Future Scenarios of Firewood Consumption for Cooking in the Mexican Tropical Region

Abstract

Within domestic food cooking, burning firewood in three-stone fires (TSF) is a common practice by more than 16 million users in the Mexican tropical climate region (CR-TR). This article aims to evaluate the implementation of improved firewood cookstoves (ICS) to replace TSF in the CR-TR by constructing firewood consumption scenarios covering 2018–2050. The results show that in CR-TR, with the implementation of ICS, the consumption of 354.95 PJ of firewood, 36.6 Mt of CO_2e, 1.29 Mt of CO, and 163.78 kt of PM_2.5 can all be avoided. The most important reduction in firewood consumption, CO_2e emissions and CO and PM_2.5 pollutants, occurs in exclusive firewood users and mixed users who utilize firewood as the primary fuel source, both of whom are low socioeconomic level in rural areas. Furthermore, most paying-for-firewood users often show negative mitigation costs and a high 50% IRR, while all non-paying-for-firewood users have mitigation costs ranging from 7.74 to 41.23 USD/tCO_2e. At the end of the results section, we perform a sensitivity analysis of the relevant parameters, which complements this study. Therefore, implementing ICS will contribute to the solution of climate change, deforestation, and facilitate the formulation of sustainable development policies for the most vulnerable population sector of the Mexican CR-TR.

1. Introduction

Worldwide, the use of traditional biomass for residential water heating and food cooking represents 26% of the total final energy consumption [1]. The energy progress report [2] estimates that around 2.6 billion people, mainly in low- and middle-income countries, use traditional biomass—firewood, charcoal, crop residues, livestock dung—to meet their cooking energy needs. The consumption of this type of solid biofuel is a subject of concern and study due to the following aspects: (i) environmental impact, (ii) health, (iii) energy poverty, and (iv) gender.

Environmental concerns stem from the unsustainable production and use of firewood, placing pressures on the regional and global environment. The unsustainable extraction of firewood has resulted in deforestation, soil erosion, and the loss of biodiversity [3,4], while the combustion of firewood in traditional stoves, such as the three-stone fires (TSF), emits a high level of pollutants, such as carbon monoxide (CO), methane (CH₄), nonmethane hydrocarbons (NMHCS), nitrous oxide (N₂O), nitrogen oxide (NO_x), particulate matter (PM), black carbon (BC), elemental carbon (EC), organic carbon (OC) and organic matter (OM). The CO₂, CH₄, N₂O, BC, and other aerosol particles increase the concentration of greenhouse gases (GHG) [5], contributing to climate change and air pollution.

Concerns about negative effects on human health are related to the exposure to air pollutants—called criteria pollutants—resulting from indoor firewood burning in which PM and CO emissions are found; these gases and compounds present a high risk of respiratory illness—including respiratory infection, lung cancer and asthma—and cardiovascular ailments [6,7]. The WHO estimated that in 2016, 3.8 million deaths were reported annually due to air pollution in households that cook with some form of solid fuel, mainly traditional biomass [8].

The concern to achieve the alleviation of energy poverty is that it is part of global goals to eradicate poverty, protect the planet, and ensure world prosperity [9]. It was established as a goal in the 2030 Sustainable Development Goal (SDG) number 7, “Affordable and clean energy”, to increase the proportion of the population that uses clean fuels and technologies. In addition, concerns about the consequences for climate change on energy poverty have gained prominence in recent years [10].

Another very important concern, which is seldom made visible, is the gender perspective, because it is mainly women are who cook at home, exposing themselves to the TSF exhaust gases [11,12,13]. Therefore, women suffer the most from the health impact mentioned in the previous paragraph. Flores et al. [14] documented that women, accompanied by children, traditionally collect firewood in some places. According to Pérez et al. [15], between 21% and 29% of Mexican households’ women are heads of family, so there is also family economic vulnerability when the women’s health is affected.

Most of the population who are traditional biomass users live in sub-Sahara Africa, central and southern Asia [2], and in some rural areas of Latin America, such as Bolivia and Nicaragua; the use of firewood is carried out in almost 100% of the households [16]. In Honduras, it is 88% [14], while in Panama and Guatemala, more than 50% of the population uses firewood [1], and in Mexico, 26%, more than a quarter of the population, use firewood for cooking [15].

Traditional biomass, when produced and used sustainably, represents a significant source of reliable, accessible, affordable, and resilient energy because it supplies energy in places where there is no or limited access to conventional fuels and technologies [17].

In order to attain a sustainable production and use of firewood, different strategies have been proposed, such as forest codes and certification programs [18,19,20]. Currently, biofuels and clean cooking technologies are new options for traditional biomass cooking, such as biogas, bioalcohols, pellets, and briquettes, as well as improved firewood cookstoves (ICS), gasifying stoves, and stoves that use biogas and bioalcohol [21,22].

ICS have significant international acceptance as a clean cooking technology option for a transition from TSF, because the ICS can reduce negative environmental impacts as well as those related to health, while ensuring reliable and affordable access to cooking fuels [4,23,24].

According to Nerini et al. [25], the transition to clean fuels such as liquid petroleum gas (LPG) and electricity is complex, as it could result in a high cost for the end user. Furthermore, the study by Fuentes-Cortes et al. [26] reached a similar conclusion, showing that diversification of the supply of primary energy sources can generate conflicts on the performance of the water–energy–environment nexus which have profound economic implications. Additionally, it has been identified that there is a segment of the population that, due to economic limitations, attachment to traditional cooking practices, or the fuel availability, will not change the use of biomass for clean fuels [27]. It has been observed that the energy transition is not unidirectional since people can return to traditional biomass in the case of rising modern energy prices, LPG, electricity, or natural gas [28]. There are also households that do not fully replace firewood with modern fuels, but rather can use multiple fuels and technologies for cooking [29].

In the study [15], three types of firewood users were identified in Mexico: exclusive users of firewood (EFU), mixed users of firewood who utilize firewood as their primary fuel source (MPU), and mixed users of firewood who utilize firewood as a secondary fuel source (MSU). The most significant number of fuelwood users is found in the southeastern Mexican states, which belong to the tropical climate region. In addition, it was identified that fuelwood consumption varies by climatic region; it also varies depending on the location of houses in rural or urban areas and the socioeconomic level of its inhabitants.

Thus, despite promoting modern fuel consumption and using clean technologies, firewood consumption will continue to be essential for a significant proportion of the population. For this reason, it is imperative to incorporate solutions to make firewood consumption sustainable through technologies that perform efficient firewood consumption, such as ICS, to satisfy energy needs while reducing the negative impacts on the environment and the health of the population that depends on this biofuel.

The objective of this article is to carry out an original evaluation of the large-scale implementation of ICS in tropical Mexican households in terms of firewood consumption, CO_2e emissions, criterion pollutants, and socioeconomic aspects, disaggregating the information by type of firewood users, socioeconomic level (SL), and size of population centers (SP).

The authors of the present study conducted this assessment by constructing scenarios from 2018 through 2050, contributing in this way to the transition towards the use of clean technologies in cooking for firewood users, which addresses environmental, health, energy poverty, and gender implications related to the use of this traditional biofuel. For this reason, the article answers the following questions: (1) What is the future trend of firewood consumption, CO_2e emissions, and criteria pollutants in the forms and devices in which firewood is currently consumed in the climatic region with the highest firewood consumption in Mexico? (2) What energy, environmental, health, and socioeconomic advantages are obtained from implementing ICS in this climate region by type of fuelwood users, socioeconomic level, and population size? (3) How does implementing ICS impact the poorest population in this region? (4) What economic benefits and mitigation costs are obtained in the alternative scenario when distinguishing between those who pay and those who do not purchase fuelwood?

This study is structured as follows: Section 2 presents the state-of-the-art analysis of ICS implementation at the global and national level through scenario building. Section 3 presents the context of fuelwood consumption in the tropical region of Mexico. Section 4 describes the methodology used to build and analyze two firewood consumption scenarios: (i) trend scenario where TSF continues to be employed, and (ii) alternative scenario where ICS is implemented. Section 5 presents the comparison between these two scenarios, the differences in CO_2e and criteria pollutants—CO and PM_2.5—emissions, cost-benefit, mitigation costs and a finance efficiency analysis. All the above is presented in a disaggregated manner considering exclusive user of firewood (EFU), mixed users of firewood who utilize firewood as their primary fuel source (MPU) and mixed users of firewood who utilize firewood as a secondary fuel source (MSU); also, at the end of this section we present a sensitivity analysis performed on the main parameters: energy consumption and CO_2e, CO and PM_2.5 emissions. Section 6 presents a discussion of the results and limitations of the study. Finally, Section 7 presents the main findings and energy policy recommendations, as future research to improve knowledge on energy transition of firewood users.

2. State of the Art Analysis of ICS Implementation Evaluation through Scenario Construction Approach

At a global level, different studies have been developed to build alternative scenarios that evaluate clean cooking options employing the various biofuels and clean technologies available for the use of biomass. These studies [4,14,15,25,27,30,31,32,33] have been carried out in countries such as Kenya, Ethiopia, Nigeria, China, Honduras, and Mexico in which the aspects of GHG emissions, air pollutant emissions, health impacts, energy saving, and economic benefits are evaluated.

In the study by Winijkul and Bond [27], the implementation of ICS and the clean fuels is analyzed, such as electricity and LPG worldwide, and estimated the effects to reduce energy consumption and emissions gases that have a local and global impact such as PM, BC, OC, CO, CO₂, NO_x, CH₄, NMHC. Their results indicated that in Latin America, Africa, and Asia, a greater reduction in energy consumption and emissions is achieved in the ICS implementation scenario, achieving a reduction of 72% of PM, 39% of CO₂, 28% of NO_x and 39% to 76% in other contaminants related to the BAU (business as usual) scenario.

