This is a self-archived – parallel published version of this article in the publication archive of the University of Vaasa. It might differ from the original. Carbon Pricing and CCUS: Evidence from China Author(s): Dutta, Anupam; Bhuiyan, Mohammad Rakib Uddin; Wang, Gang-Jin; Uddin, Gazi Salah; Ahmed, Ali Title: Carbon Pricing and CCUS: Evidence from China Year: 2024 Version: Accepted manuscript Copyright ©2024 World Scientific Publishing Company. Electronic version of an article published in Energy Transition and Carbon Neutrality in ASEAN: Developing Carbon Capture, Utilization and Storage Technologies, pp. 203-224. https://doi.org/10.1142/9789811288050_0008 Please cite the original version: Dutta, A., Bhuiyan, M. R. U., Wang, G.-J., Uddin, G. S., & Ahmed, A. (2024). Carbon Pricing and CCUS: Evidence from China. In P. Han, & R. Nepal (Eds.), Energy Transition and Carbon Neutrality in ASEAN: Developing Carbon Capture, Utilization and Storage Technologies (pp. 203-224). World Scientific. https://doi.org/10.1142/9789811288050_0008 Carbon Pricing and CCUS: Evidence from China Anupam Dutta1 Senior Researcher School of Accounting and Finance, University of VAASA Wolffintie 32, 65200 Vaasa, Finland adutta@uwasa.fi Mohammad Rakib Uddin Bhuiyan2 Doctoral Student, Division of Economics, Linköping University SE-581 83 Linköping, Sweden Associate Professor (on leave), University of Dhaka, Bangladesh Mohammad.rakib.bhuiyan@liu.se Gang-Jin Wang3 Associate Professor, Business School Hunan University Changsha 410082, P. R. China wanggangjin@hnu.edu.cn Gazi Salah Uddin4 Senior Associate Professor, Division of Economics Department of Management and Engineering Linköping University SE-581 83 Linköping, Sweden gazi.salah.uddin@liu.se Ali Ahmed5 Professor, Division of Economics Department of Management and Engineering Linköping University SE-581 83 Linköping, Sweden The Ratio Institute, Stockholm, Sweden ali.ahmed@liu.se mailto:adutta@uwasa.fi mailto:Mohammad.rakib.bhuiyan@liu.se mailto:wanggangjin@hnu.edu.cn mailto:gazi.salah.uddin@liu.se mailto:ali.ahmed@liu.se Abstract While carbon capture, utilization and storage (CCUS) technologies play a pivotal role in mitigating the adverse impact of global climate change, rising economic uncertainty, geopolitical conflicts and oil price volatility make the deployment of CCUS technologies rather sluggish. To this end, carbon emission trading mechanism can be an effective tool for promoting the deployment of CCUS projects. However, recent literature demonstrates that the emission trading scheme is still immature in a developing economy like China, where carbon pricing seems to be a key strategy to lower the CO2 discharges from power generation. In this study, we thus investigate the volatility dynamics of Chinese carbon market. In particular, we examine the presence of time-varying jumps in emission prices. Since the jump-induced volatility represents an important element of risk, precise information on such risk is important for increasing the efficiency of carbon trading. Employing the GARCH- jump process, we find that time-dependent jumps do occur in Chinese emission market and that key uncertainty indicators including economic policy uncertainty, crude oil volatility index and geopolitical risk can successfully explain the jump-induced volatility for the carbon market in China. These results offer important implications to policymakers and socially responsible investors. Keywords: CCUS; Carbon trading; Climate change; China; Time-varying jumps JEL classifications: Q54; Q56; Q58 1. Introduction Carbon capture, utilization and storage (CCUS) technologies play a pivotal role in mitigating the adverse impact of global climate change. The main goals of CCUS include managing emissions from heavy industry and exterminating carbon from the atmosphere. CCUS is now considered one of the most efficient tools for achieving net-zero targets. However, although CCUS has recently received much attention from governments, policymakers and investors, the deployment is rather sluggish. For instance, a recent report, published by the International Energy Agency (IEA), reveals that the yearly investment for promoting CCUS technologies amounts to only 0.5% of the worldwide investment in generating renewable and sustainable energy. Besides , the economic downturn and low oil prices due to COVID-19 pandemic emerge as major obstacles for the future development of CCUS technologies. To this end, carbon emission trading mechanism can be an effective tool for promoting the deployment of CCUS projects (Lin & Tan, 2021). Given that the current economic environment may not be sufficient to prompt the rapid investments in CCUS technologies, an upgraded emission trading system can significantly improve the investment value of the project. Additionally, carbon pricing offers policymakers the flexibility to determine the cheapest ways to reduce carbon pollution. However, the emission trading scheme is still immature in a developing economy like China, where carbon pricing seems to be a key strategy to lower the CO2 discharges from power generation. At the same time, the CCUS technologies in China are somewhat underdeveloped to combat climate change. In addition, the slow deployment of CCUS is also a major concern for reaching carbon neutrality. It is thus essential for the Chinese government to increase the efficiency of carbon trading market. Doing so would help the policymakers accelerate the deployment of CCUS projects. It is also worth noting that being a new financial asset class, the Chinese emission trading market could be highly volatile (Weng & Xu, 2018). Therefore, precise estimates of time-varying volatilities of carbon trading market are of utmost importance for understanding its market efficiency. Qi et al. (2022), for instance, argue that accurate forecasts of the Chinese carbon market volatility are essential for investors and regulators to make proper investment decisions, which would further help the government to reduce CO2 pollution. Besides, (Lin & Tan, 2021) shed light on the importance of improved and efficient emission trading market in China, which is a major strategy to develop the CCUS projects. Given the significance of carbon trading for the deployment of CCUS technologies, a growing body of literature investigates the volatility dynamics of Chinese emission market. For example, Wang and Guo (2018) examine the volatility linkage between carbon and energy markets. Xu (2021)) explores how energy market uncertainties impact the