3.1. Characteristics of the Households
The total sample size of the households for this study was proposed to be 600; however, 532 (94%) questionnaires out of the total sample data were complete and adequate in all respects. Thus, our final error-free sample size was 532 households. As presented in
Table 2, male-headed households were about 90.79% and female-headed households were about 9.21% of the total. The average age of the sample households is approximately 46.7 years, household size is 7.54 individuals per house, education level of the household head is 8.03 years, number of school-going children is 2.40, and the annual net income of the households is 726,500 Pakistan rupees (PKR). The main occupations for the households are crop field farming (23.12%), animal farming (18.23%), crop dusting/veterinary services (17.86%), and private small businesses (13.53%), among others who are engaged in multiple economic activities (27.15%). Further, the average livestock (cattle) holding of the households is 5.61 animals and the average landholding (in hectare) is 2.81. Pakistan is an agricultural country where credit facilities are often provided to farmers/rural households; however, such facilities are often taken by the big farmers. In our sample, the credit facilities are used by 21.99% of the households. The infrastructure of the electricity system is poor in villages and there are often technical problems (e.g., consistent grid-outage and transmission line issues). We found that 64.20% of the households were connected to the grid. However, the infrastructure of electricity distribution in the villages of district Dera Ismail Khan was slightly better than the other districts—Tank and Bhakkar. A total of 8.08% of sample households owned petrol and diesel fuel-based generators, primarily those living in off-grid areas. About 21.20% of sample households owned solar PV systems, whereas about 6.01% owned biogas systems, specifically those households engaged in animal and field crop farming. The nearest wood collection site was, on average, 50.40 min walking (round trip), and the nearest market trip takes, on average, 58.20 min of walking (round trip).
Table 3 (Panel A) presents the frequency and percentage of households using different cooking fuel sources. Out of 532 households in all three districts, 64% of the households used fuelwood as the single primary cooking fuel and 20.48% of the households simultaneously use biomass pellets, agricultural residue, and dung-cake with a flammable material such as kerosene oil or petrol.
Charcoal was used at a rate of about 4.13% at the household level; however, its use is more common in commercial activities, such as bread-making, ironing, and heating. Around 7.33% of the households use LPG as a cooking fuel, which is a comparatively a lower percentage than their urban counterparts, who often use LPG for their cooking needs. The perplexity of electricity load shedding (grid outage) in rural areas is around 14–16 h a day [
15,
17], which affects the use of electrical appliances for cooking: only 0.37% of the households use electricity for cooking. In our sample, about 6.01% of households own a biogas system; however, only 3.57% use the biogas system properly, with special biogas cooking stoves. The diffusion of biogas systems and the underlying difficulties have been discussed in the relevant studies [
7].
However, many households use a mixed fuel-portfolio (multi-fuel consumption). An analysis of the survey data shows that many rural households still prefer to use fuelwood as the primary fuel option. Fuelwood is utilized in conjunction with other fuel substitutes, which refers to the energy-staking model.
Figure 2a details the fuel-mix portfolio for cooking used by the households. About 74.19% households rely on a single source: fuelwood is the source for 48.8% of households, the simultaneous use of biomass pellets/agricultural residues/dung-cake and kerosene is the source for 16.19%, and LPG is the source for 9.2%. The rest of the households (25.81%) use mixed fuels for cooking, which is comprehensively portrayed in
Figure 2a.
Table 3 (Panel B) lays out the frequency and percentage of households’ primary energy sources for lighting. Out of 532 households, 202 (37.96%) use old-fashioned lighting sources (kerosene lamps), 147 (27.63%) use electricity, 112 (21.05%) use solar PV system/smart lamps (smart lanterns), 46 (8.64%) households use rechargeable (e.g., lithium Ion) batteries, 11 (2.06%) use biogas system/lamps, and 13 (2.44%) use petrol/diesel generators (specifically, those living off-grid areas) for lighting purposes. While 64.2% of sample households are connected to the grid, actual access to electricity is limited due to a poor distribution system, weak transmission lines, and electricity shortfall in rural areas.
Figure 2b presents households’ energy portfolio (mix substitutions) for lighting. The sample data collected in this study demonstrate that households relying on a single source of energy are about 37.7% of the total, where kerosene users are 17.76%, grid electricity users are 14.32%, and solar PV system users are 5.62%. The total proportion of grid-connected households that use a mixed-energy portfolio is 31.86%, and the aggregate proportion of off-grid households utilizing a mixed-energy portfolio is 30.44%, which is comprehensively portrayed in
Figure 2b.
