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Productivity loss through heat

Figure 1 shows the total estimated loss to GVA through productivity losses due to heat in each of the five periods studied.
Figure 2 shows observed gross value added (GVA) for the year 2005 in different sectors for Bilbao, using the statistical classification of economic activities in the European Community (abbreviated as NACE).
Figure 3 shows the estimated loss to GVA through productivity losses due to heat for a warm year in the far future for the different sectors of the economy.
Figure 4 shows the averted losses obtained by different adaptation measures, both behavioural and technical. Investigated adaptation measures are different patterns of working hours, increased ventilation, increased insulation, solar blinds and air conditioning.

Behavioural adaptation: Changing working hours
Increased ventilation: Increase in ventilation from the legal level 22m3/h/p to 50m3/h/p
Solar blinds: Installing solar blinds on the outside of windows
Insulation: Increase in insulation
Air conditioning: Use of air conditioning

Behavioural adaptation in the form of changing working hours compares the following regimes:

Baseline hours: 9h-13h; 14h-17h
Afternoon schedules: 9h-13h; 15h-18h & 9h-13h; 16h-19h & 9h13h; 17h-20h
Morning schedules: 8h-12h; 14h-17h & 7h-12h; 15h-17h & 6h-13h
Extreme: 7h-11h; 17h-20h
Figure 5 shows the averted losses obtained through changing working hours to the extreme scenario (7h-11h; 17h-20h) in the different periods studied and for different sectors of the economy.
General description:

We develop an economic cost methodology to assess the impact of specific climate change hazards through different channels of urban productive activity. We use the methodology to examine the impact of urban heat waves on transport disruptions leading to production losses across sectors of the city economy.

We define labour in terms of total quantity supplied per sector, where transport disruptions imply a decrease in time spent working. We define constant elasticity of substitution (CES) production functions for each sector that specifically encompass our estimated losses in total labour due to transport disruptions. The production functions are calibrated and aggregated at the city level according to specific weights given to each sector.

Transport disruption estimations for the UKCP09 High Emissions scenario to perform 100 x 30 year runs for daily data for the 2040-2069 time period are taken from Jenkins et al. (2012) and use information from the current transport configuration of rail, Tube, and DLR networks and 2001 Census. We evaluate the impact of one day of transport disruption on yearly GVA.