volatility Chinese emission prices. Besides, Chun (2018) estimate the volatility cross effects between the Chinese and EU emission markets. Some other notable works include (Ji et al., 2019; Lin & Chen, 2019; Zhu et al., 2020). While the number of studies focusing on the Chinese emission trading scheme continue to grow, not a single paper has investigated the jump behavior of carbon pricing. Time-varying jumps, which are often observed in this type of immature market, could cause a substantial increase in the volatility levels. Such jump-induced volatility in fact represents an important element of risk which requires special attention when modeling the volatility of carbon market1. In this study, our main objective is to examine whether time-varying jumps occur in the Chinese emission prices. In addition, we also attempt to assess if relevant variables including oil price risk, economic policy uncertainty and geopolitical risk could explain the jump-induced volatility (henceforth, JV). Identifying the factors that influence carbon price volatility would help the policymakers to precisely estimate the underlying risk linked to emission trading. Overall, this strand of research has important implications for the development of CCUS technologies in China, which is essential for achieving the net-zero targets. 1 Dutta (2018) shows that the EU emission market also experiences such jumps, which play a pivotal role in volatility modeling and risk management. Methodologically, we employ the GARCH-jump model proposed by (Chan & Maheu, 2002) and find that time-dependent jumps do occur in Chinese emission market. Our findings further show that key uncertainty indicators including economic policy uncertainty, crude oil volatility index and geopolitical risk can successfully explain the jump-induced volatility for the carbon market in China. Given that precise information on jump-induced volatility is important for increasing the efficiency of carbon emission market, our analysis offers important implications to policymakers and socially responsible investors. 2. Literature Review China, as the largest emitter of CO2 in the world, is facing ethical and normative pressure to reduce its greenhouse gas emissions and adopt sustainable energy development policies. During the UN General Assembly’s 75th session, President Xi Jinping announced that China is dedicated to achieving its CO2 emissions peak by 2030 and carbon neutrality by 2060. Furthermore, he has urged the global community to pursue innovative, coordinated, green, and open development for the betterment of humanity. Under the vision, carbon pricing can substantially reduce carbon emissions (Mo et al., 2023; Yan & Yang, 2021; Zhao et al., 2022). China’s carbon markets are evolving with commodity and financial attribution (Wen et al., 2022) and regulating the most significant emissions globally (Liao et al., 2022) . The carbon pricing mechanism in the carbon markets mainly revolves around three forms: emissions trading systems ( ETS), a.k.a. cap and trade, carbon tax, and a hybrid approach (Narassimhan et al., 2018). Carbon pricing in the form of carbon tax provides a double dividend by improving economic efficiency by redistributing collected carbon tax revenue and reducing greenhouse gas (GHG) emissions (Kojima & Asakawa, 2021). Empirical literature (Compernolle et al., 2022; Landis et al., 2021; Li et al., 2018; Mo et al., 2023; van den Bergh & Savin, 2021; Venmans et al., 2020; Wen et al., 2022) is vast on carbon pricing across the contexts and cover both classical and emerging issues of carbon economics. Lin & Jia (2019) explore the role of both carbon trading and carbon tax on China’s environment, energy, and economy and conclude that both can substantially reduce carbon emissions. However, the carbon tax is advantageous over carbon trading on emission reduction efficiency. They also point out the negative effect of carbon trading on the output of the energy industry and energy-intensive industry, including many invisible costs of setting up a carbon trading market. Thus, they suggest levying a carbon tax on fossil fuels in China to reduce CO2 emissions effectively. Mo et al. (2023) show that China’s carbon emission trading scheme (ETS) with carbon price stabilization mechanism (CPSM) has a different level of impact on Chinese firms’ productivity: an ETS with explicit price corridors could improve total factor productivity (TFP) by 0.381, whereas an ETS with no explicit price corridors could influence TFP by 0.198 while the ETS without CPSM has lessened TFP by 0.352. Extant works (Känzig, 2023; Ko & Lee, 2022; Wen et al., 2022) reveal nonlinear asymmetric and unstable effects of carbon prices on economic agents and urge for the equitable development of carbon policy and market. Essentially, carbon market development is credited to the positive acknowledgment of carbon pricing, which strengthens resource distribution and optimization and has a decisive role in the carbon trading system (Fang et al., 2018). Carbon pricing as a policy instrument for climate mitigation has been in practice in many countries though its application is heterogeneous across contexts. Political systems, domestic business nature, competition, and influence over local authorities, including international bindings and obligations, contribute significantly to the design, adoption, and implementation of carbon pricing systems (Khan & Johansson, 2022). In 2005, the European Union (EU) first launched the carbon market in its territory, which set the foundation of that emerging policy-based market in various other locations worldwide. China started its journey in the carbon energy market in 2013 by setting up seven carbon pilot markets, subsequently with a national carbon market on 16 July 2021. With heavy energy-intensive industries, China has significant potential for a robust carbon market. However, a significant gap exists between the mature carbon market in the West and the infant carbon market in China because of the differentiation in carbon price fixation, carbon quota allocation, carbon emission coverage sources, and other contextual factors (Hua & Dong, 2019). The differences between the EU emissions trading system (ETS) and China’s national emissions trading system (ETS) are not counterproductive; instead, linking these two systems can open potential economic and political gains (Zeng et al., 2016). Furthermore, the international cooperation on carbon pricing through linking carbon markets can deliver economic benefits by