TVP-VAR model is then adopted to model how these factors interact over time and explore regional disparities in carbon pricing dynamics. This study examines the effect of policy uncertainty on carbon market volatility and how green finance issuance impacts the carbon allowance demand. The current research has targeted China because of its strategic importance in carbon trading and green finance. Region 1 in China, being an economically advanced province, presents an already established carbon trading market with sophisticated regulatory frameworks and a quota auction system, which brings more liquidity into the market.

On the other hand, Region 2 provides insights into a developing carbon market. Analyzing the two regions, therefore, allows us to compare how different regional economic developments and regulatory environments shape carbon pricing dynamics. The expected results from this study will benefit policymakers interested in enhancing the efficiency and stability of carbon markets, both in China and elsewhere.

China has been chosen for this study due to its importance in the carbon trading and green finance markets. For instance, Region 1 has an advanced carbon trading system and one of the most progressive regulatory regimes in the country. Hence, it may be a good example for understanding pricing in a well-developed market. On the other hand, the emerging market of Region 2 is one of the newest carbon markets, with much government support, and may be sensitive to economic policy uncertainty.

Moreover, the researchers have sought a meaningful investigation of the relationship between green finance and economic policy uncertainty to establish strong relationships accordingly [19,20]. In light of this consideration, it is much more relevant to explore how green finance impacts carbon market volatility in the presence of unpredictable fiscal policy. The GARCH and SVAR models have been implemented to study how time-series variable variation influences the relationship between carbon market volatility and the problems it causes [21,22]. Such models go well beyond static relationships and provide relevant insights into the evolution of these linked variables.

As a result of such dynamic modeling approaches, the associated dynamics among economic policy uncertainty, green finance, and carbon market volatility can be well acquired and fully appreciated. This provides a more informative basis for decision making and policy formulation. Since it has been apparent that the carbon market volatility in China is too unstable, referring to the immaturity of the carbon trading market, the discussion of the dynamic effect among the three factors—economic policy uncertainty, green finance, and carbon market volatility—will be conducted in this section. Therefore, such a case will require a robust model incorporating time-varying relationships and heteroscedasticity for complex factors at play. In this respect, Forni et al. [23] showed that a generalized dynamic factor model can handle large-dimensional data with factor stability across conditions of time-varying volatility. The approach adopted here, similar to the one used in this paper, is chosen to ensure that such complex interrelations are captured, as in carbon pricing dynamics. The GDFM can stabilize factors despite variance discontinuities, thus providing an important methodological basis for this study.

The time-varying parameter vector autoregression model has been in vogue lately because of its capability to unravel how variables change over time and enter nonlinear relationships (refer to Jebabli et al. [24] and Gabauer and Gupta [25] for some recent applications). Although the TVP-VAR model used in this study effectively captures the time-varying dynamics and nonlinear relationships between the variables, some factors might still not be accounted for in these relationships. Although this model helps mitigate endogeneity concerns, as Bai and Ng [26] noted, there can be external shocks or omitted variables that this model does not fully capture. These can again be factored into future research by incorporating more variables or using different approaches to present more robust results. Given the nature of the TVP-VAR model, this study offers a more accurate examination of economic policy uncertainty and its dynamic effect on carbon market volatility with green finance. Therefore, this higher functionality in modeling enriches our understanding of the complex interplay among economic policy uncertainty, green finance, and carbon market volatility.

The present study’s findings provide a more accurate analysis of the dynamic impact of PU on carbon market volatility using green finance with the strengths of the TVP-VAR model. One significant strength of the model is that it produces subtle shifts in the influence of PU and green finance on carbon market volatility by allowing the parameters to change over time. This level of granularity is critically necessary to understand the evolving nature of financial markets and policy impacts so that any findings have relevance and applicability to the real world.

Our research, therefore, contributes to several dimensions of the extant literature with an extension of both theoretical and empirical understandings of carbon pricing. Firstly, we address the vital gap in the literature by focusing on the dynamic factors that determine carbon market volatility in China, with a particular emphasis on the interaction between green finance and economic policy uncertainty. While most earlier studies have examined these factors singly or through static models, our research offers a richer and more nuanced insight than previous studies by analyzing their interdependence. This represents an important gap in the literature since few studies have examined these factors simultaneously. We also extend the existing research focus from the well-researched EU carbon trading market to China, an emerging market with unique economic and regulatory features. The attention specific to China’s carbon market should further region-specific insights to inform policy decisions in China and other developing economies, allowing an extension of the scope in carbon pricing research.

From a methodological viewpoint, our work uses the TVP-VAR model, hitherto an acclaimed novelty in the extant literature. Hence, it provides a dynamic and time-varying relationship between economic policy uncertainty, green finance, and carbon market volatility. It contrasts most available studies, which assume the relationships to be time-invariant. This paper takes a temporal approach to provide new empirical evidence about how these relationships change over time amidst the gained momentum in understanding complex drivers of carbon market volatility. Further ensuring the robustness of our findings, we complement the results with the Bayesian Vector Autoregressive model, BVAR, which tends to support the relationships and further enhance the statistical reliability of our conclusions. Therefore, this approach with both methods contributes to the methodological discourse by showing how different models can be combined to study complex economic interactions.

Finally, this research provides valuable policy implications that could give governments actionable insights into developing more adaptive and effective carbon pricing mechanisms. By unveiling this time-varying influence of PU and green finance on carbon market volatility, we might be able to give policymakers the necessary knowledge to map policies that would elicit a targeted and responsive policy to sustainable environmental practices. In a nutshell, our contributions are three-fold: first, we address a significant gap in the literature; second, this study introduces new methodological insights; and third, recommendations with actionable policy implications are provided, especially regarding the carbon market of China.

The subsequent sections of this paper follow a systematic organization: Section 2 consists of the theoretical background and a thorough review of existing studies conducted on carbon market volatility. In Section 3, we introduce the TVP-VAR model and present the dataset used in our analysis. Section 4 focuses on the empirical study of our research. We examine the results obtained from applying the TVP-VAR model, and to test the robustness of our findings, the BVAR model is employed. In Section 5, we present the results drawn from our study and discuss the relevant implications.

2. Empirical and Theoretical Review of the Literature

2.1. Theoretical Literature

Changes in demand for carbon releases primarily drive the dynamics of carbon pricing. Demand theory states that there is a correlation between demand for carbon emissions and prices for carbon. In other words, an increase (or decrease) in carbon emission demand typically results in higher (or lower) carbon market volatility. Green finance and economic policy uncertainty affect carbon pricing by affecting the need for carbon emissions. Therefore, this study provides a theoretical analysis of how green finance and economic policies affect carbon emissions.

Two relatively simple theories—the indirect economic demand influence and the direct policy adjustment influence—can shed light on their mechanisms for affecting carbon emissions. The straight policy modification influence highlights that the regime’s priorities shift from protecting the environment to increasing the economy, affecting how well environmental protection measures are implemented. In other words, economic policy ambiguity is uncertainty in executing carbon emission-mitigating policies in time and comprehensively. Through an investigation of these theories, more vital knowledge is obtained concerning how the uncertainty of economic policy directly affects carbon releases and further implications on environmental sustainability. Relaxation of the government on eco-friendly oversight and governance during high economic policy uncertainty might breed other non-intended impacts. For instance, emission control companies may take advantage of this government leniency by using relatively cheaper but highly emitting sources of energy that do not meet the required standard to comply with the reduction regulations, thus further increasing corporate carbon emissions.

