This paper models 54,559 Chinese news items about education industry and scientific industry by machine learning during the COVID-19 epidemic to build China’s increased scientific research policy (ISRP) index. The result of interrupted time series analysis indicates that, the ISRP has an emphatic positive causality on the education industry advancement and promotes the development of the education industry. The ISRP also has a remarkable positive causality on the development of the scientific industry. Moreover, the result of causal network indicates that, a virtuous circle within the ISRP, the education industry and the scientific industry has been formed, which has promoted the sustainable development of the education chain.

Aguilera-Hermida, A. P. (2020). College students’ use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open, 1, 100011. https://doi.org/10.1016/j.ijedro.2020.100011

Aktas, C. B., Whelan, R., Stoffer, H., et al. (2015). Developing a university-wide course on sustainability: a critical evaluation of planning and implementation. Journal of Cleaner Production, 106, 216–221. https://doi.org/10.1016/j.jclepro.2014.11.037

Alvarezgarcia, O., Suredanegre, J., & Comasforgas, R. (2018). Assessing environmental competencies of primary education pre-service teachers in Spain. International Journal of Sustainability in Higher Education, 19(1), 15–31. https://doi.org/10.1108/ijshe-12-2016-0227

Anderson, R. M., Heesterbeek, H., Klinkenberg, D., & Hollingsworth, T. D. (2020). How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet, 395(10228), 931-934.

Blei, D. M., Ng, A. Y., Jordan, M. I. (2003). Latent Dirichlet Allocation, Journal of Machine Learning Research, 3, 993–1022.

Boehm, C. E., Flaaen, A., & Pandalai-Nayar, N. (2019). Input linkages and the transmission of shocks: firm-level evidence from the 2011 Tōhoku earthquake, Review of Economics and Statistics, 101(1), 60-75.

Cao, W., Fang, Z., Hou, G., et al. (2020). The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Research, 287, 112934. https://doi.org/10.1016/j.psychres.2020.112934

Ceulemans, K., & De Prins, M. (2010). Teacher’s manual and method for SD integration in curricula. Journal of Cleaner Production, 18(7), 645–651. https://doi.org/10.1016/j.jclepro.2009.09.014

Cheng, W. L., Chen, Y. S., Zhang, J., et al. (2007). Comparison of the Revised Air Quality Index with the PSI and AQI indices. Science of The Total Environment, 382(2–3), 191–198. https://doi.org/10.1016/j.scitotenv.2007.04.036

Didham, R. J., & Ofei-Manu, P. (2020). Adaptive capacity as an educational goal to advance policy for integrating DRR into quality education for sustainable development. International Journal of Disaster Risk Reduction, 47, 101631. https://doi.org/10.1016/j.ijdrr.2020.101631

Filho, W. L., Wu, Y. J., Brandli, L. L., et al. (2017). Identifying and overcoming obstacles to the implementation of sustainable development at universities. Journal of Integrative Environmental Sciences, 14(1), 93-108. https://doi.org/10.1080/1943815x.2017.1362007

Filho, W. L., Skanavis, C., Kounani, A., et al. (2019). The Role of Planning in Implementing Sustainable Development in a Higher Education Context. Journal of Cleaner Production, 678-687. https://doi.org/10.1016/j.jclepro.2019.06.322

Galariotis, E., Makrichoriti, P., & Spyrou, S. (2018). The impact of conventional and unconventional monetary policy on expectations and sentiment. Journal of Banking & Finance, 86, 1–20. https://doi.org/10.1016/j.jbankfin.2017.08.014

Goodell, J. W., & Huynh, T. L. D. (2020). Did Congress trade ahead? Considering the reaction of US industries to COVID-19. Finance Research Letters, 36, 101578. https://doi.org/10.1016/j.frl.2020.101578

Guan, D., Wang, D., Hallegatte, S., et al. (2020). Global supply-chain effects of COVID-19 control measures. Nature Human Behaviour, 4(6), 577–587. https://doi.org/10.1038/s41562-020-0896-8

Hellewell, J., Abbott, S., Gimma, A., et al. (2020). Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. The Lancet Global Health, 8(4), 488–496.

Hodges, C. B., Moore, S., Lockee, B. B., et al. (2020). The difference between emergency remote teaching and online learning. Educause Review, 27, 1–12.

Hsiang, S., Allen, D., Annan-Phan, S., et al. (2020). The Effect of Large-Scale Anti-Contagion Policies on the COVID-19 Pandemic. https://doi.org/10.1101/2020.03.22.20040642

Huang, S. F. (2017). A study of impacts of fiscal decentralization on smog pollution. The Journal of World Economy, 40(2), 127–152.

Isenmann, R., Landwehr-Zloch, S., & Zinn, S. (2020). Morphological box for ESD—landmark for universities implementing education for sustainable development (ESD). The International Journal of Management Education, 18(1), 100360. https://doi.org/10.1016/j.ijme.2019.100360

Khan, M., Parvaiz, G. S., Bashir, N., Imtiaz, S., & Bae, J. 2022. Students’ key determinant structure towards educational technology acceptance at universities, during COVID 19 lockdown: Pakistani perspective. Cogent Education, 9(1), 2039088.

Khan, M., Parvaiz, G. S., Dedahanov, A. T., et al. (2022). The Impact of Technologies of Traceability and Transparency in Supply Chains. Sustainability, 14(24), 16336. https://doi.org/10.3390/su142416336

Larsen, V. H., Thorsrud, L. A., & Zhulanova, J. (2020). News-driven inflation expectations and information rigidities. Journal of Monetary Economics, 117, 507–520. https://doi.org/10.1016/j.jmoneco.2020.03.004

Leng, Y., Du, S. (2016). Energy Price distortion and haze pollution: the evidence from China. Indian Economic Review, (1), 8.

