SFAM Final Project
Gen Shiraishi
2022-11-30
Overview
Objective
The objective of this exercise is to identify low-income developing countries (LIDCs) that significantly outperform or under-perform on sustainability indicators, relative to their peers.
Purpose
The purpose of this exercise is to serve as a factbook of LIDCs that are outperforming and under-performing on sustainability, which sustainable development investors (e.g. development banks, impact investors) can reference in order to create a short-list of LIDCs that pose diffentiated opportunities for sustainable development investments with high sustainable development returns on investment.
Data source
This exercise relies primarily on the World Bank’s Sovereign Environmental, Social and Governance Data, which provides historical country-level data on 67 ESG indicators (including 27 environmental indicators). The World Bank’s ESG dataset is available here: https://datatopics.worldbank.org/esg/index.html. The version of the data used in this exercise was downloaded in October, 2022.
This exercises also utilizes the IMF WB Counry Groups dataset, which includes a variety of categorizations for countries (e.g. “G20”) and enables the WB ESG dataset to be filtered for LIDCs.
Methodology
The central methodology of this exercise is to rank order LIDCs based on their performance on key sustainability indicators, in order to identify the top 10 and bottom 10 performers for each indicator.
LIDCs are defined in accordance with the IMF’s definition of the world’s 59 low-income developing countries. The IMF categorizes the rest of the countries into 40 advanced economies and 97 emerging market and middle-income economies. Further details on the IMF’s country classification can be found here: https://www.imf.org/external/datamapper/datasets/FM#
This exercise considers three categories of sustainability indicators that are available in the WB ESG dataset: Emissions & Pollution, Natural Resource Management and Energy Use.
Under Emissions & Pollution, this exercise examines LIDCs’ performance on 3 indicators: CO2 Emissions Per Capita, Methane Emissions per Capita and Nitrous Oxide Emissions per Capita.
Under Natural Resource Management, this exercise examines LIDCs’ performance on 3 indicators: Natural Resources Depletion, Net Forest Depletion and Terrestrial and Marine Protected Areas
Under Energy Use, this exercise examines LIDCs’ performance on 4 indicators: Electricity Production from Coal Source, Energy Intensity Level of Primary Energy, Renewable Electricity Output and Renewable Electricity Consumption.
The above 10 indicators were selected from the 27 environmental indicators that are available in the WB ESG dataset based on their strengths as sustainability indicators and data availability (many indicators were missing substantial volumes of data for LIDCs).
For each indicator, this exercise identifies the top 10 and bottom 10 performers among the 59 LIDCs, using the latest year that provides the most complete data. The rationale for focusing on the top 10 and bottom 10 is because LIDCs that substantially outperform or under-perform their peers are most likely to pose strong sustainable development investment opportunities with maximum sustainable development returns on investment (e.g. an LIDC that is a top performer on renewable electricity output may present a strong opportunity for follow-on investments in renewable energy, while an LIDC that is a bottom performer on terrestrial and marine protected areas may present a strong opportunity for investments that expand protected areas).
Data Preparation
Data preparation plan
The data preparation for this exercise involves reviewing, cleaning and merging the 2 datasets: the World Bank Sovereign ESG dataset and the IMF WB Country Groups dataset. The steps are as follows.
Step 1: Load each dataset and review the data.
Step 2: Convert the country_name column in the IMF WB Country Groups dataset into iso3c codes.
Step 3: Join the two datasets using the iso3c codes.
The steps above will yield a combined dataset that includes ESG indicator data by country and year, with country groupings.
Load the data sets
folder_path <- partial(here, "00_data_raw")
folder_path() %>% list.files()
WB_sovereign_ESG <- folder_path("World Bank Sovereign ESG dataset/ESGData2.csv") %>%
read_csv()
imf_wb_country_groups <- folder_path("imf_wb_country_groups.csv") %>%
read_csv()Review the WB ESG dataset
glimpse(WB_sovereign_ESG)## Rows: 16,013
## Columns: 67
## $ `Country Name` <chr> "Arab World", "Arab World", "Arab World", "Arab World…
## $ iso3c <chr> "ARB", "ARB", "ARB", "ARB", "ARB", "ARB", "ARB", "ARB…
## $ `Indicator Name` <chr> "Access to clean fuels and technologies for cooking (…
## $ `Indicator Code` <chr> "EG.CFT.ACCS.ZS", "EG.ELC.ACCS.ZS", "NY.ADJ.DRES.GN.Z…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, 30.981194, NA, NA, NA, NA, NA, NA, NA…
## $ `1962` <dbl> NA, NA, NA, NA, 30.982443, NA, NA, NA, NA, NA, NA, NA…
## $ `1963` <dbl> NA, NA, NA, NA, 31.006834, NA, NA, NA, NA, NA, NA, NA…
## $ `1964` <dbl> NA, NA, NA, NA, 31.01778, NA, NA, NA, NA, NA, NA, NA,…
## $ `1965` <dbl> NA, NA, NA, NA, 31.042245, NA, NA, NA, NA, NA, NA, NA…
## $ `1966` <dbl> NA, NA, NA, NA, 31.05018, NA, NA, NA, NA, NA, NA, NA,…
## $ `1967` <dbl> NA, NA, NA, NA, 31.103003, NA, NA, NA, NA, NA, NA, NA…
## $ `1968` <dbl> NA, NA, NA, NA, 31.133345, 14.660731, NA, NA, NA, NA,…
## $ `1969` <dbl> NA, NA, NA, NA, 31.190209, 14.295950, NA, NA, NA, NA,…
## $ `1970` <dbl> NA, NA, 6.8192031, 0.1745661, 31.2542726, 14.6376317,…
## $ `1971` <dbl> NA, NA, 6.8625877, 0.1392865, 31.3863673, 13.9126712,…
## $ `1972` <dbl> NA, NA, 8.9932942, 0.1320823, 31.4997277, 13.7760100,…
## $ `1973` <dbl> NA, NA, 12.2730219, 0.1524257, 31.4965877, 12.3616551…
## $ `1974` <dbl> NA, NA, 25.24123751, 0.08739206, 31.55058649, 7.77632…
## $ `1975` <dbl> NA, NA, 17.17663885, 0.09875801, 31.52950124, 7.98857…
## $ `1976` <dbl> NA, NA, 18.92687785, 0.06634076, 31.59973643, 7.44355…
## $ `1977` <dbl> NA, NA, 19.2409638, 0.1024133, 31.6219972, 7.1412275,…
## $ `1978` <dbl> NA, NA, 16.3069297, 0.1071046, 31.6660778, 7.1654123,…
## $ `1979` <dbl> NA, NA, 32.75748927, 0.07422815, 31.67849388, 5.67074…
## $ `1980` <dbl> NA, NA, 27.88409161, 0.05775981, 31.75886761, 4.75050…
## $ `1981` <dbl> NA, NA, 20.08867813, 0.05511109, 31.45424086, 5.07641…
## $ `1982` <dbl> NA, NA, 10.9940045, 0.1141440, 31.4801559, 5.9800629,…
## $ `1983` <dbl> NA, NA, 9.65310503, 0.08148845, 31.52860052, 6.738849…
## $ `1984` <dbl> NA, NA, 9.7577998, 0.0795408, 31.9421227, 7.3157059, …
## $ `1985` <dbl> NA, NA, 8.32055276, 0.03830571, 32.44202971, 8.068573…
## $ `1986` <dbl> NA, NA, 5.59763556, 0.08965615, 33.02639234, 9.621148…
## $ `1987` <dbl> NA, NA, 7.36173263, 0.08506867, 33.58285192, 10.27551…
## $ `1988` <dbl> NA, NA, 6.5508191, 0.0897014, 34.1868305, 10.5341524,…
## $ `1989` <dbl> NA, NA, 9.05466681, 0.08732953, 34.69763715, 11.08920…
## $ `1990` <dbl> NA, NA, 7.98566651, 0.06761466, 35.10930645, 10.38169…
## $ `1991` <dbl> NA, NA, 8.43729462, 0.07275225, 35.15972003, 12.27688…
## $ `1992` <dbl> NA, NA, 8.51357510, 0.05785795, 35.32097250, 9.225802…
## $ `1993` <dbl> NA, NA, 8.14964549, 0.04460087, 36.09585061, 9.263672…
## $ `1994` <dbl> NA, NA, 7.44516045, 0.04575975, 36.75422959, 10.22704…
## $ `1995` <dbl> NA, NA, 7.67706491, 0.06196782, 37.38644379, 10.38116…
## $ `1996` <dbl> NA, 76.61107378, 8.91043066, 0.05868878, 37.98259419,…
## $ `1997` <dbl> NA, 77.25362114, 7.75296001, 0.05524078, 38.49337516,…
## $ `1998` <dbl> NA, 78.11157597, 5.40270002, 0.07836069, 39.11826905,…
## $ `1999` <dbl> NA, 78.69106128, 7.00074834, 0.03706546, 39.64700699,…
## $ `2000` <dbl> 75.31875484, 80.73614121, 9.72850267, 0.02122214, 39.…
## $ `2001` <dbl> 76.65447960, 81.58623137, 7.51233649, 0.02455567, 39.…
## $ `2002` <dbl> 77.85548458, 81.54022203, 7.39429040, 0.02680793, 39.…
## $ `2003` <dbl> 79.04554536, 82.50815948, 8.71632561, 0.03261723, 39.…
## $ `2004` <dbl> 80.03416635, 82.50368475, 10.19516060, 0.02505279, 40…
## $ `2005` <dbl> 81.02576797, 83.21298163, 12.94744287, 0.02041118, 40…
## $ `2006` <dbl> 81.96581490, 85.45900327, 12.83170086, 0.02157661, 40…
## $ `2007` <dbl> 82.78718917, 83.76443999, 11.44096571, 0.01803353, 40…
## $ `2008` <dbl> 83.47573318, 83.38698965, 13.30778857, 0.02509408, 40…
## $ `2009` <dbl> 84.15742348, 84.31348380, 8.26486020, 0.02885193, 40.…
## $ `2010` <dbl> 84.68334427, 87.11486281, 9.45136425, 0.03003109, 40.…
## $ `2011` <dbl> 85.1927423, 87.3326612, 13.4275913, 0.0309041, 40.172…
## $ `2012` <dbl> 85.64421787, 87.03958839, 12.85922547, 0.03226624, 36…
## $ `2013` <dbl> 85.93256689, 88.99261981, 11.63839653, 0.06262475, 36…
## $ `2014` <dbl> 86.23238390, 88.01535618, 10.29789118, 0.08494782, 36…
## $ `2015` <dbl> 86.47859724, 88.68188567, 6.23770324, 0.09978365, 36.…
## $ `2016` <dbl> 86.72268517, 89.19506235, 5.20389537, 0.09505614, 36.…
## $ `2017` <dbl> 86.93793290, 90.32465949, 6.48008284, 0.09549819, 36.…
## $ `2018` <dbl> 87.04077373, 88.91074940, 8.47907008, 0.05105818, 36.…
## $ `2019` <dbl> 87.23553889, 89.99994555, 7.43714482, 0.06219453, 36.…
## $ `2020` <dbl> 87.30706752, 90.27773508, 4.37637424, 0.07908057, 36.…
## $ `2021` <dbl> NA, NA, NA, NA, NA, 5.237110, NA, NA, NA, NA, NA, NA,…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…The WB ESG dataset includes 67 columns and 16,013 rows.
The first 2 columns are the country name and the iso3c code. The next 2 columns are the indicator name and unique indicator code. Columns 5-66 include the value of the indicator for each year between 1960 and 2021. The final column 67 includes forecast values for 2050. Notably, many of the observations in columns 5-67 are blank, likely due to a lack of data.
Each row represents a unique combination of country and indicator, with the values of the indicator for the specific country included in columns 5-67. The Country Name and iso3c columns 1-2 include 239 unique countries and the indicator name and unique indicator code columns 3-4 include 67 unique indicators. The multiplication of these 239 countries and 67 indicators exactly yields the total 16,013 rows in the dataset.
Notably, multiple rows have blank values, likely due to data limitations, with some indicator rows for countries being entirely blank for each year column. Finally, the Country Name and iso3c columns include a subset of country groupings (e.g. “Arab World,” “Euro area”).
Review the IMF WB Country Groups dataset
glimpse(imf_wb_country_groups)## Rows: 2,587
## Columns: 3
## $ country_name <chr> "Australia", "Austria", "Belgium", "Canada", "Switzerlan…
## $ country_group <chr> "Advanced Economies", "Advanced Economies", "Advanced Ec…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", …The IMF WB Country Groups dataset includes 3 columns and 2,587 rows.
The columns are country name, country group (including various country groupings) and group type (an indication of whether the row corresponds to an IMF or WB grouping).
Each row is a unique combination of a country name, country group and group type. The dataset includes 218 unique country names, 66 unique country groups and 2 unique group types (IMF or WB). 19 of the country groups are tagged as IMF and 48 are tagged as WB (“Euro Area” is the only country group tagged as both IMF and WB and is thus duplicated for each country).
Notably, each country has a different number of rows, depending on the number of country groupings that the country falls under (e.g. Malawi has 15 rows as it falls under 15 country groupings while Australia has only 9 rows as it only falls under 9 country groupings). Finally, the IMF WB Country Groups dataset does not include iso3c country codes.
