A large-scale, village-level test of wood consumption patterns in a modified traditional cook stove in Kenya

Energy for Sustainable Development 49 (2019) 11–20

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Energy for Sustainable Development

A large-scale, village-level test of wood consumption patterns in a modified traditional cook stove in Kenya Mark A. Lung a,⁎, Anton Espira b a b

Eco2librium LLC, 106 N. 6th, #204, Boise, ID 83702, USA Eco2librium Kenya LTD., Kakamega 50100, Kenya

a r t i c l e

i n f o

Article history: Received 9 July 2018 Revised 31 October 2018 Accepted 11 December 2018 Available online xxxx Keywords: Improved cookstove Modified traditional cookstove Cookstove efficiency Field test Kitchen performance test Kenya

a b s t r a c t Improved cookstoves (ICS) are a common solution to problems associated with woody biomass consumption. In Kenya, wood consumption, along with settlement and agriculture, has resulted in a growing deficit between supply and demand, resulting in changes in forest cover. In response, Kenya has set goals providing 30% of households with some sort of ICS by 2020. While considerable information exists on wood savings from ICS from lab and controlled cooking tests, little is known about how well they perform in real world situations, they are often plagued by low adoption rates, and almost nothing is known at large spatial and temporal scales. This paper presents wood consumption data using kitchen performance tests over an area of 3000 km2 over 7 years on a modified traditional cookstove (Upesi ceramic stove), not commonly defined as an improved cookstove, in communities around a threatened rainforest in western Kenya. Mean (±95% CI) household wood consumption was 9.95 (0.70) kg day−1 using the traditional 3 stone fire. Using the Upesi improved cookstove significantly reduced daily wood consumption by 3.87 (0.47) kg household−1, a mean savings of 37.7%. We found that household size, numbers of tea meals cooked, and distance from the forest were the best predictors of Upesi stove wood consumption in households, while season, stove age, condition of stove, and number of food meal types had no discernable effects. We illustrate how the parameter estimates associated with the predictors can provide useful tools in predicting wood use over temporal and spatial scales. We also provide adoption and use rates. © 2018 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

Introduction Although woody biomass use has been declining as an energy source in developing countries, it is still used by almost 40% of global households, 80% of Sub-Sahara African households, and up to 90% of rural households in some African countries (Bonjour et al., 2013; Daly & Walton, 2017). And while many households globally are switching to cleaner fuels, it is predicted that the total number of biomass users in Sub-Sahara Africa will increase in the next few decades (Daly & Walton, 2017; Legros, Havet, Bruce, Bonjour, & Rijal, 2009). In Kenya, for example, biomass (primarily wood fuel in the form of firewood and charcoal) is the predominant fuel in both rural and urban households, is used by over 80% of the population, and its total use is increasing (Githiomi & Oduor, 2012; International Energy Agency, 2014). Biomass consumption is of interest because of problems created at several levels (Jeuland & Pattanayak, 2012). At the household level, biomass consumption poses a health risk from exposure to smoke (Daly & Walton, 2017; Smith-Silversten et al., 2009; Northcross, Chowdhury, & McCracken, 2010) and imposes time and money commitments in its ⁎ Corresponding author. E-mail address: [email protected] (M.A. Lung).

collection, the burdens of which often fall on women and children (UNEP (United Nations Environment Programme), 2017; Practical Action, 2016; Dohoo, Vanleeuwen, Read Guernsey, Critchle, & Gibson, 2013). At the community and national level, woodfuel (firewood and charcoal) consumption often results in the unsustainable harvesting of wood (Kirubi, Wamicha, & Laichena, 2000) which is linked to forest degradation, loss, and damage to forest ecosystem services (McCrary, Walsh, & Hammett, 2005; Madubansi & Shackleton, 2007; Taylor, Moran-Taylor, Castellanos, & Elias, 2011; Amutabi, Lung, Espira, & Gregory, 2017). In Kenya, biomass deficits between supply and demand have been reported and are expected to continue (Githiomi & Oduor, 2012). At the global level, biomass consumption releases large amounts of CO2 (when exceeding non-renewable levels) and products of incomplete combustion, which contribute to global warming (Masera, Bailis, Drigo, Ghilardi, & Ruiz-Mercado, 2015; Wathore, Mortimer, & Grieshop, 2017; Bailis, Drigo, Ghilardi, & Masera, 2015). Improved cookstoves (ICS), defined as cookstoves using certain scientific principles to better combustion and heat transfer (Kshirsagar & Kalamkar, 2014), are a common solution to the problems resulting from biomass consumption. However, ICS programs have been beset with low adoption rates by communities due primarily to financial, cultural, and technical obstacles (Lewis & Pattanayak, 2012). In other

https://doi.org/10.1016/j.esd.2018.12.004 0973-0826/© 2018 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

