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Peatlands have often been neglected in Earth system models (ESMs). Where they are included, they are usually represented via a separate, prescribed grid cell fraction that is given the physical characteristics of a peat (highly organic) soil. However, in reality soils vary on a spectrum between purely mineral soil (no organic material) and purely organic soil, typically with an organic layer of variable thickness overlying mineral soil below. They are also dynamic, with organic layer thickness and its properties changing over time. Neither the spectrum of soil types nor their dynamic nature can be captured by current ESMs. Here we present a new version of an ESM land surface scheme (Joint UK Land Environment Simulator, JULES) where soil organic matter accumulation – and thus peatland formation, degradation and stability – is integrated in the vertically resolved soil carbon scheme. We also introduce the capacity to track soil carbon age as a function of depth in JULES and compare this to measured peat age–depth profiles. The new scheme is tested and evaluated at northern and temperate sites. This scheme simulates dynamic feedbacks between the soil organic material and its thermal and hydraulic characteristics. We show that draining the peatlands can lead to significant carbon loss, soil compaction and changes in peat properties. However, negative feedbacks can lead to the potential for peatlands to rewet themselves following drainage. These ecohydrological feedbacks can also lead to peatlands maintaining themselves in climates where peat formation would not otherwise initiate in the model, i.e. displaying some degree of resilience. The new model produces similar results to the original model for mineral soils and realistic profiles of soil organic carbon for peatlands. We evaluate the model against typical peat profiles based on 216 northern and temperate sites from a global dataset of peat cores. The root-mean-squared error (RMSE) in the soil carbon profile is reduced by 35 %–80 % in the best-performing JULES-Peat simulations compared with the standard JULES configuration. The RMSE in these JULES-Peat simulations is 7.7–16.7 kg C m -3 depending on climate zone, which is considerably smaller than the soil carbon itself (around 30–60 kg C m -3). The RMSE at mineral soil sites is also reduced in JULES-Peat compared with the original JULES configuration (reduced by ∼ 30 %–50 %). Thus, JULES-Peat can be used as a complete scheme that simulates both organic and mineral soils. It does not require any additional input data and introduces minimal additional variables to the model. This provides a new approach for improving the simulation of organic and peatland soils and associated carbon-cycle feedbacks in ESMs. [ABSTRACT FROM AUTHOR]

Travel-based multitasking, i.e. using travel time to conduct enjoyable and/or productive activities, is the subject of an increasing number of theoretical and empirical studies. Most existing studies focus on modelling the choice of which activities people conduct while travelling, and a limited number of papers also focuses on their duration. The novelty of this study with respect to this literature is two-fold. Firstly, we specifically study the engagement in different online activities while travelling, and apply the state-of-the-art Multiple Discrete-Continuous Extreme Value (MDCEV) model to jointly model the choice and duration of multiple activities. We apply this model to data collected face-to-face from train passengers in the UK. We find that activity choice and duration is explained by both passenger and trip characteristics, especially trip purpose, ticket type and day/time of the trip. Secondly, we show how such modelling can assist in investment appraisal, in particular by providing insights into lower- and upper- bound estimates of the proportion of the entire travel time spent working, itself of importance in, for example, valuation of business travel time using the so-called Hensher Equation. We present a detailed discussion of how the findings from our work contribute to the broader discourse around the nature of travel time and its valuation. [ABSTRACT FROM AUTHOR]

