IDEAS home Printed from https://ideas.repec.org/p/zbw/zewdip/22006.html
   My bibliography  Save this paper

Greenwashing in the US metal industry? A novel approach combining SO2 concentrations from satellite data, a plant-level firm database and web text mining

Author

Listed:
  • Schmidt, Sebastian
  • Kinne, Jan
  • Lautenbach, Sven
  • Blaschke, Thomas
  • Lenz, David
  • Resch, Bernd

Abstract

This Discussion Paper deals with the issue of greenwashing, i.e. the false portrayal of companies as environmentally friendly. The analysis focuses on the US metal industry, which is a major emission source of sulfur dioxide (SO2), one of the most harmful air pollutants. One way to monitor the distribution of atmospheric SO2 concentrations is through satellite data from the Sentinel-5P programme, which represents a major advance due to its unprecedented spatial resolution. In this paper, Sentinel-5P remote sensing data was combined with a plant-level firm database to investigate the relationship between the US metal industry and SO2 concentrations using a spatial regression analysis. Additionally, this study considered web text data, classifying companies based on their websites in order to depict their self-portrayal on the topic of sustainability. In doing so, we investigated the topic of greenwashing, i.e. whether or not a positive self-portrayal regarding sustainability is related to lower local SO2 concentrations. Our results indicated a general, positive correlation between the number of employees in the metal industry and local SO2 concentrations. The web-based analysis showed that only 8% of companies in the metal industry could be classified as engaged in sustainability based on their websites. The regression analyses indicated that these self-reported 'sustainable' companies had a weaker effect on local SO2 concentrations compared to their 'non-sustainable' counterparts, which we interpreted as an indication of the absence of general greenwashing in the US metal industry. However, the large share of firms without a website and lack of specificity of the text classification model were limitations to our methodology.

