Modelling households’ fuel stacking behaviour for space heating in Turkey using ordered and unordered discrete choice approaches

Energy & Buildings 204 (2019) 109466

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Modelling households’ fuel stacking behaviour for space heating in Turkey using ordered and unordered discrete choice approaches ✩ Ali Kemal Çelik a,∗, Erkan Oktay b a b

Ardahan University, Faculty of Economics and Administrative Sciences, Department of Business Administration, Ardahan 75002, Turkey Atatürk University, Faculty of Economics and Administrative Sciences, Department of Econometrics, Erzurum 25400, Turkey

a r t i c l e

i n f o

Article history: Received 4 May 2018 Revised 4 August 2019 Accepted 27 September 2019 Available online 6 October 2019 Keywords: Household Fuel type Residential heating Discrete choice model Energy

a b s t r a c t The main objective of this study is to determine the factors affecting households’ fuel type choices for space heating; this is accomplished using both ordered and unordered discrete choice models utilising the data of 93,119 households. The data are drawn from Turkish Statistical Institute Household Budget Surveys (THBS) between 2003 and 2011 under three distinctive scenarios. The results reveal that there is a statistically significant and strong relationship between households’ fuel type choices for space heating and household head’s gender, occupation group, household income, household size, type of dwelling, type of space heating system, dwelling size, number of rooms in the dwelling, settlement and region. The empirical evidence indicates that households still rely on traditional fuel types, especially in the rural areas of Turkey. For the model comparison, the ordered response models outperform the unordered response models in one of the three scenarios. Thus, we suggest that ordered response models can also be performed to estimate households’ fuel type choice behaviour. As far as we know, this is the first attempt that simultaneously compares both ordered and unordered choice models using a Turkish sample. The empirical evidence can help shed light on Turkish households’ fuel stacking behaviour for future energy policies. © 2019 Elsevier B.V. All rights reserved.

1. Introduction The residential and commercial buildings sector consumes around 29% of all global energy and feedstock fuels; indeed, it is the second most important sector in terms of global energy consumption. The buildings sector shows robust growth in the demand for space cooling, lighting and electrical appliances. Globally, in 2016, the total primary energy consumption in the buildings sector was calculated as 3,840 million tonnes oil equivalent (Mtoe), and this is projected to reach 5,466 Mtoe in 2040 under the evolving transition scenario [1]. Particularly, the energy consumption of the residential sector in Turkey was 19.1 Mtoe in 2014, which accounts for 22.3% of the country’s total final energy consumption; in addition, Turkey is one of the fastest transitioning countries because its total final energy consumption is projected to reach 170.3 Mtoe in 2020. The residential sector is the third largest consuming

✩ This paper is mainly based on the doctoral dissertation of the first author under the supervision of the second author. ∗ Corresponding author. E-mail addresses: [email protected] (A.K. Çelik), [email protected] (E. Oktay).

https://doi.org/10.1016/j.enbuild.2019.109466 0378-7788/© 2019 Elsevier B.V. All rights reserved.

sector in Turkey, while residential energy demand grew by 5.8% between 2004 and 2014. The residential and commercial sectors in Turkey consume mainly natural gas and electricity, with a share of 29.8% and 27.3%, respectively. However, coal (14.7%), oil (13.3%), biofuels and waste (9.1%) still play a crucial role in energy consumption in Turkey, with coal and natural gas use being higher by 109% and 86.3%, respectively [2]. The energy consumption of the Turkish residential sector showed an annual 2% increase in 2016. However, there has been a significant improvement in energy efficiency in the residential sector because of recent incentives for implementing insulation and efficient burning systems [3]. Along with the significant impacts of several crucial determinants, including the increasing population and the level of welfare, the energy need of Turkey was above other European country averages in 2016. To accomplish certain energy goals and decrease the country’s energy dependency on external sources while increasing its energy source diversity, the Turkish Ministry of Energy and Natural Resources tends to prioritise a competitive and transparent energy policy that aims to protect consumers’ rights while also taking environmental sustainability into account. The latest National Energy Efficiency Action Plan of Turkey, released on January 2, 2018, expects a total of 23.9 Mtoe energy savings

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in terms of energy efficiency through 2023, which is equivalent to $8.4 billion in monetary terms [3]. The main objective of current global energy policies is to develop sustainable and renewable energy that can significantly reduce carbon dioxide emission and climate change issues [4]. Indeed, though, access to energy has a major role in individuals’ welfare and the economic growth of countries [5,6]. In particular, energy heating issues play a crucial role in the current and future energy policy agenda because of growing concerns about air quality and climate change [7], while household fuel transition, especially in developing countries, has increasingly been studied more often because of severe environmental and health issues [8]. Although energy consumption was found to be shifting slowly towards modern fuels in developing countries [9], future attempts to reduce carbon dioxide emissions from residential heating are considered vital in the transition towards a more sustainable energy system [10]. In this sense, access to a variety of energy sources and levels of energy consumption are key dimensions that can affect a household’s wellbeing [11]. A better understanding of households’ fuel type choice behaviour for space heating, cooking or lighting purposes would provide valuable information in estimating households’ energy use and in developing efficient fuel switching and energy saving policies; these solutions could include reducing consumption and utilising renewable energy sources [12]. However, current knowledge about the main influencing factors of households’ fuel use are limited despite a respectable number of studies [8]. Nowadays, households allocate an overwhelmingly increasing amount of their budgets to energy consumption, and because the main concern is the efficient use of energy resources, households’ behaviour regarding their energy consumption should be carefully addressed [7]. In fact, for many developed and developing countries, households’ energy choices and transitions provide an important viewpoint for future energy policies, and attempts that contribute to more efficient and environmentally friendly energy use policies have been encouraged. Hence, the regulation of efficient public policies necessitates an analysis of both rural and urban households’ energy choices and consumer behaviour [13]. The current paper aims to examine households’ energy use behaviour in Turkey, here with a particular emphasis on Turkish households’ fuel stacking behaviour. Although many earlier studies concentrate on households’ energy demand regarding the energy ladder and energy transition, the relative importance of energy stacking on energy transition deserves attention [14]. Additionally, there is a very limited number of earlier studies (e.g., [15,16]) that concentrate on Turkish households’ fuel type use, and none have explained Turkish households’ energy stacking behaviour. Using a rich data set, the current paper analyses the key determinants of Turkish households’ energy stacking behaviour using both ordered and unordered response modelling approaches. The current paper is the first attempt to examine Turkish households’ fuel stacking behaviour by using a comparison of both ordered and unordered discrete choice models. The empirical findings also differ from earlier studies using Turkish samples because the current paper compares both ordered and unordered response models using pooled data of consecutive waves. The remainder of the present paper proceeds as follows: The second section reviews the existing literature to discover the key determinants of households’ energy use behaviour. The third section provides information about the theoretical framework on two prominent models to explain household energy use behaviour and various econometric models. The fourth section introduces the data set and methodology being utilised. The fifth section presents the estimation results, and the paper concludes with a discussion of the obtained results and recommendations for forthcoming energy policies and future studies.

2. Literature review As the random utility theory states, because consumer choices are generally based on determining the highest utility among a variety of possible alternatives, a choice is a relatively complex process associated with many potential key determinants. Household fuel type choice for space heating, cooking or lighting also occurs as a result of this complex process and hence comes with many different characteristics. Looking at the literature on households’ fuel type choice, the main potential determinants of households’ fuel type choice can be classified as socio-demographic and socio-economic, dwelling, spatial and other characteristics (see also Muller and Yan [8], Kowsari and Zerriffi [5], and Swan and Ugursal [17] for a comprehensive review of the existing literature in terms of household fuel use in developing countries and a detailed discussion of the main determinants of households’ fuel switching behaviour). 2.1. Socio-demographic and socio-economic characteristics Socio-demographic and economic characteristics have a dominant role in explaining households’ fuel switching behaviour [18,19]. The household head’s demographics and the household’s characteristics can be included under the socio-demographic characteristics. Among the demographic characteristics, gender is a debated determinant of household fuel use because earlier studies fail to provide a clear consensus on households’ fuel type choice. Here, Muller and Yan [8] conclude that the role of gender in explaining household fuel type use can originate from a combination of choice characteristics, time opportunity costs and the position of women in society. In particular, men have a dominant role in terms of the household head’s gender in Turkey. Turkey is widely accepted as a patriarchal society, with men being responsible for the decision-making process, including household fuel type choice for space heating; this is especially true for those living in rural areas. On the contrary, some earlier studies carried out in developing countries [13,20,21] find that households with female household heads are more likely to choose modern types of fuels. One explanation for this is the role of women in the decision-making process because the fuel type affects cooking. The role of household heads’ age in explaining the fuel type choice is also significant although empirical findings differ. In general, it might be expected that older household heads would tend to use traditional fuel types instead of cleaner modern fuels. This behaviour may also be expected for the Turkish households’ fuel type choice behaviour for space heating because the modern fuel infrastructure in Turkey is an ongoing process, so older household heads may not have an actual awareness of the environmental advantages of modern fuels. Along similar lines, recent studies conducted in developing countries [20] indicate that households are more likely to choose traditional fuels when the household head is older. However, other studies conducted in developed and developing countries alike [13,22,23] find that an increase in the household head’s age has a statistically significant impact on the household’s modern fuel choice. Michelsen and Madlener [10] indicate that younger household heads have a higher tendency to use advanced heating systems. Muller and Yan [8] refer to the life cycle effect in terms of household heads’ age. Particularly, younger household heads may have more constrained budgets, which may force them to choose cheaper fuels, whereas older household heads tend to have more money and hence will lean toward choosing modern fuel types. In this case, younger Turkish household heads living in urban areas temporarily to attend school will tend to have constrained budgets and hence may not choose modern fuel types for space heating, even if they are aware of modern fuel’s environmental benefits.

A.K. Çelik and E. Oktay / Energy & Buildings 204 (2019) 109466

The household’s educational level has been considered a statistically significant determinant in explaining households’ fuel type choice, and higher educational levels are significantly associated with an increase in the probability of modern fuel choice [7,14,20,24]. Higher-educated households might have a higher income and increased expenditure levels; hence, the tendency to use modern fuels can also be explained by the household’s awareness of the economic and environmental benefits of modern fuel use, which is true both in developed and developing countries. Similarly, an earlier study conducted in a developing country [13] shows that literate or primary educated household heads are more likely to choose traditional or transition fuel types. Although the number of higher-educated people is steadily increasing in Turkey, the education levels of Turkish household heads are still relatively low, especially in rural areas. In this circumstance, fuel type decisions may be significantly affected by the household head’s educational level, along with their restricted access to these modern fuels as well. In addition, the number of children is associated with the household’s fuel type, while households without children are found to have a higher probability of using natural gas than households with only one adult [25]. Similarly, another study [23] also finds that households with children are more likely to use gas and electricity for space heating. As far as we know, however, none of the earlier studies conducted in Turkey have included the number of children as a variable in their estimated models when exploring household fuel type choice for space heating purposes. Thus, the actual impact of this variable deserves particular attention for Turkish households. Fortunately, recent waves of Turkish Statistical Institute Household Budget Surveys (THBS) provide valuable information on the number of children in Turkish households, and this variable was included in all of the estimated models of the present study. A household’s monthly or annual income is widely considered to be one of the most crucial determinants of household fuel type use. In fact, the energy ladder, stacking and energy transition models have been proposed based on a significant change in household income; indeed, energy poverty is principally determined using a household’s level of income or energy consumption [11]. On the other hand, in many developing countries, poverty has been frequently associated with solid fuel dependence, as well as with wealth and inequality [18]. Household energy consumption is principally expected to increase with household income [26]. The impact of Turkish households’ monthly or annual income is also expected to be very crucial when it comes to households’ fuel type choices for space heating. During the underlying sample period of the current study (2002–2011), the Turkish economy has shown a significant growth trend. In particular, the amount of GDP of the Turkish economy rose from almost $238.4 billion in 2002 to almost $832.5 billion in 2011, corresponding to an almost 6.4% and 11.1% annual growth, in 2002 and 2011, respectively. Additionally, the annual GDP per capita growth was calculated as 4.9% in 2002, while the same indicator was 9.4% in 2011, making Turkey an upper-middle-income country [27,28]. Thus, one can argue that during the sample periods, Turkish households might have experienced a significant improvement in their socio-economic status, facilitating a higher likelihood to choose modern fuel types than traditional ones. As expected, Metin Özcan, Gülay and Üçdog˘ ruk [16] state that Turkish households are more likely to choose natural gas, electricity or liquefied natural gas (LPG) than firewood in the case of a considerable change in the household’s income. Not surprisingly, many other studies in developed and developing countries [15,25,29–32] also assert that the probability of households’ fuel switching from traditional to modern fuels increases as the household’s income levels grow. Similarly, other research [13,33] finds that comparatively low-income households are more likely to choose traditional fuel types. Indeed, [34] reveal that high-