However, the indirect effect of economic demand caused by the unclear policy impact on the market is more intriguing. As economic uncertainty rises, it tends to give birth to an economic recession; in this relation, firms tend to take up risk-averse strategies, which may include a reduction in production. This reduction in economic activity could indirectly lead to lower carbon emissions due to decreased industrial output. By scanning these dynamics, we can understand how uncertain economic policy has multivalent effects on carbon emissions and their wider ramifications. A reduction in output due to an economic downturn implies that firms’ energy demand decreases, reducing carbon emissions. For example, the results reported by Bel and Joseph [27] indicated a sharp drop in the demand for and price of carbon emission permits according to the financial crisis linked with the 2008 economic crisis. Their research provided empirical evidence that indicated the direct effects of economic downturns on carbon markets. Reduced demand for carbon allowances lowered their prices.

Green finance actively supports carbon emissions by energy substitution and efficiency. Green finance issuance promotes enterprise technological innovation; empirical evidence from Mensah et al. [28] shows that technological advancement directly affects corporate carbon discharge. By stimulating technological innovation and integrating greener and more efficient energy usage, green finance helps reduce carbon emissions. The positive relation that links carbon emission reduction with green finance underlines their crucial role in effectively mitigating environmental impact and ensuring sustainable development.

Erdoğan et al. [29] noted that the impact of innovation on carbon emissions is sectoral and differs across industries in G20 countries. While it reduces emissions in the industrial sector, innovation increases emissions in the construction sector, emphasizing the need for sector-specific policies directed toward emissions. Along these lines, Ulucak and Ozcan [30] examined the effect of energy usage on environmental sustainability for OECD countries. According to the authors, the results show that non-renewable sources increase environmental degradation, while renewable sources contribute to mitigation. The critical role of sectoral approaches and energy efficiency in environmental sustainability came to light from both studies, which requires policies tailored to specific industries and energy sources.

Erdoğan et al. [31] examined the MENA region, finding a strong link between energy consumption and economic growth, particularly in oil-rich countries, where fossil fuel reliance presents significant environmental challenges. In BRICS-T countries, Erdoğan et al. [31] identified a bidirectional causal relationship between economic growth and carbon emissions, showing that both variables influence each other, complicating the balance between growth and sustainability. Similarly, Ulucak and Baloch [32] examined the United States, where natural resource rent and globalization contribute to environmental degradation, while environmental-related technologies help reduce pollution. These studies collectively emphasize the need for sustainable energy policies, stable economic frameworks, and technological innovation to address the dual challenges of economic growth and environmental sustainability.

Green finance can contribute to carbon emission reduction through two channels: energy efficiency improvement and clean energy deployment. The issuing of green finance by companies enhances technological advancement within their enterprises in terms of improving production processes and efficiency in terms of energy use. This, in turn, will reduce the energy investment of the enterprises and hence lead to a reduction in the carbon emissions for energy use. Previous research has justified this, suggesting that more intensive technology positively correlates with higher energy efficiency [33]. Secondly, through financing, green finance encourages the development and use of clean forms of energy. Since the adoption of technology in clean energy reduces enterprises’ dependence on fossil fuels, it will also cut carbon emissions. The findings by Adams and Acheampong [34] support this link between clean energy uses and reducing carbon emissions. The increasing popularity of promoting clean energy makes organizations consider clean energy sources instead of non-renewable sources because of the substitution effect. Sources like wind, solar, and oceans have a lower carbon content than non-renewable sources. As such, low carbon dioxide emissions are found when clean energy sources are used. Therefore, businesses use clean energy to minimize environmental effects and promote sustainability.

In addition, economic policy must be considered to nudge the bond market toward a greener pasture. For instance, the growth in the degree of monetary policy uncertainty weighs down enterprises and increases the volatility and uncertainty of their business environment. It increases the likelihood of poor enterprise performance. The effect this has on the stock market is that a weakening enterprise sector worsens investment risks. Given this, investors need to focus on the bond market as an alternative investment avenue. Of course, this realignment specializes in green finance, which is specially tailored to finance environmentally sustainable projects. As Haq et al. [35] reveal, an interesting perspective concerns the risk-minimizing role of green finance in economic policy uncertainty. Their research suggests that high levels of monetary policy uncertainty amplify the importance of green finance as a risk-averse investment alternative.

This has the effect of drawing investors to green finance for the sake of portfolio protection. The increasing investor interest in green finance has some important implications. First, it will improve the green finance markets’ liquidity. Augmented demand for green finance, incited by economic policy uncertainty, upsurges the nature of trading activities and market liquidity, which in turn nurtures a more robust and dynamic market environment for green finance. Consequently, Ibikunle et al. [36] mentioned that pricing efficiency is reinforced in the green finance market, reflecting a more accurate valuation of these bonds’ environmental and financial attributes.

2.2. Factors Affecting Carbon Market Volatility

Significant research has been conducted on the driving factors of carbon market volatility in the carbon trading market. The various elements that influence carbon market volatility, including the macroeconomic environment and energy costs of financial markets, have pinched much emphasis from investigators. A central focal point has been the effects of coal, oil, and natural gas since they are the major contributors to carbon emissions. Much research has indicated that energy prices influence carbon market volatility [37,38,39]. The carbon trading system of the EU has gained much attention in academic research. For example, Keppler and Mansanet-Bataller [40] noted the areas where the interaction between the carbon trading market and the energy market was taken into account. Further, their research explored an exciting relationship between energy and carbon market volatility within the framework, underlining the impact of energy costs on carbon market volatility.

Similarly, Mansanet-Bataller et al. [41] analyzed how energy prices describe carbon market volatility as the most vital element. The study pointed out the extent to which changes in energy prices contribute to fluctuations in the price of carbon. Zhu et al. [42] employed a multi-scale analysis approach in their research to explore the carbon market volatility determinant over varying time frames. A pattern was especially evident for carbon prices identified in the long and medium terms. According to Tan and Wang [43], changes in energy prices, in a way, directly affect the energy consumption of enterprises and how they request the right for carbon emissions and eventually determine the carbon market volatility. Other research has also identified the role of macroeconomic variables in determining carbon market volatility. Tang et al. [44] and Zeng et al. [22] pointed to the determination of carbon market volatility by macroeconomic conditions. Chevallier [45] revealed a direct correlation between carbon market volatility and changes in the macroeconomy. Koch et al. [46] stated that an economic recession significantly creates low carbon market volatility. When the economy is in recession, there is reduced demand for products and, therefore, a reduction in the price of carbon. Enterprises will usually lower their production and business operation levels since they are associated with costs due to such economic downturns. An activity reduction reduces the amount of carbon dioxide emissions, which in turn means a decrease in the demand for carbon emissions as long as carbon market volatility remains reduced.

Extensive research shows that carbon market volatility is highly interrelated with the performance of the financial markets. Creti et al. [47] found the stock market to be one of the significant determinants of carbon market volatility. Evidence of a complex interrelation between changes in the financial markets and carbon market volatility determination is found. The general state of the economy can usually be taken from the stock market, and high growth in stocks in an economy reflects increased demand over supply for carbon emissions. Thus, carbon market volatility is positively influenced when stock prices go up. Jiménez-Rodríguez [48] added that “the general stock market could serve as an indicator variable for the general state of the economy; a booming economy increases demand for carbon emissions.” Using a robust ARDL model, Abbasi et al. [49] conducted a similar study on carbon market volatility, emissions, and financial development. It has been observed that carbon emissions are induced by economic growth, which has an influential effect on carbon market volatility.

2.3. Carbon Market and Economic Policy Uncertainty

It is entirely legitimate to surmise that a similar type of relationship could be established between PU and the carbon market based on the more than convincing results of earlier studies that underline the effect of unsure economic policy on the dynamics of the macroeconomy. Ye et al. [50] used the MF-DCCA approach to investigate the comovement between the carbon market and monetary policy uncertainty. Their study showed strong evidence for a relationship between these two factors. Moreover, Dai et al. [51] conducted research using an innovative survey method and found a significant impact of economic policy uncertainty on variations in the carbon market. This supports the above assumption that changes in monetary policy uncertainty can dramatically change the dynamics and stability of the carbon market. Indeed, the general scholarly population has proposed various mechanisms in light of the extensive research on the impact of economic policy uncertainty on carbon emissions. For instance, according to Yu et al. [52], the effect of PU on carbon emission levels is significant and noted in a wide range of factors, such as the proportion of fossil fuels, green innovation, and energy efficiency in firms engaged in production. Another research study by Wang, Xiao and Lu [53] revealed how PU forms carbon emissions according to their impact on investment and consumption patterns.