Li, X. Y. (2016). Empirical analysis of the smog factors in Beijing-Tianjin-Hebei Region. Ecological Economy, 32(3), 144–150.

Linden, A. (2015). Conducting Interrupted Time-series Analysis for Single- and Multiple-group Comparisons. The Stata Journal: Promoting Communications on Statistics and Stata, 15(2), 480–500. https://doi.org/10.1177/1536867x1501500208

Liu, T., Cai, Y., Feng, B., et al. (2018). Long-term mortality benefits of air quality improvement during the twelfth five-year-plan period in 31 provincial capital cities of China. Atmospheric Environment, 173, 53–61. https://doi.org/10.1016/j.atmosenv.2017.10.054

Marcellino, M., & Sivec, V. (2016). Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs. Journal of Econometrics, 193(2), 335–348. https://doi.org/10.1016/j.jeconom.2016.04.010

Merritt, E., Hale, A., & Archambault, L. (2018). Changes in Pre-Service Teachers’ Values, Sense of Agency, Motivation and Consumption Practices: A Case Study of an Education for Sustainability Course. Sustainability, 11(1), 155. https://doi.org/10.3390/su11010155

Mirza, N., Naqvi, B., Rahat, B., et al. (2020). Price reaction, volatility timing and funds’ performance during Covid-19. Finance Research Letters, 36, 101657. https://doi.org/10.1016/j.frl.2020.101657

Nousheen, A., Yousuf Zai, S. A., Waseem, M., et al. (2020). Education for sustainable development (ESD): Effects of sustainability education on pre-service teachers’ attitude towards sustainable development (SD). Journal of Cleaner Production, 250, 119537. https://doi.org/10.1016/j.jclepro.2019.119537

Pappas, E., Pierrakos, O., & Nagel, R. (2013). Using Bloom’s Taxonomy to teach sustainability in multiple contexts. Journal of Cleaner Production, 48, 54–64. https://doi.org/10.1016/j.jclepro.2012.09.039

Runge, J., Nowack, P., Kretschmer, M., et al. (2019). Detecting and quantifying causal associations in large nonlinear time series datasets. Science Advances, 5(11). https://doi.org/10.1126/sciadv.aau4996

Rusinko, C. A. (2010). Integrating sustainability in higher education: a generic matrix. International Journal of Sustainability in Higher Education, 11(3), 250–259. https://doi.org/10.1108/14676371011058541

Savelyeva, T., & McKenna, J. R. (2011). Campus sustainability: emerging curricula models in higher education. International Journal of Sustainability in Higher Education, 12(1), 55–66. https://doi.org/10.1108/14676371111098302

Shao, S., Li, X., Cao, J. H., Yang, L. L. (2016). China’s economic policy choices for governing smog pollution based on spatial spillover effects. Econ. Res. J. 51(9), 73–88.

Shao, S., Li, X., Cao, J. H. (2019). Urbanization promotion and haze pollution governance in China. Econ. Res. J. 54(2), 148–165.

Shim, E., Tariq, A., Choi, W., et al. (2020). Transmission potential and severity of COVID-19 in South Korea. International Journal of Infectious Diseases, 93, 339–344. https://doi.org/10.1016/j.ijid.2020.03.031

Su, C. W., Zhang, H. G., Chang, H. L., et al. (2016). Is exchange rate stability beneficial for stabilizing consumer prices in China? The Journal of International Trade & Economic Development, 25(6), 857–879. https://doi.org/10.1080/09638199.2016.1142605

Tejedor, S., Cervi, L., Pérez-Escoda, A., et al. (2020). Digital Literacy and Higher Education during COVID-19 Lockdown: Spain, Italy, and Ecuador. Publications, 8(4), 48. https://doi.org/10.3390/publications8040048

Wagner, A. K., Soumerai, S. B., Zhang, F., et al. (2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27(4), 299–309. https://doi.org/10.1046/j.1365-2710.2002.00430.x

Wells, C. R., Sah, P., Moghadas, S. M., et al. (2020). Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak. Proceedings of the National Academy of Sciences, 117(13), 7504–7509. https://doi.org/10.1073/pnas.2002616117

Wilde, N., & Hsu, A. (2019). The influence of general self-efficacy on the interpretation of vicarious experience information within online learning. International Journal of Educational Technology in Higher Education, 16(1). https://doi.org/10.1186/s41239-019-0158-x

Yakubu, M. N., & Dasuki, S. I. (2018). Factors affecting the adoption of e-learning technologies among higher education students in Nigeria. Information Development, 35(3), 492–502. https://doi.org/10.1177/0266666918765907

Zhang, Y., & Chen, J. (2020). An empirical study of the efficiency of haze pollution governance in Chinese cities based on streaming data. Science of The Total Environment, 739, 139571. https://doi.org/10.1016/j.scitotenv.2020.139571

Zhang, H. G., Cao, T., Li, H., et al. (2021). Dynamic measurement of news-driven information friction in China’s carbon market: Theory and evidence. Energy Economics, 95, 104994. https://doi.org/10.1016/j.eneco.2020.104994

Zhang, H. G., & Li, T. (2023). News-driven bubbles in futures markets. Journal of Energy Markets. https://doi.org/10.21314/jem.2023.025