At the regional level, Dagnachew et al. [4] analyze a scenario to achieve SDG 7 in the Sub-Saharan Africa region by 2030, considering the implementation of ICS and the complete elimination of conventional biomass stoves. The results showed that in the final year, half of the population would cook in ICS or advanced biomass stoves, a quarter with LPG, and the rest of the population with a mix of biogas, natural gas, and electricity, showing a rapid transition to the use of modern fuels and clean technologies. In this way, the health impacts of infant mortality are reduced by 50% and GHG emissions are reduced by up to 64%.

In the study conducted in Kenya [33], the implementation of ICS replacing all stoves that use traditional fuels in the period 2015–2035 was analyzed. The results showed that, relating to the BAU scenario, in the ICS implementation scenario, energy consumption decreases by 9.3% and consequently GHG emissions decreases by 26.5%, CO by 54%, CH₄ by 67%, N₂O by 52%, PM_2.5 and BC by 41.9% and 13.7%, respectively; nevertheless, there is an increase of 127.3% in NO_x emissions.

Another study from Kenya [25] showed that the cost of cooking a meal is reduced when using ICS compared to cooking with TSF. Also, the study showed that the implementation of LPG and electricity has a higher cost than an ICS implementation scenario, which was analyzed towards the year 2030. This study highlights that the use of the forest area must be sustainable to achieve these benefits.

The study conducted in Nigeria [31] shows the analysis of the environmental impacts and costs of cooking in the analysis period from 2003 to 2030. The results showed that when there is a high penetration of ICS, a greater reduction in environmental impacts and a better cost that is 32% lower than the BAU scenario costs with TSF are achieved.

In Honduras [14], the ICS implementation scenario in the period from 2016 to 2030 presented low energy consumption compared to the BAU scenario, making firewood consumption more efficient and with lower GHG emissions. In the ICS and LPG implementation scenario, an increase in LPG is observed in urban and peri-urban areas while firewood consumption persists in rural areas.

The studies that have been carried out in Mexico on the implementation of ICS have reached similar conclusions to the studies carried out in other countries. Islas et al. [30] show that in the analysis period from 2005 to 2030, the large-scale implementation of ICS represents a high reduction in the demand for firewood with an average mitigation of 0.5 tC per ICS per year, thus achieving an emission reduction of 6.23 million CO_2e and 58.6 PJ energy savings by 2030, all in a high penetration scenario. This study considers the effects of unsustainable firewood consumption that causes deforestation and forest degradation.

García et al. [34] analyzed the mitigation and investment cost of replacing TSF with ICS from 2015 to 2035, thereby achieving a decrease in firewood consumption due to an increase in efficiency of the ICS, a mitigation of 23 million tons of carbon dioxide equivalent (MtCO_2e), and an investment cost of 162.4 million USD (MUSD) and a mitigation cost of 7 USD per tCO_2e.

The study made in 2018 [32] shows the GHG mitigation and BC emissions associated with the transition from TSF to clean fuels and ICS over a period from 2014 to 2030. The results show that in the BAU scenario, a small reduction of firewood consumption at the end of the analysis period, an increase in the mixed users of firewood (MU), and a decrease in exclusive firewood users (EFU). This resulted in a firewood consumption reduction of 18.4 million tons (Mt) of dry matter and GHG emissions of 364.3 MtCO₂. In the alternative scenario, where both ICS and LPG stoves replace the TSF, firewood consumption decreases by 36% and lower GHG emissions are achieved, accounting for 238 MtCO₂ compared to a scenario in which only ICS are disseminated where firewood consumption decreases 28% and GHG emissions of 257.5 MtCO₂.

Finally, Grande-Acosta and Islas-Samperio [35] show scenarios from 2011 to 2035 in which the implementation of ICS in the rural residential sector was considered with the installation of 4 million ICS. The results presented a significant reduction in the mitigation scenario compared to the BAU scenario: 43 MtCO_2e were avoided, and a mitigation cost of −5 USD/tCO₂ was achieved; thus, instead of costs, economic benefits were obtained by ICS implementation.

In general, studies carried out for Mexico show a general vision of the benefits of ICS implementation; however, there is no consensus on the results due to the different assumptions established in each of them. Also, the variability of firewood user types and their consumption patterns were not considered. Two assumptions of critical relevance in the studies mentioned above where there are large differences are the number of users and the total firewood consumption in México. Pérez et al. [15] have noted that estimates of firewood consumption in Mexico have been based on limited case studies that do not reflect accurately the current state of firewood consumption. These same authors recently reported detailed information on firewood consumption patterns for households according to three types of users (EFU, MPU and MSU), three climate regions (extreme heat, mild and tropical), and two socioeconomic levels (moderate low (ML) and low (L)) according to what we consider the best and most representative survey on firewood consumption in Mexico, which is titled National Survey of Consumption of Energy in Private Housing Units (ENCEVI, for its acronym in Spanish) [36] carried out by the Mexican National Institute for Statistics and Geography (INEGI, for its acronym in Spanish).

Pérez et al. [15] found that the highest concentration of households that use firewood is in the tropical climate region (CR-TR), coinciding with Contreras et al. [37] which reached similar conclusions. This represents Mexico’s tropical climate region which faces significant challenges in addressing the problems caused by the high dependence on firewood consumption, therefore it deserves attention in order to analyze sustainable solutions that help reduce the problem that firewood consumption implies in this climate region to the most vulnerable households.

3. Context of the Study Site: Types of Firewood Users by Socioeconomic Level and Size of Population Centers, and Evolution of the Growth of the Tropical Climate Region

The distribution of firewood users in Mexico by type user, by CR, and by SL is shown in Figure 1; in the CR-TR, 52.2% of the total firewood users are concentrated, and the majority of users are households that have a low socioeconomic level (SL-L).

As Figure 1 show, the tropical region has the highest firewood consumption in the country, representing 64.1% of the total firewood consumption, of which the EFU consume 43.08 PJ, the MPU, 18.95 PJ, and the MSU, 12.71 PJ.

In this article, the consumption of firewood was disaggregated by SP: (i) rural, villages with a population under 2500; (ii) peri-urban, towns with a population ranging between 2500 and 14,999; (iii) urban, towns/cities with a population of 15,000 or greater following the methodology reported by Pérez et al. [15]. In the same way, the number of households that pay for acquiring firewood by SP was calculated.

3.1. Number of Users and Three Stone Fires in the Tropical Region

The number of firewood users is variable among the different types of users, according to the SL and SP to which such users belong.

Table 1. Number of users and number of three stone fire in the different firewood users by socioeconomical level in the tropical climate region in 2018.

Type of User	Socioeconomical Level	Number of Users	Number of TSF
EFU	ML	537,827	140,413
	L	5,952,432	1,323,253
MPU	ML	891,157	222,812
	L	2,601,155	603,398
MSU	ML	3,401,799	823,179
	L	2,642,188	683,102

EFU: Exclusive firewood users; MPU: Mixed users of firewood who utilize firewood as their primary fuel source; MSU: Mixed users of firewood who utilize firewood as a secondary fuel source; ML: moderate low; L: low; TSF: three stone fires. Source: Own elaboration.

shows the number of users and estimated number of TSF for each type of user.

The EFUs are approximately 6.49 million (M), of which the SL-ML are 537,827 users using 0.14 M TSF, while the SL-L users are 5.95 M and the amount of TSF is almost 10 times more than in the SL-ML.

In the MPU, there are a total of 3.49 M users and the number of TSF is 43.5% less than in EFU. SL-ML users use 0.22 M TSF, almost double that of EFU of the same SL. In the SL-L, the number of TSF is reduced by almost half compared to the EFU of the same SL.

The number of MSU is almost 6.04 M users which use a slightly larger amount of TSF than in the EFU, estimated at 1.5 M TSF. The majority of TSF is used in the SL-ML (54.6%) and the rest in SL-L.

Table 2. Number of users, number of three stone fire, and percentage of households that pay and do not pay for the acquisition of firewood in the different types of firewood users broken down by size of population centers in the tropical climate region in 2018.

Type of User	Socioeconomical Level	Size of Population Centers	Number of Users	Number of TSF	% of Paying Households	% of Non-Paying Households
EFU	ML	Rural	123,247	31,574	65.1	34.9
		Peri-urban	229,799	55,509	52.6	47.4
		Urban	184,781	53,330	86.5	13.5
	L	Rural	5,050,192	1,108,386	19.6	80.4
		Peri-urban	749,204	182,266	51.7	48.3
		Urban	153,036	32,601	57.9	42.1
MPU	ML	Rural	233,699	61,040	17.2	82.8
		Peri-urban	290,286	73,028	38.3	61.7
		Urban	367,172	88,744	60.8	39.2
	L	Rural	2,043,294	474,135	28.2	71.8
		Peri-urban	485,560	110,369	55.0	45.0
		Urban	72,301	18,894	49.2	40.8
MSU	ML	Rural	584,735	158,301	19.1	80.9
		Peri-urban	1,042,450	253,817	35.5	64.8
		Urban	1,774,614	411,061	47.3	52.7
	L	Rural	1,800,795	460,030	25.8	74.2
		Peri-urban	562,986	152,341	58.2	41.8
		Urban	278,407	70,731	64.3	35.7

EFU: Exclusive firewood users; MPU: Mixed users of firewood who utilize firewood as their primary fuel source; MSU: Mixed users of firewood who utilize firewood as a secondary fuel source; ML: moderate low; L: low; TSF: three stone fires. Source: Own elaboration.

breaks down the number of firewood users and number of TSF per SP for each of the user types. The EFU of the SL-ML were almost in the same proportion between peri-urban and urban areas with 40% and 38% of the total households, respectively, while in rural areas it was 22%.

