Energy consumption in elementary and high schools in Taiwan

Accepted Manuscript Energy consumption in elementary and high schools in Taiwan

Jen Chun Wang PII:

S0959-6526(19)31345-9

DOI:

10.1016/j.jclepro.2019.04.254

Reference:

JCLP 16610

To appear in:

Journal of Cleaner Production

Received Date:

16 August 2018

Accepted Date:

19 April 2019

Please cite this article as: Jen Chun Wang, Energy consumption in elementary and high schools in Taiwan, Journal of Cleaner Production (2019), doi: 10.1016/j.jclepro.2019.04.254

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ACCEPTED MANUSCRIPT 1

Energy consumption in elementary and high schools in Taiwan

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Jen Chun Wanga,*

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aDepartment

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No.62 Shenjhong Rd., Yanchao District, Kaohsiung 82446, Taiwan R.O.C

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* Corresponding author.

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E-mail address: [email protected] (J. C. Wang)

of Industry Technology Education, National Kaohsiung Normal University,

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ACCEPTED MANUSCRIPT Energy consumption in elementary and high schools in Taiwan Running head: Energy consumption in schools ABSTRACT The establishment of energy indicators is a key aspect of promoting energy efficiency in schools worldwide. The enquiry of the present study could prove crucial for Taiwan, which has experienced significant improvement in its educational standards in recent years. This study investigated final energy consumption in 67 senior high schools, 62 junior high schools, and 102 elementary schools in Taiwan, which respectively accounted for 13.8%, 6.9%, and 4.9% of the total number of the schools at their levels. Their energy use intensity (kWh/m2/year) values were 55.8, 22.5, and 20.1, and the values of their energy usage per person (kWh/person/year) were 1163, 469, and 465, respectively. Senior high schools consumed considerably more energy than did elementary and junior high schools because of their comparatively large scale, air-conditioned classrooms, and facilities such as swimming pools, activity centers, and gymnasiums. Compared with their public counterparts, private schools exhibited substantially higher energy use intensity at each educational level, possibly because of their superior learning environments and teaching equipment and greater average number of students per class. The scale of schools was positively correlated to total energy consumption, and learning environments were significantly correlated to energy use intensity. Three multiple regression models were constructed for simple estimation of total energy consumption (kWh/year; toe/year), energy use intensity, and energy use per person in school buildings; statistically significant associations were revealed. This study demonstrated that air conditioning and lighting heavily influence the electricity consumption of school buildings; in addition, several feasible energy-saving techniques are provided for school administrators to evaluate their schools’ energy consumption and achieve greater energy efficiency. Keywords: Energy consumption; Energy audit; School buildings; Educational buildings 1

ACCEPTED MANUSCRIPT 1.

Introduction Compared with other buildings, school buildings are responsible for significant

proportions of energy consumption and carbon dioxide emissions. According to the Intergovernmental Panel on Climate Change (IPCC, 2013), building-related industries consume 40% of global energy and emit approximately 36% of CO2; as a result, reducing the energy consumption of buildings has gradually become a strategy for countries worldwide in energy saving and carbon reduction (Allouhi et al., 2015; Huo et al., 2018; Navamuel et al., 2018). In China, construction-related industries were statistically shown to account for 53.3% of the country’s total energy consumption (TEC; kWh/year; toe/year) (Lu et al., 2018). For the European Union, the energy consumption of the building sector accounts for 40% of its final energy consumption (Bourdeau et al., 2018). In the United States, power consumed by residential and commercial buildings accounts for 40% of the country’s TEC (U.S. EIA, 2017). In the United Kingdom, carbon emissions from nondomestic buildings account for 35% of the total greenhouse gases emitted there (Brady et al., 2017). For many governments, school buildings and educational enterprises are the focus of building-related energy consumption issues. A study examined energy consumption in the commercial sector of the United States and found that school buildings accounted for 13% of the total building energy consumption, ranking fourth behind the retail industry (32%), office buildings (18%), and hotels and restaurants (14%) (Perez-Lombard et al., 2008). When examining electricity consumption in the United States, school buildings were ranked third and accounted for 10.8% of the total building electricity consumption, behind office buildings (20.4%) and retail and department stores (20.4%) (U.S. EIA, 2012). Among energy-consuming business entities under the buildings category, school buildings in the United Kingdom also ranked third (Department of Energy and Climate Change, 2015). No clear data are available for Taiwan, but the 2015 energy consumption information reported by major electricity users in nonproductive industries to the 2

ACCEPTED MANUSCRIPT Bureau of Energy (BOE) of the Ministry of Economy provides some insights. The TEC of school buildings accounted for 14.4% of all electricity consumed by major electricity users in Taiwan, second only to hospital buildings (14.9%) (BOE, 2017). Compared with offices, hotels, retail stores, and other public buildings, school buildings consume a substantial amount of energy; without an extensive and reliable investigation into energy consumption, governments, institutions, and school managers may have difficulty realizing this.

2.

