Sustainable Energy Systems (United Kingdom)

The dissertation shows that while governments worldwide are driving the shift to sustainable energy, their efforts often deepen existing inequalities. Many wealthier households benefit from insulation, solar panels, and electric vehicles, while socioeconomically disadvantaged groups remain in poorly insulated homes and struggle to afford basic energy needs. Although governments increasingly acknowledge these disparities and express ambitions for energy justice, they struggle to translate those ambitions into effective action. The research investigates why this gap persists and how governments can close it. Focusing on the Dutch energy transition, it follows energy justice from its appearance in policy documents to its implementation in concrete policies. Three governance challenges emerge. • Ambiguous goals: Terms like justice or fairness are so broad that policymakers can overlook or reinterpret them. Clear, specific objectives are needed to guide decisions. • Lack of integration into policy practice: Even when objectives are defined, they often remain symbolic. Embedding them into everyday policymaking – through assessment criteria, monitoring systems, and feedback loops – is essential to ensure policies align with justice ambitions. • Ineffective policy strategies: Different governance strategies – rowing, steering, and serving – each have strengths and weaknesses. No single approach reliably delivers energy justice, and choosing among them is ultimately a political decision. The dissertation argues that achieving energy justice requires a coordinated, ongoing effort: clarifying objectives, embedding them in policy processes, and continuously learning from implementation. By bringing public administration insights into energy justice research, it offers governments practical guidance for making the energy transition fairer for those who need it most.

Global climate change and energy security have become defining challenges of the 21st century, prompting ambitious international and European Union (EU) policy frameworks such as the Paris Agreement and the European Green Deal. Within this context, energy efficiency and renewable energy (‘renewables”) are pivotal strategies for achieving climate neutrality by mid-century. Energy efficiency, often termed the “first fuel,” offers cost-effective emission reductions and system benefits, while renewables provide sustainable supply-side solutions. However, barriers such as high upfront costs, fragmented potentials, and institutional inertia hinder the uptake of energy efficiency measures. To address these challenges, two concepts have gained prominence: the Energy Efficiency First (EE1st) Principle and the Multiple Impacts (MI) concept. EE1st, enshrined in the recast Energy Efficiency Directive (EED), mandates prioritising demand-side measures over supply-side options when they deliver equal or greater value. This principle requires comprehensive cost-benefit assessments that include wider societal benefits. Similarly, the MI concept broadens the evaluation of energy efficiency beyond energy savings to encompass environmental, social, and economic co-benefits, such as improved health, energy security, and macroeconomic gains, while acknowledging potential trade-offs. Despite their relevance, gaps persist in integrating MI into standard evaluations, incorporating renewables into MI frameworks, and operationalising EE1st under legislative ambiguities. This dissertation addresses these gaps through five studies structured around four research questions: the sustainability of domestic fossil fuel extraction as an energy security strategy, the implications of interpretative flexibility in EE1st implementation, methods for embedding MI in policy evaluations, and the integration of renewables into MI frameworks to enable fair comparisons under the EE1st Principle. These all contribute to the overall research question: How can a comprehensive multiple impacts framework support the implementation of the EU’s Energy Efficiency First Principle? Findings reveal that domestic fossil fuel extraction provides substantial disadvantages in relation to its environmental and social costs, particularly compared to apparent minor and short-term contributions to energy security. Analysis of EE1st interpretation demonstrates that parameters such as discount rates and cost perspectives significantly influence whether efficiency measures are deemed cost-optimal, highlighting the need for clearer legislative guidance. Furthermore, incorporating MI into evaluation frameworks enhances the comprehensiveness and transparency of policy assessments, though data gaps and monetisation challenges persist. Finally, an extended MI framework covering both energy efficiency and renewables, implemented in the open-source MICATool, enables holistic comparisons and supports EE1st application in practice, as illustrated by a case study on Milan’s residential building stock. Overall, this research advances methodological and practical approaches for integrating MI into decision-making and operationalising EE1st. It underscores the importance of systemic, multidimensional assessments to ensure cost-effective, socially beneficial, and environmentally sustainable pathways to decarbonisation but also describes some pitfalls. Policy recommendations include refining EE1st implementation guidelines, promoting societal cost perspectives, and leveraging MI frameworks to align energy policy with climate neutrality objectives.