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income households living in Europe live in relatively large houses, yet they tend to spend less on energy per capita. Households’ expenditure can also be considered a key determinant of households’ fuel type choice, and it can even be used as a proxy for the amount of household income when the relevant data about the amount of income cannot be gathered [19]. In terms of Turkish households, the price of a fuel type may be a significant indicator of the household’s fuel type choice for space heating because the prices of traditional and modern fuels significantly differ, and multiple fuel type choice behaviour regarding fuel prices is frequently experienced in Turkey. Particularly, recent studies [14,19,20,29] show that a potential increase in a household’s expenditures has a significant effect on the household’s transition from traditional to modern fuel types. On the contrary, one study empirically finds that a considerable increase in a rural household’s expenditure leads to an increase in the quantity of all types of fuels consumed [19]. The price of any fuel type is considered a key determinant that can explain households’ fuel type choice behaviour; however, scholars still debate the actual impact of fuel prices on household energy switching behaviour [8]. However, Fazeli, Davidsdottir and Hallgrimsson [35] find a significant impact of fuel prices on Danish households’ fuel switching behaviour. From a different perspective, Heltberg [14] argues that because the price of modern fuel types continues to increase, households living in rural areas are less likely to switch to alternative fuels. The prices of other fuel types, including charcoal [21], kerosene and LPG [13], have previously been associated with households’ fuel type choice as well. Energy price sensitivity in residential energy consumption has also been shown to be higher for high-income households than for their low-income counterparts [26]. Another study conducted in a developing country [31] reveals that the price for advanced commercial energy is positively correlated with traditional biomass energy consumption. However, Greek households tend to reduce their heating consumption when oil prices rise [36]. Household size typically increases in the eastern regions of Turkey, especially for households in rural areas. Thus, household size may provide valuable information when it comes to better understanding Turkish households’ fuel type choice behaviour for space heating. Sardianou [36] finds that Greek households’ energy consumption decreases when the household size increases. The number of individuals in a dwelling has been repetitively found to have a statistically significant impact on the household’s fuel type. Accordingly, some earlier studies [13,20,29] indicate that households are more likely to choose traditional fuel types than other fuel type counterparts when the household size increases. In this circumstance, the amount of household expenditures spontaneously tends to rise, which significantly restricts households from making an attempt to switch to modern energy sources. Recent studies carried out using Turkish samples [15,16] give similar empirical evidence: Turkish households are less likely to use electricity for space heating than other fuel types when the household size increases, a phenomenon that can be explained by the household’s budget constraints as well. 2.2. Dwelling characteristics Dwelling characteristics may play a crucial role in Turkish households’ fuel type choice, especially when it comes to space heating. Dwelling characteristics can broadly encompass a variety of potential determinants of household fuel type choice, such as the type of dwelling, housing tenure, type of heating system, the year of construction, housing size and the number of rooms. Some researchers [37] argue that households’ fuel type choice is partly dependent on the type of dwelling. Therefore, some earlier contributions [7,38–40] indicate including only owner-occupied households in modelling approaches to ensure the underlying choice is

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solely made by households. Earlier empirical findings [16,30] show that people living in apartments are more likely to choose electricity than those living in smaller houses. On the other hand, people living in detached houses tend to follow a fuel stacking strategy, such as firewood and charcoal [25] or firewood and electricity [22],more than other dwelling types. Another study [23] finds that electricity consumption is relatively high in detached and semidetached houses than in row houses or apartments. Indeed, [41] provides similar evidence: living in a detached house increases households’ energy consumption. Here, one can argue that people living in detached houses can create a variety of alternatives among several fuel types, whereas those living in apartments appears to be more dependent on the type of heating system. Prior studies [22,25] find that owner-occupied households are more likely to use modern fuel types than non-owners; non-owner households have a higher tendency to choose charcoal and kerosene. Lévy and Belaïd [37] reveal that the energy consumption level increases for owner-occupied households, while Mohr [42] addresses the significant differences between owneroccupied households and renters in terms of fuel expenditure. Rehdanz [43] also puts forward that owner-occupied households are less affected by energy prices than apartments and renters in general. The type of heating system can greatly and directly influence households’ fuel type choice. Not surprisingly, households’ income levels play a major role in the choice (e.g., natural gas) of their heating system [7]. In Turkey, the role of the availability of a heating system infrastructure should also be addressed. Particularly, the natural gas infrastructure was completed mostly in the western regions of Turkey during the sample period, and it was in the construction stage for the eastern regions, especially in rural areas. Earlier studies [12,44] find that ground and district heating systems are the most popular heating systems for Finnish households. Along with the deployment of the natural gas infrastructure for all regions in Turkey, the central heating and district heating systems have been two important options for Turkish households in terms of space heating, and the rising use of natural gas as a crucial modern fuel type has become more evident. On the one hand, the central heating system may appear to be a more restricted heating system for Turkish households because the amount of energy consumption mainly depends on other households’ willingness to pay, especially in apartments. This may be a problematic issue in the eastern regions of Turkey, where the number of cold days are relatively high, and intense winter conditions lead to increasing amounts of energy consumption for space heating. Here, the district heating system may be a more suitable alternative heating system for the colder regions of Turkey because higher-income households require the intensive use of energy for space heating because of more intense winter conditions. An earlier study [45] also shows the use of extensive district systems using low-carbon fuels can provide flexibility for decarbonisation. Fazeli, Davidsdottir and Hallgrimsson [35] put forward that new district heating systems have a significant impact on Nordic households’ fuel switching from fossil fuels to district heating. The operating and investments costs were also found to decrease the probability of choosing a heating system [12,44]. In addition, increased age was found to increase the probability of choosing an electric storage heating system, while higher-educated individuals were found to less often choose a solid wood fired heating system [12]. Schleich [46] also finds that many low-income homeowners in the European Union have a relatively low tendency to adopt energy-efficient technologies. Housing size is generally associated with higher energy consumption [47]. Nesbakken [22] states that households tend to use only firewood or a fuel stacking of firewood, electricity and oil

when the dwelling is larger. On the one hand, housing size is associated with the amount of energy consumption [48] because an increase in the housing size means a higher amount of energy consumption, especially for space heating purposes. Therefore, households may tend to use cheaper traditional fuels, such as firewood. On the other hand, they may consider how much energy their fuel type produces to maintain a maximum satisfactory heating time. Because modern fuels have a greater potential for producing heat energy than traditional fuel types, households may tend to choose modern fuels and endure their higher costs. The year of construction of a dwelling may have an impact on a household’s fuel type choice because recently built dwellings generally use modern fuels (mainly advanced natural gas system) in terms of the type of heating system. Particularly, some studies [25,30] provide evidence that people living in newly built dwellings are more likely to use electricity and natural gas than other fuel types. Similarly, Brounen, Kok and Quigley [23] also display that dwellings constructed after 20 0 0 use almost 65% less gas for heating than those constructed before the 1940s. In terms of Turkish households, the availability of modern fuels in newly built dwellings with advanced heating technologies may increase the probability of choosing modern fuels, especially when there is a significant improvement in the household’s socio-economic status. Prior research [49] also shows a significant relationship among household energy consumption, the technical properties of a dwelling and the type of heating technology. The number of rooms in the dwellings may be a potential determinant of Turkish households’ fuel type choice, especially for space heating purposes, because an increase in the number of rooms is associated with increasing household expenditures for space heating. Some studies show that household energy use increases with each extra room in the dwelling [50]. Because of a household’s budget constraints, the probability of a relatively expensive modern fuel choice may decline as the number of rooms in the dwelling increase. However, households that consider the efficiency of modern fuels when it comes to heating performance for comparatively larger dwellings with more rooms may use modern fuels to lower their budgets. Earlier studies indicate people living in urban dwellings with a higher numbers of rooms are more likely to choose transition fuels (e.g., charcoal [16]) or modern fuels [14,15] than traditional fuel types.

2.3. Spatial and other characteristics The spatial characteristics that may influence households’ fuel type choice can be broadly sorted into regions and urban and rural settlements. Belaïd, Roubaud and Galariotis [51] show that regional effects are capable of explaining a significant amount of household energy consumption patterns. Earlier research [39] has indicated that spatial characteristics are often neglected in many estimation methods; however, this may be a crucial drawback for the estimated model because there are spatial differences for both the supply and consumer sides that may significantly affect households’ energy choices. On the one hand, the supply side encompasses climate conditions, accessibility to fuel types, and the environment, all of which considerably affect fuel prices. On the other hand, the demand side involves consumers’ spatial differences. There are significant differences in terms of the economic development levels of the regions in Turkey, where more developed metropolitan cities are located in the western regions of Turkey. The Marmara region is the most developed region of Turkey and includes Istanbul, which is the most populous and developed metropolitan area of the country. In 2017, in terms of provincial-based GDP calculation, the total amount of GDP for Istanbul was 31.2% of the total amount of GDP for Turkey, which was

A.K. Çelik and E. Oktay / Energy & Buildings 204 (2019) 109466

$257.3 billion1 [52,53]. In contrast, the East Anatolian and South East Anatolian regions are the least developed, and one can expect significant variations in the Turkish households’ fuel type choice for space heating based on region. In addition, the Black Sea region has also a specific feature, having the most forested areas of Turkey; Turkish households living in the Black Sea region may tend to choose traditional fuel types (e.g., fuelwood) for space heating because of their easier access to traditional fuel types. Recent studies on household fuel type choice [54] also find a statistically significant relationship between regional differences and fuel type choices. Specifically, Braun [7] states that households living in East Germany are more likely to choose natural gas in their heating system than other regions, while Laureti and Secondi [25] present a similar result because households living in Central and Northern Italy are more likely to use natural gas than other regions. Mohr [42] also highlights the significant impact of regional differences in terms of household fuel expenditure in the Northeast and South the United States. Similarly, Tso and Guan [47] find that geographical and climatological differences between the South and West in the United States lead to statistically significant differences in household residential energy demand. Turkish households’ fuel type choice may also be different depending on whether they are located in rural and/or urban areas. Households living in rural areas have comparatively limited access to modern fuel types, which increases their tendency to choose traditional fuel types (e.g., firewood). Zhang, Song, Li and Li [55] find that the gap between urban and rural settlements tended to significantly narrow between 1995 and 2011 in terms of residential energy consumption per capita. Some earlier research carried out in a developing country [13] does not include households living in rural areas because of some deficiencies in obtaining data from rural households, such as the distance to primitive fuels and time to collect these fuels. In an earlier study [16], Turkish households living in urban areas were found to be more likely to choose natural gas than firewood for space heating. Similarly, another study [15] on a Turkish sample indicates households living in rural areas are more likely to choose firewood, charcoal and dung for space heating than their urban counterparts. Indeed, this evidence is not surprising because access to traditional fuels in rural areas requires less effort than in urban areas. Some other studies in developed countries [19,20] indicate that urban households are more likely to use electricity than rural households. Household fuel type choice and climate change have been previously associated as well, while an earlier study [56] finds that households living in temperate regions rely on electricity rather than other types of fuels, including natural gas and oil. However, another study [13] shows no statistically significant evidence on the relationship between households’ fuel type choice and seasonal differences. 3. Theoretical framework 3.1. Energy ladder and energy stacking models Households, especially those in developing countries, encounter several socio-economic, cultural and environmental barriers when it comes to fuel switching or transitioning to environmentally friendly fuels, and their energy source choice is mainly limited to the cost of energy source and household budget [20]. In fact, around 2.7 million people living in developing countries still rely on traditional biomass for cooking and heating purposes [8,57]. Theoretically, the energy ladder and energy stacking models are two prominent methods for explaining households’ energy use behaviour. The energy ladder model proposes that households tend 1 $1 = TL3.77 in December 29, 2017 with respect to the foreign exchange rate of the Central Bank of the Republic of Turkey

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to establish an energy source transition when their monthly or annual income significantly changes. In the energy ladder model, the types of energy are ranked with respect to their impact on the environment, ease of use and efficiency [58,59]. Accordingly, modern fuels are classified at the highest level of the corresponding ranking, whereas firewood, animal and other waste are classified at the bottom [60]. The energy ladder model conceptualises energy switching under three main phases: The first phase is formed within a framework that depends on biomass energy sources. In the second phase, a significant raise in income level and urbanisation rate, along with a scarcity of biomass fuels, leads households to climb the hypothetical ladder and switch from traditional to transition energy sources. The final phase of the energy ladder model is experienced when households’ transition to modern energy sources (i.e., LPG, natural gas, and electricity) [61]. The energy ladder model also assumes that households tend to climb the energy ladder not only to achieve greater energy efficiency, but also to demonstrate a significant increase in their socio-economic status [62]. The energy ladder model has received serious criticism because the energy stacking model proposes that households’ energy choices cannot be solely explained by socio-economic characteristics. However, the energy ladder model is still considered valid for common observations regarding the strong income dependency of household fuel use in developing countries [8]. The energy stacking model also proposes that households may tend to choose multiple energy sources among all alternatives within a variety of determinants that may influence their choices. Here, households can make temporary transitions among energy sources, and energy switching behaviour is not considered as obvious as the energy ladder model. Some earlier studies [13] have found that households’ energy choices are in line with the energy ladder model; however, many recent works [58–60] have argued against the compatibility of the energy ladder model in real life. In fact, many prior studies [20,21,31,58,61,63,64] have found that households’ energy choice behaviour is more appropriately described by the energy switching or energy stacking models. Fig. 1 depicts a comparison of energy ladder and energy stacking behaviour. 3.2. Ordered and unordered discrete choice models A theoretical random utility framework serves as the motivation of unordered choice models [7,65]. For i = 1, ..., n, a household i chooses from a finite set of alternatives, j = 1, ..., m. According to a random utility theory, the utility of alternative j can be defined as

Ui j = β j xi + εi j where xi denote the explanatory variables, β j denotes the unknown coefficients, and ɛij denotes the error term. Thus, household i chooses alternative j, when the utility from alternative j is the highest of all the alternatives [7]. A multinomial logit model is commonly performed when explanatory variables represent characteristics of households and error terms are assumed as independently and identically distributed in regard to the type I extreme value distribution, namely, Gumbel distribution . Particularly, the probability of household i choosing fuel type j for space heating purposes can be defined as the following [7,40]:

Pr( f uel j ) = Pi j =

1+

 β j xi   for j = 1, 2, ..., J exp β j xi k=1

exp j



Following earlier contributions [7,40,66], the combinations of households’ fuel type choice for space heating purposes are assumed to represent a unique category not a combination of two other categories.