Literature Review Used by the majority of countries worldwide as an evaluation standard, energy use

intensity (EUI; kWh/m2/year) refers to the total amount of energy of all forms consumed in a full year, including fuel oil, natural gas, and electricity. To calculate EUI, the amount of energy consumed is first converted to kWh, and then this number is divided by the total floor area of a building to obtain the TEC per unit of floor area. Most countries used EUI, and the mean EUIs of junior high schools and elementary schools were between 20 and 405. The global distribution presented reveals that high-latitude countries or regions in frigid or temperate zones have a higher EUI because of the energy used for heating. In some highlatitude countries, the EUI for room heating can be higher than the final total energy consumption in low-latitude countries. For example, the EUI of heating is 100 in Slovenia (Butala and Novak, 1999) and 96 in Ireland (Hernandez et al., 2008), whereas the EUI of total energy consumption is only 67 in Portugal (Ruusala et al., 2018). By contrast, although subtropical and tropical countries have room-cooling needs, the peak of these needs occurs in summer and coincides with the school summer break; therefore, the EUI is actually lower. For example, Butala and Novak (1999) investigated 24 junior high schools and elementary schools located in the cold inland mountain area of Slovenia and found that these schools had a mean 3

ACCEPTED MANUSCRIPT EUI of 192, where the EUI for room heating reached 100 (Butala and Novak, 1999). Hernandez et al. (2008) researched junior high schools in Ireland and found that their electricity EUIs ranged between 5 and 35, and the EUIs of room heating ranged between 50 and 200, with a mean of 96. Sekki et al. (2015) studied 74 junior high and elementary schools in Espoo, Finland and revealed room-heating EUIs of 38 to 383 and electricity EUIs of 10 to 212, with a mean EUI of 214. Yoshino and Chen (2008) sampled junior high schools and elementary schools in the cold region of northeast Japan to study their electricity consumption and reported an EUI of 278. Ouf and Issa (2017) calculated the EUI of 129 junior high and elementary schools in Manitoba, Canada and observed an elementary school EUI of 270; a junior high school EUI of 264; and an EUI of 127 for K–12 schools. The mean EUI of all schools was 253, and their mean electricity EUI was 118. In Espoo, the researchers revealed that universities had an EUI of 229, which was only slightly higher than that of junior high and elementary schools (214). However, a more detailed examination of data revealed that junior high and elementary schools’ EUIs were between 10 and 212, with room-heating EUIs of 31 to 383. Although universities’ electricity EUIs were 89 to 450, their room-heating EUIs ranged from 6 to 178. This discrepancy may be explained by the longer periods of time spent using university spaces and the larger amount of electricity-dependent teaching and laboratory equipment in universities than in junior high and elementary schools. Conversely, from a physiological perspective, elementary school students are more vulnerable to cold weather than older students, and thus room heating is more essential in elementary schools than in universities; this leads to higher heating energy consumption in elementary schools than in universities. A similar study that targeted preschools found that the room-heating requirements of preschools were significantly higher than that of elementary schools: the mean preschool room-heating EUI was 200, whereas that of elementary schools was 155. The average overall room-heating EUI was 142 and the remaining electricity EUI was only 67 (Ruusala et al., 2018). Countries in frigid and 4

ACCEPTED MANUSCRIPT temperate zones are particularly concerned with their heating-related energy consumption, partly because their room-heating EUIs are higher than the EUIs for teaching equipment and lighting (Butala and Novak, 1999) for Slovenia, (Hernandez et al., 2008) for Ireland, and (Raatikainen et al., 2006; Sekki et al., 2015) for Finland. Areas with a relatively mild climate have a low EUI. In southern Greece, Santamouris et al. (2017) sampled 320 schools in Greece and obtained a room-heating EUI of 71.18. In Portugal, a country near the ocean and at a latitude similar to that of Greece, Lourenço et al. (2014) monitored the electricity consumption of eight junior high schools and obtained an overall EUI of only 67, with a fuel gas EUI of 16 and an electricity EUI of 51. Katafygiotou and Serghides (2014) surveyed 24 junior high schools in Cyprus, a Mediterranean island known for its warm and sunny winter, and obtained relatively low average EUIs (between 63 and 116). Through surveying 7 senior high schools, 11 junior high schools, and 5 elementary schools in Taiwan, a subtropical island, Wang (2016) reported EUIs of 26, 16, and 17 respectively for senior high, junior high, and elementary schools. The present study performed a literature review targeting the literature on junior high schools and elementary schools, and found EUIs of 48 to 108 for Cyprus (Katafygiotou and Serghides, 2014), 55 to 235 for Ireland (Hernandez et al., 2008), 67 for Portugal (Lourenço et al., 2014), and 405 for South Korea (Kim et al., 2012). The study on South Korea revealed significantly high EUIs: an electricity EUI of 289, fuel oil EUI of 26, and fuel gas EUI of 90. The average monthly energy consumed per student ranged from 42 to 112 kWh, primarily because the EUIs presented in this study were calculated using only energy consumed in classrooms. Elementary school students in South Korea not only attend after-school tutoring classes during the semester, but also attend tutoring at school during their summer and winter breaks. Classrooms are also often lent to the community for local residents’ culture and art classes at night or on weekends and holidays (Kim et al., 2012). By contrast, European studies 5