Convert country names to iso3c in the IMF WB Country Groups dataset
country_name_regex_to_iso3c <- function(country_name) {
country_name %>%
countrycode(origin = "country.name",
destination = "iso3c",
origin_regex = TRUE)
}
imf_wb_country_groups_iso3c <- imf_wb_country_groups %>%
mutate(iso3c = country_name_regex_to_iso3c(country_name))
imf_wb_country_groups_iso3cMerge the two datasets using the iso3c country codes
The left join function, taking the WB ESG dataset as the “left” data frame, is most suitable in this case as this will add the country groups from the IMF WB Country Groups dataset to the WB ESG dataset as new columns, while preserving all rows in the ESG dataset (including the country groupings that are already included in the ESG dataset)
WB_sovereign_ESG_country_groups <- WB_sovereign_ESG %>% left_join(imf_wb_country_groups_iso3c, by = "iso3c")
glimpse(WB_sovereign_ESG_country_groups)## Rows: 169,175
## Columns: 70
## $ `Country Name` <chr> "Arab World", "Arab World", "Arab World", "Arab World…
## $ iso3c <chr> "ARB", "ARB", "ARB", "ARB", "ARB", "ARB", "ARB", "ARB…
## $ `Indicator Name` <chr> "Access to clean fuels and technologies for cooking (…
## $ `Indicator Code` <chr> "EG.CFT.ACCS.ZS", "EG.ELC.ACCS.ZS", "NY.ADJ.DRES.GN.Z…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, 30.981194, NA, NA, NA, NA, NA, NA, NA…
## $ `1962` <dbl> NA, NA, NA, NA, 30.982443, NA, NA, NA, NA, NA, NA, NA…
## $ `1963` <dbl> NA, NA, NA, NA, 31.006834, NA, NA, NA, NA, NA, NA, NA…
## $ `1964` <dbl> NA, NA, NA, NA, 31.01778, NA, NA, NA, NA, NA, NA, NA,…
## $ `1965` <dbl> NA, NA, NA, NA, 31.042245, NA, NA, NA, NA, NA, NA, NA…
## $ `1966` <dbl> NA, NA, NA, NA, 31.05018, NA, NA, NA, NA, NA, NA, NA,…
## $ `1967` <dbl> NA, NA, NA, NA, 31.103003, NA, NA, NA, NA, NA, NA, NA…
## $ `1968` <dbl> NA, NA, NA, NA, 31.133345, 14.660731, NA, NA, NA, NA,…
## $ `1969` <dbl> NA, NA, NA, NA, 31.190209, 14.295950, NA, NA, NA, NA,…
## $ `1970` <dbl> NA, NA, 6.8192031, 0.1745661, 31.2542726, 14.6376317,…
## $ `1971` <dbl> NA, NA, 6.8625877, 0.1392865, 31.3863673, 13.9126712,…
## $ `1972` <dbl> NA, NA, 8.9932942, 0.1320823, 31.4997277, 13.7760100,…
## $ `1973` <dbl> NA, NA, 12.2730219, 0.1524257, 31.4965877, 12.3616551…
## $ `1974` <dbl> NA, NA, 25.24123751, 0.08739206, 31.55058649, 7.77632…
## $ `1975` <dbl> NA, NA, 17.17663885, 0.09875801, 31.52950124, 7.98857…
## $ `1976` <dbl> NA, NA, 18.92687785, 0.06634076, 31.59973643, 7.44355…
## $ `1977` <dbl> NA, NA, 19.2409638, 0.1024133, 31.6219972, 7.1412275,…
## $ `1978` <dbl> NA, NA, 16.3069297, 0.1071046, 31.6660778, 7.1654123,…
## $ `1979` <dbl> NA, NA, 32.75748927, 0.07422815, 31.67849388, 5.67074…
## $ `1980` <dbl> NA, NA, 27.88409161, 0.05775981, 31.75886761, 4.75050…
## $ `1981` <dbl> NA, NA, 20.08867813, 0.05511109, 31.45424086, 5.07641…
## $ `1982` <dbl> NA, NA, 10.9940045, 0.1141440, 31.4801559, 5.9800629,…
## $ `1983` <dbl> NA, NA, 9.65310503, 0.08148845, 31.52860052, 6.738849…
## $ `1984` <dbl> NA, NA, 9.7577998, 0.0795408, 31.9421227, 7.3157059, …
## $ `1985` <dbl> NA, NA, 8.32055276, 0.03830571, 32.44202971, 8.068573…
## $ `1986` <dbl> NA, NA, 5.59763556, 0.08965615, 33.02639234, 9.621148…
## $ `1987` <dbl> NA, NA, 7.36173263, 0.08506867, 33.58285192, 10.27551…
## $ `1988` <dbl> NA, NA, 6.5508191, 0.0897014, 34.1868305, 10.5341524,…
## $ `1989` <dbl> NA, NA, 9.05466681, 0.08732953, 34.69763715, 11.08920…
## $ `1990` <dbl> NA, NA, 7.98566651, 0.06761466, 35.10930645, 10.38169…
## $ `1991` <dbl> NA, NA, 8.43729462, 0.07275225, 35.15972003, 12.27688…
## $ `1992` <dbl> NA, NA, 8.51357510, 0.05785795, 35.32097250, 9.225802…
## $ `1993` <dbl> NA, NA, 8.14964549, 0.04460087, 36.09585061, 9.263672…
## $ `1994` <dbl> NA, NA, 7.44516045, 0.04575975, 36.75422959, 10.22704…
## $ `1995` <dbl> NA, NA, 7.67706491, 0.06196782, 37.38644379, 10.38116…
## $ `1996` <dbl> NA, 76.61107378, 8.91043066, 0.05868878, 37.98259419,…
## $ `1997` <dbl> NA, 77.25362114, 7.75296001, 0.05524078, 38.49337516,…
## $ `1998` <dbl> NA, 78.11157597, 5.40270002, 0.07836069, 39.11826905,…
## $ `1999` <dbl> NA, 78.69106128, 7.00074834, 0.03706546, 39.64700699,…
## $ `2000` <dbl> 75.31875484, 80.73614121, 9.72850267, 0.02122214, 39.…
## $ `2001` <dbl> 76.65447960, 81.58623137, 7.51233649, 0.02455567, 39.…
## $ `2002` <dbl> 77.85548458, 81.54022203, 7.39429040, 0.02680793, 39.…
## $ `2003` <dbl> 79.04554536, 82.50815948, 8.71632561, 0.03261723, 39.…
## $ `2004` <dbl> 80.03416635, 82.50368475, 10.19516060, 0.02505279, 40…
## $ `2005` <dbl> 81.02576797, 83.21298163, 12.94744287, 0.02041118, 40…
## $ `2006` <dbl> 81.96581490, 85.45900327, 12.83170086, 0.02157661, 40…
## $ `2007` <dbl> 82.78718917, 83.76443999, 11.44096571, 0.01803353, 40…
## $ `2008` <dbl> 83.47573318, 83.38698965, 13.30778857, 0.02509408, 40…
## $ `2009` <dbl> 84.15742348, 84.31348380, 8.26486020, 0.02885193, 40.…
## $ `2010` <dbl> 84.68334427, 87.11486281, 9.45136425, 0.03003109, 40.…
## $ `2011` <dbl> 85.1927423, 87.3326612, 13.4275913, 0.0309041, 40.172…
## $ `2012` <dbl> 85.64421787, 87.03958839, 12.85922547, 0.03226624, 36…
## $ `2013` <dbl> 85.93256689, 88.99261981, 11.63839653, 0.06262475, 36…
## $ `2014` <dbl> 86.23238390, 88.01535618, 10.29789118, 0.08494782, 36…
## $ `2015` <dbl> 86.47859724, 88.68188567, 6.23770324, 0.09978365, 36.…
## $ `2016` <dbl> 86.72268517, 89.19506235, 5.20389537, 0.09505614, 36.…
## $ `2017` <dbl> 86.93793290, 90.32465949, 6.48008284, 0.09549819, 36.…
## $ `2018` <dbl> 87.04077373, 88.91074940, 8.47907008, 0.05105818, 36.…
## $ `2019` <dbl> 87.23553889, 89.99994555, 7.43714482, 0.06219453, 36.…
## $ `2020` <dbl> 87.30706752, 90.27773508, 4.37637424, 0.07908057, 36.…
## $ `2021` <dbl> NA, NA, NA, NA, NA, 5.237110, NA, NA, NA, NA, NA, NA,…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_group <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ group_type <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…The merged dataset has 70 columns and 169,175 rows.
The columns correspond to the 67 columns in the WB ESG dataset plus the 3 columns from the IMF WB Country Groupings dataset.
The total number of rows has increased substantially because each row now represents a unique combination of country name, indicator, country group and group type. In other words, the 67 rows for each country in the WB ESG dataset was duplicated by the number of country groups that the country falls into in the IMF WB Country Groupings dataset. As an example, Australia now has 603 rows in the merged dataset because each of its 67 rows in the WB ESG dataset was duplicated 9 times (67 * 9 = 603) to create separate rows for each of the 9 country groupings that Australia falls under in the IMF WB Country Groupings dataset.
In addition, 3082 of the rows in the merged dataset are blank for the new columns 68-70 because they correspond to one of 46 “country names” in the WB ESG dataset that are actually country groupings (e.g. “Arab World”). These observations are marked as “NA” in these columns because they did not match with any observations in the iso3c column in the IMF WB Country Groupings dataset.
Finally, the blank observations in the WB ESG dataset have now been populated with “NA” in the merged dataset.
Write merged dataset to .csv
write.csv(WB_sovereign_ESG_country_groups,"C:/Users/Gen Shiraishi/Desktop/Sustainable Finance - Application and Methods/Final project/03_data_processed\\WB_sovereign_ESG_country_groups.csv", row.names = FALSE)Data Analysis
This section presents the top 10 and bottom 10 LIDCs based on their performance on the 10 sustainability indicators that are examined in this exercise. The latest year with the most complete data is used for each of the indicators. The rest of this section is divided into 3 sub-sections, each corresponding to one of the 3 categories of sustainability indicators examined in this exercise - Emissions & Pollution, Natural Resource Management and Energy Use. Commentary on the top 10 and bottom 10 LIDCs is provided for each indicator, and key findings are summarized at the end of each sub-section.
Emissions & Pollution
This sub-section presents the top 10 and bottom 10 LIDCs based on their performance on 3 Emissions & Pollution indicators: CO2 Emissions per Capita, Methane Emissions per Capita and Nitrous Oxide Emissions per Capita.
CO2 Emissions per Capita
CO2 emissions per capita is a critical sustainability indicator to measure a country’s sustainability performance because CO2 is the most common greenhouse gas that is emitted through economic activity and measuring it on per capita terms controls for population size. The WB ESG dataset’s latest available data on this indicator is from 2019. 2019 data is available for all 59 LIDCs. The top 10 and bottom 10 LIDCs for this indicator are as follows:
#Filter merged dataset for indicator and LIDCs
WB_sovereign_ESG_country_groups_CO2_LIDC <- WB_sovereign_ESG_country_groups %>%
filter(`Indicator Code` == 'EN.ATM.CO2E.PC') %>%
filter(country_group == 'Low-Income Developing Countries')
glimpse(WB_sovereign_ESG_country_groups_CO2_LIDC)## Rows: 59
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "CO2 emissions (metric tons per capita)", "CO2 emissi…
## $ `Indicator Code` <chr> "EN.ATM.CO2E.PC", "EN.ATM.CO2E.PC", "EN.ATM.CO2E.PC",…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1972` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1973` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1974` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1975` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1976` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1977` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1978` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1979` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1980` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1981` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1982` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1983` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1984` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1985` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1986` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1987` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1988` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1989` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1990` <dbl> 0.19174511, 0.11165825, 0.06628517, 0.15071562, 0.056…
## $ `1991` <dbl> 0.16768158, 0.10255767, 0.05243232, 0.24315564, 0.056…
## $ `1992` <dbl> 0.09595774, 0.10946096, 0.05251505, 0.22449839, 0.055…
## $ `1993` <dbl> 0.08472111, 0.11390962, 0.05251948, 0.24409020, 0.055…
## $ `1994` <dbl> 0.07554583, 0.12010181, 0.04900061, 0.26320490, 0.055…
## $ `1995` <dbl> 0.06846796, 0.14370070, 0.05079965, 0.29927295, 0.056…
## $ `1996` <dbl> 0.06258803, 0.14305151, 0.15916585, 0.36936420, 0.060…
## $ `1997` <dbl> 0.05682662, 0.15787209, 0.19740055, 0.34438195, 0.064…
## $ `1998` <dbl> 0.05269086, 0.15690869, 0.18700905, 0.35437242, 0.069…
## $ `1999` <dbl> 0.04015697, 0.16063628, 0.20107735, 0.34608902, 0.078…
## $ `2000` <dbl> 0.03657370, 0.16959394, 0.20681782, 0.35532153, 0.080…
## $ `2001` <dbl> 0.03378536, 0.19817246, 0.24587634, 0.38101991, 0.081…
## $ `2002` <dbl> 0.04557366, 0.20705313, 0.29881844, 0.38959457, 0.078…
## $ `2003` <dbl> 0.05151838, 0.21240196, 0.32444410, 0.41411823, 0.082…
## $ `2004` <dbl> 0.04165539, 0.22286880, 0.34193536, 0.45396980, 0.081…
## $ `2005` <dbl> 0.06041878, 0.23526364, 0.36330733, 0.49326081, 0.078…
## $ `2006` <dbl> 0.06658329, 0.25475240, 0.42230074, 0.48676307, 0.089…
## $ `2007` <dbl> 0.06531235, 0.26629679, 0.47310460, 0.48129491, 0.098…
## $ `2008` <dbl> 0.12841656, 0.28814139, 0.46223287, 0.44668716, 0.115…
## $ `2009` <dbl> 0.17186242, 0.30666481, 0.49414666, 0.47174748, 0.114…
## $ `2010` <dbl> 0.24361404, 0.34273999, 0.52504257, 0.58351399, 0.146…
## $ `2011` <dbl> 0.29650624, 0.36456659, 0.49150028, 0.85100610, 0.152…
## $ `2012` <dbl> 0.25929533, 0.38402517, 0.45635565, 0.91222407, 0.177…
## $ `2013` <dbl> 0.18562366, 0.39669703, 0.46778508, 0.94334976, 0.183…
## $ `2014` <dbl> 0.14623562, 0.41309269, 0.50452817, 0.97350263, 0.180…
## $ `2015` <dbl> 0.17289674, 0.46199743, 0.52099282, 1.05785939, 0.204…
## $ `2016` <dbl> 0.14978933, 0.47082757, 0.61993701, 1.26237605, 0.198…
## $ `2017` <dbl> 0.13169456, 0.49685185, 0.61475453, 1.30103027, 0.222…
## $ `2018` <dbl> 0.16329530, 0.51711301, 0.64605812, 1.39184189, 0.236…
## $ `2019` <dbl> 0.15982437, 0.55652945, 0.61858375, 1.37597721, 0.246…
## $ `2020` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Remove any NAs
WB_sovereign_ESG_country_groups_CO2_LIDC <- WB_sovereign_ESG_country_groups_CO2_LIDC[!(is.na(WB_sovereign_ESG_country_groups_CO2_LIDC$"2019") | WB_sovereign_ESG_country_groups_CO2_LIDC$"2019"==""), ]
glimpse(WB_sovereign_ESG_country_groups_CO2_LIDC)## Rows: 59
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "CO2 emissions (metric tons per capita)", "CO2 emissi…
