Chen et al 2025e - Revision history

Tomamil: Tomamil moved page Review 207099322276 to Chen et al 2025e

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Tomamil moved page Review 207099322276 to Chen et al 2025e

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Amanda32507 at 23:47, 29 September 2025

2025-09-29T23:47:54Z

@@ Line 82: / Line 82: @@
   '''Figure 2. Differences between the percentage of lights turned on for the no reminders, two reminders, and daily reminders groups.''' Values represent means &plusmn; SEM. Significance was preformed using ANOVA with Tukey HSD.
-As seen from Figure 2, reminder frequency did not significantly affect the percentage of lights left on overnight. It was observed that no statistically significant difference was observed in the number of lights turned on or off between the no reminder group and the two-reminder group (p = 0.11). A similar result was found between the no reminder and the daily reminder groups (p = 0.31). This meant that the implementation of reminders did not meaningfully change the percentage of lights turned on. This runs contrary to the hypothesis, which predicted that there would be a significant reduction in the percentage of lights turned on. This finding suggests that habits such as turning off lights, may be less responsive to reminder-based interventions and might require more direct encouragement for consistent energy saving behavior.
+As seen from Figure 2, reminder frequency did not significantly affect the percentage of lights left on overnight. It was observed that no statistically significant difference was observed in the number of lights turned on or off between the no reminder group and the two-reminder group (p = 0.11). A similar result was found between the no reminder and the daily reminder groups (p = 0.31). This meant that the implementation of reminders did not meaningfully change the percentage of lights turned on. This runs contrary to the hypothesis, which predicted that there would be a significant reduction in the percentage of lights turned on. This finding suggests that habits such as turning off lights, may be less responsive to reminder based interventions and might require more direct encouragement for consistent energy saving behavior.
 [[File:Review_207099322276_6048_Figure 3.jpg]]

@@ Line 74: / Line 74: @@
 [[File:Review_207099322276_3161_Figure1.png]]
-  '''Figure 1. Average electricity consumption overnight from 6:00 PM and 7:15 AM between the no-reminder, two-reminder, and daily reminder groups.''' Values represent means &plusmn; SEM. Groups labeled with the same letter (a) are not significantly different, and significance was preformed using ANOVA with Tukey HSD. There was no statistically significant difference between the no reminders and two reminders groups (p = 0.21), an indication that the use of two-reminders does not lead to a significant reduction in electricity. There was, however, a statistically significant decrease observed between the no reminders and the daily reminder groups (p < 0.05), indicating that the implementation of daily reminders did reduce the average electricity consumption of the school. This is consistent with the hypothesis, which stated that the implementation of daily reminders would lead to a reduction in the electricity consumption.
+  '''Figure 1. Average electricity consumption overnight from 6:00 PM and 7:15 AM between the no-reminder, two-reminder, and daily reminder groups.''' Values represent means &plusmn; SEM. Groups labeled with the same letter (a) are not significantly different, and significance was preformed using ANOVA with Tukey HSD.
 [[File:Review_207099322276_4295_Figure 2.png]]
-  '''Figure 2. Differences between the percentage of lights turned on for the no reminders, two reminders, and daily reminders groups.''' Values represent means &plusmn; SEM. Significance was preformed using ANOVA with Tukey HSD. It was observed that no statistically significant difference was observed in the number of lights turned on or off between the no reminder group and the two-reminder group (p = 0.11). A similar result was found between the no reminder and the daily reminder groups (p = 0.31). This meant that the implementation of reminders did not meaningfully change the percentage of lights turned on. This runs contrary to the hypothesis, which predicted that there would be a significant reduction in the percentage of lights turned on.
+  '''Figure 2. Differences between the percentage of lights turned on for the no reminders, two reminders, and daily reminders groups.''' Values represent means &plusmn; SEM. Significance was preformed using ANOVA with Tukey HSD.
