IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i11p5946-d561650.html
   My bibliography  Save this article

Did Noise Pollution Really Improve during COVID-19? Evidence from Taiwan

Author

Listed:
  • Rezzy Eko Caraka

    (Faculty of Economics and Business, Campus UI Depok, Universitas Indonesia, Depok, West Java 16426, Indonesia
    Department of Information Management, College of Informatics, Chaoyang University of Technology, Taichung 41349, Taiwan
    Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta 11480, Indonesia)

  • Yusra Yusra

    (Sekolah Tinggi Ilmu Ekonomi (STIE), Sabang, Banda Aceh 24415, Indonesia)

  • Toni Toharudin

    (Department of Statistics, Padjadjaran University, West Java, Bandung 45363, Indonesia)

  • Rung-Ching Chen

    (Department of Information Management, College of Informatics, Chaoyang University of Technology, Taichung 41349, Taiwan)

  • Mohammad Basyuni

    (Department of Forestry, Faculty of Forestry, Universitas Sumatera Utara, Medan 20155, Indonesia)

  • Vilzati Juned

    (Sekolah Tinggi Ilmu Ekonomi (STIE), Sabang, Banda Aceh 24415, Indonesia)

  • Prana Ugiana Gio

    (Department of Mathematics, Universitas Sumatera Utara, Medan 20155, Indonesia)

  • Bens Pardamean

    (Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta 11480, Indonesia
    Computer Science Department, Bina Nusantara University, Jakarta 11480, Indonesia)

Abstract

Background and objectives: The impacts of COVID-19 are like two sides of one coin. During 2020, there were many research papers that proved our environmental and climate conditions were improving due to lockdown or large-scale restriction regulations. In contrast, the economic conditions deteriorated due to disruption in industry business activities and most people stayed at home and worked from home, which probably reduced the noise pollution. Methods: To assess whether there were differences in noise pollution before and during COVID-19. In this paper, we use various statistical methods following odds ratios, Wilcoxon and Fisher’s tests and Bayesian Markov chain Monte Carlo (MCMC) with various comparisons of prior selection. The outcome of interest for a parameter in Bayesian inference is complete posterior distribution. Roughly, the mean of the posterior will be clear with point approximation. That being said, the median is an available choice. Findings: To make the Bayesian MCMC work, we ran the sampling from the conditional posterior distributions. It is straightforward to draw random samples from these distributions if they have regular shapes using MCMC. The case of over-standard noise per time frame, number of noise petition cases, number of industry petition cases, number of motorcycles, number of cars and density of vehicles are significant at α = 5%. In line with this, we prove that there were differences of noise pollution before and during COVID-19 in Taiwan. Meanwhile, the decreased noise pollution in Taiwan can improve quality of life.

