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Hydrogen energy technologies are envisioned to play a critical supporting role in global decarbonisation. While low-carbon hydrogen is primarily targeted for reducing industrial emissions, alongside decarbonising parts of the transport sector, environmental benefits could also be achieved in the residential context. Presently, gas-dependent countries such as Japan and the United Kingdom are assessing the feasibility of deploying hydrogen home appliances, as part of their national energy strategies. However, prospects for the transition will hinge on consumer acceptance, alongside an array of other socio-technical factors. To support potential ambitions for large-scale and sustained technology diffusion, this study advances a Unified Theory of Domestic Hydrogen Acceptance. Through an integrative, comparative literature review targeting hydrogen and domestic energy studies, the paper proposes a novel Domestic Hydrogen Acceptance Model (DHAM), which accounts for the cognitive and emotional dimensions of human perceptions. Through this dual interplay, the proposed framework can increase the predictive power of hydrogen acceptance models. [Display omitted] • A unified theory of domestic hydrogen acceptance is proposed. • Researchers should integrate multiple acceptance constructs into survey studies. • Comprehensive perceived risks, costs, and benefits predict attitudes toward hydrogen. • Cognitive and emotional processes influence perceptions of hydrogen homes. • Community-level factors should be incorporated into hydrogen acceptance research. [ABSTRACT FROM AUTHOR]

Archaeological data provide a potential to investigate the diffusion of technological and cultural traits. However, much of this research agenda currently needs more formal quantitative methods to address small sample sizes and chronological uncertainty. This paper introduces a novel Bayesian framework for inferring the shape of diffusion curves using radiocarbon data associated with the presence/absence of a particular innovation. We developed two distinct approaches: 1) a hierarchical model that enables the fitting of an s-shaped diffusion curve whilst accounting for inter-site variations in the probability of sampling the innovation itself, and 2) a non-parametric model that can estimate the changing proportion of the innovation across user-defined time-blocks. The robustness of the two approaches was first tested against simulated datasets and then applied to investigate three case studies, the first pair on the diffusion of farming in prehistoric Japan and Britain and the third on cycles of changes in the burial practices of later prehistoric Britain. • Bayesian methods for modelling diffusion curves with radiocarbon dates are introduced. • Sample interdependence and non-sigmoid diffusion curves can be handled. • Robustness is tested on simulated datasets and archaeological case studies. • Code and data are available on a GitHub repository and archived on Zenodo. [ABSTRACT FROM AUTHOR]