Environmental policy uncertainty and green innovation: A TVP-VAR-SV model approach

Xite Yang; Jidi Cao; Zihan Liu; Yongzeng Lai; Xite Yang; Jidi Cao; Zihan Liu; Yongzeng Lai

doi:10.3934/QFE.2022026

Quantitative Finance and Economics

2022, Volume 6, Issue 4: 604-621. doi: 10.3934/QFE.2022026

Previous Article Next Article

Research article

Environmental policy uncertainty and green innovation: A TVP-VAR-SV model approach

1.
Business School, Sichuan University, Chengdu, China
2.
Department of Mathematics, Wilfrid Laurier University, Waterloo, Ontario, Canada, N2L 3C5

Received: 18 September 2022 Revised: 27 November 2022 Accepted: 01 December 2022 Published: 12 December 2022
JEL Codes: C32, D81, Q55, Q58

This paper aims to measure the impacts of environmental policy uncertainty on green innovation and explore the transmission channel that is less understood in past scientific works. In this paper, we use a newspaper-based sentiment mining approach to establish an index of environmental policy uncertainty in China and implement web crawlers and text analysis techniques to construct a network public opinion index of the Chinese financial market. Then, we explore the relationships between environmental policy uncertainty, network public opinion, and green innovation through the time-varying parameter structural vector autoregressive with stochastic volatility (TVP-VAR-SV) model. The transmission channels of environmental policy uncertainty to green innovation are depicted by selecting different timing of policy release. Our empirical study results show that the fluctuations of environmental policy uncertainty, network public opinion, and green innovation have time-varying characteristics. Furthermore, the findings reveal interactions among the three variables: 1) The environmental policy uncertainty can influence green innovation through network public opinion. 2) The environmental policy uncertainty has both inhibited and promoted effects on network public opinion and green innovation. 3) There are differences in the direction and the degree of impulse responses among the above three variables in the context of uncertainty shocks. Besides, managerial relevance and policy implications are also provided for decision-makers facing sustainable development challenges.
- environmental policy uncertainty,
- green innovation,
- TVP-VAR-SV model,
- sentiment mining,
- network public opinion
Citation: Xite Yang, Jidi Cao, Zihan Liu, Yongzeng Lai. Environmental policy uncertainty and green innovation: A TVP-VAR-SV model approach[J]. Quantitative Finance and Economics, 2022, 6(4): 604-621. doi: 10.3934/QFE.2022026

Related Papers:

Abstract

This paper aims to measure the impacts of environmental policy uncertainty on green innovation and explore the transmission channel that is less understood in past scientific works. In this paper, we use a newspaper-based sentiment mining approach to establish an index of environmental policy uncertainty in China and implement web crawlers and text analysis techniques to construct a network public opinion index of the Chinese financial market. Then, we explore the relationships between environmental policy uncertainty, network public opinion, and green innovation through the time-varying parameter structural vector autoregressive with stochastic volatility (TVP-VAR-SV) model. The transmission channels of environmental policy uncertainty to green innovation are depicted by selecting different timing of policy release. Our empirical study results show that the fluctuations of environmental policy uncertainty, network public opinion, and green innovation have time-varying characteristics. Furthermore, the findings reveal interactions among the three variables: 1) The environmental policy uncertainty can influence green innovation through network public opinion. 2) The environmental policy uncertainty has both inhibited and promoted effects on network public opinion and green innovation. 3) There are differences in the direction and the degree of impulse responses among the above three variables in the context of uncertainty shocks. Besides, managerial relevance and policy implications are also provided for decision-makers facing sustainable development challenges.