Peatlands have often been neglected in Earth System Models (ESMs). Where they are included, they are usually represented via a separate, prescribed grid cell fraction that is given the physical characteristics of a peat (highly organic) soil. However, in reality soils vary on a spectrum between purely mineral soil (no organic material), and purely organic soil, typically with an organic layer of variable thickness overlying mineral soil below. They are also dynamic, with organic layer thickness and its properties changing over time. Neither the spectrum of soil types nor their dynamic nature can be captured by current ESMs. Here we present a new version of an ESM land surface scheme (Joint UK Land Environment Simulator, JULES) where soil organic matter accumulation - and thus peatland formation, degradation and stability - is integrated in the vertically-resolved soil carbon scheme. We also introduce the capacity to track soil carbon age as a function of depth in JULES, and compare this to measured peat age-depth profiles. This scheme simulates dynamic feedbacks between the soil organic material and its thermal and hydraulic characteristics. We show that draining the peatlands can lead to significant carbon loss along with soil compaction and changes in peat properties. However, negative feedbacks can lead to the potential for peatlands to rewet themselves following drainage. These ecohydrological feedbacks can also lead to peatlands maintaining themselves in climates where peat formation would not otherwise initiate in the model, i.e. displaying some degree of resilience. The new model produces similar results to the original model for mineral soils, and realistic profiles of soil organic carbon for peatlands. In particular the best performing configurations had root mean squared error (RMSE) in carbon density for peat sites of 7.7-16.7 kgC m−3 depending on climate zone, when compared against typical peat profiles based on 216 sites from a global dataset of peat cores. This error is considerably smaller than the soil carbon itself (around 30-60 kgC m−3) and reduced by 35-80 % compared with standard JULES. The RMSE at mineral soil sites is also smaller in JULES-Peat than JULES itself (reduced by ~30-50 %). Thus JULES-Peat can be used as a complete scheme that simulates both organic and mineral soils. It does not require any additional input data and introduces minimal additional variables to the model. This provides a new approach for improving the simulation of organic and peatland soils, and associated carbon-cycle feedbacks in ESMs, which other land surface models could follow. [ABSTRACT FROM AUTHOR]

Background The emergence of artificial intelligence (AI) provides opportunities for demand management of sexual and reproductive health services. Conversational agents/chatbots are increasingly common, although little is known about how this technology could aid services. This study aimed to identify barriers and facilitators for engagement with sexual health chatbots to advise service developers and related health professionals. Methods In January-June 2020, we conducted face-to-face, semi-structured and online interviews to explore views on sexual health chatbots. Participants were asked to interact with a chatbot, offering advice on sexually transmitted infections (STIs) and relevant services. Participants were UK-based and recruited via social media. Data were recorded, transcribed verbatim and analysed thematically. Results Forty participants (aged 18-50 years; 64% women, 77% heterosexual, 58% white) took part. Many thought chatbots could aid sex education, providing useful information about STIs and sign-posting to sexual health services in a convenient, anonymous and non-judgemental way. Some compared chatbots to health professionals or Internet search engines and perceived this technology as inferior, offering constrained content and interactivity, limiting disclosure of personal information, trust and perceived accuracy of chatbot responses. Conclusions Despite mixed attitudes towards chatbots, this technology was seen as useful for anonymous sex education but less suitable for matters requiring empathy. Chatbots may increase access to clinical services but their effectiveness and safety need to be established. Future research should identify which chatbots designs and functions lead to optimal engagement with this innovation. [ABSTRACT FROM AUTHOR]

The sophistication in the demand management approaches in both transport and energy sectors and their interaction call for modelling approaches that consider both sectors jointly. For agent-based microsimulation models of travel demand and energy consumption, this implies the necessity to ensure consistent representation of user behaviour with respect to mobility and energy consumption behaviours across the model components. Therefore this paper proposes a microeconomic framework, termed the HOT model (Home, Out-of-home, Travel) grounded in the goods-leisure paradigm, but extended to incorporate emerging activity-travel behaviour patterns and their energy consumption implications. We discuss how the model can be operationalised and embedded within agent-based frameworks with a case study using time use and energy consumption data from the UK. [ABSTRACT FROM AUTHOR]