Further investigation by Pirgaip and Dinçergök [54] illustrated, through the bootstrap panel Granger causality method, that carbon emissions are generally linked to economic policy uncertainty. These works underline the multi-faceted and complicated relationship of PU with carbon emissions. Recently, new trials have tested the influence that monetary policy uncertainty has on carbon futures, specifically its predictive capability within volatility. For example, Liu et al. [21] and Zhang et al. [55] considered different models, namely the AR-X and GARCH MIDAS models, in predicting future EUA volatility from the contributions of PU. Their results suggest that, generally, PU is most useful for predicting low future carbon volatility.

Along the same lines, Dou et al. [56] also utilized a wavelet decomposition approach to analyze the linkage of PU shocks with the volatility of daily carbon futures returns. This study showed that PU shocks were a poor predictor of the volatility in carbon futures. These studies further elaborate on how PU will affect future carbon market dynamics. Over the long run, economic policy uncertainty significantly negatively impacts future carbon market volatility returns. PU will reduce companies’ need for carbon emissions, possibly influencing carbon market volatility. However, researchers should further examine the short-term influences of policy uncertainty on carbon market volatility. A study conducted by Li et al. [6] focused on the effect of four types of PU on carbon market volatility in China. Their observations indicated that uncertainties due to PU and monetary policy increase carbon market volatility; however, adverse effects resulted from the unfavorable exchange rate policy. The study by Tiwari et al. [57], in the same vein, using a time-varying Markov switching model, concluded that higher levels of uncertain economic policy led to a reduction in carbon market volatility. These contributions build into the complex relationship between PU and carbon market volatility.

2.4. Carbon Market and Green Finance

Although the green finance market is relatively recent, it has attracted increasing academic attention regarding its interconnection with other financial markets. Other studies have focused on the associations of green finance with different markets, for example, the market of energy [58], the market of stock [59,60], and the market of national debt. In the long run, Naeem et al. [61] concluded that there is a strong relationship between crude oil and green finance. In contrast, Nguyen et al. [62] applied the rolling window wavelet correlation methodology and found a strong correlation between clean energy and green finance; the above correlation was also documented. These two studies help us understand the interlinkages of the green finance market with other finance sectors. During their research, Koch et al. [46] found that the prices of carbon emissions can be lowered when clean energy is used. On the other hand, Boersen and Scholtens [63] showed a positive relationship between shocks in oil prices and the trading price of carbon emission rights. Based on such findings, it could be assumed that carbon market volatility impacts green finance. From the review, several scholars have researched the association between the carbon market and green finance. The carbon trading market has a marked influence on the international green finance market. Based on the findings by Ren et al. [13], this has been acknowledged in a study.

Many studies are dedicated to the relationship between the green finance market and the carbon trading market in a symbiotic way. The relationship between the two markets is a mutual benefit. Still, when the green finance market is comparatively feeble, this research highlights the dynamic interaction between green finance and the carbon market, emphasizing mutual influence and interconnection. A cross-sectional evaluation by Rannou et al. [12] on European power companies found a bidirectional influence between the European carbon and green finance markets. Research has proven that green finance acts as a hedging instrument for the risks associated with carbon markets, and this is why green finance has been launched by European power businesses, although they still exercise their rights to carbon dioxide emissions. In a related study, Reboredo et al. [64] also employed a copulative function to evaluate the carbon trading and green finance markets in China and Europe. Their findings indicated an adverse movement between green and low-carbon bonds.

However, these findings show how the carbon market, green finance market, and risk management of power companies work alternatively in complicated ways. These findings reflect how carbon markets and green finance actually work in cohesion. It is observed that green finance plays an important role in offsetting the risks involved in carbon trading. Few studies have been conducted on the interlinkage of carbon market volatility with green finance prices. Sample seminal works in this domain include the works of Wang et al. [65] using the DCC-MIDAS approach. The results indicated that this relation was time-varying and featured intermittent positive and negative correlations between carbon prices and green finance. However, there is still a significant gap in the existing investigation concerning the role of green finance as one of the factors affecting carbon market volatility. More research and exploration are required to understand the prices of green finance and carbon dynamics.

3. Data and Methodology

3.1. Data Description

To comprehensively investigate green finance and the effect of economic policy uncertainty on carbon market volatility, we analyzed a carefully selected dataset from January 2015 to April 2023. We explain the rationale behind this specific time frame below. The selection process was based on several considerations, one of which is represented in

Table 1. The pilots’ status in carbon trading.

Pilot	Period	Observation Numbers
Region 1	19 December 2013–22 February 2022	1541
Region 2	18 June 2013–18 February 2022	1652
Region 3	28 November 2013–14 February 2022	1412
Region 4	26 November 2013–22 February 2022	1241
Region 5	19 June 2014–22 February 2022	851
Region 6	26 December 2013–18 January 2022	652
Region 7	28 April 2014–22 February 2022	1945

. The days of emissions permit trading in the seven most prominent regional pilots are presented in this table, from the day they opened until April 2023. The findings show that three pilots recorded 1600 trading days, reflecting a consistently robust trading environment.

As a result, the entire gathering of SZA price data could be more problematic. Other studies [66,67,68] had heavily depended on China’s emission allowances as universally accepted measures. In this study, we chose R1 and R2 prices as proxies that will genuinely reflect carbon market volatility. Moreover, China’s green finance (GF) index signifies the green finance market in the investigation. Regarding certification status, green finance falls into two categories, namely labeled and no labels, and a third-party testing body qualifies them. Combining both types, the GF index provides a comprehensive and realistic representation of the green finance market. Last, Baker et al. [1] used the news to develop China’s economic policy uncertainty index, which measures economic policy uncertainty. The index measures mentions of the terms uncertainty (U), policy (P), and economy (E) in articles in newspapers within the country. It is the most helpful measure of the economic policy uncertainty level. We sourced the data for this work from the wind database, where our sample covers January 2015 to April 2023. The dataset includes the closing prices for emission allowances of Region 1 (R1) and emission allowances of Region 2 (R2) daily, as well as daily data for green finance (GF) and monthly data for China’s economic policy uncertainty (PU). We converted the daily data into monthly data to ensure consistency in the frequency of the variables by obtaining the GF, R2, and R1 values on the final day of every month. Additionally, we calculated the change rates of GF, PU, and carbon market volatility using the formula ln(P_t/P_t−1). Figure 1 visually illustrates the temporal patterns and fluctuations of the GF, R2, R1, and PU change rates.

As depicted in Figure 1, from 2015 to 2023, notable disparities can be observed between China’s trajectory of carbon market volatility and PU (economic policy uncertainty). Policy uncertainty presents a general upward movement throughout the sample period with significant fluctuations.

Furthermore, a significant rise in economic policy uncertainty (PU) is evident during the first half of 2020, attributed to disruptive incidents like the China–US trade tension and the onset of the COVID-19 pandemic. These events necessitated continuous economic policy adjustments, resulting in an overall escalation of PU. While carbon market volatility in China remained relatively constant throughout the sample period, sporadic increases and drops occurred in response to unforeseen circumstances. Notably, the inverse association between economic policy uncertainty and carbon markets is evident. Figure 1 also showcases a steadily rising trend in the index of China’s green finance, reflecting the growing prominence of green finance over the sample time frame.

