Household biogas systems are a renewable energy technology with the potential to provide sustainable development benefits by reducing pressure on forest stocks and by shifting household time allocation towards higher value activities or long-term investments in human capital. We estimate the environmental and socioeconomic impacts of biogas expansion in Nepal using an instrumental variables approach that exploits conditional variation in access to biogas installation companies. We confirm prior evidence that biogas use significantly reduces collected fuelwood, estimating changes of approximately 800–2000 kg per year per household. We find new evidence that biogas saves time in fuelwood collection (23–47%), and results in reallocation of time away from home production and wage labor towards agricultural labor and education. We find that biogas reduced forest cover loss in the Hill region and when combined with other forest protection policies. Together the results suggest that biogas can contribute modestly to sustainable development, particularly in combination with complimentary opportunities or policies.
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In addition, Köhlin et al. (2015) argue that there is a large “know-do gap” between research on sustainable development technologies and what is actually implemented globally.
Not finding measurable impacts could also be due to missing complimentary institutions; for example, Meeks (2018) has found that formalizing property rights played a role in increasing household access to energy (and other services) in rural Peru.
Work by Chakravorty et al. (2015) suggests an important role of fuelwood markets on collection times in neighboring India.
For example, if more educated or entrepreneurial households are also more likely to install biogas and use it optimally, then analyses comparing households with and without biogas may result in overestimates of impacts. If, on the other hand, households that install biogas are more entrenched in agricultural production (e.g. have more livestock) or less entrepreneurial, the estimated impact of biogas may be downward biased.
25.2% of the population lives below the national poverty line (World Bank 2016).
AEPC works together with the Biogas Promotion Program (BSP) to support biogas expansion.
VDCs should be thought of as sub-districts—there are 3973 VDCs within the 75 districts of Nepal.
Most of the mountain area is excluded because it is too cold for biogas. We included 190 mountain VDCs that had biogas systems installed by 2011. We include a control for mountain region but group these observations with the Hill for regional comparisons. We exclude the Kathmandu District from analysis because most of the households are not suitable for biogas and there are no good counterfactuals given its unique status as the capital and major international hub of the country. Robustness checks including this district yield similar results.
Indeed, some households in Nepal cite the bio-slurry as the main reason they chose to invest in a biogas system.
Calculations are based on the 2013 exchange rate of 1 USD = 98.98 NPR.
Costs vary depending both on location and whether the household must purchase the required construction materials (brick, stone, cement, etc.) and unskilled labor or whether they can obtain them in kind.
Due to political turmoil, 2001 census enumeration was disturbed in 83 VDCs (across 12 districts); these VDCs are thus excluded.
The 2001 time allocation information was collected differently and so we could not construct the same outcome variables for this year and cannot use the data as a panel at the VDC level.
5000 of 293,000 installations (1.7%) up through 2011 could not be matched to census VDC codes.
We exclude households using electricity, LPG, kerosene, or “other” main cooking fuels.
This includes land under strict protection, conservation areas and buffer zones, and covers approximately 20% of Nepal’s land area.
This is based on the Hansen measures and excludes about 20% of VDCs, mostly located in the Terai region.
Of the 215,668 systems installed between 2001 and 2011, we matched the company branch to a location for all but 5951 installations (\(\sim 3\%\)). The remaining 73 unmatched branches either existed very briefly and thus were never included on any official list, or were incorrectly coded in the administrative data.
This is similar to the map illustrating the instrument in Madestam et al. (2013).
Robustness checks excluding the dung covariates yield similar results. Dung covariates are not included in the first stage as dung and wood together are perfectly collinear with biogas.
Household characteristics that are binary variables are aggregated to the VDC-level as proportions of the VDC with that characteristic. Household characteristics that are continuous variables are aggregated to the VDC-level as averages for the VDC.
Running the first stage using the census data limited to the NLSS 03/10 VDCs produces a Chi-squared statistic more similar to columns 1 and 2, suggesting it is an issue of sample size, not VDC selection.
We also used the Oster bounds (Oster 2013) to check for the strength of confounders needed to overturn the results. This indicated that such confounders were likely, providing additional support for using our instrumental variables as our primary empirical approach.
Many households do not both collect and purchase fuelwood. Households that report no collection amount or no expenditure are coded as having a value of zero for that variable.
We tested for the endogeneity of regressors using the endog option of ivreg2, which tests the difference between the two Sargan–Hansen statistics. Unlike the Durbin–Wu–Hausman test, this test is robust to violations of conditional homoscedasticity. We fail to reject the null in all three of the firewood analyses, suggesting that for these outcomes, OLS and IV analyses give similar answers.
As a robustness check, we also perform this analysis dropping the top and bottom 1% of VDCs (according to size). Results do not substantially change.
Testing for endogeneity using the same method as above, we find that 8/10 of the census outcomes reject the null at the 5% level and the other 2/10 reject at the 10% level.
The IV strategy had a first stage that was too weak due to the smaller number of VDCs in the sample. Although the NLSS/NLFS sample includes more households than the NLSS II/III sample, there is more overlap among sampled VDCs, so the number of VDCs is lower.
This is almost the same level as that in the Hill region (\(\sim 30\)% of time).
As described in the data section, the census collected data on economic and non-economic activities of each family member, aged 10 years or higher. A test looking separately just at the time allocation of the group aged between 10 and 18 years old did not find a significant increase in study time, and in fact showed conflicting results between the OLS and IV specifications, with the IV implying possibly significant negative impacts on time spent studying and more time on home-production. This may be due to heterogeneity in treatment effects as well as the already high proportion of time spent studying by most teenagers during this time. According to the UNICEF MICS surveys in 2010 and 2014, secondary school net attendance increased from 55.6 to 62.3% over our study period (CBS 2012b, 2015).
This would be due to apparent large percentage changes resulting from areas with very little forest cover to start.
Stock–Yogo critical values have not been tabulated for cases with more than two endogenous variables. Therefore, we also include the first stage Sanderson–Windmeijer F statistics testing for weak identification of each endogenous regressor, all of which exceed the critical values.
An alternative reason for small estimates is that neighboring households respond by collecting additional wood. For this reason, we tested for spillovers to other households without biogas in VDCs with high uptake of biogas. We did not find significant increases on average wood collection by neighboring households.
A study of technical potential of biogas in Nepal suggests 1.03 million suitable households who have not yet installed biogas (SETM 2013).
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The authors are grateful for generous funding from the ADVANCE Elizabeth Caroline Crosby Program, the University of Michigan Rackham Research Grant, and the Carnegie Corporation of NY. Brendan Hall, Wincy Poon, and Anri Chomentowska provided excellent research assistance. The authors are grateful to Johan Oldekop for sharing VDC level data on forest user groups and forest cover change. We thank David Lam, Shaun McRae, Dean Yang, Aparna Howlander, Jayash Paudel, and participants at the AERE summer meeting, the Midwest Energy Fest at Northwestern University, and the UM-MSU EEE workshop for comments.
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Meeks, R., Sims, K.R.E. & Thompson, H. Waste Not: Can Household Biogas Deliver Sustainable Development?. Environ Resource Econ 72, 763–794 (2019). https://doi.org/10.1007/s10640-018-0224-1