lowering carbon reduction costs and environmental benefits by reducing carbon emissions and leakage (Thube et al., 2021). China’s national carbon market is in its early stages and requires necessary additions and improvements. Till now, the most successful EU carbon market can provide valuable lessons for developing China’s national carbon market (J. Liu et al., 2022). For instance, carbon finance is a new trend in the global carbon market with many legal and policy complexities. However, applying the EU rules on carbon finance to the specialized regulatory regime of China’s carbon market can help it function well(Chen & Wu, 2023). More importantly, Chinese firms’ total factor productivity (TFP) can go up approximately by 22.73% if China’s carbon emissions price equals that of EU ETS (Wu & Wang, 2022). However, the sensitivity of the Chinese market to the cross-border carbon policy, especially in light of the European Union Carbon Border Adjustment Mechanism (EU CBAM), is a matter of concern as China’s exports to the EU heavily rely on carbon-intensive goods. Therefore, without carbon policy adjustment and reconciliation, cross-border trade between the EU and China may be affected (Shen et al., 2023). The carbon trading market is valuable to increase the attention of investors in the carbon capture utilization and storage (CCUS) development initiatives (Lin & Tan, 2021). CCUS systems are critically important in tackling global climate change (McLaughlin et al., 2023; Roy et al., 2023). However, CCUS is still at the early demonstration stage, and there are many uncertainties in the business model and policy incentives that the traditional method can no longer handle (Ye et al., 2022). For effective installations of CCSU technologies in a region, initiatives like setting up regional corridors for optimization of scale, societal engagement to the CCUS projects, carbon pricing, public- private partnership for CCSU financing, knowledge and technology transfer, and stakeholders’ engagement are crucial (Lau et al., 2021). 3. Status of Carbon Capture Storage and Utilization (CCUS) in China China is the first country to initiate the CCUS concept to reduce CO2 emissions globally (Qin et al., 2015), even having the most comprehensive experience of CCUS projects aiming at balancing economic expansion and carbon emissions (Yao et al., 2023). Moreover, with the CCUS installations strategy, China can avoid stranding assets from the current industry setup, including support from the business community in fighting climate challenges. An estimated 3120 GtCO2 carbon storage capacity indicates the future of CCUS in China (Xu & Dai, 2021). Chen et al. (2022) out that the the “Golden Age” of CCUS formation ranges from 2040 to 2060 globally, while 2030–2050 for China. However, the actual scale of CCUS is still not up to the mark in China, stressing the importance of continuous government support for CCUS as a component of the governmental plan for energy efficiency and transitioning towards a low-carbon future (Jiang & Ashworth, 2021). CCUS has been a vital policy part of the Chinese government over the last two decades. Chronologically, in 2007, China government enlisted CCUS as a critical technology in the battle against carbon control, with subsequent actions in 2013 to carry out CCUS demonstration projects, including environmental impact assessment and monitoring. In 2017, the emphasis was on CCUS technologies development from the government side, which got a boost in 2021 by implementing CCUS pilots in various parts of China (Xu & Dai, 2021). These initiatives led to an expectation about CCUS at various levels of economic and political agents in China (Yin & Xu, 2022). However, Ma et al. (2023) reveal several areas for improvement in China's multi-state department generated CCUS policy. First, they show that supply-side policy needs to be more balanced as it relies heavily on technology research and demonstration. Second, Environment-type policies must be revised in relevance and operability, especially legislations, standards, and incentives. Finally, demand-type policies should be present in the current policy system, essential to driving the demand for CCUS technology in national and international markets. A well-designed implementation strategy with a proper balance of CCUS technology and other objectives can work hand in hand in China. CCUS development at the maximum level can help China to achieve carbon neutrality by 2054-2058, which can defer to 2061-2064 in case of low CCUS development in China (Sun et al., 2022). Chinese oil companies have been using CO2 storage technologies for many years, constantly producing positive results, even though no large-scale CCUS technology installation has occurred in China's oil industry (Che et al., 2022). The CCUS technologies also probe grace to the China crude steel industry. Wei et al. (2022) show that the impact of CCUS will lie between 17.6%-56.5%, contingent upon the carbon price mechanism or aiding government policies for China's steel industry. Therefore, CCUS can be a catalyst for the green transition of the Chinese steel industry. Furthermore, China's coal-fired power plants can transform into CCUS projects with economic, environmental, and energy considerations. Han et al. (2023) point out that the three-dimensional model of the Chinese CCUS coal-based power plant can reach the 0.99 level in terms of the goodness-of-the-fit with a benefit ratio of 5:9:1 in the economic, environmental, and energy dimensions, respectively. Similarly, CCUS can benefit China's coal chemical, and cement industries (Guo et al., 2023; Xie et al., 2022).The projected benefit of a pipeline network model of CCUS can be a yearly emission reduction of 280 million tons of CO2, including a yearly net income of USD 2.20 billion collectively and unit wise net income of USD 7.85 per ton of CO2 (Xie et al., 2022). The public and stakeholders influence strategic CCUS investment decisions (Liu et al., 2021; Sitinjak et al., 2023) CCUS are sensitive technologies and require public understanding and acceptance. Even if a significant percentage of newsletter reports positively present CCUS technologies in the mainstream Chinese media, more is needed to tackle technical misperceptions and comprehensive subject matter of CCUS technologies (Jiang et al., 2022) General cognition positively influences mass acceptance, and perceived benefits have a more substantial effect on acceptance than perceived risks in China. Therefore, the authorities and leading clean energy players in China should strengthen the publicity and incentive system for various CCUS technology issues to make it a reality in combating carbon emissions in China ( Liu et al., 2021). 