Table 2. Variables’ descriptive statistics.

	lnPU	lnR1	lnGF	lnR2
Obs	95	95	95	95
SD	0.5342	0.1109	0.3223	0.6804
Max	4.3529	5.2091	3.9369	6.8782
Min	2.0149	4.7770	2.5757	4.3915
Mean	3.0396	5.0188	3.1772	5.9231
Kurtosis	−0.3117	−0.8423	−0.3924	−0.5611
Skewness	0.5234	−0.1653	0.0220	−0.6358

presents findings from the descriptive statistics for PU, R2, R1, and GF. We discovered that R1 has lower standard deviation, mean, median, and maximum values than R2. This shows that the carbon market volatility is more volatile and higher in R2 than in R1. The skewness statistic also indicates that R2 and R1 are skewed right, while GF and PU are skewed left. The outcomes are flatter than the distribution of values because the kurtosis statistics are negative.

3.2. Methodology

This study applies the TVP-VAR technique, which stands for Time-Varying Parameter Vector Autoregressive technique as initially proposed by Primiceri [59] and further enhanced by Nakajima [69], to scrutinize the time-changing influence of green finance and the PU index, economic policy uncertainty, on trading prices for carbon emissions. The TVP-VAR approach has the following benefits: Considering the variance–covariance matrices and time-varying coefficients allows for capturing complex nonlinear correlations among time-varying economic variables. A significant advantage of applying the TVP-VAR model in this study is its ability to reduce bias often arising from parameter instability in fixed-parameter models. Fixed-parameter models assume static relationships between variables, which can lead to biased and inaccurate estimates when the underlying economic or financial conditions change. This weakness is resolved by the TVP-VAR model, which allows parameters to change with time, accommodating dynamic shifts in the relations of economic policy uncertainty, green finance, and carbon market volatility. This flexibility gives way to more reliable estimates, making the TVP-VAR model better when analyzing time-varying interactions in the carbon market.

This, in turn, improves the fit of the evolving dynamics in the studied variables, as noted by Nakajima [69] and Primiceri [70]. Thereafter, Bayesian estimation is utilized to adjust the model parameters over time, overcoming the problem of arbitrary window size selection. In addition, it discards only a portion of the sample, making the model less prone to over-persistence issues. The TVP-VAR approach is also less volatile to the outliers’ influence [71]. Another desirable feature of the TVP-VAR model is its lower sensitivity to outliers’ effects. One of the other strong points that go to the credit of the TVP-VAR model is that, unlike regime-switching models, it considers gradual structural changes in the relationships between variables. Such financial markets, especially those affected by policy uncertainty and green finance, often face gradual shifts rather than sudden breaks. Therefore, The TVP-VAR model is exceptionally well placed to track these dynamics as they change over time, thus capturing a more nuanced and realistic understanding of how carbon market volatility responds to green finance and economic policy uncertainty. This feature adds to the robustness of the model in its assurance that gradual changes in market behavior are appropriately reflected in the results.

Another parallel application of the TVP-VAR model, as proposed in this paper, is the development of the multifactorial liquidity score as proposed by Agrrawal and Clark [72]. The multifactorial liquidity score considers gradual variation in liquidity over different market conditions. Similarly, the estimates from the TVP-VAR model show the gradual variation in the economic relationship. The findings highlight the TVP-VAR model’s ability to consider evolving market dynamics. Further confirmation of the model in studying the complex interaction of economic policy uncertainty, green finance, and carbon pricing has also been provided.

To introduce the TVP-VAR model, we start by establishing a structural VAR model as the foundation for further analysis.

$$A{y}_t=F_1{y}_{t-1}+\dots F_s y_{t-s}+u_t , t=s+1,\dots , n$$

(1)

In the TVP-VAR technique, we define yₜ as the k × 1-dimensional vector of the observed variables. The k × k-sized coefficient matrices A, F₁, …, Fs contribute to the approach, whereas the k × 1-dimensional vector of the structural shocks is indicated by uₜ. To ensure the credentials of the synchronized association among the specific structural shocks, we accept that the lower triangular matrix is matrix A with diagonal factors set to 1. It can be articulated as

$$A=\left(\begin{array}{cccc}1& 0& \cdots & 0\\ a₂₁& \ddots & \ddots & ⋮\\ ⋮& \ddots & \ddots & 0\\ a_{k1}& \cdots & a_{kk-1} & 1\end{array}\right)$$

(2)

Equation (1) can be expressed in the following form:

$$y_t=B_{1 }{y}_{t-1}+\dots B_s y_{t-s}+A^{-1} 1\Sigma {\epsilon }_t , {\epsilon }_t\sim N(0, I_k)$$

(3)

By letting $B_i$ = $A^{-1} F_i$, where i = 1, …, s, we can assemble the components in rows of $B_i$ to form β, resulting in a $k^2$s × 1-dimensional vector β. Additionally, we define $X_t$ = $I_s$ ⊗ ($y_{t-1}$, …, $y_{t-s}$), where ⊗ denotes the Kronecker product. With these definitions, the model can be simplified as follows:

$$y_t=X_t \beta +{ A}^{-1} \Sigma {\epsilon }_t$$

(4)

A more flexible TVP-VAR model can be obtained by allowing the model to change over time. This allows the TVP-VAR model to incorporate stochastic volatility, making it more suitable for capturing dynamics in the underlying variables. One of the key challenges for dynamic models such as TVP-VAR is ensuring factor stability in the presence of variance discontinuities. Approaches similar to those used by Ludvigson and Ng [73], who used factor analysis to address orthogonal factors and heteroscedasticity in financial markets, are followed in this study. Time-varying volatility adjustment ensures factor stability throughout the analysis. This is particularly critical when analyzing carbon pricing because economic and environmental factors fluctuate over time, potentially leading to variance discontinuities. Our model addresses these challenges and ensures that the dynamic interaction of factors remains stable. The possible formulation of the TVP-VAR model with stochastic volatility is as follows:

$${y }_t=X_t {\beta }_t+A^{-1}{\mathsf{\Sigma }}_t {\epsilon }_t , t=s+1,\dots , n$$

(5)

The TVP-VAR approach permits time-varying coefficients as ${\beta }^t$, a time-varying joint parameter matrix $A_t$, and a stochastic fluctuation covariance matrix ${\mathsf{\Sigma }}_t$. Following the method recommended by Nakajima [69] and Primiceri [59], the matrices of the triangle’s lower elements can be transformed and detailed as $a_t$ = ($a_{21}$, $a_{31}$, $a_{32}$, $a_{41}$, …, $a_{kk-1}$), and $h_t$ = ($h_{1t}$, …, $h_{ot}$), where$h_{it}$ signifies the fluctuation logarithm of ${\sigma }_{it}^2$ for each variable i = 1, …, k; t = s + 1.

The choice of the TVP-VAR model and its assumptions are appropriate for the paper’s aims. The chosen time lags and parameters that follow a random walk fit well in capturing evolving dynamics in the relationship between economic policy uncertainty, green finance, and carbon market volatility. In line with the work of Primiceri [59] and Nakajima [69], the random walk assumption allows for flexibility in parameter changes, ensuring that the model can adapt to the nonlinear and time-dependent nature of financial markets. HQ, LR, SC, AIC, and FPE are the usual criteria for selecting lag orders, which are then checked for their robustness via cointegration tests, as presented in

Table 3. GF, PU, R2, and R1 findings of the Johansen cointegration test.

