Time varying characteristics of factors affecting carbon price in emission trading scheme in China: Evidence from SV-TVP-VAR approach

Fang Hu; Yubo Li; Xianmei Wang; David Lee Cook

doi:10.24294/jipd.v8i7.3793

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Time varying characteristics of factors affecting carbon price in emission trading scheme in China: Evidence from SV-TVP-VAR approach

Fang Hu

Yubo Li

Xianmei Wang

David Lee Cook

Journal of Infrastructure Policy and Development 2024, 8(7), 3793; https://doi.org/10.24294/jipd.v8i7.3793

Submitted：21 Dec 2023

Accepted：26 Feb 2024

Published：17 Jul 2024

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Abstract

Analysis of the factors influencing the price of carbon emissions trading in China and its time-varying characteristics is essential for the smooth operation of the carbon trading system. We analyse the time-varying effects of public concern, degree of carbon regulation, crude oil price, international carbon price and interest rate level on China’s carbon price through SV-TVP-VAR model. Among them, the quantification of public concern and the degree of carbon emission regulation is based on microblog text and government decisions. The results show that all the factors influencing carbon price are significantly time-varying, with the shocks of each factor on carbon price rising before 2019 and turning significantly thereafter. The short-term shock effect of each factor is more significant compared to the medium- and long-term, and the effect almost disappears at a lag of six months. Thanks to public environmental awareness, low-carbon awareness and the progress of carbon market management mechanisms, public concern has had the most significant impact on carbon price since 2019. With the promulgation of relevant management measures for the carbon market, relevant regulations on carbon emission accounting, financing constraints, and carbon emission quota allocation for emission-controlled enterprises have become increasingly mature, and carbon price signals are more sensitive to market information. The above findings provide substantial empirical evidence for all stakeholders in the market, who need to recognize that the impact of non-structural factors on the price of carbon varies over time. Government intervention also serves as a key aspect of carbon emission control and requires the introduction of relevant constraints and incentives. In particular, emission-controlling firms need to focus on the policy direction of the carbon market, and focus on the impact of Internet public opinion on business production while reducing carbon allowance demand and energy dependence.

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References

An, L., Zhou, W., Ou, M., et al. (2021). Measuring and profiling the topical influence and sentiment contagion of public event stakeholders. International Journal of Information Management, 58, 102327. https://doi.org/10.1016/j.ijinfomgt.2021.102327

Bao, X., Sun, B., Han, M., et al. (2023). Quantifying the impact of CEO social media celebrity status on firm value: Novel measures from digital gatekeeping theory. Technological Forecasting and Social Change, 189, 122334. https://doi.org/10.1016/j.techfo

Batten, J. A., Maddox, G. E., & Young, M. R. (2020). Does weather, or energy prices, affect carbon prices? Energy Economics, 96, 105016. https://doi.org/10.1016/j.eneco.2020.105016

Deeney, P., Cummins, M., Dowling, M., & Smeaton, A. F. (2016). Influences from the European Parliament on EU emissions prices. Energy Policy, 88, 561–572. https://doi.org/10.1016/j.enpol.2015.06.026

Den Elzen, M. G. J., Hof, A. F., Mendoza Beltran, A., et al. (2011). The Copenhagen Accord: abatement costs and carbon prices resulting from the submissions. Environmental Science & Policy, 14(1), 28–39. https://doi.org/10.1016/j.envsci.2010.10.010

Derakhshan, A., & Beigy, H. (2019). Sentiment analysis on stock social media for stock price movement prediction. Engineering Applications of Artificial Intelligence, 85, 569–578. https://doi.org/10.1016/j.engappai.2019.07.002

Guo, J., Long, S., & Luo, W. (2022). Nonlinear effects of climate policy uncertainty and financial speculation on the global prices of oil and gas. International Review of Financial Analysis, 83, 102286. https://doi.org/10.1016/j.irfa.2022.102286

Hartvig, Á. D., Pap, Á., & Pálos, P. (2023). EU Climate Change News Index: Forecasting EU ETS prices with online news. Finance Research Letters, 54, 103720. https://doi.org/10.1016/j.frl.2023.103720

Huang, W., Wang, Q., Li, H., et al. (2022). Review of recent progress of emission trading policy in China. Journal of Cleaner Production, 349, 131480. https://doi.org/10.1016/j.jclepro.2022.131480

Huang, Y., & He, Z. (2020). Carbon price forecasting with optimization prediction method based on unstructured combination. Science of The Total Environment, 138350. https://doi.org/10.1016/j.scitotenv.2020.138350