In the SL-L of the EFUs, the highest concentration was in rural areas with 85% of the total, while 14% were found in peri-urban areas and the remaining 2% were from urban areas.

Unlike EFU, in the MPU the highest concentration of users of SL-ML and TSF users were in urban areas with 40% of the total, followed by users in peri-urban areas with 33%, and rural areas with 27%. In the SL-L, the proportion was similar to the EFU of SL-L where the highest concentration was in rural areas with 79%, followed by peri-urban areas with 18%, and the remaining 3% are in urban areas.

In the MSU of the SL-ML, half of the users were in urban areas (50%), followed by peri-urban areas (31%) and rural areas (19%). The SL-L showed the same trend as EFU and MPU, where the majority were found in rural areas (67%), followed by peri-urban areas (22%) and urban areas (10%), although this percentage is higher than of the EFU and MPU.

Of the total households, 34% that use firewood pay for this solid biofuel. The users with the highest proportion of households that pay for the acquisition of firewood, between 51.7% and 86.5%, were found in the EFUs of urban and peri-urban areas of SL, ML and L. In the MPU were in the SL-ML of urban areas and of the SL-L of peri-urban areas and, for the MSU were in the SL-L of urban and peri-urban areas.

In contrast, the highest percentage of households that do not pay for the acquisition of firewood, between 61.7% and 82.8%, were the MPU and MSU of SL-ML in peri-urban and rural areas, as well as the rural areas of the SL-L and the urban areas of the SL-ML of EFUs and, finally, the rural areas of the SL-L of the MSU.

3.2. Population Projection of the Tropical Climate Region in Mexico

Due to the lack of information on population projection disaggregated by SP (rural, peri-urban, and urban) the population projection was calculated using, as its basis, historical data the population reported in the population censuses from 1980 to 2020 by INEGI [38]. with a simple linear regression shown in Equation (1).

$$P_t=a+{bP}_{t-1}+e_t,$$

(1)

where P_t is the projection of the population in year t, a and b are the regression coefficients and P_t−1 is a previous period of P_t, assuming that the population P_t depends on population of the previous period.

Figure 2 shows the projected population growth for firewood users in the CR-TR by SP according to the average annual growth rates calculated—see Tables S1 and S2 in the Supplemental Material—based on historical data from population censuses.

In the CR-TR, in 2018, just over 16 M Inh (inhabitants) were users of firewood and a global AAGT (average annual growth rate) of 0.5% was estimated, which means that in 2050, firewood users will increase to 18.8 M Inh.

Disaggregating the number of firewood users by SP, Figure 2 shows that the rural population continues to be the most important, although its proportion decreases from 61% in 2018 to 58% in 2050. These users of firewood in rural areas grow with a small AAGR of 0.32%, going from 9.8 M users in 2018 to increasing by just 1 M users more in 2050, reaching 10.8 million users. In the case of the population of firewood users in peri-urban areas, the proportion went from 21% (3.4 M) in 2018 to 22% in 2050 (4.2 M), which means an AAGR of 0.66%. Finally, in the case of firewood users in urban areas, it had highest AAGR (0.89%) going from representing 18% in 2018 with 2.8 M users to 20% in 2050 with 3.7 M users.

4. Materials and Methods

The working hypotheses to be demonstrated in this article are the following four.

The implementation of efficient stoves to replace three-stone hearths in cooking in the tropical region results in:

• A vital potential to reduce CO_2e emissions and CO and PM_2.5 pollutants.
• A significant potential to favor sustainable development (reducing deforestation and energy poverty, health affectations related to traditional firewood consumption, and favoring an improvement in women’s wellness as the primary and direct firewood users in their homes).
• Greater positive impacts on the reduction of firewood consumption and CO_2e and CO and PM_2.5 pollutant emissions per dwelling, as well as on social development (health effects reductions related to firewood consumption, energy poverty, gender equity), for users who primarily consume firewood (EFU and MPU).
• Greater economic benefits for users who buy and are the ones who consume more firewood, especially from the SL-ML.

To test these hypotheses, we performed the following general methodology presented in Figure 3, which consist of six main phases:

4.1. Establishment of the Reference Year and Construction of the Trend Scenario

The reference year was established in 2018 because it is the year in which the most significant amount of information was available in a disaggregated manner. The authors assumed an analysis period of 32 years, meaning that the final year of analysis is 2050.

The reference year was established considering the types of firewood users (EFU, MPU, and MSU), the number of users and consumption of firewood per household that was used for the CR-TR by SL-ML, and SL-L corresponding to the year 2018, as reported by Pérez et al. [15] and disaggregated by SP: rural, peri-urban, and urban; likewise, by the number of households that pay for acquiring firewood.

To construct the trend scenario, the firewood consumption of type users analyzed was projected, considered as the main driver the population projection to the year 2050 calculated by SP (see Tables S1 and S2 of Supplementary Material on the construction of this driver for the CR-TR). In the construction of trend scenario, structural changes in equipment or the use of materials that affect firewood consumption in the residential sector were not considered. It was assumed that each of the analyzed households that use firewood for cooking does so in a TSF, so the number of households that use firewood is the same number of TSF used by each type users. The main assumptions considered in the construction of the trend scenario are presented in

Table 3. Main assumptions used in the trend scenario for calculation of firewood consumption, CO_2e and CH₄ emissions and criteria pollutants.

Assumptions		Value	Unit	References
Firewood calorific value		15.85	MJkg−1	[39]
Device where firewood is used		TSF		[15]
Specific consumption of firewood by EFU	SL-ML	14.7	GJ per device	[15]
	SL-L	31.0
Specific consumption of firewood by MPU	SL-ML	16.3
	SL-L	25.4
Specific consumption of firewood by MSU	SL-ML	5.5
	SL-L	12.0
Emission factor of firewood used in a TSF	CO2	1711.8	gCO2 kg−1	[39]
	CH4	5.5	gCH4 kg−1
		154	gCO2e kg−1
	CO	50.7	gCO kg−1
	PM2.5	4.9	gPM2.5 kg−1
CH4 Warming Potential		28		[40]
Percentage of BC in PM2.5		7.5	%	[41]
BC Warming Potential		900		[40]
Firewood non-renewability factor		34	%	[42]

TSF: Three stone fires; EFU: Exclusive firewood users; MPU: Mixed users of firewood who utilize firewood as their primary fuel source; MSU: Mixed users of firewood who utilize firewood as a secondary fuel source; SL-ML: socioeconomic level-moderate low; SL-L: socioeconomic level-low.

.

4.2. Construction of the Alternative Scenario

An alternative scenario was created to represent the implementation of ICS that replace the use of TSF in households, because ICS can provide benefits through the more efficient and cleaner use of firewood. The most important assumptions for the construction of this scenario are shown in

Table 4. Main assumptions used in the alternative scenario for the calculation of firewood consumption, CO₂ and CH₄ emissions and criterion pollutants from the implementation of improved firewood cookstoves.

Assumptions		Value	Unit	References
Firewood consumption of an Ecoestufa in relation to TSF		75	%	[39]
Goal to replace TSF with ICS at the end of the analyzed period		100	%	[32,34,35]
Ecoestufa useful life		10	year	[43]
Emission factor of firewood burned in an Ecoestufa	CO2	1691.2	gCO2kg−1	[39]
	CH4	3.2	gCH4 kg−1
	CO	48.4	gCO kg−1
	PM2.5	4.0	gPM2.5 kg−1

TSF: Three stone fires; ICS: Improved firewood cookstoves.

. The selection of the ICS that replace TSF was carried out through a bibliographic review of the ICS that are currently on the market for Mexico and that the technical and economic data of such devices are known, therefore, the ICS called “Ecoestufa” was chosen, whose technical information is reported in reference [39]. A linear ICS replacement rate was used that was calculated to reach 100% TSF substitution by the year 2050.

4.3. Energy Consumption Calculation

Firewood consumption (expressed in MJ) was calculated for each scenario and each type of firewood user found in the CR-TR, according to the annual firewood consumption per device and the population calculated for each SL group analyzed. We employed and adapted the following equations based on conventional formulas for the calculation of fuelwood energy consumption, previously used by several authors [44,45,46].

In the trend scenario:

$${FCTSF}_{tij}={FCTSF}_{i }\times {NTSF}_{tij}$$

(2)

$${NTSF}_{tij}=\frac{{PTSF}_{tij}}{{NIH}_{ij}}$$

(3)

where FCTSF_tij is the firewood consumption in an TSF in year t for SL i -ML or L- of SP j (ural, peri-urban, or urban); FCTSF_i is the firewood consumption in a TSF made by the SL i users; NTSF_tij is the number of TSF in year t for SL i of SP j. The NTSF_ij is the relationship between PTSF_tij, which is the population that uses TSF in year t for SL i of SP j; NIH_ij is the number of inhabitants per home for SL i of SP j.

