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

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 EEMs were recommended and there average savings, payback periods, and implementation costs from five Guatemalan industrial energy audits [20].

EEM Description	No. of Times Recommended	Avg. Savings (USD per yr.)	Avg. Payback Period (yrs)	Avg. Impl. Cost (USD)
Install solar panels to generate electricity	4	20,261	5.2	87,506
Upgrade thermal insulation in heated areas and components	3	2088	1.1	2328
Utilize higher-efficiency lamps and/or ballasts	2	109	1.3	142
Install kVAR systems for reactive power control and regulation in electric motors	2	1733	3.8	6595
Use most efficient type of motors	2	1935	3.6	6883
Change from bunker fuel to LPG in boiler	1	8531	0.5	4027
Install Variable Frequency Drives (VDFs) on multiple motors in the production area	1	429	3.4	1474
Practice annual verification and correction of defective steam traps	1	484	1.2	590
Practice annual verification with ultrasonic equipment to identify and correct leaks in compressed air systems	1	614	0.9	506
Reduction in energy consumption of the concrete mixing process through optimization of mixing time	1	1836	1.5	2699

. 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. 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 Description	Sector	No. Of Times Recommended	Avg. Savings (USD per yr.)	Avg. Payback Period (yrs)	Avg. Impl. Cost (USD)
Utilize higher-efficiency lamps and/or ballasts	A, T, M, C	1603	24,896	2.7	56,163
Reduce the pressure of compressed air to the minimum required	T, M, C	1266	3990	0.3	855
Eliminate leaks in inert gas and compressed air lines/valves	A, M	937	12,028	0.6	3218
Install occupancy sensors	A, T, M, C	642	15,348	7.8	20,249
Use most efficient type of motors	A, M	508	12,681	3	34,047
Installing solar panels to generate electricity	A, M	806	73,663	8.8	411,521

. 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. Breakdown of Guatemalan EEM recommendations including total annual cost savings, implementation costs, electrical usage savings, and payback periods [21].

EEM Description	Total Annual Cost Savings (USD per yr.)	Impl. Cost (USD)	Usage Savings (kWh)	Payback Period (yrs)
Install solar panels to generate electricity	2880	20,480	21,224	7.1
	189,974	909,004	125,211	4.8
	189,974	909,004	125,211	4.8
	423,193	171,959	291,176	4.1
Utilize higher-efficiency lamps and/or ballasts	833	1092	6072	1.3
	2591	5291	13,718	2.1
Upgrade standard efficiency motors with premium efficiency equipment	18,254	66,005	1,042,286	3.6
	11,549	40,000	91,674	3.5

.

Data for these same three EEMs made in the U.S. [20] are presented in Appendix A,

Table A1. Breakdown of 100 IAC audits conducted in 2022–2023 in which the installation of solar panels for electricity generation was recommended [18].