Markets		Hint Statistics				Maximum Eigenvalue
		Statistic	5% Critical Value	P		Statistic	5% Critical Value	p
Region 2	None	106.5498	28.7671	0.0000	None	48.6089	20.1216	0.0000 ***
	At most 1	57.9409	15.4947	0.0000	At most 1	41.5589	14.2646	0.0021 ***
	At most 2	16.3820	3.8415	0.0000	At most 2	16.3820	3.84147	0.0000 ***
Region 1	None	85.8061	29.7971	0.0000	None	46.6298	21.1316	0.0000 ***
	At most 1	39.1763	15.4947	0.0000	At most 1	22.3611	14.2646	0.0000 ***
	At most 2	16.8152	3.8415	0.0001	At most 2	16.8152	3.8415	0.0001 ***

*** indicates the significance at 1% significance level.

. These lags effectively capture the short-run dynamics and the long-run impacts, providing a comprehensive analysis of the dynamic relationships between the variables. Second, whereas the following analysis is based on monthly data, perhaps future studies will explore how changes in the return interval—daily or quarterly data, for instance—change the model’s results. This is because a shift in data frequency can capture information on beta variance; similar effects may emerge for the relationships scrutinized here. Knowing other frequencies might help improve the sensitivity of carbon market volatility and green finance to the policy uncertainty avenue, representing an area of important future study over diverse time horizons.

To model the time-varying factors, we assume that they follow a random traversal process, where ${\beta }_{t+1}$ = ${\beta }_t$ + ${\mu }_{\beta t}$, ${\alpha }_{t+1}$ = ${\alpha }_t$ + ${\mu }_{\alpha t}$, and $h_{t+1}$ = $h_t$+ ${\mu }_{ht}$. Furthermore, we create the following assumptions:

$$\left(\begin{array}{c}{\displaystyle \frac{{\epsilon }_t}{{\mu }_{\beta t}}}\\ {\displaystyle \frac{{\mu }_{at}}{{\mu }_{ht}}}\end{array}\right)~N\left[0 \left[\begin{array}{cccc}{\displaystyle \frac{1}{0}}& {\displaystyle \frac{0}{\sum \beta }}& {\displaystyle \frac{0}{0}}& {\displaystyle \frac{0}{0}}\\ {\displaystyle \frac{0}{0}}& {\displaystyle \frac{0}{0}}& {\displaystyle \frac{\sum \alpha }{0}}& {\displaystyle \frac{0}{\sum h}}\end{array}\right]\right], t=s+1,\dots , n$$

(6)

In Equation (6), we define the opening distributions for the time-varying limitations as follows: ${\beta }_{s+1}$tracks a normal distribution $~N$ (${\mu }_{\beta 0}$, ${\mathsf{\Sigma }}_{\beta 0}$); ${\alpha }_{s+1}$ tracks a normal distribution $~N$ (${\mu }_{\alpha 0}$, ${\mathsf{\Sigma }}_{\alpha 0}$); and $h_{s+1}$ shadows a normal distribution$~N$ (${\mu }_{h0}$, ${\mathsf{\Sigma }}_{h0}$). To manage the difficulties posed by asymmetric stochastic variations and simplify the likelihood function, we utilize MCMC, or the Markov chain Monte Carlo algorithm, as Nakajima [69] proposed. This algorithm permits us to estimate the model by simulating samples and establishing the distribution of the characteristics that need to be estimated. By leveraging the MCMC method, we can solve the approach’s complexity excellently and attain reliable evaluations for further investigation.

4. Empirical Results and Analysis

4.1. Variable Prediction Test

TVP-VAR has random volatility, and specific tests are conducted to ensure data stability. The augmented Dicky–Fuller test (ADF), proposed by Dickey and Fuller [74], and the KPSS (Kwiatkowski–Phillips–Schmidt–Shin) assessment, introduced by Kwiatkowski, Phillips, Schmidt, and Shin [75], were employed to examine the immobility of statistics. The results can be seen in

Table 4. ADF unit root assessment’s findings.

Sequence of Variable	KPSS	ADF		Stationarity
		T-Statistic	(C, T, L)
lnR2	0.3272	−0.1092	(C, 0, 6)	Unstable
D(lnR2)	0.1233 **	−4.0355 ***	(C, 0, 5)	Stable
lnR1	0.5808 *	−0.9731	(C, 0, 0)	Unstable
D(lnR1)	0.2098 ***	−11.4348 ***	(C, 0, 0)	Stable
lnGF	1.2583	−1.3262	(C, 0, 4)	Unstable
D(lnGF)	0.2024 ***	−8.8347 ***	(C, 0, 0)	Stable
lnPU	0.9602	−2.7042 *	(C, 0, 1)	Unstable
D(lnPU)	0.2153 ***	−4.6850 ***	(C, 0, 5)	Stable

Note: *, ** and *** indicate the significance at 10%, 5% and 1% significance levels respectively. Moreover, C signifies the intercept, T is the time trend, and L stands for lag.

. The results show that the R1 (Region 1 emission allowances) and R2 (Region 2 emission allowances) prices, green finance (GF), and the economic policy uncertainty index ultimately have unit roots. This indicates that their novel sequence lacks smoothness. However, after applying a first-order difference, the GF, PU, and R1 series exhibit stability at the significance level of 1%.

In comparison, the Region 2 emission allowances sequence achieves stability at the significance level of 5%. Consequently, the statistical analysis focuses on the first-order variation sequences for further investigation. Furthermore, we utilized HQ, LR, SC, AIC, and FPE criteria to determine the TVP-VAR model’s optimum lag order. We identified a lag order of 2 as the most suitable based on these criteria. Subsequently, we conducted the Johansen cointegration test to assess this lag order’s efficacy. Table 3 shows three cointegration equations at a 5% significance level for both the trace and maximum eigenvalue tests. This finding strongly suggests that the TVP-VAR model’s best possible lag order is 2, indicating a robust equilibrium relationship in the long run among green finance, prices of carbon, and an uncertain economy.

In our final step, we took precautions to confirm the accuracy of the estimation results obtained from the TVP-VAR model. We thoroughly examined the nonlinear and linear relationships linking the variables in the two datasets to achieve this. Specifically, the much-used Granger causality test was used to scrutinize whether there is a symmetric association among the factors. The convincing findings from

Table 5. Test results of Granger causality.

Markets	Null Hypothesis	F-Statistic	p
Region 2	D(lnGF) does not Granger Cause D(lnPU)	1.9866	0.1043
	D(lnPU) does not Granger Cause D(lnGF)	1.4310	0.2312
	D(lnR2) does not Granger Cause D(lnGF)	0.7653	0.5509
	D(lnGF) does not Granger Cause D(lnR2)	0.9318	0.4498
	D(lnR2) does not Granger Cause D (lnPU)	0.3804	0.8220
	D(lnPU) does not Granger Cause D (lnR2)	0.2193	0.9270
Region 1	D(lnGF) does not Granger Cause D(lnPU)	1.9866	0.1043
	D(lnPU) does not Granger Cause D(lnGF)	1.4310	0.2312
	D(lnR1) does not Granger Cause D(lnGF)	0.1832	0.9465
	D(lnGF) does not Granger Cause D(lnR1)	0.9157	0.4590
	D(lnR1) does not Granger Cause D (lnPU)	0.7735	0.5456
	D(lnPU) does not Granger Cause D (lnR1)	1.9357	0.1124

unveil that all the null hypotheses were supported at a 5% significance level. This implies that no statistically significant causal linear connection exists between Region 2 and Region 1 variables. Running such tests made it possible to reduce any distortions that might have arisen and, therefore, improved the reliability of our estimation results. Besides checking for linearity, we looked at the potential nonlinear dynamics across the variables using the nonlinear BDS test. As illustrated in

Table 6. Test results of nonlinear BDS.

