metadata: authors: 0: author: Dissanayake,Sahan
1: author: Bluffstone,Randall A.
2: author: Somanathan,E.
3: author: Luintel,Harisharan
4: author: Paudel,N. S.
5: author: Toman,Michael A.
count: Nepal
docna: 0: docna: Forest Carbon Supply in Nepal : Evidence
            from a Choice Experiment
docty: Policy Research Working Paper
subtopic: Forestry,Energy and Natural Resources,Coastal and Marine Resources,Climate Change Mitigation and Green House Gases,Forests and Forestry,Climate Change and Environment,Climate Change and Health,Science of Climate Change,Global Environment,Environmental Disasters & Degradation
teratopic: Rural Development,Energy,Water Resources,Health, Nutrition and Population,Science and Technology Development,Environment
abstracts: cdata!: This paper uses a choice experiment
            conducted in Nepal during 2013 to estimate household-level
            willingness to participate in a village-level program under
            the Reduced Emissions from Deforestation and Forest
            Degradation initiative requiring reductions in fuelwood
            collection, as a function of the price paid per unit of
            avoided carbon dioxide emissions. The analysis examines
            incentives to participate both in villages having formal
            community forest management, the core institution for
            implementing Reduced Emissions from Deforestation and Forest
            Degradation, and villages having only informal forest user
            groups. Contrary to previous findings in the literature
            about participation incentives, but in keeping with other
            recent studies of Reduced Emissions from Deforestation and
            Forest Degradation pilots in Nepal, this study finds that
            relatively little emission reduction would take place at
            prices of $1.00 to $5.00 per ton of avoided carbon
            emissions. Formal community forests will almost certainly be
            the core institution within which Reduced Emissions from
            Deforestation and Forest Degradation is implemented in Nepal
            and likely other countries. The study finds that average and
            median values of payment required for agreement to reduce
            fuelwood collection are substantially larger for formal
            forest user groups than in informal communities. This
            reflects that formal groups likely already have fuelwood
            collection restrictions in place, whereas informal groups
            may de facto permit open access extraction. The analysis
            also suggests that households that are part of informal
            groups react to Reduced Emissions from Deforestation and
            Forest Degradation very differently than households that are
            formal group members. Broadly speaking,
            "underprivileged" formal group member households,
            such as those who are landless, female-headed, and poor,
            appear to be warier of fuelwood collection restrictions and
            thus require higher payments than average respondents. This
            difference does not appear to carry over to informal group members.
geo_reg_key: 517198,517191,517212
repnme: repnme: Forest Carbon Supply in Nepal : Evidence
            from a Choice Experiment
display_title: Forest Carbon Supply in Nepal :
            Evidence from a Choice Experiment
listing_relative_url: /research/2018/11/30623633/forest-carbon-supply-nepal-evidence-choice-experiment
topicv3: BioCarbon m1325994 1851,Forestry m1326646 436,Climate Change m1326829 567,Environment, Natural Resources and Blue
            Economy m1327391 566,Deforestation m1503714 3386
publishtoextweb_dt: 2018-11-19T00:00:00Z
historic_topic: Rural Development,Energy,Water Resources,Health, Nutrition and Population,Science and Technology Development,Environment
no_of_pages: 56
author_keys: 0: author_key: 166060
model_extractions: WPS8648 Policy Research Working Paper 8648 Forest Carbon Supply in Nepal Evidence from a Choice Experiment _Sahan Dissanayake Randall A. Bluffstone E. Somanathan Harisharan Luintel N. S. Paudel Michael Toman_ Development Economics Development Research Group November 2018
Policy Research Working Paper 8648

# **Abstract**

This paper uses a choice experiment conducted in Nepal during 2013 to estimate household-level willingness to participate in a village-level program under the Reduced Emissions from Deforestation and Forest Degradation initiative requiring reductions in fuelwood collection, as a function of the price paid per unit of avoided carbon dioxide emissions. The analysis examines incentives to participate both in villages having formal community forest management, the core institution for implementing Reduced Emissions from Deforestation and Forest Degradation, and villages having only informal forest user groups. Contrary to previous findings in the literature about participation incentives, but in keeping with other recent studies of Reduced Emissions from Deforestation and Forest Degradation pilots in Nepal, this study finds that relatively little emission reduction would take place at prices of $1.00 to $5.00 per ton of avoided carbon emissions. Formal community forests will almost certainly be the core institution within which Reduced Emissions from Deforestation and Forest Degradation is implemented in Nepal and likely other countries. The study finds that average and median values of payment required for agreement to reduce fuelwood collection are substantially larger for formal forest user groups than in informal communities. This reflects that formal groups likely already have fuelwood collection restrictions in place, whereas informal groups may de facto permit open access extraction. The analysis also suggests that households that are part of informal groups react to Reduced Emissions from Deforestation and Forest Degradation very differently than households that are formal group members. Broadly speaking, “underprivileged” formal group member households, such as those who are landless, female-headed, and poor, appear to be warier of fuelwood collection restrictions and thus require higher payments than average respondents. This difference does not appear to carry over to informal group members..

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at sahan@pdx.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Forest Carbon Supply in Nepal: Evidence from a Choice Experiment

Sahan Dissanayake<sup>1</sup> Randall A. Bluffstone E. Somanathan Harisharan Luintel N. S. Paudel Michael Toman

## **JEL codes: Q23 Q54 Q56**

## **Keywords: Nepal; Carbon Sequestration; Carbon Supply; Choice Experiment**

## **Acknowledgements**

The authors acknowledge the contributions of ForestAction Nepal to the collection of field data and Alec Kretchun for the preparation of the research site map. They also gratefully acknowledge the expert research assistance of Russell Drummond, without which this paper would be on a much shakier foundation. Bluffstone thanks the Fulbright Scholar Program and USEF-Nepal, which provided support for his input into the paper. Financial support to carry out the research was provided by the World Bank’s research budget.

> 1 Corresponding author sahan@pdx.edu. Affiliations: Dissanayake, Portland State University; Bluffstone, Portland State University; Somanathan, Indian Statistical Institute; Luintel, Portland State University and ForestAction Nepal; Paudel, ForestAction Nepal; Toman, World Bank. Development Research Group.
# **1. Introduction and Key Literature**

Meeting the Paris Agreement goal to contain the increase in average surface temperature to 1.5 to 2.0 degrees Celsius will imply significant costs, but if costs differ across the world economy, there is an opportunity to improve cost effectiveness by prioritizing reductions from those who can reduce net CO2e emissions the cheapest. In the main cost estimate reported in the 5<sup>th</sup> assessment, the IPCC indeed assumes that costs are divided up in such a way as to achieve a cost-effective allocation of CO2e emissions abatement effort (IPCC, 2014). Knowledge of marginal abatement costs – and especially where in the world economy low-cost abatement opportunities exist - is therefore critical for cost effectively allocating abatement effort and reducing total costs.

Information about CO2e emissions abatement costs is particularly critical for designing payments ecosystem services (PES) programs, otherwise it is possible that compensation levels will be insufficient to spur abatement. One particularly important climate change-related PES program is Reduced Emissions from Deforestation and Forest Degradation (REDD+), which is a program by which The UN Framework Convention on Climate Change (FCCC) Annex 1 countries provide support to non-Annex 1 countries in exchange for measurable additional carbon sequestration (Angelsen, 2010; Bluffstone et al., 2013). What drives REDD+ is the notion that carbon sequestration in developing country forests is a class of particularly cost-effective climate investments; it therefore is cost-effective in a global sense for Annex 1 countries to “buy” forest carbon emission reductions from lower-income countries.

