Joanne Patterson

Advances in data science and artificial intelligence (AI) offer unprecedented opportunities to provide actionable insights, drive innovative solutions, and create long-term strategies for sustainable development in response to the triple existential crises facing humanity: climate change, pollution, and biodiversity loss. The rapid development of AI models has been the subject of extensive debate and is high on the political agenda, but at present the vast potential for AI to contribute positively to informed decision making, improved environmental risk management, and the development of technological solutions to sustainability challenges remains underdeveloped. In this paper, we consider four inter-dependent areas in which data science and AI can make a substantial contribution to developing sustainable future interactions with the environment: (i) quantification and tracking progress towards the United Nations Sustainable Development Goals; (ii) embedding AI technologies to reduce emissions at source; (iii) developing systems to increase our resilience to natural hazards; (iv) Net Zero and the built environment. We also consider the wider challenges associated with the widespread use of AI, including data access and discoverability, trust and regulation, inference and decision making, and the sustainable use of AI.

This paper presents the results for the operating energy performance of the smart operation for a low carbon energy region (SOLCER) house. The house design is based on a ‘systems’ approach, which integrates the building technologies for electrical and thermal energy systems, together with the architectural design. It is based on the concept of ‘energy positive’ buildings, utilising renewable energy systems which form part of the building envelope construction. The paper describes how the building energy model HTB2, with a range of additional ‘plugins’, has been used to simulate specific elements of the design and the overall energy performance of the house. Measurement data have been used in combination with the energy simulation results to evaluate the performance of the building together with its systems, and identifying the energy performance of individual components of the building. The study has indicated that an energy-positive performance can be achieved through an integrative systems approach. The analysis has indicated that the house, under normal occupancy, needs to import about 26% of its energy from the grid, but over the year its potential export to import ratio can reach 1.3:1. The paper discusses the performance gap between design and operation. It also considers the contribution of a transpired solar air collector (TSC) to space heating. The results have been used to gain a detailed understanding of energy-positive performance.

This paper presents an evaluation of the economic and technical feasibility of a renewable-led low carbon house in the UK. A holistic systems-based approach to achieve energy positive house has been taken. Long-term economic and technical feasibility analysis have been carried out based on a validated thermal and energy model of the house. The economic analysis employs the Return on Investment (ROI) method and considers changes to government financial support and technology progress over time. Results show that the extra investment on the house, compared with that for building a standard social house of similar size, can be paid back within the system lifespan under both the old Feed-in Tariff and its proposed replacement with reduced financial support. Variants examined in the technical feasibility analysis include housing type, orientation and location. Results show that the house can be replicated to achieve an energy positive performance for all variant combinations. Among the variants, location has the highest impact on building performance including annual electricity import, CO₂ emission and electricity self-sufficient ratio, with South UK performing better than North UK. This implies that more efficient or advanced technologies would be needed to achieve similar building or system performance in areas of unfavourable climate. The outcome of the research has demonstrated the affordability of the energy positive house, and the technical feasibility of its replication with different housing types, orientations and locations in the UK. This study supports the wide scale replication of this affordable systems-based approach in domestic building design and construction when incorporating appropriate technologies.

The first-ever legally binding global climate deal that will be adopted by 195 countries was introduced in Paris in 2015, highlighting that climate change is being recognised as a real and urgent global problem. Legislative interventions need to be accompanied by significant action across all sectors of the built environment through reducing energy demand, providing energy supply from low carbon sources and combining with this with energy storage to enable necessary targets to be met. Retrofitting existing buildings is critical to making these cuts as 80% of buildings currently in existence will still be present in 2050. These retrofits need to be undertaken rapidly using replicable and affordable solutions that benefit both the householder whilst significantly reducing emissions. This paper will present an evaluation of a £9.6 million regional scale retrofit programme funded under the Welsh Governments Arbed 1 Programme which aimed to reduce fuel poverty, reduce carbon emissions and support the energy efficiency and renewable supply chain and encourage recruitment and training in the sector. Results have been obtained from desk top data collection and energy modelling calculations. The evaluation work presents the technical, environmental and economic impacts of the programme and demonstrates lessons learnt to help improve the implementation of the other regional retrofit projects providing evidence of the impacts of a large scale retrofit programme that are necessary for the deep carbon reductions required in the near future.

To understand and facilitate modal shift to more sustainable modes of transport, there is a need to model accessibility and connectivity at an urban scale using data collection and modelling procedures that require less data and specialist input than traditional transport models. The research described in this paper uses spatial analysis modelling procedures based on space syntax to investigate the potential to model aggregate traffic flows at an urban scale. The research has demonstrated that space syntax modelling is an effective means of representing an urban scale motor traffic network; however, modifications to the original model were required to achieve a correlation between modelled and measured motor traffic flow comparable to other modelling procedures. Weighting methods were tested with ‘boundary weighting’ found to be effective at representing traffic crossing the boundary of an isolated urban sub-area, but not so effective at an urban scale. ‘Road weighting’ was found to be effective in improving model performance by representing traffic flows along routes according to a national classification scheme. The modelling approach has the potential to be extremely useful at an early planning stage to represent changes to flows across the network and to be useful for different modes.

Background: The aim of this body of research is to determine whether injuries in the home are more common in particular types of housing. Previous home injuries research has tended to focus on behaviours or the provision of safety equipment to families with young children. There has been little consideration of the physical environment. This study reports methodological developments in database linkage and analysis to improve researchers abilities to utilise large administrative and clinical databases to carry out health and health services research. Methods: The study involved linking a database of home injuries obtained from an emergency department surveillance system with an external survey of all homes in an area and population denominators for home types derived from a health service administrative database. Analysis of injury incidence by housing type was adjusted for potential biases due to deprivation and distance to hospital. For non-injured individuals data confidentiality considerations required the deprivation and distance measures be imputed. The process of randomly imputing these variables and the testing of the validity of this approach is detailed. Results: There were 14,081 first injuries in 112,248 residents living in 54,081 homes over a two-year period. The imputation method worked well with imputed and observed measures in the injured group being very similar. Re-randomisation and a repeated analysis gave identical results to the first analysis. One particular housing type had a substantially elevated odds ratio for injury occurrence, OR = 2.07 (95% CI: 1.87 to 2.30). Conclusions: The method of data linkage, imputation and statistical analysis used provides a basis for improved analysis of database linkage studies.