## $ `Indicator Code` <chr> "EN.ATM.CO2E.PC", "EN.ATM.CO2E.PC", "EN.ATM.CO2E.PC",…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1972` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1973` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1974` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1975` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1976` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1977` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1978` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1979` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1980` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1981` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1982` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1983` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1984` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1985` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1986` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1987` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1988` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1989` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1990` <dbl> 0.19174511, 0.11165825, 0.06628517, 0.15071562, 0.056…
## $ `1991` <dbl> 0.16768158, 0.10255767, 0.05243232, 0.24315564, 0.056…
## $ `1992` <dbl> 0.09595774, 0.10946096, 0.05251505, 0.22449839, 0.055…
## $ `1993` <dbl> 0.08472111, 0.11390962, 0.05251948, 0.24409020, 0.055…
## $ `1994` <dbl> 0.07554583, 0.12010181, 0.04900061, 0.26320490, 0.055…
## $ `1995` <dbl> 0.06846796, 0.14370070, 0.05079965, 0.29927295, 0.056…
## $ `1996` <dbl> 0.06258803, 0.14305151, 0.15916585, 0.36936420, 0.060…
## $ `1997` <dbl> 0.05682662, 0.15787209, 0.19740055, 0.34438195, 0.064…
## $ `1998` <dbl> 0.05269086, 0.15690869, 0.18700905, 0.35437242, 0.069…
## $ `1999` <dbl> 0.04015697, 0.16063628, 0.20107735, 0.34608902, 0.078…
## $ `2000` <dbl> 0.03657370, 0.16959394, 0.20681782, 0.35532153, 0.080…
## $ `2001` <dbl> 0.03378536, 0.19817246, 0.24587634, 0.38101991, 0.081…
## $ `2002` <dbl> 0.04557366, 0.20705313, 0.29881844, 0.38959457, 0.078…
## $ `2003` <dbl> 0.05151838, 0.21240196, 0.32444410, 0.41411823, 0.082…
## $ `2004` <dbl> 0.04165539, 0.22286880, 0.34193536, 0.45396980, 0.081…
## $ `2005` <dbl> 0.06041878, 0.23526364, 0.36330733, 0.49326081, 0.078…
## $ `2006` <dbl> 0.06658329, 0.25475240, 0.42230074, 0.48676307, 0.089…
## $ `2007` <dbl> 0.06531235, 0.26629679, 0.47310460, 0.48129491, 0.098…
## $ `2008` <dbl> 0.12841656, 0.28814139, 0.46223287, 0.44668716, 0.115…
## $ `2009` <dbl> 0.17186242, 0.30666481, 0.49414666, 0.47174748, 0.114…
## $ `2010` <dbl> 0.24361404, 0.34273999, 0.52504257, 0.58351399, 0.146…
## $ `2011` <dbl> 0.29650624, 0.36456659, 0.49150028, 0.85100610, 0.152…
## $ `2012` <dbl> 0.25929533, 0.38402517, 0.45635565, 0.91222407, 0.177…
## $ `2013` <dbl> 0.18562366, 0.39669703, 0.46778508, 0.94334976, 0.183…
## $ `2014` <dbl> 0.14623562, 0.41309269, 0.50452817, 0.97350263, 0.180…
## $ `2015` <dbl> 0.17289674, 0.46199743, 0.52099282, 1.05785939, 0.204…
## $ `2016` <dbl> 0.14978933, 0.47082757, 0.61993701, 1.26237605, 0.198…
## $ `2017` <dbl> 0.13169456, 0.49685185, 0.61475453, 1.30103027, 0.222…
## $ `2018` <dbl> 0.16329530, 0.51711301, 0.64605812, 1.39184189, 0.236…
## $ `2019` <dbl> 0.15982437, 0.55652945, 0.61858375, 1.37597721, 0.246…
## $ `2020` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Rank countries by indicator performance
WB_sovereign_ESG_country_groups_CO2_LIDC$Rank<-rank(WB_sovereign_ESG_country_groups_CO2_LIDC$'2019')
WB_sovereign_ESG_country_groups_CO2_LIDC#Filter top 10
WB_sovereign_ESG_country_groups_CO2_LIDC_top10 <- WB_sovereign_ESG_country_groups_CO2_LIDC %>%
filter(`Rank` < 11)
#Chart top 10
WB_sovereign_ESG_country_groups_CO2_LIDC_top10 %>%
ggplot(aes(fct_reorder(iso3c, -`2019`),`2019`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "CO2 Emissions per Capita - Top 10 LIDCs",
subtitle = "CO2 Emissions per Capita, Metric Tons, 2019",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
#Filter bottom 10
WB_sovereign_ESG_country_groups_CO2_LIDC_bottom10 <- WB_sovereign_ESG_country_groups_CO2_LIDC %>%
filter(`Rank` > 49)
#Chart bottom 10
WB_sovereign_ESG_country_groups_CO2_LIDC_bottom10 %>%
ggplot(aes(fct_reorder(iso3c, -`2019`),`2019`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "CO2 Emissions per Capita - Bottom 10 LIDCs",
subtitle = "CO2 Emissions per Capita, Metric Tons, 2019",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
The top performing LIDC on this indicator is the Democratic Republic of Congo, which only emits 0.04 tons of CO2 per capita. Emissions per capita increases gradually down the top 10 list until reaching 10th best performer Chad, which emits 0.14 tones of CO2 per capita.
The worst performing LIDC on this indicator is Vietnam, which emits 3.5 tones of CO2 per capita, which is notably 87.5 times worse than the top performing DRC. Notably, the worst 4 performers, which include Laos, Moldova, Uzbekistan and Vietnam have substantially higher levels of CO2 emissions per capita compared to the rest of the LIDCs.
Methane Emissions per Capita
Methane emissions per capita is a critical sustainability indicator to measure a country’s sustainability performance because methane is estimated to have 80 times the warming power of CO2 over the first 20 years of reaching the atmosphere, making it a highly harmful greenhouse gas that is estimated to be accountable for at least 25% of today’s global warming (source: https://www.edf.org/climate/methane-crucial-opportunity-climate-fight#). The WB ESG dataset’s latest available data on this indicator is from 2019. 2019 data is available for all 59 LIDCs. The top 10 and bottom 10 LIDCs for this indicator are as follows:
#Filter merged dataset for indicator and LIDCs
WB_sovereign_ESG_country_groups_Methane_LIDC <- WB_sovereign_ESG_country_groups %>%
filter(`Indicator Code` == 'EN.ATM.METH.PC') %>%
filter(country_group == 'Low-Income Developing Countries')
glimpse(WB_sovereign_ESG_country_groups_Methane_LIDC)## Rows: 59
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Methane emissions (metric tons of CO2 equivalent per…
## $ `Indicator Code` <chr> "EN.ATM.METH.PC", "EN.ATM.METH.PC", "EN.ATM.METH.PC",…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1972` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1973` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1974` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1975` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1976` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1977` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1978` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1979` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1980` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1981` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1982` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1983` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1984` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1985` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1986` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1987` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1988` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1989` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1990` <dbl> 0.5430093, 0.6422288, 0.4459184, 1.0550093, 0.7660850…
## $ `1991` <dbl> 0.5278586, 0.6284143, 0.4485876, 1.0661439, 0.7690535…
## $ `1992` <dbl> 0.4922149, 0.6221309, 0.4426268, 1.0102428, 0.7690562…
## $ `1993` <dbl> 0.4558502, 0.6116865, 0.4292109, 1.0139132, 0.7631532…
## $ `1994` <dbl> 0.4374630, 0.6073391, 0.4427555, 1.0152189, 0.7609572…
## $ `1995` <dbl> 0.43234200, 0.60206683, 0.39962393, 1.10356897, 0.763…
## $ `1996` <dbl> 0.45986293, 0.58691072, 0.44632075, 1.05268794, 0.741…
## $ `1997` <dbl> 0.48560926, 0.57914169, 0.44574318, 1.03314584, 0.749…
## $ `1998` <dbl> 0.50461628, 0.56462672, 0.45284011, 0.99224279, 0.733…
## $ `1999` <dbl> 0.53443466, 0.57017489, 0.42616393, 1.02096257, 0.714…
## $ `2000` <dbl> 0.46005872, 0.56510424, 0.44859076, 0.94752409, 0.743…
## $ `2001` <dbl> 0.3864490, 0.5572351, 0.3758799, 0.8448702, 0.7015729…
## $ `2002` <dbl> 0.45131197, 0.55828104, 0.40025222, 0.82788846, 0.708…
## $ `2003` <dbl> 0.44381814, 0.55715638, 0.37896133, 0.81230885, 0.849…
## $ `2004` <dbl> 0.42464238, 0.54830249, 0.36258050, 0.79836068, 0.850…
## $ `2005` <dbl> 0.41825388, 0.55403114, 0.39838527, 0.89403522, 0.849…
## $ `2006` <dbl> 0.4229552, 0.5571201, 0.3736205, 0.8670467, 0.8192825…
## $ `2007` <dbl> 0.42840470, 0.55747782, 0.37375264, 0.87234703, 0.814…
## $ `2008` <dbl> 0.46532966, 0.57150119, 0.35299873, 0.75936813, 0.817…
## $ `2009` <dbl> 0.47156513, 0.57474813, 0.35216335, 0.79607391, 0.821…
## $ `2010` <dbl> 0.5208064, 0.5799068, 0.3391579, 0.7877439, 0.8356183…
## $ `2011` <dbl> 0.5146525, 0.5780009, 0.3424647, 0.7788870, 0.8270159…
## $ `2012` <dbl> 0.5015824, 0.5761371, 0.3391832, 0.7554355, 0.8068189…
## $ `2013` <dbl> 0.4902448, 0.5760617, 0.3508388, 0.7603118, 0.8059608…
## $ `2014` <dbl> 0.4884509, 0.5743043, 0.3373242, 0.7092662, 0.7994983…
## $ `2015` <dbl> 0.4637701, 0.5748889, 0.3337758, 0.6731833, 0.7873835…
## $ `2016` <dbl> 0.4473896, 0.5690696, 0.3394017, 0.6651229, 0.8060559…
## $ `2017` <dbl> 0.4300736, 0.5788255, 0.3328802, 0.6706341, 0.7950718…
## $ `2018` <dbl> 0.4280112, 0.5704045, 0.3326067, 0.6362706, 0.7812079…
## $ `2019` <dbl> 0.4303166, 0.5682440, 0.3313236, 0.6159136, 0.7799666…
## $ `2020` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Remove any NAs
WB_sovereign_ESG_country_groups_Methane_LIDC <- WB_sovereign_ESG_country_groups_Methane_LIDC[!(is.na(WB_sovereign_ESG_country_groups_Methane_LIDC$"2019") | WB_sovereign_ESG_country_groups_Methane_LIDC$"2019"==""), ]
glimpse(WB_sovereign_ESG_country_groups_Methane_LIDC)## Rows: 59
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Methane emissions (metric tons of CO2 equivalent per…
## $ `Indicator Code` <chr> "EN.ATM.METH.PC", "EN.ATM.METH.PC", "EN.ATM.METH.PC",…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1972` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1973` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1974` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1975` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1976` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1977` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1978` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1979` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1980` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1981` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1982` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1983` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1984` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1985` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1986` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1987` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1988` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1989` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1990` <dbl> 0.5430093, 0.6422288, 0.4459184, 1.0550093, 0.7660850…
## $ `1991` <dbl> 0.5278586, 0.6284143, 0.4485876, 1.0661439, 0.7690535…
## $ `1992` <dbl> 0.4922149, 0.6221309, 0.4426268, 1.0102428, 0.7690562…
## $ `1993` <dbl> 0.4558502, 0.6116865, 0.4292109, 1.0139132, 0.7631532…
## $ `1994` <dbl> 0.4374630, 0.6073391, 0.4427555, 1.0152189, 0.7609572…
## $ `1995` <dbl> 0.43234200, 0.60206683, 0.39962393, 1.10356897, 0.763…
## $ `1996` <dbl> 0.45986293, 0.58691072, 0.44632075, 1.05268794, 0.741…
## $ `1997` <dbl> 0.48560926, 0.57914169, 0.44574318, 1.03314584, 0.749…
## $ `1998` <dbl> 0.50461628, 0.56462672, 0.45284011, 0.99224279, 0.733…
## $ `1999` <dbl> 0.53443466, 0.57017489, 0.42616393, 1.02096257, 0.714…
## $ `2000` <dbl> 0.46005872, 0.56510424, 0.44859076, 0.94752409, 0.743…
## $ `2001` <dbl> 0.3864490, 0.5572351, 0.3758799, 0.8448702, 0.7015729…
## $ `2002` <dbl> 0.45131197, 0.55828104, 0.40025222, 0.82788846, 0.708…
## $ `2003` <dbl> 0.44381814, 0.55715638, 0.37896133, 0.81230885, 0.849…
## $ `2004` <dbl> 0.42464238, 0.54830249, 0.36258050, 0.79836068, 0.850…
## $ `2005` <dbl> 0.41825388, 0.55403114, 0.39838527, 0.89403522, 0.849…
## $ `2006` <dbl> 0.4229552, 0.5571201, 0.3736205, 0.8670467, 0.8192825…
## $ `2007` <dbl> 0.42840470, 0.55747782, 0.37375264, 0.87234703, 0.814…
## $ `2008` <dbl> 0.46532966, 0.57150119, 0.35299873, 0.75936813, 0.817…
## $ `2009` <dbl> 0.47156513, 0.57474813, 0.35216335, 0.79607391, 0.821…
## $ `2010` <dbl> 0.5208064, 0.5799068, 0.3391579, 0.7877439, 0.8356183…
## $ `2011` <dbl> 0.5146525, 0.5780009, 0.3424647, 0.7788870, 0.8270159…
## $ `2012` <dbl> 0.5015824, 0.5761371, 0.3391832, 0.7554355, 0.8068189…
## $ `2013` <dbl> 0.4902448, 0.5760617, 0.3508388, 0.7603118, 0.8059608…
## $ `2014` <dbl> 0.4884509, 0.5743043, 0.3373242, 0.7092662, 0.7994983…
## $ `2015` <dbl> 0.4637701, 0.5748889, 0.3337758, 0.6731833, 0.7873835…
## $ `2016` <dbl> 0.4473896, 0.5690696, 0.3394017, 0.6651229, 0.8060559…
## $ `2017` <dbl> 0.4300736, 0.5788255, 0.3328802, 0.6706341, 0.7950718…
## $ `2018` <dbl> 0.4280112, 0.5704045, 0.3326067, 0.6362706, 0.7812079…
## $ `2019` <dbl> 0.4303166, 0.5682440, 0.3313236, 0.6159136, 0.7799666…
## $ `2020` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Rank countries by indicator performance
WB_sovereign_ESG_country_groups_Methane_LIDC$Rank<-rank(WB_sovereign_ESG_country_groups_Methane_LIDC$'2019')
WB_sovereign_ESG_country_groups_Methane_LIDC#Filter top 10
WB_sovereign_ESG_country_groups_Methane_LIDC_top10 <- WB_sovereign_ESG_country_groups_Methane_LIDC %>%
filter(`Rank` < 11)
#Chart top 10
WB_sovereign_ESG_country_groups_Methane_LIDC_top10 %>%
ggplot(aes(fct_reorder(iso3c, -`2019`),`2019`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Methane Emissions per Capita - Top 10 LIDCs",
subtitle = "Methane Emissions per Capita, Metric Tons of CO2 Equivalent, 2019",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
#Filter bottom 10
WB_sovereign_ESG_country_groups_Methane_LIDC_bottom10 <- WB_sovereign_ESG_country_groups_Methane_LIDC %>%
filter(`Rank` > 49)
#Chart bottom 10
WB_sovereign_ESG_country_groups_Methane_LIDC_bottom10 %>%
ggplot(aes(fct_reorder(iso3c, -`2019`),`2019`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Methane Emissions per Capita - Bottom 10 LIDCs",
subtitle = "Methane Emissions per Capita, Metric Tons of CO2 Equivalent, 2019",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
The top performing LIDC on this indicator is Sao Tome and Principe, which only emits 0.05 tons of methane (tons of CO2 equivalent) per capita. Emissions per capita increases gradually down the top 10 list until reaching 10th best performer Togo, which emits 0.35 tons of methane per capita. Notably, the variation in methane per capita between the top and 10th best performer (0.3) is much larger than the same metric for CO2 per capita (0.1). Furthermore, Burundi, Rwanda and Malawi have notably ranked among the top 10 performers for both CO2 and methane emissions per capita.
