Decisions, Science, and Values: Crafting Regulatory Alternatives Analysis

Abstract

Emerging "prevention-based" approaches to chemical regulation seek to minimize the use of toxic chemicals by mandating or directly incentivizing the adoption of viable safer alternative chemicals or processes. California and Maine are beginning to implement such programs, requiring manufacturers of consumer products containing certain chemicals of concern to identify and evaluate potential safer alternatives. In the European Union, the REACH program imposes similar obligations on manufacturers of certain substances of very high concern. Effective prevention-based regulation requires regulatory alternatives analysis (RAA), a methodology for comparing and evaluating the regulated chemical or process and its alternatives across a range of relevant criteria. RAA has both public and private dimensions. To a significant degree, alternatives analysis is an aspect of product design; that is, the process by which private industry designs the goods it sells. Accordingly, an RAA method should reflect the attributes of well-crafted product design tools used by businesses. But RAA adds health and environmental objectives to the mix of concerns taken into account by the product designer. Moreover, as part of a prevention-based regulatory regime, it implicates important public values such as legitimacy, equity, public engagement, and accountability. Thus, an RAA should reflect both private standards and public values, and be evaluated against them. This article adopts that perspective, identifying an integrated set of design principles for RAA, and illustrating the application of those principles.

Advancing Alternatives Assessment for Safer Chemical Substitution: A Research and Practice Agenda

“…The working group recommended additional research regarding the theory and practice underlying decision making in AA to improve the effect of various analytical tools and decision approaches and support tools for the deliberative process (Malloy et al 2017a). Normative standards such as fairness, accountability, and legitimacy can be drawn from the social science and regulatory literature (Olewnik and Lewis 2005;Malloy et al 2015). There is a need for standards of practice for addressing transparency in AA decision making.…”

“…Such standards are needed for both designing and evaluating decision approaches and decision support tools. Normative standards such as fairness, accountability, and legitimacy can be drawn from the social science and regulatory literature (Olewnik and Lewis 2005;Malloy et al 2015).…”

Advancing Alternative Analysis: Integration of Decision Science

“…Finding potential alternatives, collecting information about their performance, and evaluating the trade-offs posed by each alternative are all laden with problems. Those difficulties are exacerbated in the regulatory setting because of additional constraints associated with that regulatory setting, such as the need for accountability, transparency, and consistency across similar cases ( Malloy et al 2015 ). In this review, we focus on four challenges that are recognized in the decision analysis field to be of particular importance to regulatory AA: Dealing with large numbers of attributes Uncertainty in performance data Poorly understood option space Incorporating decision-maker values (sometimes called weighting of attributes) …”

Evaluating the Application of Decision Analysis Methods in Simulated Alternatives Assessment Case Studies: Potential Benefits and Challenges of Using MCDA

“…It is now fairly accepted that MCDA methods have relevance to the AA process (Ogunseitan 2016; Zheng et al 2019), including by the National Academy of Sciences (NAS 2014) and the California Department of Toxic Substances Control (DTSC 2017). Some research has started to explore conceptual, methodological, and normative issues associated with the use of MCDA in AA (Malloy et al 2017; Zheng et al 2019), and has developed criteria to guide the selection of decision‐making approaches and tools, including MCDA (Malloy et al 2015). Other work demonstrates the use of various MCDA methods in AA case studies (Zhou and Schoenung 2008; Malloy et al 2013; Tsang et al 2014).…”