Search

• A critical review of the socio-technical energy transition (STET) literature shows the importance of representing societal factors in energy modelling. • The study engages with social mechanisms that have the potential to impede or foster rapid transitions such as resistance to change and the societal diffusion of environmental values within society. • We provide modelling steps to incorporate quantified socio-technical and socio-political behavioural insights into a probabilistic, non-linear energy system model, with a focus on the UK residential heating sector. • Illustrative scenarios and research discussions ponder the potential of non-monetary drivers of societal change to affect future transition pathways, including the importance to consider the most fragile segments of society in long-term policy decision making. The achievement of national pledges that are compatible with the Paris Agreements warming limit of 1.5C is a massive challenge, as it requires not only an acceleration of technological innovation, but also a socio-economic and cultural transformation. Reducing uncertainties demands a better integration of behavioural evolutions in models exploring future energy pathways, including non-monetary barriers and drivers to technology diffusion. This study provides suggestions on incorporating social mechanisms of change such as resistance to change and the diffusion of environmental values into a UK-focused probabilistic energy system model, with a focus on people's attitudes towards residential heating technologies. We also offer a comprehensive literature review on interdisciplinary energy transitions modelling and exploratory scenarios embedding climate risks perceptions. We argue that efficient policy-making to meeting net-zero emissions targets must fully embrace whole-system approaches, support the more constrained segments of society, and account for interconnected socio-political factors. [ABSTRACT FROM AUTHOR]

New contingency policy plans are expected to be published by the United Kingdom government to set out urgent actions, such as carbon capture and storage, greenhouse gas removal and the use of sustainable bioenergy to meet the greenhouse gas reduction targets of the 4th and 5th Carbon Budgets. In this study, we identify two plausible bioenergy production pathways for bioenergy with carbon capture and storage (BECCS) based on centralized and distributed energy systems to show what BECCS could look like if deployed by 2050 in Great Britain. The extent of agricultural land available to sustainably produce biomass feedstock in the centralized and distributed energy systems is about 0.39 and 0.5 Mha, providing approximately 5.7 and 7.3 MtDM/year of biomass respectively. If this land‐use change occurred, bioenergy crops would contribute to reduced agricultural soil GHG emission by 9 and 11 MtCO2eq/year in the centralized and distributed energy systems respectively. In addition, bioenergy crops can contribute to reduce agricultural soil ammonia emissions and water pollution from soil nitrate leaching, and to increase soil organic carbon stocks. The technical mitigation potentials from BECCS lead to projected CO2 reductions of approximately 18 and 23 MtCO2/year from the centralized and distributed energy systems respectively. This suggests that the domestic supply of sustainable biomass would not allow the emission reduction target of 50 MtCO2/year from BECCS to be met. To meet that target, it would be necessary to produce solid biomass from forest systems on 0.59 or 0.49 Mha, or alternatively to import 8 or 6.6 MtDM/year of biomass for the centralized and distributed energy system respectively. The spatially explicit results of this study can serve to identify the regional differences in the potential capture of CO2 from BECCS, providing the basis for the development of onshore CO2 transport infrastructures. [ABSTRACT FROM AUTHOR]

• Representative pathways can be sought to characterise a larger pathway ensemble. • Clustering performance is sensitive to the distribution of pathways. • K-means is the most robust approach for pathway grouping. • Hierarchical clustering might perform better if pathway distribution is irregular. • Sectoral standardisation can reveal the trade-offs between metrics in each sector. Long-term decarbonisation pathways to achieve ambitious low-carbon targets involve a range of uncertainties. Different energy system modelling approaches can be used to systematically evaluate the influence of the uncertainties, but this often leads to an unmanageable number of pathways. Summarising the large ensemble through a more limited number of representative pathways, to inform stakeholders, can be challenging. This study thus explores how to identify representative decarbonisation pathways using clustering algorithms, which can assist in grouping similar data points in uncategorised datasets, such as pathway ensembles. However, the suitability of clustering algorithms for pathway characterisation has not been investigated to date. Hence, k-means, hierarchical clustering, Gaussian mixture model, spectral clustering, and density-based clustering are adopted for comparisons. An illustrative pathway ensemble for the United Kingdom is applied to evaluate their performance based on cluster validity indices. Three metric transformations, including power, standardisation and sectoral standardisation, are also applied to create three additional sets of pathways for testing. The k-means algorithm is found to outperform others consistently, although hierarchical clustering might also be applicable if the distribution of pathway proximity is uneven. The results also highlight the utility of the approach in revealing distinctive trade-offs between technologies among the identified representative pathways. For instance, the electrification of heating can be replaced by district heating in the residential sector. The described, novel approach can be applied to characterise other sets of pathways, with greater technological details generated by any energy system models, to reveal insights for long-term decarbonisation. [ABSTRACT FROM AUTHOR]