The worst performing LIDC on this indicator is Timor-Leste, which emits 3.9 tons of methane (tons of CO2 equivalent) per capita, which is 78 times worse than the top performing Sao Tome and Principe. Notably, the worst 3 performers, which include South Sudan, Chad and Timor-Leste, have substantially higher levels of methane emissions per capita compared to the rest of the LIDCs. Furthermore, Uzbekistan and the Republic of the Congo have ranked among the bottom 10 for both CO2 and methane emissions per capita.
Nitrous Oxide Emissions per Capita
Nitrous oxide emissions per capita is a critical sustainability indicator to measure a country’s sustainability performance because N2O is estimated to have 298 times the warming power of CO2, making it the most harmful greenhouse gas on per unit terms (source: https://theconversation.com/meet-n2o-the-greenhouse-gas-300-times-worse-than-co2-35204#). The WB ESG dataset’s latest available data on this indicator is from 2019. 2019 data is available for all 59 LIDCs. The top 10 and bottom 10 LIDCs for this indicator are as follows:
#Filter merged dataset for indicator and LIDCs
WB_sovereign_ESG_country_groups_N2O_LIDC <- WB_sovereign_ESG_country_groups %>%
filter(`Indicator Code` == 'EN.ATM.NOXE.PC') %>%
filter(country_group == 'Low-Income Developing Countries')
glimpse(WB_sovereign_ESG_country_groups_N2O_LIDC)## Rows: 59
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Nitrous oxide emissions (metric tons of CO2 equivale…
## $ `Indicator Code` <chr> "EN.ATM.NOXE.PC", "EN.ATM.NOXE.PC", "EN.ATM.NOXE.PC",…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1972` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1973` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1974` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1975` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1976` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1977` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1978` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1979` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1980` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1981` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1982` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1983` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1984` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1985` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1986` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1987` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1988` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1989` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1990` <dbl> 0.2288051, 0.1592487, 0.3274086, 0.2072340, 0.5368270…
## $ `1991` <dbl> 0.22031705, 0.16306953, 0.32818746, 0.20574708, 0.545…
## $ `1992` <dbl> 0.19950926, 0.16233930, 0.32259244, 0.18708199, 0.542…
## $ `1993` <dbl> 0.18524840, 0.16239144, 0.31511686, 0.20653786, 0.535…
## $ `1994` <dbl> 0.16163293, 0.16471866, 0.32375401, 0.20680385, 0.548…
## $ `1995` <dbl> 0.15902235, 0.17235401, 0.30310460, 0.22445471, 0.545…
## $ `1996` <dbl> 0.16548701, 0.17322578, 0.34294498, 0.22161852, 0.525…
## $ `1997` <dbl> 0.17719573, 0.16569495, 0.34704291, 0.21750439, 0.534…
## $ `1998` <dbl> 0.18847114, 0.16261446, 0.35238070, 0.19490483, 0.526…
## $ `1999` <dbl> 0.19830600, 0.16926319, 0.33462873, 0.20765341, 0.523…
## $ `2000` <dbl> 0.16746907, 0.16732225, 0.34663832, 0.18612080, 0.550…
## $ `2001` <dbl> 0.14208364, 0.16788544, 0.27413799, 0.16566083, 0.490…
## $ `2002` <dbl> 0.1575167, 0.1692355, 0.3097842, 0.1785642, 0.5059750…
## $ `2003` <dbl> 0.15582197, 0.16291817, 0.26992685, 0.15927625, 0.595…
## $ `2004` <dbl> 0.14842262, 0.16132985, 0.25161281, 0.17219545, 0.593…
## $ `2005` <dbl> 0.14890306, 0.16456228, 0.29315142, 0.18497281, 0.598…
## $ `2006` <dbl> 0.13846298, 0.16966935, 0.26043907, 0.16732481, 0.567…
## $ `2007` <dbl> 0.13283867, 0.16823171, 0.26257305, 0.18048560, 0.560…
## $ `2008` <dbl> 0.13995962, 0.18079866, 0.23111644, 0.16378529, 0.554…
## $ `2009` <dbl> 0.1465057, 0.1753643, 0.2370115, 0.1621632, 0.5508187…
## $ `2010` <dbl> 0.15692718, 0.17719751, 0.23371460, 0.16046634, 0.558…
## $ `2011` <dbl> 0.1553918, 0.1822163, 0.2335948, 0.1586622, 0.5416022…
## $ `2012` <dbl> 0.15146955, 0.17615225, 0.23228914, 0.15678851, 0.530…
## $ `2013` <dbl> 0.14781718, 0.17517513, 0.23789071, 0.15487831, 0.531…
## $ `2014` <dbl> 0.15252854, 0.17913842, 0.23136359, 0.15297899, 0.520…
## $ `2015` <dbl> 0.13424924, 0.18149670, 0.22125647, 0.13738434, 0.510…
## $ `2016` <dbl> 0.14272379, 0.17356940, 0.23546570, 0.13573936, 0.522…
## $ `2017` <dbl> 0.14519462, 0.17897689, 0.22102528, 0.14753951, 0.510…
## $ `2018` <dbl> 0.12617050, 0.18174865, 0.24727831, 0.14581201, 0.501…
## $ `2019` <dbl> 0.13169739, 0.17737307, 0.23048599, 0.14415000, 0.497…
## $ `2020` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Remove any NAs
WB_sovereign_ESG_country_groups_N2O_LIDC <- WB_sovereign_ESG_country_groups_N2O_LIDC[!(is.na(WB_sovereign_ESG_country_groups_N2O_LIDC$"2019") | WB_sovereign_ESG_country_groups_N2O_LIDC$"2019"==""), ]
glimpse(WB_sovereign_ESG_country_groups_N2O_LIDC)## Rows: 59
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Nitrous oxide emissions (metric tons of CO2 equivale…
## $ `Indicator Code` <chr> "EN.ATM.NOXE.PC", "EN.ATM.NOXE.PC", "EN.ATM.NOXE.PC",…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1972` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1973` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1974` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1975` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1976` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1977` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1978` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1979` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1980` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1981` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1982` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1983` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1984` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1985` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1986` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1987` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1988` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1989` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1990` <dbl> 0.2288051, 0.1592487, 0.3274086, 0.2072340, 0.5368270…
## $ `1991` <dbl> 0.22031705, 0.16306953, 0.32818746, 0.20574708, 0.545…
## $ `1992` <dbl> 0.19950926, 0.16233930, 0.32259244, 0.18708199, 0.542…
## $ `1993` <dbl> 0.18524840, 0.16239144, 0.31511686, 0.20653786, 0.535…
## $ `1994` <dbl> 0.16163293, 0.16471866, 0.32375401, 0.20680385, 0.548…
## $ `1995` <dbl> 0.15902235, 0.17235401, 0.30310460, 0.22445471, 0.545…
## $ `1996` <dbl> 0.16548701, 0.17322578, 0.34294498, 0.22161852, 0.525…
## $ `1997` <dbl> 0.17719573, 0.16569495, 0.34704291, 0.21750439, 0.534…
## $ `1998` <dbl> 0.18847114, 0.16261446, 0.35238070, 0.19490483, 0.526…
## $ `1999` <dbl> 0.19830600, 0.16926319, 0.33462873, 0.20765341, 0.523…
## $ `2000` <dbl> 0.16746907, 0.16732225, 0.34663832, 0.18612080, 0.550…
## $ `2001` <dbl> 0.14208364, 0.16788544, 0.27413799, 0.16566083, 0.490…
## $ `2002` <dbl> 0.1575167, 0.1692355, 0.3097842, 0.1785642, 0.5059750…
## $ `2003` <dbl> 0.15582197, 0.16291817, 0.26992685, 0.15927625, 0.595…
## $ `2004` <dbl> 0.14842262, 0.16132985, 0.25161281, 0.17219545, 0.593…
## $ `2005` <dbl> 0.14890306, 0.16456228, 0.29315142, 0.18497281, 0.598…
## $ `2006` <dbl> 0.13846298, 0.16966935, 0.26043907, 0.16732481, 0.567…
## $ `2007` <dbl> 0.13283867, 0.16823171, 0.26257305, 0.18048560, 0.560…
## $ `2008` <dbl> 0.13995962, 0.18079866, 0.23111644, 0.16378529, 0.554…
## $ `2009` <dbl> 0.1465057, 0.1753643, 0.2370115, 0.1621632, 0.5508187…
## $ `2010` <dbl> 0.15692718, 0.17719751, 0.23371460, 0.16046634, 0.558…
## $ `2011` <dbl> 0.1553918, 0.1822163, 0.2335948, 0.1586622, 0.5416022…
## $ `2012` <dbl> 0.15146955, 0.17615225, 0.23228914, 0.15678851, 0.530…
## $ `2013` <dbl> 0.14781718, 0.17517513, 0.23789071, 0.15487831, 0.531…
## $ `2014` <dbl> 0.15252854, 0.17913842, 0.23136359, 0.15297899, 0.520…
## $ `2015` <dbl> 0.13424924, 0.18149670, 0.22125647, 0.13738434, 0.510…
## $ `2016` <dbl> 0.14272379, 0.17356940, 0.23546570, 0.13573936, 0.522…
## $ `2017` <dbl> 0.14519462, 0.17897689, 0.22102528, 0.14753951, 0.510…
## $ `2018` <dbl> 0.12617050, 0.18174865, 0.24727831, 0.14581201, 0.501…
## $ `2019` <dbl> 0.13169739, 0.17737307, 0.23048599, 0.14415000, 0.497…
## $ `2020` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Rank countries by indicator performance
WB_sovereign_ESG_country_groups_N2O_LIDC$Rank<-rank(WB_sovereign_ESG_country_groups_N2O_LIDC$'2019')
WB_sovereign_ESG_country_groups_N2O_LIDC#Filter top 10
WB_sovereign_ESG_country_groups_N2O_LIDC_top10 <- WB_sovereign_ESG_country_groups_N2O_LIDC %>%
filter(`Rank` < 11)
#Chart top 10
WB_sovereign_ESG_country_groups_N2O_LIDC_top10 %>%
ggplot(aes(fct_reorder(iso3c, -`2019`),`2019`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Nitrous Oxide Emissions per Capita - Top 10 LIDCs",
subtitle = "Nitrous Oxide Emissions per Capita, Metric Tons of CO2 Equivalent, 2019",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
#Filter bottom 10
WB_sovereign_ESG_country_groups_N2O_LIDC_bottom10 <- WB_sovereign_ESG_country_groups_N2O_LIDC %>%
filter(`Rank` > 49)
#Chart bottom 10
WB_sovereign_ESG_country_groups_N2O_LIDC_bottom10 %>%
ggplot(aes(fct_reorder(iso3c, -`2019`),`2019`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Nitrous Oxide Emissions per Capita - Bottom 10 LIDCs",
subtitle = "Nitrous Oxide Emissions per Capita, Metric Tons of CO2 Equivalent, 2019",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
The top performing LIDC on this indicator is the Solomon Islands, which only emits 0.03 tons of nitrous oxide (tons of CO2 equivalent) per capita. Emissions per capita increases gradually down the top 10 list until reaching 10th best performer Haiti, which emits 0.14 tones of nitrous oxide per capita. Sao Tome and Principe, Kiribati, Liberia, and Cote d’Ivoire have notably ranked among the top 10 performers for both methane and nitrous oxide emissions per capita. There is no overlap among the top 10 performers for CO2 and nitrous oxide per capita.
