An integrative modelling approach to understanding human responses to hydrogen energy technologies

December 1st, 2023

By developing better statistical models that can help explain and predict human behaviour, we can create a responsible hydrogen industry.

Project Duration January 2023 – January 2026

Two researchers sitting at a model looking at statistical computer models.

Dr Mitch Scovell (L) and PhD candidate Daniel Herr (R) looking at statistical computer models.

Dr Mitch Scovell (L) and PhD candidate Daniel Herr (R) looking at statistical computer models.

The Challenge

Hydrogen, and the technologies that use it, are expected to become important components of our future energy systems. However, to develop a responsible hydrogen industry, requires considering the social environment in which it is deployed. Put simply, hydrogen’s success will be influenced by how people think about and respond to the technology.

There are many theories from the social sciences that can help explain to how people do, or will in the future, respond to hydrogen technologies. Quantitative social scientists often use statistical models to test the explanatory value of proposed theories. However, a problem often exists where statistical models does not reflect the theory being tested. To explain social phenomenon using statistical models it is important to put theory before statistics.

Statistical models are also useful because they can help with prediction. Accurate prediction is an essential aim in machine learning but it not usually a focus in the social sciences. There is, however, value in being able to both explain and predict human behaviour. This project aims to integrate these two goals to better understanding how people are likely to respond ahead of time and to solve potential problems with acceptance before they emerge.

Our response

The Hydrogen Energy Systems and Responsible Innovation Future Science Platforms are supporting a new PhD research project to tackle this challenge. The PhD is being undertaken by Daniel Herr under the supervision of Dr Mitch Scovell (CSIRO) and Dr Slava Vaisman (School of Mathematics and Physics, The University of Queensland).

The main aim of the project is to develop and test integrative models of human behaviour to better explain and predict human responses to the emerging hydrogen industry. While this project does have focus on solving practical problems, there will also be a focus on sharing knowledge between social science and computer science disciplines to develop new methodological approaches.

One specific focus of the project is using theoretically informed Bayesian networks to understand the causal mechanisms of human responses to hydrogen. An advantage of this approach is that the model can be queried to answer counterfactual questions. In other words, we can ask the question: what might happen if things were different? This has many practical implications as we can explore how changes in policy, messaging or technological development might change how people think and behave.


The project will support the responsible development of the hydrogen industry in several ways. The outcomes will help to ensure that investment in this new industry is being appropriately spent in that people are willing to buy, use and live near these technologies. The findings will also inform relevant stakeholders (e.g., industry, government, and policymakers) about the important factors that influence hydrogen acceptance and demand, and how to best address factors that may impede the successful development of the industry. Finally, the findings will help to ensure that hydrogen energy technologies are implemented in a way that aligns with community values and expectations.

Beyond the practical implications, the project aims to promote more conceptual and methodological rigor in the quantitative social science and the machine learning literatures. Whilst social scientists have the necessary theoretical understanding of people think and behave, many social scientists lack the formal training in mathematics, statistical theory and data science required to build and test formal representations of their theories. Similarly, statisticians/machine learning researchers often lack the relevant background knowledge in the theoretical and empirical concerns of the social sciences to make scientifically valid statistical inferences. By drawing on knowledge in both fields this project aims to bring insights from both disciplines to help develop new research approaches.


Daniel Herr, Mitch Scovell, Dr Slava Vaisman 

More information 

Fuel for thought: understanding public acceptance around hydrogen – Responsible Innovation Future Science Platform ( 

Hydrogen’s role in decarbonisation: ensuring a responsible transition – CSIRO 

Anticipating public attitudes towards hydrogen energy technologies – Responsible Innovation Future Science Platform (