“Liquidity spillover from carbon emission trading markets to stock markets in China”

This study delves into China’s carbon emissions trading markets, investigating the interplay between carbon price liquidity and stock liquidity. Focusing on 338 companies listed in the national and eight pilot markets of the carbon emissions trading system from August 2013 to October 2023, the empirical finding reveals a positive impact of carbon price liquidity on stock liquidity. Notably, this positive association manifests more robustly in industries characterized by low carbon intensity compared to those with high carbon intensity, is more prominent during the COVID-19 period than in preceding times, and is particularly accentuated in the Hubei Province and Chongqing, as opposed to the remaining seven regions. Intriguingly, both carbon price liquidity and stock liquidity display positive autocorrelations in vector autoregression analysis. The endogeneity concern is alleviated by the two-stage least squares regressions, using lagged carbon price liquidity as instrumental variables. This study contributes to an enhanced comprehension of the dynamic interaction between carbon price liquidity and stock liquidity contextualized within China’s evolving carbon market landscape. The insights garnered herein hold substantial value for investors and government stake-holders seeking to navigate this evolving financial terrain.


INTRODUCTION
A suite of ecological challenges, particularly involving the greenhouse effect and climate change, instigated by substantial emissions of greenhouse gases, chiefly carbon dioxide, pose significant hurdles for China and the global environment (Duan et al., 2018(Duan et al., , 2022).In the 2015 Paris Agreement, a coalition of 195 countries, including China, endorsed a global climate agreement that, for the first time, outlined a set of measures aimed at limiting global temperature rise to well below two degrees Celsius (Liobikienė & Butkus, 2017).Currently, China is the leading emitter of carbon dioxide, pushing the global climate agenda to a critical inflection point should the nation forgo a transition to a low-carbon economic framework (IEA, 2022; World Bank, 2022).Through targeted regulation of these sectors, China is expected to reach its peak carbon emissions by 2030, in alignment with achieving carbon neutrality before 2060.
The Chinese government has implemented diverse policies to regulate greenhouse gas emissions to accelerate China's path toward environmentally sustainable and low-carbon development and effectively address global climate change demands.This holistic strategy encompasses the launch of China's pilot carbon emissions markets (Zhang & Hao, 2015;Jiang et al., 2016;Wang et al., 2015).In October 2011, a critical milestone was achieved when the National Development and Reform Commission released an ambitious plan outlining China's aim to establish eight provincial carbon trading pilots in critical regions, including Beijing, Tianjin, Shanghai, Chongqing, Guangdong Province, Hubei Province, Shenzhen, and Fujian Province (Zhang & Wang, 2021).Fueled by the abundant reserves of carbon resources in China, the potential for carbon emissions trading rights and their derivative instruments holds significant relevance in the future (Wang et al., 2016).This also substantiates the claim concerning the global importance of China's carbon market.
In financial markets and related spheres, investigations into spillover effects, encompassing intra-market and inter-market dimensions, have emerged as vital endeavors to bridge existing knowledge gaps.The late establishment of China's carbon market compared to its European Union counterpart has created a research void, particularly regarding the firms regulated by the carbon markets (Chang et al., 2018b).

