Optimal Ranking and Sequencing of Non-domestic Building Energy Retrofit Options for Greenhouse Gas Emissions Reduction

Whether it is based on current emissions data or future projections of further growth, the building sector currently represent the largest and singular most important contributor to greenhouse gas (GHG) emissions globally. This notion is also supported by the Intergovernmental Panel on Climate Change based on projection scenarios for 2030 that emissions from buildings will be responsible for about one-third of total global emissions. As such, improving the energy efficiency of buildings has become a top priority worldwide. A significant majority of buildings that exist now will still exist in 2030 and beyond; therefore the greatest energy savings and carbon footprint reductions can be made through retrofit of existing buildings. A wide range of retrofit options are readily available, but methods to identify optimal solutions for a particular abatement project still constitute a major technical challenge. Investments in building energy retrofit technologies usually involve decision-making processes targeted at reducing operational energy consumption and maintenance bills. For this reason, retrofit decisions by building stakeholders are typically driven by financial considerations. However, recent trends towards environmentally conscious and resource-efficient design and retrofit have focused on the environmental merits of these options, emphasising a lifecycle approach to emissions reduction. Retrofit options available for energy savings have different performance characteristics and building stakeholders are required to establish an optimal solution, where competing objectives such as financial costs, energy consumption and environmental performance are taken into account. These key performance parameters cannot be easily quantified and compared by building stakeholders since they lack the resources to perform an effective decision analysis. In part, this is due to the inadequacy of existing methods to assess and compare performance indicators. Current methods to quantify these parameters are considered in isolation when making decisions about energy conservation in buildings. To effectively manage the reduction of lifecycle environmental impacts, it is necessary to link financial cost with both operational and embodied emissions. This thesis presents a novel deterministic decision support system (DSS) for the evaluation of economically and environmentally optimal retrofit of non-domestic buildings. The DSS integrates the key variables of economic and net environmental benefits to produce optimal decisions. These variables are used within an optimisation scheme that consists of integrated modules for data input, sensitivity analysis and takes into account the use of a set of retrofit options that satisfies a range of criteria (environmental, demand, cost and resource constraints); hierarchical course of action; and the evaluations of ‘best’ case scenario based on marginal abatement cost methods and Pareto optimisation. The steps involved in the system development are presented and its usefulness is evaluated using case study applications. The results of the applications are analysed and presented, verifying the feasibility of the DSS, whilst encouraging further improvements and extensions. The usefulness of the DSS as a tool for policy formulation and developments that can trigger innovations in retrofit product development processes and sustainable business models are also discussed. The methodology developed provides stakeholders with an efficient and reliable decision process that is informed by both environmental and financial considerations. Overall, the development of the DSS which takes a whole-life CO2 emission accounting framework and an economic assessment view-point, successfully demonstrates how value is delivered across different parts of the techno-economic system, especially as it pertains to financial gains, embodied and operational emissions reduction potential.Petroleum Technology Development Fund (PTDF), Nigeri

Enhancing life cycle product design decision-making processes: insights from normal accident theory and satisficing framework

Life Cycle Assessment (LCA), a computational tool used in sustainable product design decision making, faces challenges in the interpretation phase, where conclusions are drawn for improvement recommendations. This necessitate the need to incorporate into LCA management-relevant theoretical underpinnings to strengthen decision-making processes. Comparative LCA case studies of lead-based piezoelectric material (lead zirconate titanate – PZT) and lead-free alternatives (potassium sodium niobate – KNN, sodium bismuth titanate – NBT), was employed to demonstrate how two theoretical lenses, namely Normal Accident Theory (NAT) and Satisficing Framework, are used inductively to enhance decision making regarding unintended consequences in the value chain revealed by LCA outputs. The environmental analysis reveals NAT attributes of interactive complexity and tight coupling in piezoelectric materials, based on systems’ predictability, observability, and applicability, leading to the introduction of Environmental Impact Accident (EIA) as a new concept. EIA facilitates early assessment of the associated complexities influencing the sustainability credentials of piezoelectric materials, informing mitigation strategies. However, a conundrum is created when considering multiple objectives that conflict or trade-off between alternative piezoelectric materials with different environmental and health impacts across the value chain but was resolved using the Satisficing Framework. The paper concludes by proposing theoretical and practical policy options for incorporating LCA into product life cycle decision making