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M.A. Lung, A. Espira / Energy for Sustainable Development 49 (2019) 11–20

words, ICS are often too expensive for the target market, require significant changes in cooking behaviors, and involve technical expertise associated with use and maintenance. ICS are also often produced in developed countries, thus bypassing local people in design and job creation. There are, however, numerous examples of cookstoves that are deviations of the basic traditional cooking method (i.e. three stone fire) made from local materials and by local people, and thus may subvert the obstacles associated with ICS. But these cookstoves are usually not included in efficiency and emission tests and are rare in programs seeking to confront the challenges associated with biomass consumption. Balancing woodfuel supply and demand using cookstoves, regardless of the type of cookstove, requires understanding the woodfuel consumption patterns of these stoves in communities. This understanding includes improvements over traditional cooking stoves (e.g. 3-stone fire) and the variables that influence the consumption of woodfuel in homes. There are, however, limited studies that provide this information in communities. The cultural, demographic and environmental real-world subtleties of cookstove use mean that no two case studies are the same, and though commonalities are frequent, extrapolation across technology type, community and environment are not always suitable. Laboratory based studies (i.e. water-boiling tests) and controlled cooking tests (CCT) are available (Jetter & Kariher, 2009; Jetter et al., 2012; Ballard-Tremeer & Jawurek, 1996; Bailis et al., 2007; McCraken & Smith, 1998; Berrueta, Edwards, & Masera, 2012; Smith et al., 2007; Grimsby, Rajabu, & Treiber, 2016), but they usually focus on less traditional ICS, show mixed results of woodfuel use reductions, and generally don't translate well to field conditions (Arora & Jain, 2015; Adkins, Tyler, Want, Siriri, & Modi, 2010; Bailis & Ogle, 2007; Chagunda, Kamunda, Mlatho, Mikeka, & Palamuleni, 2017). While a few recent studies have provided improvements on controlled cooking tests to bridge this gap between lab and field tests (Medina, Berrueta, Martinez, Ruiz, & Edwards, 2017; Lombardi, Riva, Bonamini, Barbieri, & Colombo, 2017), field-based real-world tests (i.e. kitchen performance tests - KPTs) are necessary to ultimately show if cookstoves will work to reduce woodfuel consumption in households and communities. The field studies that have been done generally show woodfuel savings, but focus mainly on ICS, involve small sample sizes at small spatial scales [but see Garland et al. (2015)], have large or unknown variability, and have not explored time related effects such as seasonality and stove aging (McCraken & Smith, 1998; Smith et al., 2007; Arora & Jain, 2015; Chagunda et al., 2017; Granderson, Sandhu, Vasquez, Ramirez, & Smith, 2009; Garland et al., 2015; Khudadad, Ali, & Jan, 2013; Johnson et al., 2013; Ochieng, Tonne, & Vardoulakis, 2013; 28). In other words, we are beginning to understand the woodfuel savings that ICS can obtain in laboratory and controlled field tests but know less about how savings in lab and controlled settings translates to communities, especially with modified traditional cookstoves that are difficult to classify but may solve adoption problems (Ruiz-Mercado & Masera, 2015). Woodfuel consumption in real world settings will be influenced by many variables, and with small sample sizes and/or short durations of studies, differences may be difficult to detect, aging and seasonal effects will be missed, and inferences to larger populations ambiguous (L'Orange, Leith, & Volckens, 2015). For example, Ochieng et al. (2013) reported average household savings of an ICS of 1.3 kg day−1 compared to the traditional 3-stone fire in a paired KPT. The sample size was 37 and while they found significant savings between the ICS and the 3-stone, the 95% confidence interval was between 0.2 and 2.3 kg day−1. Ultimately, we are not interested in the sample mean and differences, but the population as a whole. Large confidence intervals make it less certain where the true population mean lies, and this ultimately influences the effectiveness of any intervention: the difference between 0.2 kg day−1 and 2.3 kg day−1 savings for households could mean the difference between a woodfuel crisis and sustainable harvests. In addition, carbon financing, which is a common mechanism to fund cookstove projects, is associated

with strict standards (i.e. Gold Standard) for estimating fuel consumption that dictates sample sizes of cookstove studies (Johnson et al., 2013). For example, Gold Standard requires fuel consumption estimates with 90% confidence intervals within 30% of the mean to use the mean in emission reduction calculations (Gold Standard, 2015). In addition, effect size and coefficient values associated with predictor variables are useful values in understanding woodfuel consumption patterns. In other words, an effective woodfuel management program would benefit from knowing how much woodfuel consumption in a cookstove changes in direct relation to fluctuations in a variable (e.g. household size). As this variable fluctuates over space or time it would then be possible to predict woodfuel consumption. In 2010, a cookstove enterprise was initiated by myclimate Foundation and Eco2librium in rural communities in the Kakamega/Nandi Forest ecosystem region of western Kenya. The cookstove being used is the ceramic Upesi stove (Fig. 3) which comes in two basic models - portable and permanently installed. In both models a mud liner is molded and kiln dried. This ceramic liner is either encased in metal (portable) or permanently installed into homes using mud and stones. The Upesi stove, used for cooking with firewood, originated in the mid-1980s through a collaborative effort between the Kenyan Ministry of Energy, the German organization GTZ (Deutsche Gesellschaft für Internationale Zusammenarbeit), and a Kenyan women's non-government organization called Maendeleo ya Wanawake. Unpublished laboratory and small field tests were conducted during the 1980–1900s (Habermehl, 1994), showing firewood savings (compared to the 3-stone) averaging 50%, but ranging from 16 to 66% and dependent on food cooked. The stove was originally name Maendeleo but was changed to Upesi in the 1990s by Intermediate Technology Development Group, (now rebranded Practical Action), which is the Swahili word for “fast.” The Upesi stove would be currently classified as either a modified traditional stove (Kshirsagar & Kalamkar, 2014) or a legacy/basic ICS (Putti, Tsan, Mehta, & Kammila, 2015). Under water boiling tests in laboratory conditions, the portable model shows thermal efficiency values ranging from 18.4 to 22.6% (high-power, cold-start) and 22.9 to 23.8% (highpower, hot-start) (Johnson et al., 2013). The permanent model, used in this enterprise and field study, shows thermal efficiency values from household water boiling tests conducted by the authors ranging from 16.5 to 19.2% (high-power, cold-start) and 21.1 to 34.6% (highpower, hot-start) (Lung, unpublished data). The cookstove enterprise was initiated in response to reports that firewood demand in the region was causing major degradation of a globally recognized and threatened rainforest (Mitchell & Bleher, 2004; Schaab & Lung, 2007; Mitchell & Schaab, 2008; Bleher, Uster, & Bergsdorf, 2006) and that b5% of households in the area use some sort of stove different than the three-stone (Eco2librium (Eco2), 2016). This cookstove enterprise, managed by Eco2librium, utilized the Upesi stove because (1) it is locally made using local materials, (2) doesn't require large cultural shifts in cooking behavior, and (3) is easily maintained and repaired. The cookstove enterprise is a commercial business that provides training and jobs for local people producing, selling, and installing the Upesi stoves, and financial and technical mechanisms for repair and replacement with the goal of providing stoves for 50% of rural households in the Kakamega Forest region by 2021. Upesi stoves prices are subsidized by carbon financing. As of December 31, 2017, stoves had been installed in approximately 43,000 households (Fig. 1) and over 500 people have received training to work as independent suppliers and installers, or are employed as supervisors and managers in the business (Eco2librium (Eco2), 2016). This paper reports on firewood consumption performance and patterns of the Upesi cookstove over relatively large temporal and spatial scales. Specifically, the objectives were to (1) assess rural household cooking fuel choices and sources; (2) measure firewood consumption and savings using the Upesi cookstove in relation to the tradition three-stone; and (3) explore the variables (including effect sizes) such as stove aging and family size that influence Upesi cookstove firewood