LITERATURE REVIEW
The integration of China into the carbon market occurred with a temporal lag relative to the European Union.Nevertheless, by capitalizing on its abundant carbon assets, carbon emission allowances and corresponding derivatives demonstrate significant promise within the Chinese market (Wang et al., 2016).During the maturation of China's carbon market, the path of carbon liquidity has exhibited incremental growth, concomitant with improvements in market infrastructure.However, the existing landscape of carbon liquidity persists in a suboptimal condition.Zhang et al.'s (2020) comprehensive analysis, segmented into three distinct chronological phases, revealed that later stages experienced reduced market efficiency compared to the initial phase.Kalaitzoglou and Ibrahim (2013) have posited evidence supporting that carbon markets characterized by elevated liquidity demonstrate superior responsiveness to market-driven news.Conversely, markets with diminished liquidity tend to manifest a delayed integration of such information.This interrelationship between liquidity and reactivity to market news is inextricably linked with the speed of news dissemination, thereby exerting additional influence on liquidity conditions.
In scrutinizing China's carbon market, Chang et al. (2018a) identified a prominent clustering effect within daily price intervals, in which intraday returns significantly impacted market liquidity.However, the current attributes of China's emergent carbon emissions trading framework reveal features of opacity, protracted inter-trade dura-tions, inefficient resource allocation, market segmentation, and price heterogeneity across trading pilots.These factors collectively give rise to prospective obstacles to liquidity and pricing efficiency within the Chinese carbon market.Song et al. (2017) and Zhang et al. (2019) also emphasize these issues, elucidating the interplay among the over-allocation of carbon emission allowances, the nascent state of China's carbon financial market, and governmental policy deficiencies contributing to this quandary.
Driven by carbon illiquidity, China's carbon market shows pronounced inefficiencies despite regional variations.The initial eight pilot markets reveal a complex efficiency and risk landscape.Shenzhen and Beijing excel in efficiency, yet overall performance remains suboptimal.Among China's key economic regions, the East outperforms, the Central converges fastest, and the West lags in both metrics (Dong et al., 2013).Concurrently, China's carbon market lags behind the European Union's in efficiency metrics, demonstrating less maturity yet higher activity, and displays leverage characteristics.However, a gradual transition from inefficiency to weak efficiency is observed (Sun et al., 2020;Zhao et al., 2017;Zhou et al., 2019;Liu et al., 2019).It is evident that China's carbon market remains nascent, and regulatory deficiencies contribute to its inefficiency (Zhu et al., 2020).While most of China's carbon markets exhibit inefficiencies, select pilot markets like Shenzhen and Beijing have attained weak efficiency.These inefficiencies may be attributable to restricted carbon liquidity.Wu  correlation between pilot market efficiency and liquidity, indicating that liquidity enhances market efficiency in the short term.
Generally, determinants of liquidity fluctuations can be categorized into macroeconomic and microeconomic variables.Stock liquidity is influenced by macroeconomic variables such as oil prices, gold prices, inflation rates, and foreign investment inflows.Oil price volatility and uncertainty exert a discernible influence on stock market liquidity.Zheng and Su (2017) establish that oil price fluctuations significantly affect stock markets: pronounced demand shocks correlate with increased liquidity, whereas the influence of oil price shifts on oil supply and aggregate demand adversely impacts stock liquidity.Zhang and Wong (2023) suggest that heightened oil uncertainty exerts a significant adverse effect on the stock liquidity of publicly traded firms.For sectors extraneous to the oil industry, the ramifications of escalating oil uncertainty are more pronounced for smaller firms than their larger counterparts.Conversely, within the oil and affiliated sectors, the stock liquidity of large oil enterprises exhibits heightened sensitivity to oil price uncertainty.
Foreign investment flows also influence stock market liquidity.Naik and Reddy (2021) indicate that foreign investment inflows and elevated gold prices contribute to decreased stock market liquidity.According to Li et al. (2022), the presence of foreign investors enhances corporate governance mechanisms, thereby mitigating stock liquidity uncertainty.They further posit that the heterogeneity among foreign investors is a pivotal determinant of stock liquidity uncertainty.Similarly, Ng et al. (2016) found that stock liquidity diminishes with increased direct foreign ownership but augments with foreign portfolio ownership.Concurrently, inflation and stock liquidity exhibit a negative correlation.Elevated inflation rates adversely impact equity liquidity and diminish market capitalization (Boyd et al., 1996;Khan, 2004).Furthermore, Jiang (2014) elucidates that rising inflation depresses stock liquidity and amplifies liquidity covariance.This effect is more pronounced for stocks with lower market capitalization and reduced liquidity than other stocks.
From a microeconomic perspective, stock liquidity is principally influenced by firm-specific determinants and certain external variables.Cheng (2007) enumerates six key factors that impact stock liquidity: firm size, ownership concentration, the extent of information asymmetry, engagement in margin trading, investors' perceptions, and the prevailing market liquidity conditions.Among these variables, firm fundamentals substantially influence stock liquidity, encompassing many factors such as profitability, operational management, and innovation.Uncertainty regarding these fundamentals can significantly affect liquidity; firms with diminished profitability often exhibit increased liquidity (Asem et al., 2016).Additionally, robust corporate governance mechanisms enhance liquidity by intensifying managerial oversight and mitigating information asymmetry (Chung et al., 2010;Prommin et al., 2014).In addition, a firm's inventive and innovative activities can contribute positively to its stock liquidity (Chen et al., 2023).Firm size and total outstanding shares also influence stock liquidity.Cheng (2007) posits that both firm size and shareholding composition are pertinent determinants: a positive correlation exists between firm size and liquidity, and greater diversification in shareholding structure is associated with enhanced liquidity.Additionally, the pronounced volatility in liquidity metrics pre-and post-stock splits corroborates the transient influence of stock splits on stock liquidity (Huang et al., 2015).
Regarding liquidity spillovers, intramarket and intermarket spillovers can be observed across various asset classes within a singular financial market.For instance, liquidity spillovers exist among diverse commodities within futures, foreign exchange, and equity markets.Zhang and Ding (2021) indicate that commodity price fluctuations within futures markets are influenced not merely by their liquidity but also by the liquidity of different commodity classes.In the foreign exchange domain, Chang et al. (2022) identify significant liquidity spillovers across currencies, noting that these spillovers are time-variant and are exacerbated by financial constraints and market instability.Recent studies by Zhou and Ye (2023) in the equity market reveal that liquidity and returns of margin-traded equities exert spillover effects on other stocks.Lim and Choi (2022) further corroborate that liquidity spillovers manifest be-tween different industry sectors within the equity market, as evidenced in an analysis of S&P 500 constituents.These studies collectively affirm the existence of liquidity spillovers within specific financial markets.
Beyond the confines of financial markets, there exists a potential for spillovers between these markets and other markets.Wen (2023) unveiled information spillovers between the stock market and other markets, with the oil market exerting more substantial spillover on the stock market than the inverse, signifying the stock market's propensity to receive information.Liquidity spillovers are also observable across distinct markets, encompassing the foreign exchange and fund markets.These spillovers can also have repercussions on the stock market.Righi and Vieira (2014) uncovered that international stock market returns could be influenced by liquidity spillovers, employing wavelet multiscale methodologies to dissect different time series meticulously.Chan et al. (2008) centered on liquidity spillovers within fund markets, highlighting that the illiquidity of funds and their underlying assets in trading markets can impact share prices.The study suggests that in integrated markets, illiquidity in one market is susceptible to spillovers, consequently influencing other markets.Furthermore, liquidity spillovers manifest in the foreign exchange and stock markets.Karnaukh et al. (2015) illustrated that foreign exchange market liquidity varies with bond and equity markets.
Carbon markets wield an influence over corporations and stock markets, with the Chinese carbon market exerting a significant impact on participating companies and their associated stocks.Zhang and Zhang (2023) show that the carbon price returns negatively affect the stock returns of enterprises covered by the Chinese regional carbon markets.According to Sun et al. (2022), a modest two-way causality is observed between the carbon and stock markets, with a 1% oscillation in one market leading to an approximately 0.15% to 0.3% oscillation in the other.Wen et al. (2020) found that the carbon emission trading market impacts the broader Chinese stock market and industries.Carbon-intensive firms' trajectory hinges on emission trading prices, prompting a distinct long-term asymmetry between the stock market and carbon trading.This is evident in the stock market's heightened responsiveness to carbon price increases versus decreases.Moreover, in China's carbon market, carbon trading positively impacts excess returns for companies involved in carbon allowance trading.These firms, marked by heightened carbon exposure, witness an increasing carbon premium on their stock returns.Variations in carbon allowances can further impact stock returns as companies with elevated carbon emissions confront heightened carbon risks, translating into higher anticipated stock returns (Oestreich & Tsiakas, 2015).
Limited research has explored the liquidity spillover dynamics between carbon and stock markets.Zhang and Han (2022) discerned an asymmetric pattern in liquidity spillovers between these markets.Notably, the one-period lagged trading volume of the carbon emission market negatively affects the future trading volume of the stock market.Nonetheless, their analysis was conducted at an aggregate market level instead of an examination at the individual firm level.In contrast, this paper explores liquidity spillover from Chinese carbon markets to the relevant stock markets at the level of individual firms.