Enhancing life cycle product design decision-making processes : insights from normal accident theory and the satisficing framework

Life Cycle Assessment (LCA), a computational tool for enabling sustainable product design decision making, faces challenges in the interpretation phase, where conclusions are drawn for improvement recommendations. This necessitate the need to incorporate into LCA management-relevant theoretical underpinnings to strengthen decision-making processes. Comparative LCA case studies of lead-based piezoelectric material (lead zirconate titanate – PZT) and lead-free alternatives (potassium sodium niobate – KNN, sodium bismuth titanate – NBT), was employed to demonstrate how two theoretical lenses, namely Normal Accident Theory (NAT) and the Satisficing Framework, are used inductively to enhance decision making regarding unintended consequences in the value chain. By operationalising NAT, which has hitherto focused on the consequences of physical accidents, as a life cycle engineering-based methodology, NAT attributes of interactive complexity and tight coupling was revealed in piezoelectric materials, based on environmental systems’ predictability, observability, and applicability. This led to the introduction of Environmental Impact Accident (EIA) as a new concept, facilitating an early assessment of the associated complexities influencing the sustainability credentials of piezoelectric materials whilst informing mitigation strategies. However, when considering multiple objectives that conflict or trade-off between alternative piezoelectric materials with different environmental and health impacts across the value chain, a conundrum is created but resolved using the Satisficing Framework. The paper concludes by proposing theoretical and practical policy options for incorporating LCA into product life cycle decision making

Comparative environmental profile assessments of commercial and novel material structures for solid oxide fuel cells

Globally, the issue of climate change due to greenhouse gas (GHG) emissions is now broadly acknowledged as one of the major challenges facing humankind that requires urgent attention. Accordingly, considerable efforts on clean energy technologies and policy recommendations have been developed to address this challenge. Solid oxide fuel cells (SOFCs) have been touted to play a role in achieving a reduction in global GHG emissions, offering numerous advantages including higher efficiencies and reduced emissions, over other conventional methods of energy generation. The increasing recognition and emphasis on fuel cells as a representative power generation system of the future has raised concerns over their environmental profile. Extensive research regarding the environmental profile of current structures of SOFCs can be found in the literature, but none consider the use of new materials to achieve lower environmental impacts. This research fills the gap and presents a comparison of the environmental profile of three SOFC structures: a commercially available structure, and two intermediate temperature structures, one using erbia-stabilised bismuth oxide electrolytes and a proposed structure using strontium-doped sodium bismuth titanate electrolytes. Using a functional unit of kg/100 kW of power output for each of the SOFC structures (excluding the interconnects), within a hybrid life cycle analysis framework, the environmental hotspots across the supply chains of each SOFC type are identified, quantified and ranked. The results show the use of these novel material combinations leads to a reduction in embodied materials and toxicological impact but higher electrical energy consumption during fabrication, in comparison to commercial SOFCs. The findings support the move to reduce the operating temperatures of SOFCs using these novel material architectures, which leads to an overall reduction in environmental impact due to the lower operational energy requirement of the chosen material constituents

Social and demographic factors associated with morbidities in young children in Egypt: A Bayesian geo-additive semi-parametric multinomial model.