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Fig. 1. Study area showing locations of stove installations between 2011 and 2017 (colored circles), forest regions (forest-dark green, gov't plantations-brown, shrublands-light green). Main town is Kakamega shown by grey star and roads as hashed lines. White squares represent 1 km2 grids for saturation analysis. Inset is satellite image of smaller area. Landcover and densities from World Resource Institute (http://www.wri.org/resources/data_sets). Satellite image from Michigan State University. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

consumption in communities. We also provide general descriptions of saturation rates (percentage of households with stoves compared with household density), usage rates (percentage of households using stoves of different ages), and stacking (i.e. continued use of prior cooking technologies – see Ruiz-Mercado & Masera (2015)). Methods Study area This study took place in rural communities within the Kakamega/ Nandi Forest ecosystem region occupying an approximate area of 3000 km2 in western Kenya (34°75′ E; 0°15′ N) (Fig. 1). Over 80% (N80%) of the population are subsistence farmers. Extreme demographic and socio-economic conditions are present including a very dense population (up to 1000 people km2) with limited land, annual growth rates around 2.5%, N50% unemployment and poverty (Kenya National Bureau of Statistics, 2010), and firewood as the primary cooking fuel (Kituyi et al., 2001). These conditions result in heavy reliance on the forest and cropland for resources to meet food, housing, and energy needs. Background surveys We collected basic household and fuel information on 775 households within the study area. Using ArcMap 10.2, random points were generated and handheld GPS units were used to navigate to the household nearest the location. Short surveys, containing questions about household size, stove and fuel types, fuel sources, and time and money spent collecting fuel, were conducted in the native language. Using ArcMap 10.2 distance tools, we calculated the distance of each surveyed household from the closest forest edge, which included native forest, government plantations, and shrublands as local people are known to

use “forest” to refer to any land with woody vegetation that is not private (Author, personal observation). Wood consumption comparison between 3-stone and Upesi stove In this component of the study, we used a field-based, paired sampling, kitchen performance test (KPT) methodology (for more details, see Ballis, Smith, & Edwards (2007)) to measure and compare firewood consumption using the traditional 3-stone and the Upesi stove. In paired sampling tests, which is the recommended sampling design for fieldbased KPT (Ballis et al., 2007), firewood consumption is measured over multiple days for both treatments (3 stone and Upesi stove) in the same household. To reduce the bias associated with time related effects (e.g. changes in household income, seasons, etc.), the treatment tests are conducted directly after one another. Households were chosen with simple random selection from the cookstove enterprise sales record spreadsheets (using random number generators to select household identification numbers). This was done at two different times (2011, 2013) for a total sample size of 155 households within an area of approximately 1000 km2. Household firewood consumption was the weight of wood consumed in a three-day (72 h) period, calculated as daily household wood consumption for each household. Per capita wood consumption was household wood consumption divided by household size which we defined as the number of people cooked for in the household. We used hand-held spring scales (accurate to a tenth of a kilogram) to weigh a stack of wood collected and set aside by the household prior to the test and at the end of the test. To reduce measurement error, scales were calibrated and tested for individual accuracy and interscale consistency using items of known and standardized weights, field assistants were trained in an all-day seminar which concluded with tests for accuracy and consistency in measurement, and families were instructed to use only wood from the weighed bundle. Although providing wood to households would help control for variables such

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Fig. 2. Relationship between household size and (A) household wood consumption and (B) per capita wood consumption.

as wood type and size, this was offset by evidence that providing wood in such tests can bias the results (Bailis et al., 2007). In addition, families also went through training and were asked to cook under a normal regime (defined as cooking meals and foods under standard conditions as

opposed to holidays and/or special events), to cook only for household members that were represented in the household size recorded in the study, to keep consistent from one part of the test (3-stone) to the next (Upesi) in both cooking episodes, behaviors, and foods, and to keep a list of all cooking episodes and foods cooked in both treatments. Wood consumption patterns in the Upesi stove

Fig. 3. Photograph of a Upesi stove in use.