The worst performing LIDC on this indicator is Cameroon, which emits 2.4 tons of nitrous oxide (tons of CO2 equivalent) per capita, which is 80 times worse than the top performing Solomon Islands. Notably, the worst 4 performers, which include Chad, South Sudan, Central African Republic and Cameroon, have substantially higher levels of nitrous oxide emissions per capita compared to the rest of the LIDCs. Furthermore, Sudan, Mauritania, Chad, South Sudan and the Central African Republic have notably ranked among the bottom 10 for both methane and nitrous oxide emissions per capita. There is no overlap among the bottom 10 performers for CO2 and nitrous oxide per capita.
Emissions & Pollution: Key Findings
Across the three emissions indicators examined in this sub-section, Burundi, Rwanda, Malawi, Sao Tome and Principe, Kiribati, Liberia and Cote d’Ivoire appear to be the top performing LIDCs as they each appear in two of the three top 10 lists.
On the other hand, Uzbekistan, the Republic of the Congo, Sudan, Mauritania, Chad, South Sudan and the Central African Republic appear to be the worst performing LIDCs as they each appear in two of the three bottom 10 lists.
For sustainable development investors, the top performing LIDCs in this category are likely to present investment opportunities that enable economic growth while limiting increases in per capita emissions. On the other hand, the worst performing LIDCs likely present investment opportunities to reduce emissions intensity while stimulating economic growth.
Natural Resource Management
This sub-section presents the top 10 and bottom 10 LIDCs based on their performance on 3 Natural Resource Management indicators: Natural Resources Depletion, Net Forest Depletion and Terrestrial and Marine Protected Areas
Natural Resources Depletion
Natural resources depletion is the sum of net forest depletion, energy depletion, and mineral depletion, expressed as a % of GNI. This is a critical sustainability indicator because it represents the extent to which a given country relies on depleting its natural resources in order to generate economic output. The WB ESG dataset’s latest available data on this indicator is from 2020. 2020 data is available for 56 of the 59 LIDCs (unavailable for Yemen, Eritrea and South Sudan). The top 10 and bottom 10 LIDCs for this indicator are as follows:
#Filter merged dataset for indicator and LIDCs
WB_sovereign_ESG_country_groups_NRD_LIDC <- WB_sovereign_ESG_country_groups %>%
filter(`Indicator Code` == 'NY.ADJ.DRES.GN.ZS') %>%
filter(country_group == 'Low-Income Developing Countries')
glimpse(WB_sovereign_ESG_country_groups_NRD_LIDC)## Rows: 59
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Adjusted savings: natural resources depletion (% of …
## $ `Indicator Code` <chr> "NY.ADJ.DRES.GN.ZS", "NY.ADJ.DRES.GN.ZS", "NY.ADJ.DRE…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> 0.285942, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `1971` <dbl> 0.3489012, NA, 5.8471656, NA, NA, NA, NA, 3.8126547, …
## $ `1972` <dbl> 0.4044896, NA, 5.3683169, NA, NA, NA, NA, 3.7030249, …
## $ `1973` <dbl> 0.8008260, 0.4915004, 7.2654359, NA, NA, NA, NA, 4.84…
## $ `1974` <dbl> 0.9584073, 0.3739375, 7.2703839, NA, NA, NA, NA, 5.13…
## $ `1975` <dbl> 1.234269, 0.555508, 8.263988, NA, NA, NA, NA, 4.66532…
## $ `1976` <dbl> 1.3907038, 0.6723388, 7.1706735, NA, NA, NA, NA, 4.85…
## $ `1977` <dbl> 1.286721, 2.442155, 11.978642, NA, NA, NA, NA, 7.0161…
## $ `1978` <dbl> 1.228503, 1.758020, 9.871873, NA, NA, NA, NA, 5.35608…
## $ `1979` <dbl> 1.5692747, 0.6387094, 7.1601077, NA, NA, NA, NA, 8.05…
## $ `1980` <dbl> 1.65708765, 0.67651685, 7.32688047, 17.73298324, 0.00…
## $ `1981` <dbl> 1.276950388, 0.523311820, 7.339551751, 14.891426740, …
## $ `1982` <dbl> NA, 0.85321483, 10.15913167, 22.47814448, 0.00000000,…
## $ `1983` <dbl> NA, 0.59954798, 8.16483582, 14.79784479, 0.00000000, …
## $ `1984` <dbl> NA, 0.49725384, 9.16055585, 10.81186814, 0.06246862, …
## $ `1985` <dbl> NA, 0.30375768, 7.52682521, 7.40283960, 0.10435357, 6…
## $ `1986` <dbl> NA, 0.53163768, 7.41896468, 10.29386419, 0.18472956, …
## $ `1987` <dbl> NA, 0.43994382, 6.65457326, 6.96983113, 0.17981601, 9…
## $ `1988` <dbl> NA, 0.44166090, 6.46820754, 6.27951239, 0.17806530, 1…
## $ `1989` <dbl> NA, 0.40194032, 7.35355671, 5.97997299, 0.09617279, 1…
## $ `1990` <dbl> NA, 0.44926131, 7.00317073, 6.60248337, 0.05261745, 1…
## $ `1991` <dbl> NA, 0.44869537, 6.69571977, 8.20642308, 0.15899305, 1…
## $ `1992` <dbl> NA, 0.459461995, 9.020507879, 8.668348304, 0.01097204…
## $ `1993` <dbl> NA, 0.372735390, 5.636218344, 7.072893879, 0.00000000…
## $ `1994` <dbl> NA, 0.34039188, 9.11628246, 5.57452648, 0.09786290, 1…
## $ `1995` <dbl> NA, 0.387166446, 9.158089222, 6.671638748, 0.06052861…
## $ `1996` <dbl> NA, 0.35209206, 8.27020290, 6.30669083, 0.09401189, 2…
## $ `1997` <dbl> NA, 0.30112925, 7.89766951, 4.51414191, 0.07622930, 2…
## $ `1998` <dbl> NA, 0.285003352, 7.500319731, 4.796488637, 0.05450754…
## $ `1999` <dbl> NA, 0.317724562, 3.216121990, 5.085892944, 0.02344117…
## $ `2000` <dbl> NA, 0.391394931, 3.347154483, 3.874847322, 0.02809417…
## $ `2001` <dbl> NA, 0.423255055, 1.764083048, 3.616764917, 0.00920239…
## $ `2002` <dbl> NA, 0.504158418, 0.526683881, 3.442783032, 0.02744460…
## $ `2003` <dbl> NA, 0.471376666, 0.014839347, 3.265931367, 0.00787698…
## $ `2004` <dbl> NA, 0.489006624, 0.000282870, 2.898660515, 0.01810973…
## $ `2005` <dbl> NA, 0.608695844, 0.000428011, 2.446920110, 0.03219485…
## $ `2006` <dbl> NA, 0.885421294, 0.001336931, 3.113633345, 0.09394937…
## $ `2007` <dbl> NA, 0.845182140, 0.001031366, 3.997329793, 0.00014751…
## $ `2008` <dbl> NA, 0.775485842, 0.001132413, 3.590784153, 0.31868380…
## $ `2009` <dbl> 0.265019028, 0.804729858, 0.001390535, 3.344222964, 1…
## $ `2010` <dbl> 0.354491193, 0.848277731, 0.001917839, 4.934621990, 3…
## $ `2011` <dbl> 0.397696521, 1.184875777, 0.002939034, 4.552379054, 5…
## $ `2012` <dbl> 0.386703580, 1.231384811, 0.002668126, 3.969690083, 4…
## $ `2013` <dbl> 0.289482199, 0.902213141, 0.001599583, 3.228200928, 2…
## $ `2014` <dbl> 0.288231475, 0.893874568, 0.001230009, 3.257500653, 2…
## $ `2015` <dbl> 0.29524234, 0.77247401, 0.03924884, 3.83803073, 1.958…
## $ `2016` <dbl> 0.35595122, 0.50149103, 0.03795196, 4.31823948, 2.923…
## $ `2017` <dbl> 0.344837071, 0.483708671, 0.068336179, 3.264792706, 3…
## $ `2018` <dbl> 0.397920793, 0.518595806, 0.092259860, 1.907963892, 3…
## $ `2019` <dbl> 0.362219986, 0.359684399, 0.082064658, 2.072428649, 2…
## $ `2020` <dbl> 0.381654456, 0.278951688, 0.038834747, 2.654465117, 3…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Remove any NAs
WB_sovereign_ESG_country_groups_NRD_LIDC <- WB_sovereign_ESG_country_groups_NRD_LIDC[!(is.na(WB_sovereign_ESG_country_groups_NRD_LIDC$"2020") | WB_sovereign_ESG_country_groups_NRD_LIDC$"2020"==""), ]
glimpse(WB_sovereign_ESG_country_groups_NRD_LIDC)## Rows: 56
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Adjusted savings: natural resources depletion (% of …
## $ `Indicator Code` <chr> "NY.ADJ.DRES.GN.ZS", "NY.ADJ.DRES.GN.ZS", "NY.ADJ.DRE…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> 0.285942, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `1971` <dbl> 0.3489012, NA, 5.8471656, NA, NA, NA, NA, 3.8126547, …
## $ `1972` <dbl> 0.4044896, NA, 5.3683169, NA, NA, NA, NA, 3.7030249, …
## $ `1973` <dbl> 0.8008260, 0.4915004, 7.2654359, NA, NA, NA, NA, 4.84…
## $ `1974` <dbl> 0.9584073, 0.3739375, 7.2703839, NA, NA, NA, NA, 5.13…
## $ `1975` <dbl> 1.234269, 0.555508, 8.263988, NA, NA, NA, NA, 4.66532…
## $ `1976` <dbl> 1.3907038, 0.6723388, 7.1706735, NA, NA, NA, NA, 4.85…
## $ `1977` <dbl> 1.286721, 2.442155, 11.978642, NA, NA, NA, NA, 7.0161…
## $ `1978` <dbl> 1.228503, 1.758020, 9.871873, NA, NA, NA, NA, 5.35608…
## $ `1979` <dbl> 1.5692747, 0.6387094, 7.1601077, NA, NA, NA, NA, 8.05…
## $ `1980` <dbl> 1.65708765, 0.67651685, 7.32688047, 17.73298324, 0.00…
## $ `1981` <dbl> 1.276950388, 0.523311820, 7.339551751, 14.891426740, …
## $ `1982` <dbl> NA, 0.85321483, 10.15913167, 22.47814448, 0.00000000,…
## $ `1983` <dbl> NA, 0.59954798, 8.16483582, 14.79784479, 0.00000000, …
## $ `1984` <dbl> NA, 0.49725384, 9.16055585, 10.81186814, 0.06246862, …
## $ `1985` <dbl> NA, 0.30375768, 7.52682521, 7.40283960, 0.10435357, 6…
## $ `1986` <dbl> NA, 0.53163768, 7.41896468, 10.29386419, 0.18472956, …
## $ `1987` <dbl> NA, 0.43994382, 6.65457326, 6.96983113, 0.17981601, 9…
## $ `1988` <dbl> NA, 0.44166090, 6.46820754, 6.27951239, 0.17806530, 1…
## $ `1989` <dbl> NA, 0.40194032, 7.35355671, 5.97997299, 0.09617279, 1…
## $ `1990` <dbl> NA, 0.44926131, 7.00317073, 6.60248337, 0.05261745, 1…
## $ `1991` <dbl> NA, 0.44869537, 6.69571977, 8.20642308, 0.15899305, 1…
## $ `1992` <dbl> NA, 0.459461995, 9.020507879, 8.668348304, 0.01097204…
## $ `1993` <dbl> NA, 0.372735390, 5.636218344, 7.072893879, 0.00000000…
## $ `1994` <dbl> NA, 0.34039188, 9.11628246, 5.57452648, 0.09786290, 1…
## $ `1995` <dbl> NA, 0.387166446, 9.158089222, 6.671638748, 0.06052861…
## $ `1996` <dbl> NA, 0.35209206, 8.27020290, 6.30669083, 0.09401189, 2…
## $ `1997` <dbl> NA, 0.30112925, 7.89766951, 4.51414191, 0.07622930, 2…
## $ `1998` <dbl> NA, 0.285003352, 7.500319731, 4.796488637, 0.05450754…
## $ `1999` <dbl> NA, 0.317724562, 3.216121990, 5.085892944, 0.02344117…
## $ `2000` <dbl> NA, 0.391394931, 3.347154483, 3.874847322, 0.02809417…
## $ `2001` <dbl> NA, 0.423255055, 1.764083048, 3.616764917, 0.00920239…
## $ `2002` <dbl> NA, 0.504158418, 0.526683881, 3.442783032, 0.02744460…
## $ `2003` <dbl> NA, 0.471376666, 0.014839347, 3.265931367, 0.00787698…
## $ `2004` <dbl> NA, 0.489006624, 0.000282870, 2.898660515, 0.01810973…
## $ `2005` <dbl> NA, 0.608695844, 0.000428011, 2.446920110, 0.03219485…
## $ `2006` <dbl> NA, 0.885421294, 0.001336931, 3.113633345, 0.09394937…
## $ `2007` <dbl> NA, 0.845182140, 0.001031366, 3.997329793, 0.00014751…
## $ `2008` <dbl> NA, 0.775485842, 0.001132413, 3.590784153, 0.31868380…
## $ `2009` <dbl> 0.265019028, 0.804729858, 0.001390535, 3.344222964, 1…
## $ `2010` <dbl> 0.354491193, 0.848277731, 0.001917839, 4.934621990, 3…
## $ `2011` <dbl> 0.397696521, 1.184875777, 0.002939034, 4.552379054, 5…
## $ `2012` <dbl> 0.386703580, 1.231384811, 0.002668126, 3.969690083, 4…
## $ `2013` <dbl> 0.289482199, 0.902213141, 0.001599583, 3.228200928, 2…
## $ `2014` <dbl> 0.288231475, 0.893874568, 0.001230009, 3.257500653, 2…
## $ `2015` <dbl> 0.29524234, 0.77247401, 0.03924884, 3.83803073, 1.958…
## $ `2016` <dbl> 0.35595122, 0.50149103, 0.03795196, 4.31823948, 2.923…
## $ `2017` <dbl> 0.344837071, 0.483708671, 0.068336179, 3.264792706, 3…
## $ `2018` <dbl> 0.397920793, 0.518595806, 0.092259860, 1.907963892, 3…
## $ `2019` <dbl> 0.362219986, 0.359684399, 0.082064658, 2.072428649, 2…
## $ `2020` <dbl> 0.381654456, 0.278951688, 0.038834747, 2.654465117, 3…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Rank countries by indicator performance
WB_sovereign_ESG_country_groups_NRD_LIDC$Rank<-rank(WB_sovereign_ESG_country_groups_NRD_LIDC$'2020')
WB_sovereign_ESG_country_groups_NRD_LIDC#Filter top 10
WB_sovereign_ESG_country_groups_NRD_LIDC_top10 <- WB_sovereign_ESG_country_groups_NRD_LIDC %>%
filter(`Rank` < 11)
#Chart top 10
WB_sovereign_ESG_country_groups_NRD_LIDC_top10 %>%
ggplot(aes(fct_reorder(iso3c, -`2020`),`2020`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Natural Resources Depletion - Top 10 LIDCs",
subtitle = "% of GNI, 2020",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
#Filter bottom 10
WB_sovereign_ESG_country_groups_NRD_LIDC_bottom10 <- WB_sovereign_ESG_country_groups_NRD_LIDC %>%
filter(`Rank` > 46)
#Chart bottom 10
WB_sovereign_ESG_country_groups_NRD_LIDC_bottom10 %>%
ggplot(aes(fct_reorder(iso3c, -`2020`),`2020`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Natural Resources Depletion - Bottom 10 LIDCs",
subtitle = "% of GNI, 2020",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
The top performing LIDCs on this indicator are Moldova, the Solomon Islands and Honduras, for which natural resources depletion represents 0% of GNI. Natural resources depletion levels remain low for the rest of the top 10, with 10th best Djibouti’s natural resources depletion rate being only 0.31% of GNI.