ACCEPTED MANUSCRIPT generally presented a more consistent and reasonable energy consumption pattern; the EUIs of southern European and Mediterranean countries were significantly lower than the EUIs of schools in Ireland, which has a cold climate. Regarding Cyprus, the mean EUI of the sampled schools was 108 in the mountain region, 56 in the inland region, and 48 the coastal region (Katafygiotou and Serghides, 2014). Notably, the coastal region had a pleasant climate all year round (8–18°C in winter, 10–20°C in spring and fall, and a maximum of 33°C in summer) and electric fans can serve as a low-energy replacement for air conditioning in subtropical conditions. Another Greek study compared elementary and junior high schools with universities and revealed that the sampled elementary schools had an EUI of 79.82, but when elementary school, junior high school, and university samples were pooled together, the mean EUI was 95 (Santamouris et al., 2017). Studies have also explored the relationships between air-conditioning energy needs and building structure. Depending on the exterior design of school buildings, the energy needs for room cooling and heating vary (Rospi et al., 2015). It was also revealed that the usage rate of a school building affected its energy consumption, and different air-conditioning designs significantly affected air conditioning energy consumption (Gamarra et al., in press; Raatikainen et al., 2016). Regarding room-heating needs, differences in the thermal conductivity of windows and exterior walls significantly affected room-heating and airconditioning needs (Capozzoli et al., 2015). Most related studies have proposed that the age of a building can significantly affect its EUI; however, few studies have argued against this claim based on increases in energy consumption due to equipment and facilities being installed in newly constructed schools. A study in Luxembourg found that all schools built after 2005 exhibited an EUI of less than 100 and a room-heating EUI of less than 50. These researchers investigated 68 schools and obtained a mean EUI of 93 with a range from 24 to 197, and asserted that newly built schools are more energy efficient than older buildings (Thewes et al., 6

ACCEPTED MANUSCRIPT 2014). Similar findings were supported by a German study that examined 105 schools in Stuttgart. This study demonstrated that for buildings constructed more recently, their thermal conductivity, as expressed by U-values, was lower than that of older buildings; the lower thermal conductivity U-values of newer buildings substantially reduced room-heating EUIs (Beusker et al., 2012). A Finnish study supported this viewpoint by surveying 71 preschools and revealing that newer school buildings had lower room-heating EUIs (Sekki et al., 2016). EU countries must comply with the Energy Performance of Buildings Directive, which has been influential in improving patterns of energy demand in newly constructed buildings after major retrofit. Nonetheless, in another study that examined 134 elementary schools, no significant correlation was found between room-heating EUIs and the age of school buildings (Ruusala et al., 2018). Studies have also presented opposing findings. For example, a Portuguese study conducted a long-term observation of schools and found that the schools originally had an average EUI of 41, but after renovation, the average EUI of the same sample increased to 67 because teaching equipment and facilities were added (Lourenço et al., 2015). A South Korean study presented similar findings. In 1992, the mean EUI of South Korean elementary schools was only 70, but in 2007, it increased substantially to 128. This increase was attributed to the addition of televisions and videocassette recorders to school classrooms (Kim et al., 2012). Because natural gas is the main energy source used for room heating, a study in Canada examined both electricity and natural gas use in school energy consumption (Ouf and Issa, 2017). They divided the surveyed schools into three groups based on the year they were built: before 2004, between 2004 and 2013, and after 2013, and found that the electricity EUIs before 2004, between 2004 and 2013, and after 2013 were 58, 116, and 125, respectively. In summary, newly built buildings tend to be energy efficient in room heating, but because of the informatisation of teaching equipment, school electricity consumption has increased. 7

ACCEPTED MANUSCRIPT 3.

Methodology

3.1. Energy consumption auditing approach The present study adopted a direct survey approach and investigated the following types of energy consumption: liquefied petroleum gas (LPG), natural gas (NG), heavy fuel, light fuel, and electricity. These data were collected from schools’ monthly expense reports, which are prepared monthly by the general affairs office of each school based on accurate information, as stipulated by the BOE of the Ministry of the Economy. Information was obtained from the database of the Ministry of Education regarding the years the schools were established, land area, total floor area of the building, building coverage ratio, floor area ratio, number of classes, number of students, and number of teaching staff members. Typically, energy consumption is reported in units of electricity use equivalents (kWh), tons oil equivalent (toe) and the TEC of a given school can be evaluated using equation (1) by referencing the numerous energy consumption factors (α).

Etotal   Ei j   i j i

(1)

j

where Etotal is TEC in kWh; toe, i denotes a given energy type, j denotes each energy type, and αi,j denotes the conversion factor for j-type energy of i-type energy. All the energy consumption units are converted into kWh. The conversion factors α are 1 kg LPG = 13.6 kWh, 1 m3 NG = 10.46 kWh, 1 litre of light fuel = 9.66 kWh, 1 litre of heavy fuel = 10.73 kWh, and 1 kWh = 8.27E-05 toe respectively (BOE, 2017). 3.2. Sampled school profiles This study sampled 231 schools, namely 67 vocational and general high schools, 62 junior high schools, and 102 elementary schools. Among the 67 general and vocational high schools studied, there were 33 public general high schools, 25 public vocational high schools, 4 private 8

ACCEPTED MANUSCRIPT general high schools, and 5 private vocational high schools. The primary objective of general high school education is to prepare students for university. The primary objective of vocational high schools, by contrast, is to provide students with job skills or to prepare them for technological college. Junior high and elementary schools provide compulsory education, and most students choose to attend public schools near their residence. Among the 62 junior high schools studied, 56 were public schools and 6 were private schools. Among the 102 elementary schools studied, only 4 were private schools. The northern metropolitan area has a higher population density than the central or southern metropolitan areas, and the population proportions in each area were considered in the school sampling strategy of this study (Fig. 1). Ultimately, this study evenly sampled schools across Taiwan.

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ACCEPTED MANUSCRIPT

Fig. 1. Distribution of the 231 studied schools; high schools,

denotes the location of the sampled senior

denotes the location of the sampled junior high schools, and

location of the sampled elementary schools. 10

denotes the

ACCEPTED MANUSCRIPT 4.