In addition to the paired sampling comparison between the 3-stone and the Upesi stove, the second component explored Upesi stove wood consumption patterns to identify important predictor variables that vary over space and time. Upesi stove wood consumption was measured in 464 additional households on six occasions between 2013 and 2018 across 3000 km2. On each occasion, households were randomly chosen from households in the sales record at that time. Thus, at any one occasion we obtained stoves of different ages. For example, in 2013 there were households in the sales record with stoves purchased between 2010 and 2013, representing stove ages between a few months and almost three years. In 2018 there were households in the sales record with stoves purchased between 2010 and 2018, representing stove ages between a few months and over 7 years. We used a 3-day KPT methodology (see above) without the 3-stone comparison group to calculate daily wood use for each household. The geographic area increases with time as stove purchases increase and spread to different areas (see Fig. 1).

M.A. Lung, A. Espira / Energy for Sustainable Development 49 (2019) 11–20 Table 1 Variables measured and analyzed to explore patterns in Upesi stove wood consumption (n = 464). Variable

Mean Range

SE SD mean

Household size (HH) Average daily number of staple meals (SM) Average daily number of root-like meals (RM) Average daily number of meat meals (MM) Average daily number of tea meals (TM) Average daily number of water heating episodes (WHE) Cooking experience (CE) Household distance from forest (DIST) - km Stove age (AGE) - yr Stove condition (COND) Season (SEAS)

5.30 1.53 0.64 0.23 1.24 0.39

1–16 0–3.33 0–3.67 0–1.67 0–2.67 0–2.0

0.10 0.02 0.02 0.01 0.03 0.02

2.21 0.55 0.46 0.27 0.63 0.45

28.40 5.47 2.26 – –

1–70 0.01–31.67 0.11–7.12 – –

0.88 0.43 0.07 – –

15.78 8.03 1.46 – –

15

age of the stove in days between installation date and date of test. Stove condition was assessed through visual inspection and placed in one of four categories (good – no visible cracks or defects; average – a few cracks and/or defects but no apparent influence on ability to cook; poor – many cracks and/or defects that may influence cooking; bad – many defects that influence cooking). It was assumed that as stoves age they would obtain defects and this would affect the efficiency of cooking. The distance of each household from a forest edge was estimated using ArcMap 10.2 distance tools, and is a proxy for wood accessibility. Wood from forests is the most logistically and financially accessible form of wood for many families. However, families also buy wood, or get wood from trees grown purposely on their own farms. Wood from the forest requires a permit (~$1.50) for an entire month of head loads and many families collect wood without a permit (Bleher et al., 2006). Stove saturation and usage rates

Although many variables may influence firewood consumption, we were interested in variables (see below) that can be obtained easily in the field or in national surveys and that would be useful in predicting spatial and temporal patterns in firewood consumption. Thus, while personal choices in cooking (e.g. pot types, simmering methods) have been shown to strongly influence fuel use (Putti et al., 2015), these variables would be difficult to assess, track over time and use in predicting changes in consumption patterns. Based on previous studies, initial observations in the field, ease of collection, and usefulness in predictive models, we recorded and analyzed eleven variables in relation to Upesi stove wood consumption (Table 1). Household size was defined as the number of people cooked for in a household. Household size was also used to calculate per capita wood use. Differences in household size over time or space are an important variable to include in firewood management analyses, and household size is also possible to predict: reductions in family size are associated with increasing education and economic status (Hager & Morawicki, 2013). From the food diary data, we calculated several variables related to amount of cooking and food types both of which are also useful indicators of socio-economic status (Beaulac, Kristjansson, & Cummins, 2009; Patrick & Nicklas, 2005). Food diversity in this area is low, with less than ten food items commonly cooked and eaten. The staple food, eaten at least once per day for almost all households, is a boiled ground maize meal (Ugali) usually served with cooked greens. Maize is also eaten in a porridge form or as boiled or grilled maize cobs. Other food items include root vegetables (cassava and potato), green bananas and beans, all of which are boiled. These are eaten less often and usually associated with times in which maize is scarce or expensive. Meat is even less common and is usually fish, chicken or beef. Tea, with or without hot milk, is prepared one to two times daily. Occasionally water is heated for bathing, drinking, or cleaning. From the list of foods and cooking events we calculated: (1) average daily number of staple meals (i.e. ugali or maize meals with greens) - SM, (2) average daily number of root-like meals (i.e. meals that included potatoes, cassava, green bananas and/or beans) - RM, (3) average daily number of meat meals (i.e. any meal that also included meat) - MM, (4) average daily number of times that tea (with or without milk) is prepared - TM, and (5) average daily number of water heating episodes (i.e. episodes in which water is heated for bathing, washing dishes, or drinking) – WHE. For each household we also recorded season, stove age, stove condition, cooking experience of main cook, and distance to a forest wood source. This ecosystem region is defined as a moist tropical rainforest with “long” rains from April to June, “short” rains from August to November, and a relative dry season from December through March. Season was thus defined as long rains (tests conducted April–June), short rains (August–November), or dry (December–March). The six occasions that data was collected fell into one of the three seasons, which could influence wood consumption through differences in wood moisture content and in food types cooked. Stove age was the