Markets	Linear VAR Variables		Regression Residuals
	X	y	X	Y
Region 2	D(lnNEWC)	D(lnPU)	2.2158 **	2.0790 **
	D(lnR2)	D(lnNEWC)	9.0045 ***	2.1099 **
	D(lnNEWC)	D(lnR2)	−3.7391 ***	2.6008 ***
Region 1	D(lnPU)	D(lnGF)	2.3158 **	2.0890 **
	D(lnR1)	D(lnPU)	4.3703 ***	2.1858 **
	D(lnR1)	D(lnGF)	4.3903 ***	2.6025 ***

** and *** indicate the significance at 5% and 1% significance levels respectively.

, the intriguing outcomes revealed that all initial expectations were decisively unsupported at the 5% or 1% significance level. The findings strongly indicate a significant asymmetric association between green finance, carbon market volatility, and an unstable economy. From these findings, the suitability and appropriateness of applying this nonlinear TVP-VAR model in understanding how economic policy uncertainty and green finance affect China’s carbon market volatility are upheld. Our study contributes to illuminating the understanding of complex dynamics in variables under investigation because of linear and nonlinear relationships and their interplay in the context of carbon market volatility.

4.2. TVP-VAR Results

4.2.1. Parameter Test TVP-VAR

Therefore, computing the proper initial values of the parameters is among the most crucial parts of correctly assessing the TVP-VAR technique under the Bayesian framework through the Markov chain Monte Carlo algorithm. Poor initialization may lead to failure in model convergence and hence yield unreliable estimates, particularly for financial markets and policy analysis, which are time-varying with oscillations and dynamic relationships between variables. The estimation results of the TVP-VAR model for the selected parameters are presented in

Table 7. Results of the TVP-VAR model’s primary parameter estimation.

Market	Parameters	Stdev	Mean	90% L	90% U	Inef	Geweke
Region 2	sa1	0.0019	0.0058	0.0107	0.0034	32.95	0.691
	sa2	0.0009	0.0045	0.0066	0.0031	16.33	0.707
	sb1	0.0003	0.0023	0.0029	0.0018	4.16	0.168
	sb2	0.0003	0.0023	0.0029	0.0018	4.87	0.624
	sh1	0.0016	0.0055	0.0094	0.0033	24.85	0.687
	sh2	0.0016	0.0055	0.0098	0.0034	27.92	0.376
Region 1	sa1	0.0014	0.0053	0.0087	0.0033	23.61	0.944
	sa2	0.0017	0.0057	0.0098	0.0034	40.70	0.537
	sb1	0.0003	0.0023	0.0029	0.0018	4.24	0.157
	sb2	0.0003	0.0023	0.0029	0.0018	4.55	0.710
	sh1	0.0017	0.0057	0.0099	0.0034	28.63	0.280
	sh2	0.0017	0.0055	0.0093	0.0034	34.06	0.179

and attest to the robustness of our method. This model yields statistically significant results that provide estimates within a 95% confidence interval, hence allowing for precision and practical applicability to real-world conditions—for example, explicating how market shocks propagate, and how policy changes influence the denounced economic variables over time, among others. Since within the 95% confidence interval, general weights have further estimation means, they are pretty exact. Moreover, the Geweke test values fall well below the 5% threshold value of 1.96 and, therefore, point to the inability to reject the null hypothesis, which states that the synthesized parameters of the posterior distribution estimated are zero. The problem of achieving this level of precision is particularly applicable to decision-makers who depend on these models for forecasts and seek quick adaptation in financial and economic policy. The presence of valid factors also shows that the sampling time used in this study—10,000 simulations—is adequate for uncorrelated samples. Such a large number of simulations ensures that the model captures all the variability features of the system, providing a solid basis for forecasts or policy interventions. Table 7 shows that the maximum invalid factor of the model is 34.06, which is higher than the impulse response analysis threshold required for model estimation. This value indicates that the model’s ability to capture impulse response dynamics is highly reliable for interpreting real-world phenomena, such as the effect of shifts in economic policy on market volatility or the impact of fiscal measures on macroeconomic stability. These results demonstrate the MCMC algorithm’s efficiency in grasping the posterior distribution well. This, in actual practice, gives policymakers and financial analysts confidence in invalid interpretation of the parameters from the TVP-VAR method, where this model gives strength and precision in unraveling the dynamics of the variables under consideration. This will strongly and precisely help to estimate the variables of the TVP-VAR technique if the MCMC algorithm is utilized correctly for the study. It could then transition into an advanced look at implications and analysis. By employing such robust techniques, our study contributes to the academic literature. It offers practical financial market regulation and policy formation tools, particularly in rapidly changing environments where time-variant relationships are critical.

In addition, the results of the simulations converge in distribution, proving the efficiency of the MCMC simulation process adopted in this study. The convergence supports that the MCMC process captured the key underlying stochastic processes governing the system by a smooth and stable distribution in terms of convergence. This also confirms that a model is robust enough to drive real-time decision making, whereby financial stability or policy adjustments are compelled by market conditions in flux. These strong results confirm that the constructed TVP-VAR model has been successful in arriving at a good measure of parameter estimation. The practical implication is that the reliable estimates obtained from this TVP-VAR model can now be used to carve out more precise policy interventions or financial strategies of high predictability. By being in a position to use such robust estimations, we are now better placed to conduct a more thorough impulse response analysis using our model and contribute to our knowledge concerning the complex interaction between the variables under investigation. This more profound understanding of impulse responses is beneficial in forecasting the effects of sudden shocks or gradual policy changes on economies or financial markets. Hence, it provides a foundation of empirical evidence for academics’ and practitioners’ decision making.

4.2.2. Impulse Response Assessment Across Varied Lag Intervals

Impulse response assessment can be carried out with the TVP-VAR model, which investigates strong impulse responses across lag periods. In this respect, we follow previous research using monthly data: Zhou et al. [76] and Huang et al. [77] explored uniform interval impulse responses representing long-term, medium-term, and short-term changes. We adopt four, eight, and twelve months to capture the changes in market dynamics over these horizons. One of the important features of this model is that it can simulate an impulse response and detect its variances over different periods. Such an analysis will give in-depth insight into the temporal dynamics of how carbon market volatility is affected by the shock in financial policies, primarily PU and green finance. This temporal understanding of these dynamics is key for policymakers to devise strategies that respond both to short-run oscillations and long-run courses.

It is important to note that the results shed light on how green finance affects carbon market volatility and how unstable economic policy varies as a time-varying characteristic. This indicates the strength and effectiveness of the TVP-VAR approach in modeling and analyzing how financial variables like green finance and PU influence market dynamics over time. These findings present critical evidence for policymakers and financial market participants regarding managing economic policy uncertainty and its spillover effects on environmental markets. Indeed, with a better understanding of the heterogeneity of these shocks in their impact on carbon pricing, regulatory bodies would be better positioned to target interventions that help further dampen market responses, especially when uncertainty is exceptionally high.

Figure 2 presents the time-variant effect of financial policy shock on carbon market volatility in Region 2 and Region 1 provinces during different periods. The impulse response functions of Regions 1 and 2 are consistently above the zero line, indicating that uncertain financial policy drives carbon market volatility positively. This could imply that during periods of economic policy uncertainty, carbon market volatility tends to soar, reflecting increased volatility and the market’s response to turbid financial conditions. Jiang et al. [78] and Yu et al. [52] pointed out the same thing: when economic policy uncertainty increases, carbon market volatility increases.

There are two significant underlying mechanisms to this. First, as economic policy uncertainty rises, governments might lighten up on their environmental regulations, which could reduce constraints on firms’ investments in conventional energy sources. This allows companies to exploit lower compliance costs, reverting to more carbon-intensive energy sources. This must be an important insight, especially for policymakers during economic turmoil, as loosening regulations to spur economic growth comes at a potential environmental cost.

Another reason uncertain economic policy raises carbon market volatility is that businesses tend to finance less in clean and green technologies and more in traditional energy foundations like oil and coal. This might be because uncertainty in the business environment has increased, and firms might react by minimizing their risks depending on cheaper, well-established energy sources rather than making capital-intensive investments in green technologies [78]. For policymakers, new incentives for green investments become necessary during periods of uncertainty or even need to be strengthened to counteract the natural tendency for businesses to revert to more traditional, high-carbon energy sources. Consequent to the shift in investment choices, carbon emissions will rise, pushing the price of carbon upwards due to increased demand for conventional energy sources. This is also supported by Li et al. [6], as they show how the preference for traditional energy during economic uncertainty can also properly drive businesses toward increasing carbon market volatility.