Low-income countries emit relatively little carbon pollution but contribute to climate change through land use change and especially loss of forest biomass. Indeed, the majority of net deforestation and forest degradation occurs in tropical and sub-tropical areas, which is the location of virtually all low-income countries (Pan et al., 2011). Forest biomass loss from tropical land use change 2
is an important source of carbon emissions and an estimated net carbon source of 2.4  0.4 Gt per year (Pan et al., 2011). Net deforestation and forest degradation make up 12% to 20% of total greenhouse gas emissions (GHG), which is greater than all transport sources combined (Saatchi et al., 2011; van der Werf, 2009).

The purpose of this paper is to estimate a nationally representative, household-level carbon supply function for rural Nepal. We model carbon supply as the marginal willingness to accept (WTA) payments for reducing fuelwood collections from common forests as part of a hypothetical REDD+ program. To estimate marginal willingness-to-accept, we use a choice experiment conducted in 2013 with 1,300 household heads or other household representatives. Choice experiments allow us to consider and decompose the effects of various aspects of our hypothetical PES program, where our particular interest in this paper is fuelwood reductions and elimination of open grazing; both these changes reduce emissions of carbon and increase sequestration.

Using this experimental framework, we find relatively high median WTA values per ton of CO2e emissions avoided ($33 - $56) when we include best available information on avoided emissions. Both sets of estimates are consistent with recent literature from Nepal, but substantially above earlier estimates. We then examine factors that shift estimated carbon supply functions by estimating correlations between the marginal willingness to accept payments and a variety of socioeconomic variables. We find that respondents who are poor, members of female-headed households and have bigger families have higher WTA values. Those who own land, use biogas and believe they will benefit from REDD+ are estimated to require lower payments to reduce fuelwood collections. We also find that the opportunity costs of CO2 emissions reductions due to eliminating grazing are likely to be relatively high.

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In most lower-income countries, such as Nepal, deforestation is less of an issue than degradation. Villagers collect forest products, such as fuelwood, forest fruits and vegetables, building materials and animal fodder, which reduces forest biomass. Fuelwood is a particularly important forest product (Cooke et al., 2008) and almost 3 billion people worldwide cook with biomass on a daily basis (Jeuland and Pattanayak, 2012). This dependence on fuelwood is expected to remain virtually constant through 2030 and decline only slightly by 2040 (IEA, 2006; 2014).

Cooking with wood contributes to climate change via the release of carbon dioxide from nonsustainably harvested wood, plus other pollution emissions, including black carbon, carbon monoxide, and methane, even if the wood is sustainably harvested (Bailis et al., 2015). A number of these pollutants are particularly potent climate forcers, with black carbon having an estimated global warming potential (GWP) central estimate, which is 900 times more potent than CO2 (Bond et al. 2013).<sup>2</sup> Cooking with wood using mainly inefficient technologies can therefore have particularly important effects on the climate.

Though the fraction of people in Nepal who use liquefied petroleum gas (LPG) for cooking increased from 8 percent in 2001 to 17 percent in 2010 (Kanel et al., 2012), biomass makes up approximately 70% of the total energy used, 86% of households use fuelwood and 75% collect it themselves (Kandel et al., 2016). The poor are most dependent on fuelwood. About 95 percent of the poorest households use fuelwood as a source of household energy, but only 58 percent of the richest quintile use fuelwood (Kanel et al., 2012).

With such a high dependence, reducing collection of forest products, such as fuelwood, to reduce greenhouse gas emissions imposes costs on those who rely on fuelwood for their cooking

> 2 There is substantial uncertainty about the GWP of black carbon, mainly due to aerosol-cloud interactions. As in this paper several GHGs other than CO2 are examined, we use CO2 and CO2 equivalent (CO2e) interchangeably, with other gases converted to CO2e using their GWPs.

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needs. These costs may come in the form of reduced convenience due to the need to conserve wood, increased monetary costs as households shift to higher cost, less GHG-intensive fuels like LPG (e.g. see Smith et al., 2000) or greater time costs as they plant trees on their own lands, because they cannot harvest enough wood from forests.

An estimated 25% of developing country forests are managed by communities (Agrawal et al., 2008; World Bank, 2009) and about 15.5% of global forest area is under the formal control of communities (RRI, 2014). These observations make formal, informal, _de jure_ and _de facto_ communitycontrolled forests (Bluffstone et al, 2013) especially important sources of GHG emissions _or_ carbon sequestration, particularly as most community forests are in low and lower- middle income countries where net forest biomass loss is occurring.

Nepal has for many decades been a leader in formal community control of forests. It has over 19,000 formal, registered forest user groups that include over 35% of the population. As of 2015, in hill areas that are home to the majority of Nepal’s population, over 78% of households were formal community forest user group (CFUG) members.<sup>3</sup> ~~.~~ For example, in the hill district of Salyan (population approximately 250,000) in 2014 there were 558 CFUGs.<sup>4</sup> These institutions have characteristics and practices that may differ from those outside the program. It is therefore important in any analysis to distinguish community forests (CFs) and non-community forests (non-CFs).

These CFUGs in Nepal implement management structures in consultation with government officials and impose collection restrictions, including on fodder, firewood and timber. Communities

> 3 National information available from the Ministry of Forest and Soil Conservation (MoFSC) Department of Forestry (DoF) Community Forestry Division website

> <u>http://dof.gov.np/dof_community_forest_division/community_forestry_dof.    Hill district information based on</u> author calculations and data from the Community Forests Database (August 2015) available at <u>http://dof.gov.np/image/data/Community_Forestry/Summary.pdf and the 2013 Nepal Statistical Yearbook (most</u> recent available), which is available at <u>http://cbs.gov.np/publications/statisticalyearbook_2013.  All accessed August 16,</u> 2016.

> 4 See <u>http://www.dfosalyan.gov.np/eng/images/pdf/database/database_of_cfugs.pdf for details.</u>

5
in our sample use a variety of mechanisms to control extractions of fuelwood, including restricting harvest technologies and number of harvesting household members (34%). They also allocate certain periods, often in winter (27%), holidays (10%) and specific days of the week, such as Saturday only (20%) for collection. Over two-thirds of non-CFs also take explicit steps to protect forests and approximately one-third even have written rules that govern extractions. Those without governance features are likely ungoverned and similar to open access.

Actual estimates of prices required to incentivize carbon sequestration in low-income countries are very limited. Strassburg et al. (2009) use simulation methods to estimate the costs to reduce GHG emissions due to forest biomass loss in the 20 most forested developing countries. They find that a price of CO2e of $8.00 per ton would reduce global emissions by 90%. Similar simulation methods were used by Kinderman et al. (2008) and they find it would cost $10-$21 per ton of CO2e emissions avoided to achieve a 50% reduction in deforestation.<sup>5</sup> These findings suggest that carbon sequestration can effectively compete with other mitigation approaches like renewable energy, energy conservation and fuel switching. At the same time, as discussed by Bluffstone et al. (2013), there is some controversy regarding the degree to which all local opportunity costs of carbon sequestration are included in such approaches (Gregorsen _et al._ 2011).