In the alternative scenario:

$${FCA}_{tij}={FCTSF}_{tij}+{FCICS}_{tij }$$

(4)

$${FCICS}_{tij}={FCICS}_{i }\times {NICS}_{tij}$$

(5)

$${NICS}_{tij}={NICSI}_{tij}+{NICS}_{t-1ij}$$

(6)

where FCA_tij is the firewood consumption of the alternative scenario in year t for SL i (ML or L) of SP j (rural, peri-urban or urban); FCICS_tij is the firewood consumption of the ICS in year t for SL i of SP j; FCICS_i is the consumption of firewood in a ICS in the SL i; NICS_tij is the number of ICS in year t for SL i of SP j; NICSI is the number of ICS installed in year t for SL i of SP j; NICS_t−1ij is the number of ICS from the year before year t for SL i of SP j.

The number of ICS that are implemented each year considers the number of TSF to be replaced at the end of the period calculated with Equation (7).

$${NICSI}_{tij}=\frac{{TSF}_{tfinal}}{n}$$

(7)

NICSI is the number of ICS installed each year for SL i of SP j; TSF_tfinal is the total number of TSFs at the end of the analyzed period; n is the number of years of the analysis period.

4.4. Calculation of CO_2e and Criterion Pollutants Emissions for the Analized Scenarios

The calculation of CO_2e emissions and criterion pollutant emissions is directly related to firewood consumption for each analyzed group. We used the following equations, which are based on standard formulas for calculating CO_2e emissions [32,35,47,48], and which we modified for this work.

Trend scenario CO_2e emissions:

$${{CO}_{2e}TSF}_{tij}=\left(\left({FCTSF}_{tij}\times {EFTSFCO}_2\times NRF\right)+\left({FCTSF}_{tij}\times {EFTSFCH}_4\right)\right)+{BCETSF}_{tij}$$

(8)

where CO_2eTSF_tij are the CO_2e emissions from burning firewood in TSF in year t for SL i of SP j; FCTSF_tij is the firewood consumption in TSF in year t for SL i of SP j; EFTSFCO₂ is the CO₂ emissions factor of firewood burned in TSF; NRF is the non-renewability factor of firewood; EFTSFCH₄ is the CH₄ emissions factor of firewood burned in TSF; BCETSF_tij are the BC emissions in CO_2e units that were obtained by estimating the percentage of BC contained in the PM_2.5 emissions from the burning of firewood in TSF in year t for SL i of SP j (See Equations (9) and (10), and Table 1).

The criteria pollutant emissions of CO and PM_2.5 were obtained using the respective emission factors:

$${COTSF}_{tij}={FCTSF}_{tij}\times EFTSFCO$$

(9)

$${PM}_{2.5}{TSF}_{tij}={FCTSF}_{tij}\times E{FTSFPM}_{2.5}$$

(10)

where COTSF_tij are the CO emissions from burning firewood in TSF in year t for SL i of SP j; FCTSF_tij is the consumption of firewood in TSF; EFTSFCO is the CO emission factor of firewood burned in TSF.

PM_2.5TSF are the PM_2.5 emissions from burning firewood in TSF in year t for SL i of SP j; EFTSFPM_2.5 is the PM_2.5 emission factor of firewood burned in TSF.

BC emissions converted into CO_2e emissions were calculated with the following equation:

$${BCETSF}_{tij}={FCTSF}_{tij}\times PBC\times GWPBC$$

(11)

where BCETSF_tij are the BC emissions from burning firewood in TSF in units of CO_2e in year t for SL i of SP j; PBC is the percentage of BC contained in PM_2.5 emissions; GWPBC is the global warming potential of BC.

For the alternative scenario:

$${{CO}_{2e}A}_{tij}={{CO}_{2e}TSF}_{tij}+{{CO}_{2e}ICS}_{tij }$$

(12)

$${{CO}_{2e}ICS}_{tij}=\left(\left({FCICS}_{tij}\times {EFICSCO}_2\times FNR\right)+\left({FCICS}_{tij}\times {EFICSCH}_4\right)\right)+{BCEICS}_{tij}$$

(13)

where CO_2eA_tij are the CO_2e emissions of the alternative scenario in year t for the SL i of SP j; CO_2eTSF_tij are the CO_2e emissions from the consumption of firewood in TSF that are not yet replaced in the year t for the SL i of SP j (see Equation (8)); CO_2eICS_tij are the CO_2e emissions from burning firewood in an ICS.

FCICS_tij is the consumption of firewood in an ICS; EFICSCO₂ is the CO₂ emission factor of firewood burned in an ICS; EFICSCH₄ is the emission factor of firewood burned in an ICS; BCEICS_tij are the BC emission in CO₂e units that are obtained by estimating PM_2.5 from firewood burned in ICS.

The emissions of criteria pollutants of CO and PM_2.5 were obtained with the following equations:

$${COA}_{tij}={COTSF}_{tij}+{COICS}_{tij}$$

(14)

$${COICS}_{tij}={FCICS}_{tij}\times EFICSCO$$

(15)

$${PM}_{2.5 }{A}_{tij}={PM}_{2.5}{TSF}_{tij}+{PM}_{2.5}{ICS}_{tij}$$

(16)

$${PM}_{2.5}{ICS}_{tij}={FCICS}_{tij}\times EFICS{PM}_{2.5}$$

(17)

where COA_tij are the CO emissions of the alternative scenario in year t for SL i of the SP j; COTSF_tij are CO emissions from burning firewood in TSF that are not yet replaced in alternative scenario in year t for SL i of the SP j (see Equation (9)); COICS_tij are the CO emissions from burning firewood in ICS in year t for SL i of the SP j. FCICS is the firewood consumption in ICS; EFICSCO is the CO emission factor of firewood burned in ICS.

PM_2.5A_tij are the PM_2.5 emissions of the alternative scenario in year t for SL i of the SP j; PM_2.5TSF_tij are PM_2.5 emissions from burning firewood in TSF that are not yet replaced in alternative scenario in year t for SL i of the SP j (see Equation (10)); PM_2.5ICS_tij are the PM_2.5 emissions from burning firewood in ICS in year t for SL i of the SP j; EFICSPM_2.5 is the PM_2.5 emission factor of firewood burned in ICS.

BC emissions converted into CO₂e emissions were calculated with the following equation:

$${BCA}_{tij}={BCETSF}_{tij}+{BCEICS}_{tij}$$

(18)

$${BCEICS}_{tij}={FCICS}_{tij }\times PBC\times GWPBC$$

(19)

where BCA_tij are the BC emissions of the alternative scenario in year t for SL i of SP j; BCETSF_tij are de BC emissions from the burning of firewood in TSF that are not yet replaced in alternative scenario in year t for SL i of SP j (see Equation (11)); BCEICS_tij are de BC emissions from the burning of firewood in ICS in units of CO_2e in year t for SL i of SP j; PBC is the percentage of BC contained in PM_2.5 emissions; GWPBC is the global warming potential of BC.

4.5. Cost-Benefit, Financial and Socioeconomic Analysis

We formulated a calculation model to conduct a financial, socioeconomic, and cost-benefit analysis for implementing ICS in substituting TSF in households. Our model can handle three user types, two socioeconomic levels, and three population sizes. Also, it distinguishes between users who pay and do not pay for the purchase of firewood and emphasizes the analysis by socioeconomic level of firewood users to locate the most relevant socioeconomic impacts. Finally, we calculate the internal rate of return (IRR) to detect the most profitable groups for implementing the ICS in the users who pay for firewood. In formulating the model, we adapted conventional formulas of economic and financial engineering and from other calculation models [35,47,48]. The equations are the following.

$${CB}_{SICSijk-STSFijk}={CostI}_{SICSijk-STSFijk}+{CostF}_{SICSijk-STSFijk}$$

(20)

where CB_{SICSijk−STSFijk} is the cost-benefit of the ICS implementation scenario related to the TSF scenario for SL i (ML or L) of SP j (rural, peri-urban or urban) and households type k (who pay or not pay for the acquisition of firewood); CostI_{SICSijk−STSFijk} is the total cumulative incremental investment cost, between the alternative scenario (SICS) in relation to the trend scenario of TSF use (STSF), for SL i of SP j in households type k; CostF_{SICSijk−STSFijk} in the total accumulated avoided cost of firewood between SICS and the STSF for SL i of SP j in households type k.

$${CostoI}_{SICSijk-STSFijk}=\sum _{t=1}^p\frac{ \left({UICICS}_{}\times {NHICS}_{SICStijk}\right)}{{(1+r)}^t}$$

(21)

where UICICS in the unit investment cost of and ICS; NHICS_SICStijk is the number of households that have an ICS in the alternative scenario in year t for SL i of SP j in users type k. TSF were considered free cost; the discount rate is the value in r and the analysis period is the value in p.

The calculation of the cost of firewood in households that pay for its acquisition is presented in Equation (22).

$${CostF}_{SICSijk-STSFijk}=\sum _{t=1}^p\frac{{CostFSICS}_{tijk}-{CostFSTSF}_{tijk}}{{(1+r)}^t}$$

(22)

where CostFSICS_tijk is the cost of firewood when households pay its acquisition in the scenario of implementation of ICS in year t for SL i of SP j; CostFSTSF_tijk is the cost of firewood when households pay its acquisition in the trend scenario of TSF use in year t for SL i of SP j.

In the case of households that do not pay for the acquisition of firewood, the value of these firewood costs are equal to zero. Section 3.1 of the results presents the percentages of households that pay and do not pay for the acquisition of firewood by type of user, SL and SP.