School characteristics and basic statistical data

4.1. School characteristics Table 1 shows that up to 96% of the energy used for lighting, air conditioning, elevators, electric fans, water supply, sewage treatment, outlets, and teaching equipment is electricity. LPG and NG are the primary heat sources for food service. Elementary schools are required to provide school lunch; some dishes are prepared at school, whereas others are prepared outside the campus and then sent to the school. Many junior high schools have a cafeteria serving hot food, but it is also acceptable for students to order lunch from external facilities. Heavy and light fuels are typically used for heating water for showers in student and staff dormitories. Air conditioners are not standard equipment for schools. Taiwan has a subtropical climate and is surrounded by the sea, the climate is thus warm and humid with an average year-round temperature between 16 and 29°C, and usually only room cooling is required. Although most general and vocational high schools have air conditioners in their classrooms and administrative offices, their students must pay for air conditioning. An electricity meter is installed in each classroom and if the entire class as a unit decides to turn on the air conditioner, the students share the electricity costs generated. The administrative offices of junior high schools and elementary schools are equipped with air conditioners, but the classrooms only have electric fans. Regarding school hours, high school students must typically be at school by 07:30. They have an 80-minute lunch break and afternoon break, and finish school at 16:30. Students can choose to have a tutoring session after school, which finishes at 17:30. With the exception of physical education class twice a week, all classes are conducted in classrooms. The schedules of junior and senior high school students are similar, except that junior high classes only last 45 minutes. Junior high school students also have eight classes a day, but if they choose to have 11

ACCEPTED MANUSCRIPT a tutoring session, they leave school at 17:00. For elementary school students, their classes are 40 minutes each. They arrive at school by 07:30 and leave at 16:00. Grades 3 to 6 finish school at noon every Wednesday, and grades 1 and 2 finish school at noon every day. There are two semesters each year for all grades, and each semester lasts 20 weeks. The semesters are separated by a 2-month summer break and a 1-month winter break. Some junior and senior high school students choose to attend a 1-month tutoring programme at school during their summer break. On average, senior high school students spend approximately 2,060 hours in school per year; junior high school students spend 1,840 hours; and elementary school students spend 1,353 hours. Table 1 Percentage of electrical and total energy ratio of sampled schools. Electrical/total (%)

Maximum

Minimum

Average

Median

Standard deviation

Senior high schools

99

88

95

95

2.2

Junior high schools

99

90

96

96

1.8

Elementary schools

99

91

97

97

1.6

4.2. Statistical data: Energy consumption auditing approach Table 2 presents descriptive statistics of the 231 schools sampled in this study. The average campus area was 7.2 hectares for general and vocational high schools, 3.4 hectares for junior high schools, and 2.3 hectares for elementary schools. Generally, larger school campuses have larger outdoor spaces; in addition, more streetlights and internal electrical facilities are available. Vocational high schools can be divided into technical, business, and agricultural schools. General and vocational high schools had an average total floor area of 40,245 m2, which was 1.8 and 2.5 times than that of junior high schools and elementary schools, respectively; this indicated that the grounds of general and vocational high schools are larger 12

ACCEPTED MANUSCRIPT on average than junior high and elementary schools. The number of students at schools of each grade level exhibited similar proportions to those observed in floor area. The average number of teaching staff at a general or vocational high school was 131, which was 1.3 and 2 times greater than that of junior high schools and elementary schools, respectively. Regarding the year the schools were established, elementary school education has been compulsory since Taiwan was under Japanese rule (before 1945); therefore, elementary schools are generally older than schools of other grades. Building coverage ratio ranged from 17.7% to 22.3% for all grades of schools, and no significant difference was observed among them. Floor area ratio also did not exhibit a significant difference among the three grades of schools, with the average ranging between 63% and 69%. However, when dividing the building coverage ratio by the floor area ratio to obtain the average number of floors in each school building, the results revealed an average of 4.1 floors for vocational and general high schools and 3.1 floors for both junior and elementary schools. For total electricity consumption, vocational and general high schools on average consumed 2,284 MWh annually, which was 4.3 times and 6.2 times greater than that of junior high and elementary schools, respectively. This proportion was considerably larger than that of the number of people or of the floor area of the three grades of schools, implying that vocational and general high schools’ EUI and energy use per person (EUP; kWh/person/year) were greater than those of junior high and elementary schools.

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Table 2 Energy consumption auditing of school building samples. School

Gross

Total floor area School

(numbera)

land

(1000m2)

history

area(ha)

Building

Floor area ratio Number of

coverage ratio (%)

Number of

Energy

Bill

students

teachers

(MWh)

(kUSD)

<100 (6)

(%)

Senior high

<4 (12)

<25 (9)

pre-1930 (9) <10 (10)

<30 (10)

<1000 (10)

<80 (9)

<1000 (6)

schools (67)

4-6 (34)

25-35 (17)

till 1950 (29) 10-15 (19)

30-60 (15)

1000-1500 (10)

80-110 (15)

1000-1600 (11) 100-150 (8)

6-8 (12)

35-45 (19)

till 1970 (15) 15-20 (16)

60-90 (24)

1500-2000 (17)

110-140 (17)

1600-2200 (21) 150-200 (20)

8-10 (1)

45-55 (13)

till 1990 (8) 20-25 (12)

90-120 (14)

2500-3000 (15)

140-170 (16)

2200-2800 (15) 200-250 (19)

>10 (8)

>55 (9)

till 2010 (6) >25 (10)

