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Article

A Comparison Between Industrial Energy Efficiency Measures in Guatemala and the United States

by
Radhika Khosla
,
Angel Martinez Rodriguez
,
Ryan J. Milcarek
* and
Patrick E. Phelan
School for Engineering of Matter, Transport, and Energy, Arizona State University, 501 East Tyler Mall, Tempe, AZ 85287-6106, USA
*
Author to whom correspondence should be addressed.
Environments 2025, 12(1), 19; https://doi.org/10.3390/environments12010019
Submission received: 20 November 2024 / Revised: 18 December 2024 / Accepted: 5 January 2025 / Published: 12 January 2025
(This article belongs to the Special Issue Environments: 10 Years of Science Together)

Abstract

:
Energy auditing has been cited as a key tool in closing the gap between the actual energy consumption in industrial facilities and what should be at an environmentally sustainable level. Several factors affect the likelihood that energy audits will be effective in closing that gap, and more analysis is needed to understand these factors, especially for developing nations. This study compares three energy efficiency measures (EEMs) frequently recommended in both the United States and Guatemala, namely, installing solar panels to generate electricity, installing higher-efficiency lighting, and upgrading to premium efficiency motors. The implementation of each of these EEMs contributes to more sustainable energy consumption, and each of these EEM’s payback periods is affected by capital costs, energy costs, and other local factors analyzed in this study. Projected payback periods for each EEM based on Guatemalan and U.S. capital cost and energy cost ranges are assessed via EEM-specific payback period calculations and compared to the energy audit data from each country. While lower capital costs incentivize EEM implementation and reduce payback periods, there is an interplay between energy cost and capital cost that impacts the trends in the U.S. and Guatemala. As in the case of the solar panel installation EEM, though Guatemalan companies pay ~110% more for electricity than U.S. companies, when Guatemalan capital costs are lower, payback periods are lower than in the U.S. Conversely, in cases where Guatemalan capital costs are higher—as for higher-efficiency lighting and motor installation—Guatemalan payback periods are roughly the same as those in the U.S. because of the higher Guatemalan energy costs.

1. Introduction

In recent years, countries around the world have been making efforts to reduce energy consumption and utilize energy more sustainably to combat climate change, but studies in the field continue to find that there is a gap between the actual energy consumption in industrial facilities and what it should be at an environmentally sustainable level [1,2]. Additionally, while the underlying principles of energy efficiency remain the same worldwide, there is no universal path to global energy efficiency improvement because each country has a different energy landscape. As such, studies that compare the application of energy efficiency improvement practices in different countries are key to closing the global energy gap. This study seeks to make such a comparison by analyzing energy auditing practices in Guatemala and the U.S.
Energy auditing—the process of identifying inefficiencies in a facility and developing energy efficiency measures (EEMs) to reduce both the facility’s energy consumption and costs, if implemented—has been cited as a key tool in closing the global energy gap [3]. An energy audit typically consists of the analysis of a facility’s metrics (e.g., size, process flow, number of employees) and utility bills, an energy survey of the facility and analysis of the energy usage data obtained, and the development of EEM recommendations that include implementation costs, energy savings, emissions reductions, and payback periods. The recommended EEMs provide energy audit clients with specific opportunities by which to close the energy efficiency gap in their facility. However, though the energy auditing process has been increasingly refined and optimized over the years, little data-driven analysis of its effectiveness has been performed.
If EEMs are not implemented, an energy audit may not have been effective in improving energy efficiency. Much of the research that focused on energy auditing in residential applications concludes that audited clients are often discouraged from adopting recommended EEMs due to long payback periods and high implementation costs [4,5]. Other studies that focused on applications in small businesses and industrial facilities hail the beneficial impacts that even the simplest EEMs, such as the installation of efficient lighting, can have on a facility’s energy usage [6,7]. Furthermore, many of the studies that seek to analyze the effectiveness of energy audit programs employ survey-driven data [8,9,10] that restrict analyses to the sampled population [6], so drawing definitive conclusions about energy auditing practices across different locations and applications becomes difficult. As such, this study seeks to investigate the relationship between regional financial factors (energy and capital costs) and the payback periods of recommended EEMs using energy audit data from Guatemala and the U.S.
Guatemala, in particular, was chosen for comparison because Guatemala is categorized as a developing country, has been undergoing an industrial boom, and is the largest economy in Central America [11]. Energy auditing practices implemented in Guatemala may very well lead the rest of the region in energy efficiency development. In evidence of this, there is an active U.S.-sponsored project focused on improving the energy efficiency of Guatemalan manufacturing facilities by supporting a local energy auditing center [12]. As the U.S. and Guatemala both have the largest economies in their respective regions (North and Central America) and because collaboration in energy auditing practices is already ongoing between the two, a comparison follows naturally.
Additionally, because the U.S. is considered a developed country and Guatemala is considered developing, a comparison between the two can provide valuable insight into the overarching motivation of this study, that the differences in each country’s energy landscape must be accounted for in global energy efficiency improvement. Many of the existing studies on energy auditing practices focus on highly developed countries [13,14,15], with less information available about energy auditing in developing countries; however, energy efficiency improvement in developing countries like Guatemala has been shown to have multi-impact benefits, allowing for economic growth and quality-of-life improvements to occur simultaneously rather than at each other’s expense [16,17,18]. Developing countries are also expected to increase their energy demand by 45% in the next 20 years, accounting for nearly 100% of projected growth in global energy demand [19].
This study therefore compares three common EEMs made both in the United States and Guatemala—installing solar panels to generate electricity, replacing current lighting with higher-efficiency lighting, and replacing old motors with premium efficiency equipment—using data obtained from U.S. industrial energy audits and from five audits of industrial facilities conducted in Guatemala [20,21]. The implementation of each of the three selected EEMs provides an actionable opportunity to improve sustainable energy consumption and reduce emissions in an industrial facility. Installing solar panels to generate electricity allows a facility to consume energy generated by a renewable energy source, as opposed to emission-intense power plant electricity generation. Installing higher-efficiency lighting and motors allows a facility to reap the same lighting levels/power output as less efficient equipment with lower energy input, cutting both energy consumption and costs.
The following analysis uses data from operating facilities, eliminating the methodological challenges posed by survey-driven data. Additionally, though the Guatemalan dataset is limited, Guatemala’s status as a key developing nation allows for an evaluation of its energy auditing practices to provide valuable insight into energy efficiency in developing nations.
Guatemala is one of the few developing countries that is experiencing steady economic growth without seeing improvement in poverty rates (Figure 1) [22]; a 2020 study [23] found that 76% of Guatemalan households could be classified as energy-poor by the United Nations definition of energy poverty [24]. Additionally, a 2021 study by Henry et al. on how renewable energy development goals will affect poverty in Guatemala found that the effect of energy development on energy prices in Guatemala could push some energy-stressed households further into energy poverty and that due to Guatemalan capital costs, fossil fuels may remain the cheapest fuel option despite the implementation of EEMs [22]. The balance between energy costs and capital costs in Guatemala therefore warrants further study, as it can provide insight into the impact of energy auditing on energy efficiency efforts in this country. Though many studies have conducted similar analyses on Latin American countries [25,26,27], beyond assessments of energy poverty [22] and the energy landscape [28], no energy efficiency studies were identified for Guatemala.