ID	Year	Cost (USD)	Savings (USD per yr.)	Payback Period (yrs.)
AM0849	2022	867,640	71,703	12.1
AS0515	2022	72,520	6571	12.2
AS0519	2022	509,120	65,138	10.4
AS0522	2022	279,240	21,588	14.1
AS0533	2022	453,050	57,834	9.2
AS0538	2022	2,223,000	722,230	3.8
AS0540	2022	97,049	9774	10.7
CI0003	2022	82,655	16,393	5.5
CI0004	2022	218,747	34,971	14.4
CI0005	2022	13,372	3625	5.0
CI0006	2022	82,655	14,859	11.1
CM1015	2022	1,455,428	139,944	11.7
CO0743	2022	84,276	9876	13.5
CO0744	2022	136,687	26,159	8.2
KS2201	2022	540,200	60,974	8.9
KS2202	2022	7992	3502	2.3
KS2204	2022	3,611,200	547,827	6.6
KS2206	2022	849,734	85,249	10.0
KS2207	2022	5,416,800	589,910	9.2
KS2208	2022	602,376	76,472	7.9
KS2209	2022	526,518	77,094	6.8
LE0529	2022	791,800	118,441	6.7
LS2201	2022	646,020	41,481	15.6
LS2202	2022	646,020	46,737	13.8
LS2208	2022	215,340	15,212	14.2
LS2209	2022	646,020	46,753	13.8
MA0828	2022	280,195	64,198	4.4
MA0831	2022	2,531,558	247,461	10.2
MA0834	2022	607,766	88,978	6.8
MA0839	2022	1,287,855	88,663	14.5
MA0840	2022	381,355	21,194	18.0
MA0841	2022	155,407	24,631	6.3
MA0842	2022	247,937	50,962	4.9
MA0843	2022	860,419	296,604	2.9
MA0844	2022	10,000	9380	1.1
MA0845	2022	906,122	108,785	8.3
MS0405	2022	43,645	6755	6.5
MS0407	2022	304,500	35,678	8.5
MS0408	2022	548,100	70,508	7.8
MZ0315	2022	836,000	138,217	6.0
MZ0317	2022	477,500	50,287	9.5
MZ0325	2022	636,000	56,407	11.3
MZ0326	2022	2,868,753	384,733	7.5
MZ0327	2022	574,750	61,626	9.3
MZ0332	2022	670,334	87,851	7.6
MZ0335	2022	957,388	106,813	9.0
OK1066	2022	191,438	13,995	13.7
OK1072	2022	1,357,737	265,528	5.1
OK1075	2022	191,438	40,136	4.8
OK1076	2022	540,000	36,196	14.9
OK1077	2022	230,880	24,215	9.5
OK1078	2022	540,200	31,608	17.1
OR0746	2022	550,846	38,605	14.3
UL2304	2023	89,882	8501	10.6
UL2305	2023	325,397	25,542	12.7
UL2315	2023	150,942	19,703	7.7
UL2316	2023	989,084	112,167	8.8
UL2318	2023	501,748	59,688	8.4
UU0216	2023	454,189	33,351	13.6
UU0222	2023	421,400	37,496	11.2
UW2312	2023	2,614,888	236,659	11.0
SU0526	2022	259,521	158,273	1.6
SU0527	2022	248,940	40,423	6.2
TR0046	2023	349,862	15,878	22.0
TR0047	2023	264,693	62,860	4.2
TR0048	2023	1,521,071	116,063	13.1
TR0049	2023	109,805	13,577	8.1
TR0050	2023	79,654	8056	9.9
TR0051	2023	1,421,000	104,742	13.6
TR0053	2023	78,403	11,908	6.6
TR0054	2023	81,000	12,166	6.7
TR0057	2023	49,916	6385	7.8
TR0058	2023	216,216	31,832	6.8
UA0283	2023	3,377,796	416,153	8.1
UC2301	2023	127,920	21,858	5.9
UC2303	2023	455,896	47,312	9.6
UC2304	2023	411,323	79,562	5.2
UC2305	2023	126,560	17,010	7.4
UC2306	2023	385,242	70,539	5.5
UC2308	2023	609,968	85,414	7.1
UC2309	2023	192,621	22,622	8.5
UC2310	2023	192,621	39,506	4.9
UC2311	2023	1,401,053	95,417	14.7
UC2312	2023	515,729	50,197	10.3
UC2314	2023	1,537,591	197,753	7.8
UC2315	2023	268,543	29,189	9.2
OK1081	2023	218,400	15,617	14.0
OK1082	2023	230,880	22,696	10.2
OK1083	2023	4,927,139	491,204	10.0
OK1084	2023	258,706	28,652	9.0
OK1085	2023	820,666	72,249	11.4
OK1086	2023	382,200	31,948	12.0
OK1087	2023	168,192	71,142	2.4
OK1088	2023	218,400	15,967	13.7
OK1090	2023	438,000	68,844	6.4
OK1091	2023	394,200	86,175	4.6
OK1092	2023	187,200	24,882	7.5
OK1093	2023	374,400	29,830	12.6
OK1094	2023	858,000	93,859	9.1
OK1095	2023	511,000	45,764	11.2

,

Table A2. Breakdown of 100 IAC audits conducted in 2022 in which the replacement of fluorescent lights with LED lights was recommended [18].