3.2. Cooking Fuel Choices: Chi-Square Analysis
The results in
Table 4 depict the relationship between explanatory variables and households’ cooking fuel choices by employing the Pearson Chi-square model.
Gender: The first treatment of Chi-square, which examines the gender factor, shows a statistically insignificant coefficient (
χ2 = 2.99,
p-value = 0.5595). This intimates that the gender differences do not influence the choice of cooking fuel source. The reason for there being no conflict in cooking fuel choices might refer to other factors, for example, cheaper or more economical sources of cooking fuel. Our findings contradict the results of Rahut et al. [
37], who found that females are more likely to choose a clean cooking fuel than their male counterparts.
Age: The results of Chi-square for the age factors are significant (χ2 = 26.93, p-value = 0.0422), indicating the importance of age as an influencing factor. In rural areas, females are often engaged in cooking activities. It is understandable that young households prefer clean energy sources, whereas senior citizens have more savings (given that they have been accumulating savings for a comparatively longer time period than the younger households), which can be used to buy/afford modern RETs, such as a biogas system.
Location: Location is another important factor, which can influence households’ cooking choices due to the numerous differences that exist in ACZs and AEZs between two different sample areas [
25]. In terms of location, as an influencing factor, our results are insignificant (
χ2 = 7.29,
p-value = 0.5056), indicating that the cooking choices across the three districts under study are largely similar: this is possible because the three selected districts in our study are geographically connected to each other and similar in terms of ACZs/AEZs. It is interesting to note that while district Dera Ismail Khan has a better infrastructure and a higher literacy rate, the households’ cooking fuel choices are yet indistinguishable across the three districts.
Income: In rural communities, income is one of the most crucial factors in making any decision, especially in rural communities in developing countries [
15,
25,
38], since these communities are financially underprivileged in developing countries. In our sample, income was a similarly significant factor (
χ2 = 91.36,
p-value = 0.0000). Our findings exhibit that wealthier households prefer to choose clean energy fuels for cooking, such as LPG. In face-to-face conversations with the households, we found that a few wealthy households also use different combinations of clean and/or traditional fuel. Conversely, households with low annual income prefer to use cheaper energy sources: fuelwood, agricultural residue, dung cake and biomass pellets. Overall, an increase in income level reduces the household’s choice to use dirty fuel for primary cooking needs.
Education level: The education level of the household head is also significant (
χ2 = 33.88,
p-value = 0.0868) in our study. Similar to many other studies that examine the education level of the household-head, we found that less-educated households are not concerned about the health issues of using dirty fuel. On the other hand, educated households prefer to use clean energy sources for cooking and are more conscious about the health issues. Our findings are in line with other, similar studies [
25,
39,
40].
Household size: The relationship between household size (the number of family members) and primary cooking fuel choices is statistically significant (
χ2 = 78.93,
p-value = 0.0000). It is commonly believed that the need for cooking fuel increases when the household number (size) increases. In rural areas, females and children are often considered an economical source of fuelwood and agricultural residues’ collection. Therefore, a large family has more chances to efficiently accumulate sufficient energy fuels. Our results corroborate the findings of other relevant studies [
41].
Occupation: The Chi-square results of household head’s occupation show (χ2 = 28.38, p-value = 0.0284) a significant association with cooking fuel choice. The households that are simultaneously engaged in both cropping and animal farming, and those that work in the government offices tend to choose clean energy sources. Further, big landlords and households with a large number of animals (cattle) were found to be the current users of a biogas system for their primary cooking needs.
Distance to the nearest market and wood-collection site: The availability of wood sources in the nearby vicinity is one of the key determinants that significantly affects households’ fuel choices. The Chi-square analysis in
Table 4 shows a significant relationship between distance to wood source and household cooking fuel choice (
χ2 = 43.45,
p = 0.0000). Distance to the nearest market is similarly a significant factor influencing households’ fuel choices. (
χ2 = 38.47,
p-value = 0.0001). Residing near to the market provides easier access to different clean energy sources, for example, LPG and electricity. In contrast, the remoteness of a location provides easier and more economical access to dirty fuels, such as agriculture residues, dung cakes, and fuelwoods.