The impacts of cookstoves are a function of their inherent characteristics (e.g. efficiency), but also their acceptance and use. We used proportion of total households per area who have purchased stoves (i.e. saturation) and proportion of households with stoves using them (i.e. usage rate) as indices of acceptance and use. Each installed stove was geolocated using hand held Garmin GPS units during purchase and installation. The intent of the stove enterprise is to provide stoves with a focus on those households near forest borders that more commonly use forest wood. We thus analyzed saturation rates after seven years since start of stove dissemination for households within 3 km of primary forest borders. We did this by randomly placing six 1 km2 grids within this area and calculated stove density within each grid using ArcGIS spatial analyst density applications (Fig. 1). This was compared with the latest household density values for the counties (KNBS 2009). Usage rates are obtained through annual visits to randomly selected households that have purchased stoves. We visited over 100 households annually between 2011 and 2016. Through evidence of stove use (e.g. coals) and questions about use and frequency, we obtained whether and how often the new stove is being used, and whether the traditional 3-stone is being used. This provides us with usage rates (%) for stoves of different ages and continued use of the 3-stone. Statistical analysis There were two primary components statistically analyzed in this study – a paired sample study to compare fuel consumption between the 3 stone and the Upesi stove and an exploration of the variables that might influence Upesi wood use over space and time. To analyze differences in the paired sample study we used a paired sample t-test with the household as the experimental unit. Even though we asked families to be consistent in cooking patterns between the two treatments, we also used the paired test to analyze differences in average daily number of cooking episodes for each meal type (see above) between the two groups. Household size and other variables (e.g. income, distance to wood source) are controlled for in paired tests as the two treatments are in same household (Field, 2013). In the second component of the study exploring Upesi wood consumption patterns using the predictor variables, we used a backwards multiple regression with probability of removal set at p = 0.10. The eleven predictor variables included were: (1) household size (HH), (2) average daily number of staple meals (SM), (3) average daily number of root-like meals (RM), (4) average daily number of meat meals (MM), (5) average daily number of tea meals (TM), (6) average daily number of water heating episodes (WHE), (7) season (SEAS), (8) stove age (AGE), (9) stove condition (COND), (10) cooking experience (CE), and (11) distance from a forest edge (DIST). Outcomes included unstandardized regression coefficients (b) and standardized regression coefficients (B) which allow us to look at both actual

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(b) and relative (B) effect sizes. All predictor variables were compared in a correlation matrix to check for autocorrelation, and all variables in resulting models were checked for multicollinearity using VIF, tolerance calculations, and eigenvalues (Di Marco et al., 2014). We used the Durbin-Watson estimation to check for independence of residuals, a case-wise diagnosis to look for bias due to extreme values, residual plots and partial plots to assess homogeneity of variance. All statistical analyses were conducted in SPSS. Unless otherwise stated, all values are reported as mean (±95% CI). Results

Table 2 Summary of multiple regression output showing remaining variables influencing Upesi wood consumption (n = 464).

Householda HH DIST TM Per capitab HH CE TM a

Fuel choices and sources

b 1 2

b1

B2

t3

p value

0.301 0.115 −0.394

0.361 0.231 −0.135

5.29 3.37 −1.97

0.000 0.001 0.051

−0.153 0.006 −0.098

−0.617 0.153 −0.113

−10.93 2.66 −1.96

0.000 0.009 0.052

r2 (adjusted r2) for model = 0.170 (0.156); F = 12.39, p = 0.000. r2 (adjusted r2) for model = 0.427 (0.417); F = 45.15, p = 0.000. Unstandardized regression coefficient. Standardized regression coefficient. t-Statistic.

Firewood was the primary fuel in 98% of households (crop residues was the remaining 2%), and was the only fuel in 64.5% of households. Charcoal was the most common secondary fuel, utilized in 29.4% of households. Kerosene, LPG, electricity, and other sources of fuel were used as secondary fuels in a combined 6% of homes. The traditional 3stone was the primary stove in 89.4% of homes. Various cook stoves were used in the remaining 10.6% of homes, but these were concentrated in one county (Nandi) to the east of the forest complex. These stoves included the chepkube (9.1%), mud stove (0.8%), Upesi (0.4%), Kenya ceramic jiko (0.3%) and rocket stove (0.1%). Families obtained firewood from a combination of three sources – the forest (which includes public plantations and shrub lands), trees on their own farm, and purchases of various sorts. Over the entire study area, most families primarily obtained wood from their own farm (48.7%). Just over one third primarily obtain wood from the forest (34.1%), and b20% primarily purchase wood (17.2%). When grouped in bins associated with distance from a forest edge, the source of wood for households significantly varied (Chi-Squared Test: Χ2 = 70.282, df = 4, p b 0.001) with the distance of household from the forest. Use of forest wood went from 66% of homes b1 km from the forest to 12% of homes N5 km from the forest. Use of wood obtained from their own farms increased with distance from the forest from 22% in households b1 km to over 60% of households located further than 5 km from the forest edge. The proportion of households that purchased wood stayed within 12–20%, and there was a slight increase in purchased wood as distance increased.

