A probabilistic scenario approach for developing improved Reduced Emissions from Deforestation and Degradation (REDD+) baselines
Performance-based payments are widely seen as a promising tool for Reduced Emissions from Deforestation and forest Degradation (REDD+) in tropical forests. Despite great advances in international REDD+ negotiations, there is a lack of consensus around the development of business-as-usual (BAU) refer...
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doaj-a0a8546996864b0689f93823e669addb2020-11-25T00:19:57ZengElsevierGlobal Ecology and Conservation2351-98942015-07-014C60261310.1016/j.gecco.2015.10.001A probabilistic scenario approach for developing improved Reduced Emissions from Deforestation and Degradation (REDD+) baselinesMalika Virah-Sawmy0Jakub Stoklosa1Johannes Ebeling2Luc Hoffmann Institute, WWF International, Avenue du Mont-Blanc, 1196 Gland, SwitzerlandSchool of Mathematics and Statistics and Evolution & Ecology Research Centre, University of New South Wales, Kensington-Sydney, NSW 2052, AustraliaBioCarbon Group, Friedrichstrasse 246, 10967 Berlin, GermanyPerformance-based payments are widely seen as a promising tool for Reduced Emissions from Deforestation and forest Degradation (REDD+) in tropical forests. Despite great advances in international REDD+ negotiations, there is a lack of consensus around the development of business-as-usual (BAU) reference scenarios or baselines to derive and quantify net carbon emission reductions. In this paper, we explore a novel approach for developing baselines (point forecasts) using exponential smoothing. Further, we introduce the concept of probabilistic BAU scenario ranges developed using this approach. We compare predictive performance with the linear trend and historical averages approaches conventionally used in policy proposals and REDD+ pilots. We empirically test the relative performance of all three approaches by forecasting BAU baselines and scenario ranges in 36 sites (consisting of 20 countries and 8 Amazonian states with and 8 countries without REDD+ schemes ). Based on two predictive performance measures (the root mean squared error and mean absolute percentage error), we find that exponential smoothing outperforms the linear trend and historical average models at predicting forest cover changes. In addition, we show how prediction intervals based on a desired confidence level generated through exponential smoothing can be used in novel ways to determine likely baseline scenario ranges. In this way it is possible to quantify the degree of variability and uncertainty in datasets. Importantly, this also provides a statistical measure of confidence to determine if REDD+ interventions have been effective. By generating robust probabilistic baseline scenarios, exponential smoothing models can facilitate the effectiveness of REDD+ payments, support a more efficient allocation of scarce conservation resources, and improve our understanding of effective forest conservation investments, also beyond REDD+.http://www.sciencedirect.com/science/article/pii/S2351989415000992AdditionalityBaselinesCarbon marketsForecastingPayments for ecosystem services |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Malika Virah-Sawmy Jakub Stoklosa Johannes Ebeling |
spellingShingle |
Malika Virah-Sawmy Jakub Stoklosa Johannes Ebeling A probabilistic scenario approach for developing improved Reduced Emissions from Deforestation and Degradation (REDD+) baselines Global Ecology and Conservation Additionality Baselines Carbon markets Forecasting Payments for ecosystem services |
author_facet |
Malika Virah-Sawmy Jakub Stoklosa Johannes Ebeling |
author_sort |
Malika Virah-Sawmy |
title |
A probabilistic scenario approach for developing improved Reduced Emissions from Deforestation and Degradation (REDD+) baselines |
title_short |
A probabilistic scenario approach for developing improved Reduced Emissions from Deforestation and Degradation (REDD+) baselines |
title_full |
A probabilistic scenario approach for developing improved Reduced Emissions from Deforestation and Degradation (REDD+) baselines |
title_fullStr |
A probabilistic scenario approach for developing improved Reduced Emissions from Deforestation and Degradation (REDD+) baselines |
title_full_unstemmed |
A probabilistic scenario approach for developing improved Reduced Emissions from Deforestation and Degradation (REDD+) baselines |
title_sort |
probabilistic scenario approach for developing improved reduced emissions from deforestation and degradation (redd+) baselines |
publisher |
Elsevier |
series |
Global Ecology and Conservation |
issn |
2351-9894 |
publishDate |
2015-07-01 |
description |
Performance-based payments are widely seen as a promising tool for Reduced Emissions from Deforestation and forest Degradation (REDD+) in tropical forests. Despite great advances in international REDD+ negotiations, there is a lack of consensus around the development of business-as-usual (BAU) reference scenarios or baselines to derive and quantify net carbon emission reductions. In this paper, we explore a novel approach for developing baselines (point forecasts) using exponential smoothing. Further, we introduce the concept of probabilistic BAU scenario ranges developed using this approach. We compare predictive performance with the linear trend and historical averages approaches conventionally used in policy proposals and REDD+ pilots.
We empirically test the relative performance of all three approaches by forecasting BAU baselines and scenario ranges in 36 sites (consisting of 20 countries and 8 Amazonian states with and 8 countries without REDD+ schemes ). Based on two predictive performance measures (the root mean squared error and mean absolute percentage error), we find that exponential smoothing outperforms the linear trend and historical average models at predicting forest cover changes. In addition, we show how prediction intervals based on a desired confidence level generated through exponential smoothing can be used in novel ways to determine likely baseline scenario ranges. In this way it is possible to quantify the degree of variability and uncertainty in datasets. Importantly, this also provides a statistical measure of confidence to determine if REDD+ interventions have been effective.
By generating robust probabilistic baseline scenarios, exponential smoothing models can facilitate the effectiveness of REDD+ payments, support a more efficient allocation of scarce conservation resources, and improve our understanding of effective forest conservation investments, also beyond REDD+. |
topic |
Additionality Baselines Carbon markets Forecasting Payments for ecosystem services |
url |
http://www.sciencedirect.com/science/article/pii/S2351989415000992 |
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