METHODOLOGY
This study employs two datasets free from survivorship bias: (1) the Wind Economic Database, encompassing regional carbon, gold, crude oil, The third step is to acquire daily carbon price and trading volume from the Wind Economic Database for the eight pilot regions and the national market.Concurrently, time series data are compiled for control variables from the same source.Daily gold price (COMEX gold, ticker code: GC.CMX), crude oil price (NYMEX WTI, ticker code: CL.NYM), natural gas price (NYMEX, ticker code: NG.NYM), and coal price index (ticker code: JFI.WI) are retrieved.These control variables are chosen due to their substantial impact on carbon emissions.Fossil energy, encompassing coal, oil, and natural gas, has historically been pivotal for China's industrial production.Additionally, gold is included as it reflects economic trends and serves as a reliable market indicator.
This study follows Amihud (2002) to define security liquidity as the average ratio of the daily absolute return to the trading volume on that day, R jmd is the return on security j on day d of month m, and Vol jmd is the daily volume.The liquidity of security j is calculated as the monthly average: where D jm is the number of days for which data are available for security j in month m.Equation ( 1) calculates monthly liquidity measures for stocks, regional carbon, gold, oil, gas, and coal prices.
Next, this study proceeds to calculate the mean liquidity for these stocks as follows: where N t is the number of ETS-covered stocks in month t, and StockLiq t is the average stock liquidity in month t.A similar approach calculates the mean carbon price liquidity across nine distinct regions.Given that the liquidity measures exhibit pronounced volatility and the presence of significant outliers, this study winsorizes the liquidity measures at the top 5% to mitigate the influence of these extreme observations.The final sample encompasses 338 firms and 123 monthly observations from August 2013 to October 2023.
Finally, this study employs the ordinary least squares regression to elucidate the linear association between the two pivotal variables: carbon price liquidity and stock liquidity.The basic model equation is as follows: where CarbonLiq t denotes carbon price liquidity in month t. β 0 is the intercept term, β 1 is the regression coefficient, and ε t is the error term with E(ε t ) = 0 and Var(ε t ) = σ 2 .
Subsequently, the control variables impacting stock liquidity are included.