Another interesting outcome is that PU significantly impacts short-run carbon market volatility, while this effect decreases in the medium and long terms. This suggests that while policy uncertainty can cause high volatility in the short run, market mechanisms and public interventions stabilize carbon market volatility over time. This has important policy implications: during periods of high uncertainty, short-term regulatory interventions may be necessary to mitigate immediate volatility in carbon pricing. Long-term stabilization is often achieved through regular verification and monitoring of corporate carbon emissions and carbon pricing, as implemented by local governments [79]. These activities help stabilize carbon market volatility by ensuring businesses adhere to regulatory frameworks, reducing the sensitivity of carbon market volatility to PU in the medium and long terms.

It is also worth emphasizing that the impact of PU on carbon market volatility varies between the provinces of Region 2 and Region 1. The influence of PU on carbon market volatility for Region 2 provinces is not as powerful as it is for Region 1 provinces. In Region 1, the highest response rate of carbon market volatility to the impact of PU is more than 0.003%, while it is below 0.001% in Region 2. Therefore, this discrepancy suggests that developing the economy and financial structure in the different regions significantly contributes to how local markets would respond to policy uncertainty. Policymakers should consider regional differences while formulating environmental and financial policies, and one-size-fits-all may not be an effective strategy. Each regional economy has unique dynamics and structure, leading to heterogeneous sensitivity to PU and a differential drive of carbon market volatility among the provinces. This is even without considering that this finding underlines region-specific policy measures to handle fluctuations in carbon pricing according to local economic conditions.

Figure 3 presents the impulse response analysis, showing the comovement of carbon market volatility with green finance in both Region 2 and Region 1 over varied time intervals. The carbon market volatility, which is influenced by green finance, shows time-varying characteristics in the short run, especially before 2017. In this period, the carbon market’s response to green finance was very volatile because the green finance market in China was at an underdeveloped stage. Market risks increase without key regulatory mechanisms, such as capital management requirements, information disclosure systems, and third-party evaluation institutions. Therefore, the influence of green finance on carbon market volatility was amplified, reflecting higher market uncertainty.

After 2017, China’s green finance market set up a series of evaluation institutions with much better information disclosure systems and more rigorous capital management requirements. These reduced systemic risks in the financial markets, and the response of carbon market volatility to green finance became more stable thereafter. Such stabilization of the green finance market is important for policymakers and investors, as it enhances the credibility of green finance as a financial instrument that supports low-carbon investments. Thus, after 2017, carbon market volatility responses became more stable and leveled off green finance, reflecting the positive impacts of market regulation.

In the short run, the analysis reveals that green finance negatively influences carbon market volatility, indicating that an increase in the green finance index leads to decreased carbon market volatility. This negative relationship may arise because green finance and carbon market volatility are influenced by similar governing fluctuations and green strategies, which could increase market risk. However, the issuance of green finance enables businesses and governments to invest in low-carbon technology innovation and renewable energy projects. It follows that the growth in the green finance index increases the carbon market risk, which leads to a decline in demand in the carbon market, subsequently cutting down the growth of carbon market volatility [11,12].

However, in the long and medium terms, the impacts of green finance on carbon market volatility are insignificant. This may be because the mechanisms for spot carbon trading have become more sophisticated, offering better control and dampening market fluctuations, thus securing long-term price stability in the green finance market. Ren et al. [13] also suggested that market dynamics, policy changes, and technological progress would help weaken the impact of green finance on carbon market volatility. Regulators should consider these factors when pursuing more resilient green finance markets that are less susceptible to short-term volatility.

We also find that the impacts of green finance in the case of Region 2 and Region 1 on carbon markets are different, being higher for the market of Region 2. This may be due to different levels of financial growth between markets and more advanced mechanisms, such as the quota auction system, in the case of Region 2. For instance, introducing the quota auction system has led to more financial carbon trading in Region 2 than in Region 1, as Wen et al. [80] documented. In this respect, the market actors of Region 2 show more commitment to staying green in their portfolios, making green finance one of the important drivers of carbon market volatility. This has important policy implications because it suggests that the maturity of financial markets may influence the perception and utilization of green finance as an investment tool. More mature markets could be less vulnerable to market shocks caused by green finance fluctuations. Even when the green finance market experiences significant disruptions, it has shown the ability to self-correct in the long and medium terms. This resilience enhances the credibility and safety of the green finance market as an investment tool, making it an attractive option for financing sustainable development.

4.2.3. Impulse Response Assessment Throughout Varying Time Horizons

Figure 4 demonstrates that the response paths of carbon market volatility in Region 2 and Region 1 to PU are nearly identical across the three time points (2015, 2018, and 2019). These periods were characterized by elevated economic policy uncertainty due to significant external shocks. During such crises, market participants, including corporations and financial institutions, experience heightened sensitivity to policy changes, directly influencing their behavior in carbon trading markets. This leads to consistent response patterns during each event. Additionally, the PU impact on carbon market volatility fluctuates between two extremes: in the short term, carbon market volatility is positively affected, but this is followed by a shift towards a negative trend, creating volatility in the market. Such a fluctuation suggests that market participants initially react by increasing carbon market volatility as a hedge against uncertainty. However, they may later adjust their positions as the crisis unfolds and new information becomes available.

We also examine how economic policy uncertainty at specific points in time and carbon market volatility sensitivity, innate to green finance, strengthen our findings. This study considers three particular points: from 2015 to June 2018, up to July–December 2019; these dates are particularly representative of benchmark stock market crises, the US–China trade tension, and the COVID-19 outbreak. These events are considered heightened periods of economic uncertainty and provide valuable contexts to assess how green finance and PU influence carbon market volatility. Figure 5 below show the impulse response diagrams, summarizing the dynamic relationships between uncertain economic policy and carbon market volatility and how carbon market volatility was affected by green finance in Region 2 and Region 1 provinces for the periods under consideration.

Initially, carbon market volatility in both markets exhibited a negative response to economic policy uncertainty. However, this quickly transitioned into a favorable reaction, reaching its peak in the second period. This shift could be explained by the delayed effects of policy interventions and market adjustments to uncertainty, as governments and firms respond to unpredictable economic conditions by adapting their environmental strategies. In the third interval, carbon market volatility again reacted negatively to PU, though the magnitude of the negative response was smaller compared to earlier intervals. These findings suggest that carbon market volatility demonstrates varied responses to PU across different time lags, reflecting China’s carbon trading market’s incomplete nature and susceptibility to external shocks.

Uncertainty in the external economic environment, significantly heightened by economic policy uncertainty, hampers enterprise production and investment, creating obstacles to overall economic growth [56]. As firms face more significant uncertainty, they reduce energy usage, which leads to a decline in carbon emissions and a subsequent reduction in carbon market volatility. This scenario posits that under specific conditions, economic policy uncertainty can exert a downward pressure on carbon market volatility, benefiting firms with reduced operational costs, though at the potential expense of long-term green investment. With appropriate adjustments, the favorable effect of PU on carbon market volatility becomes more observable over time.

Furthermore, the impact of PU on carbon market volatility tends to stabilize by the sixth period. This may be partly justified by strong government regulations and interventions in China’s carbon market. The effects of significant economic events on emission allowance prices gradually diminish over time as stringent policies and regulatory measures are introduced. Thus, carbon market volatility becomes less responsive to PU in the medium and long terms. This insight is crucial for policymakers, highlighting the need for sustained regulatory oversight to mitigate volatile responses to economic shocks. These findings suggest that during a significant financial shock or crisis, carbon market volatility may initially overreact to PU, requiring corrective adjustments. The impulse response analysis indicates that while short-term volatility is inevitable, long-term stability can be achieved through careful market observation and policy interventions [81].