In Nepal, Karky and Skutsch (2010) examine three REDD+ pilot sites and directly estimate the net benefits from three forest management scenarios, two of which include explicit mitigation within the context of a hypothetical REDD+ program. They estimate that households in all three sites benefit if the price per ton of CO2e is $5.00 and two of three benefit if the price is $1.00 per ton.

> 5 Costs to achieve a 10% reduction in deforestation were estimated to be $2 to $5 per ton of CO2e. This difference is due to increasing marginal costs of sequestration.

6
They estimate breakeven prices of $0.55 to $3.70 per ton of CO2 depending on the site and level of harvest reduction required.

Marseni et al. (2014) analyze eight REDD+ pilot projects in two districts (Chitwan and Gorkha) in Nepal. They attempt to catalogue, and to the degree possible measure, the opportunity costs associated with accepting REDD+ payments to generate carbon credits. Costs are mainly in terms of foregone forest product collections (fuelwood, grass, fodder, timber, grazing), but also include time costs. They note that within the community forestry context, to participate in REDD+ there are large meeting and forest management time costs (e.g. fire control, planting thinning, pruning, cleaning, weeding). They note that the average payment of $1,734 across the eight sites does not cover estimated opportunity costs. They also note that a price of $10/ton of CO2 (at that time the EU Emissions Trading Scheme average price) would not even cover average meeting time opportunity costs. They do not estimate breakeven costs, but express doubt that REDD+ can be truly cost effective.

Pandit et al. (2017) examine the economics of carbon sequestration in Nepal using household survey data from 47 CFUG REDD+ pilot sites in two of Nepal’s 75 districts. Half the sites are in the plains of Nepal (i.e. the Terai) and half in the hills. They estimate net benefits of the REDD+ program (as was done by Marseni et al., 2014), but also calculate average breakeven carbon prices. They find that if the opportunity cost of unharvested fuelwood is taken into account, at a carbon price of $10 per ton no CFUGs receive positive net benefits. Assuming that biomass would not have otherwise been set aside as an environmental “safeguard,” they find that the average Terai<sup>6</sup> CFUG opportunity cost ranged from $50 to $74 per ton of CO2, depending on assumptions about the ratio of above ground (and therefore harvestable) to below ground biomass. In the hills, they estimate average costs

> 6 The Terai is the plains region bordering India.

7
of $20 to $30 per ton of CO2. They conclude that opportunity costs are relatively high and significant regional heterogeneity in opportunity costs due to ecosystem productivity, opportunity costs of time, etc. may exist. Following this guidance, in our paper we also examine heterogeneous effects.

# **2. Methods and Data**

The goal of this paper is to estimate the supply of carbon sequestration in tons of CO2e from community-controlled forests as a function of hypothetical payments for reducing fuelwood harvests and grazing. Carbon supply is the behavioral response in terms of emissions reduced expected from offering a particular monetary incentive and because in rural Nepal most carbon emissions come from burning fuelwood, this paper primarily focuses on this potential source of reduced CO2 emissions. In our sample, 95% of households cook with fuelwood as their main fuel, sometimes in conjunction with other fuels and associated technologies.

In rural areas of low-income countries like Nepal, grazing is estimated to be a significant source of above ground biomass reduction and therefore carbon emissions. Based on the results of several studies (e.g. Keller, 1998), IPCC (2000) recommends an estimate of 0.4 – 0.5 tons per hectare carbon gains (1.464 – 1.83 tons of CO2) due to grazing elimination in dryer areas. Though we are unable to provide very detailed estimates of carbon supply due to grazing, we offer estimates of the cost to incentivize the elimination of open grazing and the CO2 emissions reductions that can be expected from those payments.

Less harvesting of fuelwood can potentially reduce carbon emissions if such actions result in increased efficiencies, replacing unsustainable harvest sources (e.g. open access forests) with sustainable sources, such as wood from trees planted on users’ own farms or use of biogas from existing animals. Of course, if households comply with an agreement to reduce fuelwood harvests from their own forests by simply harvesting fuelwood from another forest, then carbon sequestration 8
gains are reduced or eliminated due to such leakage. Furthermore, if open grazing elimination were to move cattle to nearby common lands, there would also be leakage. Understanding the likely extent of leakage is therefore critical for understanding the degree to which PES schemes, such as REDD+ actually achieve stated goals. In this paper, we are not able to conclusively address the possibility for leakage, but we do present descriptive results that suggest more stringent fuelwood collection limits would not cause households to primarily rely on alternative common forests.

To estimate carbon supply functions, we use marginal effects derived from a choice experiment conducted in Nepal during April – June 2013. Alpizar et al. (2003), Hanley et al. (2001), Hensher et al. (2005), and Hoyos (2010) provide reviews of the choice experiment methodology. The purpose of this experiment is to examine preferences for REDD+ contracts in 1,300 households in 130 formal and informal forest user groups across Nepal.<sup>7</sup> The sample was developed based on a nationally representative sample of 137 forests analyzed during an evaluation of the Nepal Community Forestry Program (MoFSC, 2013). We randomly chose 65 official community forests (CFs) from this sample and then matched it with 65 communities outside the program so our samples of CFs and non-CFs could be compared; half of respondents are in CFs and half in non-CFs. We limited our sample to communities in the middle hills and Terai regions of Nepal and omitted the high mountain region, which offers limited carbon sequestration potential. Figure 1 shows the sample distributed across Nepal.

> 7 Comprehensive results from the analysis can be found in Dissanayake et al. (2015), but this paper did not explicitly use the results to analyze carbon supply.  We therefore extend our earlier paper here.

9
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The context of our choice experiment is a set of hypothetical REDD+ contracts, where each contract has multiple attributes; the amount of the REDD+ payment, required fuelwood harvest reductions, required grazing closures, and the division of the payment between private households and the community. In this paper, we are interested in fuelwood and grazing ecosystem services and what level of compensation households would require to forego those services. Choice experiments are ideal for estimating the value of ecosystem services within such frameworks (Boxall et al. 1996, Louviere et al. 2000, Dissanayake and Ando 2014, Witkin 2015), because they allow the researcher to elicit marginal values for each attribute.

The attributes were selected through focus group discussions in a total of 18 communities, half CFs and half non-CFs, including both hill and _Terai_ regions.<sup>8</sup> These attributes are presented in Table 1. As shown in the table, respondents who were not CF members had four attributes; contract payment denominated per household, percentage of the payment going to the household (as opposed to the community), required reduction in fuelwood and the required reduction in grazing. The grazing restriction attribute was not included in choice experiments for those who are members of CFs, because CFs virtually always have grazing restrictions. An important implication of having different choice experiments for CF member and nonmember households is that these samples must be analyzed independently; we therefore do not pool the data.

> 8 The attributes were refined after examining the REDD+ literature and analyzing focus group results. Lancaster (1966) was one of the first to suggest that consumers get benefits from the characteristics of services rather than services themselves. Choice experiments can therefore be considered analogous to hedonic pricing, except for a stated preference setting.  As is the case with all experimental methods, hypothetical bias and lack of external validity are potentially important concerns (e.g. see Carlsson, F. and P. Martinsson, 2001).

