Social license to automate: A critical review of emerging approaches to electricity demand management

Abstract

Electricity demand-side management (DSM) programs are becoming increasingly important to energy system managers in advanced industrialized countries, especially those with high renewable energy penetration. As energy user participation is paramount for their success but has proven to be difficult to obtain, we explore the usefulness of the 'social license' concept, originally developed in the mining sector, to refer to the process of creating acceptance in DSM programs aimed at managing or controlling household energy resources such EVs, batteries, and heating and cooling devices. We argue that analyzing the attainment or lack of 'social license' may be useful to energy policy-makers and researchers for understanding public concerns with not only supply-side energy resources, but also DSM. We do so by (1) drawing attention to potential frictions between demands for flexibility on the one hand and social practices and habits on the other; (2) attending to the ways that users'

The development of social science research on smart grids: a semi-structured literature review

“…Some have also discussed the need for more theory development; for example, Khalid et al [56] reminded us that conceptualising practices as shared or socially differentiated entities in different cultural contexts calls for further theory development. In addition, Adams et al [81] critically reviewed the use of 'social license to automate' , where 'Social licence to automate' (SLA) provides a framework to understand the (mis)alignments between the expectations of actors within the energy systems and the household practices, sense of control and stake in the energy system. They argue that the concept of an SLA can bridge these domains.…”

Conceptualising socially inclusive environmental policy: a just transition to Net Zero

“… Women and LGBT+ people who are less likely to walk, cycle or take public transport in potentially unsafe conditions (Doran et al , 2021; Koskela and Pain, 2000; Lubitow et al ., 2020; Lucas et al ., 2019). People with disabilities, older people, younger people, and women who may need more support with household level changes (Adams et al ., 2021; Johnson, 2020; Snell et al ., 2018; Sovacool et al ., 2021) …”

“…People with disabilities, older people, younger people, and women who may need more support with household level changes (Adams et al ., 2021; Johnson, 2020; Snell et al ., 2018; Sovacool et al ., 2021)…”

“…For example, the increased use of flexible energy tariffs to allow the electricity grid to manage an increased dependence on electricity may disproportionately harm people with inflexible needs (e.g. people with disabilities – Calver and Simcock, 2021; Powells and Fell, 2019) or may cause household conflict in terms of who uses energy, when, and for what (Adams et a l ., 2021; Johnson, 2020). Equally, the increased emphasis on smart technology has significant implications for older people, those with visual impairments, or with limited literacy (Martiskainen et al ., 2021; Sovacool et al ., 2021).…”

Engaging with Automation: Understanding and Designing for Operation, Appropriation, and Behaviour Change

“…Automated influence, however, has also raised concerns in the ethics and policy literature, with regard to privacy in targeted advertising, autonomy in health nudges, or economic harm in dark patterns [24]. Designing such automation-based engagement systems requires careful consideration of a range of important questions among which are the given contexts within which a system will try to engage, variations in user requirements and barriers that will play a role in success and failure including underlying motivation to be engaged and expected benefits, flexibility parameters of users, and how these connect with specific user characteristics [1,5]. While a considerable body of behavior change theory and suggestions for integrating them in design is available [10], these are mostly restricted systems with classical user interaction levels, such as decision support systems, recommender systems, and gamification.…”