Behavioral sciences can advance conservation by systematically identifying behavioral barriers to conservation and how to best overcome them. Behavioral sciences have informed policy in many other realms (e.g., health, savings), but they are a largely untapped resource for conservation. We propose a set of guiding questions for applying behavioral insights to conservation policy. These questions help define the conservation problem as a behavior change problem, understand behavioral mechanisms and identify appropriate approaches for behavior change (awareness, incentives, nudges), and evaluate and adapt approaches based on new behavioral insights. We provide a foundation for the questions by synthesizing a wide range of behavior change models and evidence related to littering, water and energy conservation, and land management. We also discuss the methodology and data needed to answer these questions. We illustrate how these questions have been answered in practice to inform efforts to promote conservation for climate risk reduction. Although more comprehensive research programs to answer these questions are needed, some insights are emerging. Integrating two or more behavior change approaches that target multiple, context-dependent factors may be most successful; however, caution must be taken to avoid approaches that could undermine one another (e.g., economic incentives crowding out intrinsic incentives).

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High ambient ozone (O 3 ) concentrations are a widespread and persistent problem globally. Although studies have documented the role of forests in removing O 3 and one of its precursors, nitrogen dioxide (NO 2 ), the cost effectiveness of using peri-urban reforestation for O 3 abatement purposes has not been examined. We develop a methodology that uses available air quality and meteorological data and simplified forest structure growth-mortality and dry deposition models to assess the performance of reforestation for O 3 precursor abatement. We apply this methodology to identify the cost-effective design for a hypothetical 405-ha, peri-urban reforestation project in the Houston-GalvestonBrazoria O 3 nonattainment area in Texas. The project would remove an estimated 310 tons of (t) O 3 and 58 t NO 2 total over 30 y. Given its location in a nitrogen oxide (NO x )-limited area, and using the range of Houston area O 3 production efficiencies to convert forest O 3 removal to its NO x equivalent, this is equivalent to 127-209 t of the regulated NO x . The cost of reforestation per ton of NO x abated compares favorably to that of additional conventional controls if no land costs are incurred, especially if carbon offsets are generated. Purchasing agricultural lands for reforestation removes this cost advantage, but this problem could be overcome through cost-share opportunities that exist due to the public and conservation benefits of reforestation. Our findings suggest that peri-urban reforestation should be considered in O 3 control efforts in Houston, other US nonattainment areas, and areas with O 3 pollution problems in other countries, wherever O 3 formation is predominantly NO x limited.air pollution | ecosystem services | natural infrastructure | state implementation plan

Remote sensing offers an increasingly wide array of imagery with a broad variety of spectral and spatial resolution, but there are relatively few comparisons of how different sources of data impact the accuracy, cost, and utility of analyses. We evaluated the impact of satellite image spatial resolution (1 m from Digital Globe; 30 m from Landsat) on land use classification via ArcGIS Feature Analyst, and on total suspended solids (TSS) load estimates from the Soil and Water Assessment Tool (SWAT) for the Cambori u watershed in Southeastern Brazil. We independently calibrated SWAT models, using both land use map resolutions and short-term daily streamflow (discharge) and TSS load data from local gauge stations. We then compared the predicted TSS loads with monitoring data outside the model training period. We also estimated the cost difference for land use classification and SWAT model construction and calibration at these two resolutions. Finally, we assessed the value of information (VOI) of the higher-resolution imagery in estimating the cost-effectiveness of watershed conservation in reducing TSS at the municipal water supply intake. Land use classification accuracy was 82.3% for 1 m data and 75.1% for 30 m data. We found that models using 1 m data better predicted both annual and peak TSS loads in the full study area, though the 30 m model did better in a subwatershed. However, the 1 m data incurred considerably higher costs relative to the 30 m data ($7000 for imagery, plus additional analyst time). Importantly, the choice of spatial resolution affected the estimated return on investment (ROI) in watershed conservation for the municipal water company that finances much of this conservation, although it is unlikely that this would have affected the company's decision to invest in the program. We conclude by identifying key criteria to assist in choosing an appropriate spatial resolution for different contexts.

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