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**Table 1: Attributes and Levels in the Choice Experiment**

|**Attributes**|**Description**|**Levels**|
|---|---|---|
|REDD + payments (Rs. per<br>household per year)|Annual total REDD+ payment to<br>your community.| Rs. 1000<br> Rs. 2000<br> Rs. 3000<br> Rs. 4000<br> Rs. 5000|
|Portion of the REDD+<br>payment<br>going<br>to<br>the<br>household.|The<br>portion<br>of<br>REDD+<br>payments that go to communities<br>for community projects and /or<br>equally<br>divided<br>between<br>households in your group.| 100% community<br> 50% community and<br>50% household<br> 100% household|
|Reduction in amount of fuel<br>wood collected|Required<br>fuelwood<br>reduction<br>measured as a portion of your<br>current use.| 25%<br> 50%<br> 75%<br> 100%|
|**Only Households w**<br>|**ho are Not CF Members**<br>||
|Grazing restrictions<br>(only<br>for<br>non-CF<br>households)|Open grazing is prohibited| Yes<br> No|

Once an initial list of attributes was developed, we pretested the instrument with potential respondents. The final choice experiment contains background information about the REDD+ program, a description of the attributes and levels, seven choice sets, and a small demographic questionnaire. Appendix A provides the actual background information document used for the choice experiments. For each of the choice sets, the respondents chose between two alternatives and the status quo. Figure 2 presents an example of a choice set, with the first row showing the payment, the second presenting the distribution of the payment (100% household, 100% community and 50:50) and the last indicating the required fuelwood reduction. All attributes are depicted pictorially as well as verbally, because many respondents were illiterate.

When conducting a choice experiment, the respondent makes choices over bundles of attributes and it is necessary to generate choice cards and identify the levels of each attribute that will 12
appear in each choice set. We follow standard practice in the choice modeling literature (Adamowicz et al. 1997, Adamowicz et al. 1998, Louviere et al. 2000) and create an efficient experimental design that allows both main effects and interaction effects to be estimated. The designs for the choice experiments were generated following Kuhfeld (2010)<sup>9</sup> and achieve a 100% D-efficiency (Huber and Zwerina 1996, Carlsson and Martinsson 2003).

The experimental design yielded 30 unique choice profiles for CF households and 60 unique choice profiles for non-CF households (Kuhfeld 2010, Vermeulen et al. 2008). We create a block design giving five unique experiments for CFs and 10 unique experiments for non-CFs, where each experiment has six choice questions. Carlsson et al. (2010) test for learning and ordering effects in CE surveys and show that dropping the first choice question can decrease the error variance of estimates. We therefore duplicate the first choice set, yielding a total of seven choices made by respondents, and drop the first choice before conducting the analyses to account for possible learning effects.

> 9 The experiment design was conducted using the SAS experiment design macro (Kuhfeld 2010).

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_X_  where _qit_ is a vector of non-stochastic explanatory variables. The parameter _qi_ represents an intrinsic preference for the alternative (also called the alternative specific constant, ASC). We include an alternative specific constant (ASC) term in the estimation to account for the fact that option A and option B are both REDD+ contract options that can be viewed as closer substitutes with each other than with the status quo option (Haaijer, et al _._ 2001; Blaeij et al. 2007). In this setting the unconditional choice probability for individual _q_ is given by the conditional choice probability of individual _q_ choosing alternative _i_ in choice situation _t_ is _Lq_ (  _q_ ) This choice probability allows for the coefficients to vary and thereby relaxes the assumption that each individual has identical preferences. We estimate the model using the mixlogit command in STATA (Hole 2007).

Given that the mixlogit model identifies a distribution for each parameter, it is possible to use the results to generate individual level coefficients. We do so by following Revelt and Train (2000), Hensher and Green (2003), and Hole (2007) and calculate the likelihood of a respondent choosing a specific set of choices from a set of alternatives. It is important to note that the individual estimates we generate are the conditional means of the coefficient distribution for the respondents who made identical choices when faced with the same choice set (Campbell et al. 2006; Hole 2007). The expected value of  _q_ conditional on a given response pattern _p_ and a set of alternatives _a_ is given by 15
The value of _E_ [  | _pq_ , _aq_ ) can be approximated using simulation and we use the _mixlbeta_ command in STATA to derive the individual parameter estimates (Hole 2007). For the results included in the text of the paper, we assume the preferences for attributes are the payment vehicle and fuelwood reduction attributes are random and lognormally distributed. This assumption assures that willingness to accept compensation for fuelwood reductions is always positive. As robustness checks, we present results that assume attributes are normally distributed and that only fuelwood reduction (not the payment vehicle) is lognormally distributed. As the findings are not appreciably different from those presented in the paper, we only briefly mention these results in the text.

These marginal effects are the willingness to accept payment (in Nepali Rupees) for a 1-percent reduction in fuelwood collections from common forests. As shown in Table 1, these are derived from four discrete percentage reduction possibilities. We use the percentage reduction metric, in contrast to a pure quantity-based measure, because it is relevant to all households regardless of their circumstances. It is also true, though, that different households have different family sizes, food consumption patterns, live at different altitudes, exist in environments of different qualities and characteristics, etc. They therefore will have different meanings of 1-percent reductions. We address this issue below.

Choice experiments only produce a single estimated WTP or in our case, WTA, value for each household. To examine small reductions that are greater than 1%, we must assume homothetic 16
preferences for cash (in the form of REDD+ payments) and fuelwood collections up to the fuelwood collection reduction percentage required by the simulated program. This implies that if we increase the total payment by a given percentage, households will reduce their fuelwood collections by this same percentage.

This assumption, which is necessitated by our use of the choice experiment methodology, therefore implies a scaling of the payment and response by the same factor. It also implies that each household’s willingness-to-accept per ton of fuelwood reductions is unresponsive to the level of the reduction program. Our carbon supply function estimates are therefore conservative (i.e. less reduction generated for a given payment), because in reality we would expect that individual households require higher prices to supply more carbon reductions; this is consistent with standard increasing marginal cost if greater levels of carbon sequestration are to be supplied, which we expect not only across households as we analyze here, but also within households. Because we assume that household-level opportunity cost functions are constant, we do not also assume those estimates apply to highly non-marginal fuelwood reductions.

We therefore discuss our results in two ways. First, we present the proportion of households who are willing to participate in a hypothetical REDD+ fuelwood reduction program at various prices. We then simulate the quantity effects for a hypothetical nationwide program that reduces fuelwood collections by 25% from current levels, assuming constant household-level marginal costs. We chose this value after consultation with Nepalese forestry experts, because it would be viewed as significant for households, but would not be considered an overwhelming reduction. At the mean, this reduction would be about 75 kg per month or about two basket loads per household (average size about 6 persons). Reported responses, including fuel substitutions, associated with such reduction requirements are discussed below.

17
Though a percentage reduction will have relevance to all respondents, people will have different meanings of 1-percent reductions.<sup>11</sup> We ideally would like to customize these meanings to each household. Our data indeed include information on average monthly fuelwood consumption and across our sample we find that the average fuelwood consumption is reported to be 298 kilograms per month. Only three-quarters of our sample respondents reported their fuelwood consumption, however, and we are concerned that those who did not provide information differ systematically from those who provided information. We also worry that consumption may differ from fuelwood collections, which is our interest in this paper. For example, some households may buy fuelwood, which would cause collections and consumption to differ.

To create a standard meaning of a 1-percent reduction in fuelwood collections, we utilize a nationally representative estimate from the literature. We argue that this approach is appropriate, because our communities are randomly chosen from a nationally representative random sample, with half CFs and half non-CFs, half of which are in the hills and half in the _Terai_ , which are the two most important and relevant stratification dimensions. Households within communities are also chosen randomly, so the moments of the sample should be representative of the population. Using these features of our sampling frame, we assume that a 1-percent fuelwood collection reduction in our sample on average has the same meaning as a 1-percent reduction in average, national, rural household fuelwood collections.