Emissions in Guatemala and the U.S.

To further highlight the importance of a comparison between energy efficiency applications in the U.S. and Guatemala, key differences between each country’s national emissions can be identified, as the energy usage reductions offered by EEM implementation also reduce global emissions to mitigate climate change. Figure 2 and Figure 3 below provide a breakdown of the U.S. and Guatemala’s national carbon dioxide (CO2) emissions in 2021, created from International Energy Agency data (IEA) [29,30]. As can be seen in Figure 2 and Figure 3, the transportation, electricity and heat production, and industrial sectors are responsible for generating the majority of CO2 emissions in both countries, which serves as an interesting point for comparison, especially given that the U.S. and Guatemala differ in their energy sources. Notably, the U.S. relies mainly on fossil fuels [29], while Guatemala relies equally heavily on both fossil fuels and hydropower, as well as biomass [30].
Targeting energy auditing practices to the same high-emitting sectors in each of these countries could serve to reduce national emissions for each, but the same recommended EEM would have a different impact in each country due to their differing energy landscapes, especially considering that the level of CO2 emissions from developing countries can exceed that of developed countries due to rapid urbanization [31]. Should an EEM focused on reducing fossil fuel consumption in an industrial facility be made in Guatemala, the contribution to national CO2 emissions reductions may be less impactful than in the U.S. This further serves to highlight not only the need for energy auditing comparison between Guatemala and the U.S. but between recommended EEMs that are common to energy audits conducted in both countries.

2. Materials and Methods

The process for selecting the three EEMs to be compared as well as the governing equations for payback period calculation and the assumptions employed for the purposes of this analysis are detailed below. The balance between capital costs and energy costs in the U.S. and Guatemala is then presented by plotting projected payback periods for each EEM with U.S. and Guatemalan energy audit data. All U.S. energy audit data were collected from a database of industrial and commercial energy audits, developed by the U.S. Department of Energy Industrial Training and Assessment Center Program [32] hosted by Rutgers University [20], and all Guatemalan energy audit data were collected from the recently developed Partnerships for International Energy Efficiency (PIEE) database, developed by the authors [21].

2.1. Energy Efficiency Measure (EEM) Selection

To make the most robust comparison between energy auditing in the U.S. and Guatemala, the most frequently recommended Guatemalan EEMs that were also frequently recommended in major U.S. industrial sectors were selected for comparison and analysis. Data for all EEM recommendations made in Guatemalan energy audits recorded in the PIEE database are presented in Table 1. The total number of times the EEM was recommended across all recorded Guatemalan energy audits, average annual savings, payback periods, and average implementation costs for each are documented. Note that EEMs related to sustainable energy use and improved efficiency—such as installing solar panels, installing lighting with higher-efficiency, and upgrading motor efficiency—are among the most frequently recommended.
Data for six frequently recommended EEMs from energy audits conducted in the agricultural (A), transportation (T), manufacturing (M), and commercial (C) sectors in the U.S. are presented in Table 2. These data were synthesized by comparing the top ten most frequently recommended EEMs in energy audits conducted in each of these sectors and selecting all EEMs that were common to at least two. As in Table 1, the total number of times the EEM was recommended in U.S. energy audits in each sector, average annual savings, average payback periods, and average implementation costs for each EEM are documented.
As energy auditing is a growing field in Guatemala and data are limited, the Guatemalan data shown in Table 1 are the limiting factor in the analysis of recommended EEMs. The five EEMs most frequently recommended by the Guatemalan Energy Auditing Center, as shown in Table 1, are installing solar panels, installing higher-efficiency lighting, upgrading thermal insulation, installing kVAR systems, and upgrading motor efficiency. As shown in Table 2, EEMs to install solar panels, install higher-efficiency lighting, and upgrade motor efficiency are frequently recommended in U.S. energy audits. As such, the three EEMs selected to be compared in the following analysis are (1) installing solar panels to generate electricity, (2) installing more efficient lighting, and (3) upgrading motors for maximum efficiency.

2.2. Analysis of Frequently Recommended EEMs in the U.S. and Guatemala

The three recommended EEMs selected above will be used to determine how the impact of recommended EEMs varies between Guatemala and the U.S. and why. By comparing the average savings and payback period of each of the three EEMs in Guatemala and the U.S., the potential that each EEM is implemented can be assessed, as EEMs with higher savings and lower payback periods are typically more attractive to audited facilities [15,33]. As stated previously, energy auditing is only truly impactful in achieving energy efficiency if EEMs are implemented. Projected payback periods for each selected EEM in each country can then be calculated to model exactly how energy cost and capital cost affect the impact an EEM can have.
The specific implementation cost, implementation savings, electricity usage savings, and payback period for each of the solar panel installation, higher-efficiency lighting, and motor upgrade EEM recommendations made in Guatemalan energy audits are presented in Table 3.
Data for these same three EEMs made in the U.S. [20] are presented in Appendix A, Table A1, Table A2 and Table A3. For each selected EEM, a sample size of 100 randomly selected energy audits conducted by ITACs in recent years (2020–2023) with available energy audit data for each of the three selected EEMs at the time of this analysis was used for data collection. The maximum, minimum, and average payback periods for each recommended EEM are shown in Table 4.