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Fig. 1. Household energy transition process. Adapted from van der Kroon et al. (2013) [59].

For a particular attribute, ordered categorical variables are assumed to represent the rank order, which the underlying rank order does not necessarily exhibit the actual magnitudes. Ordinal variables enable to vary distances across adjacent values which makes them more general than continuous variables [67]. In other words, the ordered choice models provide non-linear effects of any variable on the probabilities related to each ordered level [68]. Ordered probability models are introduced by the definition of an unobserved variable z, which represents a modelling approach for ordinal ranking of data. Thus, the unobserved variable is practically considered as a linear function for each observation as z = βX + ε where X denotes a vector of variables that determine the discrete ordering for observation n; β denotes a vector of parameters that can be estimated, and finally ɛ denotes a random disturbance term. Accordingly, the observed ordinal variable y can be described as

y=1

if z ≤ μ0

e(α j +X 1i β 1+X 2i β 2+X 3i β 3 j )



1 + e(α j +X 1i β 1+X 2i β 2+X 3i β 3 j )

,

j = 1, 2, ..., M − 1

(6)

The heteroskedastic ordered logit (HOLOGIT) model allows researchers to examine the determinants of the conditional variance. For an ordered variable y with M categories, the full heterogeneous choice model can be written as





xik βk − κm   P (yi > m ) = invlogit exp j zi j γ j 

k xik βk − κm = invlogit , m = 1, 2, ..., M − 1, k

σi



if

σi = exp

y = ... y=I

P (Yi > j ) = g(X β j ) =

(7)

where the variance equation can be defined as

μ0 < z ≤ μ1 y = 3 if μ1 < z ≤ μ2 y=2

PPO model which enables the β s for X3 to differ [71]:



zi j γ j

(8)

j

if z ≥ μI−2 ,

(1)

where the μ’s denote thresholds that is utilized to define y, that corresponds to integer ordering, and I denotes the highest integer ordered response [65]. Parallel lines assumption is a critical assumption of the standard ordered logit (OLOGIT) and standard ordered probit (OPROBIT) models that considers the slope coefficients in z = βX + ε do not differentiate with respect to the deprivation outcome being evaluated [69]. When restrictive parallel lines assumption of OLOGIT or OPROBIT model is violated, alternative ordered response models including generalized ordered logit (GOLOGIT) and partial proportional odds ratio (PPO) models are considered as less restrictive models than a standard OLOGIT model. They are also more parsimonious than methods that neglect a combined ordering of categories [70]. Let Y be an ordinal variable with M categories, the GOLOGIT can be defined as the following [70,71]:

P (Yi > j ) =

e(α j +Xi β j ) 1 + e(α j +Xi β j )

, j = 1, 2, ..., M − 1

(5)

In fact, the OLOGIT model can be considered as a special case of GOLOGIT since the betas are analogous for each j in Eq. (5). On the other hand, for the PPO model, some betas are again same for all values of j and others can be different [70]. Eq. (5) illustrates a

For any given response, the full heterogeneous choice model in Eq. (7) presents how the choice and variance equations are combined to produce the probability [72,73]. Elasticities are frequently calculated to measure the magnitude of a specific variable’s impact on outcome probabilities. Elasticity can be computed from the partial derivative for each observation n

ExPki(i ) =

∂ P (i ) xki x ∂ xki P (i )

(9)

where P(i) denotes the probability of outcome i and xki denotes the value of variable k for outcome i. By taking the partial derivative, Eq. (9) becomes the following:

ExPki(i ) = [1 − P (i )]βki xki

(10)

However, elasticity in Eq. (10) is only convenient for continuous variables and is not valid for indicator variables. For indicator variables, a pseudo-elasticity can be calculated to estimate an approximate elasticity of the variables, which gives the incremental change in frequency associated with changes in the indicator variables. The pseudo-elasticity can be defined as  exp [ βi xi ] ∀I exp(βkI xkI )   Exλiki = − 1, (11) exp [ (βi xi )] ∀In exp(βkI xkI ) + ∀I=In exp(βkI xkI )

A.K. Çelik and E. Oktay / Energy & Buildings 204 (2019) 109466

where xki is the value of variable k for outcome i; λi is the pected frequency for observation i; β i is a vector of estimable rameters; Xi is a vector of explanatory parameters; In is the of alternate outcomes with xk in the function that determines outcome, and I is the set of all possible outcomes [65].

expaset the

4. Materials and methods 4.1. Study design, sample and data collection The current paper utilised comprehensive data derived from the THBS that encompass nine consecutive waves between 2003 and 2011 [74–82]. The THBS is a representative longitudinal dataset that comprises annual surveys since 1987, as one of the most comprehensive surveys across the country. The THBS has been consecutively conducted since 2002. The THBS is mainly designed to give information about households’ and individuals’ socio-economic characteristics, quality of life and consumption patterns. Thus, the surveys investigate the effectiveness of current socio-economic policies in terms of settlement (i.e., rural and urban), regions and socio-economic groups. The THBS is periodically administered to selected households across Turkey, with varying numbers of samples given in every month during the one-year period. The THBS involves three separate data sets (i.e., household, individual and consumption) that can be matched using unique bulletin numbers. The sampling design of every THBS consists of two stages. The first stage is the selection of the sampling units, namely blocks, which are determined by the population of the surveyed settlements using the National Address Database. The second stage is a determination of the final sampling units, namely households. Thus, the THBS follows a two-stage cluster sampling, and the coefficients that correspond to the weights are calculated by taking up-to-date population projections with respect to the Address Based Population Registration System [83]. The dependent variable of all the fitted models was the fuel types chosen by households for space heating purposes. In every dataset of the THBS, households’ fuel type options are sorted into the categories of firewood, charcoal, natural gas, fuel-oil, diesel, kerosene, LPG, electricity, dung and others. The THBS data set also provides information about households’ primary, secondary and tertiary fuel type choices. As stated earlier, households generally rely on traditional fuel types in developing countries, leading to serious health and environmental issues, and a household’s potential fuel transition from traditional to environmentally friendly modern fuels may dramatically decrease such issues. In this context, the main objective of the current study was to examine Turkish households’ fuel transition behaviour from traditional to modern fuels based on the energy ladder and energy stacking models, hence determining the key factors influencing Turkish households’ fuel type choices. As a secondary purpose, the current study analysed households’ primary and secondary fuel type choices among 10 fuel types under the energy ladder and stacking models. Because many households have not attempted to choose a tertiary fuel type choice, their tertiary choices were excluded from the estimation models. Following several commonly accepted fuel type classifications by the International Energy Agency [84] and in light of the energy ladder and slacking models, firewood and dung were included in traditional; charcoal and kerosene in transition; and natural gas, fuel-oil, diesel, LPG and electricity in modern fuels. When households’ primary and secondary fuel type choices were evaluated under these three classifications, there were nine possibilities for all the alternative fuel type choices. Among the nine alternatives, the most frequently chosen fuel type classes were ‘only traditional’ fuels, ‘traditional and transition’ fuels, ‘only transition’ fuels, and ‘only modern’ fuels. Because the remaining five classes

7

were considerably less frequently chosen, selecting items from all nine classes may have led to an extremely skewed sample. Therefore, any estimation results may have been significantly biased because of the impact of this unusual skewness, and the least-used five categories were excluded from the estimation models to obtain unbiased and more reliable estimation results. Thus, the final categories for the households’ fuel type choice were classified as ‘only traditional’ fuels (i.e., both primary and secondary fuel type choices were made from traditional fuels), ‘traditional and transition’ fuels (i.e., primary fuel type choice was made from traditional fuel, and the secondary fuel type choice was made from transition fuel), ‘only transition’ fuels (i.e., both the primary and secondary fuel type choices were made from transition fuels) and ‘only modern’ fuels (i.e., both the primary and secondary fuel type choices were made from modern fuels). Here, household fuel type choices were analysed using both ordered and unordered response models. The ordered nature of the relevant categories stemmed from the assumption that households tend to climb up from traditional to modern fuel types (or climb down from modern to traditional fuels) when there are considerable changes to the household’s socioeconomic status (for both the energy ladder and energy stacking models) and to other determinants (for the energy stacking model). Fig. 2 summarises the two-phase fuel type selection of Turkish households for space heating purposes. Because the type of energy source can also be sorted based on its impact on the environment and health, ranging from harmful (i.e., traditional) to environmentally friendly (i.e., modern), one can argue that there is a ranking among the types of fuels as well. Therefore, the current paper considers households’ primary choice as the basis of this ranking. For instance, the only traditional fuel choice is ranked at the bottom because households’ choices are made from traditional fuel types only, and the only modern fuel choice is ranked at the top because all choices are selected from modern fuels. Because households make multiple fuel choices without any sharp fuel switching among fuels, their behaviour can be best explained by the energy stacking model instead of the energy ladder model. Similarly, recent studies [13,21] also have considered household fuel type choice as an ordered discrete variable. Fig. 3 illustrates the hypothetical model proposed for Turkish households’ fuel stacking behaviour during the sample period. As shown in Fig. 3, the Turkish households’ fuel type choice behaviour can also be estimated using ordered discrete choice models because there is an ordered nature of fuel types, ranging from primitive fuels only to modern fuels only. Following Michelsen and Madlener [40], the independent variables used in the estimated models were classified into four main groups: socio-demographic and socio-economic, dwelling, spatial and other characteristics. All the prices, monthly income and expenditures used in the current study were transformed into real terms with respect to December 2011 by removing the factor of inflation through consumer price indexes [85,86]. The present paper initially intended to include the other waves of the THBS, including 1994, 2002, 2012, 2013 and 2014. However, the sample could not have encompassed these waves for several reasons. For instance, the earlier waves of the THBS dataset, namely 1994 and 2002, could not have matched with the sample period in terms of some specific categories, while subsequent waves of the THBS, including 2012, 2013 and 2014, do not give adequate information about monthly income or urban or rural settlements. Table 1 presents the number of observations used in the current study. As shown in Table 1, the final sample size is 93,119 households in Turkey between the years 2003 and 2011.2 Because the number of observa-

2 The waves of the THBS after the year 2014 have not been released by Turkish Statistical Institute for data estimation purposes when the present paper was

8

A.K. Çelik and E. Oktay / Energy & Buildings 204 (2019) 109466

Fig. 2. Two-phase fuel type choice for Turkish households.

Fig. 3. Hypothetical fuel stacking model for Turkish households.

tions in the 2003 THBS wave was relatively higher than the other selected years, this year was selected as the base year for all estimated models to allow for a more efficient model comparison. In the current paper, the underlying dataset was evaluated based on three different scenarios to utilise all of the potential determinants highlighted in the literature, and all three scenarios were estimated using both ordered and unordered response models using a maximum likelihood approach. Among all the estimated ordered and unordered response models, the best model fit was explored in terms of goodness-of-fit and convenient information criteria, including Akaike information criterion (AIC), and Bayesian information criteria (BIC). For brevity, all interpretations and recommendations were made using the empirical evidence from the best model. All estimated models were tested against for multicollinearity issues, and the results revealed that none of the estimated models involved any serious multicollinearity issues.3 The ordered response models were the OLOGIT, ordered probit OPROBIT, GOLOGIT, PPO and HOLOGIT models. The unordered response models were the multinomial logit (MNL) and multinomial probit (MNP) models. When the standard ordered logits violated restrictive parallel lines, as proposed by Brant [87], alternative or-

Table 1 The number of observations used in the present study. Wave

The number of observations in THBS

2003 2004 2005 2006 2007 2008 2009 2010 2011 Total

25,754 8,544 8,556 8,556 8,543 8,549 10,046 10,082 9,918 98,548

The number of observations used in the present study 24,897 8,234 8,106 8,072 8,082 7,990 9,298 9,397 9,043 93,119

written. As seen in Table 1, some observations outside the final dependent variable categories were excluded from the estimated models. 3 In this study, a total of twenty-four estimations were made using ordered and unordered response models. The multicollinearity test results with respect to variance inflation factor values were not presented in the text for brevity. There were no serious multicollinearity issue for any of estimated models. All standard OLOGIT models in this study have been estimated for comparison purposes and their results were not presented in the text. Parallel regression test results were not presented in the text for brevity, while all alternative ordered model selections were made in regard to the violation of this assumption. In addition, since all standard OLOGIT models outperformed standard OPROBIT models, the results of the latter model were not presented for brevity. Results of all tests and models can be provided upon request.