Globally, the burden of mortality in children, especially in poor developing countries, is alarming and has precipitated concern and calls for concerted efforts in combating such health problems. Examples of diseases that contribute to this burden of mortality include diarrhoea, cough, fever, and the overlap between these illnesses, causing childhood morbidity and mortality. Methods: To gain insight into these health issues, we employed the 2008 Demographic and Health Survey Data of Egypt, which recorded details from 10,872 children under five. This data focused on the demographic and socio-economic characteristics of household members. We applied a Bayesian multinomial model to assess the area-specific spatial effects and risk factors of co-morbidity of fever, diarrhoea and cough for children under the age of five. Results: The results showed that children under 20 months of age were more likely to have the three diseases (OR: 6.8; 95% CI: 4.6-10.2) than children between 20 and 40 months (OR: 2.14; 95% CI: 1.38-3.3). In multivariate Bayesian geo-additive models, the children of mothers who were over 20 years of age were more likely to have only cough (OR: 1.2; 95% 2 CI: 0.9-1.5) and only fever (OR: 1.2; 95% CI: 0.91-1.51) compared with their counterparts. Spatial results showed that the North-eastern region of Egypt has a higher incidence than most of other regions. Conclusions: This study showed geographic patterns of Egyptian governorates in the combined prevalence of morbidity among Egyptian children. It is obvious that the Nile Delta, Upper Egypt, and south-eastern Egypt have high rates of diseases and are more affected. Therefore, more attention is needed in these areas. Funding: The authors have no support or funding to report. Competing Interests: The authors have declared that no competing interests exist

Measuring the Environmental Sustainability Performance of Global Supply Chains: a Multi-Regional Input-Output analysis for Carbon, Sulphur Oxide and Water Footprints

Measuring the performance of what an environmentally sustainable supply chain has become a challenge despite the convergence of the underlining principles of sustainable supply chain management. This challenge is exacerbated by the fact that supply chains are inherently dynamic and complex and also because multiple measures can be used to characterize performances. By identifying some of the critical issues in the literature regarding performance measurements, this paper contributes to the existing body of literature by adopting an environmental performance measurement approach for economic sectors (primary, secondary and tertiary sectors). It uses economic sectors and evaluates them on a sectoral level in specific countries as well as part of the Global Value Chain based on the established multi-regional input-output (MRIO) modelling framework. The MRIO model has been used to calculate direct and indirect (that is supply chain or upstream) environmental effects such as CO2, SO2, biodiversity, water consumption and pollution to name just a few of the applications. In this paper we use MRIO to calculate emissions and resource consumption intensities and footprints, direct and indirect impacts, and net emission flows between countries. These are exemplified by using carbon emissions, sulphur oxide emissions and water use in two highly polluting industries; Electricity production and Chemical industry in 33 countries, including the EU-27, Brazil, India and China, the USA, Canada and Japan from 1995 to 2009. Some of the results highlights include: On average, direct carbon emissions in the electricity sector across all 27 member states of the EU was estimated to be 1368 million tonnes and indirect carbon emissions to be 470.7 million tonnes per year representing 25.6% of the EU-27 total carbon emissions related to this sector. It was also observed that from 2004, sulphur oxide emissions intensities in electricity production in India and China have remained relatively constant at about 62.8 gSOx/$and 84.4 gSOx/$ although being higher than in other countries. In terms of water use, the high water use intensity in China (1040.27 litres/$) and India (961.63 litres/$), which are among the highest in the sector in the electricity sector is exacerbated by both countries being ranked as High Water Stress Risk countries. The paper also highlights many merits of the MRIO including: a 15-year time series study (which provides a measurement of environmental performance of key industries and an opportunity to assess technical and technological change during the investigated time period), a supply chain approach that provides a consistent methodological framework and accounts for all upstream supply chain environmental impacts throughout entire global supply chains. The paper also discusses the implications of the study to environmental sustainability performance measurement in terms of the level of analysis from a value chain hierarchy perspective, methodological issues, performance indicators, environmental exchanges and policy relevance

Roadmap on energy harvesting materials

Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere

Integrating economic considerations with operational and embodied emissions into a decision support system for the optimal ranking of building retrofit options

