Modeling deforestation and examining trade-offs among carbon stocks and benefits to livelihoods and biodiversity for REDD+ planning in Kenya
<p>Reducing deforestation and degradation (REDD&plus;) can lower anthropogenic carbon emissions by 9-11% and is therefore integral to mitigating climate change. REDD&plus; is a proposed scheme in which developed countries pay developing countries for reduced deforestation. Keny...
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| Format: | Thesis |
| Language: | English |
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2015
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| _version_ | 1797074554119520256 |
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| author | Formica, A |
| author2 | Grenyer, R |
| author_facet | Grenyer, R Formica, A |
| author_sort | Formica, A |
| collection | OXFORD |
| description | <p>Reducing deforestation and degradation (REDD&plus;) can lower anthropogenic carbon emissions by 9-11% and is therefore integral to mitigating climate change. REDD&plus; is a proposed scheme in which developed countries pay developing countries for reduced deforestation. Kenya is one such country participating in REDD&plus;. As such, Kenya must establish a reference emission level (REL) against which it can compare avoided emissions from reduced deforestation. Developing a spatial model of future deforestation will determine the REL for Kenya and provide complementary information on where forests are at risk and drivers of deforestation. REDD&plus; also offers benefits of poverty alleviation and biodiversity conservation, referred to as "co-benefits." Since the spatial distributions of carbon and co-benefits may not be congruent, there may be trade-offs among them whereby increasing one decreases others. To assist Kenya in its REDD&plus; preparations, I build the first spatially explicit deforestation model for the whole of Kenya parametrized with national data. I apply a novel Bayesian inference method which propagates uncertainty in the estimated parameters to the predictions. In addition, I conduct a novel analysis of trade-offs among carbon and co-benefits in three dimensions. I find that predicted forest loss is 1.68% ± 0.06% from 2013 to 2018 (cross-validated AUC = 0.8). There is high deforestation in the Rift Valley and coastal regions attributed to drivers related to accessibility, extraction, and agriculture. Analysis of three-dimensional trade-offs reveals that REDD&plus; sites could simultaneously include 86% of the maximum possible number of people in poverty and 88% of the maximum possible average proportion of ranges of rare species with the smallest 25% of global ranges with a 10% reduction in carbon. The results presented here may help Kenya spatially target REDD&plus; interventions and address particular deforestation drivers as well as plan for securing high levels of carbon stocks and co-benefits.</p> |
| first_indexed | 2024-03-06T23:37:53Z |
| format | Thesis |
| id | oxford-uuid:6e48fc43-0b43-421a-8007-3b752c7b7c22 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:37:53Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | oxford-uuid:6e48fc43-0b43-421a-8007-3b752c7b7c222022-03-26T19:23:25ZModeling deforestation and examining trade-offs among carbon stocks and benefits to livelihoods and biodiversity for REDD+ planning in KenyaThesishttp://purl.org/coar/resource_type/c_bdccuuid:6e48fc43-0b43-421a-8007-3b752c7b7c22GeographyEnglishORA Deposit2015Formica, AGrenyer, R<p>Reducing deforestation and degradation (REDD&plus;) can lower anthropogenic carbon emissions by 9-11% and is therefore integral to mitigating climate change. REDD&plus; is a proposed scheme in which developed countries pay developing countries for reduced deforestation. Kenya is one such country participating in REDD&plus;. As such, Kenya must establish a reference emission level (REL) against which it can compare avoided emissions from reduced deforestation. Developing a spatial model of future deforestation will determine the REL for Kenya and provide complementary information on where forests are at risk and drivers of deforestation. REDD&plus; also offers benefits of poverty alleviation and biodiversity conservation, referred to as "co-benefits." Since the spatial distributions of carbon and co-benefits may not be congruent, there may be trade-offs among them whereby increasing one decreases others. To assist Kenya in its REDD&plus; preparations, I build the first spatially explicit deforestation model for the whole of Kenya parametrized with national data. I apply a novel Bayesian inference method which propagates uncertainty in the estimated parameters to the predictions. In addition, I conduct a novel analysis of trade-offs among carbon and co-benefits in three dimensions. I find that predicted forest loss is 1.68% ± 0.06% from 2013 to 2018 (cross-validated AUC = 0.8). There is high deforestation in the Rift Valley and coastal regions attributed to drivers related to accessibility, extraction, and agriculture. Analysis of three-dimensional trade-offs reveals that REDD&plus; sites could simultaneously include 86% of the maximum possible number of people in poverty and 88% of the maximum possible average proportion of ranges of rare species with the smallest 25% of global ranges with a 10% reduction in carbon. The results presented here may help Kenya spatially target REDD&plus; interventions and address particular deforestation drivers as well as plan for securing high levels of carbon stocks and co-benefits.</p> |
| spellingShingle | Geography Formica, A Modeling deforestation and examining trade-offs among carbon stocks and benefits to livelihoods and biodiversity for REDD+ planning in Kenya |
| title | Modeling deforestation and examining trade-offs among carbon stocks and benefits to livelihoods and biodiversity for REDD+ planning in Kenya |
| title_full | Modeling deforestation and examining trade-offs among carbon stocks and benefits to livelihoods and biodiversity for REDD+ planning in Kenya |
| title_fullStr | Modeling deforestation and examining trade-offs among carbon stocks and benefits to livelihoods and biodiversity for REDD+ planning in Kenya |
| title_full_unstemmed | Modeling deforestation and examining trade-offs among carbon stocks and benefits to livelihoods and biodiversity for REDD+ planning in Kenya |
| title_short | Modeling deforestation and examining trade-offs among carbon stocks and benefits to livelihoods and biodiversity for REDD+ planning in Kenya |
| title_sort | modeling deforestation and examining trade offs among carbon stocks and benefits to livelihoods and biodiversity for redd planning in kenya |
| topic | Geography |
| work_keys_str_mv | AT formicaa modelingdeforestationandexaminingtradeoffsamongcarbonstocksandbenefitstolivelihoodsandbiodiversityforreddplanninginkenya |