A.K. Çelik and E. Oktay / Energy & Buildings 204 (2019) 109466

dered response models, namely, GOLOGIT, PPO and HOLOGIT models, were estimated. All econometric models were found to be statistically significant at the 1% significance level, and the pseudoR2 values of all the estimated models were at the levels recommended by Louviere, Hensher and Swait [88]. All estimations were performed using Stata 14.2/MP [89], and all alternative ordered response models were fitted using two user-written programmes (gologit2- and –oglm-) [71,73]. Following earlier studies [16,21], the first scenario involves pooling all the data between 2003 and 2011 into only one dataset. Wooldridge [90] argues that pooling repeated crosssectional data dramatically increases the sample size; he also asserts that the estimations from using pooled cross-sectional data can have more robust estimators, and he also suggests using pooled cross-sectional data for estimation purposes by adding the year dummy to the fitted model. This type of pooling resembles a pseudo-panel data set meant to seek dynamic effects, as recommended by Verbeek [91]. There may be a statistically significant relationship between households’ fuel type choice and regional differences. However, the relevant information about regional differences was only present in the THBS wave released in 2003. Therefore, the second scenario of the current study involves only the 2003 THBS wave to explore regional differences among Turkish households’ fuel type choice. Additionally, earlier research [56] shows a statistically significant association between climate change and households’ fuel type choice. Unfortunately, the Turkish Statistical Institute does not share data regarding climate change in any of its THBS waves. Furthermore, the THBS waves do not give information about which month the survey was conducted. On the other hand, the THBS waves do give information about consumer price indexes so that all the income and expenditures can be translated into real terms with respect to the December values of all observed years, here assuming the consumer price indexes of all December values are one. At that point, all consumer price indexes that correspond to one implicitly refer to the month of December. In the current study, emphasis is placed on households’ fuel type choice space heating, and the available information about a specific month in a winter season (i.e., December) may be relatively valuable because December is probably the most important month because it is generally when all households’ must make a fuel type choice for the upcoming winter. Therefore, the third scenario only involves the pooled dataset from December between 2003 and 2011 to concentrate on the impact of seasonal changes on Turkish households’ fuel type choice. This specific scenario also allows for observing the influence of unit fuel prices and fuel expenditures. Accordingly, the amount of monthly fuel expenditures drawn from individual budget surveys was included in the household-level data, while the unit fuel prices were derived from other official data sources [92–95]. Williams [70] recommends considering the weighted coefficients in survey-based studies because the probability of every choice may differ with each case. The THBS also provides information about weighted coefficients regarding population projections. The use of weighted coefficients is also beneficial to follow a twostage sampling methodology for every wave of the THBS. However, the use of weighted coefficients usually does not allow for calculating the particular values of AIC, BIC and the likelihood ratio, which would hence not allow for a model comparison [70]. In the present study, weighted coefficients have been utilised because the estimated models also calculate the underlying goodness-offit values. Otherwise, the unweighted coefficients are presented to allow for a model comparison. Rao and Reddy [29] suggest that the unweighted coefficients also present very similar results as the weighted coefficients.

9

5. Estimation results 5.1. Estimation results for the first scenario The first scenario involves data from 93,119 households between the years 2003 and 2011. Table A1 presents the descriptive statistics for both the dependent variable and explanatory variables. As shown in Table A1, Turkish households tended to take advantage of multiple fuel choices over the sample period, and 48% of chose traditional fuels and transition fuels as their primary and secondary fuels, respectively. Because there is no evident switching among fuel types, this behaviour mainly conforms to the fuel stacking model instead of the proposed energy ladder model. A relatively intensive use of both traditional and transition fuels instead of their modern counterparts may be because a respectable number of Turkish households still rely on traditional and transition fuels. Table 2 presents the summary statistics for both the ordered and unordered response models. As seen in Table 2, the GOLOGIT and PPO models have statistically higher explanation power than the other models, while both the AIC and BIC values confirm that the PPO model has the best model fit among the estimated ordered response models. Although the MNL model was performed as well, its results are not presented because the underlying model violates the restrictive independence of the irrelevant alternatives (IIA) assumption. An attempt to compare both the PPO and MNP models in terms of goodness-of-fit shows that the MNP has the best model fit for explaining the first scenario. Because the PPO and MNP models have the best model fit among the ordered and unordered discrete choice models, respectively, both the estimation results of the PPO and MNP models will be utilised for discussing the first scenario. Table A2 introduces the estimation results of the PPO and MNP models. However, the coefficients of the explanatory variables presented in Table A2 only give information about the direction of a statistically significant relationship, not any evidence about the magnitude of this association. In this sense, the marginal effects of the explanatory variables should be obtained. Washington, Karlaftis and Mannering [65] suggest that the average direct pseudo-elasticities may provide more precise evidence compared with the standard marginal effects for estimation models using dummy variables. Hence, Table 3 introduces the average direct pseudo-elasticities for both the PPO and MNP models. If the computed average direct pseudo-elasticity is greater than one, the explanatory variable is said to be ‘elastic’, implying a statistically strong association between the dependent variable and the explanatory variables. They are interpreted by multiplying the actual pseudo-elasticity value by 100 [65]. Table 3 presents only the statistically significant variables for simplicity, and only the relevant explanatory variable with the highest impact on Turkish households’ fuel type choice was interpreted here. As shown in Table 3, the households’ real annual income has the strongest relationship with fuel type choice for both the PPO and MNP models, implying that household real annual income is an elastic variable for both models. As expected, the probability of choosing only modern fuels significantly increases (with a pseudo-elasticity value of 9.114 for the MNP model) when there is a remarkable increase in the household’s annual income. However, when household size increases, the probability of choosing only traditional fuels increases by almost 96% for the MNP model. The household head’s occupational status has a statistically significant impact on the household’s fuel type choice. When household heads were unemployed, the households were almost 38% less likely to choose only traditional fuels for the MNP model. For the MNP model, the probability of choosing only traditional fuels decreases by 14% and 10% when the household head’s occupation was craftsman and other related jobs and lawmaker, top manager or director, respectively. The estimation results for the PPO

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A.K. Çelik and E. Oktay / Energy & Buildings 204 (2019) 109466

Table 2 Summary statistics for ordered and unordered response models in the first scenario. Summary statistics

HOLOGIT

GOLOGIT

PPO

MNP

Number of observations Log-likelihood (full) Likelihood ratio chi-square Degrees of freedom McFadden pseudo-R2 AIC BIC

93,119 −73,080.8 87,683.6 91 0.375 146,343.6 147,202.7

93,119 −70,158 93,529.2 144 0.400 140,603.9 141,963.5

93,119 −70,166.1 93,512.9 134 0.400 140,600.2 141,865.4

93,119 −69,896.5 39,900.9 144 0.443 140,081.0 141,440.6

Table 3 Average direct pseudo-elasticities for the PPO and MNP models in the first scenario. Explanatory variables

[1]

Average direct pseudo-elasticities for PPO model Socio-demographic and socio-economic characteristics Household Male 0.103∗ Household head’s age-group <35 years 0.049∗ 35 – 44 years 45 – 54 years Household head’s occupation group Law makers, top managers and directors −0.069∗ Service and sales workers −0.061∗ Crafts and other related-jobs −0.110∗ Plant and machine operatives and assemblers −0.069∗ Elementary occupations −0.059∗∗ Others −0.281∗ Household head’s educational level Tertiary education Household real annual income (log) −4.291∗ Household size (log) 0.760∗ Household type Elementary with two children −0.039∗ Patriarchal/Large families −0.051∗ Dwelling characteristics Type of dwelling Apartment – Basement/Ground floor −0.049∗ Apartment – Normal floor −0.338∗ Housing tenure Owner-occupied 0.230∗ Renter −0.058∗ Type of heating system Stove 2.202∗ Joint/Central heating −0.134∗ Housing size (log) 1.168∗ The number of rooms (log) −1.023∗ Spatial characteristics Settlement Rural 0.202∗ Other characteristics Year dummy 2004 −0.027∗ 2005 2006 2007 2008 −0.056∗ 2009 2010 Average direct pseudo-elasticities for MNP model Socio-demographic and socio-economic characteristics Household head’s gender Male 0.141∗ Household head’s age-group <35 years 0.054∗ 35 – 44 years 45 – 54 years Household head’s occupation group Law makers, top managers and directors −0.096∗ Service and sales workers −0.079∗ Crafts and other related-jobs −0.140∗ Plant and machine operatives and assemblers −0.094∗ Elementary occupations −0.083∗

[2]

[3]

[4]

head’s gender −0.224∗ −0.058∗ −0.046∗ −0.026∗

−2.541∗

2.511∗ −0.167∗

0.038∗ 8.037∗ −0.824∗

0.076∗ 0.614∗

−0.157∗

−0.125∗

1.557∗ 0.089∗ 0.262∗ ∗ 0.426∗

−2.210∗

−0.477∗

−2.810∗ −0.409∗ −1.780∗ −0.407∗

−0.349∗

0.020∗ 0.035∗ 0.032∗ 0.054∗ 0.029∗ 0.026∗

−0.223∗ −0.049∗ −0.055∗ −0.051∗

(continued on next page)

A.K. Çelik and E. Oktay / Energy & Buildings 204 (2019) 109466

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Table 3 (continued) Explanatory variables Others Household head’s educational level Tertiary education Household real annual income (log) Household size (log) Household type Elementary family with two children Patriarchal/Large families Dwelling characteristics Type of dwelling Apartment – Basement/Ground floor Apartment– Normal floor Housing tenure Owner-occupied Renter Type of heating system Stove Joint/Central heating Housing size (log) The number of rooms (log) Spatial characteristics Settlement Rural Other characteristics Year dummy 2004 2005 2006 2007 2008 2009 2010

[1]

[2]

−0.379

[3]

∗

−5.866∗ 0.961∗

[4] 0.189∗

−1.213∗

−1.066∗ 0.195∗

0.047∗ 9.114∗ −0.964∗

−0,047∗ −0.045∗

−0.057∗ −0.399∗

0.068∗ 0.550∗

−0.089∗

0.259∗ −0.057∗ 0.910∗ −0.119∗ 1.326∗ −0.864∗

0.278∗

−0.047∗

−0,088∗

−0.121∗

1.195∗ 0.046∗ −0.295∗ 0.567∗

1.159∗ 0.201∗ 1.919∗ −0.836∗

−3.163∗ −0.332∗ −2.234∗ −0.331∗

0.062∗

−0.331∗

0.051∗ 0.068∗ 0.065∗ 0.077∗ 0.059∗ 0.054∗

∗

p < 0.01; [1] only traditional fuel choice; [2] traditional and transition fuels; [3] only transition fuels; [4] only modern fuels; empty cells denote the pseudo-elasticity value is not statistically significant; normal floor refers to all floors except for basement, ground, or roof floors.

model also reveal that male household heads were 22% less likely to choose only modern fuels than their female counterparts. The PPO estimation results also indicate that younger household heads were 6% less likely to choose only modern fuels than only traditional fuel. For the MNP model, higher-educated household heads were 5% more likely to choose only modern fuels than secondary educated household heads. The same model estimation results reveal that households with two children or households with patriarchal/large families were 5% less likely to choose only traditional fuels. The MNP estimation results reveal that when housing size increases, the probability of choosing only modern fuels decreases by 223%. For the PPO model, on the condition that households use a stove as their heating system, the probability of choosing multiple fuel types, such as traditional and transition fuels, increases by approximately 156%. On the other hand, the PPO model’s estimation results indicate that households that use joint or central heating systems were almost 13% less likely to choose only traditional fuels than only modern fuels. The MNP estimation results exhibit that an increase in the number of rooms in dwellings leads to a higher probability of using multiple fuel types (i.e., traditional fuels as the primary and transition fuels as the secondary), going up by almost 57%. In addition, for the PPO model, households with normal floors (refers to all floors except for basement, ground, or roof floors) were 61% more likely to choose only modern fuels than only traditional fuels. The same probability also increases by almost 8% for households on the basement/ground floor. The empirical evidence indicates that for the MNP model, owner-occupied households were 26% more likely to choose only traditional fuels than only modern fuels. For the PPO model, households living in rural areas were 35% less likely to choose only modern fuels. Finally, the

Table 4 Summary statistics for ordered and unordered response models in the second scenario. Summary statistics

HOLOGIT

GOLOGIT

PPO

MNL

Number of observations Wald chi-square Degrees of freedom McFadden pseudo-R2

24,897 15,000.9 83 0.3846

24,897 8,963.6 141 0.4146

24,897 8,868.0 101 0.4133

24,897 7,335.0 141 0.4152

MNP estimation results reveal that the probability of choosing only traditional fuels decreased by 5% and 9% in 2004 and 2008, respectively, whereas the probability of choosing only transition fuels increased by 8% and 7% in 20 06 and 20 08, respectively. 5.2. Estimation results for the second scenario The second scenario involves the data from the 2003 waves of the THBS, here concentrating on the impact of regional differences on Turkish households’ fuel type, along with other key determinants. Table A3 shows the descriptive statistics of the dependent and explanatory variables used in the second scenario. As seen in Table A3, the annual household income was considered in terms of four 25% portions, which is in line with earlier research [15,16]. The second scenario involves a total of 24,897 observations, and the data were estimated using both ordered and unordered response models. The MNL model does not violate the IIA assumption and outperforms the MNP model in terms of model fit. Weighted coefficients were used in the second scenario because all goodness-offit tests were successfully obtained. Table 4 presents the summary statistics for both the ordered and unordered response models to

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A.K. Çelik and E. Oktay / Energy & Buildings 204 (2019) 109466

determine the best fit. As seen in Table 4, the GOLOGIT model has the best model fit among the ordered response models in terms of pseudo-R2 values. However, as shown in Table 4, the MNL outperforms the GOLOGIT model in terms of explanation power. Hence, the MNL model was determined to be the most parsimonious one. Table A4 presents the estimation results of the GOLOGIT and MNL models. The interpretation of the estimated model will again be performed using the pseudo-elasticities presented in Table 5, which shows only the statistically significant variables for simplicity reasons. Because the PPO and MNP models have the best model

fit among the ordered and unordered discrete choice models, respectively, only the relevant explanatory variable with the highest impact on Turkish households’ fuel type choice was interpreted. The MNL model’s estimation results indicate that male households were 30% more likely to choose only modern fuels than their female counterparts. For the MNL model, younger household heads were 9% more likely to choose only modern fuels than older household heads. For the MNL model, the probability of choosing only modern fuels increased by 7% for higher-educated household heads. When the annual income of households was at the