Investing. (2023). Real-time quotation. Available online: https://cn.investing.com/ (accessed on 21 June 2023)

Kearney, C., & Liu, S. (2014). Textual sentiment in finance: A survey of methods and models. International Review of Financial Analysis, 33, 171–185. https://doi.org/10.1016/j.irfa.2014.02.006

Khan, J., & Johansson, B. (2022). Adoption, implementation and design of carbon pricing policy instruments. Energy Strategy Reviews, 40, 100801. https://doi.org/10.1016/j.esr.2022.100801

Kim, J., Dong, H., Choi, J., & Chang, S. R. (2022), Sentiment change and negative herding: Evidence from microblogging and news, Journal of Business Research, 142, 364-376, https://doi.org/10.1016/j.jbusres.2021.12.055

Li C, Qi Y, Liu S, et al. (2022). Do carbon ETS pilots improve cities’ green total factor productivity? Evidence from a quasi-natural experiment in China. Energy Economics, 108. https://doi.org/10.1016/j.eneco.2022.105931

Li, C., Ma, X., Fu, T., & Guan, S. (2020). Does public concern over haze pollution matter? Evidence from Beijing-Tianjin-Hebei region, China. Science of The Total Environment, 142397. https://doi.org/10.1016/j.scitotenv.2020.142397

Li, H., Huang, X., Zhou, D., & Guo L. (2023). The dynamic linkages among crude oil price, climate change and carbon price in China. Energy Strategy Reviews, 48, 101123. https://doi.org/10.1016/j.esr.2023.101123

Li, M., Weng, Y., & Duan, M. (2019). Emissions, energy and economic impacts of linking China’s national ETS with the EU ETS. Applied Energy, 235, 1235–1244. https://doi.org/10.1016/j.apenergy.2018.11.047

Lin, B., & Jia, Z. (2019). What are the main factors affecting carbon price in Emission Trading Scheme? A case study in China. Science of The Total Environment, 654, 525–534. https://doi.org/10.1016/j.scitotenv.2018.11.106

Lin, B., & Xu, B. (2021). A non-parametric analysis of the driving factors of China’s carbon prices. Energy Economics, 104, 105684. https://doi.org/10.1016/j.eneco.2021.105684

Liu, Y., Zhang, J., & Fang, Y. (2023). The driving factors of China’s carbon prices: Evidence from using ICEEMDAN-HC method and quantile regression. Finance Research Letters, 54, 103756. https://doi.org/10.1016/j.frl.2023.103756

Liu, Y., Zhou, Y., & Wu, W. (2015). Assessing the impact of population, income and technology on energy consumption and industrial pollutant emissions in China. Applied Energy, 155, 904–917. https://doi.org/10.1016/j.apenergy.2015.06.051

Nakajima, J. (2011). Time-varying parameter VAR model with stochastic volatility: an overview of methodology and empirical applications. Monetary and Economic Studies, 29, 107–142.

Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. The Review of Economic Studies, 72(3), 821–852. https://doi.org/10.1111/j.1467-937x.2005.00353.x

Qiao, S., Dang, Y., Ren, Z., & Zhang, K. (2023). The dynamic spillovers among carbon, fossil energy and electricity markets based on a TVP-VAR-SV method. Energy, 266, 126344. https://doi.org/10.1016/j.energy.2022.126344

Seifert, J., Uhrig-Homburg, M., & Wagner, M. (2008). Dynamic behavior of CO2 spot prices. Journal of Environmental Economics and Management, 56(2), 180–194. https://doi.org/10.1016/j.jeem.2008.03.003

Song, Y., Liu, T., Li, Y., et al. (2022). Paths and policy adjustments for improving carbon-market liquidity in China. Energy Economics, 115, 106379. https://doi.org/10.1016/j.eneco.2022.106379

Song, Y., Liu, T., Ye, B., et al. (2019). Improving the liquidity of China’s carbon market: Insight from the effect of carbon price transmission under the policy release. Journal of Cleaner Production, 239, 118049. https://doi.org/10.1016/j.jclepro.2019.1

Sun, Y., Liu, X., Chen, G., et al. (2019). How Mood Affects the Stock Market: Empirical Evidence from Microblogs. Information & Management, 103181. https://doi.org/10.1016/j.im.2019.103181

Tang, Y. E., Fan, R., Cai, A. Z., et al. (2023). Rethinking personal carbon trading (PCT) mechanism: A comprehensive review. Journal of Environmental Management, 344, 118478. https://doi.org/10.1016/j.jenvman.2023.118478