2.3. Calculating Projected Payback Periods for the Three Selected EEMs

With the data presented in Table 2, Table 3 and Table 4, the balance between energy costs and capital costs in U.S. and Guatemalan payback periods for the three selected EEMs can now be determined [34].
Generally, the payback period for any recommended EEM can be calculated as (Equation (1)):
P a y b a c k P e r i o d = T o t a l I m p l . C o s t U S D T o t a l A n n u a l S a v i n g s U S D y r
where total implementation cost and total annual savings are functions of a specific EEM’s parameters. Note that payback period calculations typically include demand savings, the reduction in electricity demand resulting from the implementation of the EEM. All calculations in this work have been performed in USD, with an exchange rate of 0.13 USD/Guatemalan Quetzal (GTQ). Demand savings have been neglected in this analysis as demand calculation and billing are dependent on geographic location, a facility’s operating schedule, and the utility provider’s billing structure. Additionally, Guatemalan utility providers currently do not typically bill by demand, so this is a reasonable assumption for this case study.
The payback period for installing a solar photovoltaic (PV) system for electricity generation can be calculated as (Equation (2)):
S o l a r P a y b a c k P e r i o d = S y s t e m C a p a c i t y k W P V I n s t a l l a t i o n C o s t U S D k W U s a g e S a v i n g s k W h U s a g e C o s t U S D k W h
Usage savings in Equation (2) is the reduction in electricity a facility must purchase from a utility provider due to the amount of electricity produced by a solar panel system. Usage savings from solar panel installation can be calculated as (Equation (3)):
U s a g e S a v i n g s = I P a n e l A r e a m 2 μ
where I is annual solar irradiation (kWh/m2yr) and μ is solar panel efficiency. For this study, IGuatemala = 824.62 kWh/m2yr, IU.S. = 754.49 kWh/m2yr [35,36], and μ = 0.25 (25%). Additionally, this analysis assumes a solar PV system with a total capacity of 2000 kW, a module-specific capacity of 500 W, and a module area of 2.2 m2 for the purposes of calculation.
The payback period for installing higher-efficiency lighting can be calculated as (Equation (4)) [34]:
H i g h e r E f f i c i e n c y L i g h t i n g P a y b a c k P e r i o d = U C S + R C S L i g h t C o s t U S D l i g h t N + L C
where UCS is usage cost savings, RCS is replacement cost savings, N is the number of lights to be installed, and LC is labor cost. RCS is the expense saved by eliminating the need to replace the existing lights, as higher-efficiency lights may have longer lifetimes, and LC is the labor cost of removing existing lighting and installing new, higher-efficiency lighting. UCS can be calculated as (Equation (5)):
U C S = N P E P R e p O H E n e r g y C o s t U S D k W h
where PE is the power rating of the existing lamps, PRep is the power rating of the higher-efficiency replacement lamps, and OH is the annual operating hours of the lamps. RCS (Equation (6)) and LC (Equation (7)) can be calculated as:
R C S = O H N C E + L C h T R L E
L C = O H N C E + L C h T R L E
where CE is the cost of the existing bulbs (USD 20.40 per fluorescent bulb for this analysis), LCh is the labor cost per hour, TR is the time it takes in hours to replace one bulb, LE is the lifetime of the existing bulbs in hours, and LRep is the lifetime of the higher-efficiency replacement bulbs in hours. This analysis assumes that 100 fluorescent bulbs (15,000 h lifetime) that consume 240 W each are being replaced with LED lights that consume 32 W each (50,000 h lifetime) [37]. Additionally, according to the U.S. Bureau of Labor Statistics, it costs roughly LCh = USD 19.24 to replace a light bulb [38].
The payback period for replacing old motors with premium efficiency motors can be calculated as (Equation (8)):
M o t o r U p g r a d e P a y b a c k P e r i o d = T o t a l m o t o r P o w e r k W I n s t a l l e d M o t o r C o s t U S D k W U s a g e S a v i n g s k W h U s a g e C o s t U S D k W h
where usage savings, enumerating the expense saved by the reduced energy consumption of premium efficiency motors, can be calculated as (Equation (9)):
U s a g e S a v i n g s = M o t o r P o w e r k W L F O H E C E P
where LF is the load factor of the motors, OH is the annual motor operating hours, Ec is the efficiency of the motors being replaced, and Ep is the efficiency of the premium efficiency replacement motors. For the purposes of this analysis, it has been assumed that a 50 HP (37.3 kW) electric motor with 87% efficiency will be replaced with the National Electrical Manufacturers Association (NEMA) premium motor with 93% efficiency [39] and that the motors run for 8,760 h per year.
Finally, from the energy audit data [20,21], typical energy cost ranges for each country are USD 0.05–0.09 per kWh in the U.S. and USD 0.15–0.20 per kWh in Guatemala. The capital cost ranges for each of the three EEMs above in each country are presented in Table 5.
Equations (2)–(9), in conjunction with the energy cost and capital cost ranges presented in Table 5, are used to calculate projected payback periods in each country and determine how the balance of capital cost expenditures and energy cost savings affects each EEM’s payback period.