4. Methodology The jump approach has received enormous attention in recent studies (Dutta, 2017; Dutta et al., 2022; Dutta & Das, 2022; Fowowe, 2013; Gronwald, 2019; Kuttu, 2017; Zhou et al., 2019). Following (Chan & Maheu, 2002), we employ the GARCH-jump model as follwos2: 𝑟𝑡 = 𝜋 + 𝜇𝑟𝑡−1 + 𝜖𝑡 (1) where 𝑟𝑡 indicates the logarithmic difference for the CEP index at time t, and 𝜖𝑡 refers to the innovation term specified as: 𝜖𝑡 = 𝜖1𝑡 + 𝜖2𝑡 (2) where 𝜖1𝑡 will follow the GARCH (1,1) specification: 𝜖1𝑡 = √ℎ𝑡𝑧𝑡, 𝑧𝑡~𝑁𝐼𝐷(0,1) ℎ𝑡 = 𝜔 + 𝛼𝜖1𝑡−1 2 + 𝛽ℎ𝑡−1 (3) In addition, 𝜖2𝑡 denotes a jump innovation defined as: 𝜖2𝑡 = ∑ 𝐽𝑡𝑙 𝑛𝑡 𝑙=1 − 𝜃𝜆𝑡 (4) where 𝐽𝑡𝑙 is the jump size with a mean value 𝜃 and a variance 𝜗2, ∑ 𝐽𝑡𝑙 𝑛𝑡 𝑙=1 refers to the jump factor, and 𝑛𝑡 represents the jump frequency at time t, following a Poisson distribution given by: 𝑃(𝑛𝑡 = 𝑗|𝐼𝑡−1) = 𝑒−𝜆𝑡𝜆𝑡 𝑗 𝑗! , 𝑗 = 0,1,2, … (5) 2 The choice of the AR(1) specification based on the Akaike information criterion. with an autoregressive conditional jump intensity (ARJI) given as: 𝜆𝑡 = 𝜆0 + 𝜌𝜆𝑡−1 + 𝛾𝜉𝑡−1 (6) In equation 6, 𝜆𝑡 indicates the time-varying conditional jump intensity parameter, 𝜆0 is the constant jump intensity, and 𝜉𝑡−1 denotes the intensity residual. (Chan & Maheu, 2002) assume that 𝜆𝑡 > 0, 𝜆0 > 0, 𝜌 > 0, and γ > 0. We define the log-likelihood as: 𝐿(Θ) = ∑ log 𝑓(𝑋𝑡|𝐼𝑡−1; Θ) 𝑇 𝑡=1 where Θ = (π, 𝜇, ω, α, β, θ, 𝜗, 𝜆0, ρ, γ) and 𝐼𝑡−1is the information set. Note that the jump-induced volatility (JV) is given as: 𝐽𝑉𝑡 = √(𝜃2 + 𝜗2)𝜆𝑡 (7) Next, we examine whether crude oil volatility index, clean energy asset price volatility and the EU emission price volatility could explain the jump-induced volatility defined in Equation 7. To serve this purpose, we estimate the following model: 𝐽𝑉𝑡 = 𝜑0 + 𝜑1𝑈𝑡−1 + 𝜀𝑡 (8) where, U indicates a vector of uncertainty indexes. If case when 𝜑1is significantly different from zero for a particular uncertainty measure, we can infer that it may explain the jump-induced risk linked to the Chinese emission prices3. 3 Many recent studies have found significant linkages between the Chinese carbon and global energy markets (see Ji et al., 2019; Lin and Chen, 2019; Zhu et al., 2020; Gong et al., 2021; Xu, 2021 etc.). 5. Data In this study, we use the information on Shenzhen carbon emission trading price (henceforth, CEP) due to its high quality and long duration. While there are currently eight carbon emission trading pilots in China, the Shenzhen carbon emission trading market has received much attention in recent studies (H. Chen & Xu, 2022; Zheng et al., 2021). The sample incudes daily observations spanning from August, 2013 to August, 2022. As mentioned earlier, our analysis has considered several influencing factors including crude oil volatility index (OVX), economic policy uncertainty (EPU) and geopolitical risk (GPR). The data on OVX are collected from the DataStream database, while the GPR index is found in the website of economic policy uncertainty. Notably, the EPU index for China has been recently introduced by (Lee et al., 2023). We obtain the data from their website (twitterchnepu.github.io). Next, Table 1 shows the descriptive statistics for emission prices, OVX, EPU and GPR. It is observed that carbon market returns are positively skewed. This is also true for all the uncertainty indicators. Besides, all the series are found to be leptokurtic and hence they violate the normality assumption. The Jarque- Bera test also leads to the same conclusion. Table 2 displays the outcomes of the augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests. The null hypothesis of these tests is that the data follow a non-stationary process. The results are reported for both levels and first differences. These findings suggest that all the uncertainty measures are stationary even at levels. The carbon price index, on the other hand, appears to be non-stationary at levels when the ADF test is employed. After considering the logarithmic differences for the CEP index, both ADF and PP tests find it stationary. Fig.1, which depicts the Chinese emission price index, demonstrates that carbon prices fall significantly during the COVID-19 pandemic periods, while the prices have increased substantially during the ongoing Russia-Ukraine war periods. It is also observed that during the period 2014-2015, when the global crude oil markets witness a downturn, carbon prices drop significantly. it thus seems that carbon prices in China maintain a close positive correlation with traditional energy prices. 6. Empirical findings 6.1. Findings of GARCH-jump process Table 3 shows the estimates of the GARCH-jump process. For comparison purposes, we have also included the estimates of GARCH(1,1) model and constant jump intensity(CJI) process. The CJI process, developed by (Jorion, 1988), assumes that 𝜆𝑡 = 𝜆0, (i.e., the jump intensity parameter is constant with respect to time). These results demonstrate that the GARCH parameters (𝛼, 𝛽) are statistically significant in all these models. We also notice that the sum of 𝛼̂ and 𝛽̂ are close to unity, showing the evidence of volatility persistence. When looking at the CJI process, we find that the mean parameter (𝜃) for jumps is insignificant, whereas the variance parameter (𝜗) is statistically significant. Notably, the positive coefficient of the jump variance indicates that the volatility of carbon prices increases with the increment in the volatility caused by abnormal information. Moving to the GARCH-ARJI process, we observe two interesting results. Firstly, the parameter 𝜆0, which appears to be significant in the CJI model, is no longer significant. Secondly, the ARJI parameters (𝜌, 𝛾) are strongly significant at 1% level, suggesting that jumps exist in the Chinese carbon market and that such jumps evolve over time. Note that the parameter 𝜌 being 0.9973 reveals that the intensity of jumps is highly persistent. Besides, the significant value of 𝛾 suggests a diminishing effect (0.0456) on the intensity of future jumps. It is also noteworthy that the ARJI parameters (𝜌, 𝛾) are found to be exceeding the zero value, satisfying the positivity constraints. The log-likelihood values shown in the last row also confirm the superiority of the GARCH-ARJI process. Fig.2 depicts the jump intensities for the Chinese carbon market. This graph evidently shows that the intensity of jumps tends to be time-dependent. More importantly, 𝜆𝑡 seems to increase during the crisis period. For example, we witness several huge spikes throughout the COVID-19 pandemic period. Previous studies (Dutta et al., 2021; Dutta et al., 2022) also claim that jumps in energy prices tend to rise amid the periods of market downturns. Our findings are consistent with earlier research. Dutta (2018), for instance, shows that time-varying jumps represent a common event in the EU carbon emission market. The author also argues that modeling such jumps plays a pivotal role in understanding the volatility of carbon prices more precisely. We, therefore, recommend the application of GARCH-ARJI process when studying the volatility dynamics of carbon prices in China. 