Diffusion of low carbon innovations (WILSONU16ERC2)

Widespread adoption of low carbon innovations is needed to tackle climate change. Diffusion is the process by which innovations spread through a population of adopters [1]. Diffusion is influenced by both the characteristics of a technology and adoption contexts, including policy and geography [2-4].

The aim of this project is to analyse the diffusion of low carbon innovations. Where do they begin? How fast do they spread? How can this be measured? Does social influence accelerate diffusion? Is diffusion dependent on innovation policy or climate policy? Are there spatial characteristics to diffusion?

This project will focus on ‘disruptive’ low carbon innovations. Disruptive innovations challenge prevailing technologies or practices [5]. Examples include: car clubs, car sharing networks, and car-free communities as an alternative to private car ownership; or smart homes, net zero energy homes, and off-grid homes as alternative ways of heating and powering domestic life.

Activities
There is considerable scope for the successful applicant to choose which innovations to analyse, and at what scale (UK, EU, global). Specific research activities include:
(1) compiling data series describing the diffusion of disruptive low carbon innovations
(2) analysing technology, socioeconomic and policy variables explaining diffusion
(3) mapping spatial diffusion and quantifying spatial clustering effects

Training
The successful applicant will be a core part of a larger project team working on disruptive low carbon innovations. They will be integrated into the Tyndall Centre, a world-leading inter-disciplinary research institute on the causes, impacts and responses to climate change. They will also have available comprehensive skills and employment training through the EnvEast doctoral training programme.

Person Specification
Applicants should have, or be working towards, a good MSc degree or equivalent research experience that demonstrates interest and capabilities in innovation, diffusion, mapping, and/or modelling. Please contact [Email Address Removed] for any further information.