 [[File:Review_207099322276_6048_Figure 3.jpg]]
-  '''Figure 3. Average electricity consumption (All day long) between the no reminders, two reminders, and daily reminders groups.''' alues represent means &plusmn; SEM. Significance was preformed using ANOVA with Tukey HSD.The results show no significant difference between the no reminders and the daily reminders groups (p = 0.33). This is not the case between the no reminders and two reminders groups, though, since a statistically significant difference was noted between the no reminder and two reminders groups (p < 0.05). The statistically significant difference was, however, not a decrease but an increase. This meant that the implementation of the two-reminder feedback system not only proved ineffective in reducing electricity consumption, but also led to an increase in the average electricity consumption. The results here do not align with the perceived hypothesis, which stated that implementation of daily reminders would decrease electricity consumption.
+  '''Figure 3. Average electricity consumption (All day long) between the no reminders, two reminders, and daily reminders groups.'''
 [[File:Review_207099322276_5005_Figure 4.png]]
-  '''Figure 4. Comparison between the percentage of lights on and electricity consumption during 6:00 P.M. to 7:15 A.M. during Weeks 1 and 2.''' During the ten days, there were no reminders sent to the custodians. On Day 5, heating began in the school, causing a spike in electricity consumption. As a result, the first four days of data were disregarded and excluded from the figure (Figure 4). It can be inferred that the percentages of lights on do not correlate with electricity consumption, as there were no proportional changes between the respective figures (Figure 4). This indicates that lighting likely had less of a substantial impact on overall electricity consumption than previously hypothesized.
+  '''Figure 4. Comparison between the percentage of lights on and electricity consumption during 6:00 P.M. to 7:15 A.M. during Weeks 1 and 2.'''
 [[File:Review_207099322276_3859_Figure 5.png]]
-  '''Figure 5. Comparison between the percentage of lights on with the electricity consumption from 6:00 P.M. to 7:15 A.M. during weeks 3 and 4.''' During these ten days, there were 2 reminders sent to the custodians on Monday and Friday. From Day 11- 13, the percentage of lights on showed a slight correlation with the electricity consumption. This was seen through the corresponding decreases and increases between the lights and electricity. After Day 13, a large, inconsistent, spike in electricity consumption was observed. As there were no corresponding increases in percentage of lights on, it can be inferred that lights are not a major contributor of electricity consumption. Other factors most likely contribute more to electricity consumption in this time period.
+  '''Figure 5. Comparison between the percentage of lights on with the electricity consumption from 6:00 P.M. to 7:15 A.M. during weeks 3 and 4.'''
 [[File:Review_207099322276_7380_Figure 6.png]]
-  '''Figure 6. Comparison between the percentage of lights on and the electricity consumption from 6:00 P.M. to 7:15 A.M. during weeks 5 and 6.''' During the two week period, reminders were sent to custodians daily. Days 29 and 30 saw a noticeable decrease in electricity consumption as school holidays canceled all classes. As a result, Days 29 and 30 were removed from the figure to prevent an artificial drop in data. A slight correlation was observed between the percentage of lights on and electricity consumption, as seen on Days 23 and 27, where increases in lighting usage coincided with small spikes in electricity use (Figure 6). These findings suggest that while lighting contributes to electricity consumption, its overall impact is minor, once again indicating other factors are responsible for the majority of the energy use during this period.
+  '''Figure 6. Comparison between the percentage of lights on and the electricity consumption from 6:00 P.M. to 7:15 A.M. during weeks 5 and 6.'''
 [[File:Review_207099322276_7175_Figure 7.png]]
-  '''Figure 7. Comparison of the percentage of lights on and electricity consumption across Weeks 1 to 6, from 6:00 P.M. to 7:15 A.M. and over a full day, from 12:00 A.M. to 12:00 A.M.''' The orange line represents total daily electricity consumption (12:00 AM - 12:00 PM), while the blue line reflects usage during the evening and overnight hours (6:00 PM - 7:15 AM). The correlation between the two lines indicates that changes in electricity consumption during the evening and overnight period significantly influence overall daily consumption (Figure 7). This highlights the substantial contribution of evening and overnight electricity use to total energy usage. Thus, decreasing electricity consumption is best done by affecting the electricity usage from 6:00 P.M. to 7:15 A.M. Boxed areas in the figure show when reminders were sent, highlighting their potential impact on energy consumption patterns (Figure 7).
+  '''Figure 7. Comparison of the percentage of lights on and electricity consumption across Weeks 1 to 6, from 6:00 P.M. to 7:15 A.M. and over a full day, from 12:00 A.M. to 12:00 A.M.'''