The cost of firewood when households pay its acquisition in the scenario of implementation of ICS—CostFSICSt_ijk—was calculated with the Equations (23)–(25).

$${CostFSICS}_{tijk}={CostFTSF}_{tijk}+{CostFICS}_{tijk}$$

(23)

where CostFTSFt_ijk is the cost of firewood when households pay for its acquisition that is used in TSF in alternative scenario in year t for SL i of SP j; CostFICSt_ijk is the cost incurred for the use of firewood in ICS in households that pay for its acquisition of alternative scenario in year t for SL i of SP j.

$${CostFTSF}_{tijk}=\left({NHPTSF}_{tijk}\times {FCTSF}_{tijk}\times {AFE}_i\right)$$

(24)

where NHPICS_ijk is the number of households that pay or do not pay for acquisition of firewood and that have a TSF in year t for SL i of SP j; FCTSF is the consumption of firewood in TSF in year t for SL i of SP j; AFE_i is the annual expenditure for purchasing firewood in SL i.

$${CostFICS}_{tijk}=\left({NHPICS}_{tijk}\times {FCICS}_{tijk}\times {AFE}_i\right)$$

(25)

where NHPICS_ijk is the number of households that pay or do not pay for acquisition of firewood and that have an ICS in year t for SL i of SP j; FCICS is the consumption of firewood in ICS year t for SL i of SP j.

To measure the financial efficiency of the investments for implementing ICS, we use the internal rate of return (IRR), an essential indicator of the economic and financial engineering literature, in this case, is applied to those users who pay to acquire firewood, distinguishing SL i and SP j. To calculate it, we employ the following equation to find the value of r (see Equations (21) and (22)), identified as the IRR:

$${CB}_{SICSijk-STSFijk}=0$$

(26)

where CB_{SICSijk−STSFijk} is the cost benefit due the ICS implementation related to use of TSF for SL i and SP j of households that pay for acquisition of firewood.

Table 5. Main assumptions for the cost calculation of trend and alternative scenarios.

Assumptions		Value	Units	References
Annual expenditure for purchasing firewood in EFU	SL-ML	153.90	USD 2017	[15]
	SL-L	153.37
Annual expenditure for purchasing firewood in MPU	SL-ML	137.92
	SL-L	124.08
Annual expenditure for purchasing firewood in MSU	SL-ML	68.16
	SL-L	87.87
Unit investment cost of ICS		130	USD 2017	[43]
Discount rate		10	%	[34,35]

EFU: Exclusive firewood users; MPU: Mixed users of firewood who utilize firewood as their primary fuel source; MSU: Mixed users of firewood who utilize firewood as a secondary fuel source; SL-ML: socioeconomic level-moderate low; SL-L: socioeconomic level-low; ICS: Improved firewood cookstove.

shows the main assumptions, the values for the annual expenditure on purchasing firewood by households reported in Pérez et al. [15], values for UICICS, and the discount rate considered.

The percentage of households that pay and do not pay for the acquisition of firewood is shown in Table 2.

4.6. Comparison of the Alternative Scenario with the Trend Scenario

Finally, a comparison of firewood consumption, CO_2e emissions, criterion pollutants, and cost-benefit and socioeconomic analysis is carried out between the trend scenario and alternative scenario, which is a large-scale implementation of the ICS for efficient and clean use of firewood.

5. Results

5.1. BAU Scenario of Exclusive Firewood Users

The total number of EFU in 2018 was 6,490,259 distributes across 1,463,666 households. Figure 4 shows the energy consumption and emissions of the trend scenario by SL and SP. According to the results, in 2018, the EFU consumed 43.1 PJ of firewood in TSF generating 2.9 MtCO_2e, 137.8 thousands of tons (kt) of CO, and 13.2 kt of PM_2.5.

The largest number of households belong to the SL-L found in rural areas (76%), which consequently are those with the highest firewood consumption, CO_2e emissions, and criterion pollutants at proportion of 80% of the total.

Following the trend to the year 2050, the total number of households increases to 1,669,992 where rural areas represent 73% and peri-urban areas, 13%. In this way, total firewood consumption increases by 2050 to 48.9 PJ and emissions to 3.3 MtCO_2e, 156.5 kt of CO and 15 kt of PM_2.5, where rural areas contribute 78% and peri-urban areas with 14%.

In this scenario, it is observed that EFU households will continue to increase with high firewood consumption and therefore with high CO_2e emissions and criteria pollutants, especially in rural areas and those that belong to the SL-L which will continue, in this scenario, depending on firewood with its consequent environmental, health, and socioeconomic problems.

5.2. BAU Scenario for Mixed Users of Firewood Who Utilize Firewood as Their Primary Fuel Source

The number of MPU households was 3,492,312 distributed in 826,210 households in 2018, which represents close to half of the EFU households. Like the EFU users, in these MPU the majority belongs to the SL-L who are found in rural areas (57%) and in peri-urban areas (13%), although a significant 11% was also found in the urban areas of the SL-ML.

Figure 5 presents the BAU scenario for firewood consumption with its respective CO_2e emissions and criterion pollutants for MPU by 2050. In 2018, the total consumption of firewood is 19 PJ with emissions of 1.3 MtCO_2e, 60 kt of CO and 5.8 kt of PM_2.5, which SL-L households in rural areas contribute 64% and peri-urban areas with 15%, while SL-ML households in urban areas contribute 8%.

In the trend to year 2050, the number of households increases by 14% reaching 962,131 households where the SL-L in rural areas represents 55%, peri-urban areas, 14% and urban areas, 3%, while the SL-ML of rural areas represents 7% and peri-urban areas and urban areas, 9% and 12%, respectively. In this way, firewood consumption by 2050 is estimated at 21.9 PJ with emissions of 1.5 MtCO_2e, 70.1 kt of CO and 6.7 kt of PM_2.5, of which SL-L households in rural areas contribute 61% and peri-urban and urban areas with 16% and 3%, respectively. While SL-ML households in urban areas contribute 8%, peri-urban areas, 7% and rural areas 5%.

5.3. BAU Scenario for Mixed Users of Firewood Who Utilize Firewood as a Secondary Fuel Source

The number of MSU in 2018 is 6,043,987 distributed across 1,506,281 households, which is a slightly larger number than EFU. Of these households, although the majority are located in SL-L of rural areas (31%), a considerable proportion are found in the SL-ML of urban (27%) and peri-urban (17%) areas. In a smaller proportion, this type of user was found in rural areas (11%) of the SL-ML as well as in peri-urban areas (10%) and urban areas of the SL-L with 5%.

In 2018, as seen in Figure 6, firewood consumption was 12.7 PJ with emissions of 0.9 MtCO_2e, 40.7 kt of CO, and 3.9 kt of PM_2.5, of which SL-L households in rural areas contribute 43% of CO_2e emissions and peri-urban areas with 14%, while SL-ML households in urban areas contribute 18% and peri-urban areas with 11%; this proportion is similar for CO and PM_2.5 emissions.

In the year 2050, the number of households will increase to 1,826,757 where the majority of households will be concentrated in the urban areas of SL-ML with 30% followed by households in rural areas of SL-L with 28%, the households from the SL-ML of peri-urban areas with 17%, the rural areas of SL-ML and peri-urban areas of SL-L with 10%, and the remaining 5% from the urban areas of SL-L. For this same year, firewood consumption at the end of the analysis period is estimated at 152.2 PJ with emissions of 1.0 MtCO_2e, 48.6 kt of CO, and 4.6 kt of PM_2.5, of which the CO_2e contribution from households of SL-L in rural areas will represent 40% and SL-ML households in urban areas represent 20%, while the peri-urban areas of SL-L and ML will contribute 15% and 11%, respectively. The rural areas of SL-ML and urban areas of SL-L will contribute 6% and 7%, respectively. The contribution of CO and PM_2.5 show similar percentages.

In this BAU scenario, both types of MPU and MSU users are observed to increase the number of households that consume firewood in urban and peri-urban areas. Although the consumption of firewood is lower than in the EFU, mixed users of firewood will continue to have a significant dependence on the use of firewood and its respective consequences of CO_2e emissions and criteria pollutants.

5.4. Alternative Scenario of Exclusive Firewood Users

With the implementation of ICS that replaces the TSF, a significant reduction in firewood consumption and its corresponding emissions of CO_2e and criteria pollutants are shown. Figure 7 presents the alternative EFU scenario where 100% of TSF is replaced by ICS in the analyzed period.

The EFUs recorded the highest consumption of firewood in 2018. By 2050 with the implementation of ICS firewood consumption reduces to 36.7 PJ, emissions of CO_2e reduces to 2.2 MtCO_2e, and emissions of CO and PM_2.5 reduces to 112.1 kt and 9.3 kt, respectively. This represents a global reduction, related to the initial year, of 17% in firewood consumption, 33% in CO_2e emissions, 23% in CO emissions, and 41% in PM_2.5 emissions, respectively. In this way, compared to the base scenario, the consumption of 12.2 PJ of firewood is avoided and, consequently, 1.1 MtCO_2e, 44.4 kt of CO, and 5.6 kt of PM_2.5 are avoided.

The greatest reduction occurs in rural areas, especially in SL-L which is the group with the greatest implementation of ICS avoiding the consumption of 9.51 PJ of firewood, 0.87 MtCO_2e, 34.54 kt of CO, and 4.38 kt of PM_2.5; this means that 77.7% of firewood consumption and emissions are avoided.

In peri-urban areas, SL-L households are the second group that has the highest firewood consumption and avoided emissions with approximately 14.3% of the total.

5.5. Alternative Scenario for Mixed Users of Firewood Who Utilize Firewood as Their Primary Fuel Source

The consumption of firewood and its corresponding emissions, CO_2e and criteria pollutants, in alternative scenario of the MPU is presented in Figure 8, where it is observed that, like the EFU, the users of SL-L have the greatest potential to reduce firewood consumption and its respective emissions with the implementation of ICS.