In the UK, 87% of dwellings and 60% of non-domestic buildings that will be standing in 2050 have already been built. Therefore, the greatest energy savings and emissions reductions will be achieved through retrofit of existing buildings. This usually involves decision-making processes targeted at reducing operational energy consumption and maintenance bills. For this reason, retrofit decisions by building stakeholders are typically driven by financial considerations. However, recent trends towards environmentally conscious design and retrofit have focused on the environmental merits of these options, emphasising a lifecycle approach to emissions reduction. Building stakeholders cannot easily quantify and compare the sustainability impacts of retrofit options since they lack the resources to perform an effective decision analysis. In part, this is due to the inadequacy of existing methods to assess and compare the cost, operational performance and environmental merit of the options. Current methods to quantify these parameters are considered in isolation when making decisions about energy conservation in buildings. To effectively manage the reduction of lifecycle environmental impacts, it is necessary to link financial cost with both operational and embodied emissions. This paper presents a robust Decision Support System which integrates economic and net environmental benefits (including embodied and operational emissions) to produce optimal decisions based on marginal abatement cost methods and Pareto optimisation. The implication of the DSS within the current climate change policies is also discussed. Overall, the methodology developed provides stakeholders with an efficient and reliable decision process that is informed by both environmental and financial considerations

Macroeconomic Policy effects on development transition – Views from Agent based model

Assessing the impact of a policy before implementation has often been a difficult feat to achieve, both at the macroeconomic and microeconomic levels. This challenge becomes even more daunting in the context of a developing country and has encouraged enormous amount of research over an extended period of time using different models. Traditional models for assessing the impact of policy implementation are fragmented given the assumption that factors affecting such policies are homogeneous whilst neglecting the interactions between various markets. Agent-based modelling can overcome this limitation given its capability to provide a micro-founded macroeconomic analysis of policy, within a variety of economic conditions and policy objectives to facilitate the understanding of the observed response. Against this backdrop, the current work adopts an agent based framework to investigate three distinct policies that have been employed by some advanced countries towards achieving sustainable development goals. This is carried out to derive lessons and explore opportunities for enhancing policy implementation in developing countries. Agent representation involve decisions by involve manufacturers, households (final goods consumers), banks (loan issues & bankruptcy warning), central bank (Basel monitor & monetary policy activity), government (fiscal policy role) and singular energy market supplier, which enables consideration of: the impact of unemployment benefits on the labour market; the impact of capital investment subsidy on investment levels; and the impact of energy taxes (in the form of an increase in the energy cost structure) on a developing country’s macroeconomic system. Results shows that an increase in unemployment benefits led to improvements in the labour market and reduction in wage margin, with a limitation threshold of 50%. Additionally, it was observed that the economy becomes more sensitive to energy tax due to higher unemployment benefits, although the diminishing nature of the relationship was quite noticeable

Numerical and random forest modelling of the impact response of hierarchical auxetic structures

Major sources of concern when auxetic protective structures are deployed in service of mission-critical applications encompass the triggering of high impact stress and the weakening of the structures’ elastic strength in response to the impact events. The current prevailing approach to assessing the impact resistance of these structures broadly hinges on mechanics-informed nonlinear finite element (FE) analysis. However, this method is computationally expensive and ill-suited for tackling the implementation of automated condition monitoring schemes. To address these issues, first, this paper proposes a hybrid hierarchical auxetic structure named Hybrid-Hierarchical Re-entrant Honeycomb (HHRH) that is endowed with impact stress-reducing capabilities. Next, using explicit FE, the investigation uncovers the interplay between the key geometric features of this HHRH auxetic structure and the impact performance under low, intermediate, and high crushing velocities. The outcome of the nonlinear explicit FE simulations is then unified with random forests (RF) scheme towards the establishment of intelligent auxetic structural systems. The results revealed that the developed HHRH maintained the auxeticity of the regular re-entrant auxetic and exhibited superior performance in some crushing strain regions. Moreover, the HHRH structure manifests up to an 85 % reduction in peak stress and the proposed reinforcement boosts the auxetic property by up to 23 % when compared to the regular re-entrant auxetic structure under high-velocity applications. Regarding the established data-driven RF-enabled machine learning model, its predictive strength with optimally-tuned hyperparameters is demonstrated to excellently capture the nonlinear multi-modal crushing stress response at various crushing strains, velocities, and geometric variations. Data Availability: The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study