Table 5 Average direct pseudo-elasticities for GOLOGIT and MNL models in the second scenario. Explanatory variables Average direct pseudo-elasticities for GOLOGIT model Socio-demographic and socio-economic characteristics Household head’s gender Male Household head’s age-group < 35 years 45 – 54 years Household head’s occupation group Law makers, top managers and directors Service and sales workers Crafts and other related-jobs Plant and machine operatives and assemblers Elementary occupations Others Household head’s educational level Tertiary education Household real annual income Second 25% group Third 25% group Fourth 25% group Household size (log) Household type Elementary family with three and more children Patriarchal/Large families Dwelling characteristics Type of dwelling Apartment – Basement/Ground floor Apartment – Normal floor Housing tenure Owner-occupied Renter Type of heating system Stove Joint/Central heating The age of dwelling ≤ 5 year(s) 6 – 10 years 11 – 15 years 16 – 20 years 21 – 25 years Housing size (log) The number of rooms (log) Spatial characteristics Settlement Rural Geographical region Aegean Region Mediterranean Region Black Sea Region Central Anatolia Region East Anatolia Region Southeast Anatolia Region Average direct pseudo-elasticities for the MNL model Socio-demographic and socio-economic characteristics Household head’s gender Male Household head’s age-group <35 years 35 – 44 years 45 – 54 years

[1]

[2]

[3]

[4]

−0.275∗

0.062∗

−0.070∗ −0.048∗

−0.087∗ −0.065∗ −0.112∗ −0.061∗ −0.051∗ −0.271∗

0.123∗ 0.074∗ 0.123∗ 0.068∗ 0.102∗ 0.451∗ 0.049∗

−0.089∗ −0.130∗ −0.209∗ 0.890∗

0.074∗ 0.105∗ 0.214∗ −0.584∗

0.114∗ 0.220∗ 0.418∗ −1.054∗

0.378∗

0.056∗ 0.597∗

−0.048∗ −0.067∗

−0.243∗

−0.082∗

0.272∗ −0.072∗ 0.621∗

−1.806∗ −0.608∗

0.166∗

−3.670∗ −0.051∗

1.473∗ ∗ −0.630∗

−3.305∗ −0.278∗ −0.056∗ −0.063∗ −0.036∗ ∗ −0.042∗ −0.033∗ ∗

0.404∗

0.221∗ 0.092∗ −0.166∗ 0.089∗

−0.133∗

0.043∗

−0.191∗ −0.167∗ −0.149∗

−0.163∗ 0.036∗ −0.355∗ −0.080∗ −0.137∗ −0.086∗

−0.297∗ 0.054∗

−0.094∗ −0.076∗ ∗ −0.061∗ ∗ (continued on next page)

A.K. Çelik and E. Oktay / Energy & Buildings 204 (2019) 109466

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Table 5 (continued) Explanatory variables Household head’s occupation group Law makers, top managers and directors Service and sales workers Crafts and other related-jobs Plant and machine operatives and assemblers Elementary occupations Others Household head’s educational level Tertiary education Household real annual income Second 25% group Third 25% group Fourth 25% group Household size (log) Household type Elementary family with three and more children Patriarchal/Large families Dwelling characteristics Type of dwelling Apartment – Basement/Ground floor Apartment– Normal floor Housing tenure Owner-occupied Renter Type of heating system Stove Joint/Central heating The age of dwelling ≤ 5 year(s) 6 – 10 years 11 – 15 years 16 – 20 years 21 – 25 years Housing size (log) The number of rooms (log) Spatial characteristics Settlement Rural Geographical region Aegean Region Mediterranean Region Black Sea Region Central Anatolia Region East Anatolia Region Southeast Anatolia Region ∗

[1]

[2]

[3]

−0.093∗ −0.066∗ −0.118∗ −0.065∗ −0.054∗ −0.286∗

[4] 0.128∗ 0.071∗ 0.111∗ 0.061∗ 0.096∗ 0.422∗ 0.067∗

−0.093∗ −0.129∗ −0.192∗ 0.935∗

0.051∗ ∗ 0.068∗ ∗ 0.102∗ −0.414∗ ∗

−0.052∗ −0.067∗

−0.237∗

0.121∗ 0.223∗ 0.457∗ −1.136∗

−0.058∗ ∗

−0.046∗

0.202∗

0.058∗ 0.655∗

0.247∗ −0.064∗ 0.684∗ −0.201∗

−2.365∗ −0.425∗ ∗

0.509∗ ∗

0.220∗

0.385∗

0.215∗ 0.123∗ −0.134∗ 0.091∗

0.056∗

2.660∗ −1.108∗

−4.524∗ −0.229∗ −0.061∗ −0.073∗ −0.056∗ −0.060∗ −0.050∗

0.080∗

−0.486∗

0.076∗

−0.178∗ 0.045∗ −0.454∗ −0.157∗ −0.211∗ −0.090∗

−0.164∗ −0.213∗ −0.145∗

∗∗

p < 0.01; p < 0.05; [1] only traditional fuel; [2] traditional and transition fuels; [3] only transition fuels; [4] only modern fuels; empty cells denote the pseudo-elasticity value is not statistically significant.

highest quartile, they were 46% more likely to choose only modern fuels. The same probability also increased by approximately 12% and 22% when the annual household income was at the second and third quartiles, respectively. For the MNL model, the estimation results reveal that an increase in household size significantly decreased the probability of choosing only modern fuels by 114%. The MNL model estimation results also indicate that patriarchal or large families were 7% less likely to choose only environmentally friendly fuels. Households living in normal floors were 66% more likely to choose only modern fuels compared with households living in detached houses. The probability of choosing only traditional fuels increased by 27% for owner-occupied dwellings in the GOLOGIT model, whereas the probability of only choosing modern fuels decreased by 7% for renters. For the MNL model, households were 452% less likely to choose only modern fuels when their heating system was a stove. For the GOLOGIT model, when the type of heating system was joint or central heating, the probability of choosing only modern fuels decreased by 28%. The estimation results show the impact of regional differences on households’ fuel type choice for space heating. On the one hand, for the MNL model, rural households were 49% less likely to

choose only modern fuels than urban households. For the GOLOGIT model, rural households were found to be 40% more likely to choose only traditional fuels. On the other hand, households living in the Black Sea region were found to be 45% less likely to choose only modern fuels than households living in the Marmara region. For the MNL model, this probability also decreased by 21%, 18%, 16% and 9% for households living in East Anatolia, Aegean, Central Anatolia, and Southeast Anatolia, respectively. 5.3. Estimation results for the third scenario The third scenario uses the December data between 2003 and 2011. In this third scenario, additional specific variables were included in the estimated models, including monthly fuel prices, monthly expenditures per capita and monthly fuel type expenditures. In the winter season, the fuel demand increases in Turkey, and hence, the regional and climatically differences between fuel expenditures tend to decrease compared with the other three seasons. The third scenario involves a total of 9,275 observations between 2003 and 2011. Table A5 shows the descriptive statistics for the dependent and explanatory variables used in the estimated models. Table 6 presents the summary statistics for the ordered

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Table 6 Summary statistics for ordered and unordered response models in the third scenario. Summary statistics

HOLOGIT

GOLOGIT

PPO

MNL

Log-likelihood (null) Log-likelihood (full) Likelihood ratio chi-square Degrees of freedom McFadden pseudo-R2 AIC BIC

−11,654.5 −6,379.4 10,550.1 88 0.4526 12,934.8 13,562.7

−11,654.5 −5,807.3 11,694.3 162 0.5017 11,938.7 13,094.6

−11,654.5 −5,827.8 11,653.4 118 0.5000 11,891.5 12,733.5

−11,654.5 −5,747.5 11,813.9 162 0.5068 11,819.0 12,974.9

Table 7 Average direct pseudo-elasticities for PPO and MNL models in the third scenario. Explanatory variables Average direct pseudo-elasticities for PPO model Socio-demographic and socio-economic characteristics Household head’s occupation group Law makers, top managers and directors Office and customer services Service and sales workers Crafts and other related-jobs Plant and machine operatives and assemblers Elementary occupations Others Household head’s educational level Secondary education Tertiary education Household real monthly income (log) Household real monthly expenditures per capita (log) Household real monthly electricity expenditures (log) Household real monthly natural gas expenditures (log) Household real monthly solid fuel expenditures (log) Household real monthly liquefied hydrocarbon exp. (log) Household size (log) Dwelling characteristics Type of dwelling Apartment – Basement/Ground floor Apartment – Normal floor Housing tenure Owner-occupied Type of heating system Stove Joint/Central heating The age of dwelling 6 – 10 years The number of rooms (log) Spatial characteristics Settlement Rural Other characteristics Year dummy 2004 2005 2006 2007 2008 2009 2010 Average direct pseudo-elasticities for MNL model Socio-demographic and socio-economic characteristics Household head’s age-group <35 years Household head’s occupation group Law makers, top managers and directors Office and customer services Service and sales workers Crafts and other related-jobs Plant and machine operatives and assemblers Elementary occupations Others Household head’s educational level

[1]

−0.085∗ −0.034∗ 0.069∗ −0.142∗ −0.073∗ −0.055∗ −0.276∗

−0.069∗ ∗

−2.209∗ −0.109∗

−0.351∗

[2]

[3]

[4]

0.052∗

0.061∗

0.025∗ 0.031∗

−0.146∗

0.098∗

−1.250∗ 1.797∗ −0.277∗ 0.145∗ −0.447∗

0.141∗ 0.120∗

0.255∗ 0.103∗ 0.098∗ 1.000∗ 6.021∗ 0.424∗ 0.630∗ −0.990∗ −0.122∗

0.048∗ 0.434∗

−0.178∗

0.173∗ 1.793∗ 1.644∗

−1.275∗ 0.080∗

−2.255∗ −0.396∗

0.021∗

−0.039∗ ∗

−0.840∗

0.179∗

0.122∗

−0.042∗

−0.030

−0.102∗ −0.056∗ −0.080∗ −0.153∗ −0.093∗ −0.066∗ −0.338∗

−0.036∗

0.015∗ 0.018∗ 0.046∗ 0.053∗ 0.023∗ 0.010 0.014∗ ∗

−0.083∗

0.061∗

−0.083∗ ∗

0.065∗

0.052∗ ∗

0.107∗

0.053∗ 0.052∗

−0.054∗

0.097∗

0.153∗ ∗

(continued on next page)

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Table 7 (continued) Explanatory variables

[1]

Secondary education Tertiary education Household real monthly income (log) Household real monthly expenditures per capita (log) Household real monthly electricity expenditures (log) Household real monthly natural gas expenditures (log) Household real monthly solid fuel expenditures (log) Household real monthly liquefied hydrocarbon exp. (log) Household size (log) Dwelling characteristics Type of dwelling Apartment – Basement/Ground floor Apartment– Normal floor Housing tenure Owner-occupied Type of heating system Stove Joint/Central heating The age of dwelling 6 – 10 years Housing size (log) The number of rooms (log) Spatial characteristics Settlement Rural Other characteristics Year dummy 2004 2005 2006 2007 2008 2009 2010

−0.080∗ ∗ −2.884∗

0.134∗

[2]

−0.747∗ 1.166∗ −0.110∗ −0.057∗ 0.217∗

[3]

−0.260∗ 0.194∗

−0.210∗

−0.384∗

[4] 0.066∗ 0.092∗ 1.435∗ 6.382∗ 0.506∗ 0.597∗ −0.990∗ −0.149∗

0.056∗ 0.490∗

−0.086∗

0.167∗ 2.898∗

−0.437∗

1.231∗

0.480∗

−1.531∗ 0.174∗

−2.641∗ −0.316∗

0.068∗ 1.738∗ ∗ −0.663∗ ∗

−0.056∗

0.147∗

−0.107∗

−0.037∗

−0.026∗ ∗ −0.047∗

0.047∗ 0.070∗ 0.124∗ 0.129∗ 0.072∗ 0.079∗ 0.076∗

−0.078∗

0.078∗

∗ p < 0.01; ∗ ∗ p < 0.05; [1] only traditional fuel choice; (2) traditional and transition fuels choice; [3] only transition fuels choice; [4] only modern fuels choice; empty cells denote the average direct pseudo-elasticity value is not statistically significant; household solid, liquid, and liquefied hydrocarbon expenditures (in logs) were considered as proxy variables to explain traditional, transition, and modern fuels.

and unordered response models in the third scenario to determine the best model fit. As shown in Table 6, the PPO model has the best fit among the ordered response models. The PPO model outperforms the MNL model, becoming the most parsimonious model among all the estimated models, and both the MNL and PPO model estimation results will be used for interpretations. Table A6 indicates the estimation results for the MNL model, and Table 7 shows the average direct pseudo-elasticities of the MNL model for the third scenario. Table 7 presents only the statistically significant variables for simplicity purposes. Because all statistically significant variables are analogous for both models, only the relevant explanatory variable with the highest impact on Turkish households’ fuel type choice was interpreted. The estimation results using the MNL approach reveal that the probability of choosing only traditional fuels decreased by almost 34% when the household head was unemployed. Household heads aged under 35 were 8% less likely to choose only modern fuels. For the PPO model, the secondary and higher-educated household heads were 10% more likely to choose only modern fuels. When monthly income increases, the households were 144% more likely to choose only modern fuels. As expected, an increase on the amount of monthly expenditures per capita meant that the probability of choosing only modern fuels significantly increased by 638.2%, which also implies that the relationship between choosing modern fuel type and household real monthly expenditures per capita was elastic. For the MNL model, households living in apartments and normal floors were 49% more likely to choose only modern fuels than households living in detached houses. For the

PPO model, owner-occupied households were 17% more likely to choose only traditional fuels than other housing tenure types. For the GOLOGIT model, the probability of choosing both traditional and transition fuels increased by 179% when the Turkish households used stoves as their heating system. For the MNL model, households were 264% less likely to choose only modern fuels when the heating system was a stove. For the GOLOGIT model, the Turkish households having joint or central heating systems in their dwellings were 40% less likely to choose only modern fuels. For the MNL model, when housing size increased, the probability of choosing only transition fuels increased by 173.8% as well. When the number of rooms increased, the probability of choosing only traditional fuels significantly decreased by 66%. When households’ monthly natural gas expenditures increased, the probability of choosing only transition fuels increased by approximately 19% for the MNL model, and in this case, the probability of choosing only modern fuels increased by 63% for the PPO model. Similarly, when households’ monthly electricity expenditures increased, they were 51% more likely to choose only modern fuels. When households’ monthly solid fuel expenditures increased, the probability of choosing only modern fuels increased by 99%. When households’ monthly hydrocarbon expenditures increased, they were 15% less likely to choose only modern fuels. When the age of the dwelling was between 6 and 10 years, households were 6% less likely to choose only modern fuels. For the PPO model, rural households were 12% less likely to use modern fuels than their urban counterparts. The households were 8% less likely to choose only modern fuels in 2006 and were 7% more likely to choose only modern fuels in 2009 when compared with the year 2003.