Of the ten predictor variables, only household size (HH), average daily number of tea meals (TM), and distance from forest (DIST) remained as significant predictors of Upesi stove wood consumption (Table 2). Daily household Upesi wood consumption increased with household size and distance from forest and decreased with average daily number of tea meals. Based on the standardized coefficient, household size (B = 0.361) was the strongest relative predictor of wood use. Based on the unstandardized coefficients (b), one-unit changes in distance from forest, household size and average daily number of tea meals predicts 0.115, 0.301, and −0.394 kg changes, respectively, in wood use. Daily per capita Upesi wood consumption significantly decreased with household size and significantly increased with cooking experience (CE) and decreased (with marginal significance) with average daily number of tea meals. Based on the standardized coefficient, household size (B = −0.617) was the strongest predictor of wood use, which was over four times that of cooking experience (B = 0.153) and average daily number of tea meals (B = −0.113). Further analysis of the effect of household size showed that household wood consumption increased with household size in a linear function, but per capita wood consumption decreased with household size with a power function (Fig. 2A and B).

3-stone and Upesi wood consumption comparison

Saturation and usage rates

Mean daily household wood consumption using the 3-stone was 9.95 (±0.70) kg. Mean per capita daily wood consumption was 2.25 (±0.22) kg. Mean daily household wood consumption using the Upesi stove was 6.08 (±0.50) kg. Mean per capita daily wood consumption was 1.36 (±0.07) kg. The Upesi stove significantly (t: 17.53, t crit: 1.98, p(1 tail) b 0.001) decreased wood consumption by 3.87 (±0.47) kg household−1 day−1. This is a mean wood savings of 38.9% (±3.4%). Only one cooking variable - average daily number of water heating episodes (WHE), was significantly different (p = 0.02) between the 3stone and Upesi tests. During the Upesi portion of the test, households heated water 0.60 times per day as compared to 0.51 times per day for the 3-stone portion.

Within three kilometers of forest borders the proportion of total households with stoves (i.e. saturation rate) at the end of 2016 ranged from 82.5% to 90.1% (Fig. 1). The proportion of households using stoves (i.e. usage rate) for stoves of all ages averaged 95.8% and declined with age of stove from 98.4% for stoves less than two years old to 92.9% for stoves four years and older. Ninety-one percent (91%) of households use the stove N4 days week−1. Only 3.6% of households with stoves are still using the 3-stone daily for cooking.

Upesi wood consumption patterns and predictors To explore predictor variables on Upesi stove use, we performed backward multiple regressions on Upesi daily (a) household and (b) per capita wood consumption. No predictor variables had Pearson correlation coefficients over 0.45, therefore all variables were used in the regression. Eigenvalues suggested no evidence of multicollinearity, plots of standardized residuals and standardized predicted values showed no signs of heteroscedasticity, a Durbin-Watson value of 1.8 suggested independence of residuals, and case-wise diagnostics showed b3.5% of cases outside two standard deviations.

3

Discussion Firewood was the primary fuel in almost all households. The primary sources of this woodfuel demand included primary forest and associated forest patches and plantations (national land), household private land, and purchases from woodlots or vendors (in which the source of wood was not explored in this study). Almost 60% of families living ≤1 km of forest edges used the forest for firewood, while b5% living N5 km used the forest. The use of forest wood was essentially zero for households living N7.5 km. This decline in using the forest as a wood source with distance is associated with an increase in use of wood from family's own farms: about one quarter of families living within a 1 km band around forest areas used trees on their own land and this increased to two-thirds of families living further than 5 km. Forest wood is a relatively free source of energy but involves considerable time as

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households get further from the sources. Although collecting of dead fall wood by permit occurs in certain areas, much of the collection is illegal and involves the taking of live trees (Amutabi et al., 2017; Bleher et al., 2006). However, about 25% of these families living N5 km are buying wood, and much of this wood may come from the forest (Bleher et al., 2006). We did not observe much use of alternative fuels such as sawdust and animal dung in our study region, probably because wood is preferably, trees are plentiful and easy to grow in this region, and programs to encourage farmers to plant trees have been visible in Kenya and this area for some time. Average household daily wood consumption using the traditional 3stone was 9.95 (±0.70) kg. Per capita daily wood consumption using the 3-stone was 2.25 kg, which is consistent with other studies of rural wood consumption in Kenya (Kituyi et al., 2001; Habermehl, 1994). Three and a half tonnes of wood consumed per year (assuming a simple extrapolation of our daily measurements to estimate annual use by multiplication of 365) is considerable in the context of current population density, population growth rates, and wood resources in this region. In some areas adjacent to the forest, there are over 100 rural households in every km2 (Kenya National Bureau of Statistics, 2010) and about 60% were using the forest for wood. This amounts to consuming about 218 t of forest wood annually for every km2 of households. Pimentel, Warneke, Teel, & Schwab (1988) calculated that a typical household in rural Kenya would need 1.2–2.4 acres (0.005–0.010 km2) of farm land devoted to trees to supply wood demands, while the average holding size for households in our study area ranges from 1.2 to 3.4 acres (0.005–0.014 km2) (Kenya National Bureau of Statistics, 2010). However, Eucalyptus spp. are one of the most common planted trees in this area and have mean above ground biomass annual increments (for fuelwood removal) in the range of 9.2–10.8 tonnes acre−1 (2272–2668 tonnes km2–1) (IPCC, 2006). A rough analysis (i.e. productivity is usually measured in dry matter, but wood used by households is air dried in various degrees) using these figures shows that households in this area, using the traditional 3-stone, could meet wood fuel demands with 0.33–0.39 acres of Eucalyptus spp. Here, we make no evaluations of land devoted to crops or other values of trees, only provide a rough picture of the wood supply needed. Average household daily wood consumption using the Upesi cook stove was 6.08 (±0.50) kg. Compared to cooking with the 3-stone, the Upesi reduced household daily wood consumption by 3.87 (±0.47) kg, which is an average fuel savings of 37.7% with a 95% confidence interval between 35.8 and 42.2%. The savings obtained using the Upesi stove are within the range found for ICS used in Africa and South America (e.g. Ballard-Tremeer & Jawurek, 1996; McCraken & Smith, 1998; Berrueta et al., 2012; Ochieng et al., 2013), which obtain savings between 19% and 67%. Most of these studies, again, were either lab tests, controlled field studies or small sample (N b 25) kitchen performance tests. While these are important types of studies, the ultimate objective is to provide wood savings to large populations. Knowing the values in the population within a range of certainty and with some explanation of the variation, while difficult (L'Orange et al., 2015), is valuable as it provides a reasonably accurate step in determining wood demand in a region, especially in the context of woodfuel supply in threatened ecosystems. Although we paid careful attention to methods to reduce error by following the strict protocols of KPTs (e.g. field technicians were local people and well trained; all technicians were tested for accuracy and consistency; scales were calibrated and tested for individual accuracy and inter-scale consistency using items of known and standardized weights; considerable time was spent educating participating households prior to, during, and after the tests), error due to bias and measurement are difficult to completely minimize in large scale field studies. It is virtually impossible to guarantee that all participants in the test will strictly adhere to the protocols of the stove testing procedures. However, every effort was made to ensure the tests are reliable. Part of the testing protocol was a rigorous training of the participants that emphasizes the need to not modify their every-day cooking