 == Discussion ==

@@ Line 7: / Line 7: @@
 '' 1.1 Issues of Electricity Consumption''
-Electricity contributes greatly to carbon emissions. Carbon dioxide and other greenhouse gasses trap heat in the form of solar radiation within the atmosphere, leading to an increase in the global water temperature and overall global temperature [1]. An increase in global temperature can disrupt the equilibrium of Earth’s ecosystems, leading to adverse effects for both people and animals. For instance, as a result of increasing carbon dioxide levels, oceans have become more acidic. The increased acidity makes the ocean uninhabitable to marine life and upsets the balance of the ocean ecosystems[2].
+Electricity contributes greatly to carbon emissions. Carbon dioxide and other greenhouse gasses trap heat in the form of solar radiation within the atmosphere, leading to an increase in the global water temperature and overall global temperature [1]. An increase in global temperature can disrupt the equilibrium of Earth’s ecosystems, leading to adverse effects for both people and animals. For instance, as a result of increasing carbon dioxide levels, oceans have become more acidic. The increased acidity makes the ocean uninhabitable to marine life and upsets the balance of the ocean ecosystems [2].
 In the U.S., 32% of the overall amount of carbon emissions came from electricity usage [3], making it an important factor to consider if the US wants to reduce emission rate by 30% before 2030 [4]. It was also found that, in the EU, 20% of all carbon dioxide produced comes from electricity use, also making it important in curbing the carbon emissions in Europe [5].
@@ Line 15: / Line 15: @@
 ''1.2 Purpose and hypothesis''
-The objective of this study is to investigate the effects of energy-efficient behavior reminders on electricity conservation in a New York City Public High School setting. It was hypothesized that energy-saving actions implemented through behavior will reduce electricity consumption in a high school setting when they are implemented daily. Schools, a major source of energy usage, contribute to a large amount of the current carbon dioxide output. This large contribution can be attributed to the fact that 12.5% of New York’s population is made up of students[8,9]. To reduce energy consumption as well as the resulting carbon output, New York schools must decrease the amount of electricity used. By using a New York public high school as a model, the effectiveness of altering behavior on electricity use can be gauged in a public school setting and could potentially be applied to several public high schools both in and outside of New York.
+The objective of this study is to investigate the effects of energy-efficient behavior reminders on electricity conservation in a New York City Public High School setting. It was hypothesized that energy-saving actions implemented through behavior will reduce electricity consumption in a high school setting when they are implemented daily. Schools, a major source of energy usage, contribute to a large amount of the current carbon dioxide output. This large contribution can be attributed to the fact that 12.5% of New York’s population is made up of students [8,9]. To reduce energy consumption as well as the resulting carbon output, New York schools must decrease the amount of electricity used. By using a New York public high school as a model, the effectiveness of altering behavior on electricity use can be gauged in a public school setting and could potentially be applied to several public high schools both in and outside of New York.
 '' 1.3 Literature Review ''
@@ Line 21: / Line 21: @@
 There have been several studies that have attempted to reduce electricity usage or develop more environmentally friendly, energy-efficient devices. For instance, it was found that by replacing compact fluorescent lamps (CFL) with light-emitting diodes (LED), a 33-35% reduction in lighting energy expenditure can be seen. A further reduction of 13-15% can be seen if smart LEDs were used instead of LEDs [10]. The reduction in electric lighting energy usage can lead to a decrease in carbon emissions. Several studies have also made improvements to heating, ventilation, and air conditioning systems (HVAC). One such instance is the use of the Berkley Retrofitted and Inexpensive HVAC Testbed for Energy Efficiency (BRITE) on the Berkeley campus, which adjusts depending on the temperature to prevent a room from overcooling. BRITE was found to be 30-70% more energy efficient compared to two-position control, another way HVAC is controlled [11]. Another example can be seen in the development of solar-powered air conditioners. Solar-powered air conditioners rely on solar energy, which does not result in the production of carbon dioxide. In addition, solar-powered air conditioners are more energy efficient than conventional air conditioners, providing an incentive for their use [12]. The development of environmentally friendly devices and the illustration that they work demonstrate that there is great potential in reducing the amount of electricity consumed.
