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    Spatial modeling of the biophysical and economic values of ecosystem services in Maryland, USA2020

    CAMPBELL E., CONN C., MARKS R.Journaux et Revues (scientifiques)

    valeur non marchande ou de non usage, GIS, biens et services écosystémiques, aide à la décision

    Ecosystem Services
    Volume 43, June 2020, 101093

    Highlights
    • We found the annual economic value of the seven non-market services assessed for the State of Maryland to be $8 billion.

    • Coastal wetlands were found to have the highest average value, followed by inland wetlands and forests.

    • The information here is being used by Maryland to inform land purchasing for conservation and compensation for impacts on state lands.


    Abstract
    Mapping ecosystem services is increasingly a focal area in ecosystem service research. Mapping services allows spatial variability in the benefits being received to be observed and incorporated in decision-making. Services vary in their biophysical supply as well as their economic value due to differences in ecological function and demand for the service. We map and present both vectors of change for seven ecosystem services for the state of Maryland. Economic value is estimated by observing previous instances of payment for increasing, or avoiding loss, to ecosystem services in Maryland or adjacent states. We find the economic value of ecosystem services from natural lands in Maryland to be significant, totaling $8 billion every year for seven non-market services. Storm-water mitigation is the largest service obtained from natural systems, totaling $3.1 billion annually, followed by wildlife habitat at $2.6 billion. Groundwater recharge, nitrogen removal, surface water protection, carbon sequestration, and air pollutant removal services are also valued and mapped. This work has several active or planned applications for the state’s prioritization of land acquisitions and ecological restoration funding. Results from this work can be viewed online-

    http://geodata.md.gov/greenprint/

    https://doi.org/10.1016/j.ecoser.2020.101093

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