Trends and drivers of end-use energy demand and the implications for managing energy in food supply chains: Synthesising insights from the social sciences

AbstractThe Climate Change Act commits the UK Government to an ambitious 80% reduction in greenhouse gas emissions by 2050; this paper provides a consumer focused framework to devise, inform and evaluate potential interventions to reduce energy demand and emissions in food supply chains. Adopting a Life cycle Assessment (LCA) framing we explore the relationship between production and consumption by reviewing trends in the food sector with implications for energy demand. Secondly, a multidisciplinary review of the literature on sustainable consumption is structured around the ISM (Individual, Social, Material Contexts) framework devised by Southerton et al., bringing insights from a range of theoretical perspectives. Combined, these frameworks complement LCA approaches to mapping and quantifying emissions hotspots in a supply chain in two ways.First, production and consumption must be considered with the ‘consumer’ interactive throughout, one of many factors affecting energy use at each stage, rather than restricted to the end of a supply chain. Second, when considering consumption patterns and how they might be changed, drawing on the insights of multiple disciplines allows for a fuller array of potential interventions to be identified. Given the complexity of the food system and the range of relevant sustainability goals, there are several areas in which the ‘preferred trajectories’ for ‘more sustainable’ consumption patterns are unclear, particularly where data on variation, causal relationships and longitudinal change is lacking. Technical and social understandings of ‘desirable’ change in the food sector must continue to be developed in parallel to achieve such challenging reductions in emissions

Low energy catering strategy: insights from a novel carbon-energy calculator

This paper presents a highly original carbon-energy calculator, designed with the aim of realistically and holistically evaluating the carbon and energy impacts of different food preparation options in delivering a restaurant menu. Its design (based on life-cycle principles) brings the customer demand (number, type and timings of meals served) during typical, peak and special weeks together with the food storage, warewashing, ventilation, cooking and hot holding appliance capacities, carbon emissions and energy usage in various states. An assessment of separate and specific behavioural, equipment maintenance, preparation and cooking strategies are performed. The baseline energy use results were validated to within 0.65% of the findings from an extensive and detailed monitoring study of a leading operator of UK public houses and restaurants [1]. Seven energy reduction scenarios were then assessed using the developed calculator. Potential energy savings of 58% (195 MWh) and emissions savings of 46% (55,224 kgCO2e) per year were indicated from replacing the chargrill, fryers and microwave combi ovens with two combi steam ovens and reducing freezing demand in the case study restaurant. This scenario projects reductions in energy use of 37.77 million kWh (£2 million) per year for the whole restaurant chain and up to 346 million kWh (£18.3 million) if applied to the whole case study organisation

The use of Gaussian process regression for wind forecasting in the UK

Wind energy has experienced remarkable growth in recent years, both globally and in the UK. As a low carbon source of electricity this progress has been, and continues to be, encouraged through legally binding targets and government policy. However, wind energy is non-dispatchable and difficult to predict in advance. In order to support continued development in the wind industry, increasingly accurate prediction techniques are sought to provide forecasts of wind speed and power output. This thesis develops and tests a hybrid numerical weather prediction (NWP) and Gaussian process regression (GPR) model for the prediction of wind speed and power output from 3 hours to 72 hours in advance and considers the impact of incorporating atmospheric stability in the prediction model. In addition to this, the validity of the model as a probabilistic technique for wind power output forecasting is tested and the economic value of a forecast in the UK electricity market is discussed. To begin with, the hybrid NWP and GPR model is developed and tested for prediction of 10 m wind speeds at 15 sites across the UK and hub height wind speeds at 1 site. Atmospheric stability is incorporated in the prediction model first by subdividing input data by Pasquill-Gifford-Turner (PGT) stability class, and then by using the predicted Obukhov length stability parameter as an input in the model. The model is developed further to provide wind power output predictions, both for a single turbine and for 22 wind farms distributed across the UK. This shows that the hybrid NWP and GPR model provide good predictions for wind power output in comparison to other methods. The hybrid NWP and GPR model for the prediction of near-surface wind speeds leads to a reduction in mean absolute percentage error (MAPE) of approximately 2% in comparison to the Met office NWP model. Furthermore, the use of the Obukhov length stability parameter as an input reduces wind power prediction errors in comparison to the same model without this parameter for the single turbine and for offshore wind farms but not for onshore wind farms. The inclusion of the Obukhov length stability parameter in the hub height wind speed prediction model leads to a reduction in MAPE of between 2 and iv 5%, dependent on the forecast horizon, over the model where Obukhov length is omitted. For the prediction of wind power at offshore wind farms, the inclusion of the Obukhov length stability parameter in the hybrid NWP and GPR model leads to a reduction in normalised mean absolute error (NMAE) of between 0.5 and 2%. The performance of the hybrid NWP and GPR model is also evaluated from a probabilistic perspective, with a particular focus on the appropriate likelihood function for the GPR model. The results suggest that using a beta likelihood function in the hybrid model for wind power prediction leads to better probabilistic predictions than implementing the same model with a Gaussian likelihood function. The results suggest an improvement of approximately 1% in continuous ranked probability score (CRPS) when the beta likelihood function is used rather than the Gaussian likelihood function. After considering new techniques for the prediction of wind speed and power output, the final chapter in this thesis considers the economic benefit of implementing a forecast. The economic value of a wind power forecast is evaluated from the perspective of a wind generator participating in the UK electricity market. The impact of forecast accuracy and the change from a dual imbalance price to a single imbalance price is investigated. The results show that a reduction in random error in a wind power forecast does not have a large impact on the average price per MWh generated. However, it has a more significant impact on the variation in price received on an hourly basis. When the systematic bias in a forecast was zero, a forecast with NMAE of 20% of capacity results in less than £0.05 deviation in mean price per MWh in comparison with a perfect forecast. However, the same forecast leads to an increase in standard deviation of up to £21/MWh. This indicates that whilst a reduction in random error in a forecast might not lead to an improvement in mean price per MWh, it can lead to a more stable income stream. In addition to this, Chapter 6 considers the use of the probabilistic and deterministic forecasts developed throughout this thesis to choose an appropriate value to bid in the UK electricity market. This shows that using a probabilistic forecast can limit a generator’s exposure to variable prices and decrease the standard deviation in hourly prices