3. Results

Figure 4, developed from the data presented in Table 3 and Table A1, shows the histogram distribution of payback periods for the recommended EEM to install solar panels to generate electricity in the U.S. and Guatemala. The payback periods for installing solar panels in Guatemala are far lower than the U.S. payback periods; the average Guatemalan payback period is ~5 years, while the average U.S. payback period is ~10 years, despite U.S. energy costs being USD ~0.10 per kWh lower than Guatemalan energy costs. From Table 5, this is correlated with Guatemalan capital costs, which are USD ~0.40 per kW lower than U.S. capital costs for solar panel installation.
Figure 5, developed from the data presented in Table 3 and Table A2, shows the histogram distribution of payback periods for the recommended EEM to replace lighting with higher-efficiency lighting (i.e., LEDs) in the U.S. and Guatemala. The average Guatemalan payback period is ~1.7 years and the U.S. average is ~1.9 years. From Table 5, Guatemalan capital costs for installing higher-efficiency LED lighting are more than twice the U.S. capital costs. Thus, in this case, lower Guatemalan energy costs and higher capital costs are correlated with similar payback periods, as seen in U.S. energy audits.
Figure 6, developed from the data presented in Table 3 and Table A3, shows the histogram distribution of payback periods for the recommended EEM to replace standard efficiency motors with premium efficiency equipment. The average Guatemalan payback period is ~3 years and the U.S. average is ~2.3 years. From Table 5, Guatemalan capital costs for this EEM are ~50% greater than U.S. capital costs. Similar to the results in Figure 5, higher Guatemalan capital costs and lower energy costs in this case are correlated with similar payback periods, as seen in the U.S. Additionally, the difference between U.S. and Guatemalan capital costs is greater in the higher-efficiency lighting case than in the motor upgrade case, and the difference between Guatemalan and U.S. payback periods for installing higher-efficiency lighting is lower than that of the recommended EEM to upgrade motors.
The correlations between capital and energy costs in a recommended EEM’s payback period demonstrated by Figure 4, Figure 5 and Figure 6 are now further investigated by plotting projected payback period curves for each EEM recommendation calculated from Equations (2)–(9). Figure 7 plots the expected payback period for the recommended EEM to install solar panels to generate electricity in the U.S. and Guatemala over the energy and capital cost ranges presented in Section 2.3 and Table 5. Energy audit data for each country [20,21] are plotted with the projected payback period curves, and note that only 50 data points of the 100 presented in Table A1, Table A2 and Table A3 are plotted for figure clarity. The plotted curves in Figure 7 indicate that payback periods can be expected to decrease with increasing energy costs and increase with increasing capital costs, and the correlations seen in Figure 5 are corroborated by Figure 7. For solar panel installation, the projected payback period area for each country in Figure 7 is bounded by energy costs of USD 0.05–0.09 per kWh and capital costs of USD 0.8–1 per kW for the U.S. and by energy costs of USD 0.15–0.2 per kWh and capital costs of USD 1.2–1.4 per kW for Guatemala. As can be seen in Figure 7, these projected areas indeed indicate that U.S. payback periods can be expected to be ~5 years longer than Guatemalan payback periods, which is reflected in the energy audit data from each country [20,21].
Figure 8 plots the expected payback period for the recommended EEM to replace current lighting with higher-efficiency lighting in the U.S. and Guatemala over the energy and capital cost ranges presented in Section 2.3 and Table 5. The projected payback period areas in Figure 8 are bounded by energy costs of USD 0.05–0.09 per kWh and capital costs of USD 50–95 per LED light for the U.S. and by energy costs of USD 0.15–0.2 per kWh and capital costs of USD 130–195 per LED light for Guatemala. As expected based on Figure 5, these projected areas indicate that U.S. payback periods for this EEM can be expected to be roughly the same as Guatemalan payback periods, a trend confirmed by the collected energy audit data [20,21].
Finally, Figure 9 plots the expected payback period for the recommended EEM to replace old motors with premium efficiency equipment in the U.S. and Guatemala over the energy and capital cost ranges presented in Section 2.3 and Table 5. The projected payback period areas in Figure 9 are bounded by energy costs of USD 0.05–0.09 per kWh and capital costs of USD 70–90 per motor for the U.S. and by energy costs of USD 0.15–0.2 per kWh and capital costs of USD 100–120 per motor for Guatemala. As expected based on Figure 6, these projected areas indicate that U.S. payback periods for this EEM can be expected to be similar and up to ~1 year shorter than Guatemalan payback periods, which is again reflected by the energy audit data [20,21].
The trends in Figure 7, Figure 8 and Figure 9 can be tied directly to the motivating question of this study—how impactful is energy auditing for energy efficiency in developed vs. developing countries—by considering the existing work focused on EEM implementation. A 2012 study conducted by Fleiter et al. [13] analyzing the barriers to the adoption of EEMs based on German energy audit data found that 80% of 160 facilities that received an energy audit reported high investment costs as a critical factor in the decision to forgo the implementation of an EEM. Further regression analysis also revealed that a lack of capital and low profitability held the highest statistical weight in the prediction of EEM adoption. A 2004 study [28] analyzing the EEM adoption decisions of U.S. manufacturing facilities that received energy audits also found that the probability of EEM adoption declines logarithmically with increasing payback period. Numerous other studies in addition to these two cite implementation cost and payback period as critical factors in the adoption, and hence effectiveness, of a recommended EEM [9,10,16,28]. In combination with the results presented here, it is clear that across developing and developed countries, payback periods must be low for the sustainability-related benefits offered by an energy audit to be realized via EEM adoption, and that payback periods hinge on the balance of capital and energy cost.

4. Discussion

Guatemala’s energy sector is characterized by a lower per capita energy consumption than the U.S. In 2022, Guatemala’s per capita energy consumption was 0.714 MWh per person per year, which is significantly lower than the U.S.’s 12.87 MWh per person per year [29,30]. Additionally, a Guatemalan facility pays ~110% more on average for electricity than a facility in the United States [29,30]. This results in more favorable payback periods for Guatemalan EEM recommendations that reduce overall electricity consumption, and because Guatemalan electricity costs are higher, a facility in Guatemala would have a similar or lower payback period than a U.S. facility for the same EEM. This expected trend is corroborated by the results presented in Figure 7, Figure 8 and Figure 9; as shown in the case of the solar panel installation EEM, the combination of lower capital cost and higher energy cost resulted in shorter payback periods in Guatemala. This also indicates that the balance of energy and capital cost is important in payback period calculation; though higher energy costs result in higher savings from reductions in energy consumption, higher capital costs can result in similar payback periods in some cases.
In the case of upgrading motors, the Guatemalan EEMs have roughly the same payback periods as the U.S. EEMs despite the higher savings that Guatemalan facilities would reap from their higher energy costs due to capital cost differences of only USD 30–50 per horsepower. Numerous studies have also reported that up-front EEM implementation costs have been found to hold more weight in the decision to implement an EEM than future energy savings [27,40,41]. Given that nearly all of the projected future increase in global energy demand is expected to occur in developing countries like Guatemala, it is, therefore, important to consider—in light of the results presented here—that changing energy landscapes in developing countries will impact the effectiveness of EEMs recommended in energy audits. A 2021 study modeling how energy costs in Guatemala would be impacted by the adoption of renewable energy technology found that while solar power has the potential to meet projected Guatemalan energy demand for the lowest cost, the added cost of renewable energy generation translates to a 20–40% increase in energy expenditures [22]. Should energy prices rise in Guatemala, according to the analysis presented above, the cost savings offered by reductions in energy use would increase, but capital costs have the potential to rise with increasing commercial demand as well.
The results presented here offer insight into the effect of energy technology subsidies on EEM payback periods as well, as they factor into the associated costs. The energy auditing services provided in the U.S. and resulting technology upgrades are occasionally subsidized, which has been found to reduce short-term implementation costs [27]. However, while energy subsidies have been found to reduce energy inequality in developed countries with stable energy costs, in the case of developing countries, Henry et al. [22] found that tax subsidies for EEM adoption may lower costs in the short term but also have the potential to raise energy prices in the long term, as new development costs trickle down to electricity consumers. The comparisons made here thus indicate that while higher Guatemala energy prices currently shorten payback periods, energy development in Guatemala—and other developing countries by extension—may shift the balance, especially given that lower short-term costs have long-term implications for energy costs.
In addition to providing insight into the effect of energy subsidies on energy efficiency, the results of this study can have implications for energy-related policy in developing countries as compared to developed countries. The establishment of energy audit programs has long been considered a useful instrument in improving energy efficiency—hence the U.S. involvement in Guatemalan energy auditing programs that serves as motivation for this study—but lack of incentive for EEM implementation presents a barrier to its effectiveness [42]. As the successful implementation of EEMs can contribute significantly to closing a country’s energy efficiency gap while also having the effect of bolstering business and alleviating poverty for developing countries [43], the results of this study can serve to inform what exactly the barriers to EEM implementation are given a country’s specific local energy and financial landscapes. For example, the results demonstrate that lowering capital costs through incentive programs can reduce the payback period and make EEM implementation more attractive in Guatemala, especially given the high energy costs.