>120 (4)

>3000 (15)

>170 (10)

>2800 (14)

>280 (14)

Avg

7.2

40.2

1951

17.7

72

1997

131

2284

225

Std

5.8

15.9

20

9.1

41

1058

44

1194

114

Max

30.1

89.4

2003

52.8

260

6771

288

6992

661

Min

3.1

8.9

1927

4.6

13.6

279

43

367

52

Junior high

<2.5 (3)

<8 (6)

pre-1930 (0) <10 (10)

<30 (12)

<500 (14)

<50 (10)

<200 (15)

<10 (7)

schools (62)

2.5-3 (8) 8-16 (12)

till 1950 (19) 10-18 (16)

30-50 (9)

500-1000 (13)

50-80 (11)

200-400 (12)

10-30 (13)

3-3.5 (15) 16-24 (16)

till 1970 (12) 18-26 (18)

50-70 (16)

1000-1500 (18)

80-110 (15)

400-600 (12)

30-50 (12)

14

3.5-4 (33) 24-32 (18)

till 1990 (23) 26-34 (12)

70-90(14)

1500-2000 (8)

110-140 (12)

600-800 (10)

50-70 (15)

>4 (3)

>32 (10)

till 2010 (8) >34 (6)

>90 (11)

>2000 (9)

>140 (14)

>800 (13)

>70 (15)

Avg

3.44

21.8

1966

20.5

63

1152

104

530

526

Std

0.51

11.2

20

9.3

34

711

54

397

38

Max

4.63

49.1

2005

48.0

187

2922

223

2175

213

Min

1.98

1.82

1931

5.7

7.2

26

5

26

3.1

Elementary

<1.5 (2)

<2 (5)

pre-1930

<10 (17)

<30 (20)

<300 (29)

<30 (31)

<100 (20)

<5 (11)

(40)

schools (102) 1.5-2 (16) 2-10 (32)

till 1950 (29) 10-18 (22)

30-50 (11)

300-800 (25)

30-60 (22)

100-250 (28)

5-15 (19)

2-2.5 (67) 10-18 (25)

till 1970 (17) 18-26 (22)

50-70 (26)

800-1300 (24)

60-90 (25)

250-400 (17)

15-30 (24)

2.5-3 (12) 18-26 (22)

till 1990 (14) 26-34 (26)

70-90 (20)

1300-1800 (15)

90-120 (13)

400-550 (17)

30-45 (20)

>3 (5)

>26 (18)

till 2010 (2) >34 (15)

>90 (25)

>1800 (9)

>120 (11)

>550 (20)

>45 (28)

Avg

2.28

16.3

1944

22.3

69

835

64

366

36.1

Std

0.38

11.5

24

10.1

43

710

46

330

32.8

Max

3.53

54.1

2002

39.7

190

3198

228

1626

156.3

Min

1.21

1.20

1897

3.3

6.7

19

6

12.5

1.8

15

ACCEPTED MANUSCRIPT

5.

Results

5.1. Energy consumption by school grade Table 3 shows the EUI and EUP among different grades of schools. Vocational and general high schools had an average EUI of 55.8, which was 2.5 and 2.8 times that of junior high and elementary schools, respectively. Vocational and general high schools’ average EUP reached 1,163, which was 2.4 and 2.5 times that of junior high and elementary schools. An analysis of variance (ANOVA) indicated that results were significant, and a Scheffe’s test (posthoc) was subsequently conducted. The results revealed that the EUI and EUP of general and vocational high schools were both significantly higher than those of junior high and elementary schools. Notably, the time spent in school by vocational and general high school students was only 1.1 and 1.5 times that of junior high and elementary school students, respectively. Vocational and general high schools generally had more laboratory and hands-on practical equipment than junior high or elementary schools. The greatest difference was that most vocational and general high school classrooms were equipped with air conditioners and electric fans, but in junior high and elementary schools, air conditioning was only likely to be available in a few private schools. Most public elementary and junior high school classrooms relied on electric fans for cooling. Therefore, air conditioning was the key factor contributing to the difference observed in energy consumption among the three grades of schools. Table 3 Analysis of the EUI and EUP among different grades of schools.

Senior high schools (A)

Average

16

EUI

Energy use per student

(kWh/m2/year)

(kWh/person/yr)

55.8

1163

ACCEPTED MANUSCRIPT (n=67)

Std. deviation

12.5

187

Junior high schools (B)

Average

22.5

469

(n=62)

Std. deviation

9.3

199

Elementary schools (C)

Average

20.1

465

(n=102)

Std. deviation

6.5

132

F test

333

405

p-value

.000**

.000**

Scheffe’s test

A>B,C

A>B,C

** denotes statistical significance when p < 0.01 (two-tailed) 5.2. Comparison of energy consumption between public and private schools In Taiwan, 97% of elementary schools, 90% of junior high schools, and 82% of vocational and general high schools are public schools. The primary source of funding for private schools is tuition fees, with very little government subsidisation. Because of limited funding, the number of students per class in private schools is higher on average than in public schools. Consequently, private schools of all grades in this study were found to have higher EUIs than their public counterparts. Table 4 shows that the independent sample t test results were all statistically significant. Nonetheless, regarding EUP, the amount of energy consumed per student in private schools was not substantially higher than that of public schools due to the high student density in private schools. Among vocational and general high schools, public and private schools had similar EUPs. For junior high and elementary schools, the EUP of urban schools tended to be higher than that of rural schools, which was likely due to urban schools having superior equipment, higher tuition fees, and air-conditioned classrooms. However, this difference was only borderline significant in junior high schools and nonsignificant in elementary schools, possibly because of the small number of students per class in rural schools. 17