Suggested Citation

  • Schmidt, Sebastian & Kinne, Jan & Lautenbach, Sven & Blaschke, Thomas & Lenz, David & Resch, Bernd, 2022. "Greenwashing in the US metal industry? A novel approach combining SO2 concentrations from satellite data, a plant-level firm database and web text mining," ZEW Discussion Papers 22-006, ZEW - Leibniz Centre for European Economic Research.
  • Handle: RePEc:zbw:zewdip:22006
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/250390/1/1793893977.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bongsug (Kevin) Chae & Eunhye (Olivia) Park, 2018. "Corporate Social Responsibility (CSR): A Survey of Topics and Trends Using Twitter Data and Topic Modeling," Sustainability, MDPI, vol. 10(7), pages 1-20, June.
    2. Richard Schmalensee & Robert N. Stavins, 2013. "The SO 2 Allowance Trading System: The Ironic History of a Grand Policy Experiment," Journal of Economic Perspectives, American Economic Association, vol. 27(1), pages 103-122, Winter.
    3. Shuhua Ma & Zongguo Wen & Jining Chen, 2012. "Scenario Analysis of Sulfur Dioxide Emissions Reduction Potential in China's Iron and Steel Industry," Journal of Industrial Ecology, Yale University, vol. 16(4), pages 506-517, August.
    4. World Commission on Environment and Development,, 1987. "Our Common Future," OUP Catalogue, Oxford University Press, number 9780192820808.
    5. Jan Kinne & Janna Axenbeck, 2020. "Web mining for innovation ecosystem mapping: a framework and a large-scale pilot study," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2011-2041, December.
    6. repec:rre:publsh:v:37:y:2007:i:1:p:5-27 is not listed on IDEAS
    7. M Tiefelsdorf & D A Griffith & B Boots, 1999. "A Variance-Stabilizing Coding Scheme for Spatial Link Matrices," Environment and Planning A, , vol. 31(1), pages 165-180, January.
    8. Julian Schwierzy & Robert Dehghan & Sebastian Schmidt & Elisa Rodepeter & Andreas Stoemmer & Kaan Uctum & Jan Kinne & David Lenz & Hanna Hottenrott, 2022. "Technology Mapping Using WebAI: The Case of 3D Printing," Papers 2201.01125, arXiv.org.
    9. Abdullah Gök & Alec Waterworth & Philip Shapira, 2015. "Use of web mining in studying innovation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 653-671, January.
    10. Ki‐Hoon Lee, 2017. "Does Size Matter? Evaluating Corporate Environmental Disclosure in the Australian Mining and Metal Industry: A Combined Approach of Quantity and Quality Measurement," Business Strategy and the Environment, Wiley Blackwell, vol. 26(2), pages 209-223, February.
    11. Jan Kinne & David Lenz, 2021. "Predicting innovative firms using web mining and deep learning," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-18, April.
    12. Dörr, Julian Oliver & Kinne, Jan & Lenz, David & Licht, Georg & Winker, Peter, 2021. "An integrated data framework for policy guidance in times of dynamic economic shocks," ZEW Discussion Papers 21-062, ZEW - Leibniz Centre for European Economic Research.
    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. Dörr, Julian Oliver & Kinne, Jan & Lenz, David & Licht, Georg & Winker, Peter, 2021. "An integrated data framework for policy guidance in times of dynamic economic shocks," ZEW Discussion Papers 21-062, ZEW - Leibniz Centre for European Economic Research.
    2. Rammer, Christian & Es-Sadki, Nordine, 2023. "Using big data for generating firm-level innovation indicators - a literature review," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    3. Axenbeck, Janna & Breithaupt, Patrick, 2022. "Measuring the digitalisation of firms: A novel text mining approach," ZEW Discussion Papers 22-065, ZEW - Leibniz Centre for European Economic Research.
    4. Julian Schwierzy & Robert Dehghan & Sebastian Schmidt & Elisa Rodepeter & Andreas Stoemmer & Kaan Uctum & Jan Kinne & David Lenz & Hanna Hottenrott, 2022. "Technology Mapping Using WebAI: The Case of 3D Printing," Papers 2201.01125, arXiv.org.
    5. Motohashi, Kazuyuki & Zhu, Chen, 2023. "Identifying technology opportunity using dual-attention model and technology-market concordance matrix," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    6. Christoph Stich & Emmanouil Tranos & Max Nathan, 2023. "Modeling clusters from the ground up: A web data approach," Environment and Planning B, , vol. 50(1), pages 244-267, January.
    7. Antonio Corvino & Silvio Bianchi Martini & Federica Doni, 2021. "Extinction accounting and accountability: Empirical evidence from the west European tissue industry," Business Strategy and the Environment, Wiley Blackwell, vol. 30(5), pages 2556-2570, July.
    8. Kinne, Jan & Dehghan, Robert & Schmidt, Sebastian & Lenz, David & Hottenrott, Hanna, 2024. "Location factors and ecosystem embedding of sustainability-engaged blockchain companies in the US: A web-based analysis," ZEW Discussion Papers 24-023, ZEW - Leibniz Centre for European Economic Research.
    9. Lucie Kvasničková Stanislavská & Ladislav Pilař & Klára Margarisová & Roman Kvasnička, 2020. "Corporate Social Responsibility and Social Media: Comparison between Developing and Developed Countries," Sustainability, MDPI, vol. 12(13), pages 1-19, June.
    10. Chenxi Liu & Zhenghong Peng & Lingbo Liu & Shixuan Li, 2023. "Innovation Networks of Science and Technology Firms: Evidence from China," Land, MDPI, vol. 12(7), pages 1-21, June.
    11. Dahlke, Johannes & Beck, Mathias & Kinne, Jan & Lenz, David & Dehghan, Robert & Wörter, Martin & Ebersberger, Bernd, 2024. "Epidemic effects in the diffusion of emerging digital technologies: evidence from artificial intelligence adoption," Research Policy, Elsevier, vol. 53(2).
    12. Khosroshahi, Hossein & Azad, Nader & Jabbarzadeh, Armin & Verma, Manish, 2021. "Investigating the level and quality of the information in the environmental disclosure report of a corporation considering government intervention," International Journal of Production Economics, Elsevier, vol. 235(C).
    13. Breithaupt, Patrick & Hottenrott, Hanna & Rammer, Christian & Römer, Konstantin, 2023. "Mapping employee mobility and employer networks using professional network data," ZEW Discussion Papers 23-041, ZEW - Leibniz Centre for European Economic Research.
    14. CHEN, Helen S.Y., 2020. "Designing Sustainable Humanitarian Supply Chains," OSF Preprints m82ar, Center for Open Science.
    15. Denise Ravet, 2011. "Lean production: the link between supply chain and sustainable development in an international environment," Post-Print hal-00691666, HAL.
    16. Mara Del Baldo, 2012. "Corporate social responsibility and corporate governance in Italian SMEs: the experience of some “spirited businesses”," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 16(1), pages 1-36, February.
    17. Michael Howes & Liana Wortley & Ruth Potts & Aysin Dedekorkut-Howes & Silvia Serrao-Neumann & Julie Davidson & Timothy Smith & Patrick Nunn, 2017. "Environmental Sustainability: A Case of Policy Implementation Failure?," Sustainability, MDPI, vol. 9(2), pages 1-17, January.
    18. Parnphumeesup, Piya & Kerr, Sandy A., 2011. "Stakeholder preferences towards the sustainable development of CDM projects: Lessons from biomass (rice husk) CDM project in Thailand," Energy Policy, Elsevier, vol. 39(6), pages 3591-3601, June.
    19. Chin-Shan Lu & Kuo-Chung Shang & Chi-Chang Lin, 2016. "Examining sustainability performance at ports: port managers’ perspectives on developing sustainable supply chains," Maritime Policy & Management, Taylor & Francis Journals, vol. 43(8), pages 909-927, November.
    20. Kebede, Yohannes, 1993. "The Limits to Common Resource Management: The Bypassed Commons or Commons without Tragedy," MPRA Paper 662, University Library of Munich, Germany, revised 01 May 1993.

    More about this item

    Keywords

    Sentinel-5P; air pollution; natural language processing; spatial regression;
    All these keywords.

    JEL classification:

    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:zbw:zewdip:22006. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/zemande.html .

    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.