• Energy Research
• 2021-2025close

Cities consume over 75% of natural resources, produce over 50% of global waste, and emit 60–80% of greenhouse gases. The scenario that by 2050 two thirds of the world population will live in cities, highlights how cities are still responsible for the growing consumption characterized by linear economic processes, with the production of various types of waste. In this unsustainable framework, the Circular Economy offers the opportunity to shape the urban system by means of rethinking the possibility to produce and use goods and services, exploring new ways to ensure long-term prosperity. The Circular City paradigm contains in fact all the principles of the Circular Economy: recovery, recycling, and sharing. In particular, Circular City also introduces actions related to the development of renewable energy communities, use of green materials, CO2 absorption approaches, and Proximity Cities. This work aims to develop a methodology to build a composite index (Circular City Index) capable of measuring the degree of implementation of urban policies that may enable an ecological transition of public assets. Circular City Index was applied to the military cluster of the city of Cagliari (Sardinia, Italy), a significant case study to guide circular policies in public properties for civil and military uses.

Abstract The introduction of Time of Use (ToU) tariffs may affect residential electricity consumers differently depending not only on their financial position but also time availability. The aim of this paper is to identify socio-demographic groups which may be financially advantaged or disadvantaged by the introduction of ToU tariffs. Assuming no behavioural change, we impose ToU tariffs on UK half hourly smart meter data and the synthetic demand profiles for different household composition generated using the 2014–2015 UK Time Use Survey data and optimisation of energy consumption per activity against the smart meter data. The distributional effects of ToU tariffs are obtained for customer segmentation and socio-demographic groups, and presented in terms of peak to off-peak ratios and impacts on the synthetic demand profiles. Findings on the distributional effects of ToU tariffs reveal regional differences (e.g. positive effects for high income groups in London) and household composition similarities (e.g. positive effects for households with children not in the high-income group).

Models simulating household energy demand based on different occupant and household types and their behavioral patterns have received increasing attention over the last years due the need to better understand fundamental characteristics that shape the demand side. Most of the models described in the literature are based on Time Use Survey data and Markov chains. Due to the nature of the underlying data and the Markov property, it is not sufficiently possible to consider long-term dependencies over several days in occupant behavior. An accurate mapping of longterm dependencies in behavior is of increasing importance, e.g. for the determination of flexibility potentials of individual households urgently needed to compensate supplyside fluctuations of renewable based energy systems. The aim of this study is to bridge the gap between social practice theory, energy related activity modelling and novel machine learning approaches. The weaknesses of existing approaches are addressed by combining time use survey data with mobility data, which provide information about individual mobility behavior over periods of one week. In social practice theory, emphasis is placed on the sequencing and repetition of practices over time. This suggests that practices have a memory. Transformer models based on the attention mechanism and Long short-term memory (LSTM) based neural networks define the state of the art in the field of natural language processing (NLP) and are for the first time introduced in this paper for the generation of weekly activity profiles. In a first step an autoregressive model is presented, which generates synthetic weekly mobility schedules of individual occupants and thereby captures long-term dependencies in mobility behavior. In a second step, an imputation model enriches the weekly mobility schedules with detailed information about energy relevant at home activities. The weekly activity profiles build the basis for multiple use cases one of which is modelling consistent electricity, heat and mobility demand profiles of households. The approach developed provides the basis for making high-quality weekly activity data available to the general public without having to carry out complex application procedures.

In order to have the best possible chance of achieving ‘decent work’ and ‘climate action’ as laid forth in the UN Sustainable Development Goals, government and policy makers must pay close attention to current time-use patterns, as well as the way these might change in the near future. Here we contribute to the existing literature on time-use behaviour through a systematic exploration of the relationship between working patterns and energy consumption from the perspective of time-use. Our starting point is the premise that different work arrangements impact the timing of energy demand not only in workplaces, but also at home. Using the data from the 2014–2015 UK time-use survey, we were able to capture patterns of time-use behaviours and to assess their relationship with daily energy consumption. We propose a systematic time-use-based approach for estimating residential energy consumption with regards to activity timing, activity location, activity coordination, and appliance type. We use this method to discover patterns in residential activities and energy consumption, as well as the causal relationship between residential energy consumption and work patterns. In this study, we unpack the heterogeneity in the work–energy relationship, particularly when comparing full-time and part-time workers. Our results suggest that full-time employees have a higher potential to reduce their energy use compared to part-time employees. We also discover a non-linear change in total energy consumption for respondents with varying levels of work time. Energy consumption reductions associated with differences in work schedules are greatest during the first few hours of the workday, but then level off. Our findings suggests that time-use data can provide useful insights for evaluating and possibly designing energy and labour-market policies.