Accurate prediction of temperature-dependent liquid viscosity is essential for process design and molecular screening but remains challenging for novel hydrocarbons. Purely data-driven graph neural networks (GNNs) can achieve good in-range accuracy yet often lack thermodynamic consistency and become unstable under temperature shifts or sparse calibration data. This work introduces ExPUFFIN (Extended Path-Unifying Feed-Forward Interfaced Network), a hybrid GNN framework that embeds established viscosity–temperature correlations directly into the output layer by learning molecule-specific parameters of Andrade-type and empirical formulations. The approach is evaluated on a curated dataset of 305 pure hydrocarbons using standard test metrics, interpolation analysis, Leave-One-Temperature-Out evaluation, and extrapolation beyond the experimental range. While the baseline model attains comparable test errors, it exhibits oscillatory and inconsistent behaviour outside the training domain. In contrast, the ExPUFFIN variants preserve smooth, monotonic viscosity–temperature trends and substantially reduce extrapolation errors. The results show that incorporating inductive bias into the network output improves robustness and reliability without compromising predictive accuracy. • ExPUFFIN integrates GNNs with inductive viscosity–temperature correlations to improve prediction reliability. • The hybrid architecture yields smoother, more consistent viscosity–temperature curves. • ExPUFFIN enhances interpolation stability and extrapolation performance compared to a purely data-driven baseline.

Background The swift advancement of Internet of Things (IoT) technology has revolutionized smart home settings; the prevalent automation systems are limited by their need for specific device identification and rigid rule-based configurations. These constraints impede natural human-device interaction, especially in dynamic or communal environments where spatial context is more instinctive than predetermined naming conventions. Current solutions frequently neglect spatial reasoning and multimodal inputs, resulting in heightened cognitive demands and diminished accessibility. The proposed work develops a spatial context-aware control system aimed at facilitating intuitive, vision-driven, and language-based interaction with smart devices to overcome these problems. Methods The proposed model is a modular, multimodal framework that integrates computer vision, natural language processing, and spatial inference for context-aware smart device control. The system comprises six core components: (i) an Onboarding Inference Engine for extracting device information via natural language input, (ii) Zero-Shot Device Detection using Open-World Localization–Vision Transformer (OWL-ViT) for object identification without prior training, (iii) Metadata Refinement and Filtering for structured annotation and disambiguation, (iv) a Geospatial Device Visualizer for annotated visual feedback, (v) Spatial Topology Inference using Generative Pre-trained Transformer 4 omni (GPT-4o) for reasoning about physical layouts, and (vi) Intent-Based Command Synthesis with Gemini Flash to generate precise, executable control commands. The final Agentic Execution Module interfaces with the Tuya Smart Device Application Programming Interface (API), ensuring vendor-agnostic actuation. The system supports multilingual input and adapts to various environmental contexts, including smart homes and assisted living facilities. Results A user study involving 15 participants (aged 18–80, diverse educational backgrounds) was conducted to evaluate the effectiveness of the proposed method in comparison to the Google Home Assistant. Quantitative findings demonstrate a statistically significant reduction in cognitive workload, with NASA Task Load Index (TLX) scores decreasing by an average of 13.17 points ( p = 0.0013, Cohen’s d = 1.0381). Participants rated the proposed method higher in terms of ease of use (mean = 4.67) compared to Google Home (mean = 3.8) on a 5-point Likert scale. Qualitative feedback highlighted the intuitive nature of spatial context commands, reduced cognitive burden due to the elimination of device name memorization, and enhanced accessibility via support for regional languages. 93.3% of users preferred the proposed method over the baseline system. These results affirm the feasibility and user-centric benefits of integrating vision-language models for context-aware smart device control.

