Many countries have adopted large-scale tree-planting programs as a climate mitigation strategy and to support local livelihoods. We evaluate a series of large-scale tree planting 23 programs using data collected from historical Landsat imagery in the state of Himachal Pradesh 24 in Northern India. Using this panel dataset, we use an event study design to estimate the 25 socioeconomic and biophysical impacts over decades of these programs. We find that tree plantings have not, on average, increased the proportion of forest canopy cover, and have modestly shifted forest composition away from the broadleaf varieties valued by local people.Further cross-sectional analysis, from a household livelihood survey, shows that tree planting supports little direct use by local people. We conclude that decades of expensive tree planting programs in this region have not proved effective. This result shows that large-scale tree planting may sometimes fail to achieve its climate mitigation and livelihood goals.3 MainMany countries have begun adopting large-scale tree-planting programs based on the potential of forests to absorb carbon and support local livelihoods 1-3 . As of 2015, the extent of 35 global tree cover from planted forests is estimated at 280 million hectares, and 12 million 36 hectares lie within India 4 . Despite the broad appeal of planting trees, some researchers and practitioners have raised concerns about potential negative impacts of large-scale tree-planting programs on vulnerable people and diverse ecosystems [5][6][7] . Restoration ecologists have cautioned 39 that tree planting should not be equated with forest restoration, but instead countries should 40 consider diverse restoration strategies in diverse ecosystems 7 . However, forest restoration commitments made under international agreements like the Bonn Challenge and UNFCCC Paris Accords demand nationally-coordinated efforts to achieve ambitious restoration targets at immense scale 8 . As a result, much of the current nationally-pledged restoration area is set aside 44 for large-scale tree planting 2,9 . For example, the Indian National Determined Contributions 45 (NDC) from the Paris Accords commits "To create an additional carbon sink of 2.5 to 3 billion 46 tonnes of CO2 equivalent through additional forest and tree cover by 2030" 10 . Understanding the 47

This study evaluates the effectiveness of a Stakeholder Engagement (SE) intervention in improving outcomes for communities affected by oil and gas extraction in Western Uganda. The study design is a randomized controlled trial where villages are randomly assigned to a treatment group (participating in SE) or a control group (not participating). Data are collected via household surveys at baseline and end line in 107 villages in the Albertine Graben. We find that SE improves transparency, civic activity, and satisfaction with issues that most concern the people under study. While satisfaction has improved, it is too early to ascertain whether these interventions improve long-term outcomes. These results are robust when controlling for spillover effects and other subregional fixed effects.

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Myriad scholars, policymakers, and practitioners advocate tree planting as a climate mitigation strategy and to support local livelihoods. But, is the broad appeal of tree planting supported by evidence? We report estimated impacts from decades of tree planting in Northern India. We find that tree plantings have not, on average, increased the proportion of dense forest cover, and have modestly shifted species composition away from the broadleaf varieties valued by local people. Supplementary analysis from household livelihood surveys show that, in contrast to narratives of forest dependent people being supported by tree planting, there are few direct users of these plantations and their dependence is low. We conclude that decades of expensive tree planting programs have not proved effective.

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A growing number of studies seek to identify global priority areas for conservation and restoration. These studies often produce maps that highlight the benefits of concentrating such activity in the tropics. However, the potential equity implications of using these prioritization exercises to guide global policy are less often explored and articulated. We highlight those equity issues by examining a widely publicized restoration priority map as an illustrative case. This map is based on a prioritization analysis that sought to identify places where restoration of agricultural land might provide the greatest biodiversity and carbon sequestration benefits at the lowest cost. First, we calculate the proportion of agricultural land in countries around the world that the map classifies as a top 15% restoration priority. A regression analysis shows that this map prioritizes restoration in countries where displacing agriculture may be most detrimental to livelihoods: countries that are poorer, more populated, more economically unequal, less food secure, and that employ more people in agriculture. Second, we show through another regression analysis that a similar pattern appears sub-nationally within the tropics: 5 km × 5 km parcels of land in the tropics that are less economically developed or more populated are more likely to be top 15% restoration priorities. In other words, equity concerns persist at a subnational scale even after putting aside comparisons between the tropics and the Global North. Restorative activity may be beneficial or harmful to local livelihoods depending on its conceptualization, implementation, and management. Our findings underline a need for prioritization exercises to better attend to the risks of concentrating potentially negative livelihood impacts in vulnerable regions. We join other scholars calling for greater integration of social data into restoration science.

This paper reports the results of a field experiment to assess the collaborative effects of community participation in the Ugandan oil and gas sector. Our research design assesses collaborative impacts as relational between community members and different decision-makers in the sector and measures these impacts from the point of view of local people. Local people often face power imbalances in collaborative governance. Decision-makers are increasingly attempting to mitigate such imbalances to improve outcomes for communities, but little experimental evidence exists showing the impact of such efforts. Using multilevel ordered logit models, we estimate positive treatment effects, finding that encouraging the equitable participation of communities improves collaboration with other actors. Next, we use machine-learning techniques to demonstrate a method for targeting communities most likely to benefit from the intervention. We estimate that purposefully targeting communities that would benefit most yields a treatment effect about twice as large, relative to pure random assignment. Our results provide evidence that interventions mindful of community needs can improve collaborative governance and shows how such communities can be most effectively targeted. The experiment took place across 107 villages (53 treatment and 54 control) and the unit of statistical analysis is the household, where we report outcomes measured from 6,062 household surveys (approximately half at baseline and half at endline).

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