Access to grid electricity: The
χ2 of access to grid electricity (
χ2 = 5.89,
p-value = 0.2075) is insignificant, indicating that the access to grid electricity does not affect the cooking fuel choice in our sample. The main reasons for its insignificance are the consistently increasing prices of electricity in Pakistan [
14], with grid outages of up to 16 h a day in the rural areas [
15], and a low voltage that does not support many electricity appliances [
17]. Therefore, despite having access to grid-connected electricity, the households do not consider this as a cooking fuel choice. In summary, the access to grid electricity does not substantially impact the choice of cooking fuels: while our results are contrary to the findings of some relevant studies [
39,
40], they are also supported by a few other relevant studies [
25].
Biogas system ownership: The last explanatory variable in our analysis is biogas system ownership. The Chi-square results (
χ2 = 43.51,
p-value = 0.0000) indicate that the relationship between biogas system ownership and households’ cooking fuel choices is significant. Given that biogas system owners are comparatively wealthier and more educated households, the influence of biogas system ownership on fuel choice is evident, consistent with the literature [
2,
7,
23]. The biogas awareness program launched by the provincial and federal governments has played an important role in educating people about the benefits of installing a biogas system.
In addition to the explanatory variables discussed above, few households are also found to be culturally addicted to traditional cooking style and taste; for example, the use of traditional (sand/mud made) cooking pots that can only be used with fuelwood. Similarly, we found that households prefer to use traditional biomass sources (i.e., agri-residues and fuelwoods) to cook milk tea and some other specific traditional food, even when clean energy sources are available. However, such habitual and cultural cooking styles and taste consciousness are not part of this study (for brevity). During face-to-face interviews and discussions with the households, we found that the steadily increasing prices of clean cooking fuels (LPG and electricity) have discouraged the household shift towards modern/clean energy sources. Likewise, an increase in the prices of clean energy fuels also increased the spread between the prices of clean and traditional fuels, making unclean traditional fuels more cost-effective and attractive.
3.3. Households’ Energy Choices for Lighting: Multivariate Probit (MVP) Model Approach
In this section, we analyze households’ energy choices for cooking using the MVP model. Choosing the MVP model with robust standard errors allows for us to understand the key factors influencing households’ decision when choosing among different energy sources for lighting. Multicollinearity between explanatory variables (so-called independent or influencing factors) can result in sporous findings, which can mislead readers and policymakers. Following a common practice, we first examined the Variance Inflation Factor (VIF) and checked whether the values were within the acceptance range that is commonly used (i.e., the threshold value of VIF < 10). Except for the distance to road factor, the remaining variables were below the threshold value of 10 (
Table 5). To normalize the data, Z-scores were first calculated and then a cut-off value of ±3 was used (as suggested by the relevant literature [
25]) to normalize the data and drop outliers. Note that the higher values of Z-score indicate more unusual observations, whereas 0 indicates a value that equals the mean.
Table 6 presents pairwise correlation results of the binary dependent variables: two unclean energy sources (kerosene and petrol/diesel generator) and four clean energy sources (electricity, rechargeable batteries, solar PV system and biogas system). The likelihood ratio is statistically significant—Chi
2(15) = 76.24, Prob > Chi
2 = 0.0000—rejecting the null hypothesis that the selected six energy choices are independent. The correlations between kerosene and electricity, rechargeable batteries, solar PV system, and biogas system and between petrol/diesel generator and electricity, rechargeable batteries, and solar PV system are all negative. More precisely, the correlation coefficients show a negative relationship between unclean energy sources and clean energy sources, indicating a substitution effect among them.
On the contrary, the relationship between kerosene oil and petrol/diesel generator is positive, indicating a complementary relationship between these sources: in off-grid areas, households use a combination of energy choices. The correlation coefficient among clean energy sources for lighting contrasts both positive and negative effects. For example, the correlation of (i) rechargeable batteries with electricity and solar PV system and the correlation between (ii) electricity and biogas system are positive. This complementarity between the clean fuels is understandable as the rechargeable batteries need power (using electricity or solar PV system) for re-charging and the use of biogas technology fundamentally complements the grid-connected electricity (energy mix portfolio). The correlation of solar PV system with electricity and biogas systems, and the correlation between rechargeable batteries and the biogas system were found to be negative, because the likelihood of such an energy mix (combination) for lighting may not be technically and economically feasible.