The worst performing LIDC on this indicator is the Republic of the Congo, with its natural resources depletion representing 30% of its GNI. This is significantly higher than the rest of the bottom 10, which have natural resources depletion percentages ranging from 8.1% to 17.7% of GNI. Notably, the Republic of the Congo was among the worst performing LIDCs on the emissions indicators in the previous sub-section.
Net Forest Depletion
Net Forest Depletion measures the extent to which the harvest rate exceeds the rate of natural growth (expressed as a % of GNI), and is a critical sustainability indicator of the extent to which a given country replies on depleting its forests in order to generate economic output. The WB ESG dataset’s latest available data on this indicator is from 2020. 2020 data is available for 56 of the 59 LIDCs (unavailable for Yemen, Eritrea and South Sudan). The top 10 and bottom 10 LIDCs for this indicator are as follows:
#Filter merged dataset for indicator and LIDCs
WB_sovereign_ESG_country_groups_NFD_LIDC <- WB_sovereign_ESG_country_groups %>%
filter(`Indicator Code` == 'NY.ADJ.DFOR.GN.ZS') %>%
filter(country_group == 'Low-Income Developing Countries')
glimpse(WB_sovereign_ESG_country_groups_NFD_LIDC)## Rows: 59
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Adjusted savings: net forest depletion (% of GNI)", …
## $ `Indicator Code` <chr> "NY.ADJ.DFOR.GN.ZS", "NY.ADJ.DFOR.GN.ZS", "NY.ADJ.DFO…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> 0.2794117, NA, 6.7579811, NA, 0.0000000, 6.9655562, 0…
## $ `1971` <dbl> 0.3375673, NA, 5.8471656, NA, 0.0000000, 5.5839502, 0…
## $ `1972` <dbl> 0.3892899, NA, 5.3683169, NA, 0.0000000, 6.6137245, 0…
## $ `1973` <dbl> 0.7517331, 0.4902521, 7.2654359, NA, 0.0000000, 8.970…
## $ `1974` <dbl> 0.7831858, 0.3712633, 7.2703839, NA, 0.0000000, 8.678…
## $ `1975` <dbl> 0.7956867, 0.5521918, 8.2639881, NA, 0.0000000, 9.911…
## $ `1976` <dbl> 0.7569508, 0.6634578, 7.1706735, NA, 0.0000000, 8.187…
## $ `1977` <dbl> 0.589740, 2.431331, 11.978642, NA, 0.000000, 12.67032…
## $ `1978` <dbl> 0.73497110, 1.75248996, 9.87187338, NA, 0.00000000, 1…
## $ `1979` <dbl> 0.55993684, 0.62961579, 7.16010772, NA, 0.00000000, 8…
## $ `1980` <dbl> 0.58168484, 0.66297509, 7.32688047, 17.73283092, 0.00…
## $ `1981` <dbl> 0.43426277, 0.51986939, 6.97322977, 14.89102357, 0.00…
## $ `1982` <dbl> NA, 0.85208142, 10.02046687, 22.47767530, 0.00000000,…
## $ `1983` <dbl> NA, 0.5842947, 7.7791764, 14.7973075, 0.0000000, 6.59…
## $ `1984` <dbl> NA, 0.47811205, 7.46749654, 10.81112139, 0.00000000, …
## $ `1985` <dbl> NA, 0.28629993, 5.46353237, 7.40179954, 0.00000000, 6…
## $ `1986` <dbl> NA, 0.51409623, 6.86825062, 10.29285281, 0.00000000, …
## $ `1987` <dbl> NA, 0.4202785, 5.7680982, 6.9687188, 0.0000000, 9.552…
## $ `1988` <dbl> NA, 0.42199116, 6.09031361, 6.27812106, 0.00000000, 1…
## $ `1989` <dbl> NA, 0.38592916, 6.87235042, 5.97830309, 0.00000000, 1…
## $ `1990` <dbl> NA, 0.41394344, 6.51627052, 6.60075459, 0.00000000, 1…
## $ `1991` <dbl> NA, 0.42337777, 6.44474534, 8.20435432, 0.00000000, 1…
## $ `1992` <dbl> NA, 0.41700364, 7.85104945, 8.61563633, 0.00000000, 1…
## $ `1993` <dbl> NA, 0.32618866, 4.83203758, 7.04205136, 0.00000000, 1…
## $ `1994` <dbl> NA, 0.28628482, 8.17498615, 5.53488815, 0.00000000, 1…
## $ `1995` <dbl> NA, 0.32152129, 8.84051347, 6.60810170, 0.00000000, 2…
## $ `1996` <dbl> NA, 0.26167467, 8.06390287, 6.25656632, 0.00000000, 2…
## $ `1997` <dbl> NA, 0.21644452, 7.81107885, 4.48413591, 0.00000000, 2…
## $ `1998` <dbl> NA, 0.20706543, 7.43227857, 4.77278193, 0.00000000, 2…
## $ `1999` <dbl> NA, 0.20921291, 3.15801806, 5.06886720, 0.00000000, 1…
## $ `2000` <dbl> NA, 0.19467844, 3.27238423, 3.85700725, 0.00000000, 1…
## $ `2001` <dbl> NA, 0.19136793, 1.71405726, 3.57335007, 0.00000000, 1…
## $ `2002` <dbl> NA, 0.1800911, 0.4722776, 3.4143105, 0.0000000, 24.55…
## $ `2003` <dbl> NA, 0.18624791, 0.00000000, 3.24479926, 0.00000000, 4…
## $ `2004` <dbl> NA, 0.17270367, 0.00000000, 2.87657257, 0.00000000, 3…
## $ `2005` <dbl> NA, 0.15556564, 0.00000000, 2.40016564, 0.00000000, 2…
## $ `2006` <dbl> NA, 0.20962145, 0.00000000, 3.06018539, 0.00000000, 2…
## $ `2007` <dbl> NA, 0.31012130, 0.00000000, 3.94234153, 0.00000000, 3…
## $ `2008` <dbl> NA, 0.24350582, 0.00000000, 3.45796000, 0.00000000, 3…
## $ `2009` <dbl> 0.22502632, 0.20929553, 0.00000000, 3.31889090, 0.000…
## $ `2010` <dbl> 0.2886741, 0.3407615, 0.0000000, 4.8793261, 0.0000000…
## $ `2011` <dbl> 0.24932279, 0.32422872, 0.00000000, 4.49048825, 0.000…
## $ `2012` <dbl> 0.21373476, 0.26352378, 0.00000000, 3.91944849, 0.000…
## $ `2013` <dbl> 0.21375019, 0.18943587, 0.00000000, 3.20399757, 0.000…
## $ `2014` <dbl> 0.21713041, 0.18106063, 0.00000000, 3.24468252, 0.000…
## $ `2015` <dbl> 0.24327159, 0.19645972, 0.00000000, 3.82570825, 0.000…
## $ `2016` <dbl> 0.28325658, 0.20491448, 0.00000000, 4.30944637, 0.000…
## $ `2017` <dbl> 0.23157224, 0.15489765, 0.00000000, 3.25427278, 0.000…
## $ `2018` <dbl> 0.24297920, 0.07910001, 0.00000000, 1.89473204, 0.000…
## $ `2019` <dbl> 0.26850821, 0.07908477, 0.00000000, 2.05273817, 0.000…
## $ `2020` <dbl> 0.30825414, 0.08763023, 0.00000000, 2.64047052, 0.000…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Remove any NAs
WB_sovereign_ESG_country_groups_NFD_LIDC <- WB_sovereign_ESG_country_groups_NFD_LIDC[!(is.na(WB_sovereign_ESG_country_groups_NFD_LIDC$"2020") | WB_sovereign_ESG_country_groups_NFD_LIDC$"2020"==""), ]
glimpse(WB_sovereign_ESG_country_groups_NFD_LIDC)## Rows: 56
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Adjusted savings: net forest depletion (% of GNI)", …
## $ `Indicator Code` <chr> "NY.ADJ.DFOR.GN.ZS", "NY.ADJ.DFOR.GN.ZS", "NY.ADJ.DFO…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> 0.2794117, NA, 6.7579811, NA, 0.0000000, 6.9655562, 0…
## $ `1971` <dbl> 0.3375673, NA, 5.8471656, NA, 0.0000000, 5.5839502, 0…
## $ `1972` <dbl> 0.3892899, NA, 5.3683169, NA, 0.0000000, 6.6137245, 0…
## $ `1973` <dbl> 0.7517331, 0.4902521, 7.2654359, NA, 0.0000000, 8.970…
## $ `1974` <dbl> 0.7831858, 0.3712633, 7.2703839, NA, 0.0000000, 8.678…
## $ `1975` <dbl> 0.7956867, 0.5521918, 8.2639881, NA, 0.0000000, 9.911…
## $ `1976` <dbl> 0.7569508, 0.6634578, 7.1706735, NA, 0.0000000, 8.187…
## $ `1977` <dbl> 0.589740, 2.431331, 11.978642, NA, 0.000000, 12.67032…
## $ `1978` <dbl> 0.73497110, 1.75248996, 9.87187338, NA, 0.00000000, 1…
## $ `1979` <dbl> 0.55993684, 0.62961579, 7.16010772, NA, 0.00000000, 8…
## $ `1980` <dbl> 0.58168484, 0.66297509, 7.32688047, 17.73283092, 0.00…
## $ `1981` <dbl> 0.43426277, 0.51986939, 6.97322977, 14.89102357, 0.00…
## $ `1982` <dbl> NA, 0.85208142, 10.02046687, 22.47767530, 0.00000000,…
## $ `1983` <dbl> NA, 0.5842947, 7.7791764, 14.7973075, 0.0000000, 6.59…
## $ `1984` <dbl> NA, 0.47811205, 7.46749654, 10.81112139, 0.00000000, …
## $ `1985` <dbl> NA, 0.28629993, 5.46353237, 7.40179954, 0.00000000, 6…
## $ `1986` <dbl> NA, 0.51409623, 6.86825062, 10.29285281, 0.00000000, …
## $ `1987` <dbl> NA, 0.4202785, 5.7680982, 6.9687188, 0.0000000, 9.552…
## $ `1988` <dbl> NA, 0.42199116, 6.09031361, 6.27812106, 0.00000000, 1…
## $ `1989` <dbl> NA, 0.38592916, 6.87235042, 5.97830309, 0.00000000, 1…
## $ `1990` <dbl> NA, 0.41394344, 6.51627052, 6.60075459, 0.00000000, 1…
## $ `1991` <dbl> NA, 0.42337777, 6.44474534, 8.20435432, 0.00000000, 1…
## $ `1992` <dbl> NA, 0.41700364, 7.85104945, 8.61563633, 0.00000000, 1…
## $ `1993` <dbl> NA, 0.32618866, 4.83203758, 7.04205136, 0.00000000, 1…
## $ `1994` <dbl> NA, 0.28628482, 8.17498615, 5.53488815, 0.00000000, 1…
## $ `1995` <dbl> NA, 0.32152129, 8.84051347, 6.60810170, 0.00000000, 2…
## $ `1996` <dbl> NA, 0.26167467, 8.06390287, 6.25656632, 0.00000000, 2…
## $ `1997` <dbl> NA, 0.21644452, 7.81107885, 4.48413591, 0.00000000, 2…
## $ `1998` <dbl> NA, 0.20706543, 7.43227857, 4.77278193, 0.00000000, 2…
## $ `1999` <dbl> NA, 0.20921291, 3.15801806, 5.06886720, 0.00000000, 1…
## $ `2000` <dbl> NA, 0.19467844, 3.27238423, 3.85700725, 0.00000000, 1…
## $ `2001` <dbl> NA, 0.19136793, 1.71405726, 3.57335007, 0.00000000, 1…
## $ `2002` <dbl> NA, 0.1800911, 0.4722776, 3.4143105, 0.0000000, 24.55…
## $ `2003` <dbl> NA, 0.18624791, 0.00000000, 3.24479926, 0.00000000, 4…
## $ `2004` <dbl> NA, 0.17270367, 0.00000000, 2.87657257, 0.00000000, 3…
## $ `2005` <dbl> NA, 0.15556564, 0.00000000, 2.40016564, 0.00000000, 2…
## $ `2006` <dbl> NA, 0.20962145, 0.00000000, 3.06018539, 0.00000000, 2…
## $ `2007` <dbl> NA, 0.31012130, 0.00000000, 3.94234153, 0.00000000, 3…
## $ `2008` <dbl> NA, 0.24350582, 0.00000000, 3.45796000, 0.00000000, 3…
## $ `2009` <dbl> 0.22502632, 0.20929553, 0.00000000, 3.31889090, 0.000…
## $ `2010` <dbl> 0.2886741, 0.3407615, 0.0000000, 4.8793261, 0.0000000…
## $ `2011` <dbl> 0.24932279, 0.32422872, 0.00000000, 4.49048825, 0.000…
## $ `2012` <dbl> 0.21373476, 0.26352378, 0.00000000, 3.91944849, 0.000…
## $ `2013` <dbl> 0.21375019, 0.18943587, 0.00000000, 3.20399757, 0.000…
## $ `2014` <dbl> 0.21713041, 0.18106063, 0.00000000, 3.24468252, 0.000…
## $ `2015` <dbl> 0.24327159, 0.19645972, 0.00000000, 3.82570825, 0.000…
## $ `2016` <dbl> 0.28325658, 0.20491448, 0.00000000, 4.30944637, 0.000…
## $ `2017` <dbl> 0.23157224, 0.15489765, 0.00000000, 3.25427278, 0.000…
## $ `2018` <dbl> 0.24297920, 0.07910001, 0.00000000, 1.89473204, 0.000…
## $ `2019` <dbl> 0.26850821, 0.07908477, 0.00000000, 2.05273817, 0.000…
## $ `2020` <dbl> 0.30825414, 0.08763023, 0.00000000, 2.64047052, 0.000…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Rank countries by indicator performance
WB_sovereign_ESG_country_groups_NFD_LIDC$Rank<-rank(WB_sovereign_ESG_country_groups_NFD_LIDC$'2020')
WB_sovereign_ESG_country_groups_NFD_LIDC#Filter top 10
WB_sovereign_ESG_country_groups_NFD_LIDC_top10 <- WB_sovereign_ESG_country_groups_NFD_LIDC %>%
filter(`Rank` < 11)
#Chart top 10
WB_sovereign_ESG_country_groups_NFD_LIDC_top10 %>%
ggplot(aes(fct_reorder(iso3c, -`2020`),`2020`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Net Forest Depletion - Top 10 LIDCs",
subtitle = "% of GNI, 2020",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
#Filter bottom 10
WB_sovereign_ESG_country_groups_NFD_LIDC_bottom10 <- WB_sovereign_ESG_country_groups_NFD_LIDC %>%
filter(`Rank` > 46)
#Chart bottom 10
WB_sovereign_ESG_country_groups_NFD_LIDC_bottom10 %>%
ggplot(aes(fct_reorder(iso3c, -`2020`),`2020`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Net Forest Depletion - Bottom 10 LIDCs",
subtitle = "% of GNI, 2020",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
19 countries tied as the top performer on this indicator because they all reported 0% net forest depletion rates, implying that the rate of natural growth was higher than the harvest rate in their countries.