We utilize the average fuelwood collection estimate of 2.40 tons per household per year from Somanathan and Bluffstone (2015), which is derived from the nationally-representative 2010/2011 Nepal Living Standards Survey (NLSS). This estimate is slightly lower than the 2.54 tons per household per year from Nepal et al. (2010), which used the 2003/2004 NLSS and is about 8% below

> 11 This is not unique to our analysis, but is inherent in all carbon forestry PES programs implemented in primarily subsistence agriculture settings.

18
the estimate of Baland et al. (2010), which uses the 1995/1996 survey. Our estimate is also 17% below those for Dolakha District by Kandel et al. (2016). We therefore see our estimate of a 1-percent decrease in fuelwood collections (in tons) as reasonable, but erring on the conservative side, because each 1% reduction implies less wood reduced than in our consumption data and in much of the literature.

Only preservation of non-renewable biomass generates additional carbon savings and as we saw in the previous section, in our sample CFs implement a variety of restrictions to increase sustainability. We cannot know the sustainability implications of each household’s reduction in fuelwood collections or even the average portion of collections that are currently sustainable across our sample. To estimate the percentage of biomass harvests that are currently unsustainable, we use estimates from Bailis et al. (2015) and Drigo et al. (2014), who calculate nonrenewable biomass estimates by country and region within country using the Woodfuel Integrated Supply/Demand Overview Mapping (WISDOM) model. The estimate for Nepal is 52%, which is the estimate we employ in this paper, implying that slightly more than half of fuelwood harvest reductions generate additional carbon savings.

Each ton of unsustainably harvested fuelwood that is not collected would have been burned to generate heat for cooking and perhaps heating. Not all the biomass in fuelwood is carbon and for our analysis we use the IPCC (2013) emissions factor (0.50) to convert fuelwood not burned to carbon not emitted. We then apply the standard factor of 3.67 tons of CO2 per ton of carbon to convert carbon to CO2, which is the metric for international carbon transactions.

In Nepal, only about 4% of households who cook with fuelwood use improved biomass cookstoves (Nepal et al., 2011). Much of the rest use traditional, inefficient stoves, such as the Nepali _chulo_ , which is made of mud and stone, and typically vents waste gases within the home. Such 19
traditional stoves not only emit CO2, but also other gases, including aerosols that are estimated to have negative global warming potentials (GWP) and black carbon, which is a short-lived, but potent greenhouse gas. Indeed, about 25% of black carbon emitted into the atmosphere comes from millions of small, typically traditional stoves throughout the developing world (Bond _et al._ 2013). Smith _et al._ (2000) indeed find that, depending on the timeline examined, the GWP of a meal cooked with biomass can be significantly higher than the GWP of a meal cooked using fossil fuels.

We utilize calculations from Somanathan and Bluffstone (2015), based on emissions factors and estimated GWPs taken from IPCC (2013), Pandey et al. (2014) and Grieshop et al. (2011), combined with the estimated nonrenewable biomass harvest estimate from Drigo et al. (2014) and Bailis et al. (2015), to derive 1.6047 tons of CO2e saved per ton of fuelwood not harvested. These calculations are presented in Table 2.

Table 2: Emission Factors and 100-Year Global Warming Potentials in Tons of CO2e.

|Pollutant|Emission Factor (g of<br>Pollutant/Kg of wood)|100-year Global<br>Warming Potential<br>(tCO2e)|Tons of CO2e saved per<br>ton of wood|
|---|---|---|---|
|CO2|1358|0.52|0.7062|
|BC|0.7|900|0.63|
|OC|1.9|-46|-0.0874|
|SO2|0.1|-76|-0.0076|
|CO|76|1.8|0.1368|
|NMVOC|6.9|8.8|0.06072|
|CH4|4.9|28|0.1372|
|N2O|0.1|265|0.0265|
|**TOTAL**|||**1.6047**|

Sources: Emission factors: (Pandey et al. 2014), Table SI3. Global Warming Potentials: IPCC (2013), Appendix 8.A, except for SO2 which is from Grieshop et al. (2011). The GWP for CO2 is adjusted to account for an estimated 48% of all fuelwood that is sustainably harvested (Drigo et al., 2014 and Bailis et al., 2015).

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We estimate participation at various CO2 prices based on our sample as well as a national rural CO2 supply function for a program that asks rural households to reduce fuelwood collections by 25%. We scale our experimental results using the 2011 estimated national rural population of 4.4 million rural households (CBS, 2012; 2016) and the 1.45 million rural households who are members of CFs (MoFSC, 2017).

Finally, we are interested in potential carbon supply shifters. We therefore run OLS regressions of marginal WTA values on key household and community variables. We emphasize that the cross-section nature of our data does not allow us to convincingly infer causality, because it is possible that omitted variables simultaneously affect choice experiment responses and key household and community variables. Nevertheless, the experimental nature of our dependent variable virtually rules out reverse causality, though perhaps not all possible confounders and we do not seek to infer causality. We merely would like to know which respondent characteristics, technologies previously adopted, assets and perceptions of REDD+ are associated with higher WTA. Correlations are therefore sufficient for our purposes.

# **3. Results**

We begin our discussion with a brief overview of descriptive statistics, followed by estimates of household participation in programs to reduce fuelwood collections and then present national level supply functions for CO2 reductions from a program that requires all households to reduce fuelwood collections by 25%. We do this for CF members and those outside the CF program and then consider the costs of carbon sequestration due to open grazing closures in non-CF communities.<sup>12</sup> Finally, we present our OLS analysis of carbon supply shifters.

> 12 In the appendix, for the assumed normally distributed models, we trim 12 of 650 observations for non-CFs and 2 of 650 for CFs that have WTA estimates that are above $1000 and below -$1000 for 1% (average 24 kg/year) reductions in

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# <u>Overview of the Sample</u>

Table 3 below presents key descriptive statistics from our sample of 1,300 households. These are the variables we use as potential carbon supply function shifters. Respondents were mainly male, but with a significant minority (34%) of respondents being female. The sample includes a variety of ethnic groups, including _Dalit_ (i.e. lowest) castes (16%) and 38% of households are classified as poor or ultra-poor. Virtually all households use fuelwood, but 9% also use biogas and 15% each use LPG or improved biomass cookstoves. Virtually all households own some land and 42% say that REDD+ will very likely or extremely likely benefit them personally.

Table 3: Descriptive Statistics of Sample and Potential Carbon Supply Shifters

|Variable|Mean|Standard Deviation|
|---|---|---|
|Member of CF (1 = yes, 0 otherwise)|0.50|0.50|
|Respondent age (years)|50.61|13.76|
|Respondent is female (1 = yes, 0 otherwise)|0.34|0.48|
|Female-headed households (1 = yes, 0 otherwise)|0.16|0.37|
|Household size (number of people)|5.96|2.52|
|Household is classified as poor or ultra-poor (1 = yes, 0 otherwise)|0.38|0.48|
|Dalit ethnic group (1 = yes, 0 otherwise)|0.16|0.37|
|Indigenous or Newar ethnic group (1 = yes, 0 otherwise)|0.41|0.49|
|Brahmin or Chetri ethnic group (1 = yes, 0 otherwise)|0.40|0.49|
|Madheshi ethnic group (1 = yes, 0 otherwise)|0.02|0.14|
|Respondent migrated to site from another location (1 = yes, 0 otherwise)|0.25|0.44|
|Uses biogas (1 = use, 0 otherwise)|0.09|0.29|
|Uses LPG (1 = use, 0 otherwise)|0.15|0.36|
|Uses improved biomass cooking stove (1 = use, 0 otherwise)|0.15|0.36|
|Uses firewood (1 = use, 0 otherwise)|0.95|0.22|
|Household owns land (1 = yes, 0 otherwise)|0.94|0.24|
|Walking distance from respondent’s house to road < 2 hours (1 = yes,<br>0 otherwise)|0.70|0.46|

fuelwood collections. The May 2013 exchange rate of the Nepali Rupee with the $US was RS. 85/$US. As no observations meet this criterion, the lognormal results are not trimmed.

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|Respondent says they are very likely or extremely likely to benefit<br>personally from REDD+ (1 = yes, 0 otherwise)|0.42|0.49|
|---|---|---|
|Reported firewood used per month (kilograms)|298.87|394.84|
|Number of formal CFUGs (number of groups)|1.3|0.73|

We now present illustrative information on the possibility for leakage as fuelwood collection restrictions are put in place. This information comes from our household survey. Respondents were directed to indicate all options that would apply, given restrictions of 25%, 50%, 75% and 100%. Figure 3 shows that faced with a 25% fuelwood collection restriction, over half of respondents would shift to fuels produced on their private lands. About 30% would include reductions in their energy consumption in their response portfolio, about 25% would gather from other forests and substantial portions would shift to biogas and LP gas or gather in CFs. Very limited portions of households would use crop residues, kerosene or dung (not shown).

Figure 3 Alternative Energy Sources Identified for 25% Fuelwood Restrictions in Respondents’ Main Forests 23
Figure 4 shows identification of biogas and LP gas as alternative fuels for 25%, 50% and 100% fuelwood collection restrictions in respondents’ main forests. Other alternative energy sources used are roughly the same as in Figure 3 as restrictions increase. For example, 54% to 56% of respondents say they would rely on their own land for fuel and 25% - 28% say they will gather from government forests. In contrast to reliance on other fuels, which does not vary as hypothetical fuelwood collection restrictions increase, respondents report that biogas and LPG use will increase as fuelwood from main forests is more stringently regulated. When no fuelwood can be collected from respondents’ main common forests, indeed over 25% of respondents say they would include LPG in their fuel portfolios.

Figure 4 Reported Additional Use of Biogas and LPG at Various Fuelwood Restrictions in Respondents’ Main Forests This indicative evidence suggests that some leakage will occur if a PES scheme to sequester additional carbon were put in place, because about a quarter of respondents say that they would gather additional wood from government forests and just under 20% say they would gather from community 24
forests. We do not know anything about the amounts they would gather as responses are prospective. It is also striking, though, that so many respondents also point to their own land as an alternative source of fuel. These extractions would presumably not represent leakage as households are likely to manage their biomass resources in their best interests and replant as appropriate to maintain flows. Cleaner fuels like biogas and LPG are also expected to be important fuel sources if REDD+ programs were to be instituted in rural Nepal.

# <u>Carbon Supply by CF Member Households</u>

Figure 5 presents the estimated participation of CF member households in programs to reduce fuelwood collections at various CO2 prices, with CO2 emissions reductions credited as in table 2. Estimated prices to incentivize participation in the program range from $7.00 to $655 per ton of CO2, with a median of $48 and an average of $73. Assuming normally distributed payment vehicle and fuelwood reduction attributes, the median WTA is $56 with an average of $63. Approximately 25% of households are estimated to participate at a price of $30 per ton and 75% participate at a price of $93 ($27 and $86 assuming normal distributions). These results suggest that estimated costs of reducing fuelwood collections are relatively high, particularly considering that the 2015 global social cost of carbon is estimated at approximately $40 per ton (USIAWG, 2015).

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Figure 5: CF Household REDD+ Program Participation at Various CO2 Prices <!-- Start of picture text --> Participation of CF Households in Program Requiring Fuelwood<br>Reductions at Various per‐ton CO2 Prices *<br>$700.00<br>$600.00<br>$500.00<br>$400.00<br>$300.00<br>$200.00<br>$100.00<br>$0.00<br>Participation in Program<br>0.16% 2.95% 5.75% 8.54% 11.34% 14.13% 16.93% 19.72% 22.52% 25.31% 28.11% 30.90% 33.70% 36.49% 39.29% 42.08% 44.88% 47.67% 50.47% 53.26% 56.06% 58.85% 61.65% 64.44% 67.24% 70.03% 72.83% 75.62% 78.42% 81.21% 84.01% 86.80% 89.60% 92.39% 95.19% 97.98% 100.78%<br><!-- End of picture text -->

* Assumes Lognormal Distribution of Payment and Fuelwood Reduction Attributes

In Figure 6, we present our estimate of the CO2 supply function for a program that requires CF member households to reduce fuelwood collections by 25%. It is estimated that 1.45 million households are members of the CF Programme and it is therefore for this population that we estimate the supply of carbon. As discussed in our Methods and Data section, the moments of the price distribution are constant, because we estimate willingness to accept using a choice experiment, which only produces one marginal cost value for each respondent; this implies that the carbon supply price per ton does not increase when we evaluate a larger-than-1%-increase. For this reason, we only consider a program that requires a 25% reduction in fuelwood collections, which is likely to be at the upper end of “marginal,” but still significant enough to be monitorable. For reference, results for a 10% reduction program are included in the Appendix, but such a program seems too insignificant.

At the median willingness to accept value of $48 per ton of CO2 reduced, we estimate that 695,877 additional tons will be sequestered by the population of 1.45 million households, with a total 26
revenue of $33.5 million or about $23 per household per year. At a price of $40, which is consistent with contemporary estimates of the social cost of carbon, approximately 570,142 additional tons are estimated to be sequestered by CF households, with total revenue of $22.8 million or about $16/household per year. At a price of $10 per ton, we estimate that very little carbon will be sequestered (less than 10,000 tons).

Figure 6: CF CO2 Sequestration Supply at Various CO2 Prices <!-- Start of picture text --> National CF CO2 Supply Function<br>25% Fuelwood Reduction Program<br>$700.00<br>$600.00<br>$500.00<br>$400.00<br>$300.00<br>$200.00<br>$100.00<br>$0.00<br>Cumulative Tons of CO2 in National Rural Population<br>2167.84 41188.96 80210.08 119231.20 158252.32 197273.45 236294.57 275315.69 314336.81 353357.93 392379.05 431400.17 470421.29 509442.41 548463.54 587484.66 626505.78 665526.90 704548.02 743569.14 782590.26 821611.38 860632.50 899653.63 938674.75 977695.87 1016716.99 1055738.11 1094759.23 1133780.35 1172801.47 1211822.59 1250843.72 1289864.84 1328885.96 1367907.08 1406928.20<br><!-- End of picture text -->

* Assumes Lognormal Distribution of Payment and  Fuelwood Reduction Attributes

# <u>Carbon Supply by Households outside the Community Forestry Program</u>

We find that households who are outside the Nepal Community Forestry Program have lower willingness to accept fuelwood reductions (Figure 7). We estimate that the median cost per ton of CO2 sequestered by those outside the CF program is $41/ton and the average is $40 per ton ($33 and $40 assuming normal distribution of all attributes). For a given CO2 price, households outside the CF program are estimated to be willing to sequester more carbon than CF households. At the median, 27
the cost difference is approximately 40% and whereas CF households required a carbon price of $27 for 25% of households to participate, non-CF households are estimated to require only $16.40, or 41% less ($24 assuming lognormal distribution). Rather than requiring $86/ton for three-quarters of households to participate, those outside the CF program are estimated to demand only $55 or 36% less ($45 assuming lognormal). At $10 per ton, roughly 18% of households are willing to participate, compared with 9.6% for CF households (9% with lognormal distribution). Approximately 7% of respondents report negative willingness to accept values.

Figure 7: Non-CF Household REDD+ Program Participation at Various CO2 Prices <!-- Start of picture text --> Participation of Non‐CF Households in Program Requiring Fuelwood<br>Reductions at Various per‐ton CO2 Prices *<br>$1,200.00<br>$1,000.00<br>$800.00<br>$600.00<br>$400.00<br>$200.00<br>$0.00<br>Participation in Program<br>0.16% 2.80% 5.43% 8.07% 10.71% 13.35% 15.99% 18.63% 21.27% 23.91% 26.55% 29.19% 31.83% 34.47% 37.11% 39.75% 42.39% 45.03% 47.67% 50.31% 52.95% 55.59% 58.23% 60.87% 63.51% 66.15% 68.79% 71.43% 74.07% 76.71% 79.35% 81.99% 84.63% 87.27% 89.91% 92.55% 95.19% 97.83% 100.47%<br><!-- End of picture text -->

* Assumes Lognormal Distribution of Payment and Fuelwood Reduction Attributes

In addition to getting more reductions per monetary unit paid, there are also many more households outside the CF program than inside it (2.95 million households versus 1.45 million). Implementing a program to reduce fuelwood collections by 25% therefore generates much more sequestration. As shown in Figure 8, at the median WTA of $33 per ton of CO2, 1.43 million tons are estimated to be sequestered, raising revenues of $48 million. As this amount is spread over 2.95 million households, an average household receives just over $16 per year. At a price of $40/ton (consistent with the 2015 estimate of the global social cost of carbon), 1.67 million tons are estimated 28
to be sequestered and generate revenues of $67.2 million or $23 per household. At $10 per ton, roughly 500,000 tons are sequestered and generate about $5 million in revenues.

Figure 8 Non-CF Household CO2 Sequestration Supply at Various CO2 Prices <!-- Start of picture text --> National Non‐CF CO2 Supply Function<br>25% Fuelwood Reduction Program<br>$1,200.00<br>$1,000.00<br>$800.00<br>$600.00<br>$400.00<br>$200.00<br>$0.00<br>Cumulative Tons of CO2 in National Rural Population<br>2167.84 43356.80 84545.76 125734.72 166923.68 208112.65 249301.61 290490.57 331679.53 372868.49 414057.45 455246.41 496435.37 537624.34 578813.30 620002.26 661191.22 702380.18 743569.14 784758.10 825947.06 867136.02 908324.99 949513.95 990702.91 1031891.87 1073080.83 1114269.79 1155458.75 1196647.71 1237836.68 1279025.64 1320214.60 1361403.56 1402592.52<br><!-- End of picture text -->

* Assumes Lognormal Distribution of Payment and  Fuelwood Reduction Attributes

We now provide indicative estimates of the costs of eliminating open grazing for non-CF households, with the goal of characterizing whether opportunity costs might be competitive with other mitigation options. To accurately apportion shares of grazing to each respondent is impossible. We therefore cannot estimate household-specific carbon supply functions for grazing elimination and our findings should therefore be viewed largely as suggestive of whether opportunity costs are high or low. In contrast to fuelwood reductions, for grazing, we assume normal distributions of grazing reduction and the payment vehicle, because assuming lognormal distributions generated 76 WTA values greater than $1,000; we consider these values, which represent 12% of our observations, as outliers. Assuming normal distributions, the range of WTA values by the NCF respondents is -$805 29
to $3,746 per year. The average WTA per year to forego open grazing is $46 and the median is $20. In making these calculations we drop the highest and lowest 3 observations as potential outliers (<1% of observations).

Non-CF forest user communities on average have 296 households (maximum 1,881) and mean forest area per household is 0.75 hectares (maximum 9.25). We assume that respondent opportunity costs represented by WTA depend primarily on factors other than forest area, such as outside income-earning opportunities and non-forest fodder production/grazing possibilities and divide each household WTA by the mean forest per household of 0.75. This give us the respondentspecific WTA per hectare of forest, which is a potential estimate of the average opportunity cost should open grazing be eliminated. We then recalibrate these values to be per ton of CO2 sequestered rather than emitted, using the IPCC (2000) centroid value of 1.647 tons of CO2 per hectare.

With the above assumptions, we find that the mean WTA is $37.24 per ton of CO2 and at the median, the estimated opportunity cost of grazing closure is $16.19. These costs are similar to the costs of reducing carbon via fuelwood collection restrictions in non-CF settings.

# <u>Carbon Supply Shifters</u>

Table 4 presents an OLS regression of the potential carbon supply shifters in Table 2 on WTA of CF member households for reducing fuelwood collections; this approach allows us to consider heterogeneous effects and explain the distribution of WTA values in terms of potential carbon supply shifters. As our goal is to explain the distribution of WTA estimates rather than estimate the cost of carbon sequestration measured as CO2, we do not constrain WTA estimates to be positive. We therefore assume normal distributions. Estimates assuming lognormally distributed payment vehicle and fuelwood reduction attributes are presented in the Appendix.

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We see that households that are poor, large, female-headed, use improved biomass cookstoves and are larger (likely indicating more children) require more compensation to be induced to participate in the REDD+ program that involves fuelwood reductions. Compared with the mean, female-headed households require roughly 30% more than average households and the same is true for households that use an improved biomass cookstove. Female respondents require roughly 15% more than the average and the effect is approximately the same for poor households. Each additional household member above the average adds about 4% to the WTA compared with the mean. Leaving aside the effect of having an improved cookstove as potentially endogenous, a household that is female-headed, poor, with 8 household members rather than the mean of 6 and the respondent is female, is therefore expected to have a WTA about 69% above the mean.

Several variables have negative and statistically significant correlations with WTA values. If respondents are of Madheshi ethnicity, which means they are of Indian origin and likely live in the Terai, WTA is estimated to be much lower than the mean. Households who use biogas on average have estimated WTA values that are about 35% below the mean and the same is true for those who own land. All else equal, those who believe that REDD+ is very likely or extremely likely to benefit them personally on average have WTA values 19% below the mean.

Table 4: OLS Regression of Potential Supply Shifters on Marginal WTA (in Nepali Rupees) of CF Member Households to Participate in REDD+ Programs Requiring Reduction in Fuelwood Collections

|Variable|Regression Coefficient|P Value|
|---|---|---|
|Respondent age(years)|-0.96|0.21|
|Respondent is female(1 =yes,0 otherwise)|34.35<sup>*</sup>|0.10|
|Female-headed households(1 =yes,0 otherwise)|63.71<sup>***</sup>|0.01|
|Household size(number ofpeople)|8.61<sup>**</sup>|0.03|
|Household is classified as poor or ultra-poor (1 = yes, 0<br>otherwise)|35.83<sup>*</sup>|0.09|
|Dalit ethnicgroup (1 =yes,0 otherwise)|-49.09|0.12|
|Indigenous or Newar ethnic group (1 = yes, 0<br>otherwise)|-3.46|0.87|
|Madheshi ethnicgroup (1 =yes,0 otherwise)|-105.45<sup>***</sup>|0.03|

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|Respondent migrated to site from another location (1 =<br>yes,0 otherwise)|-34.10|0.16|
|---|---|---|
|Uses biogas(1 = use,0 otherwise)|-72.04<sup>***</sup>|0.01|
|Uses LPG(1 = use,0 otherwise)|-21.21|0.32|
|Uses improved biomass cooking stove (1 = use, 0<br>otherwise)|68.95<sup>*</sup>|0.08|
|Uses firewood(1 = use,0 otherwise)|-130.67|0.43|
|Household owns land(1 =yes,0 otherwise)|-79.26<sup>**</sup>|0.02|
|Walking distance from respondent’s house to road < 2<br>hours(1 =yes,0 otherwise)|12.35|0.68|
|Respondent says they are very likely or extremely likely<br>to benefit personally from REDD+ (1 = yes, 0<br>otherwise)|-41.28<sup>**</sup>|0.03|
|Reported firewood usedper month(kilograms)|0.01|0.40|
|Number in formal CFUGs(number ofgroups)|-4.28|0.80|
|Constant|532.40<sup>***</sup>|0.01|

***, **, * indicate significant at 1%, 5% and 10% levels.  N= 597, R<sup>2</sup> =0.10, F=3.01, prob>F=0.0007 Assumes normally distributed payment vehicle and fuelwood reduction attributes

Table 5 presents results of the same model using the subsample of respondents who are not members of CFs. We see that the model does not offer us nearly as many insights into heterogeneous effects and supply shifters as noted in Table 4 for CFs. The model is only marginally jointly significant and while the signs of the estimated regression coefficients are broadly similar to those in Table 4, only two estimated coefficients, household size and use of biogas, are significantly different from zero, with the same signs and rough magnitudes as in Table 4. These findings suggest that with the exception of these two variables, the shifters of WTA for reductions in fuelwood collections are fundamentally different in CFs and non-CFs; indeed, in non-CFs the hypothesized variables do not seem to matter for the placement of the carbon supply function.

Table 5: OLS Regression of Potential Supply Shifters on Marginal WTA (in Nepali Rupees) of Non-CF Households to Participate in REDD+ Program Requiring Reduction in Fuelwood Collections

|Variable|Regression Coefficient|P Value|
|---|---|---|
|Respondent age(years)|0.93|0.38|
|Respondent is female(1 =yes,0 otherwise)|-20.24|0.56|
|Female-headed households(1 =yes,0 otherwise)|16.98|0.76|
|Household size(number ofpeople)|12.24<sup>*</sup>|0.09|
|Household is classified as poor or ultra-poor (1 = yes, 0<br>otherwise)|47.78|0.17|
|Dalit ethnicgroup (1 =yes,0 otherwise)|-5.59|0.89|

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|Indigenous or Newar ethnic group (1 = yes, 0<br>otherwise)|20.92|0.59|
|---|---|---|
|Madheshi ethnicgroup (1 =yes,0 otherwise)|19.86|0.70|
|Respondent migrated to site from another location (1 =<br>yes,0 otherwise)|-20.99|0.52|
|Uses biogas(1 = use,0 otherwise)|-73.20<sup>*</sup>|0.03|
|Uses LPG(1 = use,0 otherwise)|48.95|0.35|
|Uses improved biomass cooking stove (1 = use, 0<br>otherwise)|11.30|0.83|
|Uses firewood(1 = use,0 otherwise)|10.60|0.78|
|Household owns land(1 =yes,0 otherwise)|-34.11|0.54|
|Walking distance from respondent’s house to road < 2<br>hours(1 =yes,0 otherwise)|-22.47|0.55|
|Respondent says they are very likely or extremely likely<br>to benefit personally from REDD+ (1 = yes, 0<br>otherwise)|-9.94|0.62|
|Reported firewood usedper month(kilograms)|-0.04|0.39|
|Number in formal CFUGs(number ofgroups)|-4.53|0.81|
|Constant|91.93|0.41|

***, **, * indicate significant at 1%, 5% and 10% levels.  N= 608, R<sup>2</sup> =0.023, F=1.77, prob>F=0.05 Assumes normally distributed payment vehicle and fuelwood reduction attributes

# **<u>4</u>** **~~6.~~ Discussion and Conclusions**

In this paper, we use a choice experiment conducted in 2013 to estimate household-level willingness to participate in a REDD+ program that requires reductions in fuelwood collections, as a function of CO2 prices. We find that for both CF and non-CF households, robust participation occurs at prices that are higher than much of the literature and grazing reduction opportunity costs per ton of CO2 are similar to those for fuelwood reductions in non-CF settings. Rather than prices of $1.00 to $5.00 incentivizing participation, we find that relatively little carbon would be supplied at such prices. This basic finding is in line with two recent published papers focusing on REDD+ pilots in Nepal. These findings in combination with our results, which use very different techniques, suggest that optimism regarding carbon supply in community forestry settings could be somewhat misplaced. Our use of an experimental method to elicit WTA is novel in the recent literature, which has largely used bottom-up methods to estimate WTA within the context of REDD+.

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CFs will almost certainly be the core institution within which REDD+ is implemented in Nepal. We find that average and median WTA values for CFs are substantially greater than for nonCFs, which is not surprising given that while non-CFs may be open access, CFs have typically already imposed fuelwood collection restrictions; REDD+ will just increase those restrictions to generate additional carbon sequestration. Our analysis of carbon supply shifters indeed suggests that non-CF households react to REDD+ very differently than households who are CF members. Broadlyspeaking, “underprivileged” CF member households, such as those who are landless, female-headed and poor, appear to be warier of fuelwood collection restrictions within the context of REDD+. They therefore require higher payments, all else equal, than average respondents.

Our choice experiment methodology has the important advantage that WTA values come from household members themselves, rather than being constructed based on assumed explicit costs. In this regard, our estimates may include a broader array of difficult-to-measure costs, such as inconvenience and transaction costs, that have been found in the literature to be significant and may be associated with - or suspected to be part of – REDD+. As we have noted, though, a downside of using the choice experiment methodology is that it can only provide us with one marginal (and average) WTA value for each household. We therefore can only construct our carbon supply functions across households and do not have the capacity to fully incorporate intra-household marginal costs of carbon supply. Because the estimates are for 1% decreases in fuelwood collections, they are likely underestimates of true marginal WTA for larger reductions in fuelwood collections (e.g. 25% as we have simulated). Furthermore, as is true for any experimental methodology, there is the potential that estimates might not be externally valid outside the experimental framework and even that the hypothetical nature of the estimation procedure may yield biased WTA estimates.

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Future research on carbon supply from households should prioritize estimating carbon supply functions that incorporate both intra-household and inter-household marginal costs. Both are valid elements of carbon supply functions. We have included the latter, which we see as a contribution to a literature that has generally estimated at most a few points rather than anything like a “function.” People who are part of a formal collective action institution, the CF program, react very differently to REDD+ and perhaps other PES schemes than those without such affiliations. We see this point as important, because even in Nepal where the CF program is very large, it only covers about 35% of the rural population and those who are unaffiliated may be able to offer lower-cost carbon sequestration. Additional work on the sources and implications of differences is therefore likely to be very useful.

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