5. Conclusions

The Guatemalan and U.S. energy audit data available in the IAC and PIEE databases [20,21] provide a unique opportunity to build on previous work investigating the effectiveness of energy auditing via EEM implementation by providing case study insight into the mechanisms that dictate EEM payback periods in both a developed and a developing country. It was found in previous work that shorter payback periods for recommended EEMs are correlated with higher implementation rates, and that lower implementation costs tend to be prioritized over future energy-saving potential by audited facilities.
This work finds that lower costs have the effect of reducing EEM payback periods and that an interplay between energy cost and capital cost exists in payback period calculation. In the case of solar panel installation, lower Guatemalan capital costs and higher energy costs resulted in payback periods that are nearly half of U.S. payback periods. However, as in the case of installing higher-efficiency lighting and upgrading motors, higher Guatemalan energy costs combined with higher capital costs resulted in payback periods very similar to those in the U.S. for the same EEMs, and the larger the difference between Guatemalan and U.S. capital costs, the lower the difference between each country’s payback periods. This indicates that capital costs and energy costs have opposite effects on EEM payback periods; as energy costs increase, payback periods decrease, while increases in capital costs increase payback periods. This also indicates that it is not capital costs alone that reduce payback periods but higher energy prices as well, so much so that even when Guatemalan capital costs are higher than in the U.S. (as for the higher-efficiency lighting and motor upgrade EEMs), higher energy costs prevent Guatemalan payback periods from increasing beyond those in the U.S.
In developing countries, as energy costs are less stable than in developed countries, lower short-term costs can have the effect of raising energy prices in the long term. Given the results of this study, this may indicate that for developing countries like Guatemala, payback periods may shorten further in the long term, but as a result of an overall increase in costs rather than a result of improved energy economics. Therefore, it follows that when citing energy auditing as a measure of energy efficiency, not only the energy audit data collected but also the socioeconomic context in which an energy audit was performed must be considered; tailored energy efficiency recommendations that consider local conditions and infrastructure can enhance energy savings. By leveraging the unique opportunities and addressing the specific challenges within a given country’s energy sector, substantial progress can be made towards sustainable energy consumption and environmental preservation.
It is additionally important to note that while this study provides insights for energy auditors in both Guatemala and the U.S. and emphasizes the need for context-specific energy strategies to achieve optimal results, it suffers from a lack of historical Guatemalan energy audit data. While data from hundreds of energy audits with the studied EEMs are available for the U.S., only data from five Guatemalan energy audits were available. Potential future work in this space may therefore benefit from an expanded data set, and a follow-up study comparing IAC data with EEM data from a variety of developing countries could serve to strengthen the comparisons made here. Additionally, as all of the Guatemalan data employed in this analysis are from audits conducted in 2023 and 2024, the implementation status of each analyzed EEM is currently unknown. Future work comparing the implementation of EEMs across developing countries could thus serve as an insightful extension of this study.

Author Contributions

Conceptualization, R.K. and A.M.R.; methodology, R.K. and A.M.R.; software, R.K. and A.M.R.; validation, R.K., A.M.R., R.J.M., and P.E.P.; formal analysis, R.K.; investigation, R.K.; resources, R.K.; data curation, R.K. and A.M.R.; writing—original draft preparation, R.K.; writing—review and editing, R.K., A.M.R., R.J.M., and P.E.P.; visualization, R.K.; supervision, R.J.M. and P.E.P.; project administration, R.J.M. and P.E.P.; funding acquisition, R.J.M. and P.E.P. All authors have read and agreed to the published version of the manuscript.

Funding

This material is based upon work supported by the American People through the United States Agency for International Development (USAID) Cooperative Agreement AID-OAA-A-17-00010 as the prime sponsor and from the United State Energy Agency award number USEA/USAID-2021-801-01. This material is also based upon work supported by the U.S. Department of Energy under award number DE-EE0009732. The information expressed does not necessarily reflect the views of the United States Agency for International Development or the United States Government.

Data Availability Statement

This study makes use of energy data from publicly available databases [20,21]. The U.S. energy audit data employed can be found at https://iac.university/searchAssessments (accessed on 20 November 2024), and the Guatemalan energy audit data employed can be found at https://piee.eec.asu.edu/ (accessed on 20 November 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Savings, Cost, and Payback Period Data for 100 IAC Audits Conducted in 2020–2023 in Which the Three Analyzed EEMs Were Recommended

