IDEAS home Printed from https://ideas.repec.org/a/vrs/offsta/v35y2019i3p625-651n6.html
   My bibliography  Save this article

A Lexical Approach to Estimating Environmental Goods and Services Output in the Construction Sector via Soft Classification of Enterprise Activity Descriptions Using Latent Dirichlet Allocation

Author

Listed:
  • Keogh Gerard

    (Central Statistics Office, Ardee Road, Rathmines, Dublin 6, Ireland.)

Abstract

The research question addressed here is whether the semantic value implicit in environmental terms in an activity description text string, can be translated into economic value for firms in the construction sector. We address this question using a relatively new applied statistical method called Latent Dirichlet Allocation (LDA). We first identify a satellite register of firms in construction sector that engage in some form of environmental work. From these we construct a vocabulary of meaningful words. Then, for each firm in turn on this satellite register we take its activity description text string and process this string with LDA. This softly-classifies the descriptions on the satellite register into just seven environmentally relevant topics. With this seven-topic classification we proceed to extract a statistically meaningful weight of evidence associated with environmental terms in each activity description. This weight is applied to the associated firm’s overall output value recorded on our national Business Register to arrive at a supply side estimate of the firm’s EGSS value. On this basis we find the EGSS estimate for construction in Ireland in 2013 is about EURO 229m. We contrast this estimate with estimates from other countries obtained by demand side methods and show it compares satisfactorily, thereby enhancing its credibility. Our method also has the advantage that it provides a breakdown of EGSS output by EU environmental classifications (CEPA/CReMA) as these align closely to discovered topics. We stress the success of this application of LDA relies greatly on our small vocabulary which is constructed directly from the satellite register.

Suggested Citation

  • Keogh Gerard, 2019. "A Lexical Approach to Estimating Environmental Goods and Services Output in the Construction Sector via Soft Classification of Enterprise Activity Descriptions Using Latent Dirichlet Allocation," Journal of Official Statistics, Sciendo, vol. 35(3), pages 625-651, September.
    • Handle: RePEc:vrs:offsta:v:35:y:2019:i:3:p:625-651:n:6
    DOI: 10.2478/jos-2019-0026
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/jos-2019-0026
    Download Restriction: no
    File URL: https://libkey.io/10.2478/jos-2019-0026?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
    Full references (including those not matched with items on IDEAS)

    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. Sandra Wankmüller, 2023. "A comparison of approaches for imbalanced classification problems in the context of retrieving relevant documents for an analysis," Journal of Computational Social Science, Springer, vol. 6(1), pages 91-163, April.
    2. Arsenyan, Jbid & Mirowska, Agata & Piepenbrink, Anke, 2023. "Close encounters with the virtual kind: Defining a human-virtual agent coexistence framework," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    3. Hong Joo Lee & Hoyeon Oh, 2020. "A Study on the Deduction and Diffusion of Promising Artificial Intelligence Technology for Sustainable Industrial Development," Sustainability, MDPI, vol. 12(14), pages 1-15, July.
    4. Maksym Polyakov & Morteza Chalak & Md. Sayed Iftekhar & Ram Pandit & Sorada Tapsuwan & Fan Zhang & Chunbo Ma, 2018. "Authorship, Collaboration, Topics, and Research Gaps in Environmental and Resource Economics 1991–2015," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(1), pages 217-239, September.
    5. Stefano Sbalchiero & Maciej Eder, 2020. "Topic modeling, long texts and the best number of topics. Some Problems and solutions," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(4), pages 1095-1108, August.
    6. Martin Baumgaertner & Johannes Zahner, 2021. "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics 202130, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    7. Daoud, Adel & Kohl, Sebastian, 2016. "How much do sociologists write about economic topics? Using big data to test some conventional views in economic sociology, 1890 to 2014," MPIfG Discussion Paper 16/7, Max Planck Institute for the Study of Societies.
    8. Shr-Wei Kao & Pin Luarn, 2020. "Topic Modeling Analysis of Social Enterprises: Twitter Evidence," Sustainability, MDPI, vol. 12(8), pages 1-20, April.
    9. Hsia-Ching Chang, 2016. "The Synergy of Scientometric Analysis and Knowledge Mapping with Topic Models: Modelling the Development Trajectories of Information Security and Cyber-Security Research," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-33, December.
    10. Ozgun, Burcu & Broekel, Tom, 2021. "The geography of innovation and technology news - An empirical study of the German news media," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    11. Anita Mendiratta & Shveta Singh & Surendra Singh Yadav & Arvind Mahajan, 2023. "Bibliometric and Topic Modeling Analysis of Corporate Social Irresponsibility," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(3), pages 319-339, September.
    12. Eric. W. K. See-To & Yang Yang, 2017. "Market sentiment dispersion and its effects on stock return and volatility," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 283-296, August.
    13. Lüdering Jochen & Winker Peter, 2016. "Forward or Backward Looking? The Economic Discourse and the Observed Reality," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(4), pages 483-515, August.
    14. Sabrina L. Woltmann & Lars Alkærsig, 2018. "Tracing university–industry knowledge transfer through a text mining approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 449-472, October.
    15. Cho, Yung-Jan & Fu, Pei-Wen & Wu, Chi-Cheng, 2017. "Popular Research Topics in Marketing Journals, 1995–2014," Journal of Interactive Marketing, Elsevier, vol. 40(C), pages 52-72.
    16. Kun Sun & Haitao Liu & Wenxin Xiong, 2021. "The evolutionary pattern of language in scientific writings: A case study of Philosophical Transactions of Royal Society (1665–1869)," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1695-1724, February.
    17. Nicolas Jouvin & Pierre Latouche & Charles Bouveyron & Guillaume Bataillon & Alain Livartowski, 2021. "Greedy clustering of count data through a mixture of multinomial PCA," Computational Statistics, Springer, vol. 36(1), pages 1-33, March.
    18. Ramit Debnath & Ronita Bardhan, 2020. "India nudges to contain COVID-19 pandemic: A reactive public policy analysis using machine-learning based topic modelling," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-25, September.
    19. Prithwiraj Choudhury & Dan Wang & Natalie A. Carlson & Tarun Khanna, 2019. "Machine learning approaches to facial and text analysis: Discovering CEO oral communication styles," Strategic Management Journal, Wiley Blackwell, vol. 40(11), pages 1705-1732, November.
    20. Wang, Jason & Weiss, Robert E., 2022. "Local and global topics in text modeling of web pages nested in web sites," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).

    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:vrs:offsta:v:35:y:2019:i:3:p:625-651:n:6. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.