کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6556624 | 161840 | 2016 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Global use of ecosystem service models
ترجمه فارسی عنوان
استفاده جهانی از مدل خدمات اکوسیستم
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کلمات کلیدی
خدمات محیط زیستی، مدل سازی، پشتیبانی تصمیم ظرفیت سازی، حفاظت،
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
علوم کشاورزی و بیولوژیک (عمومی)
چکیده انگلیسی
Spatial models of ecosystem services inform land use and development decisions. Understanding who uses these models and conditions associated with use is critical for increasing their impact. We tracked use of The Natural Capital Project's InVEST models and observed 19 different models were run 43,363 times in 104 countries over a 25-month period. Models for regulating services were most commonly used. We analyzed relationships between country-level variables and use of models and found capacity (population, GDP, Internet and computer access, and InVEST trainings), governance, biodiversity, and conservation spending are positively correlated with use. Civic involvement in conservation, carbon project funding, and forest cover are not correlated with use. Using multivariate statistical models, we analyzed which combinations of country-level variables best explain use of InVEST and found further evidence that variables related to capacity are the strongest predictors. Finally, we examined InVEST trainings in detail and found a significant effect of trainings on subsequent use of InVEST models. Our results indicate the general capacity of a country may limit uptake and use of decision support tools such as InVEST. Model-specific trainings are only one form of capacity building likely required for models to have desired levels of use and policy impact.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ecosystem Services - Volume 17, February 2016, Pages 131-141
Journal: Ecosystem Services - Volume 17, February 2016, Pages 131-141
نویسندگان
Stephen Posner, Gregory Verutes, Insu Koh, Doug Denu, Taylor Ricketts,