6.2. Determinants of jump-induced volatility In this section, we intend to examine whether leading uncertainty measures could explain the jump- induced volatility for the Chinese emission market. Table 4 shows the impacts of OVX, EPU and GPR on the volatility of jumps. These findings show that all the uncertainty indicators have substantial effects on the jump-induced volatility as the corresponding parameters are statistically significant at 1% level. Notably, OVX and EPU influence the jump-induced volatility positively, whereas GPR has a negative impact on the same. Moreover, the R2 statistics show that the volatility of jumps is better explained by the economic uncertainty in China when compared to crude oil volatility or geopolitical risk. These significant linkages could be due to the high correlations between energy and emission prices, geopolitics for oil and inflation, investors’ concern for climate change, and financial market integration. Our findings are in line with earlier literature (Dutta, 2018; Viteva et al., 2014). For example, Dutta (2018) also shows that OVX exerts a positive impact on the volatility of EU emission prices. One plausible explanation is that with the increase in crude oil volatility, energy prices tend to decrease, which in turn promotes the usage of traditional energy sources, resulting in increased volatility for the emission prices. This finding also establishes that energy and emission prices move in the same direction. In addition, we observe that the EU and Chinese emission prices react in a similar manner to crude oil volatility. Moreover, the positive association between economic policy uncertainty and jump-induced volatility implies that rising economic uncertainty tends to raise the policy uncertainty for sustainable investments, which would lift the volatility levels for the emission market in China . Finally, the negative association between GPR and jump-induced volatility could be attributed to the fact that with the upsurge in geopolitical risk, crude oil, which is highly sensitive to such risk (Tiwari et al., 2020), seems to be replaced by renewable energies, leading to a rise in emission prices. As a consequence, a significant drop in the levels of volatility is observed. 7. Additional tests: Do jumps exist for outlier-free carbon prices? We now explore whether the Chinese carbon prices involve outliers. We then check if jumps still exist after correcting for such outliers. In doing so, we employ the methodology, proposed by (Ané et al., 2008), to identify the potential outliers. Suppose, 𝑅𝑡 denotes the log return for carbon price on day t. Then the AR(1)-GARCH(1,1) process is defined as: 𝑅𝑡 = 𝑏0 + 𝑏1𝑅𝑡−1 + 𝜀𝑡 (9) 𝜎𝑡 2 = 𝑎0 + 𝑎1𝜀𝑡−1 2 + 𝑎2𝜎𝑡−1 2 (10) where 𝜀𝑡 = 𝜎𝑡𝑧𝑡 with 𝑧𝑡 being an i.i.d. process such as 𝑧𝑡/𝐼𝑡−1~𝐼𝐼𝑁(0, 1); 𝐼𝑡−1 indicates the filtration of information at time t−1. Then 𝑅𝑡+1 is assumed to be an outlier if it does not fall in the following interval: 𝑅𝑡+1 ∈ [𝑅𝑡,𝑡+1 ± 𝐹(1 − 𝛼 2 )𝜎𝑡,𝑡+1] where, 𝑅𝑡,𝑡+1 refers to the one-step ahead prediction, which is modelled as: 𝑅𝑡,𝑡+1 = 𝐸(𝑅𝑡+1/𝐼𝑡) = 𝑏0 + 𝑏1𝑅𝑡 + 𝑏2𝑅𝑡−1 and 𝜎𝑡,𝑡+1 2 is the one-step ahead risk prediction specified as: 𝜎𝑡,𝑡+1 2 = var(𝑅𝑡+1/𝐼𝑡) = 𝑎0 + (𝑎1 + 𝑎2)𝜎𝑡 2 Additionally, 𝐹 (1 − 𝛼 2 ) = 𝑃(𝑧𝑡 ≤ 1 − 𝛼 2⁄ ) refers to a fractile of the presumed conditional distribution. This process keeps rolling over until the end of our sample. Our empirical analysis has detected several outliers in emission prices. These outliers mainly occur during the crisis periods such as 2014 oil market downturns, COVID-19 pandemics, etc. We now re- estimate the jump process after deleting these outliers from our original dataset and report the results in Table 5. We find that time-dependent jumps exit even after removing the outliers. This result holds for both CJI and ARJI models. Hence, we conclude that modelling jumps is important for risk assessment purpose. Besides, detecting such jumps is also crucial for increasing the efficiency of carbon trading in China. 8. Conclusions policy implications While carbon capture, utilization and storage (CCUS) technologies are now considered one of the most efficient tools for achieving net-zero targets, rising economic uncertainty, geopolitical conflicts and oil price volatility make the deployment of CCUS technologies rather sluggish. To this end, carbon emission trading mechanism can be an effective tool for promoting the deployment of CCUS projects. However, recent literature demonstrates that the emission trading scheme is still immature in a developing economy like China, where carbon pricing seems to be a key strategy to lower the CO2 discharges from power generation. In this study, we thus investigate the volatility dynamics of Chinese carbon market. In particular, we examine the presence of time-varying jumps in emission prices. Since the jump-induced volatility represents an important element of risk, precise information on such risk is important for increasing the efficiency of carbon trading. Employing the GARCH- jump process, we find that time-dependent jumps do occur in Chinese emission market and that key uncertainty indicators including economic policy uncertainty, crude oil volatility index and geopolitical risk can successfully explain the jump-induced volatility for the carbon market in China. For investors, this research has significant implications given that precise estimates of financial market volatilities always play a key role in risk management. Our study suggests that when investing in the emission market in China, financial institutions should pay careful attention to time-varying jumps occurring in carbon prices given that the jump-induced volatility might carry important information for understanding the potential risk. Since jumps could