-While studies testing electricity depletion and behavior have been done previously, there are few instances where the impact of energy-efficient behavior reminders on electricity consumption was investigated in a high school setting. Several studies have observed how habits such as turning off appliances[13] or one’s political ideology[14]have influenced electricity conservation, but these were often done in offices or residential buildings and were not explored previously in a New York City public high school. By investigating this context, the study seeks to fill a critical gap in the research done on electricity consumption in school settings, exploring whether behavior reminders can be effectively tailored and implemented in educational institutions to promote energy conservation. The unique aspect of this study is its focus on the effectiveness of behavior reminders in a high school setting, particularly within a New York City public high school. This focus on a high school setting underscores the importance of understanding how energy-saving interventions have to be modified in order to fit different institutional contexts due to how they differ in size, layout, and management compared to other contemporary settings, such as a residential building or office. A school building is designed not only to provide education, but also for extracurricular activities or programs such as sports or clubs. Additionally, most of the rooms within a high school are classrooms, which lack the typical amenities seen in residential buildings such as hydraulic systems and are not always in operation. Moreover, the focus on daily reminders adds novelty to this research as by examining the behavior changes in an educational institution, this study can help identify methods to reduce electricity usage and carbon emission in schools. Furthermore, the study's use of real-time data collected over days, weeks, and months provides a more accurate and credible assessment of electricity consumption compared to traditional methods that rely solely on monthly electricity bills.
+While studies testing electricity depletion and behavior have been done previously, there are few instances where the impact of energy-efficient behavior reminders on electricity consumption was investigated in a high school setting. Several studies have observed how habits such as turning off appliances [13] or one’s political ideology [14] have influenced electricity conservation, but these were often done in offices or residential buildings and were not explored previously in a New York City public high school. By investigating this context, the study seeks to fill a critical gap in the research done on electricity consumption in school settings, exploring whether behavior reminders can be effectively tailored and implemented in educational institutions to promote energy conservation. The unique aspect of this study is its focus on the effectiveness of behavior reminders in a high school setting, particularly within a New York City public high school. This focus on a high school setting underscores the importance of understanding how energy-saving interventions have to be modified in order to fit different institutional contexts due to how they differ in size, layout, and management compared to other contemporary settings, such as a residential building or office. A school building is designed not only to provide education, but also for extracurricular activities or programs such as sports or clubs. Additionally, most of the rooms within a high school are classrooms, which lack the typical amenities seen in residential buildings such as hydraulic systems and are not always in operation. Moreover, the focus on daily reminders adds novelty to this research as by examining the behavior changes in an educational institution, this study can help identify methods to reduce electricity usage and carbon emission in schools. Furthermore, the study's use of real-time data collected over days, weeks, and months provides a more accurate and credible assessment of electricity consumption compared to traditional methods that rely solely on monthly electricity bills.
 == Methods ==

@@ Line 7: / Line 7: @@
 '' 1.1 Issues of Electricity Consumption''
-Electricity contributes greatly to carbon emissions. Carbon dioxide and other greenhouse gasses trap heat in the form of solar radiation within the atmosphere, leading to an increase in the global water temperature and overall global temperature (Raval & Ramanathan, 1989). An increase in global temperature can disrupt the equilibrium of Earth’s ecosystems, leading to adverse effects for both people and animals. For instance, as a result of increasing carbon dioxide levels, oceans have become more acidic. The increased acidity makes the ocean uninhabitable to marine life and upsets the balance of the ocean ecosystems (Cornwall, 2021).
+Electricity contributes greatly to carbon emissions. Carbon dioxide and other greenhouse gasses trap heat in the form of solar radiation within the atmosphere, leading to an increase in the global water temperature and overall global temperature [1]. An increase in global temperature can disrupt the equilibrium of Earth’s ecosystems, leading to adverse effects for both people and animals. For instance, as a result of increasing carbon dioxide levels, oceans have become more acidic. The increased acidity makes the ocean uninhabitable to marine life and upsets the balance of the ocean ecosystems[2].
-In the U.S., 32% of the overall amount of carbon emissions came from electricity usage (U.S. Energy Information Administration, 2020), making it an important factor to consider if the US wants to reduce emission rate by 30% before 2030 (International Energy Agency, 2023). It was also found that, in the EU, 20% of all carbon dioxide produced comes from electricity use, also making it important in curbing the carbon emissions in Europe (Eurostat, 2023).