References

[1]	Albino V, Balice A, Dangelico, RM (2009) Environmental strategies and green product development: an overview on sustainability-driven companies. Bus Strategy Environ 18: 83–96. https://doi.org/10.1002/bse.638 doi: 10.1002/bse.638
[2]	Aramonte S, Carl M (2016) Firm-level R&D after periods of intense technological innovation: the role of investor sentiment. http://dx.doi.org/10.2139/ssrn.2324958
[3]	Ambec S, Lanoie P (2008) Does it pay to be green? A systematic overview. Acad Manag Perspect, 45–62. https://doi.org/10.5465/amp.2008.35590353 doi: 10.5465/amp.2008.35590353
[4]	Antweiler W, Frank MZ (2004) Is all that talk just noise? The information content of internet stock message boards. J Financ 59: 1259–1294. https://doi.org/10.1111/j.1540-6261.2004.00662.x doi: 10.1111/j.1540-6261.2004.00662.x
[5]	Bahrini R, Filfilan A (2020) Impact of the novel coronavirus on stock market returns: evidence from GCC countries. Quant Financ Econ 4: 640-652. https://doi.org/10.3934/QFE.2020029 doi: 10.3934/QFE.2020029
[6]	Baker SR, Bloom N, Davis SJ (2016) Measuring economic policy uncertainty. Q J Econ 131: 1593–1636. http://dx.doi.org/10.2139/ssrn.2198490 doi: 10.2139/ssrn.2198490
[7]	Barrett S(1991) Environmental regulation for competitive advantage. Bus Strateg Rev 2: 1–15.
[8]	Brogaard J, Detzel A (2015) The asset-pricing implications of government economic policy uncertainty. Manage Sci 61: 3–18. https://doi.org/10.1287/mnsc.2014.2044 doi: 10.1287/mnsc.2014.2044
[9]	Brown JR, Martinsson G, Petersen BC (2013) Law, stock markets, and innovation. J Financ 68: 1517–1549. https://doi.org/10.1111/jofi.12040 doi: 10.1111/jofi.12040
[10]	Calel R (2020) Adopt or innovate: Understanding technological responses to cap-and-trade. Am Econ J-Econ Polic 12: 170–201. https://doi.org/10.1257/pol.20180135 doi: 10.1257/pol.20180135
[11]	Chen MY, Chen TH (2019) Modeling public mood and emotion: Blog and news sentiment and socio-economic phenomena. Future Gener Comput Syst 96: 692–699. https://doi.org/10.1016/j.future.2017.10.028 doi: 10.1016/j.future.2017.10.028
[12]	Da Z, Engelberg J, Gao P (2015) The sum of all FEARS investor sentiment and asset prices. Rev Financ Stud 28: 1–32. https://doi.org/10.1093/rfs/hhu072 doi: 10.1093/rfs/hhu072
[13]	Dang TV, Xu Z (2018) Market sentiment and innovation activities. J Financ Quant Anal 53: 1135–1161. https://doi.org/10.1017/S0022109018000078 doi: 10.1017/S0022109018000078
[14]	Dicks D, Fulghieri P (2021) Uncertainty, investor sentiment, and innovation. Rev Financ Stud 34: 1236–1279. http://dx.doi.org/10.2139/ssrn.2676854 doi: 10.2139/ssrn.2676854
[15]	Feng Y, Chen S, Wang X, et al. (2021) Time-varying impact of US financial conditions on China's inflation: a perspective of different types of events. Quant Financ Econ 5: 604–622. https://doi.org/10.3934/QFE.2021027 doi: 10.3934/QFE.2021027
[16]	Ferguson A, Lam P (2016) Government policy uncertainty and stock prices: The case of Australia's uranium industry. Energy Econ 60: 97–111. https://doi.org/10.1016/j.eneco.2016.08.026 doi: 10.1016/j.eneco.2016.08.026
[17]	Huang Y, Luk P (2020) Measuring economic policy uncertainty in China. China Econ Rev 59: 101367. https://doi.org/10.1016/j.chieco.2019.101367 doi: 10.1016/j.chieco.2019.101367
[18]	Kalamova M, Johnstone N, Haščič I (2012) Implications of policy uncertainty for innovation in environmental technologies: the case of public R&D budgets. In: Costantini V, Mazzanti M (eds) The Dynamics of Environmental and Economic Systems, Springer, Dordrecht, 99–116. https://doi.org/10.1007/978-94-007-5089-0_6
[19]	Kim SH, Kim D(2014) Investor sentiment from internet message postings and the predictability of stock returns. J Econ Behav Organ 107: 708–729. https://doi.org/10.1016/j.jebo.2014.04.015 doi: 10.1016/j.jebo.2014.04.015
