Electronic waste—a modern form of risk? On the consequences of the delay between the increasing generation of electronic waste and regulations to manage this increase

ABSTRACT

This article addresses the relation between the current proliferation of Information and Communication Technology (ICT) goods and services and the development of different kinds of risk assessments and policy documents. The ambition is to shed light on a, hithertho, less-discussed aspect of the development of risk assessments and policy documents, namely the time span between the development of new technologies and the development of regulatory frameworks. The concept of risk and danger can be seen as a potential means through which we can start to think about the consequences of the delay between the increased generation of electronic waste (e-waste) and the regulations to manage this increase. By using e-waste as a case study, this article provides the basis for a more general understanding of the relation between the development of new technologies and the development of regulatory frameworks. While it might be difficult to pin down the effects that this delay had/has for the subsequent development of ICTs, this article highlights the importance of taking into account not only how and by whom risk assessments and policy documents are developed, but also when they are developed in relation to the technologies that they serve to regulate.

KEYWORDS:

• Information and Communication Technology (ICT)
• risk assessment
• policy making
• electronic waste
• risk

Notes

¹ The term soft law refers to a non-treaty law, where the rules of conduct, in principle, have no legally binding force, but that nevertheless may have practical effects (Tallacchini 2009).

² Luhmann treats risk, not as an object of “a first-order observation” (“Beobachtung erster Ordnung”) (Luhmann 1991: 23) (which he terms “danger”) but as a concept of “a second-order observation” (“Beobachtung zweiter Ordnung”) (Luhmann 1991: 23).

³ Depending on their disciplinary background, experts from diverse academic fields define scientific uncertainty differently (Landström et al. 2015). According to Schettler and Raffensperger's (2004) typology of uncertainties, the “model uncertainty” is inherent in technologies with multiple variables interacting in complex ways; “statistical uncertainty” occurs as a result of not knowing the value of a specific variable at a point in time or space, but being able to determine the probability distribution of the variable; and, “fundamental uncertainty” which extends the degree of indeterminacy into ignorance. In the case of the last uncertainty, people do not even know what they do not know (Schettler and Raffensperger 2004: 68).