ACCEPTED MANUSCRIPT Table 4 Analysis of the energy consumption of and differences between national and private schools. EUI

Energy use per student

(kWh/m2/year)

(kWh/person/yr)

National senior high schools

Average

53.5

1160

(n=59)

Std. deviation

10.4

191

Private senior high schools

Average

73.3

1183

(n=8)

Std. deviation

14.0

158

t test

4.84

.38

p-value

.005**

.712

National junior high schools

Average

20.8

452

(n=56)

Std. deviation

7.3

195

Private junior high schools

Average

38.2

630

(n=6)

Std. deviation

13.1

179

t test

3.21

2.13

p-value

.022*

.037*

National elementary schools

Average

19.3

458

(n=98)

Std. deviation

4.7

122

Private elementary schools

Average

39.1

639

(n=4)

Std. deviation

14.3

255

t test

7.44

1.42

p-value

.000**

.250

* denotes statistical significance when p < 0.05 (two-tailed) ** denotes statistical significance when p < 0.01 (two-tailed)

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ACCEPTED MANUSCRIPT 5.3. Relationship between energy consumption and class size in junior high and elementary schools Low birth rates are a serious problem in Taiwan, and they pose a considerable challenge to junior high and elementary schools in rural areas. Apart from school administration problems, this phenomenon is also highlighted in energy consumption-related issues. Table 5 compares public junior high and elementary schools but excludes private schools because private elementary and junior high schools usually have more than 30 (or even 40) students per class, and their school equipment is often superior to that of their public counterparts. The results demonstrate that the EUIs of the 77 schools with more than 25 students per class were significantly higher than those of schools with fewer students per class. Small rural schools with fewer than 10 students per class on average had significantly higher EUPs than those of other schools. Table 5 Analysis of the EUI and EUP of national junior high and elementary schools among different class sizes. Class size

EUI

Energy use per student

(kWh/m2/year)

(kWh/person/yr)

Higher than 30 (A)

Average

29.7

480

(n=4)

Std. deviation

3.9

86

25-30 (B)

Average

22.0

416

(n=73)

Std. deviation

4.9

95

20-25 (C)

Average

18.5

433

(n=43)

Std. deviation

4.5

94

15-20 (D)

Average

16.1

521

19

ACCEPTED MANUSCRIPT (n=17)

Std. deviation

6.0

161

10-15 (E)

Average

17.2

494

(n=8)

Std. deviation

4.4

154

Lower than 10 (F)

Average

13.9

707

(n=9)

Std. deviation

4.9

375

p-value

.000**

.000**

Scheffe’s test

A>B>C,D,E,F

F>B,C

** denotes statistical significance when p < 0.01 (two-tailed) 5.4. Vocational and general high school energy consumption High school is equivalent to grades 10 to 12 in the United States, and can be divided into two types: general and vocational. For general high schools, the primary purpose is to prepare students for college, and teaching methods are mostly classroom lectures. In vocational high schools, students have hands-on training at factories in addition to classroom lectures. Half of vocational high school graduates are likely to enter the workforce, and the other half may proceed to higher education. Table 6 shows that the EUIs of private high schools were higher than those of public high schools. Nevertheless, the results revealed that public general high schools were larger in mean student size than public vocational high schools, but that private vocational high schools were larger in size than private general high schools. Because most public general high schools focus on preparing students for entrance to college, it is common for grade 12 students to stay after school ends at 17:30 to study independently until 21:00. However, because the focus of public vocational high schools is on technical training, students typically leave school after 16:30. For private schools, because vocational high school students with strong technical performance can receive preferential treatment for college entrance requirements, private vocational high schools often offer their students extra technical training 20

ACCEPTED MANUSCRIPT after classes end to improve their chances of entering college. Therefore, private vocational high schools were found to have a high EUI. However, no significant EUP difference was observed among the four school types. Table 6 Analysis of EUI and EUP between senior high and vocational schools. Class size

EUI

Energy use per student

(kWh/m2/year) (kWh/person/yr) National senior high schools Average

57.3

1128

8.2

137

National vocational schools (B) Average

47.8

1207

(n=24)

Std. deviation

10.8

247

Private senior high schools (C)

Average

68.7

1081

(n=3)

Std. deviation

5.0

204

Private vocational schools (D)

Average

76.0

1245

(n=5)

Std. deviation

17.6

101

p-value

.000**

.258

Scheffe’s test

D,C>B,A

--

(A) (n=35)

Std. deviation

** denotes statistical significance when p < 0.01 (two-tailed) 5.5. Relationship between monthly electricity consumption and outdoor temperature of schools of all grades Fig. 2 shows less energy was consumed in January and February and in July and August, which is primarily because of the 1-month winter break and 2-month summer break, respectively. Senior and junior high school students typically have a 1-month tutoring 21

ACCEPTED MANUSCRIPT programme during their summer break but elementary school students do not. As a result, although the energy consumption of all three grades of schools in July and August was lower than in June and September, the magnitude of the reduction varied: senior high schools had a reduction of 28%; junior high schools saw a reduction of 38%; and elementary schools had a reduction of 70%. This discrepancy can be attributed to the fact that only on-duty teachers stay at elementary schools during the summer break and only some administrative offices are in normal use. Furthermore, the figure also reveals that senior high schools exhibited an energy consumption pattern typical to air-conditioned buildings: high electricity consumption in May, June, September, and October.