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6. Conclusion 6.1. Policy implications Although energy demand has rapidly increased in almost all industries, including the residential sector, the energy poverty of many individuals worldwide continues, especially in developing countries. As an upper-middle-income developing country, many rural households in Turkey still rely on traditional fuels. Despite these issues, the number of studies concentrating on households’ energy behaviour in Turkey is limited, and prior empirical research on Turkish samples only performed an MNL approach. As far as we know, the current paper is the first attempt at performing standard and alternative ordered response models in a Turkish sample and at comparing both ordered and unordered response models. Because the use of ordered response models in the existing literature is extremely limited, the empirical evidence gathered from the current study contributes to this field. In addition, this is the first attempt to empirically examine the fuel stacking behaviour of Turkish households using the highest number of observations. Because multiple fuel type choices were concentrated on the simultaneously intensive use of traditional and transition fuels, further energy policies can be improved to encourage the choice of modern fuel types. The estimation evidence shows that Turkish households tend to make a transition from traditional to modern fuels when there is a considerable increase in their socio-economic status, which is in line with both energy ladder and fuel stacking models. The underlying result is consistent with many earlier studies [13,14,21,61,96]. In addition, the third scenario indicates that an increase in households’ monthly income per capita and expenditures leads to a statistically significant positive impact on households’ modern fuel type choices; this is also in line with previous research [15,25,29,30]. Moreover, one noteworthy result indicates that an increase in households’ monthly natural gas and electricity expenditures also increases the probability of choosing modern fuels. Here, more efficient energy policies are needed to encourage households’ fuel transition. After the recent general elections in Turkey, minimum wages have significantly improved, which may be considered an important starting point to improve the welfare of low-income households and their ability to access modern fuels. Further policies that intend to decrease the income gap between individuals and increase monthly income may accelerate the transition process of low-income households. Recent forecasts from the Ministry of Energy and Natural Resources indicate that further energy policies should concentrate on forthcoming investments in electricity. Further energy policies that enable the price of electricity to decline within governmental budget constraints may stimulate households’ modern fuel choice behaviour. A more permanent solution to Turkey’s dependence on oil and natural gas will strengthen the Turkish economy financially and allow it to carry out more efficient household energy policies in the long run. On the other hand, the natural gas infrastructure in Turkey has almost been completed, overcoming the remaining barriers to access to another important modern fuel alternative. A recent energy perspective evaluation by the Ministry of Energy and Natural Resources shows that the electricity consumption costs on a minimum wage income accounts for 6.3% of the income, while the corresponding share was 20.1% in January 2002. Thus, one can argue that earlier energy policies have succeeded in decreasing the price of electricity. Additionally, Turkey has ninth without taxes and seventh with taxes among European Union members and candidate states, respectively, in terms of dwelling electricity prices (in €/kWh). Similarly, the share of natural gas consumption costs on a minimum wage income was 32.2% in January 2002, and it successfully declined to 10.5%. Turkey is in third

without taxes and fourth with taxes among European Union member and candidate states, respectively, in terms of dwelling natural gas prices (in €/kWh). Finally, the share of charcoal consumption costs on a minimum wage income has declined from 6.8% to 5.3% since January 2002 [97]. In light of these numbers, although there is a significant favourable performance in terms of decreasing electricity and natural gas prices, the share of charcoal consumption cost on a minimum wage income is not adequate, implying households’ energy transition to modern fuels has not been completely accomplished. For the years 2008 and 2009, the statistically significant dummy year variable, which is in line with earlier research [21], exhibits an increase in choosing only modern fuels. These years also correspond to the years when Turkey’s natural gas infrastructure accelerated. However, natural gas expenditures are relatively higher for households living in Eastern Turkey because of tough winter conditions. By taking advantage of Turkey’s geopolitical location in terms of further energy policies, the recent milestone attempt at decreasing natural gas prices may be encouraging in terms of increasing households’ modern fuel choices. Turkey has successfully chaired the 2017 Energy Club of Shanghai Cooperation Organization, and very recent energy policies were taken in cooperation with the Russian Federation. Along with the completion of the natural gas infrastructure, this modern fuel would be an actual alternative among other modern fuels as well. The intensive use of both natural gas and electricity in Turkey would also play a key role in significant declines in CO2 and greenhouse gas emissions. Recent environment- and climate-related goals outlined by the Ministry of Energy and Natural Resources [97,98] emphasise that crucial developments have been accomplished to maintain a sustainable investment environment for the electricity, natural gas and oil industries, while future energy policies will also encourage the efficient use of clean energy within the framework of sustainable economic growth. A scenario study [99] of further energy policies indicates that the country should concentrate on power plant construction to ward off a potential electric energy deficit. As expected, the estimation results of the present study find a statistically significant association between the type of heating system and households’ fuel type choice for space heating. However, households’ reliance on traditional and transition fuels is particularly prevalent when stoves are used for space heating. Further energy policies that provide the use of modern fuels in central heating systems may encourage households’ to choose modern fuels. As stated in the above recommendations, the introduction of natural gas as an actual modern fuel alternative may contribute to accelerating households’ modern fuel choice processes, moving them from stoves to other types of heating systems. On the other hand, rural households are still more likely to choose traditional fuels such as firewood because of their easy access. Here, the transformation process from stoves to other types of heating systems may also contribute to avoiding a potential deforestation issue. Takama, Tsephel and Johnson [100] investigate the use of alternative fuel types for stoves, and further studies may concentrate on the use of modern fuels in stoves to decrease the intensive use of firewood for space heating. The estimation results also reveal that an increase in the number of people living in the household decreases the probability of choosing modern fuels; this is consistent with many earlier studies [13,20,21,29]. In fact, when other individuals in households save for the household heads are unemployed, the households may choose inexpensive traditional fuels instead of modern fuels. Hence, further employment policies in Turkey may help unemployed individuals find a job and contribute to the household income. Thus, households may tend to choose modern instead of traditional fuels. The estimation results indicate that household heads currently em-

A.K. Çelik and E. Oktay / Energy & Buildings 204 (2019) 109466

ployed at other occupations increase the likelihood of choosing traditional fuel. Because other occupation groups also involve retirees, this tendency might have been increased. Further the THBS surveys may consider the other occupation groups to represent more specific occupation groups. The empirical evidence obtained from the current study indicates that larger dwelling sizes and more rooms significantly decrease the probability of choosing only modern fuels, which is in line with earlier research [22]. This can be explained by the fact that households experience several difficulties when trying to heat larger dwellings. Controlling dwelling insulation and making sure all dwellings have insulation may be crucial to maintain heating in many buildings. Adding insulation is a continuing process in Turkey, and it may contribute to choosing modern fuels. Buildings aged between six and 10 years have a negative impact on households’ modern fuel choice, which is in line with earlier studies [25,30]. However, the impact of this variable deserves further attention because the underlying evidence was only found in 2003. Rural households tend to be less likely to choose modern fuels than their urban counterparts. In line with earlier research [15,16,20], this evidence shows that rural households still rely on traditional fuels. Bioenergy production in the rural areas of Turkey may increase households’ tendency to choose modern energy fuels. This type of production should be carried out with caution so as not to cause possible climate changes [101]. The current study also shows that regional differences in Turkish households’ fuel type choice are in line with earlier studies [7,25]. Particularly, households’ living in the Black Sea region are not less likely to choose modern fuels. Households in the Black Sea region have easier access to firewood because of the comparatively high forestation levels. Further energy policies can follow particular policies to increase these households’ awareness and explain potential deforestation threats that could occur because of the higher use of traditional fuels, including firewood. The estimation results also indicate that households living in apartments and normal floors were more likely to choose only modern fuels. This result is in line with previous studies [16,30]. Further energy policies may provide for the construction of new dwellings that successfully use modern fuels in their space heating systems. These forthcoming energy policies could take owneroccupied households’ fuel type behaviour into account because these houses were less likely to choose modern fuels, a result that contradicts earlier work [25]. Male households also are less likely to choose modern fuels, which is in line with some previous studies [13,20,21]; this result is noteworthy because Turkish society is patriarchal, and transforming male households’ into

17

dwellings that choose modern fuels may significantly contribute to the energy transition process. Younger households are also less likely to choose modern fuels, and further policies should consider their future fuel choice behaviour. In line with previous research [14,21,22,29], the current study indicates that highereducated households are more likely to choose modern fuels. In this sense, the importance of education and energy awareness policies are key, and these successful policies should be maintained in the future. 6.2. Study limitations and recommendations for further studies As recommended by Farsi, Filippini and Pachauri [13], further studies should consider the time spent on accessing traditional fuels and the opportunity costs of choosing modern fuels as other explanatory variables for explaining households’ fuel type choice. As the utmost authorised institution on repeated cross-sectional surveys, the Turkish Statistical Institute should consistently provide more specific information about the time (i.e., month or season), unit prices, expenditures and region to enable more comprehensive model estimations without using proxy variables. The Turkish Statistical Institute could also concentrate on conducting energyoriented surveys that take into account the crucial role of energy in the Turkish economy. A random parameters logit or a nested logit model may be other alternative unordered models that can be performed in future studies. Further studies can also attempt to use pseudo-panel datasets that enable dynamic effects and then compare their results with standard cross-sectional data. Another research avenue can be to pay attention and observe the impact of other insignificant variables, potentially estimating households’ fuel type choices, even for simply cooking and lighting purposes. Future studies using a cross-country comparison for developing countries can attempt to better understand how the key determinants of households’ fuel type choices differ with respect to each country. Declaration of Competing Interest None. Appendix A Tables A1–A6

Table A1 Descriptive statistics of variables in the first scenario. Variables Discrete variables Household fuel type choice Only traditional fuels Traditional and transition fuels Only transition fuels Only modern fuels∗ Socio-demographic and socio-economic characteristics Household head’s gender Male Female∗ Household head’s age-group < 35 years 35 – 44 years 45 – 54 years

Frequency

Percent

14,249 44,212 13,114 21,544

15.30 47.48 14.08 23.14

82,607 10,502

88.71 11.29

17,812 25,116 22,792

19.13 26.97 24.48

(continued on next page)

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Table A1 (continued)

∗

Variables

Frequency

Percent

55 – 64 years ≥ 65 years∗ Household head’s occupational group Lawmakers, top managers, and directors Professional occupational groups Assistant professional occupational groups Office and customer services Service and sales workers Skilled agricultural, animal husbandry, forestry and fishery workers∗ Crafts and other related works Plant and machine operatives and assemblers Elementary occupations Others (Retiree, student, house woman, etc.) Household head’s educational level Illiterate Literate (without school diploma) Primary education∗ Secondary education Tertiary education Household type Elementary family with one child Elementary family with two children Elementary family with three and more children Family without children Patriarchal/Large family One-adult family or individuals living together∗ Dwelling characteristics Type of dwelling Detached houses∗ Apartment – Basement/Ground floor Apartment – Normal floor Others Housing tenure Owner-occupied Renter Others∗ Type of heating system Stove Joint/Central heating Flat heating/Combi/Others∗ The age of dwelling ≤ 5 year(s) 6 – 10 years 11 – 15 years 16 – 20 years 21 – 25 years > 25 years∗ Spatial characteristics Settlement Rural Urban∗ Other characteristics Year dummy 2003∗ 2004 2005 2006 2007 2008 2009 010 2011 Continuous variables Socio-demographic and socio-economic characteristics Household real annual income (log) Household size (log) Dwelling characteristics Housing size (m2 ) (log) The number of rooms (log) Number of observations

14,431 12,968

15.50 13.93

9,270 4,169 3,431 2,912 6,152 12,667 10,287 7,561 7,943 26,174

9.96 4.48 3.68 3.13 6.61 13.60 11.05 8.12 8.53 28.11

6,627 5,060 55,545 15,895 9,992

7.12 5.43 59.65 17.07 10.73

16,801 20,694 15,910 13,138 16,913 9,663

18.04 22.22 17.09 14.11 18.16 10.38

43,862 5,106 42,328 1,823

47.10 5.48 45.46 1.96

62,938 20,703 9,478

67.59 22.23 10.18

68,377 10,895 13,757

73.43 11.80 14.77

7,766 12,878 15,475 14,002 10,887 32,111

8.34 13.83 16.62 15.04 11.69 34.48

31,231 61,888

33.54 66.46

24,897 8,234 8,106 8,072 8,082 7,990 9,298 9,397 9,043 Mean

26.74 8.84 8.70 8.67 8.68 8.58 9.99 10.09 9.71 Std. Err.

4.27 0.55

0.31 0.21

1.99 0.53 93,119

0.12 0.11

denotes base category.