[20]	Li K, Guo Z, Chen Q (2021) The effect of economic policy uncertainty on enterprise total factor productivity based on financial mismatch: Evidence from China. Pac-Basin Financ J 68: 101613. https://doi.org/10.1016/j.pacfin.2021.101613 doi: 10.1016/j.pacfin.2021.101613
[21]	Li W, Wang J, Chen R (2019) Innovation-driven industrial green development: The moderating role of regional factors. J Clean Prod 222: 344–354. https://doi.org/10.1016/j.jclepro.2019.03.027 doi: 10.1016/j.jclepro.2019.03.027
[22]	Li X, Hu Z, Zhang Q (2021) Environmental regulation, economic policy uncertainty, and green technology innovation. Clean Technol Environ Policy 23: 2975–2988. https://doi.org/10.1007/s10098-021-02219-4 doi: 10.1007/s10098-021-02219-4
[23]	Luo B, Zeng J, Duan J (2016) Emotion space model for classifying opinions in stock message board. Expert Syst Appl 44: 138–146. https://doi.org/10.1016/j.eswa.2015.08.023 doi: 10.1016/j.eswa.2015.08.023
[24]	Marcus AA (1981) Policy uncertainty and technological innovation. Acad Manage Rev 6: 443–448. https://doi.org/10.1016/j.pacfin.2021.101542 doi: 10.1016/j.pacfin.2021.101542
[25]	Nakajima J (2011) Time-varying parameter VAR model with stochastic volatility: An overview of the methodology and empirical applications. Monetary Econ Studies.
[26]	Porter M (1996) America's green strategy. Bus Environ: a Reader 33: 1072.
[27]	Porter ME, Van der Linde C (1995) Toward a new conception of the environment-competitiveness relationship. J Econ Perspect 9: 97–118. https://doi.org/10.1257/jep.9.4.97 doi: 10.1257/jep.9.4.97
[28]	Primiceri GE (2005) Time varying structural vector autoregressions and monetary policy. Rev Econ Stud 72: 821–852. https://doi.org/10.1111/j.1467-937X.2005.00353.x doi: 10.1111/j.1467-937X.2005.00353.x
[29]	Simmons BA, Marcos-Martinez R, Law EA (2018) Frequent policy uncertainty can negate the benefits of forest conservation policy. Environ Sci Policy 89: 401–411. https://doi.org/10.1016/j.envsci.2018.09.011 doi: 10.1016/j.envsci.2018.09.011
[30]	Sirmon DG, Hitt MA, Ireland RD (2007) Managing firm resources in dynamic environments to create value: Looking inside the black box. Acad Manage Rev 32: 273–292. https://doi.org/10.5465/amr.2007.23466005 doi: 10.5465/amr.2007.23466005
[31]	Song M, Wang S, Sun J (2018) Environmental regulations, staff quality, green technology, R&D efficiency, and profit in manufacturing. Technol Forecast Soc Change 133: 1–14. https://doi.org/10.1016/j.techfore.2018.04.020 doi: 10.1016/j.techfore.2018.04.020
[32]	Stein LC, Stone E (2013) The effect of uncertainty on investment, hiring, and R&D: Causal evidence from equity options.
[33]	Teeter P, Sandberg J (2017) Constraining or enabling green capability development? How policy uncertainty affects organizational responses to flexible environmental regulations. Brit J Manage 28: 649–665. https://doi.org/10.1111/1467-8551.12188 doi: 10.1111/1467-8551.12188
[34]	Vinodhini G, Chandrasekaran RM (2016) Sentiment analysis and opinion mining: a survey. Int J Comput Appl 2: 282–292.
[35]	Wang CY, Wu J (2015) Media Tone, Investor sentiment, and IPO pricing. J Financ Res 423: 174–189.
[36]	Wang Hui, Sun Hui, Xiao Hanyue (2020) Relationship between environmental policy uncertainty, two-way FDI, and low-carbon TFP. China Popul Resour Environ 30: 75–86.
[37]	You J, Zhang B, Zhang L (2018) Who captures the power of the pen? Rev Financ Stud 31: 43–96.
[38]	Zhu Y, Sun Z, Zhang S (2021) Economic Policy Uncertainty, Environmental Regulation, and Green Innovation—An Empirical Study Based on Chinese High-Tech Enterprises. Int J Environ Res Public Health 18: 9503. https://doi.org/10.3390/ijerph18189503 doi: 10.3390/ijerph18189503

Reader Comments

Your name:*

Email:*
© 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)