Suggested Citation

  • Rezzy Eko Caraka & Yusra Yusra & Toni Toharudin & Rung-Ching Chen & Mohammad Basyuni & Vilzati Juned & Prana Ugiana Gio & Bens Pardamean, 2021. "Did Noise Pollution Really Improve during COVID-19? Evidence from Taiwan," Sustainability, MDPI, vol. 13(11), pages 1-12, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:5946-:d:561650
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/11/5946/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/11/5946/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shuai Pan & Jia Jung & Zitian Li & Xuewei Hou & Anirban Roy & Yunsoo Choi & H. Oliver Gao, 2020. "Air Quality Implications of COVID-19 in California," Sustainability, MDPI, vol. 12(17), pages 1-14, August.
    2. Warwick McKibbin & Roshen Fernando, 2021. "The Global Macroeconomic Impacts of COVID-19: Seven Scenarios," Asian Economic Papers, MIT Press, vol. 20(2), pages 1-30, Summer.
    3. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
    4. Youngjo Lee & John A. Nelder, 2006. "Double hierarchical generalized linear models (with discussion)," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 139-185, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Toni Toharudin & Resa Septiani Pontoh & Rezzy Eko Caraka & Solichatus Zahroh & Panji Kendogo & Novika Sijabat & Mentari Dara Puspita Sari & Prana Ugiana Gio & Mohammad Basyuni & Bens Pardamean, 2021. "National Vaccination and Local Intervention Impacts on COVID-19 Cases," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    2. Mohammed Hashim Ameen & Huda Jamal Jumaah & Bahareh Kalantar & Naonori Ueda & Alfian Abdul Halin & Abdullah Saeb Tais & Sarah Jamal Jumaah, 2021. "Evaluation of PM2.5 Particulate Matter and Noise Pollution in Tikrit University Based on GIS and Statistical Modeling," Sustainability, MDPI, vol. 13(17), pages 1-14, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. A. G. Aganbegyan & A. N. Klepach & B. N. Porfiryev & M. N. Uzyakov & A. A. Shirov, 2020. "Post-Pandemic Recovery: The Russian Economy and the Transition to Sustainable Social and Economic Development," Studies on Russian Economic Development, Springer, vol. 31(6), pages 599-605, November.
    2. Piotr Sorokowski & Agata Groyecka & Marta Kowal & Agnieszka Sorokowska & Michał Białek & Izabela Lebuda & Małgorzata Dobrowolska & Przemysław Zdybek & Maciej Karwowski, 2020. "Can Information about Pandemics Increase Negative Attitudes toward Foreign Groups? A Case of COVID-19 Outbreak," Sustainability, MDPI, vol. 12(12), pages 1-10, June.
    3. Rubén Muñoz Pavón & Antonio A. Arcos Alvarez & Marcos G. Alberti, 2020. "Possibilities of BIM-FM for the Management of COVID in Public Buildings," Sustainability, MDPI, vol. 12(23), pages 1-21, November.
    4. Akhtaruzzaman, Md & Boubaker, Sabri & Sensoy, Ahmet, 2021. "Financial contagion during COVID–19 crisis," Finance Research Letters, Elsevier, vol. 38(C).
    5. Claudia Salceanu & Mariana Floricica Calin, 2022. "The Pandemic Context and Quality of Life for Youth in Constanta County," Technium Social Sciences Journal, Technium Science, vol. 27(1), pages 687-696, January.
    6. Yanguas Parra, Paola & Hauenstein, Christian & Oei, Pao-Yu, 2021. "The death valley of coal – Modelling COVID-19 recovery scenarios for steam coal markets," Applied Energy, Elsevier, vol. 288(C).
    7. repec:osf:socarx:n49hv_v1 is not listed on IDEAS
    8. Di Bartolomeo, Giovanni & D'Imperio, Paolo & Felici, Francesco, 2022. "The fiscal response to the Italian COVID-19 crisis: A counterfactual analysis," Journal of Macroeconomics, Elsevier, vol. 73(C).
    9. Brum, Matias & De Rosa, Mauricio, 2021. "Too little but not too late: nowcasting poverty and cash transfers’ incidence during COVID-19’s crisis," World Development, Elsevier, vol. 140(C).
    10. Yanyuan Ma & Marc G. Genton, 2010. "Explicit estimating equations for semiparametric generalized linear latent variable models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 475-495, September.
    11. Anna Sokolova, 2023. "Marginal Propensity to Consume and Unemployment: a Meta-analysis," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 813-846, December.
    12. Oleksiuk Adam & Pleśniak Agnieszka, 2022. "Environment Characteristics and Internationalization of SMEs: Insights from a Polish and Finnish Sample," Journal of Management and Business Administration. Central Europe, Sciendo, vol. 30(3), pages 175-194, September.
    13. Matías Brum & Mauricio de Rosa, 2020. "Too little but not too late. Nowcasting poverty and cash transfers' incidence in Uruguay during COVID-19's crisis," Documentos de Trabajo (working papers) 20-09, Instituto de Economía - IECON.
    14. Hasan, Iftekhar & Horvath, Roman & Mares, Jan, 2020. "Finance and wealth inequality," Journal of International Money and Finance, Elsevier, vol. 108(C).
    15. Galharret, Jean-Michel & Philippe, Anne, 2023. "Bayesian analysis for mediation and moderation using g−priors," Econometrics and Statistics, Elsevier, vol. 27(C), pages 161-172.
    16. Philipp Piribauer & Jesús Crespo Cuaresma, 2016. "Bayesian Variable Selection in Spatial Autoregressive Models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 11(4), pages 457-479, October.
    17. James Chapman & Ajit Desai, . "Using payments data to nowcast macroeconomic variables during the onset of Covid-19," Journal of Financial Market Infrastructures, Journal of Financial Market Infrastructures.
    18. Ley, Eduardo & Steel, Mark F. J., 2007. "On the effect of prior assumptions in Bayesian model averaging with applications to growth regression," Policy Research Working Paper Series 4238, The World Bank.
    19. Emmanuel Apergis & Nicholas Apergis, 2021. "The impact of COVID-19 on economic growth: evidence from a Bayesian Panel Vector Autoregressive (BPVAR) model," Applied Economics, Taylor & Francis Journals, vol. 53(58), pages 6739-6751, December.
    20. Forte, Anabel & Peiró-Palomino, Jesús & Tortosa-Ausina, Emili, 2015. "Does social capital matter for European regional growth?," European Economic Review, Elsevier, vol. 77(C), pages 47-64.
    21. Sadananda Prusty & Anubha & Saurabh Gupta, 2021. "On the Road to Recovery: The Role of Post-Lockdown Stimulus Package," FIIB Business Review, , vol. 11(2), pages 206-224, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:5946-:d:561650. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.