RESULTS
Table 1 presents the descriptive statistics for all examined variables.The average liquidity of stocks is quantified at 0.0257, over twice its standard deviation, measured at 0.0099.In contrast, the average liquidity of carbon pricing is 0.0850, nearly equivalent to its standard deviation of 0.0768, signifying a substantial degree of variation compared to stock liquidity.Gold price liquidity exhibits an average of 0.0130, twice its standard deviation of 0.0069, indicative of lesser variability.The liquidity averages for crude oil, natural gas, and coal are documented at 0.0452, 0.0410, and 0.0031, respectively, with all averages approximating their respective standard deviations.
Table 2 presents the pairwise correlations among the variables under investigation.Stock price li-quidity is positively associated with carbon liquidity, yielding a correlation coefficient of 0.2125, significant at the 5% significance level.Conversely, stock price liquidity is inversely correlated with the liquidity of gas and coal prices, exhibiting coefficients of -0.1742, significant at the 10% level, and -0.3699 at the 1% level, respectively.The empirical evidence suggests that a decline in carbon price liquidity is positively linked to reduced liquidity in ETS-covered stock prices.
Table 3 elucidates the baseline regressions, employing Equations ( 3) and ( 4).The dependent variable is the mean liquidity of the 338 ETS-covered stocks, with carbon price liquidity as the primary explanatory variable.The estimated coefficient for carbon price liquidity registers at 0.0275, achieving statistical significance at the 5% level.Upon the inclusion of control variables, namely the liquidity of gold price, coal price, natural gas price, and crude oil price, the coefficient of carbon price liquidity adjusts to 0.0247, retaining statistical significance at the 5% level.This adjustment substantiates the robustness of the baseline findings after accounting for relevant commodity price liquidity.
The paper thus posits a positive influence of car-     spillover effects transpiring from the carbon market to the equity market.This could be attributed to a depletion in stock market liquidity and an intensification of cross-market systematic risk induced by the pandemic.Figure 1 displays impulse response functions delineating the interrelations between stock liquidity and carbon price liquidity after executing vector autoregression analyses.These graphical outcomes corroborate the empirical evidence presented in Table 7.The impulse response function gradually decays when quantifying auto-correlations in carbon price liquidity.Conversely, the impulse response function exhibits rapid attenuation when assessing the cross-relationship explanatory power between carbon price liquidity and equity liquidity.
Endogeneity issues emerge in the baseline regression specified by Equation ( 4).Reverse causality is conceivable, wherein elevated levels of stock illiquidity induce corporate distress, In the first step, Equation ( 5) regression predicts the current carbon price liquidity using the oneand two-month lagged carbon price liquidity as the instrumental variables.The second step employs the first-step predicted value to forecast stock liquidity in Equation (6).From prior research, lagged independent variables may be considered valid instrumental variables (Heckman, 1997).
Table 8 delineates the results of the two-stage least squares regression, utilizing lagged carbon price liquidity as the instrumental variables.The firststage findings, consistent with Equation ( 5), indicate that the coefficient associated with the one-month lagged carbon liquidity stands at 0.3734 and achieves statistical significance at the 1% level, thus mitigating concerns of weak instrument variables.Following Equation ( 6), the second-stage findings reveal a statistically significant, positive association between the predicted carbon liquidity and stock liquidity, manifesting a coefficient of 0.0493 that is statistically significant at the 10% level.These two-stage least squares regression outcomes are congruent with the antecedent regression results.