lead to a sudden increase in the volatility levels, they should be detected properly. Moreover, as we find that the volatility of jumps could be explained by the key uncertainty indicators, the information on such measure could also be useful for market participants when making investment decisions during the periods of high jumps. Hence, our analysis could be useful for deriving appropriate asset pricing models which could minimize the carbon price uncertainty. This strand of research also provides several important suggestions for the government and policymakers. Firstly, the government should focus on how to increase the efficiency of the Chinese carbon market. An improved and efficient emission trading market is crucial for accelerating the deployment of CCUS projects in China. To this end, identifying the time-dependent jumps in emission prices would help the policymakers increase the market efficiency. Developing an efficient market is also important for attracting eco-friendly investors, which is a key requirement for the reduction of CO2 releases. Secondly, in addition to improved carbon market, oil market stability is also a precondition for the successful implementation of CCUS projects. To stabilize oil prices, major steps such as uplifting oil reserves, updating the monitoring policy of crude oil futures market and increasing carbon taxes should be taken into account. Besides, the promotion of renewable energies is also important for reducing the impact of oil price uncertainty and geopolitical conflicts. Thirdly, increasing the efficiency of carbon market should not be the only strategy for encouraging the investors towards sustainable investments. The government in China must concentrate on other promotional activities (such as financial incentives, higher carbon taxes) as well for the deployment of CCUS technologies. Finally, in order to decrease the probability of project failure, the Chinese government should invest more on the research and development department. Doing so would improve the CCUS technologies further. References Ané, T., Ureche-Rangau, L., Gambet, J.-B., & Bouverot, J. (2008). Robust outlier detection for Asia–Pacific stock index returns. Journal of International Financial Markets, Institutions and Money, 18(4). https://doi.org/10.1016/j.intfin.2007.03.001 Chan, W. H., & Maheu, J. M. (2002). Conditional Jump Dynamics in Stock Market Returns. Journal of Business & Economic Statistics, 20(3). https://doi.org/10.1198/073500102288618513 Che, X., Yi, X., Dai, Z., Zhang, Z., & Zhang, Y. (2022). Application and Development Countermeasures of CCUS Technology in China’s Petroleum Industry. Atmosphere, 13(11). https://doi.org/10.3390/atmos13111757 Chen, B., & Wu, R. (2023). Legal and Policy Pathways of Carbon Finance: Comparative Analysis of the Carbon Market in the EU and China. European Business Organization Law Review, 24(1). https://doi.org/10.1007/s40804-022-00259-x Chen, H., & Xu, C. (2022). The impact of cryptocurrencies on China’s carbon price variation during COVID-19: A quantile perspective. Technological Forecasting and Social Change, 183. https://doi.org/10.1016/j.techfore.2022.121933 Chen, S., Liu, J., Zhang, Q., Teng, F., & McLellan, B. C. (2022). A critical review on deployment planning and risk analysis of carbon capture, utilization, and storage (CCUS) toward carbon neutrality. Renewable and Sustainable Energy Reviews, 167. https://doi.org/10.1016/j.rser.2022.112537 Chun, S. (2018). Spillover effects of price fluctuation on China’s carbon market and EU carbon market. The Journal of Industrial Economics, 37. Compernolle, T., Kort, P. M., & Thijssen, J. J. J. (2022). The effectiveness of carbon pricing: The role of diversification in a firm’s investment decision. Energy Economics, 112. https://doi.org/10.1016/j.eneco.2022.106115 Dutta, A. (2017). Oil price uncertainty and clean energy stock returns: New evidence from crude oil volatility index. Journal of Cleaner Production, 164, 1157–1166. https://doi.org/10.1016/j.jclepro.2017.07.050 Dutta, A. (2018). Modeling and forecasting the volatility of carbon emission market: The role of outliers, time-varying jumps and oil price risk. Journal of Cleaner Production, 172. https://doi.org/10.1016/j.jclepro.2017.11.135 Dutta, A., Bouri, E., Saeed, T., & Vo, X. V. (2021). Crude oil volatility and the biodiesel feedstock market in Malaysia during the 2014 oil price decline and the COVID-19 outbreak. Fuel, 292, 120221. https://doi.org/10.1016/j.fuel.2021.120221 Dutta, A., & Das, D. (2022). Forecasting realized volatility: New evidence from time‐varying jumps in VIX. Journal of Futures Markets, 42(12). https://doi.org/10.1002/fut.22372 Dutta, A., Soytas, U., Das, D., & Bhattacharyya, A. (2022). In search of time-varying jumps during the turmoil periods: Evidence from crude oil futures markets. Energy Economics, 114. https://doi.org/10.1016/j.eneco.2022.106275 Fang, G., Tian, L., Liu, M., Fu, M., & Sun, M. (2018). How to optimize the development of carbon trading in China—Enlightenment from evolution rules of the EU carbon price. Applied Energy, 211. https://doi.org/10.1016/j.apenergy.2017.12.001 Fowowe, B. (2013). Jump dynamics in the relationship between oil prices and the stock market: Evidence from Nigeria. Energy, 56. https://doi.org/10.1016/j.energy.2013.04.062 Gronwald, M. (2019). Is Bitcoin a Commodity? On price jumps, demand shocks, and certainty of supply. Journal of International Money and Finance, 97. https://doi.org/10.1016/j.jimonfin.2019.06.006 Guo, Y., Luo, L., Liu, T., Hao, L., Li, Y., Liu, P., & Zhu, T. (2023). A review of low-carbon technologies and projects for the global cement industry. Journal of Environmental Sciences, 136. https://doi.org/10.1016/j.jes.2023.01.021 Han, J., Li, J., Tang, X., Wang, L., Yang, X., Ge, Z., & Yuan, F. (2023). Coal-fired power plant CCUS project comprehensive benefit evaluation and forecasting model study. Journal of Cleaner Production, 385. https://doi.org/10.1016/j.jclepro.2022.135657 Hua, Y., & Dong, F. (2019). China’s Carbon Market Development and Carbon Market Connection: A Literature Review. Energies, 12(9). https://doi.org/10.3390/en12091663 Ji, Q., Xia, T., Liu, F., & Xu, J.