Assessment ID	Assessment Year	Cost (USD)	Savings (USD per yr.)	Payback Period (yrs.)
AM0831	2022	1243	318	3.9
AM0833	2022	9462	6337	1.5
AM0833	2022	1930	1000	1.9
AS0515	2022	28,033	18,528	1.5
AS0516	2022	170,361	55,838	3.1
MA0831	2022	42,058	34,657	1.2
MI0405	2022	39,398	26,714	1.5
MI0407	2022	7899	4140	1.9
MI0409	2022	363	222	1.6
MI0413	2022	5249	3013	1.7
MS0395	2022	18,748	10,723	1.7
MS0398	2022	6769	5227	1.3
MS0399	2022	31,953	18,456	1.7
MS0401	2022	570	253	2.3
MS0405	2022	2866	1475	1.9
MU0002	2022	1113	610	1.8
MU0003	2022	4005	3005	1.3
OK1076	2022	20,754	10,234	2.0
OK1077	2022	7590	3255	2.3
ORC001	2022	29,612	9145	3.2
IC0254	2022	19,578	9289	2.1
IC0255	2022	4100	3153	1.3
LE0530	2022	5500	4986	1.1
LE0530	2022	6000	3313	1.8
LE0530	2022	17,000	8695	2.0
LE0531	2022	10,414	8036	1.3
LE0531	2022	135,000	88,444	1.5
LE0532	2022	5160	4393	1.2
LE0532	2022	80,000	30,125	2.7
LE0532	2022	15,000	4896	3.1
LE0532	2022	99,432	32,155	3.1
TR0030	2022	2422	133	1.5
TR0030	2022	549	6145	1.1
TR0030	2022	68,078	3744	2.1
TR0031	2022	8278	488	2.6
TR0031	2022	19,868	1172	1.2
UA0266	2022	22,132	2132	1.3
WM0184	2022	6200	4492	1.4
WM0184	2022	3265	1517	2.2
WM0184	2022	850	844	1.0
WM0184	2022	1030	779	1.3
TT0265	2022	9000	8704	1.0
TT0266	2022	6147	2458	2.5
TT0270	2022	2770	2877	1.0
TT0270	2022	2240	1201	1.9
TT0270	2022	1200	881	1.4
TT0274	2022	9150	5185	1.8
TT0274	2022	2040	1507	1.4
TT0274	2022	1700	1129	1.5
MZ0317	2022	12,626	6392	2.0
MZ0329	2022	20,439	7529	2.7
MZ0331	2022	25,757	23,162	1.1
MZ0331	2022	46,350	18,037	2.6
MZ0332	2022	1330	1676	0.8
MZ0333	2022	21,815	8379	2.6
MZ0334	2022	7780	2140	3.6
MZ0335	2022	1330	759	1.8
MU0004	2022	2773	845	3.3
MU0008	2022	210	263	0.8
MU0009	2022	795	348	2.3
MU0010	2022	2452	842	2.9
MU0012	2022	425	415	1.0
MU0013	2022	1484	781	1.9
MU0014	2022	4307	4680	0.9
MU0017	2022	555	364	1.5
MU0020	2022	5949	5404	1.1
MUCM01	2022	3319	32,030	0.1
MUCM02	2022	1776	6230	0.3
MUCM03	2022	232	1067	0.2
MUCM06	2022	934	343	2.7
MUCM06	2022	12,451	4458	2.8
MUCM09	2022	15,420	10,348	1.5
TT0273	2022	1926	1072	1.8
TT0276	2022	24,750	15,344	1.6
TT0277	2022	2790	1116	2.5
UA0253	2022	14,236	9681	1.5
UA0261	2022	7168	3719	1.9
UA0262	2022	44,464	46,023	1.0
UA0268	2022	17,628	9089	1.9
UC2201	2022	48,285	20,799	2.3
UC2205	2022	2641	2027	1.3
UC2207	2022	464	378	1.2
UD1033	2022	6000	5524	1.1
UD1038	2022	29,375	13,100	2.2
UD1040	2022	58,100	43,060	1.3
UD1044	2022	2940	2125	1.4
UD1045	2022	32,900	15,554	2.1
UD1046	2022	554,028	317,041	1.7
UD1047	2022	26,460	14,755	1.8
UF0570	2022	35,369	11,674	3.0
UF0574	2022	25,258	7896	3.2
UF0579	2022	12,753	5095	2.5
UF0582	2022	79,204	37,793	2.1
UF0583	2022	33,000	14,746	2.2
UF0588	2022	17,178	17,751	1.0
UL2202	2022	12,941	6861	1.9
UL2203	2022	37,026	37,530	1.0
UL2207	2022	83,246	31,984	2.6
UL2207	2022	18,120	7572	2.4

and

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].

