the unit opportunity cost of time (discussed further below). Because the amount of time spent cooking may differ across technologies, with fast-cooking fuel-technology packages offering time savings benefits compared with other cooking methods, we denote the opportunity cost of cooking time as _CTi w_ , where _CTi_ is the amount of cooking time. In addition, some stoves may cause more disamenities than others, with amount of household smoke being a primary example. That cost is denoted by _Di_ . Finally, we denote by _dij_ the annual value of natural resource damages of type _j_ (e.g., deforestation) from the use of stove-and-fuel combination _i_ ; _pij_ is the annual emission of pollutant type _j_ from stove-and-fuel combination _i;_ and the unit health damage cost of pollutant _j_ is _hj_ . Then the total opportunity cost of natural resource and health damages for combination _i_ is given by ∑��� ���������.

One way to apply (1) to different stove-fuel combinations, given a comparable level of cooking activity, is through controlled cooking tests, in which fuel consumption, cooking time, and (to the extent possible) air quality in the vicinity of the cooking location are measured while cooking the same meals with different methods. Satisfaction surveys can be used in connection with the controlled cooking tests to gauge advantages and disamenities (Beyene et al., 2015), and natural resource damage estimates can be made. This is obviously a time consuming and expensive approach. A simpler but less precise method is to apply (1) to different cooking methods, holding constant the total cooking heat produced as a way of approximating comparable levels of cooking activity.<sup>6</sup> Information about fuel costs and fuel gathering and cooking times also can be collected. This can provide information about the more direct costs borne by the household, but it will not shed light on natural resource damages, health effects, or amenity and disamenity values.

# **4.  Challenges in Assessing Social Costs**

The most fundamental challenge in estimating the social cost of cooking technology and fuel combinations is that even direct cost information is often not easily available. Locally relevant information is needed for commercial fuel prices, time spent collecting fuel, and actual costs of stoves in the market. Information on stove performance is also needed, but this can be difficult given the lack of internationally recognized performance standards and testing.

Missing markets for accessing financing can increase the cost for improved stove acquisition or a switch to modern fuels by raising the effective cost of borrowing (Binswanger and Sillers 1983; Yesuf and Bluffstone, 2009). Low-income households may need to make significant investments to take advantage of electricity or LPG availability, and large investments are often needed to establish biogas plants. However, the problem can arise even with much less costly technologies. Bensch et al. (2015) investigate low uptake of improved biomass cookstoves in Burkina Faso and find that the main barrier is affordability – even though the stove model investigated costs between

> 6 For _i_ ≥ 1, let _Qi_ be total fuel use (purchased and/or collected), _HCi_ is the heat content of the relevant fuel (joules/kg), and _SEi_ is a dimensionless number reflecting the stove’s conversion efficiency (joules of cooking heat output per joule of energy input).  Given the definitions of _Q_ , _HC_ , and _SE_ , we can write total cooking heat output as ���∗��∗��. Setting _Hi_ = _H0_ yields �� � ������������� �.    One difficulty with this approach is that it does not allow for what is typically referred to as “rebound effects,” in which households enjoy part of fuel savings in the form of additional cooking. Relatively little seems to be known about the size of such cooking rebound effects in lower income settings.

**5 |** P a g e