Keywords
carbon offsets; integrated modelling; landscape suitability; forest expansion; tree planting
Start Date
7-7-2022 1:40 PM
End Date
7-7-2022 2:00 PM
Abstract
There is considerable interest worldwide in offsetting greenhouse gas (GHG) emissions through new forest planting. Yet existing policy is poorly informed by inadequate data, with landscapes assumed to be spatially homogenous, and little consideration given to different yields (and hence carbon captured) across tree species. Few studies examine the effect of the changing climate on the evolution of projected planting schemes, and differences between mechanised and less-mechanised planting approaches are rarely considered. In this contribution we present an integrated modelling approach that aims to address these shortcomings for the case of Scotland, UK. We model spatial variability in landscape suitability for forest expansion in Scotland at a high level of detail, using a range of high-quality datasets. We use the UK yield class index estimates for 11 commonly planted tree species to identify the potential productivity of even-aged stands of trees. We apply a machine learning and general additive regression model to estimate their growth, tree biomass and carbon sequestration potential under changing climatic and hydrological soil conditions, including options for mechanised and non-mechanised planting regimes. We show that while non-mechanised planting options show effective carbon gains from tree planting across many parts of Scotland, upland ecosystems with carbon rich soils are highly vulnerable to net carbon loss, in particular under ordinary commercial planting methods. Overall, our results show the carbon offset achievable over 30 years to be modest, around 1% of the total UK carbon footprint or around 12% of that for Scotland. We highlight the importance of systematic aggregation of high quality data and integrated land use modelling approaches for effective evaluation of new forest planting strategies for carbon sequestration.
Integrated modelling of landscape suitability for carbon offsetting from new forest planting. Case of Scotland, UK
There is considerable interest worldwide in offsetting greenhouse gas (GHG) emissions through new forest planting. Yet existing policy is poorly informed by inadequate data, with landscapes assumed to be spatially homogenous, and little consideration given to different yields (and hence carbon captured) across tree species. Few studies examine the effect of the changing climate on the evolution of projected planting schemes, and differences between mechanised and less-mechanised planting approaches are rarely considered. In this contribution we present an integrated modelling approach that aims to address these shortcomings for the case of Scotland, UK. We model spatial variability in landscape suitability for forest expansion in Scotland at a high level of detail, using a range of high-quality datasets. We use the UK yield class index estimates for 11 commonly planted tree species to identify the potential productivity of even-aged stands of trees. We apply a machine learning and general additive regression model to estimate their growth, tree biomass and carbon sequestration potential under changing climatic and hydrological soil conditions, including options for mechanised and non-mechanised planting regimes. We show that while non-mechanised planting options show effective carbon gains from tree planting across many parts of Scotland, upland ecosystems with carbon rich soils are highly vulnerable to net carbon loss, in particular under ordinary commercial planting methods. Overall, our results show the carbon offset achievable over 30 years to be modest, around 1% of the total UK carbon footprint or around 12% of that for Scotland. We highlight the importance of systematic aggregation of high quality data and integrated land use modelling approaches for effective evaluation of new forest planting strategies for carbon sequestration.
Stream and Session
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