DISCUSSION
The paper posits a robustly positive association between the liquidity of stocks and carbon prices.The baseline regression results presented in Furthermore, Hubei Province and Chongqing represent some of the more recent additions to regional carbon markets, in contrast to their counterparts, and are situated inland, as opposed to other markets located along the coast.A plausible interpretation of these phenomena is that these two markets predominantly rely on businesses such as manufacturing, construction, and transportation, making them more susceptible to variations in carbon price liquidity.Notably, the national market does not exhibit a discernible spillover effect, a phenomenon potentially ascribable to the constraints of a limited sample period.Collectively, these observations highlight regional disparities in the liquidity spillover effects under scrutiny.to stock markets, a conclusion that is intuitively sound and corroborated by existing literature.Ren et al. (2023) explore the dynamics of spillovers and information transmission across carbon, crude oil, and stock markets during the third phase of the European Union ETS, demonstrating that information flows robustly from the crude oil market to the stock market under normal conditions but dissipates in extremely bearish or bullish stock market scenarios.Concurrently, Sun et al. (2022) uncover a modest bidirectional causality between Chinese carbon prices and four energy-intensive stock indices, quantifying the impact of a 1% fluctuation in one market as inducing a 0.15%−0.3%fluctuation in the other.In a related study, Zhang and Han (2022) scrutinize the spillover effects between these two markets, concluding that the carbon market exerts a more substantial influence on the stock