Energy demand modelling has been widely applied in various contexts, including power plant generation, building energy simulation and demand-side management. However, it is still an ongoing research topic in terms of the choice of modelling method, feature engineering for data-driven methods, the application contexts and the type of data used. In the residential sector, survey-based and meter-based approaches are categorised according to the type of input data used, i.e. the activity records from the time use survey and energy consumption from meters respectively. These two paradigms are not necessarily easy to combine, which warrants the questions of when one may be preferred over the other and whether they need to be combined despite the significant data requirements. Other details also have a huge impact on the data structure and performance of the energy demand model, including the choice of influential factors, the historical time window of factors selected, the split between training and test data, and the choice of machine learning (ML) algorithm. There is a lack of comparative research to guide researchers and practitioners in developing energy demand modelling capability, specifically as it pertains to these issues. This study analyses three groups of test scenarios in a multi-household residential context based in the UK. Six ML algorithms (LightGBM, Random forest, ANN, SVM, KNN and LSTM), with eight sets of various influential features, at four different historical time window widths and two train-test splits were compared. An appropriate methodology was designed to capture the temporal impact of activities on energy demand and represent the overlap and interaction of activities. The results show that the combination of meter-based and survey-based energy demand models performs better in terms of modelling accuracy and robustness against sudden load variation. Particularly, integrating energy tariffs, household and individual attributes, appliance usage and general activity features can improve the energy demand model. Among the ML algorithms, LightGBM and ANN perform better than other algorithms while LSTM and SVM may not be suitable in this multi-household short monitoring context. [ABSTRACT FROM AUTHOR]

Planning and managing an energy system in a district require a comprehensive understanding and accurate modelling of people's occupancy and circulation among multiple buildings. Due to the lack of occupancy modelling tools for district scale analysis, energy models still use simplified occupancy patterns provided in building codes and standards. However, the simplified information restricts the reflection of complex occupancy patterns driven by urban heterogeneity. This paper fills this research gap and presents a hazard-based model combined with nested copula dependence to describe the complex occupants' interactions between buildings in a district, enabling the characterisation of irregular occupancy patterns in special cases. The proposed model is calibrated using Wi-Fi authentication data from the Imperial College London (UK) South Kensington campus and is validated using the following days of the same data by evaluating the performance of predicted occupancy patterns both on average and day by day. The validation results demonstrate that the model can accurately capture the effects of the urban environment on occupancy duration and choice of transition within a district. Mean Absolute Percentage Errors (MAPEs) of average-pattern predictions are between 7% and 16% for most buildings, though a bit lower in accuracy for the Library and Food Hall predictions with MAPEs of 32%–36%. We also discuss the contributions of the proposed occupancy model to potential future applications, including efficient building space use, local energy planning and management. • A hazard-based model with copulas is proposed to model occupancy at district level. • The model permits inter-building dependence in occupancy patterns using copulas. • The proposed model well predicts regular and irregular building occupancy patterns. • Combining urban heterogeneity into occupancy simulation helps strategic planning. • Empirical results prove exogenous impacts on inter-building occupancy dependence. [ABSTRACT FROM AUTHOR]

The ability to accurately model and predict timing and duration of activities for different individuals is essential for successful and widespread Demand Side Response (DSR) policies, especially in the residential sector. Understanding what people do during the day and what factors influence their activity participation decisions is important for planning an effective DSR strategy to harness the end-user flexibility. The recent Covid-19 pandemic has shown how much activities can be shifted to a virtual mode in the presence of mobility restrictions. Further, participation in activities via digital devices (virtual activity participation) has spread across society. Such virtual activities, including teleworking, online shopping, and virtual social interactions, are observed to explicitly impact travel behaviour and activity scheduling. And yet, activity-based models of mobility and energy demand do not accommodate the trade-offs between activity types, location and virtual activity participation. This paper presents a model of activity participation that captures the relationship between the three dimensions of: activity type (such as work, study, shopping), activity location (in-home, out-of-home), and activity modality (in-person, virtual). A Multiple-Discrete Continuous Extreme Value (MDCEV) model structure is applied, and the empirical analysis is undertaken using the 2015 United Kingdom Time Use Survey (UKTUS). The model results provide insights for better understanding of the trade-offs made by individuals as they participate in and allocate time across a set of activity type-location-modality alternatives, and the heterogeneity in these trade-offs. Further, holdout sample validation and policy scenario analysis exercises are presented to demonstrate the reliability and suitability of the model for policy implications. The empirical results presented in our paper suggest that this framework embedded in an activity and agent-based simulator of energy demand will enable us to test a variety of scenarios related to end-user flexibility and activity behaviour change, including consideration of virtual activity participation trends. [ABSTRACT FROM AUTHOR]