A.K. Çelik and E. Oktay / Energy & Buildings 204 (2019) 109466

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Table A2 The estimation results of the PPO and MNP models in the first scenario. Explanatory variables The estimation results of the PPO model Socio-demographic and socio-economic characteristics Household head’s gender Male Household head’s age-group < 35 years 35 – 44 years 45 – 54 years 55 – 64 years Household head’s occupational group Lawmakers, top managers, and directors Professional occupational groups Assistant professional occupational groups Office and customer services Service and sales workers Crafts and other related works Plant and machine operatives and assemblers Elementary occupations Others Household head’s educational level Illiterate Literate (without school diploma) Secondary education Tertiary education Household real annual income (log) Household size (log) Household type Elementary family with two children Elementary family with three and more children Family without children Patriarchal/Large family Dwelling characteristics Type of dwelling Apartment – Basement/Ground floor Apartment – Normal floor Others Housing tenure Owner-occupied Renter Type of heating system Stove Joint/Central heating The age of dwelling ≤ 5 year(s) 6 – 10 years 11 – 15 years 16 – 20 years 21 – 25 years Housing size (m2 ) (log) The number of rooms (log) Spatial characteristics Settlement Rural Other characteristics Year dummy 2004 2005 2006 2007 2008 2009 2010 2011 Constant term The estimation results of the MNP model Socio-demographic and socio-economic characteristics Household head’s gender Male Household head’s age-group < 35 years 35 – 44 years 45 – 54 years 55 – 64 years

[1] Coefficient

[2] Coefficient

[3] Coefficient

−0.122∗

−0.200∗

−0.282∗

−0.268∗

−0.163∗

−0.341∗

−0.112∗ ∗ −0.164∗ −0.109∗ 0.344∗ 0.483∗ 0.382∗ 0.438∗ 0.330∗ 0.478∗ 0.453∗ 0.641∗

−0.243∗ −0.214∗ −0.189∗ 0.468∗ 0.264∗ 0.737∗ 0.662∗ 0.637∗ 0.568∗ 0.662∗ 0.700∗ 0.878∗

−0.455∗ −0.254∗ 0.171∗ 0.392∗ 1.058∗ −1.447∗

−0.237∗ −0.254∗ 0.171∗ 0.392∗ 1.556∗ −1.027∗

−0.492∗ −0.254∗ 0.171∗ 0.392∗ 2.109∗ −1.671∗

0.184∗ 0.070∗ ∗ −0.103∗ 0.295∗

0.070∗ ∗ −0.103∗ −0.186∗

0.070∗ ∗ −0.103∗ −0.164∗

0.941∗ 0.781∗

1.019∗ 0.931∗ 0.319∗

1.546∗ 1.508∗ 0.970∗

−0.357∗ 0.272∗

−0.177∗

−0.206∗

−3.146∗ 1.193∗

−5.411∗ −1.494∗

−4.275∗ −3.872∗

0.729∗ 0.730∗ 0.950∗ 1.124∗ 0.962∗ 1.042∗ 0.890∗ 0.728∗ 1.047∗

−0.147∗ −0.077∗ ∗

−0.154∗ −0.115∗

−0.616∗ 2.027∗

0.107∗ 0.097∗ 0.069∗ ∗ −0.415∗ −1.457∗

−0.633∗

−0.490∗

−1.164∗

0.327∗

0.536∗

0.224∗ 0.859∗

0.194∗ 0.266∗ 0.471∗ 0.421∗ 1.002∗ 0.451∗ 0.407∗ 0.512∗ −1.386∗

−0.104∗ ∗ 0.568∗ 0.235∗ 0.222∗ 0.307∗ −4.641∗

0.266∗

0.194∗

0.201∗

0.345∗ 0.201∗ 0.180∗ 0.149∗

0.164∗ 0.141∗ 0.165∗ 0.144∗

0.233∗ 0.209∗ 0.180∗ 0.160∗

−0.105∗

−0.173∗ 0.687∗

−1.000∗ −0.858∗

(continued on next page)

20

A.K. Çelik and E. Oktay / Energy & Buildings 204 (2019) 109466 Table A2 (continued) Explanatory variables Household head’s occupational group Lawmakers, top managers, and directors Professional occupational groups Assistant professional occupational groups Office and customer services Service and sales workers Crafts and other related works Plant and machine operatives and assemblers Elementary occupations Others Household head’s educational level Illiterate Literate (without school diploma) Secondary education Tertiary education Household real annual income (log) Household size (log) Household type Elementary family with two children Elementary family with three and more children Family without children Patriarchal/Large family Dwelling characteristics Type of dwelling Apartment – Basement/Ground floor Apartment – Normal floor Others Housing tenure Owner-occupied Renter Type of heating system Stove Joint/Central heating The age of dwelling ≤ 5 year(s) 6 – 10 years 11 – 15 years 16 – 20 years 21 – 25 years Housing size (m2 ) (log) The number of rooms (log) Spatial characteristics Settlement Rural Other characteristics Year dummy 2004 2005 2006 2007 2008 2009 2010 2011 Constant term

[1] Coefficient

[2] Coefficient

−0.784∗ −0.614∗ −1.073∗ −1.096∗ −–1.022∗ −1.010∗ −0.997∗ −0.896∗ −1.280∗

−0.163∗ −0.348∗ −0.239∗ −0.242∗ −0.138∗ −0.261∗ −0.274∗ −0.449∗

−0.207∗ −0.198∗ −0.172∗ −0.165∗ −0.144∗ −0.243∗ −0.357∗

0.663∗ 0.320∗ −0.253∗ −0.468∗ −2.281∗ 2.245∗

0.301∗

0.427∗ 0.144∗

−0.130∗ −0.421∗ −1.696∗ 1.229∗

[3] Coefficient

−0.166∗ −1.614∗ 1.421∗

−0.156∗ −0.105∗ ∗ 0.143∗

0.017 −0.014 0.091∗ 0.237∗

−0.064∗ ∗ −0.085∗ ∗ 0.076∗ ∗

−1.470∗ −1.350∗ −0.495∗

−0.989∗ −0.989∗ −0.495∗

−0.840∗ −0.813∗ −0.778∗

0.355∗ −0.150∗

0.089∗

0.147∗ 0.077∗ ∗

3.635∗ 1.262∗

4.377∗ 2.246∗

1.789∗ 3.087∗

0.098∗ ∗ 0.086∗ ∗

1.165∗ −0.595∗

0.618∗ 1.403∗

0.239∗ 0.224∗ 0.119∗ 0.078∗ ∗ 0.099∗ 1.431∗ −0.683∗

1.171∗

0.724∗

0.791∗

−0.574∗

−0.306∗ −0.089∗ ∗ −0.164∗ −0.088∗ ∗ −0.591∗ −0.351∗ −0.256∗ −0.365∗ 2.377∗

−0.159∗ 0.481∗ 0.617∗ 0.617∗ 0.300∗ 0.285∗ 0.297∗ 0.121∗ 1.634∗

−0.083∗ −0.066∗ ∗

−0.961∗ −0.233∗ −0.205∗ −0.425∗ 4.160∗

∗ p < 0.01; ∗ ∗ p < 0.05; [1] only traditional fuel; [2] traditional and transition fuels; [3] only transition fuels; [4] only modern fuels; empty cells denote the pseudo-elasticity value is not statistically significant.

Table A3 Descriptive statistics of variables in the second scenario. Explanatory variables Discrete variables Household fuel type choice Only traditional fuels Traditional and transition fuels Only transition fuels Only modern fuels∗ Socio-demographic and socio-economic characteristics Household head’s gender Male Female∗

Frequency

Percent

4,657 14,044 2,422 3,774

18.71 56.41 9.73 15.16

22,510 2,387

90.41 9.59

(continued on next page)

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Table A3 (continued) Explanatory variables Household head’s age-group < 35 years 35 – 44 years 45 – 54 years 55 – 64 years ≥ 65 years∗ Household head’s occupational group Lawmakers, top managers, and directors Professional occupational groups Assistant professional occupational groups Office and customer services Service and sales workers Skilled agricultural, animal husbandry, forestry and fishery workers∗ Crafts and other related works Plant and machine operatives and assemblers Elementary occupations Others (Retiree, student, house woman, etc.) Household real annual income First 25% group∗ Second 25% group Third 25% group Fourth 25% group Household head’s educational level Illiterate Literate (without school diploma) Primary education∗ Secondary education Tertiary education Household type Elementary family with one child Elementary family with two children Elementary family with three and more children Elementary family without children Patriarchal/Large family One-adult family or individuals living together∗ Dwelling characteristics Type of dwelling Detached houses∗ Apartment – Basement/Ground floor Apartment – Normal floor Others Housing tenure Owner-occupied Renter Others∗ Type of heating system Stove Joint/Central heating Flat heating/Combi/Others∗ The age of dwelling ≤ 5 year(s) 6 – 10 years 11 – 15 years 16 – 20 years 21 – 25 years > 25 years∗ Spatial characteristics Settlement Rural Urban∗ Geographical region Marmara∗ Aegean Mediterranean Black Sea Central Anatolia East Anatolia Southeast Anatolia Continuous variables Socio-demographic and socio-economic characteristics Household size (log) Dwelling characteristics Housing size (m2 ) (log) The number of rooms (log) Number of observations ∗

denotes base category.

Frequency

Percent

4,937 7,106 5,887 3,596 3,371

19.83 28.54 23.65 14.44 13.54

2,342 1,137 776 754 1,644 3,837 2,969 1,908 1,987 7,543

9.41 4.57 3.12 3.03 6.60 15.41 11.93 7.66 7.98 30.30

6,225 6,224 6,225 6,223

25.00 25.00 25.00 25.00

1,732 1,364 15,322 4,143 2,336

6.96 5.48 61.54 16.64 9.38

4,222 5,505 4,758 3,247 4,828 2,337

16.96 22.11 19.11 13.04 19.39 9.39

12,218 1,067 10,868 744

49.07 4.29 43.65 2.99

17,951 5,401 1,545

72.10 21.69 6.21

20,675 2,732 1,490

83.04 10.97 5.98

2,318 3,766 4,462 3,258 3,401 7,692

9.31 15.13 17.92 13.09 13.66 30.90

7,326 17,571

29.43 70.57

6,486 3,833 2,990 3,564 4,239 1,634 2,151 Mean

26.05 15.40 12.01 14.31 17.03 6.56 8.64 Std. Err.

0.17

0.38

1.99 0.53 24,897

0.11 0.10

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Table A4 The weighted estimation results of the GOLOGIT and the MNL models in the second scenario. Explanatory variables The estimation results of the GOLOGIT model Socio-demographic and socio-economic characteristics Household head’s gender Male Household head’s age-group < 35 years 45 – 54 years 55 – 64 years Household head’s occupational group Lawmakers, top managers, and directors Professional occupational groups Assistant professional occupational groups Office and customer services Service and sales workers Crafts and other related works Plant and machine operatives and assemblers Elementary occupations Others Household head’s educational level Illiterate Literate (without school diploma) Secondary education Tertiary education Household real annual income Second 25% group Third 25% group Fourth 25% group Household size (log) Household type Elementary family with two children Elementary family with three and more children Elementary family without children Patriarchal/Large family Dwelling characteristics Type of dwelling Apartment – Basement/Ground floor Apartment – Normal floor Others Housing tenure Owner-occupied Renter Type of heating system Stove Joint/Central heating The age of dwelling ≤ 5 year(s) 6 – 10 years 11 – 15 years 16 – 20 years 21 – 25 years Housing size (m2 ) (log) The number of rooms (log) Spatial characteristics Settlement Rural Geographical region Aegean Mediterranean Black Sea Central Anatolia East Anatolia Southeast Anatolia Constant term The estimation results of the MNL model Socio-demographic and socio-economic characteristics Household head’s gender Male Household head’s age-group < 35 years 35 – 44 years 45 – 54 years 55 – 64 years

[1] Coefficient

[2] Coefficient

[3] Coefficient

−0.319∗ −0.331∗

−0.302∗ ∗

−0.370∗ −0.216∗ ∗ −0.319∗

0.930∗ 1.001∗ 0.748∗ 1.135∗ 1.031∗ 0.984∗ 0.816∗ 0.699∗ 0.957∗

0.920∗ 0.474∗ ∗ 1.044∗ 0.736∗ 0.602∗ 0.570∗ 0.596∗ 0.714∗ 0.956∗

1.300∗ 1.007∗ 1.663∗ 1.337∗ 1.165∗ 1.075∗ 0.899∗ 1.391∗ 1.583∗

−0.406∗ −0.254∗ ∗ 0.236∗

−0.512∗ −0.283∗ ∗ 0.194∗ 0.540∗

−0.604∗

0.388∗ 0.539∗ 0.818∗ −1.653∗

0.420∗ 0.641∗ 1.200∗ −1.503∗

0.493∗ 0.907∗ 1.629∗ −1.944∗

0.172∗ ∗ 0.266∗ −0.292∗ 0.370∗

0.185∗ ∗ 0.530∗

−0.251∗

0.582∗ 0.577∗ −0.405∗

1.256∗ 1.187∗ 0.435∗

1.378∗ 1.411∗ 0.540∗

−0.397∗

−0.184∗ ∗

−0.221∗ −0.350∗

−3.634∗

−5.261∗ −1.274∗

−4.199∗ −2.816∗

−0.427∗ −0.348∗ −0.198∗ −0.238∗ −0.232∗

−0.633∗ −0.439∗ −0.206∗ ∗ −0.333∗ −0.256∗ ∗

0.958∗ 1.225∗

−1.168∗

−1.290∗

−0.632∗

1.541∗

−1.110∗ −2.568∗ −1.105∗ −2.528∗ −0.878∗ 3.073∗

−1.112∗ 0.299∗ −3.380∗ −0.528∗ −2.052∗ −1.370∗ 2.034∗

0.333∗ ∗

0.332∗

0.457∗

0.745∗ 0.367∗ ∗ 0.352∗ ∗

0.475∗

0.491∗

−1.844∗ −0.886∗ 1.097∗ 0.383∗ −1.442∗ 3.651∗

0.276∗ ∗ 0.289∗ ∗ (continued on next page)