+In the U.S., 32% of the overall amount of carbon emissions came from electricity usage [3], making it an important factor to consider if the US wants to reduce emission rate by 30% before 2030 [4]. It was also found that, in the EU, 20% of all carbon dioxide produced comes from electricity use, also making it important in curbing the carbon emissions in Europe [5].
-Behavior, habits, and awareness relating to electricity usage play a role in electricity conservation. One study revealed that providing feedback on electricity through a website led to a 15% reduction in electricity use in residential households (Vassileva et al., 2012). Another study found a 13% reduction in electricity usage when a feedback system that allowed office workers to compare electricity use with others was used (Gulbinas & Taylor, 2014). Therefore, it is clear that altering behavior can be an effective strategy to conserve electricity.
+Behavior, habits, and awareness relating to electricity usage play a role in electricity conservation. One study revealed that providing feedback on electricity through a website led to a 15% reduction in electricity use in residential households [6]. Another study found a 13% reduction in electricity usage when a feedback system that allowed office workers to compare electricity use with others was used [7]. Therefore, it is clear that altering behavior can be an effective strategy to conserve electricity.
 ''1.2 Purpose and hypothesis''
-The objective of this study is to investigate the effects of energy-efficient behavior reminders on electricity conservation in a New York City Public High School setting. It was hypothesized that energy-saving actions implemented through behavior will reduce electricity consumption in a high school setting when they are implemented daily. Schools, a major source of energy usage, contribute to a large amount of the current carbon dioxide output. This large contribution can be attributed to the fact that 12.5% of New York’s population is made up of students. (Anonymous, 2022) (US Census Bureau, 2023). To reduce energy consumption as well as the resulting carbon output, New York schools must decrease the amount of electricity used. By using a New York public high school as a model, the effectiveness of altering behavior on electricity use can be gauged in a public school setting and could potentially be applied to several public high schools both in and outside of New York.
+The objective of this study is to investigate the effects of energy-efficient behavior reminders on electricity conservation in a New York City Public High School setting. It was hypothesized that energy-saving actions implemented through behavior will reduce electricity consumption in a high school setting when they are implemented daily. Schools, a major source of energy usage, contribute to a large amount of the current carbon dioxide output. This large contribution can be attributed to the fact that 12.5% of New York’s population is made up of students[8,9]. To reduce energy consumption as well as the resulting carbon output, New York schools must decrease the amount of electricity used. By using a New York public high school as a model, the effectiveness of altering behavior on electricity use can be gauged in a public school setting and could potentially be applied to several public high schools both in and outside of New York.
 '' 1.3 Literature Review ''
-There have been several studies that have attempted to reduce electricity usage or develop more environmentally friendly, energy-efficient devices. For instance, it was found that by replacing compact fluorescent lamps (CFL) with light-emitting diodes (LED), a 33-35% reduction in lighting energy expenditure can be seen. A further reduction of 13-15% can be seen if smart LEDs were used instead of LEDs (Moadab et al., 2021). The reduction in electric lighting energy usage can lead to a decrease in carbon emissions. Several studies have also made improvements to heating, ventilation, and air conditioning systems (HVAC). One such instance is the use of the Berkley Retrofitted and Inexpensive HVAC Testbed for Energy Efficiency (BRITE) on the Berkeley campus, which adjusts depending on the temperature to prevent a room from overcooling. BRITE was found to be 30-70% more energy efficient compared to two-position control, another way HVAC is controlled (Aswani et al., 2011). Another example can be seen in the development of solar-powered air conditioners. Solar-powered air conditioners rely on solar energy, which does not result in the production of carbon dioxide. In addition, solar-powered air conditioners are more energy efficient than conventional air conditioners, providing an incentive for their use (Zheng et al., 2018). The development of environmentally friendly devices and the illustration that they work demonstrate that there is great potential in reducing the amount of electricity consumed.