The chemical industry accounts for around 10% of global greenhouse gas emissions, making it a critical sector in the transition to net-zero emissions. Like chemical reactions that break and reform bonds, the industry itself must fundamentally restructure by replacing fossil-based feedstocks and fuels with sustainable alternatives. This transformation is particularly challenging due to high energy intensity, reliance on fossil carbon, tight integration within industrial clusters, narrow profit margins, long-lived capital assets, and global competition. Yet the sector remains indispensable, supplying materials that underpin global value chains. This dissertation develops an open-source, multi-scale optimization framework to support strategic decision-making for net-zero chemical clusters. The framework integrates three analytical levels: the plant level, capturing detailed process design and operations; the cluster level, representing shared utilities, by-product exchanges, and infrastructure; and the system level, linking clusters to the national energy system. A mixed-integer linear programming formulation enables simultaneous optimization of investment and operation at high temporal resolution, accounting for interdependencies within clusters and with external infrastructure. Long-term brownfield investment decisions are modeled through a rolling-horizon approach reflecting a myopic investor perspective. A hard link to a national energy system model ensures cross-scale consistency and reveals trade-offs and synergies between local feasibility and national climate objectives. Results show that flexible operation of electrified plants, particularly through expanded operating envelopes, improves both cost and emission performance at plant and system levels. While cluster integration can constrain operational flexibility, it enhances cost-effective CO2 mitigation. Infrastructure availability emerges as a key bottleneck, highlighting the need for early, coordinated investments in electricity and CO2 networks. Substantial emission reductions are achievable using near-market technologies that combine electrification, green hydrogen, and carbon capture and storage. However, absolute zero emissions are not feasible with these options alone. Achieving net-zero requires alternative fossil-free feedstocks, such as bio-based and synthetic carbon, whose competitiveness depends strongly on future policy frameworks, creating uncertainty about the long-term feedstock mix. Feedstock-flexible technologies, including electric steam methane reforming and methanol-to-olefins, consistently emerge as low-regret options that enhance system resilience. Technologies capable of exploiting low-cost variable electricity or feedstocks, such as CO2 electrolyzers, also offer strategic value. Overall, the analysis demonstrates that legacy assets strongly shape transition pathways, but net-zero chemical clusters are technically feasible and economically viable under supportive policy conditions. Early coordination between industrial and national strategies substantially reduces costs and accelerates emission reductions, whereas delayed action increases long-term expenditures. Methodologically, the framework bridges process- and system-level modeling; strategically, it identifies robust and cost-effective technology pathways. While uncertainty remains, postponing investment in anticipation of clarity is counterproductive. Timely deployment of flexible, low-regret technologies combined with coordinated infrastructure development provides the most resilient pathway toward climate-neutral chemical clusters.

Purpose The paper examines how low-constraint investors (LCIs) design, select and govern strategic real estate investment partnerships. LCIs are defined by long-horizon mandates, balance-sheet structures anchored by long-dated liabilities and investment missions and risk budgets that may accommodate illiquidity, volatility, complexity and duration. This group includes selected sovereign wealth funds, public pension schemes, life insurers, endowments and large family offices. LCI institutions are typically characterised by stable and expanding capital bases and the capacity to act as natural liquidity providers. Many are positive cash-flow generators. LCIs commonly access real estate markets through external execution partnerships. Well-structured partnership models enhance LCI optionality, supporting flexibility in investment pacing, recovery planning and programme extension or contraction, while providing execution partners with capital stability, flexible deployment windows and broader investment scopes. These features support relationship dynamics that favour long-term collaboration over transactional engagement. The paper discusses how LCIs evaluate alternative partnering models, establish alignment with external execution partners and govern strategic relationships over time. It offers practical guidance for execution partners on structuring and maintaining effective partnerships with LCI capital. Design/methodology/approach The paper adopts an expert opinion approach, drawing on insights from two senior industry practitioners. The first is a global real estate executive with over 25 years' leadership experience as CEO, CIO and Global Head of Real Estate across multi-billion-dollar investment platforms. The second is a co-founder of a real estate investment and asset management boutique (later acquired by a listed global firm) with 25 years of sector experience. Their combined perspectives inform a synthesis of institutional practices, behavioural patterns and governance models observed in real-world partnering arrangements and strategic programme design. Findings Strategic partnering is a core mechanism for LCIs to translate long-term objectives into executable investment programmes. The paper identifies primary partnering archetypes, mapping their suitability against dimensions such as control, integration depth, governance complexity and scalability. Partner selection is shown to hinge on behavioural alignment, operational infrastructure and strategic compatibility. Maintaining alignment over time requires deliberate incentive design, adaptive governance and institutional oversight capacity. In their absence, partnerships risk style drift, misalignment and performance erosion. Practical implications The paper offers practical tools to help LCIs and counterparties align capabilities with intent, select appropriate partnering models and implement governance structures that reinforce strategic continuity. It clarifies how to manage the behavioural and institutional expectations inherent in such relationships. Originality/value The paper reframes strategic partnerships not as vehicles for transactional performance but as structural components of institutional investment strategy. It provides LCIs and their partners with a framework for understanding and governing collaboration as a durable, evolving system.