-H. (2019). The information spillover between carbon price and power sector returns: Evidence from the major European electricity companies. Journal of Cleaner Production, 208. https://doi.org/10.1016/j.jclepro.2018.10.167 Jiang, K., & Ashworth, P. (2021). The development of Carbon Capture Utilization and Storage (CCUS) research in China: A bibliometric perspective. Renewable and Sustainable Energy Reviews, 138. https://doi.org/10.1016/j.rser.2020.110521 Jiang, K., Ashworth, P., Zhang, S., & Hu, G. (2022). Print media representations of carbon capture utilization and storage (CCUS) technology in China. Renewable and Sustainable Energy Reviews, 155. https://doi.org/10.1016/j.rser.2021.111938 Jorion, P. (1988). On Jump Processes in the Foreign Exchange and Stock Markets. Review of Financial Studies, 1(4). https://doi.org/10.1093/rfs/1.4.427 ’Känzig, D. (2023). THE UNEQUAL ECONOMIC CONSEQUENCES OF CARBON PRICING (No. 31221). Khan, J., & Johansson, B. (2022). Adoption, implementation and design of carbon pricing policy instruments. Energy Strategy Reviews, 40. https://doi.org/10.1016/j.esr.2022.100801 Ko, I., & Lee, T. (2022). Carbon pricing and decoupling between greenhouse gas emissions and economic growth: A panel study of 29 European countries, 1996–2014. Review of Policy Research, 39(5). https://doi.org/10.1111/ropr.12458 Kojima, S., & Asakawa, K. (2021). Expectations for Carbon Pricing in Japan in the Global Climate Policy Context (T. Arimura & S. Matsumoto, Eds.). Springer. https://doi.org/10.1007/978-981-15-6964-7_1 Kuttu, S. (2017). Time-varying conditional discrete jumps in emerging African equity markets. Global Finance Journal, 32. https://doi.org/10.1016/j.gfj.2016.06.004 Landis, F., Fredriksson, G., & Rausch, S. (2021). Between- and within-country distributional impacts from harmonizing carbon prices in the EU. Energy Economics, 103. https://doi.org/10.1016/j.eneco.2021.105585 Lau, H. C., Ramakrishna, S., Zhang, K., & Radhamani, A. V. (2021). The Role of Carbon Capture and Storage in the Energy Transition. Energy & Fuels, 35(9). https://doi.org/10.1021/acs.energyfuels.1c00032 Lee, K., Choi, E., & Kim, M. (2023). Twitter-based Chinese economic policy uncertainty. Finance Research Letters, 53. https://doi.org/10.1016/j.frl.2023.103627 Li, M., Zhang, D., Li, C.-T., Mulvaney, K. M., Selin, N. E., & Karplus, V. J. (2018). Air quality co-benefits of carbon pricing in China. Nature Climate Change, 8(5). https://doi.org/10.1038/s41558-018-0139-4 Liao, H., Wu, D., Wang, Y., Lyu, Z., Sun, H., Nie, Y., & He, H. (2022). Impacts of carbon trading mechanism on closed-loop supply chain: A case study of stringer pallet remanufacturing. Socio-Economic Planning Sciences, 81. https://doi.org/10.1016/j.seps.2021.101209 Lin, B., & Chen, Y. (2019). Dynamic linkages and spillover effects between CET market, coal market and stock market of new energy companies: A case of Beijing CET market in China. Energy, 172. https://doi.org/10.1016/j.energy.2019.02.029 Lin, B., & Jia, Z. (2019). Impacts of carbon price level in carbon emission trading market. Applied Energy, 239. https://doi.org/10.1016/j.apenergy.2019.01.194 Lin, B., & Tan, Z. (2021). How much impact will low oil price and carbon trading mechanism have on the value of carbon capture utilization and storage (CCUS) project? Analysis based on real option method. Journal of Cleaner Production, 298. https://doi.org/10.1016/j.jclepro.2021.126768 Liu, B., Xu, Y., Yang, Y., & Lu, S. (2021). How public cognition influences public acceptance of CCUS in China: Based on the ABC (affect, behavior, and cognition) model of attitudes. Energy Policy, 156. https://doi.org/10.1016/j.enpol.2021.112390 Liu, J., Hou, J., Fan, Q., & Chen, H. (2022). China’s national ETS: Global and local lessons. Energy Reports, 8. https://doi.org/10.1016/j.egyr.2022.03.097 Ma, Q., Wang, S., Fu, Y., Zhou, W., Shi, M., Peng, X., Lv, H., Zhao, W., & Zhang, X. (2023). China’s policy framework for carbon capture, utilization and storage: Review, analysis, and outlook. Frontiers in Energy. https://doi.org/10.1007/s11708-023-0862-z McLaughlin, H., Littlefield, A. A., Menefee, M., Kinzer, A., Hull, T., Sovacool, B. K., Bazilian, M. D., Kim, J., & Griffiths, S. (2023). Carbon capture utilization and storage in review: Sociotechnical implications for a carbon reliant world. Renewable and Sustainable Energy Reviews, 177. https://doi.org/10.1016/j.rser.2023.113215 Mo, J., Tu, Q., & Wang, J. (2023). Carbon pricing and enterprise productivity-The role of price stabilization mechanism. Energy Economics, 120. https://doi.org/10.1016/j.eneco.2023.106631 Narassimhan, E., Gallagher, K. S., Koester, S., & Alejo, J. R. (2018). Carbon pricing in practice: a review of existing emissions trading systems. Climate Policy, 18(8). https://doi.org/10.1080/14693062.2018.1467827 Qi, S., Cheng, S., Tan, X., Feng, S., & Zhou, Q. (2022). Predicting China's carbon price based on a multi-scale integrated model. Applied Energy, 324, 119784. https://doi.org/10.1016/j.apenergy.2022.119784 Roy, P., Mohanty, A. K., & Misra, M. (2023). Prospects of carbon capture, utilization and storage for mitigating climate change. Environmental Science: Advances, 2(3). https://doi.org/10.1039/D2VA00236A Shen, H., Yang, Q., Luo, L., & Huang, N. (2023). Market reactions to a cross-border carbon policy: Evidence from listed Chinese companies. The British Accounting Review, 55(1). https://doi.org/10.1016/j.bar.2022.101116 Sitinjak, C., Ebennezer, S., & Ober, J. (2023). Exploring Public Attitudes and Acceptance of CCUS Technologies in JABODETABEK: A Cross-Sectional Study. Energies, 16(10). https://doi.org/10.3390/en16104026 Sun, L.-L., Cui, H.-J., & Ge, Q.-S. (2022). Will China achieve its 2060 carbon neutral commitment from the provincial perspective? Advances in Climate Change Research, 13(2). https://doi.org/10.1016/j.accre.2022.02.002 Tiwari, A. K., Aye, G. C., Gupta, R., & Gkillas, K. (2020). Gold-oil dependence dynamics and the role of geopolitical risks: Evidence from a Markov-switching time-varying copula model. Energy Economics, 88, 104748. https://doi.org/10.1016/j.eneco.2020.104748 Thube, S., Peterson, S., Nachtigall, D., & Ellis, J. (2021). The economic and environment benefits from international co-ordination on carbon pricing: a review of economic modelling studies *. Environmental Research Letters, 16(11). https://doi.org/10.1088/1748-9326/ac2b61 van den Bergh, J., & Savin, I. (2021). Impact of Carbon Pricing on Low-Carbon Innovation and Deep Decarbonisation: Controversies and Path Forward. Environmental and Resource Economics, 80(4). https://doi.org/10.1007/s10640-021-00594-6 Venmans, F., Ellis, J., & Nachtigall, D. (2020). Carbon pricing and competitiveness: are they at odds? Climate Policy, 20(9). https://doi.org/10.1080/14693062.2020.1805291 Viteva, S., Veld-Merkoulova, Y. V., & Campbell, K. (2014). The forecasting accuracy