CHAPTER 7: ASSESSMENT OF IMPACT OF URBAN LAND SURFACE
7.4 Results and discussion
7.4.5 Effect of urban heat island on energy demand in Harare
Figure 7.6 shows that mean energy consumption in residential areas of Harare increased as minimum and maximum temperature decreased during the winter season (May to September).
During the summer season (October to March of the next year) energy consumption increased
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as temperatures rose. Highest energy consumption in residential areas in the summer season (above 1.05X106KWh) corresponded with highest maximum temperature in October and in January. However, energy consumption was higher in winter than in summer. This suggests that during the winter season consumption is increased due to use of heaters as well as warm water for bathing in all residential areas. Even the urban poor who mostly characterize the high density residential areas who do not afford air conditioning facilities can warm water for bathing. The slightly lower energy consumption during the summer season suggests that some parts of the season are comfortable or residents especially in low income residential areas use natural ventilation to remove heat. This may also imply that, although maximum temperatures will cause discomfort, a large proportion of the residents do not afford air conditioning facilities and hence are vulnerable. This concurs with Mushore, et al. (2017a) who observed that heat vulnerability in Harare is high in high density residential areas due to factors which included low household income levels, high population density and physical exposure. Energy consumption in industrial areas was also higher in winter than in summer although responses to maximum temperature in summer were not as pronounced as in residential areas.
Figure 7.6: Response of energy consumption to monthly temperature changes in Harare The mean daytime HDD values for the cool season were decreasing with time regardless of built-up density between 1984 and 2015. The decline in heat requirements for space heating increased with built-up density; largest in the CBD and high density residential areas where there was a decrease by 1 degree day and smallest in the low density residential areas where the decrease was about 0.5 degree days (Table 7.6). The general decrease in winter heating energy requirement concurs with observation of reduction in the number of cold days in
0 5 10 15 20 25 30 35
- 20 000 000 40 000 000 60 000 000 80 000 000 100 000 000 120 000 000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
T em p eratu re (
oC)
E n er g y con su m p ti o n (K W h )
T(Max) T(Min) Residential Industrial
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Zimbabwe (Chagutah, 2010). Mean HDD values were higher in low and medium density residential areas than in high density residential areas and the CBD in all years. This was because low and medium density residential areas have lower temperatures than other residential with higher built-up density. According to Kamusoko, et al. (2013), low density high income residential areas are characterized by high vegetation cover fraction. The vegetation which includes trees and lawns reduce the temperatures in these areas by evaporation cooling (Odindi, et al., 2015). Furthermore, the buildings are also spaced out, allowing cooling by advection due to low resistance to wind flow. Therefore, the low temperatures result in higher requirement of energy for indoor heating in the low density than other built-up areas during the cool season.
On the contrary, energy demand for cooling during daytime in summer increased between 1984 and 2015 as indicated by rising CDD in all residential types. For example, CDD increased from 7.71 to 9.43 degree days in the CBD and industrial areas while it increased from 5.73 to 9.28 in the low density residential areas. This was in tandem with (Blake, Curitiba, et al., 2011) who showed an increase in temperature since 1978 based on in-situ observations in the city. In consistence with Vardoulakis, et al. (2013) we also found that elevation of temperatures resulted in increases in CDD values hence leading to a rising trend in energy requirement for indoor cooling in the hot season. Throughout summer, daytime cooling energy requirements were larger in the CBD and high density residential areas than in the low-medium density residential areas. For example, in 2015, the CDD was 8.90 degree days in high density residential areas while it was 7.27 degree days in low density residential areas. This was because of the UHI effect which causes higher temperatures in areas within the CBD and high density of buildings (Guan, 2011; Salvati, 2015). Salvati (2015) noted that increases in temperature leads to increase in energy demand, which vary with urban density. Hirano, et al.
(2009) also reported that energy consumption increased with total floor area such that it was high in densely built up areas, with buildings with more than two floors, hence very high daytime HDD in the CBD. Consistent with UHI spatial distribution, low-medium density residential areas have larger heating and lower cooling energy requirements. Mushore, et al.
(2016) and Kamusoko, et al. (2013) established a high vegetation fraction, which increase cooling by latent heat transfer in these areas. The range of CDD values was consistent with mean midday CDD obtained using data from NOAA Advanced Very High Resolution Radiometer (AVHRR) in Athens, Greece (Stathopoulou, et al., 2006).
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Table 7.6: Changes in energy requirement for air conditioning Energy
(KWh)
Average daytime HDD Average daytime CDD 1984 1993 2001 2015 1986 1993 2001 2015 CBD/Industrial - -3.04 -3.01 -3.40 -4.14 6.94 7.71 8.26 9.43 High density 480 -2.97 -2.99 -3.49 -4.05 6.87 6.91 7.76 8.90 Medium density 768 -2.79 -2.56 -2.87 -3.73 6.61 6.67 7.06 8.12 Low density 1440 -2.39 -2.18 -2.18 -3.30 6.05 6.16 6.26 7.27
*Energy=current mean monthly energy consumption per residential type
However, although the CDD values showed that higher cooling energy requirements were in the high density residential than in the low-medium density residential areas, household income seemed to influence actual energy consumption differences. For example, Table 7.6 shows that mean energy consumption per household was inversely related to population density. As such low density residential areas had the highest mean monthly energy usage (1440KWh) while the lowest was in high density residential areas (480KWh). This is in tandem with Arifwidodo and Chandrasiri (2015) who observed a strong positive correlation between income, number of air-conditioning units in a house and energy consumption in Bangkok. Therefore, in Harare, the CDD can also be linked to heat health risks because heating requirement is high in the high density residential areas where the majority of residents are low-income earners (Wania, et al., 2014). Similarly, in Indonesia the ratio of electricity need to income was a measure of vulnerability to temperature extremes (Batih & Sorapipatana, 2016). Therefore, residents in low CDD low-medium density residential areas have the potential to utilize larger amounts of energy in air conditioning due to high income. Although this was not determined as it fell outside the scope of the study, houses in low-medium density residential areas are generally more spacious and have wealthier occupants than in the high density residential areas. The high consumption of energy by residents with large houses and high income was associated with the capacity to own sophisticated air conditioning facilities (Ewing & Rong, 2008; Batih &
Sorapipatana, 2016).
Figure 7.7 shows that warming has reduced daytime requirements for space heating in the cool season and increased heat requirements for space cooling in buildings. Therefore, relative to the 18oC threshold for human comfort, urban warming has increased household requirement of energy for cooling in summer in Harare. The increase in requirement for space heating was larger than the decrease in energy requirement for space cooling, implying a net increase in energy requirement for air conditioning. The summer CDD trends are in agreement with projections that household energy consumption in Zimbabwe would increase from 133221TJ
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in 1994 to 147190TJ in 2010 and further increase to 313045 in 2050 (Ministry of mines environment and tourism., 1998). In Rome and Barcelona, temperature elevation increased energy demand from 10 to 33% (Salvati, 2015).
Figure 7.7: Estimated impact of urban warming on daytime household energy consumption
7.4.6 In-situ observed long-term changes in space cooling and heating requirements