The 3 worst performing LIDCs on this indicator are Liberia, Somalia and Burundi, with Net Forest Depletion levels representing 12.3%, 14.9% and 17.7% of GNI respectively. Unsurprisingly, 5 of the bottom 10 worst performers in terms of Net Forest Depletion also appeared in the bottom 10 list for Natural Resources Depletion (Republic of the Congo, the Democratic Republic of the Congo, Guinea-Bissau, Burundi and Somalia)
Terrestrial and Marine Protected Areas (% of total territorial area)
Terrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine protected areas are areas of intertidal or subtidal terrain–and overlying water and associated flora and fauna and historical and cultural features–that have been reserved by law or other effective means to protect part or all of the enclosed environment.
Terrestrial and Marine Protected Areas, expressed as a % of a country’s total territorial area, is a critical sustainability indicator because protected terrestrial and marine areas play a substantial role in maintaining and growing biodiversity. The WB ESG dataset’s latest available data on this indicator is from 2020. 2020 data is available for 58 of the 59 LIDCs (unavailable for Somalia). The top 10 and bottom 10 LIDCs for this indicator are as follows:
#Filter merged dataset for indicator and LIDCs
WB_sovereign_ESG_country_groups_TMPA_LIDC <- WB_sovereign_ESG_country_groups %>%
filter(`Indicator Code` == 'ER.PTD.TOTL.ZS') %>%
filter(country_group == 'Low-Income Developing Countries')
glimpse(WB_sovereign_ESG_country_groups_TMPA_LIDC)## Rows: 59
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Terrestrial and marine protected areas (% of total t…
## $ `Indicator Code` <chr> "ER.PTD.TOTL.ZS", "ER.PTD.TOTL.ZS", "ER.PTD.TOTL.ZS",…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1972` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1973` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1974` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1975` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1976` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1977` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1978` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1979` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1980` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1981` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1982` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1983` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1984` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1985` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1986` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1987` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1988` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1989` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1990` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1991` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1992` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1993` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1994` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1995` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1996` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1997` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1998` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1999` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2000` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2001` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2002` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2003` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2004` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2005` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2006` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2007` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2008` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2009` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2010` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2011` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2012` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2013` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2014` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2015` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2016` <dbl> 0.1047070, 4.8886765, 28.8719450, 48.0073122, 15.0848…
## $ `2017` <dbl> 0.1047070, 4.8886486, 23.4719086, 48.0078510, 14.9228…
## $ `2018` <dbl> 0.1047070, 4.8886486, 23.4719086, 48.0078510, 14.9228…
## $ `2019` <dbl> 0.1047070, 4.8885560, 23.4567013, 48.0078507, 14.8904…
## $ `2020` <dbl> 3.6372566, 4.8885560, 23.4571056, 49.6693344, 16.4016…
## $ `2021` <dbl> 3.6372566, 4.8885560, 23.4877701, 49.6693344, 16.4263…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Remove any NAs
WB_sovereign_ESG_country_groups_TMPA_LIDC <- WB_sovereign_ESG_country_groups_TMPA_LIDC[!(is.na(WB_sovereign_ESG_country_groups_TMPA_LIDC$"2020") | WB_sovereign_ESG_country_groups_TMPA_LIDC$"2020"==""), ]
glimpse(WB_sovereign_ESG_country_groups_TMPA_LIDC)## Rows: 58
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Terrestrial and marine protected areas (% of total t…
## $ `Indicator Code` <chr> "ER.PTD.TOTL.ZS", "ER.PTD.TOTL.ZS", "ER.PTD.TOTL.ZS",…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1972` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1973` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1974` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1975` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1976` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1977` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1978` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1979` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1980` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1981` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1982` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1983` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1984` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1985` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1986` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1987` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1988` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1989` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1990` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1991` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1992` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1993` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1994` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1995` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1996` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1997` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1998` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1999` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2000` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2001` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2002` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2003` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2004` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2005` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2006` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2007` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2008` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2009` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2010` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2011` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2012` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2013` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2014` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2015` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2016` <dbl> 0.1047070, 4.8886765, 28.8719450, 48.0073122, 15.0848…
## $ `2017` <dbl> 0.1047070, 4.8886486, 23.4719086, 48.0078510, 14.9228…
## $ `2018` <dbl> 0.1047070, 4.8886486, 23.4719086, 48.0078510, 14.9228…
## $ `2019` <dbl> 0.1047070, 4.8885560, 23.4567013, 48.0078507, 14.8904…
## $ `2020` <dbl> 3.6372566, 4.8885560, 23.4571056, 49.6693344, 16.4016…
## $ `2021` <dbl> 3.6372566, 4.8885560, 23.4877701, 49.6693344, 16.4263…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Rank countries by indicator performance
WB_sovereign_ESG_country_groups_TMPA_LIDC$Rank<-rank(WB_sovereign_ESG_country_groups_TMPA_LIDC$'2020')
WB_sovereign_ESG_country_groups_TMPA_LIDC#Filter top 10
WB_sovereign_ESG_country_groups_TMPA_LIDC_top10 <- WB_sovereign_ESG_country_groups_TMPA_LIDC %>%
filter(`Rank` > 48)
#Chart top 10
WB_sovereign_ESG_country_groups_TMPA_LIDC_top10 %>%
ggplot(aes(fct_reorder(iso3c, `2020`),`2020`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Terrestrial and Marine Protected Areas - Top 10 LIDCs",
subtitle = "% of Total Territorial Area, 2020",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
#Filter bottom 10
WB_sovereign_ESG_country_groups_TMPA_LIDC_bottom10 <- WB_sovereign_ESG_country_groups_TMPA_LIDC %>%
filter(`Rank` < 11)
#Chart bottom 10
WB_sovereign_ESG_country_groups_TMPA_LIDC_bottom10 %>%
ggplot(aes(fct_reorder(iso3c, `2020`),`2020`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Terrestrial and Marine Protected Areas - Bottom 10 LIDCs",
subtitle = "% of Total Territorial Area, 2020",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
The 2 top performing LIDCs on this indicator are Bhutan and Zambia, which protect 49.7% and 41.3% of their territorial areas respectively. Notably, 4 of the top 10 LIDCs are neighboring Zambia, Tanzania, Zimbabwe and Malawi, which share a common characteristic as tourist destinations for safari tours.
The 2 worst performing LIDCs on this indicator are the Solomon Islands and Sao Tome and Principe, which only protect 0.15% and 0.25% of their territorial areas respectively. Notably, both of these countries, along with 3 other countries in the bottom 10 list (Comoros, Papua New Guinea, Haiti), are island nations with considerable marine territories.
Natural Resource Management: Key Findings
In terms of Natural Resources Depletion and Net Forest Depletion the top performing LIDCs have resource depletion levels that represent 0% of their GNIs. Particularly impressive are the 19 LIDCs that have Net Forest Depletion rates equivalent to 0% of GNI, indicating that forests are growing on net in those countries. On the other hand, the worst performing LIDCs have resource depletion rates that are in the double digits in terms of % of GNI. The Republic of the Congo, the Democratic Republic of the Congo, Guinea-Bissau, Burundi and Somalia are in the bottom 5 of both measures of resource depletion, and the Republic of the Congo is particularly notable given its poor performance on the emissions indicators examined in the previous sub-section.
For sustainable development investors, the worst performing LIDCs in terms of natural resource depletion likely present investment opportunities with high sustainable development returns (e.g. the Republic of the Congo). Investments can be made to help diversify these economies away from unsustainable reliance on resource depletion.
In terms of terrestrial and marine protected areas, LIDCs that generate income from protected terrestrial areas appear to be the top performers (e.g. Zambia generates income from its national parks through safari tourism).On the other hand, LIDCs with large marine areas appear to under-perform.
For sustainable development investors, this suggests that there is potential opportunity in making investments that replicate income-generating terrestrial protection models in low-income island nations (e.g. by developing ocean safari tourism).
Energy Use
This sub-section presents the top 10 and bottom 10 LIDCs based on their performance on 4 Energy Use indicators: Electricity Production from Coal Source, Energy Intensity Level of Primary Energy, Renewable Electricity Output and Renewable Electricity Consumption.