ID	Year	Cost (USD)	Savings (USD per yr.)	Payback Period (yrs.)
AM0838	2022	10,152	2070	4.9
IC0268	2022	1040	315	3.3
KS2203	2022	59,793	10,802	5.5
LE0520	2022	50,000	7958	6.3
MA0829	2022	7	1594	3.2
MU0021	2022	1877	438	4.3
MU0022	2022	683	147	4.6
OR0747	2022	7587	1708	4.4
IP0131	2020	1430	428	3.3
LS2101	2021	26,500	4907	5.4
AS0489	2020	28,466	6899	4.1
BS0135	2020	21,800	3828	5.7
MZ0292	2020	45,300	9760	4.6
NL0051	2020	13,737	3333	4.1
UW2217	2022	22,148	2705	3.4
SD0608	2021	6	544	3.1
SD0591	2020	5661	3520	3.3
UF0579	2022	105,185	42,152	2.5
OK1022	2020	23,434	5748	4.1
AM0806	2020	24,685	9025	2.7
AM0828	2021	8890	3950	2.3
CW0126	2022	54,963	19,047	2.9
IC0240	2020	11,623	47,833	0.8
SD0592	2020	12,585	2751	2.1
SD0599	2020	1555	7370	1.7
SD0601	2020	4000	714	2.2
SD0603	2020	2850	2190	1.8
SD0605	2020	2000	2006	1.4
SD0605	2020	540	639	3.1
SD0607	2021	1695	487	1.1
MS0407	2022	1944	293	6.6
MS0409	2022	582	96	6.1
MS0400	2022	398	53	7.5
MI0387	2021	9016	3862	2.3
MS0400	2022	398	53	7.5
MS0402	2022	20,845	7486	2.8
AM0806	2020	24,685	9025	2.7
AM0828	2020	8890	3950	2.3
MZ0292	2020	45,300	9760	4.6
MZ0305	2021	93,000	27,350	3.4
MZ0313	2021	33,446	34,967	1
NL0051	2020	13,737	3333	4.1
SD0599	2020	12,585	7370	1.7
SD0611	2021	10,350	5709	1.8
SD0612	2021	9545	4826	2.0
SD0614	2021	4700	4829	1.0
UD1010	2020	7457	4616	1.6
UD1017	2020	16,720	8360	2.0
UF0544	2020	42,592	46,696	0.9
UF0545	2020	20,230	67,090	0.3
UF0546	2020	15,494	14,436	1.1
UF0547	2020	17,893	18,816	1.0
UF0548	2020	74,548	23,699	3.1
UF0549	2020	68,138	73,018	0.9
UF0551	2020	4938	6254	0.8
UF0554	2020	25,232	19,805	1.3
UF0555	2020	3900	1821	2.1
UF0557	2020	25,529	33,993	0.8
UF0558	2020	19,045	20,597	0.9
UF0559	2020	3317	2600	1.3
UF0561	2020	2343	11,358	0.2
UF0562	2020	4086	11,635	0.4
UF0564	2021	14,132	52,001	0.3
UF0565	2021	9223	52,797	0.2
UF0567	2021	42,764	42,803	1.0
UF0568	2021	12,247	11,182	1.1
UF0569	2021	5229	12,766	0.4
UF0570	2022	32,610	12,034	2.7
AM0866	2023	3605	630	5.7
AM0877	2023	74,067	18,290	4.0
AS0568	2023	7680	822	9.3
CI0012	2023	2400	1166	2.1
CI0015	2023	1310	1262	1.0
CI0016	2023	6270	13,733	0.5
CI0020	2023	2870	6503	0.4
IC0287	2023	734	277	2.7
LE0553	2023	91,800	20,000	4.6
LS2023	2023	14,800	8548	1.7
LS2304	2023	56,400	13,867	4.1
LS2311	2023	3700	3096	1.2
LS2314	2023	89,400	25,291	3.5
LS2318	2023	43,200	17,311	2.5
MS0411	2023	3690	537	6.9
MU0043	2023	319	116	2.8
NL0083	2023	4945	8182	0.6
OK1079	2023	3624	800	4.5
OR0762	2023	145,425	94,540	1.5
SD0627	2023	41,842	17,281	2.4
SD0631	2023	10,494	2583	4.1
SD0633	2023	66,845	28,972	2.3
SD0636	2023	37,377	24,771	1.5
SD0638	2023	104,792	59,928	1.7
SD0639	2023	31,410	9572	3.3
SD0640	2023	1749	641	2.7
SJ0005	2023	4556	1251	3.6
SJ0008	2023	436	126	3.5
TT0288	2023	28,000	11,036	2.5
UF0606	2023	79,800	38,799	2.1
UW2301	2023	77,405	13,590	5.7
UF0594	2023	18,243	6320	2.9

. 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. 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 Description	Maximum Payback Period (yrs)	Minimum Payback Period (yrs)	Average Payback Period (yrs)
Install solar panels to generate electricity	22	1.1	9.3
Utilize higher-efficiency lamps and/or ballasts	3.9	0.1	1.8
Upgrade standard efficiency motors with premium efficiency equipment	9.3	0.2	2.8

.

