Fuel poverty and domestic retrofit: a socio-technical optimization approach to reduce heating carbon emissions in the UK

The global imperative to contrast but also adapt to climate change necessitates a rapid transition towards decarbonisation and a reduction in energy consumption. In the United Kingdom (UK), where 30% of greenhouse gas (GHG) emissions stem from the domestic sector, (UK CCC, 2019) (BEIS, 2018c) a targeted and efficient strategy is essential. This doctoral thesis addresses the pressing need to retrofit the UK's building stock.

The challenge of retrofitting goes beyond technical considerations. It must be socially sustainable, overcoming economic barriers, safeguarding the vulnerable, and minimising disparities. Cost emerges as a significant barrier to retrofit adoption, especially its impact on households across different income deciles, showing how the social sustainability of retrofitting for energy efficiency is intricately linked with the issue of fuel poverty.

Fuel poverty is the condition in which households cannot afford to keep their homes adequately heated. It can be identified as a critical lens through which to address the social sustainability of retrofit initiatives. In 2022, 13% of England’s households were classified as fuel-poor (DESNZ, 2023a). Although government policies and schemes acknowledge the importance of mitigating fuel poverty through retrofit, they lack a cohesive strategy for delivering energy efficiency measures (EEMs). Moreover, there is a need for a defined analytical methodology to integrate fuel poverty risk prevention with EEM delivery. In response to these challenges, this thesis introduces a novel socio-technical optimization approach. This approach aims to evaluate the impact of fuel poverty on regional-scale retrofit optimization to reduce GHG emissions derived from heating. The optimization process offers decision-makers and policy-makers results that represent optimal trade-offs, factoring in the complexities of fuel poverty through purposeful constraints.

Based on the English Housing Survey (EHS) database, a parametric energy model was populated to simulate the energy demand of 4 million dwellings in England. Passive and active retrofit strategies were implemented, including walls and roof insulation, window replacement, and Air-to-Water Heat Pump (AWHP) installations, and the consequent reduction in heating carbon emissions was calculated. A two-stage multi-objective optimization algorithm minimised annualised investment, operational costs, and heating carbon emissions at both dwelling and regional levels. This approach identifies optimal solutions through an exhaustive search at the dwelling level and utilises the Evolutionary Genetic Algorithm NSGA-II for global optimal solutions.

Fuel poverty constraints were implemented in the workflow based on three indicators: the 10% indicator, the Low-Income High-Cost (LIHC) indicator, and the Low-Income Low-Energy Efficiency (LILEE) indicator. The study compares unconstrained and constrained solutions, considering four cost evaluation scenarios: a base case scenario, a "tenure discretionary", a "net investment," and a hybrid approach, analysing three regional building stocks - the East Midlands, the North East region, and the Greater London area. A heuristic multi-scenario approach was applied to the results analysis.

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The examination of Pareto fronts, solution effectiveness, and average cost per household provides nuanced insights into the regional dynamics of retrofit optimization. The analyses on EEM installation rate across all sets of optimal solutions provided information on the relationship between retrofit strategies and their impact on fuel poverty. Dwellings and household characteristics of the optimal solutions were also investigated to identify which were more susceptible to the constraints.

The findings reveal a significant impact of fuel poverty constraints on the trade-offs. While addressing fuel poverty, constrained optimal solutions incur higher costs, diminished effectiveness, and impact retrofit delivery. A discernible hierarchy among different fuel poverty indicators emerges, emphasising the significance of socio-demographic characteristics such as income and house tenure in mitigating fuel poverty risk.

The influence of the "tenure discretionary" approach on the optimization process is highlighted, particularly in enhancing retrofit outcomes for rented properties. On a global scale, the research demonstrates that not accounting for energy savings derived from retrofit does not compromise overarching objectives or increase the risk of fuel poverty on a large scale.

Delving into regional differences, the research elucidates how variables such as retrofit potential and expenditure capacity affect the constraining process. This study identifies regional nuances and quantifies the risk of retrofit-induced fuel poverty for each distinct region. Socio-demographic characteristics, particularly income and house tenure, are identified as pivotal drivers of the fuel poverty risk associated with this workflow.

Acknowledging limitations in the evaluation of Energy Performance Certificates (EPCs) in the UK, the study underscores its relationship with fuel poverty indicators.

In conclusion, this PhD thesis contributes to bridging the gap between environmental sustainability and social equity in the context of domestic retrofit. By analysing and analytically incorporating fuel poverty in national retrofit optimization, this research proposed a potential tool but also provides actionable insights for policymakers and local authorities. The findings pave the way for designing retrofit deployment schemes that are not only environmentally responsible but also attuned to the diverse socio-economic fabric of the UK.