Fig. 2. Monthly energy consumption of all samples investigated and mean outdoor temperatures in 2017. 5.6. Correlation analysis of energy consumption determinants Variables that represented school size, including campus area, total floor area, and number of students, were positively correlated with TEC (Table 7). Variables that represented the 22

ACCEPTED MANUSCRIPT crowdedness of a school building, building coverage ratio, and floor area ratio, were also correlated with energy consumption. A weak and negative correlation was observed between the age of a school and its TEC. EUI and EUP were positively correlated with the campus area, total floor area, and number of students, but some of the correlation coefficients were relatively low, primarily due to the polarised development of school size. Despite the low birth rates, large schools in urban areas are not affected by this because more people are moving cities than previously. Conversely, rural schools are losing students; large schools remain large and small schools shrink in size. Large schools could also receive more government subsidies. In particular, many elite high schools in urban areas possess abundant teaching equipment and resources, and receive teaching support or subsidisation from neighbouring universities; this resulted in a higher EUP. Table 7 Correlation analysis of different colleges regarding energy consumption.

TEC

EUI

Gross

Total

School Building Floor

Number

land

floor

history coverage area

of

area

area

ratio

ratio

students

Pearson correlation .381** .873** -.142* .203**

.483** .860**

p-value

.000

.000

.000

.031

.002

.000

Pearson correlation .308** .655** .172** .008

.322** .633**

p-value

.901

.000

.000

.000

.000

.009

Energy use per

Pearson correlation .437** .506** .135*

-.201**

.026

.366**

person

p-value

.002

.694

.000

.000

.000

.040

*denotes statistical significance when p < 0.05 (two-tailed) ** denotes statistical significance when p < 0.01 (two-tailed)

23

ACCEPTED MANUSCRIPT 5.7. Multiple regression analysis for predicting energy consumption in schools This study used step-wise multiple regression analysis to construct three prediction models in order to estimate the TEC, EUI, and EUP of junior high and elementary schools. Factors with their significant values (p values) against annual values of TEC as well as EUIs higher than 0.10 were excluded from this analysis. The results are presented in Table 8. To calculate TEC, variables that represented school size, namely the total floor area, number of students, and campus area, were included in the prediction model. The schools were divided into eight categories, and each school category had its distinctive characteristics. Because floor area ratio represented the crowdedness of a school building, school category and floor area ratio were also incorporated into the prediction model. The TEC prediction model had an average error rate of 33.8%. When the top 10% of samples with the highest error rates (N = 23) were removed, the average error rate was reduced to 24.2%. For the EUI prediction model, the category of a school was the most important variable. Floor area ratio, campus area, and the number of students were also incorporated into the prediction model. When this prediction model was used to estimate a school’s annual EUI, the average error rate was 17.8%, and when the top 10% of samples with the highest error rates were removed, the average error rate was reduced to 13.6%. For the EUP prediction model, the category of the school was also the most important variable. The average number of students per class and number of students at the school were also incorporated into the prediction model. The average error rate of this annual EUP prediction equation was only 18.6%. Moreover, when the top 10% of samples with the highest error rates were eliminated, the average error rate dropped to 14.5%. The adjusted R2 values of these three models were 0.914, 0.869, and 0.809 for the TEC model, EUI model, and EUP model, respectively, which indicated that the explained variance of the variables was 91%, 87%, and 81% for the TEC model, EUI model, and EUP model, respectively. This study also applied the Durbin–Watson test on the three regression models, and the results were 1.78, 1.71, 24

ACCEPTED MANUSCRIPT and 1.79 for the TEC model, EUI model, and EUP model, respectively. These values were all greater than the threshold of Du,0.05 and yielded significant p values, indicating that the three models possessed regressional relationships that were statistically robust .

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Table 8 Step-wise multiple regression analysis of TEC, EUI, and EUP in high schools and elementary schools. Dependent variable: TEC Priority of

Coefficient of R

R2 change (ΔR)

F value

F change

determination (R2)

independent Gross floor area Property 1 Number of students Gross land area Floor area ratio TEC (MWh) =

Coefficient of

.873 .932 .945 .947 .957

.762 .869 .893 .897 .916

Standardized coefficients

.761 .108 .024 .004 .019

731 756 631 493 492

731 187 51 9 51

.569 .367 .441 -.255 -.325

–232 + 0.038×Gross floor area (m2) + 193×Prooerty 1 + 0.507×Number of students – 75.1×Gross land area (ha) – 897×Floor area ratio (%) R2=0.914

(p-value=.000)

TEC (toe) = –19.3 + 3.17×Gross floor area (1000m2) + 16.0×Prooerty 1 + 0.042×Number of students – 6.23×Gross land area (ha) – 74.4×Floor area ratio (%) Dependent variable: EUI Priority of independent

Coefficient of R

Coefficient of

R2 change (ΔR)

determination (R2)

F value

F change

Standardized coefficients

26

Property 1

.905

.819

.819

1038

1038

.868

Floor area ratio

.925

.856

.037

677

58

.013

Gross land area

.930

.864

.008

482

14

-.163

Number of students .933

.871

.007

382

11

.201

EUI (kWh/m2/year)= 8.213 + 7.578×Prooerty 1 + 0.584×Floor area ratio – 0.796×Gross land area (ha) + 0.004×Number of students R2=0.869

(p-value=.000)

