Rethinking embodied carbon accounting of construction materials: from attributional snapshots to consequential causal modelling

About the Project

The construction sector is one of the largest global sources of greenhouse gas emissions, primarily due to the production of carbon-intensive materials such as cement and steel. Efforts to reduce these emissions (through building standards, procurement rules, labelling schemes, and corporate reporting) rely almost entirely on attributional life cycle assessment (LCA) data and methods. Attributional LCA describes average emissions within fixed boundaries, treating supply chains as static systems.

However, real-world design and policy decisions are inherently consequential: they aim to change production systems, not merely describe them. Their true impact depends on how suppliers adjust output, how co-products constrain material availability, how markets and trade respond, and how technologies evolve. This creates a fundamental methodological gap: current policy initiatives attempt to drive system change using data that assume the system does not change. As a result, decisions based on attributional data can appear to reduce emissions at the project level while failing, or even backfiring, at the system scale, particularly when co-production limits exist, such as slag-limited GGBS in concrete or scrap-limited recycled steel.

This PhD will develop a new framework for consequential analysis of embodied carbon decisions in the construction sector. Using causal probabilistic graphical models, the research will capture the dynamic interactions between material stocks and flows, physical co-production constraints, market behaviour, and policy or procurement interventions. Rather than asking what the average carbon footprint of a material is, the project will investigate how different decision frameworks affect total system emissions over time. It will explore what happens when demand for low-carbon materials exceeds the physical supply of their co-products, how time lags in scrap or residue generation shape long-term emission outcomes, and under what conditions policies based on attributional benchmarks truly deliver system-wide decarbonisation.

The project will critically evaluate current embodied carbon accounting frameworks and the limitations of existing consequential models such as Computable General Equilibrium approaches. It will then construct and test a causal model of key material supply chains (e.g., cement, steel, and timber) to simulate alternative policy and procurement scenarios. Through these analyses, the research will identify when and why attributional-data-driven decisions succeed or fail to achieve genuine emissions reductions in the built environment.

Academic qualifications

Have, or expect to achieve by the time of start of the studentship a first-class honours degree, or a distinction at master level, ideally in Data Science, Engineering or Mathematics or equivalent with a good fundamental knowledge of causal inference, life cycle assessment, and environmental systems analysis.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

• Only a first-class honours degree, or a distinction at master level in a subject relevant to the PhD project will be considered, or equivalent achievements.
• Computational skills: experience with programming in one of the following: Python, R, MATLAB.
• Basic knowledge in probability theory and causal reasoning: ability to work with random variables, conditional probabilities, Bayesian inference, and causal graphical models.
• Only a first-class honours degree, or a distinction at master level in a subject relevant to the PhD project will be considered, or equivalent achievements.

Desirable attributes:

• Fundamental knowledge of LCA and embodied carbon: understanding attributional vs consequential LCA, environmental product data, and carbon accounting frameworks.
• Problem-solving and critical thinking: ability to design methodological frameworks, identify limitations of existing models, and propose innovative solutions.
• Communication and collaboration skills: ability to explain complex technical concepts to interdisciplinary teams and contribute to academic publications.
• Motivation and independence: strong drive to undertake rigorous research, learn new methods, and work autonomously within a structured PhD program.
• Practical experience in research or industry will be considered an advantage.

APPLICATION CHECKLIST

• Completed application form
• CV
• 2 academic references, using the Postgraduate Educational Reference Form (download)
• Research project outline of 2 pages (list of references excluded). The outline may provide details about:
  1. Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
  2. Research questions or objectives.
  3. Methodology: types of data to be used, approach to data collection, and data analysis methods.
  4. List of references.

The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.

• Statement no longer than 1 page describing your motivations and fit with the project.
• Evidence of proficiency in English (if appropriate)

To be considered, the application must use

• the advertised title as project title

For informal enquiries about this PhD project, please contact Dr D'amico email B.DAmico@napier.ac.uk