The MPU had a global reduction related to the initial year of 15.2% of firewood consumption, 30.8% of CO_2e emissions, 20.7% of CO emissions, and 38.7% of PM_2.5 emissions. This means that by 2050, firewood consumption in this type of user will be reduced to 16.4 PJ, CO_2e emissions to 1.0 MtCO_2e, CO emissions to 50.2 kt, and PM_2.5 emissions to 4.2 kt. In this way, compared to the trend scenario, the consumption of 5.5 PJ of firewood, 0.5 MtCO_2e, 19.9 kt of CO, and 2.5 kt of PM_2.5 is avoided.

The greatest reduction occurs in rural areas in the SL-L where 3.33 PJ of firewood, 0.3 MtCO_2e, 12.1 kt of CO, and 1.53 kt of PM_2.5 are avoided. This means that with the implementation of ICS in this group, it is possible to avoid 60.8% of firewood consumption as well as CO_2e emissions and the criteria pollutants of CO and PM_2.5.

SL-L users in peri-urban areas are the second group that have the highest firewood consumption and highest avoided emissions, with 15.8% of the total, while in urban areas the avoided emissions are 11.7%.

5.6. Alternative Scenario for Mixed Users of Firewood Who Utilize Firewood as a Secondary Fuel Source

The consumption of firewood and emissions of CO_2e and criteria pollutants in the alternative scenario of the MSU is presented in Figure 9, in which it is observed that the SL-L, as in the EFU and the MPU, has the greatest potential to avoid the consumption of firewood and its respective emissions with the implementation of ICS.

For the MSU global reduction related to the initial year are estimated, 11.6% in firewood consumption, 29.3% in CO_2eemissions, 16.9% in CO, and 34.4% in PM_2.5. In this way by 2050, firewood consumption is reduced to 11.4 PJ, CO_2e emissions to 0.7 MtCO_2e, CO to 34.8 kt, and PM_2.5 to 2.9 kt. Compared to the trend scenario, 3.8 PJ of firewood to 0.4 MtCO_2e, 13.8 kt of CO and 1.8 kt of PM_2.5 are avoided.

The greatest reduction occurs in rural areas in the SL-L: 1.52 PJ of firewood, 0.14 MtCO_2e, 5.54 kt of CO, and 0.70 kt of PM_2.5. In this way, approximately 40% of firewood consumption and emissions are generally reduced.

In users in peri-urban areas in the SL-L and SL-ML, there is also a present significance of firewood consumption and avoided emissions, being 14.9% for SL-L and 11.4% for SL-ML. It is also notable to highlight that in urban areas the SL-ML achieves 19.8% of avoided emissions.

5.7. Cost-Benefit, Financial and Socioeconomic Analysis Results

This section presents the results of the cost benefit, financial and socioeconomic analysis of the implementation of the ICS in the tropical region of Mexico. These results are disaggregated by type of user, SL and SP, considering households who pay a monetary amount for the acquisition of firewood and those who do not.

5.7.1. Households That Pay for the Acquisition of Firewood

The case of households that pay to obtain firewood is shown in

Table 6. Avoided emissions in the alternative scenario and costs involved in trend and alternative scenarios, investment and firewood costs, cost-benefit and mitigation costs and financial efficiency of ICS in households who pay for acquisition of firewood.

Type of User	Socioeconomical Level	Size of Population Centers	Trend Scenario	Alternative Scenario		Emissions of CO2e Avoided (MtCO2e)	Cost-Benefit			Financial Efficiency IRR (%)
			Firewood Cost (MUSD)	Investment Cost (MUSD)	Firewood Cost (MUSD)		Incremental Cost of Firewood (MUSD)	Total Incremental Cost (MUSD)	Mitigation Cost (USD/tCO2e)
EFU	ML	Rural	31.07	1.35	28.62	0.08	−2.45	−1.10	−13.36	39.16
		Peri-urban	45.61	2.14	41.72	0.13	−3.89	−1.75	−13.36	39.16
		Urban	73.67	3.63	67.07	0.22	−6.61	−2.97	−13.36	39.16
	L	Rural	327.84	14.29	301.96	1.85	−25.89	−11,60	−6.29	38.93
		Peri-urban	146.78	7.02	134.27	0.91	−12.51	−5.49	−6.05	37.83
		Urban	30.06	1.49	27.36	0.19	−2.69	−1.21	−6.29	38.93
MPU	ML	Rural	14.20	0.69	13.08	0.05	−1.12	−0.43	−9.27	32.37
		Peri-urban	39.14	2.05	35.81	0.14	3.34	−1,29	−9.27	32.37
		Urban	77.25	4.25	70.32	0.29	−6.93	−2.68	−9.27	32.37
	L	Rural	162.79	8.77	149.94	0.93	−12.85	−4.08	−4,40	26.72
		Peri-urban	76.39	3.09	69.88	0.33	−6.51	−3.42	−10.43	50.19
		Urban	11.97	0.73	10.89	0.08	−1.07	−0.34	−4.40	26.72
MSU	ML	Rural	20.27	1.99	18.67	0.05	−1.60	0.39	8.03	−1.96
		Peri-urban	61.82	6.54	56.55	0.16	−5.27	1.28	8.03	−1.96
		Urban	137.50	15.31	125.17	0.37	−12.33	2.98	8.03	−1.96
	L	Rural	102.49	7.80	94.40	0.41	−8.09	−0.30	−0.72	11.57
		Peri-urban	79.06	3.93	72.33	0.21	−6.74	−2.81	−13.52	35.44
		Urban	41.48	3.58	37.76	0.19	−3.72	−0.14	−0.72	11.57

EFU: Exclusive firewood users; MPU: Mixed users of firewood who utilize firewood as their primary fuel source; MSU: Mixed users of firewood who utilize firewood as a secondary fuel source; ML: moderate low; L: low. Source: Own elaboration.

. It is observed that all households of the SL of EFU and MPU present significant avoided costs due to the implementation of ICS.

In the EFU, SL-L households in rural areas have the highest cost for purchasing firewood in the BAU scenario, with a value of 327.84 million dollar (MUSD) followed by peri-urban areas with a value of 146.78 MUSD. These high costs are mainly due to the fact that these are the areas where the highest concentration of users is found. The highest investment cost to implement the ICS are found in urban areas with an investment cost of 14.29 MUSD, followed by peri-urban areas with an investment cost of 7.02 MUSD.

Our results also show that users in rural and peri-urban areas have the highest firewood avoided cost of 25.89 MUSD and 12.51 MUSD, respectively. If these avoided costs and the investment costs to implement ICS are considered. This mitigation measure represents a total benefit for the EFU of 11.60 MUSD in rural areas and 5.94 MUSD in peri-urban areas.

Given the benefits found for these EFU, the mitigation cost is consequently negative. The group of EFU from the SL-ML is shown to have −13.36 USD for each tCO_2e avoided, while it is −6.05 to −6.29 for users in rural and urban areas that belong to the SL-L.

In the trend scenario of the MPU, households that belong to the SL-L in rural areas have the highest cost of firewood with a value of 162.79 MUSD, followed by SL-ML users in urban areas with 77.25 MUSD. On the other hand, the investment cost of implementing ICS in users who belong to the SL-L in rural areas is 8.77 MUSD, while in households in the SL-ML in urban areas, it is almost half (4.25 MUSD). Considering that the implementation of ICS translates into avoided firewood costs, −12.85 MUSD for MPU users in rural areas of SL-L and −6.93 MUSD in urban areas of SL-ML; a total benefit of 4.08 MUSD is achieved in the rural areas of SL-L and 2.68 MUSD in the urban areas of SL-ML.

The mitigation cost is negative in all MPU groups that pay for firewood as well as in the EFU, although these are slightly lower. Users of the SL-ML have a value of −9.27 USD for each tCO_2e avoided. For users who belong to the SL-L in rural and urban areas, this value is −4.40 USD/tCO_2e each, while for peri-urban areas, a value of −10.43 USD/tCO_2e is reached.

The results in MSU behave very differently from the case of MPU. In the trend scenario, MSU belonging to the SL-ML in urban areas have the highest cost of firewood with a value of 137.50 MUSD, followed by users from the SL-L in rural areas with a value of 102.49 USD. In the alternative scenario, the investment cost of implementing ICS in SL-ML households in urban areas is 15.31 MUSD, while in SL-L households in rural areas this cost is 7.80 MUSD. Due to the existence of an avoided firewood cost when implementing ICS (−2.33 MUSD in urban areas of SL-ML and −8.09 MUSD in rural areas of SL-B), benefits of 2.98 MUSD are achieved in the urban areas of SL-ML and 0.30 MUSD in the rural areas of the SL-L.

It is important to note that the mitigation cost is positive only for the MSU of the SL-ML for all SP, at 8.03 USD/tCO_2e, while for the SL-L users, these costs are negative; in peri-urban areas the mitigation cost is −13.52 USD/tCO_2e while in rural and urban areas the mitigation cost is −0.72 USD/tCO_2e.

Regarding the financial efficiency of ICS investments, we chose the IRR to evaluate users who pay when purchasing firewood. See the last column of Table 6, where the highest profitability is found in the MPU users of the SL-L of peri-urban areas with an IRR of 50.19%, five times the discount rate used in this article. Also showing high rentabilities of between 26.72% and 39% were all EFUs in the SL-ML, MPUs of all SPs, SL-L of rural and urban areas, and the SL-L MSUs of peri-urban areas. These users who showed the highest profitability would be the priority when establishing financial programs for ICS implementation.

