Publications

Lois Player, AAPS CDT student, co-authors a study that finds that whilst climate anxiety is low amongst the UK public, it may be an important driver of climate action such as cutting down on waste.

The study published in the Journal of Environmental Psychology coincides with a new briefing paper from the Centre for Climate Change & Social Transformations focused on UK public preferences for low-carbon lifestyles. Its analysis suggests that lifestyle changes (for example, reducing car use or eating less meat), are increasingly seen as both feasible and desirable.

In the paper, the authors emphasise the importance of the media as a motivating force for the lifestyle changes required as we decarbonise. They suggest that the media and public discourse about climate anxiety has the power to create a positive vision for a greener, cleaner future which is significantly less dependent on fossil fuels.

Lois explained: “Our results suggest that the media could play an important role in creating positive pro-environmental behaviour change, but only if they carefully communicate the reality of climate change without inducing a sense of hopelessness.”

As ambient air pollution increases, governments are imposing traffic management strategies to improve air quality. A common strategy is the implementation of Low Emission Zones (LEZs), which have generated considerable public debate. Nonetheless, little research has explored which factors determine their public acceptability. Previous empirical studies have also typically lacked power for regression analyses and have not determined the relative importance of different predictors. After conducting a large online survey in a UK city, well-powered multiple regression and dominance analyses demonstrated that psychological factors, such as environmental moral obligation, were the most important predictors of LEZ acceptability. However, travel-related and socio-demographic factors, such as distance lived from the LEZ and having dependent children, were also unique and important predictors. Overall, we argue that, whilst psychological factors are important, travel-related and socio-demographic barriers must not be overlooked during LEZ implementation.

Environmental knowledge is considered an important pre-cursor to pro-environmental behaviour. Though several tools have been designed to measure environmental knowledge, there remains no concise, psychometrically grounded measure. We validated an existing measure in a British sample, confirming that it had good one- and three-factor structures in line with previous literature. For the first time in this field, we built upon previous Classical Test Theory approaches and used discrimination values derived from Item Response Theory to select the best items, resulting in the 19-Item Environmental Knowledge Test (EKT-19). This measure retained a clear factor structure and had moderate-to-good internal reliability, indicating that it is a parsimonious and psychometrically robust measure for the assessment of overall and specific types of environmental knowledge. The theoretical implications and real-world applications of this measure are discussed.

Machine-assisted approaches for free-text analysis are rising in popularity, owing to a growing need to rapidly analyse large volumes of qualitative data. In both research and policy settings, these approaches have promise in providing timely insights into public perceptions and enabling policymakers to understand their community’s needs. However, current approaches still require expert human interpretation – posing a financial and practical barrier for those outside of academia. For the first time, we propose and validate the Deep Computational Text Analyser (DECOTA) - a novel Machine Learning methodology that automatically analyses large free-text datasets and outputs concise themes. Building on Structural Topic Modelling (STM) approaches, we used two fine-tuned Large Language Models (LLMs) and sentence transformers to automatically derive ‘codes’ and their corresponding ‘themes’, as in Inductive Thematic Analysis. To automate the process, we designed and validated a novel algorithm to choose the optimal number of ‘topics’ following STM. This approach automatically derives key codes and themes from free-text data, the prevalence of each code, and how prevalence varies with covariates such as age and gender. Each code is accompanied by three representative quotes. Four datasets previously analysed using Thematic Analysis were triangulated with DECOTA’s codes and themes. We found that DECOTA is approximately 378 times faster and 1920 times cheaper than human coding, and consistently yields codes in agreement with or complementary to human coding (averaging 91.6% for codes, and 90% for themes). The implications for evidence-based policy development, public engagement with policymaking, and the development of psychometric measures are discussed.