Last updated: 2021-05-06
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 232737c | Johannes Schielein | 2021-05-06 | update portfolio |
| html | 232737c | Johannes Schielein | 2021-05-06 | update portfolio |
| Rmd | 46d99d6 | Johannes Schielein | 2021-05-04 | update portfolio analysis |
| html | 46d99d6 | Johannes Schielein | 2021-05-04 | update portfolio analysis |
| html | ec29620 | Johannes Schielein | 2021-05-03 | update areas |
| Rmd | 36ba24f | Johannes Schielein | 2021-04-22 | minor changes portfolio |
| html | 36ba24f | Johannes Schielein | 2021-04-22 | minor changes portfolio |
| html | 0c069d8 | Johannes Schielein | 2021-03-27 | update portfolio |
| Rmd | aae8e69 | Johannes Schielein | 2021-03-19 | first assessment |
| html | aae8e69 | Johannes Schielein | 2021-03-19 | first assessment |
Conservation finance is an important field in the KfW development bank with considerable investment into Protected Areas (Short PAs). The evaluation department, together with operational departments tries to learn more about our conservation projects by using our project documentation as well as open geo-datasets to assess the relevance and effectiveness of supported areas in Latin America. The main impact goals of our conservation financing efforts can be summarized under three broad topics:
Conservation finance has increased in importance for German development cooperation and considerably more financial resources had been spent in Latin America since 2004.
We machted our Latin America portfolio with the World Database on Protected Areas - WDPA (IUCN) and used data from the multiple different open data-sources to make an assessment of our portfolio and evaluate the impacts of our projects. Our database currently comprises 398 PAs in Latin America which are situated in 15 different countries. Those areas can be broadly categorized into 337 terrestrial, 19 marine, and 42 partial marine/terrestrial protected areas. They cover a total surface of 0.989 Mio. km2 which is about 2.8 times the size of Germany.Conservation planning needs careful consideration on how to allocate limited financial resources in order to preserve the most relevant, remaining natural landscapes. In order to put money to work where it is most urgently needed, targeted ares should be high in biodiversity and endemism, as well as ecosystem functionality (and the degree to which they are threatened). Therefore, we need to use an adequate conceptual model that characterizes different natural areas in terms of biodiversity and ecosystem functionality. This will allow us to see how our financial resources are allocated among distinctive biotas and if we have an overall bias towards specific ecosystems while other relevant areas slip our attention.
To that end we utilize the Terrestrial Ecoregions of the World (TEOW) classification system from Olson et al..1 The TEOW system is based on biogeographic knowledge from thousands of experts and designed with the purpose of conservation planning in mind. It differentiates between 867 ecoregions which are nested in 14 large biomes. Ecoregions are defined as relatively large units of land containing a distinct assemblage of natural communities and species, with boundaries that approximate the original extent of natural communities prior to major land-use change (Olson et al.). Each ecoregion is part of a terrestrial biome which is a major ecosystem division that shares a common climate, vegetation type (e.g. tundra or forest), soiltype and wildlife.
KfW supports PAs that cover 101 different ecosystems and 10 biomes. This corresponds to 66% of all terrestrial ecoregions in our 15 partnering countries. This relatively high share indicates a broad diversification of the investment portfolio. Nevertheless we should compare total area size of supported ecoregions to get a more adequate representation of the utilized funds. The graphic below allows us to see the actual numbers for each ecoregion grouped by larger biomes.
We can clearly see that our support is heavily concentrated in tropical and subtropical moist broadleaf forest areas. This biome alone makes up 84% of the total supported areas. Most of these areas are concentrated in the Amazon Basin. Nevertheless, we see that within this huge biome very different ecoregions are supported to differing degrees as well.Note: It seems here that e.g. very few mountaineous regions are supported, relatively few mangrove areas and few Savannas. It would be interesting if there are any global priorities discussed currently in terms of biomes or ecoregions.
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
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[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggthemes_4.2.0 plotly_4.9.2.1 RColorBrewer_1.1-2 leaflet_2.0.3
[5] sf_0.9-3 janitor_2.0.1 readxl_1.3.1 forcats_0.5.0
[9] stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4 readr_1.3.1
[13] tidyr_1.1.3 tibble_3.1.1 ggplot2_3.3.1 tidyverse_1.3.0
loaded via a namespace (and not attached):
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[4] modelr_0.1.8 assertthat_0.2.1 blob_1.2.1
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[28] generics_0.1.0 ellipsis_0.3.2 withr_2.4.2
[31] lazyeval_0.2.2 cli_2.5.0 magrittr_2.0.1
[34] crayon_1.4.1 evaluate_0.14 fs_1.5.0
[37] fansi_0.4.2 lwgeom_0.2-4 xml2_1.3.2
[40] class_7.3-16 tools_4.0.0 data.table_1.12.8
[43] hms_0.5.3 lifecycle_1.0.0 munsell_0.5.0
[46] reprex_0.3.0 compiler_4.0.0 e1071_1.7-3
[49] rlang_0.4.11 classInt_0.4-3 units_0.6-6
[52] grid_4.0.0 rstudioapi_0.13 htmlwidgets_1.5.3
[55] crosstalk_1.1.0.1 labeling_0.4.2 rmarkdown_2.2
[58] gtable_0.3.0 DBI_1.1.0 R6_2.5.0
[61] lubridate_1.7.8 knitr_1.28 utf8_1.2.1
[64] workflowr_1.6.2 rprojroot_1.3-2 KernSmooth_2.23-16
[67] stringi_1.5.3 Rcpp_1.0.6 vctrs_0.3.8
[70] dbplyr_1.4.4 tidyselect_1.1.1 xfun_0.14 REF - Olson et al: https://academic.oup.com/bioscience/article/51/11/933/227116↩︎