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)):

$$\begin{array}{c}Payback Period = {\displaystyle \frac{Total\mathrm{ }Impl.\mathrm{ }Cost\mathrm{ }\left(\mathrm{U}\mathrm{S}\mathrm{D}\right)}{Total\mathrm{ }Annual\mathrm{ }Savings\mathrm{ }\left(\frac{\mathrm{U}\mathrm{S}\mathrm{D}}{\mathrm{y}\mathrm{r}}\right)}}\end{array}$$

(1)

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)):

$$\begin{array}{c}Solar Payback Period = {\displaystyle \frac{System\mathrm{ }Capacity\mathrm{ }\left(\mathrm{k}\mathrm{W}\right)\cdot PV\mathrm{ }Installation\mathrm{ }Cost\mathrm{ }\left(\frac{\mathrm{U}\mathrm{S}\mathrm{D}}{\mathrm{k}\mathrm{W}}\right)}{Usage\mathrm{ }Savings\mathrm{ }\left(\mathrm{k}\mathrm{W}\mathrm{h}\right)\cdot Usage\mathrm{ }Cost\mathrm{ }\left(\frac{\mathrm{U}\mathrm{S}\mathrm{D}}{\mathrm{k}\mathrm{W}\mathrm{h}}\right)}}\end{array}$$

(2)

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)):

$$\begin{array}{c}Usage Savings = I\cdot Panel Area \left({\mathrm{m}}^2\right)\cdot \mu \end{array}$$

(3)

where I is annual solar irradiation (kWh/m²yr) and μ is solar panel efficiency. For this study, I_Guatemala = 824.62 kWh/m²yr, I_U.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]:

$$\begin{array}{c}Higher Efficiency Lighting Payback Period ={\displaystyle \frac{UCS+RCS}{\left(Light\mathrm{ }Cost\mathrm{ }\left(\frac{\mathrm{U}\mathrm{S}\mathrm{D}}{\mathrm{l}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}}\right)\cdot N\right)+LC}}\end{array}$$

(4)

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)):

$$\begin{array}{c}UCS = N\cdot \left(P_E-P_{Rep}\right)\cdot OH\cdot Energy Cost\left({\displaystyle \frac{\mathrm{U}\mathrm{S}\mathrm{D}}{\mathrm{k}\mathrm{W}\mathrm{h}}}\right)\end{array}$$

(5)

where P_E is the power rating of the existing lamps, P_Rep 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:

$$\begin{array}{c}RCS = OH\cdot N\cdot \left({\displaystyle \frac{C_E+LCh\cdot TR}{L_E}}\right)\end{array}$$

(6)

$$\begin{array}{c}LC = OH\cdot N\cdot \left({\displaystyle \frac{C_E+LCh\cdot TR}{L_E}}\right)\end{array}$$

(7)

where C_E 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, L_E is the lifetime of the existing bulbs in hours, and L_Rep 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)):

$$\begin{array}{c}Motor Upgrade Payback Period = {\displaystyle \frac{Total\mathrm{ }motor\mathrm{ }Power\mathrm{ }\left(\mathrm{k}\mathrm{W}\right)\cdot Installed\mathrm{ }Motor\mathrm{ }Cost\mathrm{ }\left(\frac{\mathrm{U}\mathrm{S}\mathrm{D}}{\mathrm{k}\mathrm{W}}\right)}{Usage\mathrm{ }Savings\mathrm{ }\mathrm{ }\left(\mathrm{k}\mathrm{W}\mathrm{h}\right)\cdot Usage\mathrm{ }Cost\mathrm{ }\left(\frac{\mathrm{U}\mathrm{S}\mathrm{D}}{\mathrm{k}\mathrm{W}\mathrm{h}}\right)}}\end{array}$$

(8)

where usage savings, enumerating the expense saved by the reduced energy consumption of premium efficiency motors, can be calculated as (Equation (9)):

$$\begin{array}{c}Usage Savings = Motor Power \left(\mathrm{k}\mathrm{W}\right)\cdot LF\cdot OH\cdot \left(E_C-E_P\right)\end{array}$$

(9)

where LF is the load factor of the motors, OH is the annual motor operating hours, E_c is the efficiency of the motors being replaced, and E_p 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. 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 Country	Recommended EEM	Unit	Capital Cost (USD per Unit)
U.S.	Install Solar Panels	kW	1.20–1.40
	Install LEDs	LED lamp	50–95
	Upgrade Motors	HP	70–90
Guatemala	Install Solar Panels	kW	0.80–1
	Install LEDs	LED lamp	130–195
	Upgrade Motors	HP	100–140

.

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.