Table A1. Breakdown of 100 IAC audits conducted in 2022–2023 in which the installation of solar panels for electricity generation was recommended [18].
Table A1. Breakdown of 100 IAC audits conducted in 2022–2023 in which the installation of solar panels for electricity generation was recommended [18].
IDYearCost (USD)Savings (USD per yr.)Payback Period (yrs.)
AM08492022867,64071,70312.1
AS0515202272,520657112.2
AS05192022509,12065,13810.4
AS05222022279,24021,58814.1
AS05332022453,05057,8349.2
AS053820222,223,000722,2303.8
AS0540202297,049977410.7
CI0003202282,65516,3935.5
CI00042022218,74734,97114.4
CI0005202213,37236255.0
CI0006202282,65514,85911.1
CM101520221,455,428139,94411.7
CO0743202284,276987613.5
CO07442022136,68726,1598.2
KS22012022540,20060,9748.9
KS22022022799235022.3
KS220420223,611,200547,8276.6
KS22062022849,73485,24910.0
KS220720225,416,800589,9109.2
KS22082022602,37676,4727.9
KS22092022526,51877,0946.8
LE05292022791,800118,4416.7
LS22012022646,02041,48115.6
LS22022022646,02046,73713.8
LS22082022215,34015,21214.2
LS22092022646,02046,75313.8
MA08282022280,19564,1984.4
MA083120222,531,558247,46110.2
MA08342022607,76688,9786.8
MA083920221,287,85588,66314.5
MA08402022381,35521,19418.0
MA08412022155,40724,6316.3
MA08422022247,93750,9624.9
MA08432022860,419296,6042.9
MA0844202210,00093801.1
MA08452022906,122108,7858.3
MS0405202243,64567556.5
MS04072022304,50035,6788.5
MS04082022548,10070,5087.8
MZ03152022836,000138,2176.0
MZ03172022477,50050,2879.5
MZ03252022636,00056,40711.3
MZ032620222,868,753384,7337.5
MZ03272022574,75061,6269.3
MZ03322022670,33487,8517.6
MZ03352022957,388106,8139.0
OK10662022191,43813,99513.7
OK107220221,357,737265,5285.1
OK10752022191,43840,1364.8
OK10762022540,00036,19614.9
OK10772022230,88024,2159.5
OK10782022540,20031,60817.1
OR07462022550,84638,60514.3
UL2304202389,882850110.6
UL23052023325,39725,54212.7
UL23152023150,94219,7037.7
UL23162023989,084112,1678.8
UL23182023501,74859,6888.4
UU02162023454,18933,35113.6
UU02222023421,40037,49611.2
UW231220232,614,888236,65911.0
SU05262022259,521158,2731.6
SU05272022248,94040,4236.2
TR00462023349,86215,87822.0
TR00472023264,69362,8604.2
TR004820231,521,071116,06313.1
TR00492023109,80513,5778.1
TR0050202379,65480569.9
TR005120231,421,000104,74213.6
TR0053202378,40311,9086.6
TR0054202381,00012,1666.7
TR0057202349,91663857.8
TR00582023216,21631,8326.8
UA028320233,377,796416,1538.1
UC23012023127,92021,8585.9
UC23032023455,89647,3129.6
UC23042023411,32379,5625.2
UC23052023126,56017,0107.4
UC23062023385,24270,5395.5
UC23082023609,96885,4147.1
UC23092023192,62122,6228.5
UC23102023192,62139,5064.9
UC231120231,401,05395,41714.7
UC23122023515,72950,19710.3
UC231420231,537,591197,7537.8
UC23152023268,54329,1899.2
OK10812023218,40015,61714.0
OK10822023230,88022,69610.2
OK108320234,927,139491,20410.0
OK10842023258,70628,6529.0
OK10852023820,66672,24911.4
OK10862023382,20031,94812.0
OK10872023168,19271,1422.4
OK10882023218,40015,96713.7
OK10902023438,00068,8446.4
OK10912023394,20086,1754.6
OK10922023187,20024,8827.5
OK10932023374,40029,83012.6
OK10942023858,00093,8599.1
OK10952023511,00045,76411.2
Table A2. Breakdown of 100 IAC audits conducted in 2022 in which the replacement of fluorescent lights with LED lights was recommended [18].
Table A2. Breakdown of 100 IAC audits conducted in 2022 in which the replacement of fluorescent lights with LED lights was recommended [18].
Assessment IDAssessment YearCost (USD)Savings (USD per yr.)Payback Period (yrs.)
AM0831202212433183.9
AM08332022946263371.5
AM08332022193010001.9
AS0515202228,03318,5281.5
AS05162022170,36155,8383.1
MA0831202242,05834,6571.2
MI0405202239,39826,7141.5
MI04072022789941401.9
MI040920223632221.6
MI04132022524930131.7
MS0395202218,74810,7231.7
MS03982022676952271.3
MS0399202231,95318,4561.7
MS040120225702532.3
MS04052022286614751.9
MU0002202211136101.8
MU00032022400530051.3
OK1076202220,75410,2342.0
OK10772022759032552.3
ORC001202229,61291453.2
IC0254202219,57892892.1
IC02552022410031531.3
LE05302022550049861.1
LE05302022600033131.8
LE0530202217,00086952.0
LE0531202210,41480361.3
LE05312022135,00088,4441.5
LE05322022516043931.2
LE0532202280,00030,1252.7
LE0532202215,00048963.1
LE0532202299,43232,1553.1
TR0030202224221331.5