Wang, P., Liu, J., Tao, Z., & Chen, H. (2022). A novel carbon price combination forecasting approach based on multi-source information fusion and hybrid multi-scale decomposition. Engineering Applications of Artificial Intelligence, 114, 105172. https://d

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, J., Zhang, L., Yang, R., & Song, M. (2023). A new perspective to promote sustainable low-carbon consumption: The influence of informational incentive and social influence. Journal of Environmental Management, 327, 116848. https://doi.org/10.1016/j.je

Wen, F., Zhao, H., Zhao, L., & Yin, H. (2022). What drive carbon price dynamics in China? International Review of Financial Analysis, 79, 101999. 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, 613–619. https://doi.org/10.1016/j.rser.2018.04.026

Wilson, K. A., Davis, K. J., Matzek, V., & Kragt, M. (2018). Concern about threatened species and ecosystem disservices underpin public willingness to pay for ecological restoration. Restoration Ecology. https://doi.org/10.1111/rec.12895

Wu, Q. (2022). Price and scale effects of China’s carbon emission trading system pilots on emission reduction. Journal of Environmental Management, 314, 115054. https://doi.org/10.1016/j.jenvman.2022.115054

Wu, Q., Tan, C., Wang, D., et al. (2023). How carbon emission prices accelerate net zero: Evidence from China’s coal-fired power plants. Energy Policy, 177, 113524. https://doi.org/10.1016/j.enpol.2023.113524

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, 105990. https://doi.org/10.1016/j.eneco.2022.105990

Wu, W., Wang, W., & Zhang M. (2022). Does internet public participation slow down environmental pollution? Environmental Science & Policy, 137, 22-31. https://doi.org/10.1016/j.envsci.2022.08.006

Xian, Y., Wang, K., Wei, Y. M., & Huang, Z. (2020). Opportunity and marginal abatement cost savings from China’s pilot carbon emissions permit trading system: Simulating evidence from the industrial sectors. Journal of Environmental Management, 271, 11097

Yang, J., Wan, Y., & Shen, S. (2023). Research on the impact of exchange rates and interest rates on carbon price changes in the context of sustainable development., Frontiers in Ecology and Evolution, 10, 1122582. https://doi.org/10.3389/fevo.2022.112258

Ye, J., & Xue, M. (2021). Influences of sentiment from news articles on EU carbon prices. Energy Economics, 101, 105393. https://doi.org/10.1016/j.eneco.2021.105393

Zhang, F., & Xia, Y. (2022). Carbon price prediction models based on online news information analytics. Finance Research Letters, 2022, 46, 102809. https://doi.org/10.1016/j.frl.2022.102809

Zhang, S., Li, Y., Hao, Y., & Zhang, Y. (2018). Does public opinion affect air quality? Evidence based on the monthly data of 109 prefecture-level cities in China. Energy Policy, 116, 299–311. https://doi.org/10.1016/j.enpol.2018.02.025

Zhang, W., Li, J., Li, G., & Guo, S. (2020). Emission reduction effect and carbon market efficiency of carbon emissions trading policy in China. Energy, 196, 117117. https://doi.org/10.1016/j.energy.2020.117117

Zhang, Y. J., Wang, A. D., & Tan, W. (2015). The impact of China’s carbon allowance allocation rules on the product prices and emission reduction behaviors of ETS-covered enterprises. Energy Policy, 86, 176–185. https://doi.org/10.1016/j.enpol.2015.07.004

Zhang, Y., Li, Y., & Shen, D. (2021). Investor Attention and the Carbon Emission Markets in China: A Nonparametric Wavelet-Based Causality Test. Asia-Pacific Financial Markets, 29(1), 123–137. https://doi.org/10.1007/s10690-021-09348-2

Zhong, M., Zhang, R., & Ren, X. (2023). The time-varying effects of liquidity and market efficiency of the European Union carbon market: Evidence from the TVP-SVAR-SV approach. Energy Economics, 123, 106708. https://doi.org/10.1016/j.eneco.2023.106708

Zhou, K., & Li, Y. (2019). Influencing factors and fluctuation characteristics of China’s carbon emission trading price. Physica A: Statistical Mechanics and Its Applications, 524, 459–474. https://doi.org/10.1016/j.physa.2019.04.249

Zhu, B., Ye, S., Han, D., et al. (2018). A multiscale analysis for carbon price drivers. Energy Economics, 2018, 78, 202–216. https://doi.org/10.1016/j.eneco.2018.11.007

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