+There have been several studies that have attempted to reduce electricity usage or develop more environmentally friendly, energy-efficient devices. For instance, it was found that by replacing compact fluorescent lamps (CFL) with light-emitting diodes (LED), a 33-35% reduction in lighting energy expenditure can be seen. A further reduction of 13-15% can be seen if smart LEDs were used instead of LEDs [10]. The reduction in electric lighting energy usage can lead to a decrease in carbon emissions. Several studies have also made improvements to heating, ventilation, and air conditioning systems (HVAC). One such instance is the use of the Berkley Retrofitted and Inexpensive HVAC Testbed for Energy Efficiency (BRITE) on the Berkeley campus, which adjusts depending on the temperature to prevent a room from overcooling. BRITE was found to be 30-70% more energy efficient compared to two-position control, another way HVAC is controlled [11]. Another example can be seen in the development of solar-powered air conditioners. Solar-powered air conditioners rely on solar energy, which does not result in the production of carbon dioxide. In addition, solar-powered air conditioners are more energy efficient than conventional air conditioners, providing an incentive for their use [12]. The development of environmentally friendly devices and the illustration that they work demonstrate that there is great potential in reducing the amount of electricity consumed.
-While studies testing electricity depletion and behavior have been done previously, there are few instances where the impact of energy-efficient behavior reminders on electricity consumption was investigated in a high school setting. Several studies have observed how habits such as turning off appliances (G. Kavulya, B. Becerik-Gerber, 2012) or one’s political ideology (Gromet et al., 2013) have influenced electricity conservation, but these were often done in offices or residential buildings and were not explored previously in a New York City public high school. By investigating this context, the study seeks to fill a critical gap in the research done on electricity consumption in school settings, exploring whether behavior reminders can be effectively tailored and implemented in educational institutions to promote energy conservation. The unique aspect of this study is its focus on the effectiveness of behavior reminders in a high school setting, particularly within a New York City public high school. This focus on a high school setting underscores the importance of understanding how energy-saving interventions have to be modified in order to fit different institutional contexts due to how they differ in size, layout, and management compared to other contemporary settings, such as a residential building or office. A school building is designed not only to provide education, but also for extracurricular activities or programs such as sports or clubs. Additionally, most of the rooms within a high school are classrooms, which lack the typical amenities seen in residential buildings such as hydraulic systems and are not always in operation. Moreover, the focus on daily reminders adds novelty to this research as by examining the behavior changes in an educational institution, this study can help identify methods to reduce electricity usage and carbon emission in schools. Furthermore, the study's use of real-time data collected over days, weeks, and months provides a more accurate and credible assessment of electricity consumption compared to traditional methods that rely solely on monthly electricity bills.
+While studies testing electricity depletion and behavior have been done previously, there are few instances where the impact of energy-efficient behavior reminders on electricity consumption was investigated in a high school setting. Several studies have observed how habits such as turning off appliances[13] or one’s political ideology[14]have influenced electricity conservation, but these were often done in offices or residential buildings and were not explored previously in a New York City public high school. By investigating this context, the study seeks to fill a critical gap in the research done on electricity consumption in school settings, exploring whether behavior reminders can be effectively tailored and implemented in educational institutions to promote energy conservation. The unique aspect of this study is its focus on the effectiveness of behavior reminders in a high school setting, particularly within a New York City public high school. This focus on a high school setting underscores the importance of understanding how energy-saving interventions have to be modified in order to fit different institutional contexts due to how they differ in size, layout, and management compared to other contemporary settings, such as a residential building or office. A school building is designed not only to provide education, but also for extracurricular activities or programs such as sports or clubs. Additionally, most of the rooms within a high school are classrooms, which lack the typical amenities seen in residential buildings such as hydraulic systems and are not always in operation. Moreover, the focus on daily reminders adds novelty to this research as by examining the behavior changes in an educational institution, this study can help identify methods to reduce electricity usage and carbon emission in schools. Furthermore, the study's use of real-time data collected over days, weeks, and months provides a more accurate and credible assessment of electricity consumption compared to traditional methods that rely solely on monthly electricity bills.
 == Methods ==
@@ Line 122: / Line 122: @@
 == References  ==
 Raval, A., & Ramanathan, V. (1989). Observational determination of the greenhouse effect. Nature, 342(6251), 758–761.
 Cornwall, C. E., Comeau, S., Kornder, N. A., Perry, C. T., van Hooidonk, R., DeCarlo, T. M., ... & Lowe, R. J. (2021). Global declines in coral reef calcium carbonate production under ocean acidification and warming. Proceedings of the National Academy of Sciences, 118(21), e2015265118.