Dependent variable: EUP Priority of

Coefficient of R

Coefficient of

R2 change (ΔR)

F value

F change

determination (R2)

independent

Standardized coefficients

Property 1

.845

.714

.714

573

573

.995

Class size

.867

.752

.038

346

35

-.311

Gross floor area

.873

.763

.010

243

10

.879

Number of students .895

.802

.039

228

44

-.739

Gross land area

.813

.012

196

14

-.153

.902

EUP (kWh/person/year) = 479 + 169×Prooerty 1 – 12.8×Class size + 0.0192×Gross floor area (m2) – 0.275×Number of students – 14.6×Gross 27

land area R2 = 0.809 (p-value=.000) 11

for National elementary schools, 2 for National junior high schools, 3 for Private junior high schools, 4 for Private elementary schools, 5

for National vocational schools, 6 for National senior high schools, 7 for Private senior high schools, 8 for Private vocational schools.

28

ACCEPTED MANUSCRIPT

6.

Discussion The energy consumption of junior high and elementary schools was low (average EUIs

were 22.5 and 20.1, respectively) because air conditioning was not used at these schools. Although high schools had air conditioners, their average EUI was only 55.8. Compared with students in other countries, students in Taiwan spend more time at school; however, the schools’ EUIs were not higher than those of European schools in temperate or frigid zones (Hernandez et al., 2008, in Ireland; Katafygiotou and Serghides, 2014, in Cyprus; Sekki et al., 2015, in Finland; Ouf and Issa, 2017, in Canada). In terms of senior high school power consumption, to save energy in senior high schools, air conditioning-related energy saving must be prioritized. Many administrative offices use heating, ventilation, and air conditioning (HVAC). Therefore, HVAC energy-saving methods, including variable air volume, variable water volume, total heat recovery, and CO2 concentration control systems, can be adopted to reduce the HVAC chiller load. Second, because many senior high schools have gymnasiums and swimming pools, heat pump system that can provide both hot showers and room cooling. Many of the senior high school personnel interviewed for this study suggested that installing a heat pump system significantly reduced their electricity expenses. Numerous studies have also proven that installing a heat pump system in schools can result in significant energy-saving benefits (Allaerts et al., 2017; Zhang et al., 2018). Regarding energy saving for lighting, many schools still rely on traditional T8 fluorescent tubes; only schools built in the past 10 years use T5, T85, or light-emitting diode (LED) tubes. Switching to LED lighting can save energy and has been employed as one approach for designing energy efficient schools (AlFaris et al., 2016). This also implies that campuses still have room for improvement regarding energy saving. A study examined energy-saving strategies for eight school buildings in Matera, Southern Italy 29

ACCEPTED MANUSCRIPT and found that improving the opaque envelope design can reduce energy consumption by 5% to 84%, whereas improving the transparent envelope reduced energy consumption by 3% to 59% (Rospi et al., 2017). A South Korean study evaluated the use of active equipment or facilities for energy saving such as air conditioning, energy-saving lighting, and electricity supply planning and monitoring, and found that the aforementioned methods can reduce a school’s energy consumption by 6% to 29% (Chung and Rhee, 2014). The Guangdong Province of China implemented a demonstrative project promoting green campuses and found that by reducing the electricity consumption of facilities and equipment and decreasing the electricity used by air conditioning cooling, the EUI became 23.3% lower on green campuses than on conventional campuses (Zhou et al., 2013). 7.

Conclusion Numerous studies have explored differences in energy consumption among schools built

at different times. Several studies suggested that new buildings have lower thermal conductivity U-values and superior thermal resistance performance compared to older ones, and are therefore more energy efficient (Beusker et al., 2012; Ruusala et al., 2018; Thewes et al., 2014). Nevertheless, these studies were undertaken in countries in the temperate zone, and although the aforementioned features can reduce the energy required for heating, they may not be applicable in Taiwan. Older school buildings in Taiwan have red brick walls constructed using 23 cm-thick bricks, and the brick walls are covered by 1.5 cm-thick cement mortar on both sides and then paved with tiles on the outside. The U-value of these buildings is between 2.4 and 2.6 (W/m2K). The walls of school buildings constructed in the last 20 years are made of 18 cm-thick reinforced concrete for building structure safety, covered by cement mortar, and paved with tiles. The U-value of these newer buildings is between 2.8 and 3.0. Regarding window glass, both old and new school buildings have single-pane glass windows 5 to 6 mm 30

ACCEPTED MANUSCRIPT in thickness. These building structures illustrate that thermal insulation is not a major consideration in Taiwan. Because of the lack of difference in the thermal performance of structural envelopes, this study did not determine any significant correlation between the age of school buildings. Evidently, considerable energy-saving potential exists in improvements to both new and existing school buildings. Finally, three multiple regression models were constructed for simple estimation of total energy consumption, energy use intensity, and energy use per person in school buildings; statistically significant associations were revealed. The results of this paper could serve as a reference for government authorities in formulating energy conservation regulations for school buildings. In addition, school administrators may gain greater understanding of the current energy consumption situation. Acknowledgements This study was financially supported by the Ministry of Science and Technology of Taiwan under project no. 106-2221-E-017-004-MY2.

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ACCEPTED MANUSCRIPT Highlights 

Study final energy consumption in senior and junior high and elementary schools in Taiwan



Measure annual energy use intensity (EUI) and energy consumption per student



Private schools consumed more energy than did public schools



Propose 3 multiple regression models for a simple energy consumption estimation