ABSTRACT Metal halide perovskite indoor photovoltaics (IPVs) are the top contenders in terms of efficiency among emerging IPV technologies. The state‐of‐the‐art perovskite IPVs have already achieved reported efficiencies above 44%, indicating their significant potential. However, only a small percentage of reports discuss stability measurements, with approximately 7% adopting the International Summit on Organic PV Stability (ISOS) protocol for stability evaluation. A standard for the stability assessment of emerging thin film IPVs still lags. This research area remains largely unexplored, yet it is essential to the commercialization of IPV technologies. This review focuses on perovskite‐based IPVs, with an emphasis on device stability. It provides discussions of the origins of degradation in perovskite materials and their corresponding IPV devices. This is followed by an overview of various structure–property–stability strategies, including compositional, interface, and device design engineering for perovskite materials to improve their performance. Finally, the review outlines some existing stability test protocols that could apply to perovskite IPVs, including addressing mitigation issues, such as encapsulation, draws the attention of researchers in the field, and calls for the development of standardized stability test protocols for perovskite IPVs and for understanding how these tests correlate with actual indoor lifespans to enable the commercialization of perovskite IPVs.

Nitrous oxide (N 2 O) emissions from cropland soils are a significant source of greenhouse gases in Sub-Saharan Africa (SSA), yet fluxes and drivers remain poorly quantified. This review synthesizes findings from 39 field-based studies across Sub-Saharan Africa to assess current knowledge, key drivers, and research gaps. Additionally, this review offers a focused analysis on the effect of nitrogen fertilizer application on N 2 O emissions. The main finding is that emissions were strongly influenced by nitrogen input rates, precipitation, cropping systems, soil types, and measurement durations. Emission factors often diverge from IPCC Tier 1 defaults, suggesting the need for context-specific values. However, research is unevenly distributed: Studies from Kenya dominate the literature, while large areas, especially in Central Africa, lack data. Methodological inconsistencies further limit comparability across studies. We identify priorities for future research, including geographic expansion, standardized protocols, and improved integration of climate and agronomic variables. Strengthening the regional evidence base is essential for accurate inventories and for developing climate-smart agricultural policies tailored to SSA’s diverse agroecosystems.

Phase change materials (PCMs) are promising for solar-thermal energy storage, yet polymeric PCMs such as PEG suffer from supercooling, low thermal conductivity, and limited cycling stability. Here we investigate Co, Ni, and Co-Ni-doped 1,3,5-benzenetricarboxylate metal–organic frameworks (BTC MOFs) as porous matrices for PEG and systematically assess how metal substitution tunes the local coordination environment, melting behavior, and enthalpy of the resulting MOF–PCM composites. We find that disorder–order phase transitions are decisive in governing energy storage performance. Among the materials studied, Ni-BTC/PEG delivers the best results, enabled by its higher specific surface area, larger pore volume, and abundant surface hydroxyl groups that promote strong hydrogen bonding with PEG. Ni-BTC/PEG attains a high latent heat of 156 J/g, reduced supercooling (ΔT = 19.0 °C), and stable performance over 200 melt–freeze cycles, while its thermal conductivity increases by ∼38% relative to pure PEG. The composite also achieves a solar-thermal energy storage efficiency of 72.2%. These results identify the specific surface area and pore-size distribution as dominant, design-controllable parameters for MOF–PCM systems and position Ni-BTC/PEG as a strong candidate for practical solar-thermal energy storage applications.