Upholding labour productivity under climate change: an assessment of adaptation options

Changes in labour productivity feed through directly to national income. An external shock, like climate change, which may substantially reduce the productivity of workers is therefore a macroeconomic concern. The biophysical impact of higher temperatures on human performance is well documented. Less well understood are the wider effects of higher temperatures on the aggregate productivity of modern, diversified economies, where economic output is produced in contexts ranging from outdoor agriculture to work in air-conditioned buildings. Working conditions are at least to some extent the result of societal choices, which means that the labour productivity effects of heat can be alleviated through careful adaptation. A range of technical, regulatory/infrastructural and behavioural options are available to individuals, businesses and governments. The importance of local contexts prevents a general ranking of the available measures, but many appear cost-effective. Promising options include the optimization of working hours and passive cooling mechanisms. Climate-smart urban planning and adjustments to building design are most suitable to respond to high base temperature, while air conditioning can respond flexibly to short temperature peaks if there is sufficient cheap, reliable and clean electricity. Key policy insights The effect of heat stress on labour productivity is a key economic impact of climate change, which could affect national output and workers’ income. Effective adaptation options exist, such as shifting working hours and cool roofs, but they require policy intervention and forward planning. Strategic interventions, such as climate-smart municipal design, are as important as reactive or project-level adaptations. Adaptation solutions to heat stress are highly context specific and need to be assessed accordingly. For example, shifting working hours could be an effective way of reducing the effect of peak temperatures, but only if there is sufficient flexibility in working patterns

London School Building Stock Model for Cognitive Performance Assessment

Climate change is one of the biggest challenges facing humankind in the 21st century. In the building sector, a warming climate will significantly affect building heating and cooling loads, as well as building occupant health, comfort and wellbeing. School buildings in the UK, in particular, might face additional challenges, such as indoor overheating risks due to high internal gains in classrooms, and their current reliance on natural ventilation, which might offer limited cooling capacity in the future. This paper presents a secondary school building stock indoor environment modelling framework for London. The aim of the present study is to explore the impacts of ongoing and future climate change on schoolchildren’s cognitive performance levels. Using the PDSP (Property Data Survey Programme) dataset and a basic set of school building archetypes for London, a parametric stock modelling framework was developed. Weather files based on existing Test Reference Years (TRY) incorporating the UK Climate Projections 2009 scenarios were used. This study provides a detailed assessment of school building stock indoor thermal performance and students’ cognitive performance. It was found that building thermal properties and ventilation rates can function as reliable predictors of students’ cognitive performance, and their impacts were quantified in this study. A sensitivity analysis aiming to identify the relative importance of these factors will be conducted as part of ongoing research

Energy retrofit and passive cooling: overheating and air quality in primary schools

While building stock modelling has been used previously to investigate the space heating demand implications of national energy efficiency retrofitting, there are also implications for indoor overheating and air quality, particularly in schools, with highly intermittent occupancy patterns. This paper assesses indoor overheating risk and air quality within an English classroom stock model containing 111 archetypes, based on the analysis of the nationwide Property Data Survey Programme (PDSP) containing 9629 primary school buildings in England. Metrics for indoor temperatures, heating demand and concentrations of three contaminants (CO_{2}, NO_{2}, PM_{2.5}) were estimated in naturally ventilated classrooms, while exploring future climate projections, retrofit and overheating mitigation scenarios to analyse school stock resilience. Classrooms with a south-east orientation experience around four to six times the overheating-hours compared with those with a northern orientation. Post-1976 archetypes are most susceptible to overheating, indicative of the conflict between better insulated and airtight classrooms and overheating prevention. A range of retrofit and passive cooling measures can mitigate against overheating alone, although mechanically driven cooling and filtration may be required towards the 2080s. While no single measure predicted universally positive effects for building performance, night ventilation and overhangs were found to be particularly effective passive overheating mitigation methods across the school stock

Overheating in English dwellings: comparing modelled and monitored large-scale datasets

Monitoring and modelling studies of the indoor environment indicate that there are often discrepancies between simulation results and measurements. The availability of large monitoring datasets of domestic buildings allows for more rigorous validation of the performance of building simulation models derived from limited building information, backed by statistical significance tests and goodness-of-fit metrics. These datasets also offer the opportunity to test modelling assumptions. This paper investigates the performance of domestic housing models using EnergyPlus software to predict maximum daily indoor temperatures over the summer of 2011. Monitored maximum daily indoor temperatures from the English Housing Survey's (EHS) Energy Follow-Up Survey (EFUS) for 823 nationally representative dwellings are compared against predictions made by EnergyPlus simulations. Due to lack of information on the characteristics of individual dwellings, the models struggle to predict maximum temperatures in individual dwellings and performance was worse on days when the outdoor maximum temperatures were high. This research indicates that unknown factors such as building characteristics, occupant behaviour and local environment makes the validation of models for individual dwellings a challenging task. The models did, however, provide an improved estimate of temperature exposure when aggregated over dwellings within a particular region

Climatic, energy retro-fit and IEQ mitigation scenario modelling of the English classroom stock model

Health and cognitive performance in UK school classrooms is dependent on building fabric performance as well as heating and ventilation system operation in maintaining Indoor Environmental Quality (IEQ), comprising thermal comfort and air quality. While archetype models can be used to simulate IEQ for different stock-wide location and construction eras, a predictive approach also necessitates the use of longitudinal scenarios. As a key component of the UK’s decarbonisation strategy, these scenarios should account for fabric retro-fit adaptations to reduce carbon emissions, and changes in operation of the building for overheating mitigation as well as changes in external climatic conditions. The IEQ of three representative classroom archetypes, representing the stock of 18,000 English schools, have been analysed for 24 pair-wise retro-fit and operational scenarios across three climatic scenarios. Retro-fitting, while effective in reducing energy demand, may risk compromising indoor air by requiring ventilation at times of the day when external conditions are least conducive to air quality and overheating. Additionally, while North facing classrooms can tackle overheating through single effective IEQ mitigation measures, South facing and 2080 climates will necessitate cumulative effects of multiple measures to be realised. Future work involves incorporating educational and construction stakeholder preferences through multi-criteria decision analysis, to derive suitable metrics

A validated methodology for the prediction of heating and cooling energy demand for buildings within the Urban Heat Island: Case-study of London

This is the post-print version of the final paper published in Solar Energy. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper describes a method for predicting air temperatures within the Urban Heat Island at discreet locations based on input data from one meteorological station for the time the prediction is required and historic measured air temperatures within the city. It uses London as a case-study to describe the method and its applications. The prediction model is based on Artificial Neural Network (ANN) modelling and it is termed the London Site Specific Air Temperature (LSSAT) predictor. The temporal and spatial validity of the model was tested using data measured 8 years later from the original dataset; it was found that site specific hourly air temperature prediction provides acceptable accuracy and improves considerably for average monthly values. It thus is a very reliable tool for use as part of the process of predicting heating and cooling loads for urban buildings. This is illustrated by the computation of Heating Degree Days (HDD) and Cooling Degree Hours (CDH) for a West–East Transect within London. The described method could be used for any city for which historic hourly air temperatures are available for a number of locations; for example air pollution measuring sites, common in many cities, typically measure air temperature on an hourly basis.EPSR