of implied volatility from ECX carbon options. Energy Economics, 45. https://doi.org/10.1016/j.eneco.2014.08.005 Wang, Y., & Guo, Z. (2018). The dynamic spillover between carbon and energy markets: new evidence. Energy, 149, 24-33. https://doi.org/10.1016/j.energy.2018.01.145 Wei, X., Mohsin, M., & Zhang, Q. (2022). Role of foreign direct investment and economic growth in renewable energy development. Renewable Energy, 192, 828–837. https://doi.org/10.1016/j.renene.2022.04.062 Wen, F., Zhao, H., Zhao, L., & Yin, H. (2022). What drive carbon price dynamics in China? International Review of Financial Analysis, 79. https://doi.org/10.1016/j.irfa.2021.101999 Weng, Q., & Xu, H. (2018). A review of China’s carbon trading market. Renewable and Sustainable Energy Reviews, 91. https://doi.org/10.1016/j.rser.2018.04.026 Wu, Q., & Wang, Y. (2022). How does carbon emission price stimulate enterprises’ total factor productivity? Insights from China’s emission trading scheme pilots. Energy Economics, 109. https://doi.org/10.1016/j.eneco.2022.105990 Xie, J., Li, X., & Gao, X. (2022). Pipeline Network Options of CCUS in Coal Chemical Industry. Atmosphere, 13(11). https://doi.org/10.3390/atmos13111864 Xu, S., & Dai, S. (2021). CCUS As a second-best choice for China’s carbon neutrality: an institutional analysis. Climate Policy, 21(7). https://doi.org/10.1080/14693062.2021.1947766 Xu, Y. (2021). Risk spillover from energy market uncertainties to the Chinese carbon market. Pacific-Basin Finance Journal, 67. https://doi.org/10.1016/j.pacfin.2021.101561 Yan, J., & Yang, J. (2021). Carbon pricing and income inequality:A case study of Guangdong Province, China. Journal of Cleaner Production, 296. https://doi.org/10.1016/j.jclepro.2021.126491 Yao, J., Han, H., Yang, Y., Song, Y., & Li, G. (2023). A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China. Applied Sciences, 13(2). https://doi.org/10.3390/app13021169 Ye, J., Yan, L., Liu, X., & Wei, F. (2022). Economic feasibility and policy incentive analysis of Carbon Capture, Utilization, and Storage (CCUS) in coal-fired power plants based on system dynamics. Environmental Science and Pollution Research, 30(13). https://doi.org/10.1007/s11356-022-24888-4 Yin, C., & Xu, H. (2022). Assessing the niche development of carbon capture and storage through strategic niche management approach: The case of China. International Journal of Greenhouse Gas Control, 119. https://doi.org/10.1016/j.ijggc.2022.103721 Zeng, Y., Weishaar, S. E., & Couwenberg, O. (2016). Absolute vs. Intensity-based Caps for Carbon Emissions Target Setting. European Journal of Risk Regulation, 7(4). https://doi.org/10.1017/S1867299X00010187 Zhao, Y., Wang, C., & Cai, W. (2022). Carbon pricing policy, revenue recycling schemes, and income inequality: A multi-regional dynamic CGE assessment for China. Resources, Conservation and Recycling, 181. https://doi.org/10.1016/j.resconrec.2022.106246 Zheng, Y., Zhou, M., & Wen, F. (2021). Asymmetric effects of oil shocks on carbon allowance price: Evidence from China. Energy Economics, 97. https://doi.org/10.1016/j.eneco.2021.105183 Zhou, C., Wu, C., & Wang, Y. (2019). Dynamic portfolio allocation with time-varying jump risk. Journal of Empirical Finance, 50. https://doi.org/10.1016/j.jempfin.2019.01.003 Zhu, B., Huang, L., Yuan, L., Ye, S., & Wang, P. (2020). Exploring the risk spillover effects between carbon market and electricity market: A bidimensional empirical mode decomposition based conditional value at risk approach. International Review of Economics & Finance, 67. https://doi.org/10.1016/j.iref.2020.01.003 Fig.1: Chinese emission price index 0 20 40 60 80 100 120 140 Chinese emission prices Fig.2: Jump intensity for the Chinese emission price index 0 0,05 0,1 0,15 0,2 0,25 0,3 0,35 0,4 0,45 0,5 Jump-intensity for the Chinese emission market Table 1: Descriptive statistics CEP returns OVX EPU GPR Mean 0.0033 36.6066 135.2457 145.4613 Std. Dev. 0.3389 13.61 267.55 1152.40 Skewness 0.3915 2.23 6.66 35.74 Kurtosis 23.03 20.65 71.72 1346.66 Jarque-Bera 30942.00*** 24532.16*** 362653.30*** 134000.01*** Note: This Table presents the summary statistics for all the variables. *** indicates statistical significance at 1% level. Table 2: Unit root tests Augmented Dickey-Fuller test Phillips–Perron test Level 1st Difference Level 1st Difference CEP -2.55 -27.59*** -4.51*** -83.93*** OVX -4.40*** -23.34*** -6.15*** -59.21*** EPU -8.45*** -21.82*** -40.30*** -394.70*** GPR -42.16*** -19.34*** -42.15*** 1781.14*** Note: This Table presents the unit root test results for all the variables. We consider logarithmic differences only for the CEP index. *** indicates statistical significance at 1% level. Table 3: Estimates of different GARCH models Models → GARCH (1,1) CJI ARJI Estimate S.E. Estimate S.E. Estimate S.E. 𝜋 0.0008 0.0020 -0.0015 0.0016 -0.0023 0.0016 𝜇 -0.3252*** 0.0255 -0.2606*** 0.0231 -0.2407*** 0.0243 𝜔 0.0001*** 0.00002 0.0003*** 0.0001 0.0007*** 0.0001 𝛼 0.1229*** 0.0108 0.3276*** 0.0347 0.4021*** 0.0415 𝛽 0.8952*** 0.0072 0.6365*** 0.0297 0.4831*** 0.0430 𝜃 0.0796 0.0886 0.0089 0.0538 𝜗2 0.7190*** 0.0975 0.6518*** 0.0725 𝜆0 0.0392*** 0.0081 0.0002 0.0002 𝜌 0.9973*** 0.0025 𝛾 0.0456*** 0.0147 Log-likelihood 853.07 1034.36 1070.65 Note: This Table presents the findings of different GARCH models for the Chinese emission index. *** indicates statistical significance at 1% level. Table 4: Determinants of jump-induced volatility Estimate Standard Error t-statistic p-value R2 Panel A: OVX 39.54 Constant 0.0302*** 0.0049 6.08 0.00 𝜑1 0.0008*** 0.0001 8.21 0.00 Panel B: EPU 41.49 Constant 0.0495*** 0.0019 25.99 0.00 𝜑1 0.0001*** 0.00001 10.01 0.00 Panel C: GPR 38.61 Constant 0.1093*** 0.0178 6.12 0.00 𝜑1 -0.0111*** 0.0038 -2.89 0.00 Notes: 𝜑1 measures the impact of OVX/EPU/GPR on the jump-induced volatility for the emission market. *** indicates statistical significance at 1% level. Table 5: Testing for jumps for the outlier-free carbon market data Models → CJI ARJI Estimate S.E. Estimate S.E. 𝜋 -0.0020 0.0016 -0.0026*** 0.0004 𝜇 -0.2819*** 0.0234 -0.2573*** 0.0271 𝜔 0.0003*** 0.0001 0.0008*** 0.0002 𝛼 0.3224*** 0.0346 0.4002*** 0.0418 𝛽 0.6468*** 0.0287 0.4758*** 0.0392 𝜃 0.1034 0.1012 0.0134 0.0482 𝜗2 0.7137*** 0.0872 0.6562*** 0.0414 𝜆0 0.0395*** 0.0083 0.0004*** 0.0001 𝜌 1.0002*** 0.0004 𝛾 0.0062*** 0.0002 Log-likelihood 955.76 1000.32 Note: This Table presents the findings of different jump models for the Chinese emission index after correcting for outliers. *** indicates statistical significance at 1% level.