A nexus perspective on competing land demands: Wider lessons from a UK policy case study

As nations develop policies for low-carbon transitions, conflicts with existing policies and planning tools are leading to competing demands for land and other resources. This raises fundamental questions over how multiple demands can best be managed. Taking the UK as an empirical example, this paper critiques current policies and practices to explore the interdependencies at the water-energy-food nexus. It considers how current land uses and related policies affect the UK’s resilience to climate change, setting out an agenda for research and practice relevant to stakeholders in land-use management, policy and modelling. Despite recent progress in recognising such nexus challenges, most UK land-related policies and associated science continue to be compartmentalised by both scale and sector and seldom acknowledge nexus interconnections. On a temporal level, the absence of an over-arching strategy leaves inter-generational trade-offs poorly considered. Given the system lock-in and the lengthy policy-making process, it is essential to develop alternative ways of providing dynamic, flexible, practical and scientifically robust decision support for policy-makers. A range of ecosystem services need to be valued and integrated into a resilient land-use strategy, including the introduction of non-monetary, physical-unit constraints on the use of particular services

Reimagining spaces of innovation for water efficiency and demand management: An exploration of professional practices in the English water sector

Social practice theories have established an important counter narrative to conventional accounts of demand. The core argument of this body of research is that, having focused on informing and incentivising behavioural change, demand management has largely neglected the social and material dimensions of everyday action that shape how and why resources are used. Despite making a compelling case for reframing demand management, there is limited evidence of practice-based approaches having gained a foothold in policy and business practices. This raises important questions regarding how and why certain modes of intervention are pursued at the expense of others and, more broadly, the factors that shape the pace and direction of innovation in demand management. In this paper we turn a practice-lens towards the professional practices of demand management. Using mixed methods, we demonstrate how specific modes of intervention emerge as priorities within a social, political, semiotic and material landscape of professional practice. Our empirical analysis highlights four particular contingencies of demand management that constrain the scope of interventions pursued. These are industry expectations and ideals; modes of collaboration; processes of evidencing action; and hydrosocial disturbances. We discuss the implications of these findings, making suggestions as to how innovation in the practices of demand management might be facilitated, and the role of academic research in this process

Engaging stakeholders in research to address water-energy-food (WEF) nexus challenges

The water–energy–food (WEF) nexus has become a popular, and potentially powerful, frame through which to analyse interactions and interdependencies between these three systems. Though the case for transdisciplinary research in this space has been made, the extent of stakeholder engagement in research remains limited with stakeholders most commonly incorporated in research as end-users. Yet, stakeholders interact with nexus issues in a variety of ways, consequently there is much that collaboration might offer to develop nexus research and enhance its application. This paper outlines four aspects of nexus research and considers the value and potential challenges for transdisciplinary research in each. We focus on assessing and visualising nexus systems; understanding governance and capacity building; the importance of scale; and the implications of future change. The paper then proceeds to describe a novel mixed-method study that deeply integrates stakeholder knowledge with insights from multiple disciplines. We argue that mixed-method research designs—in this case orientated around a number of cases studies—are best suited to understanding and addressing real-world nexus challenges, with their inevitable complex, non-linear system characteristics. Moreover, integrating multiple forms of knowledge in the manner described in this paper enables research to assess the potential for, and processes of, scaling-up innovations in the nexus space, to contribute insights to policy and decision making