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      Managing nature-based solutions in fire-prone ecosystems: Competing management objectives in California forests evaluated at a landscape scale

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      Frontiers in Forests and Global Change
      Frontiers Media SA

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          Abstract

          California’s cap-and-trade compliance offset market incentivizes forest managers to maintain elevated carbon stocks. It provides these incentives without enforcing standardized fire mitigation practices despite many projects being located in fire prone regions. Here, we evaluated the difference between management actions in California forests that participated in the carbon offset market versus those that engaged with state programs to reduce wildfire risk via fuel reduction treatments. Using remotely sensed data from the California Forest Observatory and the Moderate Resolution Imaging Spectroradiometer, we compared the vertical forest structure and vegetation canopy trends on forest offsets with forests that are receiving fuel treatment. We found California forests managed for carbon under the Improved Forest Management (IFM) program by the California Air Resources Board had higher levels of biomass than forests managed for fire risk reduction as indicated by 2016 lidar-estimated fuel loads. In addition, IFM-participating forests did not reduce their fuel loads between 2016 and 2020, whereas lands receiving grants for fuel management did, indicating that on average, the IFM projects were not engaging in fuel reduction efforts. However, despite the differences in fuel management between IFM projects and active fuel treatments, we found that both types of management saw a declining trend in vegetation greenness between 2015 and 2021. While declining greenness is expected of active fuel treatments associated with vegetation removal, such a trend in the case of IFM indicates additional wildfire risk. Managing forests for long-term carbon storage and sequestration requires consideration of fire risk mitigation. Given the little evidence of fuel reduction in the first decade of IFM projects implementation we question whether the century-long duration of carbon stocks in these offsets is realistic. We recommend that policymakers reevaluate the incentives directed at carbon stock preservation or expansion to better encompass the growing wildfire risk in California.

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          An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

          The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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            The Shuttle Radar Topography Mission

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              Development of gridded surface meteorological data for ecological applications and modelling

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                Author and article information

                Journal
                Frontiers in Forests and Global Change
                Front. For. Glob. Change
                Frontiers Media SA
                2624-893X
                October 19 2022
                October 19 2022
                : 5
                Article
                10.3389/ffgc.2022.957189
                d1c7b7a7-e823-43ed-964b-bdc34d5305cf
                © 2022

                Free to read

                https://creativecommons.org/licenses/by/4.0/

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