A.K. Çelik and E. Oktay / Energy & Buildings 204 (2019) 109466

23

Table A4 (continued) Explanatory variables Household head’s occupational group Lawmakers, top managers, and directors Professional occupational groups Assistant professional occupational groups Office and customer services Service and sales workers Crafts and other related works Plant and machine operatives and assemblers Elementary occupations Others Household head’s educational level Illiterate Literate (without school diploma) Secondary education Tertiary education Household real annual income Second 25% group Third 25% group Fourth 25% group Household size (log) Household type Elementary family with three and more children Elementary family without children Dwelling characteristics Type of dwelling Apartment – Basement/Ground floor Apartment – Normal floor Others Housing tenure Owner-occupied Renter Type of heating system Stove Joint/Central heating The age of dwelling ≤ 5 year(s) 6 – 10 years 11 – 15 years 16 – 20 years 21 – 25 years Housing size (m2 ) (log) The number of rooms (log) Spatial characteristics Settlement Rural Geographical region Aegean Mediterranean Black Sea Central Anatolia East Anatolia Southeast Anatolia Constant term Hausman IIA test result Only traditional fuels Traditional and transition fuels Only transition fuels

[1] Coefficient

[2] Coefficient

[3] Coefficient

−2.231∗ −1.714∗ −2.258∗ −2.228∗ −2.062∗ −1.915∗ −1.589∗ −1.954∗ −2.373∗

−1.301∗ −0.710∗ ∗ −1.648∗ −1.185∗ −1.041∗ −0.887∗ −0.763∗ ∗ −1.238∗ −1.442∗

−1.126∗ −0.770∗ ∗ −1.351∗ −1.207∗ −1.017∗ −0.856∗

1.005∗ 0.469∗ ∗ −0.466∗ −0.917∗

0.637∗ −0.252∗ −0.740∗

−0.226∗ ∗ −0.507∗

−0.885∗ −1.385∗ −2.421∗ 3.652∗

−0.521∗ −0.918∗ −1.793∗ 2.056∗

−0.612∗ −1.325∗ 1.274∗

−1.390∗ −1.143∗

−0.346∗ ∗ 0.398∗

−1.920∗ −2.014∗

−1.474∗ −1.583∗ −0.627∗

−0.820∗ −1.023∗

0.308∗ ∗

0.384∗

5.946∗

6.322∗ 2.513∗

1.929∗ 2.796∗

0.721∗ 0.435∗ 0.404∗ 0.396∗

0.662∗ 0.502∗ 0.331∗ 0.479∗ 0.382∗

0.930∗ 0.639∗ 0.365∗ 0.574∗ 0.458∗ 1.827∗ −1.716∗

2.390∗

1.272∗

1.552∗

1.072∗ 1.350∗ 5.253∗

0.665∗

3.036∗ 2.781∗ −4.358∗

1.315∗ −0.614∗ 4.504∗ 1.340∗ 3.514∗ 1.367∗ −5.397∗

2.641∗ −0.357∗ 0.943∗ 1.074∗ −4.880∗

Chi-square 45.49 47.69 1.53

Sig. 1.000 1.000 1.000

Result for Ho for Ho for Ho

0.560∗

IIA denotes independence of irrelevant alternatives; ∗ p < 0.01; ∗ ∗ p < 0.05; [1] only traditional fuel; [2] traditional and transition fuels; [3] only transition fuels; [4] only modern fuels; empty cells denote the pseudo-elasticity value is not statistically significant.

Table A5 Descriptive statistics of variables in the third scenario. Explanatory variables

Frequency

Percent

Discrete variables Household fuel type choice Only traditional fuels Traditional and transition fuels Only transition fuels

1,259 4,192 1,259

13.57 58.77 13.57

(continued on next page)

24

A.K. Çelik and E. Oktay / Energy & Buildings 204 (2019) 109466

Table A5 (continued) Explanatory variables

Frequency

Percent

Only modern fuels∗ Socio-demographic and socio-economic characteristics Household head’s gender Male Female∗ Household head’s age-group < 35 years 35 – 44 years 45 – 54 years 55 – 64 years ≥ 65 years∗ Household head’s occupational group Lawmakers, top managers, and directors Professional occupational groups Assistant professional occupational groups Office and customer services Service and sales workers Skilled agricultural, animal husbandry, forestry and fishery workers∗ Crafts and other related works Plant and machine operatives and assemblers Elementary occupations Others Household head’s educational level Illiterate Literate (without school diploma) Primary education∗ Secondary education Tertiary education Household type Elementary family with one child Elementary family with two children Elementary family with three and more children Elementary family without children Patriarchal/Large family One-adult family or individuals living together∗ Dwelling characteristics Type of dwelling Detached houses∗ Apartment – Basement/Ground floor Apartment – Normal floor Others Housing tenure Owner-occupied Renter Others∗ Type of heating system Stove Joint/Central heating Flat heating/Combi/Others∗ The age of dwelling ≤ 5 year(s) 6 – 10 years 11 – 15 years 16 – 20 years 21 – 25 years > 25 years∗ Spatial characteristics Settlement Rural Urban∗ Other characteristics Year dummy 2003∗ 2004 2005 2006 2007 2008 2009 2010 2011 Continuous variables Socio-demographic and socio-economic characteristics Household real monthly income (log)

2,565

27.65

8,290 985

89.38 10.62

1,700 2,582 2,302 1,478 1,213

18.33 27.84 24.82 15.94 13.08

949 481 354 334 561 1,086 1,076 782 755 2,677

10.23 5.19 3.82 3.60 6.05 11.71 11.60 8.43 8.14 28.86

530 426 5,627 1,595 1,097

5.71 4.59 60.67 17.20 11.83

1,772 2,119 1,551 1,329 1,628 876

19.11 22.85 16.72 14.33 17.55 9.44

3,974 517 4,642 142

42.85 5.57 50.05 1.53

6,201 2,167 907

66.86 23.36 9.78

6,476 1,135 1,664

69.82 12.24 17.94

738 1,379 1,646 1,471 1,025 3,016

7.96 14.87 17.75 15.86 11.05 32.52

3,031 6,244

32.68 67.32

1,907 985 674 1,095 1,181 650 1,085 954 744 Mean

20.56 10.62 7.27 11.81 12.73 7.01 11.70 10.29 8.02 Std. Err.

3.17

0.32

(continued on next page)

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Table A5 (continued) Explanatory variables

Frequency

Percent

Household real monthly expenditures per capita (log) Household real monthly electricity expenditures (log) Household real monthly natural gas expenditures (log) Household real monthly solid fuels expenditures (log) Household real monthly liquid fuels expenditures (log) Household real monthly liquefied hydrocarbon expenditures (log) Household real unit fuel type prices Household size (log) Dwelling characteristics Housing size (m2 ) (log) The number of rooms (log) Number of observations

3.20 1.48 0.38 0.56 0.09 0.81 0.34 0.55

0.28 0.63 0.79 0.88 0.42 0.86 0.43 0.21

1.99 0.53 9,275

0.12 0.10

∗

denotes base category.

Table A6 The estimation results of the PPO and MNL models in the third scenario. Explanatory variables The estimation results of the GOLOGIT model Socio-demographic and socio-economic characteristics Household head’s gender Male Household head’s age-group 55 – 64 years Household head’s occupational group Lawmakers, top managers, and directors Professional occupational groups Assistant professional occupational groups Office and customer services Service and sales workers Crafts and other related works Plant and machine operatives and assemblers Elementary occupations Others (Retiree, student, house woman, etc.) Household head’s educational level Illiterate Secondary education Tertiary education Household real monthly income (log) Household real monthly expenditures per capita (log) Household real monthly electricity expenditures (log) Household real monthly natural gas expenditures (log) Household real monthly solid fuels expenditures (log) Household real monthly liq. hydroc. expenditures (log) Household size (log) Household type Elementary family without children Patriarchal/Large family Dwelling characteristics Type of dwelling Apartment – Basement/Ground floor Apartment – Normal floor Housing tenure Owner-occupied Renter Type of heating system Stove Joint/Central heating The age of dwelling ≤ 5 year(s) 6 – 10 years The number of rooms (log) Spatial characteristics Settlement Rural Other characteristics Year dummy 2004 2005

[1] Coefficient

[2] Coefficient

[3] Coefficient

−0.256∗ 0.863∗ 0.473∗ 0.869∗ 0.973∗ 1.187∗ 1.275∗ 0.904∗ 0.708∗ 0.991∗ −0.408∗ ∗ 0.609∗ ∗ −0.316∗ 0.866∗ 1.183∗ 0.201∗ −0.605∗ ∗ 0.332∗ 0.303∗ ∗ 0.858∗ 0.729∗ −0.269∗ 0.270∗ −2.444∗ 1.637∗ −0.570∗ 0.414∗ −0.302∗ ∗ 2.605∗

−0.256∗ 0.304∗ ∗ 1.168∗ 0.473∗ 0.869∗ 0.973∗ 1.187∗ 0.773∗ 0.904∗ 0.708∗ 0.991∗ 0.424∗ 0.617∗ 1.262∗ 1.392∗ −0.404∗ −0.158∗ 1.279∗ −0.250∗ −0.600∗ 0.858∗ 0.702∗ −0.269∗ −4.657∗ −0.297∗ 0.414∗ 0.399∗ 0.375∗ 0.660∗ 0.774∗ 0.499∗ 0.414∗ −2.497∗

−0.256∗ 0.675∗ 0.473∗ 0.869∗ 0.973∗ 1.187∗ 0.767∗ 0.904∗ 0.708∗ 0.991∗ −0.853∗ 0.675∗ 0.933∗ 0.355∗ ∗ 2.551∗ 0.322∗ 1.864∗ −1.989∗ −0.168∗ 0.858∗ 0.974∗ −0.269∗ −3.627∗ −3.633∗ −0.609∗ −0.291∗ ∗ −1.411∗ ∗ −0.418∗ 0.414∗ −0.787∗ 0.521∗ 1.031∗ 0.429∗ ∗ 0.685∗ −6.067∗

(continued on next page)

26

A.K. Çelik and E. Oktay / Energy & Buildings 204 (2019) 109466

Table A6 (continued) Explanatory variables 2006 2007 2008 2009 2010 2011 Constant term The estimation results of the MNL model Socio-demographic and socio-economic characteristics Household head’s gender Male Household head’s age-group < 35 years Household head’s occupational group Lawmakers, top managers, and directors Assistant professional occupational groups Office and customer services Service and sales workers Crafts and other related works Plant and machine operatives and assemblers Elementary occupations Others Household head’s educational level Illiterate Secondary education Tertiary education Household real monthly income (log) Household real monthly expenditures per capita (log) Household real monthly electricity expenditures (log) Household real monthly natural gas expenditures (log) Household real monthly solid fuels expenditures (log) Household real monthly liq. hydroc. expenditures (log) Household type Elementary family without children Patriarchal/Large family Dwelling characteristics Type of dwelling Apartment – Basement/Ground floor Apartment – Normal floor Type of heating system Stove Joint/Central heating The age of dwelling ≤ 5 year(s) 6 – 10 years 16 – 20 years 21 – 25 years The number of rooms (log) Spatial characteristics Settlement Rural Other characteristics Year dummy 2004 2005 2006 2007 2008 2009 2010 2011 Constant term Small-Hsiao IIA test result Only traditional fuels Traditional and transition fuels Only transition fuels

[1] Coefficient

[2] Coefficient

[3] Coefficient

0.501∗ ∗ 0.574∗ ∗ −1.498∗ −1.671∗ −2.023∗ −1.898∗ −1.683∗ −1.611∗ −1.301∗ −1.701∗ 0.753∗ ∗ −0.614∗ −1.456∗

0.783∗ −0.723∗ ∗

−0.857∗ −0.396 −0.642∗ ∗ −0.714∗

−3.497∗ −0.431∗ −2.031∗ 1.891∗ 0.173∗ ∗

−0.418∗ −0.842∗ −0.689∗ −2.849∗ −0.416∗ −1.723∗ 2.151∗ 0.234∗

0.482∗ ∗ 0.165

0.542∗ ∗

−1.737∗ −1.783∗

−1.260∗ −1.151∗

−0.794∗ −0.950∗

4.874∗ 2.153∗

5.545∗ 2.916∗

1.590∗ 4.009∗

0.652∗ ∗ 0.430∗ ∗ 0.417∗ ∗

0.509∗ ∗

0.972∗ 0.831∗ 0.453∗ ∗ 0.444∗ ∗ −0.444

1.701∗ ∗

0.937∗

0.363∗ ∗

−0.756∗

−0.245

0.744∗ −0.297 −0.585∗ ∗ −0.896∗

0.459∗ ∗

−0.664∗ ∗ 6.213∗ Chi-square 132.97 109.68 115.59

−0.588∗ ∗ −1.075∗ −0.649∗ −0.802∗ 6.691∗ Sig. 0.052 0.432 0.291

−0.493∗ −0.938∗ −2.702∗ −0.381∗ −2.260∗ 2.110∗ 0.198∗

1.371∗ 1.710∗ 1.156∗ 0.815∗

3.313∗ ∗ Result for Ho for Ho for Ho

IIA denotes independence of irrelevant alternatives; ∗ p < 0.01; ∗ ∗ p < 0.05; [1] only traditional fuel; [2] traditional and transition fuels; [3] only transition fuels; [4] only modern fuels; empty cells denote the pseudo-elasticity value is not statistically significant.

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