Table 7 provides the estimated coefficients (
βi), while
Table 8 illustrates the marginal probability effect (
Yi = 1) of factors explaining households’ energy choices for lighting. The Wald Chi
2 (102) = 597.42 (Prob > chi
2 = 0.0000) is statistically significant at any commonly referred to conventional significance levels (e.g., at a 1% level; α = 0.01). Thus, the results of the model can be considered reliable.
Gender: In
Table 7 and
Table 8, both the coefficients and marginal probability effects of gender (female) are statistically insignificant for all lighting energy choices, except for the PV system, which is positive and significant. This implies that the females in rural areas usually stay at home during the daytime, and the unavailability of grid (electricity) creates numerous hurdles for them when attempting to manage their housework in a timely manner. Thus, the female-headed households have a higher tendency towards the use of solar PV systems compared to other lighting choices. Consistent with the previous studies [
14,
36], our results suggest that gender plays an important role in the energy choice for lighting: female-headed households are more likely to choose clean energy sources for lighting than their male counterparts.
Age: The coefficients and probability estimations of the household head’s age are positive and significant for the kerosene and biogas system. The former relationship indicates that older households may possibly be more comfortable with old-fashioned lighting choices (i.e., kerosene), whereas the latter relationship suggests that older households may have more resources allowing for the installation of a biogas system than the younger households. While our results are in line with the findings of Ali et al. [
34], they contradict the findings of Kelebe et al. [
42].
Education level: Table 7 illustrates that the education level of the household-head has a positive and significant association with rechargeable batteries, grid electricity, solar PV system, and biogas system. This clearly indicates the importance of literacy in the use of clean and modern energy sources. A negative and statistically significant association between household head’s education level and kerosene further strengthen our earlier argument. A substantially large number of studies examining the determinants of adoption for clean energy sources show similar findings [
19,
23,
25].
Household size: Household size is another important factor in rural areas: since a large family size (up to some extent) has numerous comparative advantages in rural communities, at least in Pakistan. The results of both the coefficients and marginal probability estimates for household size are positive for kerosene, biogas system, and rechargeable batteries. This means that households with a larger family size, on average, need more energy and arranging multiple clean energy sources may not be a feasible option. Therefore, these households use kerosene lamps and rechargeable batteries to meet their basic lighting needs instantly, using easily available substitutes. Similarly, large families have different advantages regarding the cost-effective collection of animal manure (for the biogas system). In sum, the electricity shortfall in rural areas has pushed households towards the use of multisource energy, including from dirty, health-hazard sources.
Children in schools:
Table 7 shows positive and significant relationships between children in school and both electricity and solar PV systems. Since school-going children represent comparatively more educated and wealthier households and households living nearer main roads than their counterparts, their choice to use electricity and a solar PV system is, therefore, justified. Further, it is also important to know that both electricity and solar PV systems are more user-friendly and have brighter lighting options than kerosene or rechargeable batteries; therefore, such households prefer to adopt these clean energy sources, which can also help the school-going children in their studies.
Households’ income level: The coefficients of household income are positive and significant for electricity, solar PV system, biogas system, and petrol/diesel generator. Households with a high income—both off-grid and grid-connected households—are likely to adopt a combination of different energy sources (energy mix) for lighting. The marginal probability shows significant results for petrol/diesel generators as an energy choice because households with a high income can easily afford substitutes that can fulfill their energy needs when electricity is unavailable. For off-grid areas, wealthy households have to rely on petrol/diesel generators. In contrast, the coefficient and marginal probability of households’ income portray a negative but significant relationship with kerosene and rechargeable batteries. As discussed above, households with a high income already own a more reliable energy portfolio, and, therefore, do not rely on dirty sources or rechargeable batteries.
Landholding: The coefficients of landholding size are positive and significant for solar PV systems, biogas systems, and petrol/diesel generators. The households with larger landholdings also have more available space, more livestock (in most cases), and more crop-residuals; therefore, installing a biogas system, solar PV system, and using petrol/diesel generators (in off-grid areas) for lighting needs are all favorable choices for them, compared to those households who own smaller (or no) landholdings. Our results are in line with the findings of other studies conducted in different regions [
42,
43].
Livestock holding: The coefficient of livestock holding was found to be positive and significant for biogas systems and petrol/diesel generators. It is evident that households owning large numbers of livestock have a sufficient availability of animal manure for biogas production. Furthermore, during the interview and questionnaire stage, we found that few households owning large numbers of livestock live in remote areas, which are mostly off-grid; thus, they use petrol/diesel generators as one of the primary energy sources for lighting in addition to biogas plants.
