Charlie Wilson

Domestic automation is extending across everyday household life, but uptake remains fragmented and uneven across activities. Little is known about how experiences of automation in one activity shape acceptance in others. This paper investigates whether, and under what conditions, acceptance of automation spills over across activities of daily life. Drawing on behavioural spillover theory and an adapted Automation Acceptance Model (AAM), we report findings from two in-home automation trials. Using pre- and post-trial mixed methods, we examine how acceptance develops through use, feedback mechanisms, and subsequent changes in willingness to automate additional activities.,Results show that acceptance is closely associated with experiences during use rather than with initial attitudes. Experienced usefulness, compatibility with household routines, trust and perceived burden were consistently associated with positive evaluations. Acceptance spillover was observed but was selective and conditional rather than universal. It occurred more readily for automation that executed low-risk, routine activities, whereas acceptance of planning-dominant automated activities remained contested, with participants emphasising the need for approval, flexibility, and control. Overall, the findings demonstrate that acceptance of automation can spill over across activities following real-world use, mediated by experiential mechanisms consistent with AAM constructs. The paper advances understanding of how automation diffuses within households by identifying the micro-level experiential pathways shaping acceptance spillover. These insights have implications for the design of user-centred automation and for understanding how everyday digital technologies are adopted in domestic life.

Digitalisation in daily life can reduce carbon emissions, but its climate potential depends on public acceptance. This paper proposes examining acceptance of technology across different aspects of daily life as an aggregate construct. This paper reports two-parts study with UK residents (n = 2,029) using the Technology Acceptance Model (TAM) to examine intentions to adopt digitalisation in daily life in general (DDL) and specifically for carbon reduction (DDL+CC). The first part shows that general DDL intentions are shaped by attitudes, perceived usefulness, neighbourhood effects, and perceived benefits. The second part extends the model to climate-focused digitalisation, showing that attitudes, neighbourhood effects, and perceived usefulness for emission reduction are the strongest predictors of DDL+CC intentions. Intentions to use DDL were higher than intention to use DDL+CC. Attitudes and neighbourhood influence consistently emerged as central drivers, underscoring the importance of social visibility and positive evaluations in shaping sustainable digital behaviours. In general, results indicate greater reported intentions to digitalise daily life in general than for the specific purpose of carbon reduction. These findings suggest people may consider climate-focused digitalisation more sophisticated or complex. Overall findings demonstrate how community engagement and attitudinal framing can accelerate climate-oriented digital adoption.

On-demand digital services provide convenient and fast fulfilment of consumption. From their emergence in media, on-demand digital services have expanded in ride-hailing, food delivery and retail. While there is abundant research on digitalisation in various consumption sectors, few have identified the interrelations between digital services across multiple consumption sectors and their implications for consumption behaviours. Using domestication theory, this article explores the interconnections between households’ cognitive, practical and symbolic learning of various on-demand digital services to understand how they shape the understanding of and responses to convenience in consumption. Findings from qualitative interviews in the United Kingdom show linkages between how households learn about, use, and develop meanings around online media, food delivery, ride-hailing and retail services. Learning from one service affects households’ awareness, skills, routinisation and meaning-making of other services. These multi-sited learnings engender shared expectations of convenience within and across digital services that are interpreted and negotiated through households’ identities. The dynamic process between households and technologies organised around convenience shapes households’ temporal and spatial coordination of their consumption. Recognising the similarities and interdependencies in how households engage with various digital services in media, retail, food and ride-hailing helps us understand digitalisation as an overarching transformation of consumption.

Digitalisation is a double-edged sword for energy transitions and climate change mitigation. Digital applications can improve the energy efficiency of processes and systems, but can also induce demand for energy-hungry activity. Digital infrastructure like data centres also has a large energy footprint. AI is amplifying and accelerating these impacts, both for better and for worse. How does this affect the feasibility of long-term climate targets? We use a global integrated modelling framework to quantify scenarios to 2050 describing alignment or misalignment between digitalisation and climate goals. We combine empirical estimates of the energy impacts of digital and AI applications in buildings, transport, and industry sectors with digitalisation’s role in the integration of renewables, storage, and demand flexibility in electricity systems. We show digitalisation uncertainties for future energy demand and CO ₂ emissions are large - similar in magnitude to the effect of all global demographic, economic, and technological uncertainties quantified in future climate pathways. We also show that available near-term assessments of AI impacts within this uncertainty space are optimism biased. Energy transition and emission risks from digital and AI applications in transport and buildings are a factor five of higher than from data centres and other infrastructure. Climate policy is the most effective driver of emission reductions to meet Paris targets but climate-aligned digitalisation reduces energy investment needs by a factor of two down to $35 trillion and brings the global net-zero year forward to as early as 2047. Conversely, climate-misaligned digitalisation drives up energy demand and sees lost opportunities to build a reliable and renewable future electricity system. Resulting increases in near-term cumulative CO ₂ emissions mean the probability of limiting warming to 2°C almost falls below the ‘likely’ threshold, increasing risk of temperature overshoot. Digital transformation can undermine or enable the feasibility and affordability of long-term climate targets delivered by decarbonised energy systems. Entwining digital and low-carbon energy transitions requires strategic governance and institutional innovation to link climate and digital concerns.

Artificial Intelligence (AI) may impact greenhouse gas emissions in both positive and negative ways, yet these impacts are not represented in recent assessments of climate change mitigation. Uncertainty surrounding future AI capability and adoption, its impacts on energy systems, and limited data availability complicates the inclusion of AI impacts. We conduct an expert-led process, ascertaining plausible impacts, and quantify ranges in their magnitude to 2040. We present a new scenario framework for AI and climate change that considers interactions between AI growth, climate policy, and AI policy. We translate elicited insights into illustrative scenarios and assess their implications using an integrated assessment model. Cumulative global CO2 emissions by 2040 range from +11% above to 1.4% below a baseline without AI impacts, depending on the scale of AI growth and policy intervention. Our results highlight emissions risks and opportunities from AI and the need for explicit representation in mitigation scenarios.

The corpus of national climate policies continues to grow - but to what effect? Using data on 3,917 policy instruments across 43 OECD countries and major emerging economies from 2000-2022, we show that national climate policy portfolios specializing in instrument types and sectors are associated with faster reductions in fossil CO ₂ emission intensity. Supported by exemplar country case studies, we also provide quantitative evidence that the effectiveness of climate policy is amplified by long-term emission reduction targets and the presence of dedicated governmental bodies including ministries and intergovernmental organisations. The cumulative effect of all climate policy portfolios over our study period amounts to 3.1 GtCO ₂ fewer emissions in 2022 relative to a no-policy counterfactual - substantially less than what’s needed to stay on track for the Paris Agreement goals.

The ERL Focus Issue on Technology and Global Change brings together 19 research articles, 4 perspectives, and 2 topical reviews on wide-ranging topics including innovation, policy, poverty alleviation, digitalisation, and modelling. Collectively, these 25 new articles help advance understanding of technology and global change, building on work 25 years ago by Arnulf Grübler whose system perspective in turn built on pioneering scholarship in evolutionary and institutional economics. In this editorial essay, we survey the 25 contributions to this Focus Issue and draw out some of the themes and ideas that define the research frontiers in this field.