On the contrary, the MSU in the SL-ML for all SP show IRR with negative values because these groups have lower per capita consumption of firewood, so the implementation of ICS are not high and sufficient to cover the initial cost of the ICS. In this case, the ICS implementation is not profitable and will require the support of some other financial mechanism.

5.7.2. Households Who Do Not Pay for Firewood Acquisition

In the case of users who do not pay for the acquisition of firewood, the cost-benefit analysis includes only the investment cost of implementing ICS in the alternative scenario.

Table 7. Investment cost, avoided emissions, and mitigation costs for users who do not pay for firewood acquisition.

Type of User	Socioeconomical Level	Size of Population Centers	Investment Cost (MUSD)	Emissions of CO2e Avoided (MtCO2e)	Mitigation Cost (USD/tCO2e)
EFU	ML	Rural	0.72	0.04	16.33
		Peri-urban	1.93	0.12	16.33
		Urban	0.57	0.03	16.33
	L	Rural	58.45	7.55	7.74
		Peri-urban	6.33	0.82	7.74
		Urban	1.08	0.14	7.74
MPU	ML	Rural	3.32	0.23	14.73
		Peri-urban	3.30	0.22	14.73
		Urban	2.74	0.19	14.73
	L	Rural	22.35	2.36	9.45
		Peri-urban	4.99	0.53	9.45
		Urban	0.76	0.08	9.45
MSU	ML	Rural	8.40	0.20	41.23
		Peri-urban	12.04	0.29	41.23
		Urban	17.08	0.41	41.23
	L	Rural	22.39	1.28	18.90
		Peri-urban	7.23	0.38	18.90
		Urban	1.99	0.11	18.90

EFU: Exclusive firewood users; MPU: Mixed users of firewood who utilize firewood as their primary fuel source; MSU: Mixed users of firewood who utilize firewood as a secondary fuel source; ML: moderate low; L: low. Source: Own elaboration.

presents these costs as well as the mitigation cost for each type of user, SL and SP.

All types of firewood users present positive mitigation cost for each tCO_2e avoided. In the EFU, all SL-ML households have the highest mitigation cost with a value of 16.33 USD/tCO_2e, while for SL-L households in rural areas, its value is 7.74 USD/tCO_2e and, in fact, this group have the lowest mitigation costs of all types of users who do not pay for acquisition of firewood. This is because in this socioeconomic level, rural areas have the largest number of users but, at the same time, this group has the highest consumption of firewood per household. Although this group has the highest investment cost in ICS of all user types (58.45 MUSD), each of the ICSs reduce more CO_2e emissions per household. Consequently, this results in lower costs related to the rest of the users that do not pay for firewood acquisition.

The trend is similar in MPU and MSU, where the mitigation cost is higher in the SL-ML level than in SL-L. The mitigation cost for MSU belonging to the SL-ML is 14.73 USD/tCO_2e and 9.45 USD/tCO_2e for SL-L users, 35% lower than SL-ML. The MSU users belonging to the SL-ML have the highest mitigation cost of all types of users (41.23 USD/tCO_2e) because this group have the lowest firewood consumption per household of all user types. In users belonging to the SL-L, the mitigation cost is 54% lower than the SL-ML (18.90 USD/tCO_2e).

Among all users, those that pay for firewood and those who do not, other social benefits that until now have not been analyzed must be taken into account, such as the reduction in respiratory diseases due to the reduction of CO and PM_2.5 indoor emissions, as well as the corresponding avoided health services cost of the exposed population. Also, the avoided cost related to the reduction of the number of labor days lost must be considered.

Additionally, and taking into account that mostly women are those who have the greatest direct contact with cooking devices and that a considerable percentage of women are heads of family, the social benefits that result from reducing firewood consumption would contribute to improving aspects of gender equity and energy poverty.

5.8. Regional Results

At a regional level in Mexico in 2018, firewood consumption was 74.77 PJ and emissions were 5.03 MtCO_2e, 239.87 kt of CO, and 22.88 kt of PM_2.5. By the year 2050 in the trend scenario, which is characterized by the use of TSF for cooking, firewood consumption increases up to 86.04 PJ, and emissions to 5.78 MtCO_2e, 275.27 kt of CO, and 26.33 kt of PM_2.5. These relatively mild increases are due to a decrease in the growth rate of the firewood user population in this region, especially in the rural areas where most of the firewood users are located.

By the year 2050, the alternative scenario, with the implementation of ICS, firewood consumption will decrease to 64.54 PJ when compared to the year 2018, and, consequently, CO_2e emissions will decrease to 3.81 MtCO_2e, CO to 197.12 kt, and PM_2.5 to 6.47 kt, which corresponds to decreases of 14%, 24%, 18%, and 28% in energy, CO_2e emission and criteria pollutants, respectively, compared to the base year.

When comparing the base scenario and the alternative scenario in the analysis period, results of 21.51 PJ of firewood consumption were avoided, and we achieved reductions of 1.8 MtCO_2e, 78.14 kt of CO, and 9.93 kt of PM_2.5 are avoided for year 2050. This means reductions of 25% in firewood consumption, 34% in CO_2e emissions, 28% in CO and 38% in PM_2.5. For the entire period, results of 354.95 PJ of accumulated firewood consumption were avoided, and the implementation of alternative scenario achieves accumulated reductions of 36.6 MtCO_2e, 1.29 Mt of CO, and 163.78 kt of PM_2.5 are avoided cumulatively.

For users who pay for the acquisition of firewood, there is a total avoided cost of 123.61 MUSD for acquiring firewood and a total investment cost for implementing the ICS of 88.65 MUSD, which translates into an average mitigation cost equal to −3.35 USD for each tCO_2e avoided. For users who do not pay for the acquisition of firewood, the investment cost of implementing the ICS is 175.67 MUSD; this translates into a mitigation cost of 11.73 USD/tCO_2e.

5.9. Sensitivity Analysis

We performed a sensitivity analysis to identify the effects of three parameters on avoided energy consumption and avoided CO_2e, CO, and PM_2.5 emissions by type of user (EFU, MPU, and MSU). These parameters are (1) fuelwood non-renewability factor (NRF), (2) the improvement in the efficiency of the ICS, and (3) the calorific value of fuelwood. The following subsections show the resulting sensitivity analysis.

5.9.1. Impacts on Avoided Energy Consumption

We analyzed the avoided energy consumption due to variations in the NRF ranging from −10% to 10%; when the percentage is negative, it means that there is an improvement in the renewability of firewood, and when it is positive, it means the opposite, i.e., that the use of non-renewable firewood increases. For this parameter, we found no sensitivity in avoided energy consumption for the three firewood user types because there is no distinction between renewable and non-renewable firewood during firewood consumption.

Regarding variations in improving the efficiency of ICS compared to TSF, the results show that, for the EFU, if there is a variation of −10%, the avoided energy consumption has the same variation. If it varies by 10%, the analysis shows it has the same variation (10%) in avoided energy. Our results show the same effect for the other two types of users (MPU and MSU). According to these results, the avoided energy consumption is highly sensitive to improving the ICS efficiency.

Finally, when the firewood calorific value varies (from −10% to 10%), as in the case of NRF, it was also found that there is no sensitivity in the avoided energy consumption of firewood, so only the volumes of this biofuel will vary.

5.9.2. Impacts on Avoided Emissions of CO_2e, CO, and PM_2.5

The results of the impact of FNR variations on avoided CO_2e, CO, and PM_2.5 emissions show that this parameter only impacts avoided CO_2e emissions for the three types of users (EFU, MPU, and MSU) (see

Table 8. Sensitivity analysis values due to parameter variations in: NRF, ICS energy efficiency, and calorific value related to avoided emissions of CO_2e, CO y PM_2.5.

Varied Parameter	User Type	−10%			10%
		CO2e	CO	PM2.5	CO2e	CO	PM2.5
Firewood non-renewability factor	EFU	−4.69%	0%	0%	4.69%	0%	0%
	MPU	−4.17%	0%	0%	4.17%	0%	0%
	MSU	−4.02%	0%	0%	4.02%	0%	0%
ICS energy efficiency	EFU	−5.70%	−8.40%	−5.51%	5.70%	8.40%	5.51%
	MPU	−4.17%	−8.41%	−5.51%	4.17%	8.41%	5.51%
	MSU	−6.13%	−8.41%	−5.51%	6.13%	8.41%	5.51%
Firewood calorific value	EFU	9.03%	11.11%	11.11%	−7.39%	−9.09%	−9.09%
	MPU	11.11%	11.11%	11.11%	−9.09%	−9.09%	−9.09%
	MSU	11.11%	11.11%	11.11%	−9.09%	−9.09%	−9.09%

Source: Own elaboration.

). Indeed, for EFU, a −10% variation in NRF causes a −4.69% variation in avoided CO_2e emissions, while for MPU and MSU, we see variations of −4.17% and −4.02%, respectively

Concerning the impact of the variations of ICS energy efficiency, we found that for the EFU, if this parameter is varied by −10%, the avoided CO emissions are reduced by 8.40%; for PM_2.5 and CO_2e, these decreases are 5.51%, and 5.70%, respectively. On the contrary, if this parameter improves by 10%, these values improve with the same variations, increasing the avoided emissions.

In the case of the MPUs, when the improvement in the ICS efficiency varies −10%, the variations for avoided CO and PM_2.5 emissions are very similar to those found for the EFU, while for avoided CO_2e emissions, the reduction is less than in the EFU and equal to 4.17%. For these same users, if the improvement parameter in the efficiency of the ICS were to vary by 10%, the results would be symmetrical to those already mentioned for a variation of −10%.