 U.S. Energy Information Administration. (2020). How much of US carbon dioxide emissions are associated with electricity generation? https://www.eia.gov/tools/faqs/faq.php?id=77&t=11
 International Energy Agency. (2023, February 1). Methane emissions remained stubbornly high in 2022 even as soaring energy prices made actions to reduce them cheaper than ever. IEA News. https://www.iea.org/news/methane-emissions-remained-stubbornly-high-in-2022-even-as-soaring-energy-prices-made-actions-to-reduce-them-cheaper-than-ever
 Eurostat. (2023, May 15). EU economy greenhouse gas emissions: -4% in Q4 2022. Eurostat News. https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20230515-2
 Vassileva, I., Odlare, M., Wallin, F., & Dahlquist, E. (2012). The impact of consumers’ feedback preferences on domestic electricity consumption. Applied Energy, 93, 575–582.
 Gulbinas, R., & Narayanan, S. (2014). Effects of real-time eco-feedback and organizational network dynamics on energy-efficient behavior in commercial buildings. Energy and Buildings.
 Anonymous. (2022). DOE data at a glance. NYC Department of Education. Retrieved January 18, 2023, from https://www.schools.nyc.gov/about-us/reports/doe-data-at-a-glance
 U.S. Census Bureau. (n.d.). QuickFacts: New York City, New York. Retrieved March 20, 2023, from https://www.census.gov/quickfacts/newyorkcitynewyork
 Moadab, N. H., Olsson, T., Fischl, G., & Aries, M. (2021). Smart versus conventional lighting in apartments: Electric lighting energy consumption simulation for three different households. Energy and Buildings, 244, 111009.
 Aswani, A., Master, N., Taneja, J., Culler, D., & Tomlin, C. (2011). Reducing transient and steady state electricity consumption in HVAC using learning-based model-predictive control. Proceedings of the IEEE, 100(1), 240-253.
-Aksoezen, M., Daniel, M., Hassler, U., & Kohler, N. (2015). Building age as an indicator for energy consumption. Energy and Buildings, 87, 74–86.
+Zheng, L., Zhang, W., Xie, L., Wang, W., Tian, H., & Chen, M. (2019). Experimental study on the thermal performance of solar air conditioning system with MEPCM cooling storage. International Journal of Low-Carbon Technologies, 14(1), 83–88.
-Anonymous. (2023, April 19). National Climate Task Force. The White House. https://www.whitehouse.gov/climate/#:~:text=Reducing%20Emissions%20and%20Accelerating%20Clean%20Energy
+Kavulya, G., & Becerik-Gerber, B. (2012). Understanding the influence of occupant behavior on energy consumption patterns in commercial buildings. In Proceedings of Computing in Civil Engineering 2012(pp. 569–576).
-Zheng, L., Zhang, W., Xie, L., Wang, W., Tian, H., & Chen, M. (2019). Experimental study on the thermal performance of solar air conditioning system with MEPCM cooling storage. International Journal of Low-Carbon Technologies, 14(1), 83–88.
+Gromet, D. M., Kunreuther, H., & Larrick, R. P. (2013). Political ideology affects energy-efficiency attitudes and choices. Proceedings of the National Academy of Sciences, 110(23), 9314-9319
-−
-Kavulya, G., & Becerik-Gerber, B. (2012). Understanding the influence of occupant behavior on energy consumption patterns in commercial buildings. In Proceedings of Computing in Civil Engineering 2012(pp. 569–576).
-−
 Gromet, D. M., Kunreuther, H., & Larrick, R. P. (2013). Political ideology affects energy-efficiency attitudes and choices. Proceedings of the National Academy of Sciences, 110(23), 9314-9319

@@ Line 80: / Line 80: @@
   '''Figure 2. Differences between the percentage of lights turned on for the no reminders, two reminders, and daily reminders groups.''' Values represent means &plusmn; SEM. Significance was preformed using ANOVA with Tukey HSD. It was observed that no statistically significant difference was observed in the number of lights turned on or off between the no reminder group and the two-reminder group (p = 0.11). A similar result was found between the no reminder and the daily reminder groups (p = 0.31). This meant that the implementation of reminders did not meaningfully change the percentage of lights turned on. This runs contrary to the hypothesis, which predicted that there would be a significant reduction in the percentage of lights turned on.