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procedure in any way at all, as well as teaching the participants how to record data and observations. Every effort was also made to ensure a testing supervisor is present at the household for at least some of the test. To further scrutinize the testing procedure, random monitoring of the individual households was done by test supervisors during the test, with the aim of ensuring that participants do not simply make up figures, and do not deviate from the test protocol significantly, making it difficult for the family to anticipate how and when to modify their cooking for whatever reason they may decide to do so. In addition, data was scrutinized in detail at the end of the test looking for anomalies that might reflect changes in the behavior of the participant. Such anomalies include better than expected wood savings figures that deviate from expected “daily” averages for each family as well as higher than expected wood consumption. When such anomalies were noted, the participants were visited and questioned in person to determine what may have caused such an anomaly. Possible causes that have been encountered include visiting guests, cold weather, unattended stoves during cooking, and children cooking. Overall, such errors are mostly unintentional and just as often will inflate as decrease the wood savings calculated. Unless the result is seen to be too erroneous to justify inclusion, all such data was included in the results even if it significantly decreased the difference in wood use between 3-stone and Upesi cooking. Further to that, participants are for the most part already busy, and as much as they may appreciate the attention given to them through the test, they are unlikely to make the effort to modify their daily routine in benefit of the tests. If anything, in some case, families will decline to participate in the test due to time constraints suggesting that these tests are unlikely to be viewed by community members as a way to ingratiate themselves with project proponents, and are just as likely to be viewed as a small inconvenience. In fact, given that these tests add to the participants an extra layer of activity around their cooking routine, it is not entirely beyond possibility that wood savings observed during such tests may be lower than actual wood savings realized by women cooking in an uninterrupted daily routine where they are not observed and are not asked to take notes. As such, it is just as likely that the participants fret so much about the test procedure itself (“Did I record the right details”, “Did I spell the food type correctly”, “Can they read my handwriting”, “Do I really understand how to weigh the wood”) that they neglect to carry out small extra activities that may increase wood savings. Lastly, the participants would gain nothing by modifying their cooking activities to better reflect on the project because they view themselves as customers of the independent stove sales and installation groups rather than participants in an external intervention. This is an important emphasis placed by Eco2librium in executing the project and is one of several reasons why Eco2librium structured this project via a private company with independent stove suppliers and installers, rather than a not-for-profit enterprise. Because the users purchase the stoves not from Eco2librium management but from independent sales people and installers, they have a clear investment in its ownership and cultivate a clear feeling of a right to use the stove as they see fit. Given this, as much as they may be positively predisposed towards the enterprise for increasing accessibility to the stoves, not all participants are likely to view themselves as behold to the “good will” of the project proponents. Overall, it is possible that some participants will act in such ways as to increase wood savings during the testing procedure to better reflect on the Upesi stove. However, it is equally likely that some participants will make mistakes or modify their routine in such a way that wood consumption would be higher than in ideal conditions. Given the sample size and the temporal spread of the tests, it is accurate to say that these participants will even out the other and the results reported in this paper are a close reflection of the wood savings accrued during normal and routine everyday cooking. We were interested in exploring the variables that influence wood consumption using the Upesi stove, especially those variables that