Figure 5 shows the response of carbon market volatility to the shocks of green finance in three specific periods. Usually, at these three time points, the response patterns to the carbon market volatility of green finance are stable. This is consistent with the broader economic crises and turmoil in the markets for green finance and carbon market volatility, where businesses’ decisions and issuance of green finance depend on exogenous shocks. Similar response trajectories from different time points reflect similar ways market participants adjust their strategies across periods of crisis.

Similar to the response to PU, the carbon market volatility response to green finance in Region 2 and Region 1 fluctuates between negative and positive in the short run. This suggests that in the initial stages of a crisis, green finance may worsen market volatility as firms and investors reassess the value of green projects in the face of uncertainty. However, the reaction tends to approach zero in the medium and long terms, indicating a diminishing effect. Notably, the adverse impact of green finance on carbon market volatility is more apparent in the short run. Specifically, in the first interval, green finance negatively influences carbon market volatility. This might be because the immediate market perception is that in periods of turmoil, green investments carry more risks, which would then reduce demand for green finance and, consequently, carbon market volatility. This negative effect transitions into a positive impact in the following intervals, though it weakens over time.

The impact of green finance on carbon market volatility stabilizes in the sixth interval and disappears in the eighth. This stability reflects the resilient green finance market, as market mechanisms and regulatory interventions support dampening short-term shocks and restoring investor confidence in green projects. Many diversified uses of green finance funds also explain their positive influence on prices in different carbon lag periods. These funds are typically spent on various green initiatives, such as sewage treatment, resource recycling, and renewable energies [82].

Enterprises adjust their investment ratios among different projects based on prevailing market conditions, leading to the rational allocation of green resources. In cases where firms prioritize non-energy-related green projects, such as water management or waste reduction, the allocation of green finance funds can negatively affect clean energy initiatives, resulting in a temporary decline in carbon market volatility. As firms refocus their investments on non-energy green projects, the influence of green finance on carbon market volatility diminishes [82]. However, in the medium and long runs, as market confidence in green investments grows, the influence of green finance stabilizes, contributing to a more predictable carbon pricing environment.

During the three time periods, the sensitivity of the carbon market volatility to green finance in response is more dramatic in Region 1 than in Region 2. It can then be suggested that within Region 1—the carbon-trading market with a later establishment date compared with Region 2—the participants have less experience in managing risks in the market. Consequently, market participants in Region 1 are more sensitive to financial variations and thus respond strongly to volatility with respect to green finance. In contrast, a more developed and financialized carbon market in Region 2 could better manage such fluctuations of green finance disruption. Region 2 participants may be less risk-averse and less sensitive to changes in their financial status due to their greater experience in carbon trading [83].

5. Conclusions and Implications

Therefore, considering PU and green finance as factors of carbon market volatility is an integral part of the development of the present study. We conduct systematic and dynamic analysis based on the advanced TVP-VAR model in investigating time-varying impacts that China’s PU and green finance have on carbon market volatility in Region 2 and Region 1 provinces. The dataset ranges from January 2015 to April 2023. This allows robustness in the proper investigation of said relationships. The TVP-VAR model is applied here as it is known for capturing gradual structural changes and reducing bias from instability in estimated parameters; hence, it considers time-varying interactions between financial and economic variables, which are very important in any study under quantitative finance.

Our research makes several critical contributions to understanding the dynamic relationships between economic policy uncertainty, green finance, and carbon market volatility—factors highly pertinent to financial market participants. The results show that PU’s and green finance’s impact on carbon market volatility declines over time, while both shocks have substantial short-run impacts. Although green finance tends to dampen carbon market volatility, economic policy uncertainty drives carbon market volatility up in the short term. These insights are vital for policymakers, as they offer a clear view of the short-term volatility caused by policy changes or fluctuations in green finance markets. With this understanding, policymakers can design more effective response strategies to help stabilize carbon markets.

Meanwhile, the development of relationships between green finance and carbon market volatility in Chinese provinces could be determined by international-level developments expressed in the form of functioning international carbon markets, world energy prices, and external events. These may range from alterations in global energy prices to changes in international carbon markets, resulting in variations in carbon credit prices and portraying the impact on carbon finance in China. Geopolitical events, such as trade friction or climate policy taken at the global level, may further exacerbate such price volatility across the carbon trading system. Though not directly included in the current analysis, these external factors are important in understanding the broad context in which carbon pricing exists. Therefore, when generalizing the findings, these aspects need to be considered as well. Exclusion itself is a limitation of this study. Policymakers must consider these external factors when developing policies on carbon market volatility stability since global shifts in market dynamics could directly impact domestic carbon markets.

The current study is limited in scope to the provinces of Region 2 and Region 1, key carbon trading markets in China. Future studies should extend this research to other regions within China and global markets to develop better insights into carbon pricing dynamics. Furthermore, future research needs to incorporate external factors, such as international carbon markets, global energy prices, and geopolitical events, to show the impact of these factors on carbon trading dynamics. This would offer a more general understanding of how global and domestic factors interact to shape carbon market volatility, making the findings more widely applicable. Expanding the study in this direction will enable future research to provide deeper insights into regional and global carbon markets and better inform policy decisions.

Our analysis also indicates that the responses of carbon market volatility to both PU and green finance shocks vary across crises and periods of turbulence in the financial markets. These critical events mold the behaviors of carbon market volatility and highlight the complexity of their interaction with policy uncertainty and green finance. We also find significant regional heterogeneity in the impact of these factors on carbon market volatility, especially with the influence of green finance being more substantial in Region 1 than Region 2. Policymakers will thus need to jettison the one-size-fits-all approach to policy formulation by developing informed regional variations that could be more effective in dampening market volatility and achieving regional stability. This is relevant for quantitative finance professionals, particularly in regional market differences. It also helps a manager understand how local economic conditions contribute to market volatility, which can be used for portfolio management, regional risk analysis, and asset allocation strategies.

The key advantage of the TVP-VAR model adopted in this study is that it allows for determining time-varying coefficients and gradual structural changes, making it well suited for capturing dynamic relationships in financial markets. This methodological approach aligns with both quantitative finance research and the needs of policymakers, providing actionable insights into how policy shifts may influence market stability and carbon price dynamics. While the proposed model is highly relevant to policymakers, it also has practical implications for financial market participants who need to anticipate and manage risks associated with carbon price volatility.

Based on the statistical results and the specific context of China, the following policy implications are deduced, carrying significance for market participants:

• Improving carbon market stability and efficiency with consistent financial policies: Policies’ stability eliminates fluctuations in carbon market volatility due to economic instability. As such, policymakers should ensure that such policies aim to develop a predictable environment, an important factor for both enterprises and market actors involved in carbon trading. This will nurture a more balanced and orderly carbon market, provide a stable environment for market participants, and reduce systemic risks.
• Encouraging green finance issuance for market efficiency: Governments should promote using green finance as an effective financing tool for carbon reduction projects. For policymakers, supporting the green finance market through regulatory improvements and incentives will pave the way for broader participation in green finance and enhance the overall effectiveness of the carbon market. From a quantitative finance perspective, the emerging green finance market offers new investment opportunities and risk management strategies. The green finance market can become a key contributor to sustainable finance by improving transparency, rating standards, and regulatory frameworks.
• Establishing a unified national carbon trading market to reduce regional differences in carbon market volatility and ensure a level playing field: This will be one of the significant policy recommendations that guarantee the smooth functioning of a national carbon market, improving liquidity and market efficiency. This is particularly relevant for market participants, as one market will foster liquidity, price discovery, and better trading and investment opportunities.

These policy measures help improve the carbon market in China and bring stability and efficiency, offering valuable lessons to financial market participants. In this regard, the measures implemented by policymakers will lead to a more predictable and stable carbon market, aligning with national sustainability goals and supporting long-term economic growth.