-[[File:Review_207099322276_4196_Screen Shot 2025-06-23 at 4.39.54 PM.png]]
+[[File:Review_207099322276_6048_Figure 3.jpg]]
   '''Figure 3. Average electricity consumption (All day long) between the no reminders, two reminders, and daily reminders groups.''' alues represent means &plusmn; SEM. Significance was preformed using ANOVA with Tukey HSD.The results show no significant difference between the no reminders and the daily reminders groups (p = 0.33). This is not the case between the no reminders and two reminders groups, though, since a statistically significant difference was noted between the no reminder and two reminders groups (p < 0.05). The statistically significant difference was, however, not a decrease but an increase. This meant that the implementation of the two-reminder feedback system not only proved ineffective in reducing electricity consumption, but also led to an increase in the average electricity consumption. The results here do not align with the perceived hypothesis, which stated that implementation of daily reminders would decrease electricity consumption.
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   '''Figure 6. Comparison between the percentage of lights on and the electricity consumption from 6:00 P.M. to 7:15 A.M. during weeks 5 and 6.''' During the two week period, reminders were sent to custodians daily. Days 29 and 30 saw a noticeable decrease in electricity consumption as school holidays canceled all classes. As a result, Days 29 and 30 were removed from the figure to prevent an artificial drop in data. A slight correlation was observed between the percentage of lights on and electricity consumption, as seen on Days 23 and 27, where increases in lighting usage coincided with small spikes in electricity use (Figure 6). These findings suggest that while lighting contributes to electricity consumption, its overall impact is minor, once again indicating other factors are responsible for the majority of the energy use during this period.
-[[File:Review_207099322276_3268_Screen Shot 2025-06-23 at 5.17.17 PM.png]]
+[[File:Review_207099322276_7175_Figure 7.png]]
   '''Figure 7. Comparison of the percentage of lights on and electricity consumption across Weeks 1 to 6, from 6:00 P.M. to 7:15 A.M. and over a full day, from 12:00 A.M. to 12:00 A.M.''' The orange line represents total daily electricity consumption (12:00 AM - 12:00 PM), while the blue line reflects usage during the evening and overnight hours (6:00 PM - 7:15 AM). The correlation between the two lines indicates that changes in electricity consumption during the evening and overnight period significantly influence overall daily consumption (Figure 7). This highlights the substantial contribution of evening and overnight electricity use to total energy usage. Thus, decreasing electricity consumption is best done by affecting the electricity usage from 6:00 P.M. to 7:15 A.M. Boxed areas in the figure show when reminders were sent, highlighting their potential impact on energy consumption patterns (Figure 7).

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	'''Figure 1. Average electricity consumption overnight from 6:00 PM and 7:15 AM between the no-reminder, two-reminder, and daily reminder groups.''' Values represent means ± SEM. Groups labeled with the same letter (a) are not significantly different, and significance was preformed using ANOVA with Tukey HSD. There was no statistically significant difference between the no reminders and two reminders groups (p = 0.21), an indication that the use of two-reminders does not lead to a significant reduction in electricity. There was, however, a statistically significant decrease observed between the no reminders and the daily reminder groups (p < 0.05), indicating that the implementation of daily reminders did reduce the average electricity consumption of the school. This is consistent with the hypothesis, which stated that the implementation of daily reminders would lead to a reduction in the electricity consumption.		'''Figure 1. Average electricity consumption overnight from 6:00 PM and 7:15 AM between the no-reminder, two-reminder, and daily reminder groups.''' Values represent means ± SEM. Groups labeled with the same letter (a) are not significantly different, and significance was preformed using ANOVA with Tukey HSD. There was no statistically significant difference between the no reminders and two reminders groups (p = 0.21), an indication that the use of two-reminders does not lead to a significant reduction in electricity. There was, however, a statistically significant decrease observed between the no reminders and the daily reminder groups (p < 0.05), indicating that the implementation of daily reminders did reduce the average electricity consumption of the school. This is consistent with the hypothesis, which stated that the implementation of daily reminders would lead to a reduction in the electricity consumption.