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may change considerably over space and time. Household daily wood consumption using the Upesi stove is highly variable, ranging from 1.33 to 14.50 kg. Three variables emerged as important predictors. These were household size (HH), average daily number of tea meals (TM), and distance from forest (DIST). Wood use goes up with household size and distance from forest. Of the three variables, household size had the highest standardized coefficient (B). We interpret this as meaning that, of the variables we tested, household size is the strongest predictor of wood use, followed by distance from forest and tea meals. The values for the unstandardized coefficients (b) provide an important way to predict wood use in space or time. For example, a one person decrease in family size (b = 0.301) would result in decreases in wood use of 110 kg annually (0.301 ∗ 365). Ochieng et al. (2013), studying an ICS (rocket stove) in a nearby region, reported that age of main cook and socioeconomic status (indexed by floor type), along with cooking duration and number of people eating, influenced wood consumption but gave no indication of the statistical significance or the size of the effect. Household size influenced both household and per capita wood consumption but with different functions and in inverse ways. A linear and positive relationship exists between household size and household wood consumption, and only 15% of the variation is explained by household size. A power and negative relationship exists between household size and per capita consumption, and over 40% of the variation is explained by household size. This is an interesting and explainable finding: cooking for more people would result in presumably more food and thus more fuel, while there is a measure of scale as cooking for one or two people still requires a certain amount of wood. This has potential ramifications for how wood use is reported. Since per capita wood use is not stable across household sizes until a certain household size is reached, reporting per capita wood use may increase uncertainty especially if used to extrapolate to larger populations. A number of cooking events and types of foods have been used as proxies for socioeconomic status (Beaulac et al., 2009; Patrick & Nicklas, 2005) which can be easily obtained in the field or from national data. Because of the nature of different foods, we assumed that some cooking episodes would require longer time (e.g. root vegetables and meat meals) than other cooking episodes (e.g. preparing tea) and this would require more wood. However, we did not detect any significant influences of number of different food types cooked, except for number of tea meals. Upesi wood use decreased with number of tea meals (b = −0.394), but this was only marginally significant (p = 0.052). This is difficult to explain but may be an artifact of our data organization. Many times, tea is cooked with a meal and we counted these cooking events as both a food meal and a tea meal. In these cases, preparing a supplementary serving of tea might not alter the amount of wood use, while a family that cooks several tea meals during a day in addition to food meals would probably use more wood. Forests are a common source of woodfuel (firewood and charcoal) in many regions and, although contested, the level of subsistence use of woodfuel, in the setting of population growth, agriculture, and poverty in developing countries, the combination is a real and growing threat to forests (Di Marco et al., 2014). In addition, the social and economic burden to women and children associated with collecting wood is emerging as an important topic (Dohoo et al., 2013; International Energy Agency, 2014; Practical Action, 2016). For many families, the collection of forest wood takes considerable time and effort due to increasing distances needed to travel to wood sources. In situations like this, one would expect families to make decisions (e.g. how many meals cooked) to reduce wood use. We found, however, that families that live further from forest regions use more wood. We believe this can be explained. First, our survey found that families that live far from the forest do not use forest wood but get wood from trees grown on their own farms. There is evidence that agroforestry practices on farms in this region for timber and firewood have been encouraged for its high potential

and embraced by farmers (Holmgren, Masakha, & Sjoholm, 1994; Kindt, Simons, & Van Damme, 2004). In fact, Lung et al. (in review) found an average of 80 trees per farm in this region, with most of the tree species being Eucalyptus, an important firewood species in this area. Families that live close to the forest still must purchase permits and spend considerable time collecting wood and this might encourage efficient use of wood. Families that live far from the forest have an accessible and plentiful supply of wood in their yards and this might result in less efficient wood use. Season, stove age and condition, cooking experience, and number of staple, root and meat meals were not found to be significant predictors of wood use. Given the sample size and associated variances of these variables similar to the significant predictors, our ability to detect differences if present was adequate. Stove age and condition were reasoned to be convenient proxies that would influence the efficiency of cooking. As stoves age, they get cracks and breaks and their condition worsens, which we assumed would make them less efficient and thus require more wood to cook the same meal. Stove age was not found as a significant predictor presumably because age has two separate, opposite effects on wood consumption: older stoves are associated with poorer condition and this may reduce stove efficiency and require more wood, but older stoves could also be associated with households getting better at using the new cooking technology, thus increasing efficiency. In addition, stoves may not have been old enough at the time of the study to show big changes in efficiency: the oldest stoves were 7 years old and the Upesi has an expected, but untested, life of 4–7 years (Habermehl, 1994). This is further supported because stove condition was not detected as an important variable and we had numerous stoves in poor condition. Although significant predictors of wood use emerged with relatively large effect sizes, they explained b16% of the total variance in Upesi household wood consumption. This suggests that other important variables were not considered and/or measurement error was large. Individual cooking behaviors (e.g. type of pots, simmering behaviors) are the most important predictors of fuel consumption in developed countries (Hager & Morawicki, 2013), but little is known in developing countries. It is reasonable to assume that these would influence wood consumption, but using individual behaviors to map spatial and temporal wood use patterns and apply to woodfuel management plans would be difficult and these were not explored in this study. Other variables such as socio-economic status and cooking for guests and laborers we feel were captured in variables such as number and type of meals cooked. Conclusions Here we present large-scale spatial and temporal field data on firewood consumption efficiencies and patterns of the Upesi cookstove in Kenya. The Upesi cookstove does not fit well into current classifications and would be considered either a modified traditional cookstove or a basic/legacy ICS, and it showed field fuel savings in the higher range of those ICS studies conducted. This has significant implications as ICS's have been shadowed with poor adoption rates due to cultural, financial and technical obstacles, which are unlikely to plague locally-made, traditional cookstoves. In fact, we found high adoption and use rates of the Upesi stove in our study area. From this study, we provide a wood savings value with a confidence interval of b10% and have isolated predictor variables and estimated their coefficients. We also provide field data for Sub-Saharan Africa, which is needed as this area is not experiencing the declining biomass use seen in many other developing regions. These outcomes can provide a method to predict future changes in wood demand in the population as socio-economic variables (ex. family size, amount of meat in diet) change over time or space. However, there is still considerable variation in Upesi wood consumption that was not explained. Additional studies exploring the source of this variation would be valuable.

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