JOURNAL ARTICLE

Who Is Affected? Defining Nuclear Territories and Their Borders: A Historical Perspective on the Nuclearization of the Rhône River from the 1970s to the 1990s.

  • Published In: Historical Social Research, 2024, v. 49, n. 1. P. 148 1 of 3

  • Database: Historical Abstracts with Full Text 2 of 3

  • Authored By: Fagon, Louis 3 of 3

Abstract

Through the example of two nuclear power plants (Superphénix and Saint-Alban) in France along the Rhône River, in the Isère département, 1 I show that the areas involved and potentially "affected" by nuclear power at the local level do not overlap historically and that they are the result of a scientific, political, and administrative construction based on nuclear risk. I suggest that the various zones established around nuclear power plants (potentially affected by an accident, involved in public inquir- ies, included in various committees in charge of information and control, al- lowed to collect taxes) tend to grow under the influence of anti-nuclear pro- test, of local populations, and also of elected officials who are exposed to the effective or potential effects of nuclear power plants. Despite the difficulty of framing the nuclear risk spatially, it delimits a growing nuclear territory sur- rounding each nuclear power plant, from several municipalities at the begin- ning of the 1970s to an entire region at the beginning of the 1990s. The nu- merous maps available in French local archives thus shed historical light on the construction of nuclear territories. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Historical Social Research. 2024/01, Vol. 49, Issue 1, p148
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:0172-6404
  • DOI:10.12759/hsr.49.2024.07
  • Accession Number:175941491
  • Copyright Statement:Copyright of Historical Social Research is the property of GESIS - Leibniz-Institute for the Social Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.