CONCLUSION
This study explores the liquidity spillover effects of carbon pricing on ETS-covered stocks in China.Leveraging a comprehensive dataset of 338 publicly listed, ETS-covered firms spanning nine distinct markets in China from 2013 to 2023, this study documents a statistically significant, positive spillover effect from carbon price liquidity to associated equity liquidity.Notably, this positive liquidity spillover effect exhibits a greater magnitude in industries with lower carbon intensity than those with higher carbon intensity.Regional analysis reveals that the liquidity spillover phenomenon is significantly manifest solely within Hubei Province and Chongqing, thus evincing regional heterogeneity.Temporal partitioning indicates that the spillover effect is more prominent during COVID-19 relative to early epochs.Vector autoregressive analyses corroborate that liquidity spillovers are chiefly concurrent rather than inter-temporal.The outcomes of the two-stage least squares estimations effectively mitigate endogeneity concerns.
This study offers multiple contributions to existing literature.Firstly, it augments the body of work focused on the development and effectiveness of China's carbon emission trading markets, elucidating a direct conduit through which policy influences the equity market.Secondly, introducing an innovative inter-market spillover channel enhances the literature on stock liquidity determinants.Lastly, the study fortifies the overarching literature on liquidity spillovers across diverse markets.It furnishes novel empirical insights into the cross-market liquidity dynamics between carbon and equity markets in China, bridging a significant research gap.
In summary, the present research is pivotal in elucidating the interconnected dynamics between China's nascent carbon markets and its equity market.As global carbon pricing mechanisms proliferate, the prospect for additional scholarly inquiry is pronounced.Grasping these complex interlinkages is imperative for regulatory entities, investment practitioners, and corporate actors steering the course of energy transformation and climate risk mitigation.Nevertheless, the study has limitations that future research could redress.The dataset, confined to a temporal window commencing with initiating the ETS pilots in 2013, could benefit from longitudinal expansion as these markets evolve, potentially yielding more robust empirical findings.Moreover, scrutinizing liquidity spillover effects across different industries may furnish supplementary insights.Additionally, the merit exists for exploring alternative transmission conduits between carbon and equity markets, extending beyond solely liquidity effects.
Investment Management and Financial Innovations, Volume 20, Issue 4, 2023 gas, and coal price variables, and (2) the China Stock Market & Accounting Research database, capturing individual stock returns, trading volume, and company information. http://dx.doi.org/10.21511/imfi.20(4).2023.19natural GoldLiq t denotes gold price liquidity in month t, OilLiq t denotes crude oil price liquidity in month t, GasLiq t denotes natural gas price liquidity in month t, and CoalLiq t denotes coal price liquidity in month t.

Table 1 .
Descriptive statistics Note: This table presents descriptive statistics for 338 firms across nine ETS markets, spanning August 2013 to October 2023.

Table 4 .
Low-and high-carbon-intensity industries Note: This table shows the regression of ETS-covered stock liquidity on the carbon price liquidity and a series of control variables for two subsamples: the subsample firms in the low-carbon-intensity industries and the subsample firms in high-carbon-intensity industries.The standard errors are reported in parentheses below the estimated coefficients.***, **, and * denote statistical significance levels of 1%, 5%, and 10%, respectively.

Table 5 .
Different ETS markets

Table 7 .
Vector autoregression analysis This table shows the vector autoregression of ETS-covered stock liquidity, carbon price liquidity, and a series of control variables.The standard errors are reported in parentheses below the estimated coefficients.***, **, and * denote statistical significance levels of 1%, 5%, and 10%, respectively Note: This figure shows the impulse function and changing trend between stock liquidity and carbon price liquidity after applying the vector autoregression analysis.

Table 8 .
Two-stage least squares regression results This table presents the results derived from the two-stage least squares regressions, examining the association between carbon liquidity and stock liquidity, utilizing one-and two-period lagged carbon price liquidity as the instrumental variables.The standard errors are reported in parentheses below the estimated coefficients.***, **, and * denote statistical significance levels of 1%, 5%, and 10%, respectively. Note: