Research Article

# Cash for carbon: A randomized trial of payments for ecosystem services to reduce deforestation

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Science  21 Jul 2017:
Vol. 357, Issue 6348, pp. 267-273
DOI: 10.1126/science.aan0568

### Tables

• Table 1 Summary statistics for treatment and control groups.

Observations for treatment and control: PFOs, 564 and 535; villages, 60 and 61. Subsample means with standard deviations in brackets. The last column reports the regression-adjusted difference in means between the treatment and control subsample divided by the pooled standard deviation. None of the differences has a P < 0.10. The standardized difference and P values are based on a regression with subcounty fixed effects and clustering at the village level. IHS denotes inverse hyperbolic sine. Tree cover in village, percentage of village with tree cover, and percentage change in vegetation in village are at the village level. The data source for variables with Tree cover in the variable name is the baseline QuickBird satellite image. The source for percentage change in vegetation is 1990 and 2010 Landsat satellite images. The source for all other variables is the baseline survey.

 Treatment Control ND Household head’s age 47.499 [13.605] 47.589 [14.659] 0.003 Household head’s years of education 7.715 [4.003] 7.931 [4.187] –0.056 IHS of self-reported land area (ha) 4.062 [1.021] 4.004 [0.968] 0.053 Self-reported forest area (ha) 1.727 [3.318] 2.068 [12.413] –0.042 Cut any trees in the last 3 years 0.845 [0.362] 0.858 [0.350] –0.031 Cut trees to clear land for cultivation 0.236 [0.425] 0.241 [0.428] –0.016 Cut trees for timber products 0.704 [0.457] 0.721 [0.449] –0.037 Cut trees for emergency/lumpy expenses 0.25 [0.433] 0.292 [0.455] –0.088 IHS of total revenue from cut trees 1.238 [2.118] 1.397 [2.248] –0.085 Rented any part of land 0.163 [0.370] 0.198 [0.399] –0.091 Dispute with neighbor about land 0.218 [0.413] 0.206 [0.405] 0.035 Involved in any environmental program 0.100 [0.301] 0.111 [0.315] –0.035 Agree: Deforestation affects the community 0.539 [0.499] 0.548 [0.498] –0.014 Agree: Need to damage environ. to improve life 0.064 [0.245] 0.043 [0.204] 0.089 Tree cover in village (ha) 134.515 [108.800] 159.18 [178.011] –0.169 Percentage of village with tree cover 0.247 [0.122] 0.263 [0.132] –0.122 Percentage change in vegetation in village, 1990–2010 0.036 [0.041] 0.041 [0.033] –0.128 Tree cover in PFO land circle (ha) 4.355 [12.466] 3.845 [9.178] 0.050 Percentage of PFO land circle with tree cover 0.199 [0.161] 0.209 [0.157] –0.044 Percentage change in vegetation in PFO land circle, 1990–2010 0.035 [0.066] 0.037 [0.058] –0.016
• Table 2 Program enrollment, compliance, and payments.

All columns include subcounty fixed effects and the four village-level baseline variables used to balance the randomization: number of PFOs in baseline sample, average weekly earnings per capita, distance to the nearest main road, and average size of the reported land nearest the dwelling. Amount paid is in units of 10,000 UGX. Calculated values are means; standard errors are clustered by village. Asterisks denote significance: ***P < 0.01. Outcome data are from CSWCT administrative data.

 Enrolled Enrolled and deemed to have conserved forest Amount paid Proportion of eligible amount paid Treatment group 0.320*** 0.282*** 9.023*** 0.238*** [0.030] [0.028] [1.872] [0.024] Control group 0.009 0.009 0.417 0.007 Observations 1099 1095 1095 1095
• Table 3 Effect of the PES program on tree cover.

All regressions and means are weighted by the proportion of available tree-classification data for the observation. All columns include subcounty fixed effects and the four village-level baseline variables used to balance the randomization. Columns 2, 3, 5, and 6 also control for dummy variables for the date of the baseline satellite image, and columns 2 and 3 control for 1990 and 2010 area covered by photosynthetic vegetation within the village polygon and in aggregate in PFO land circles for the village; columns 5 and 6 control for 1990 and 2010 area covered by photosynthetic vegetation within the village polygon and in the PFO’s land circle. Standard errors are heteroskedasticity-robust in columns 1 to 3 and clustered by village in columns 4 to 6. Significance: *P < 0.10, **P < 0.05, ***P < 0.01.

 Village boundaries PFO-level land circles ΔTree cover (ha) ΔTree cover (ha) ΔLog of tree cover ΔTree cover (ha) ΔTree cover (ha) ΔIHS of tree cover (1) (2) (3) (4) (5) (6) Treatment group 5.549* 5.478** 0.0521** 0.245** 0.267** 0.0447* [2.888] [2.652] [0.021] [0.110] [0.106] [0.023] Control group –13.371 –13.371 –0.095 –0.349 –0.349 –0.073 Control variables No Yes Yes No Yes Yes Observations 121 121 121 995 995 995
• Table 4 Effects of the PES program on secondary outcomes.

All columns include subcounty fixed effects and the four village-level baseline variables used to balance the randomization. Columns 1, 4, 5, and 6 control for the baseline value of the outcome. Baseline data on the outcomes in columns 2 and 3 were not collected. IHS denotes inverse hyperbolic sine. For observations where the baseline outcome is missing, the value is imputed as the sample mean, and the regression includes an indicator variable for whether the baseline value is imputed. Standard errors are clustered by village. Significance: *P < 0.10, **P < 0.05, ***P < 0.01.

 Cut any trees in the past year Allow others to gather firewood from own forest Increased patrolling of the forest in last 2 years Has any fence around land with natural forest IHS of food expend. in past 30 days IHS of nonfood expend. in past 30 days (1) (2) (3) (4) (5) (6) Treatment group –0.140*** –0.170*** 0.109*** 0.036 0.065 0.156** [0.034] [0.033] [0.039] [0.033] [0.074] [0.066] Lee bound (lower) –0.161*** –0.185*** 0.094** 0.007 –0.029 0.053 [0.034] [0.033] [0.039] [0.033] [0.070] [0.064] Lee bound (upper) –0.104*** –0.148*** 0.132*** 0.055 0.144* 0.215*** [0.033] [0.032] [0.039] [0.034] [0.075] [0.064] Control group mean 0.453 0.427 0.378 0.667 2.524 4.363 Control group SD [0.498] [0.495] [0.485] [0.472] [1.177] [1.354] Observations 1018 9767 984 1020 1020 1020 Observations (Lee bounds) 994 957 965 998 998 998
• Table 5 Cost-benefit analysis.

The costs of the PES program compared with the social benefit of delayed CO2, both measured per MT of averted CO2. The base case assumes (i) an average 3-year delay in deforestation (treatment effects undone over 4 years), (ii) no further treatment effects during the 0.5 years between endline QuickBird data collection and program end, (iii) average time from tree-cutting to CO2 emissions of 10 years, and (iv) a monitoring rate of 2 spot checks per monitor per day. Row 2 modifies (i) to assume a 1-year delay in deforestation (treatment effects undone immediately when the program ends). Row 3 modifies (i) to assume the averted deforestation and all subsequent deforestation are delayed by the 2-year duration of the program. Row 4 maintains the base case assumptions but uses the treatment effect estimated using PFO land circles instead of village boundaries. Row 5 modifies (ii) to assume the treatment effects accumulate at the same rate in the final months as we observe in the period before endline data collection. Rows 6 and 7 modify (iii) to shorten and lengthen the gap between tree-cutting and emissions. Row 8 modifies (iv) to assume one spot check per monitor per day. See SM section 4 for further details.

 Scenario Benefit per MT of CO2 ($) Cost per MT of CO2 ($) Benefit-cost ratio 1. Base case: Program effects undone over 4 years 1.11 0.46 2.4 2. Program effects undone immediately 0.37 0.46 0.8 3. Deforestation resumes at normal rate (permanent delay) 0.74 0.05 14.8 4. Base case except using effect size from PFO-level analysis 1.11 0.63 1.8 5. Program effects accumulate for final 6 months 1.11 0.34 3.2 6. Average time until emissions is halved to 5 years 1.17 0.46 2.6 7. Average time until emissions doubled to 20 years 1.00 0.46 2.2 8. Monitoring rate of 1 spot check per day per staff person 1.11 0.53 2.1

### Supplementary Materials

• Cash for carbon: A randomized trial of payments for ecosystem services to reduce deforestation

Seema Jayachandran, Joost de Laat, Eric F. Lambin, Charlotte Y. Stanton, Robin Audy, Nancy E. Thomas