In contrast, the relationship between livestock holding and kerosene was found to be negative but significant. This is understandable because households with larger livestock holdings, their own biogas system, and a petrol/diesel generator, among other energy sources; therefore, the use of kerosene lamps has no interest. Our results corroborate the findings of Kabir et al. [
44]: an increase in livestock size positively influences the households to take advantage of animal manure for the biogas system.
Price of solar PV system: Table 7 and
Table 8 illustrate positive and significant associations between the prices of the solar PV system and kerosene, rechargeable batteries, and electricity. This shows that an increase in the price of solar PV systems leads rural households to shift towards either traditional/unclean sources (i.e., kerosene) or cheaper/economical sources (i.e., rechargeable batteries). Although the solar PV system is clean and not very complex for lighting purposes, households in rural areas cannot afford such technologies.
Price of kerosene: The estimated coefficients and marginal probabilities in
Table 7 and
Table 8 show that an increase in the price of kerosene oil noticeably reduces the use of kerosene lamps for lighting. The positive and significant relationship between kerosene oil prices and rechargeable batteries, electricity, and solar PV systems further suggests that an increase in the price of kerosene fuel leads households to adopt alternative sources. In other words, when kerosene fuel is be more expensive, households will either choose a cheaper option (rechargeable batteries) or other clean alternatives (solar PV system or electricity), given that the increase in the price of kerosene oil would shrink the spread between clean and dirty lighting fuels.
Price of generators and fossil fuel (petrol/diesel): It is obvious that an increase in the price of a petrol/diesel generator will substantially reduce the demand (or purchasing power) for petrol/diesel generators (households), as indicated by the negative and statistically significant coefficient in
Table 7 and
Table 8. On the contrary, the results for kerosene, grid electricity, and solar PV system, were found to be positive and significant, implying that the price of the petrol/diesel generator creates a spillover effect between cheaper energy sources and other comparable options (e.g., solar PV system). Similar results were found for the prices of fossil fuels in Pakistan.
Access to credit: The relationship between access to credit and solar PV system, biogas system, and petrol/diesel generators were found to be positive and significant. Recent renewable energy promotion campaigns and other motivational measures taken by both private and government entities to increase the use of RETs and clean/modern energy sources can be examined by looking at the results in
Table 7 and
Table 8. Our results also correlate with the findings of Berhe et al. [
45]. In sum, we found that access to credit facilities positively and significantly influences the adoption of biogas systems as an energy lighting source in Pakistan.
Distance to market: The coefficient and marginal probability estimates are insignificant for kerosene. The availability of kerosene is relatively high in all three districts; therefore, distance to market was not an influencing factor. However, the coefficients were found to be found negative and significant for rechargeable batteries, electricity, and solar PV systems, indicating that households residing far from the market have limited access to clean energy compared to those residing nearby the market.
Distance to road: Interestingly, the results of
Table 7 and
Table 8 show that the distance to road was significant for only one factor: electricity. The electricity infrastructure follows the main and link roads of each district; therefore, households residing near the road have easy access to electricity. The estimated coefficients and marginal probability effects for other energy fuels are insignificant. Our results contradict the findings of Ali et al. [
34] and Kelebe et al. [
42], who found that easy access to roads increases households’ willingness to use clean energy.
Location: Although ACZs/AEZs have a similar nature across the three districts under study, demographic, socio-economic, and infrastructural factors vary. Our district-wise results show the dissimilarity of lighting energy choices: kerosene, solar PV systems, petrol/diesel generators, and biogas systems. The coefficients of kerosene and petrol/diesel generators are positive and significant for district Tank and Bhakkar, illustrating that households residing in Tank and Bhakkar use more unclean sources. A possible explanation for such choices is that the villages in these two districts are comparatively more remote than the villages in district Dera Ismail Khan. In contrast, the lighting energy choices of households in district Dera Ismail Khan are tilted towards solar PV systems and electricity. However, the propensity to choose the biogas system is more pronounced in district Bhakkar. It is well documented in the literature that the government of the Punjab province is consistently encouraging (in the form of providing subsidies) its rural residents to install biogas systems [
7,
20]; therefore, it is not surprising to find that households in district Bhakkar—which lies in the Punjab province—are more inclined towards biogas technology than the households in the other two districts, which are administrated by Khyber Pakhtunkhwa (KPK) province.