TR0030202254961451.1
TR0030202268,07837442.1
TR0031202282784882.6
TR0031202219,86811721.2
UA0266202222,13221321.3
WM01842022620044921.4
WM01842022326515172.2
WM018420228508441.0
WM0184202210307791.3
TT02652022900087041.0
TT02662022614724582.5
TT02702022277028771.0
TT02702022224012011.9
TT0270202212008811.4
TT02742022915051851.8
TT02742022204015071.4
TT02742022170011291.5
MZ0317202212,62663922.0
MZ0329202220,43975292.7
MZ0331202225,75723,1621.1
MZ0331202246,35018,0372.6
MZ03322022133016760.8
MZ0333202221,81583792.6
MZ03342022778021403.6
MZ0335202213307591.8
MU0004202227738453.3
MU000820222102630.8
MU000920227953482.3
MU0010202224528422.9
MU001220224254151.0
MU0013202214847811.9
MU00142022430746800.9
MU001720225553641.5
MU00202022594954041.1
MUCM012022331932,0300.1
MUCM022022177662300.3
MUCM03202223210670.2
MUCM0620229343432.7
MUCM06202212,45144582.8
MUCM09202215,42010,3481.5
TT02732022192610721.8
TT0276202224,75015,3441.6
TT02772022279011162.5
UA0253202214,23696811.5
UA02612022716837191.9
UA0262202244,46446,0231.0
UA0268202217,62890891.9
UC2201202248,28520,7992.3
UC22052022264120271.3
UC220720224643781.2
UD10332022600055241.1
UD1038202229,37513,1002.2
UD1040202258,10043,0601.3
UD10442022294021251.4
UD1045202232,90015,5542.1
UD10462022554,028317,0411.7
UD1047202226,46014,7551.8
UF0570202235,36911,6743.0
UF0574202225,25878963.2
UF0579202212,75350952.5
UF0582202279,20437,7932.1
UF0583202233,00014,7462.2
UF0588202217,17817,7511.0
UL2202202212,94168611.9
UL2203202237,02637,5301.0
UL2207202283,24631,9842.6
UL2207202218,12075722.4
Table A3. Breakdown of 100 IAC audits conducted in 2020-2023 in which the use of the most efficient type of motor was recommended [18].
Table A3. Breakdown of 100 IAC audits conducted in 2020-2023 in which the use of the most efficient type of motor was recommended [18].
IDYearCost (USD)Savings (USD per yr.)Payback Period (yrs.)
AM0838202210,15220704.9
IC0268202210403153.3
KS2203202259,79310,8025.5
LE0520202250,00079586.3
MA08292022715943.2
MU0021202218774384.3
MU002220226831474.6
OR07472022758717084.4
IP0131202014304283.3
LS2101202126,50049075.4
AS0489202028,46668994.1
BS0135202021,80038285.7
MZ0292202045,30097604.6
NL0051202013,73733334.1
UW2217202222,14827053.4
SD0608202165443.1
SD05912020566135203.3
UF05792022105,18542,1522.5
OK1022202023,43457484.1
AM0806202024,68590252.7
AM08282021889039502.3
CW0126202254,96319,0472.9
IC0240202011,62347,8330.8
SD0592202012,58527512.1
SD05992020155573701.7
SD0601202040007142.2
SD06032020285021901.8
SD06052020200020061.4
SD060520205406393.1
SD0607202116954871.1
MS0407202219442936.6
MS04092022582966.1
MS04002022398537.5
MI03872021901638622.3
MS04002022398537.5
MS0402202220,84574862.8
AM0806202024,68590252.7
AM08282020889039502.3
MZ0292202045,30097604.6
MZ0305202193,00027,3503.4
MZ0313202133,44634,9671
NL0051202013,73733334.1
SD0599202012,58573701.7
SD0611202110,35057091.8
SD06122021954548262.0
SD06142021470048291.0
UD10102020745746161.6
UD1017202016,72083602.0
UF0544202042,59246,6960.9
UF0545202020,23067,0900.3
UF0546202015,49414,4361.1
UF0547202017,89318,8161.0
UF0548202074,54823,6993.1
UF0549202068,13873,0180.9
UF05512020493862540.8
UF0554202025,23219,8051.3
UF05552020390018212.1
UF0557202025,52933,9930.8
UF0558202019,04520,5970.9
UF05592020331726001.3
UF05612020234311,3580.2
UF05622020408611,6350.4
UF0564202114,13252,0010.3
UF05652021922352,7970.2
UF0567202142,76442,8031.0
UF0568202112,24711,1821.1
UF05692021522912,7660.4
UF0570202232,61012,0342.7
AM0866202336056305.7
AM0877202374,06718,2904.0
AS0568202376808229.3
CI00122023240011662.1
CI00152023131012621.0
CI00162023627013,7330.5
CI00202023287065030.4
IC028720237342772.7
LE0553202391,80020,0004.6
LS2023202314,80085481.7
LS2304202356,40013,8674.1
LS23112023370030961.2
LS2314202389,40025,2913.5
LS2318202343,20017,3112.5
MS0411202336905376.9
MU004320233191162.8
NL00832023494581820.6
OK1079202336248004.5
OR07622023145,42594,5401.5
SD0627202341,84217,2812.4
SD0631202310,49425834.1
SD0633202366,84528,9722.3
SD0636202337,37724,7711.5
SD06382023104,79259,9281.7
SD0639202331,41095723.3
SD0640202317496412.7
SJ00052023455612513.6
SJ000820234361263.5
TT0288202328,00011,0362.5
UF0606202379,80038,7992.1
UW2301202377,40513,5905.7
UF0594202318,24363202.9