In the case of the MSU, concerning avoided CO and PM_2.5 emissions, the same variations are reported for the MPU; however, for avoided CO_2e emissions, the variation is more significant than for the other two types of users, reaching 6.13%. Given these values, modifications in this parameter contribute significantly to increased avoided emissions of climate change and criteria pollutants.

Regarding the impact of variations of firewood heat value, from −10% to 10%, the results show that for a variation of −10% of this parameter, regarding EFU, the avoided emissions of CO and PM_2.5 have an increase of 11.11% in both pollutants. While avoided emissions of CO_2e have an increase of 9.03%. On the other hand, if the firewood heat value increases by 10%, there is a reduction in avoided CO and PM_2.5 emissions of 9.09% each, and for avoided CO_2e emissions, the reduction is 7.39%.

Concerning the MPU, when the variation is −10%, the avoided emissions of CO_2e, CO, and PM_2.5 have a reduction of 11.11% each. On the contrary, when the firewood calorific value. For the opposite case, a 10% variation produces a 9.09% decrease in avoided emissions of the three pollutants (CO_2e, CO, and PM_2.5). Finally, for the MSU, results equal to those of the MPU were found when this parameter varied from −10% to 10%. According to these results, the avoided CO_2e, CO, and PM_2.5 emissions are very sensitive to this parameter.

The sensitivity analysis results show that improving the ICS energy efficiency is relevant for reducing energy consumption. For avoided CO_2e, PM_2.5, and CO emissions, our results show that the most relevant parameter is the firewood calorific value, followed by the improvement in ICS efficiency, while the NRF has a significant impact on CO_2e emissions.

6. Discussion

The ICS implementation shows that firewood consumption can be avoided without compromising the satisfaction of the cooking in homes that depend on firewood, as well as avoiding CO_2e emission and criterion pollutants. These reductions are in agreement with previous studies reported for countries with high firewood consumption in which the ICS implementation has been analyzed [14,25,31].

In Kenya, the study of ICS implementation from 2015–2035 performance by Carvalho et al. [33] obtained a reduction in firewood consumption of up to 9.3% in an alternative scenario related to a BAU scenario. Concerning emission reductions, CO_2e was reduced by 26.5%, CO by 54%, and PM_2.5 by 41.9%.

Our results showed that in Mexican RC-TR, the resulting reduction of fuelwood consumption to the year 2035 is 14%, 5 points higher than Kenya study. Regarding CO_2e, CO, and PM_2.5 emissions in our study, we found reductions of 19%, 16%, and 21%, respectively, when comparing the alternative scenario to the BAU scenario, where the reductions in CO_2e emissions are lower by 7.5 points than those reported for Kenya. At the same time, the reductions in CO y PM_2.5 are 38 points and 21 points lower, respectively. These differences could originate from the different biomass types, ICS efficiency and firewood renewability factor considered in both studies, which affect the estimates of CO_2e, CO, and PM_2.5 emissions.

Previous studies in Mexico have been carried out at a national level, where the analyzed projection period has been 2030 and 2035 and has only the analysis of fuelwood consumption and CO_2e emissions was covered. In the study by [30], they proposed a national ICS implementation in the period 2005–2030, and obtained a reduction of 15.87% in firewood consumption and 8.28% in the reduction of CO_2e emissions related to a BAU scenario. On the other hand, the study by [32], whose period of analysis is from 2014 to 2030 in the scenario of implementation only of ICS, the reduction in firewood consumption is 28% and 29.3% in the CO_2e emissions. The variation in these results between the latter two authors is due to the different assumptions considered, especially those related to structural changes from exclusive firewood users to mixed firewood and LPG users in the study by [32].

Compared to study [30], our results show a percentage reduction in the year 2030 of firewood consumption of 5 points, mainly because the assumption was that all users were EFU, resulting in higher per capita consumption. Compared to the study [32], their results show a reduction in firewood consumption towards 2030, 18 points higher than in the present article. This difference lies in the assumption of a structural change from EFU to mixed users from firewood to LPG, in addition to improving the efficiency of the ICS 50%, the double of the considered value in the present article. These considerations also explain the differences between the CO_2e reduction percentages, we obtained a 14% reduction by 2030.

Concerning the analysis of CO and PM_2.5 criteria pollutants emissions from the use of firewood for cooking, our study contributes, unlike the authors mentioned, knowledge about the magnitude of these pollutants that have negative impacts on the human health of users. We recommend further studies in this area.

Concerning the cost-benefit analysis, the present study shows for the first time a distinction between households that pay for firewood and those that do not pay.

Regarding mitigation cost compared to other studies conducted in Mexico, our results show, at a regional level, that the case of users who pay for the acquisition of firewood have no mitigation cost (−3.35 USD/tCO₂) compared to those reported by [35] which is at the national level, with a value of 6.55 USD/tCO_2e, and those reported by [34] of 7 USD/tCO_2e. In the case of users who do not pay for the acquisition of firewood, this mitigation cost is higher in the present article (11.73 USD/tCO_2e).

This article also presents for the first time the trend of firewood consumption disaggregated by user type, socioeconomic level, and size population centers, identifying a diversity of benefits that are detailed in the article, when moving towards clean cooking technologies. Thus, obtaining an integral vision to provide solutions to problems derived from the use of firewood in cooking that have an impact on health, energy poverty, and gender equity.

The limits of this study include the lack of specific data for CR-TR on the type of firewood used in that region, the location of the TSF—inside or outside the dwellings—and the efficiency of ICS with field values. In the absence of such information, we used a homogeneous composition and calorific value of firewood, and a homogeneous ICS efficiency value. Also, we considered that the TSF was used and located indoors.

Another significant limitation is the absence of other historical surveys, such as the ENCEVI, in order to have data that would provide greater certainty in constructing the baseline scenario. As far as we know, it is impossible to define whether the users who pay for the acquisition of firewood do so in its entirety or only as a part of their consumption due to the absence of historical data.

Finally, there is the limitation that this study does not consider other technological options such LPG and electricity for cooking, it only focuses on devices that consume firewood.

7. Conclusions

The analysis of firewood consumption scenarios carried out in the CR-TR, where, in 2018, there was the most significant number of firewood users (16,026,258 Inh.) and the highest consumption of firewood for cooking in Mexico (74.77 PJ), shows the possibility of making important reductions in firewood consumption and consequently in CO_2e, CO and PM_2.5 emissions with the implementation of ICS.

Most of the firewood users in this region, 11.05 M Inh., are of low socioeconomical level, the poorest in Mexico, of which 5.95 M Inh. are EFU, 2.6 M Inh. are MPU, and 2.08 M Inh. are MSU. Our results show that, particularly in rural areas of Mexico, where 8,894,281 Inh. are concentrated, a government program to promote ICS would have the best and broadest impacts of reducing firewood consumption, mitigating CO_2e, and criteria pollutants. Furthermore, promoting ICS in the aforementioned areas would have the greatest socioeconomic, health, energy poverty-positive effects, and would also achieve the incorporation of gender equity criteria.

More than one-third of firewood users (34%) pay to acquire this traditional biofuel and mostly live in peri-urban, urban areas and, to a lesser extent, in rural areas. According to our results since the beginning of the ICS implementation, these users retain high economic benefits per household, the most significant being for the EFU and MPU, and, consequently, this group has, in general, no mitigation costs and a high financial return. These results suggest that a governmental ICS implementation program would be successful in urban and peri-urban environments, where a high percentage of firewood users pay for this biofuel. Its social cost would be null and void and it would have potential positive impacts on energy poverty, health aspects, due to respiratory diseases reduction, and the consequent decrease in labor absences that can represent important costs. Gender aspects would also see an improvement because of a reduction of CO and PM_2.5 pollutants, due to ICS implementation, have a more significant impact on the health of women, who are primarily in charge of cooking. Financially, there would be an alleviation on the purchasing power aspects of these firewood users due to the savings compared to firewood from ICS, which will allow these users to have more resources to satisfy other social needs.

These users who pay for firewood also reveal the existence of numerous local firewood markets that are primarily informal and where firewood can be related to forest deforestation, for which there is no institutional regulation. Based on the results of this article, it is estimated that by 2050, these users would pay a cumulative total of 1475 MUSD for firewood. This indicates that these local firewood markets are not a minor matter and are very likely to grow in the future, thus having a negative impact on the income of users, and an impact on the issue of energy poverty and deforestation. Therefore, we suggest developing national legislation to regulate them with sustainable development criteria, combating climate change; this could be done, on the one hand, by identifying, registering, and regulating the aforementioned firewood markets as well as their commercial scale. On the other hand, requiring that all firewood sold in these already formalized markets be certified through a standard from the Environment Ministry of Mexico (SEMARNAT, for its acronym in Spanish), in terms of whether its origin is from a forest with management or from an equivalent use of biomass.

The sensitivity analysis shows that improving the ICS’s efficiency is relevant to reducing energy consumption from solid biofuels. At the same time, the calorific value of biomass is relevant for avoided emissions of CO_2e, PM_2.5, and CO. This leads us to consider that a policy to promote the use of solid biofuels should encourage the use of ICS with condensed biofuels that have a higher calorific value, such as pellets and briquettes, as a better option to combat climate change, the health problems associated with firewood, and the deforestation of forests associated with this consumption.

The authors consider that the issues of barriers to financing ICS implementation, health impact, gender equity, energy poverty, and deforestation associated with firewood consumption in Mexico are relevant and require future research.