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Figure 1. Map of Guatemalan departments showing: (A) the overall percentage of households in each department with the highest levels of poverty in 2014 and (B) the change in poverty percentage of households in each department between 2006 and 2014 [22] (© 2021 Henry, C. L.; Baker, J. S.; Shaw, B. K.; Kondash, A. J.; Leiva, B.; Castellanos, E.; Wade, C. M.; Lord, B.,; van Houtven, G.; and Redmon, J. H. Published by Elsevier B.V. Link to the license: https://creativecommons.org/licenses/by/4.0/ (accessed on 30 March 2024)).
Figure 1. Map of Guatemalan departments showing: (A) the overall percentage of households in each department with the highest levels of poverty in 2014 and (B) the change in poverty percentage of households in each department between 2006 and 2014 [22] (© 2021 Henry, C. L.; Baker, J. S.; Shaw, B. K.; Kondash, A. J.; Leiva, B.; Castellanos, E.; Wade, C. M.; Lord, B.,; van Houtven, G.; and Redmon, J. H. Published by Elsevier B.V. Link to the license: https://creativecommons.org/licenses/by/4.0/ (accessed on 30 March 2024)).
Environments 12 00019 g001
Figure 2. CO2 emissions per sector in the U.S. in 2021 [30].
Figure 2. CO2 emissions per sector in the U.S. in 2021 [30].
Environments 12 00019 g002
Figure 3. CO2 emissions per sector in Guatemala in 2021 [30].
Figure 3. CO2 emissions per sector in Guatemala in 2021 [30].
Environments 12 00019 g003
Figure 4. Histogram showing payback period distribution for the recommended EEM to install solar panels to generate electricity from Guatemalan and U.S. energy audit data [20,21].
Figure 4. Histogram showing payback period distribution for the recommended EEM to install solar panels to generate electricity from Guatemalan and U.S. energy audit data [20,21].
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Figure 5. Histogram showing payback period distribution for the recommended EEM to install higher-efficiency lighting from Guatemalan and U.S. energy audit data [20,21].
Figure 5. Histogram showing payback period distribution for the recommended EEM to install higher-efficiency lighting from Guatemalan and U.S. energy audit data [20,21].
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Figure 6. Histogram showing payback period distribution for the recommended EEM to replace old motors with premium efficiency motors from Guatemalan and U.S. energy audit data [20,21].
Figure 6. Histogram showing payback period distribution for the recommended EEM to replace old motors with premium efficiency motors from Guatemalan and U.S. energy audit data [20,21].
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Figure 7. Variation in projected payback period with energy cost for various Guatemalan and U.S. capital costs for recommended EEM to install solar panels, plotted with Guatemalan and U.S. energy audit data [20,21]. The shaded areas indicate the range of projected payback periods for each country based on typical electricity costs and capital costs.
Figure 7. Variation in projected payback period with energy cost for various Guatemalan and U.S. capital costs for recommended EEM to install solar panels, plotted with Guatemalan and U.S. energy audit data [20,21]. The shaded areas indicate the range of projected payback periods for each country based on typical electricity costs and capital costs.
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Figure 8. Variation in projected payback period with energy cost for various Guatemalan and U.S. capital costs for recommended EEM to replace current lighting with higher-efficiency lighting, plotted with Guatemalan and U.S. energy audit data [20,21]. The shaded areas indicate the range of projected payback periods for each country based on typical electricity costs and capital costs.
Figure 8. Variation in projected payback period with energy cost for various Guatemalan and U.S. capital costs for recommended EEM to replace current lighting with higher-efficiency lighting, plotted with Guatemalan and U.S. energy audit data [20,21]. The shaded areas indicate the range of projected payback periods for each country based on typical electricity costs and capital costs.
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Figure 9. Variation in projected payback period with energy cost for various Guatemalan and U.S. capital costs for recommended EEM to replace old motors with premium efficiency equipment, plotted with Guatemalan and U.S. energy audit data [20,21]. The shaded areas indicate the range of projected payback periods for each country based on typical electricity costs and capital costs.
Figure 9. Variation in projected payback period with energy cost for various Guatemalan and U.S. capital costs for recommended EEM to replace old motors with premium efficiency equipment, plotted with Guatemalan and U.S. energy audit data [20,21]. The shaded areas indicate the range of projected payback periods for each country based on typical electricity costs and capital costs.
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Table 1. The total number of times EEMs were recommended and there average savings, payback periods, and implementation costs from five Guatemalan industrial energy audits [20].
Table 1. The total number of times EEMs were recommended and there average savings, payback periods, and implementation costs from five Guatemalan industrial energy audits [20].
EEM DescriptionNo. of Times RecommendedAvg. Savings (USD per yr.)Avg. Payback Period (yrs)Avg. Impl. Cost
(USD)
Install solar panels to generate electricity420,2615.287,506
Upgrade thermal insulation in heated areas and components 320881.12328
Utilize higher-efficiency lamps and/or ballasts21091.3142
Install kVAR systems for reactive power control and regulation in electric motors 217333.86595
Use most efficient type of motors219353.66883
Change from bunker fuel to LPG in boiler185310.54027
Install Variable Frequency Drives (VDFs) on multiple motors in the production area14293.41474
Practice annual verification and correction of defective steam traps14841.2590
Practice annual verification with ultrasonic equipment to identify and correct leaks in compressed air systems16140.9506
Reduction in energy consumption of the concrete mixing process through optimization of mixing time118361.52699
Table 2. Most frequently recommended EEMs in the agricultural (A), transportation (T), manufacturing (M), and commercial (C) sectors in the U.S. from energy audits [21].
Table 2. Most frequently recommended EEMs in the agricultural (A), transportation (T), manufacturing (M), and commercial (C) sectors in the U.S. from energy audits [21].
EEM DescriptionSectorNo. Of Times RecommendedAvg. Savings (USD per yr.)Avg. Payback Period (yrs)Avg. Impl. Cost (USD)
Utilize higher-efficiency lamps and/or ballastsA, T, M, C160324,8962.756,163
Reduce the pressure of compressed air to the minimum requiredT, M, C126639900.3855
Eliminate leaks in inert gas and compressed air lines/valvesA, M93712,0280.63218
Install occupancy sensorsA, T, M, C64215,3487.820,249
Use most efficient type of motorsA, M50812,681 334,047
Installing solar panels to generate electricityA, M80673,6638.8411,521
Table 3. Breakdown of Guatemalan EEM recommendations including total annual cost savings, implementation costs, electrical usage savings, and payback periods [21].
Table 3. Breakdown of Guatemalan EEM recommendations including total annual cost savings, implementation costs, electrical usage savings, and payback periods [21].
EEM DescriptionTotal Annual Cost Savings (USD per yr.)Impl. Cost (USD)Usage Savings (kWh)Payback Period (yrs)
Install solar panels to generate electricity288020,48021,2247.1
189,974909,004125,2114.8
189,974909,004125,2114.8
423,193171,959291,1764.1
Utilize higher-efficiency lamps and/or ballasts833109260721.3
2591529113,7182.1
Upgrade standard efficiency motors with premium efficiency equipment18,25466,0051,042,2863.6
11,54940,00091,6743.5
Table 4. Maximum, minimum, and average payback periods for each of the three selected EEM recommendations from 100 energy audits conducted in the U.S. [20].
Table 4. Maximum, minimum, and average payback periods for each of the three selected EEM recommendations from 100 energy audits conducted in the U.S. [20].
EEM DescriptionMaximum Payback Period (yrs)Minimum Payback Period (yrs)Average Payback Period (yrs)
Install solar panels to generate electricity221.19.3
Utilize higher-efficiency lamps and/or ballasts3.90.11.8
Upgrade standard efficiency motors with premium efficiency equipment9.30.22.8
Table 5. Capital costs for the installation of solar panels, the installation of LED light installation, and upgrading motors in the U.S. and Guatemala [20,21].
Table 5. Capital costs for the installation of solar panels, the installation of LED light installation, and upgrading motors in the U.S. and Guatemala [20,21].
Energy Audit CountryRecommended EEMUnitCapital Cost (USD per Unit)
U.S.Install Solar PanelskW1.20–1.40
Install LEDsLED lamp50–95
Upgrade MotorsHP70–90
GuatemalaInstall Solar PanelskW0.80–1
Install LEDsLED lamp130–195
Upgrade MotorsHP100–140
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Khosla, R.; Martinez Rodriguez, A.; Milcarek, R.J.; Phelan, P.E. A Comparison Between Industrial Energy Efficiency Measures in Guatemala and the United States. Environments 2025, 12, 19. https://doi.org/10.3390/environments12010019

AMA Style

Khosla R, Martinez Rodriguez A, Milcarek RJ, Phelan PE. A Comparison Between Industrial Energy Efficiency Measures in Guatemala and the United States. Environments. 2025; 12(1):19. https://doi.org/10.3390/environments12010019

Chicago/Turabian Style

Khosla, Radhika, Angel Martinez Rodriguez, Ryan J. Milcarek, and Patrick E. Phelan. 2025. "A Comparison Between Industrial Energy Efficiency Measures in Guatemala and the United States" Environments 12, no. 1: 19. https://doi.org/10.3390/environments12010019

APA Style

Khosla, R., Martinez Rodriguez, A., Milcarek, R. J., & Phelan, P. E. (2025). A Comparison Between Industrial Energy Efficiency Measures in Guatemala and the United States. Environments, 12(1), 19. https://doi.org/10.3390/environments12010019

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