Electricity Production From Coal Sources (% of total)
The percentage of total electricity production that is derived from coal sources is a critical sustainability indicator because coal is the most toxic source of energy production, estimated to emit 50% to 60% more emissions compared to natural gas (source: https://www.gasvessel.eu/news/natural-gas-vs-coal-impact-on-the-environment/#:~:text=Natural%20gas%20is%20a%20fossil,a%20typical%20new%20coal%20plant.). The WB ESG dataset’s latest available data on this indicator is from 2015. Unfortunately, 2015 data is only available for 32 of the 59 LIDCs. The top 10 and bottom 10 LIDCs for this indicator among these 32 LIDCs are as follows:
#Filter merged dataset for indicator and LIDCs
WB_sovereign_ESG_country_groups_EPFC_LIDC <- WB_sovereign_ESG_country_groups %>%
filter(`Indicator Code` == 'EG.ELC.COAL.ZS') %>%
filter(country_group == 'Low-Income Developing Countries')
glimpse(WB_sovereign_ESG_country_groups_EPFC_LIDC)## Rows: 59
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Electricity production from coal sources (% of total…
## $ `Indicator Code` <chr> "EG.ELC.COAL.ZS", "EG.ELC.COAL.ZS", "EG.ELC.COAL.ZS",…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> NA, 0, NA, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA…
## $ `1972` <dbl> NA, 0, NA, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA…
## $ `1973` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1974` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1975` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1976` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1977` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1978` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1979` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1980` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1981` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1982` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1983` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1984` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1985` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1986` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1987` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1988` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1989` <dbl> NA, 0, 0, NA, NA, NA, NA, 0, NA, NA, NA, 0, 0, 0, NA,…
## $ `1990` <dbl> NA, 0.0000, 0.0000, NA, NA, NA, NA, 0.0000, NA, NA, N…
## $ `1991` <dbl> NA, 0.00000, 0.00000, NA, NA, NA, NA, 0.00000, NA, NA…
## $ `1992` <dbl> NA, 0.000000, 0.000000, NA, NA, NA, NA, 0.000000, NA,…
## $ `1993` <dbl> NA, 0.000000, 0.000000, NA, NA, NA, NA, 0.000000, NA,…
## $ `1994` <dbl> NA, 0.00000, 0.00000, NA, NA, NA, NA, 0.00000, NA, NA…
## $ `1995` <dbl> NA, 0.000000, 0.000000, NA, NA, NA, 0.000000, 0.00000…
## $ `1996` <dbl> NA, 0.000000, 0.000000, NA, NA, NA, 0.000000, 0.00000…
## $ `1997` <dbl> NA, 0.000000, 0.000000, NA, NA, NA, 0.000000, 0.00000…
## $ `1998` <dbl> NA, 0.000000, 0.000000, NA, NA, NA, 0.000000, 0.00000…
## $ `1999` <dbl> NA, 0.000000, 0.000000, NA, NA, NA, 0.000000, 0.00000…
## $ `2000` <dbl> NA, 0.000000, 0.000000, NA, NA, NA, 0.000000, 0.00000…
## $ `2001` <dbl> NA, 0.000000, 0.000000, NA, NA, NA, 0.000000, 0.00000…
## $ `2002` <dbl> NA, 0.000000, 0.000000, NA, NA, NA, 0.000000, 0.00000…
## $ `2003` <dbl> NA, 0.000000, 0.000000, NA, NA, NA, 0.000000, 0.00000…
## $ `2004` <dbl> NA, 0.000000, 0.000000, NA, NA, NA, 0.000000, 0.00000…
## $ `2005` <dbl> NA, 0.616327, 0.000000, NA, NA, NA, 0.000000, 0.00000…
## $ `2006` <dbl> NA, 0.9346111, 0.0000000, NA, NA, NA, 0.0000000, 0.00…
## $ `2007` <dbl> NA, 2.389937, 0.000000, NA, NA, NA, 0.000000, 0.00000…
## $ `2008` <dbl> NA, 3.214682, 0.000000, NA, NA, NA, 0.000000, 0.00000…
## $ `2009` <dbl> NA, 2.937300, 0.000000, NA, NA, NA, 2.211690, 0.00000…
## $ `2010` <dbl> NA, 1.892621, 0.000000, NA, NA, NA, 3.100000, 0.00000…
## $ `2011` <dbl> NA, 1.873032, 0.000000, NA, NA, NA, 3.207547, 0.00000…
## $ `2012` <dbl> NA, 1.927433, 0.000000, NA, NA, NA, 2.580195, 0.00000…
## $ `2013` <dbl> NA, 2.306099, 0.000000, NA, NA, NA, 9.505062, 0.00000…
## $ `2014` <dbl> NA, 1.9697377, 0.0000000, NA, NA, NA, 28.1841933, 0.0…
## $ `2015` <dbl> NA, 1.689516, 0.000000, NA, NA, NA, 48.396634, 0.0000…
## $ `2016` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2017` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2018` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2019` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2020` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Remove any NAs
WB_sovereign_ESG_country_groups_EPFC_LIDC <- WB_sovereign_ESG_country_groups_EPFC_LIDC[!(is.na(WB_sovereign_ESG_country_groups_EPFC_LIDC$"2015") | WB_sovereign_ESG_country_groups_EPFC_LIDC$"2015"==""), ]
glimpse(WB_sovereign_ESG_country_groups_EPFC_LIDC)## Rows: 32
## Columns: 70
## $ `Country Name` <chr> "Bangladesh", "Benin", "Cambodia", "Cameroon", "Congo…
## $ iso3c <chr> "BGD", "BEN", "KHM", "CMR", "COD", "COG", "CIV", "ERI…
## $ `Indicator Name` <chr> "Electricity production from coal sources (% of total…
## $ `Indicator Code` <chr> "EG.ELC.COAL.ZS", "EG.ELC.COAL.ZS", "EG.ELC.COAL.ZS",…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> 0.000000, NA, NA, 0.000000, 0.000000, 0.000000, 0.000…
## $ `1972` <dbl> 0.00000000, NA, NA, 0.00000000, 0.00000000, 0.0000000…
## $ `1973` <dbl> 0.000000, 0.000000, NA, 0.000000, 0.000000, 0.000000,…
## $ `1974` <dbl> 0.000000, 0.000000, NA, 0.000000, 0.000000, 0.000000,…
## $ `1975` <dbl> 0.000000, 0.000000, NA, 0.000000, 0.000000, 0.000000,…
## $ `1976` <dbl> 0.000000, 0.000000, NA, 0.000000, 0.000000, 0.000000,…
## $ `1977` <dbl> 0.000000, 0.000000, NA, 0.000000, 0.000000, 0.000000,…
## $ `1978` <dbl> 0.000000, 0.000000, NA, 0.000000, 0.000000, 0.000000,…
## $ `1979` <dbl> 0.000000, 0.000000, NA, 0.000000, 0.000000, 0.000000,…
## $ `1980` <dbl> 0.000000, 0.000000, NA, 0.000000, 0.000000, 0.000000,…
## $ `1981` <dbl> 0.000000, 0.000000, NA, 0.000000, 0.000000, 0.000000,…
## $ `1982` <dbl> 0.000000, 0.000000, NA, 0.000000, 0.000000, 0.000000,…
## $ `1983` <dbl> 0.00000000, 0.00000000, NA, 0.00000000, 0.00000000, 0…
## $ `1984` <dbl> 0.0000000, 0.0000000, NA, 0.0000000, 0.0000000, 0.000…
## $ `1985` <dbl> 0.00000000, 0.00000000, NA, 0.00000000, 0.00000000, 0…
## $ `1986` <dbl> 0.00000000, 0.00000000, NA, 0.00000000, 0.00000000, 0…
## $ `1987` <dbl> 0.00000000, 0.00000000, NA, 0.00000000, 0.00000000, 0…
## $ `1988` <dbl> 0.00000000, 0.00000000, NA, 0.00000000, 0.00000000, 0…
## $ `1989` <dbl> 0.00000000, 0.00000000, NA, 0.00000000, 0.00000000, 0…
## $ `1990` <dbl> 0.00000000, 0.00000000, NA, 0.00000000, 0.00000000, 0…
## $ `1991` <dbl> 0.00000000, 0.00000000, NA, 0.00000000, 0.00000000, 0…
## $ `1992` <dbl> 0.00000000, 0.00000000, NA, 0.00000000, 0.00000000, 0…
## $ `1993` <dbl> 0.0000000, 0.0000000, NA, 0.0000000, 0.0000000, 0.000…
## $ `1994` <dbl> 0.0000000, 0.0000000, NA, 0.0000000, 0.0000000, 0.000…
## $ `1995` <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0…
## $ `1996` <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0…
## $ `1997` <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0…
## $ `1998` <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0…
## $ `1999` <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0…
## $ `2000` <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0…
## $ `2001` <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0…
## $ `2002` <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0…
## $ `2003` <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0…
## $ `2004` <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.0…
## $ `2005` <dbl> 0.616327, 0.000000, 0.000000, 0.000000, 0.000000, 0.0…
## $ `2006` <dbl> 0.9346111, 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ `2007` <dbl> 2.3899371, 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ `2008` <dbl> 3.2146823, 0.0000000, 0.0000000, 0.0000000, 0.0000000…
## $ `2009` <dbl> 2.937300, 0.000000, 2.211690, 0.000000, 0.000000, 0.0…
## $ `2010` <dbl> 1.892621, 0.000000, 3.100000, 0.000000, 0.000000, 0.0…
## $ `2011` <dbl> 1.873032, 0.000000, 3.207547, 0.000000, 0.000000, 0.0…
## $ `2012` <dbl> 1.927433, 0.000000, 2.580195, 0.000000, 0.000000, 0.0…
## $ `2013` <dbl> 2.3060994, 0.0000000, 9.5050619, 0.0000000, 0.0000000…
## $ `2014` <dbl> 1.9697377, 0.0000000, 28.1841933, 0.0000000, 0.000000…
## $ `2015` <dbl> 1.689516, 0.000000, 48.396634, 0.000000, 0.000000, 0.…
## $ `2016` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2017` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2018` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2019` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2020` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Bangladesh", "Benin", "Cambodia", "Cameroon", "Congo…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Rank countries by indicator performance
WB_sovereign_ESG_country_groups_EPFC_LIDC$Rank<-rank(WB_sovereign_ESG_country_groups_EPFC_LIDC$'2015')
WB_sovereign_ESG_country_groups_EPFC_LIDC#Filter top 10
WB_sovereign_ESG_country_groups_EPFC_LIDC_top10 <- WB_sovereign_ESG_country_groups_EPFC_LIDC %>%
filter(`Rank` < 12)
#Chart top 10
WB_sovereign_ESG_country_groups_EPFC_LIDC_top10 %>%
ggplot(aes(fct_reorder(iso3c, -`2015`),`2015`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Electricity Production from Coal - Top 10 LIDCs",
subtitle = "% of Total Electricity Production, 2015",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
#Filter bottom 10
WB_sovereign_ESG_country_groups_EPFC_LIDC_bottom10 <- WB_sovereign_ESG_country_groups_EPFC_LIDC %>%
filter(`Rank` > 22)
#Chart bottom 10
WB_sovereign_ESG_country_groups_EPFC_LIDC_bottom10 %>%
ggplot(aes(fct_reorder(iso3c, -`2015`),`2015`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Electricity Production from Coal - Bottom 10 LIDCs",
subtitle = "% of Total Electricity Production, 2015",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
In terms of the top 10, 22 of the 32 countries for which data is available are tied for #1 on this indicator, because they do not derive any of their electricity production from coal sources.
The bottom 10 LIDCs range from Honduras, which only derives just over 1% of its total electricity from coal sources, to Cambodia, which derives 48.4% of its total electricity from coal sources. Most notably, the bottom 5 performers derive over 10% of their total electricity from coal sources, and appear to be the priority LIDCs for any investments that aim to transition energy production away from coal.
Energy Intensity Level of Primary Energy (MJ/$2017 PPP GDP)
Energy intensity level of primary energy is the ratio between energy supply and gross domestic product measured at purchasing power parity. This is a critical sustainability indicator because it is an indication of how much energy is used to produce one unit of economic output. A lower ratio indicates that less energy is used to produce one unit of output. The WB ESG dataset’s latest available data on this indicator is from 2019. 2019 data is available for 56 of the 59 LIDCs (unavailable for South Sudan, Eritrea and Yemen). The top 10 and bottom 10 LIDCs for this indicator are as follows:
#Filter merged dataset for indicator and LIDCs
WB_sovereign_ESG_country_groups_EIL_LIDC <- WB_sovereign_ESG_country_groups %>%
filter(`Indicator Code` == 'EG.EGY.PRIM.PP.KD') %>%
filter(country_group == 'Low-Income Developing Countries')
glimpse(WB_sovereign_ESG_country_groups_EIL_LIDC)## Rows: 59
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Energy intensity level of primary energy (MJ/$2017 P…
## $ `Indicator Code` <chr> "EG.EGY.PRIM.PP.KD", "EG.EGY.PRIM.PP.KD", "EG.EGY.PRI…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1972` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1973` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1974` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1975` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1976` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1977` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1978` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1979` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1980` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1981` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1982` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1983` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1984` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1985` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1986` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1987` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1988` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1989` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1990` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1991` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1992` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1993` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1994` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1995` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1996` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1997` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1998` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1999` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2000` <dbl> 1.640000, 3.140000, 4.880000, 18.420000, 5.490000, 10…
## $ `2001` <dbl> 1.740000, 3.270000, 4.930000, 17.290000, 5.150000, 10…
## $ `2002` <dbl> 1.400000, 3.230000, 5.050000, 15.990000, 4.210000, 10…
## $ `2003` <dbl> 1.400000, 3.200000, 5.150000, 14.960000, 4.030000, 10…
## $ `2004` <dbl> 1.200000, 3.100000, 5.150000, 14.150000, 4.800000, 9.…
## $ `2005` <dbl> 1.410000, 3.050000, 5.080000, 13.610000, 5.550000, 9.…
## $ `2006` <dbl> 1.500000, 3.050000, 5.760000, 12.990000, 5.600000, 9.…
## $ `2007` <dbl> 1.530000, 2.980000, 5.910000, 11.300000, 5.720000, 8.…
## $ `2008` <dbl> 1.94000, 2.94000, 5.76000, 11.12000, 5.72000, 8.45000…
## $ `2009` <dbl> 2.25000, 2.94000, 5.91000, 10.69000, 5.66000, 8.03000…
## $ `2010` <dbl> 2.460000, 2.930000, 6.000000, 10.110000, 5.420000, 7.…
## $ `2011` <dbl> 3.230000, 2.850000, 5.820000, 9.470000, 5.200000, 7.7…
## $ `2012` <dbl> 2.610000, 2.770000, 5.610000, 9.510000, 5.150000, 7.4…
## $ `2013` <dbl> 2.460000, 2.730000, 5.710000, 9.410000, 5.040000, 7.4…
## $ `2014` <dbl> 2.250000, 2.660000, 5.540000, 9.320000, 4.910000, 7.0…
## $ `2015` <dbl> 2.370000, 2.690000, 5.790000, 8.700000, 4.940000, 7.3…
## $ `2016` <dbl> 2.24, 2.61, 6.22, 8.30, 4.76, 7.56, 4.74, 4.65, 9.12,…
## $ `2017` <dbl> 2.30, 2.50, 6.11, 8.06, 4.72, 7.62, 4.59, 4.53, 8.80,…
## $ `2018` <dbl> 2.44, 2.30, 6.05, 8.22, 4.60, 7.71, 4.61, 4.43, 8.56,…
## $ `2019` <dbl> 2.41, 2.36, 5.69, 7.91, 4.49, 7.62, 4.68, 4.33, 8.39,…
## $ `2020` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Remove any NAs
WB_sovereign_ESG_country_groups_EIL_LIDC <- WB_sovereign_ESG_country_groups_EIL_LIDC[!(is.na(WB_sovereign_ESG_country_groups_EIL_LIDC$"2019") | WB_sovereign_ESG_country_groups_EIL_LIDC$"2019"==""), ]
glimpse(WB_sovereign_ESG_country_groups_EIL_LIDC)## Rows: 56
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Energy intensity level of primary energy (MJ/$2017 P…
## $ `Indicator Code` <chr> "EG.EGY.PRIM.PP.KD", "EG.EGY.PRIM.PP.KD", "EG.EGY.PRI…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1972` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1973` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1974` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1975` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1976` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1977` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1978` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1979` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1980` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1981` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1982` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1983` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1984` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1985` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1986` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1987` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1988` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1989` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1990` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1991` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1992` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1993` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1994` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1995` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1996` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1997` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1998` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1999` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2000` <dbl> 1.64, 3.14, 4.88, 18.42, 5.49, 10.37, 7.91, 6.23, 7.7…
## $ `2001` <dbl> 1.74, 3.27, 4.93, 17.29, 5.15, 10.52, 7.51, 6.04, 7.5…
## $ `2002` <dbl> 1.40, 3.23, 5.05, 15.99, 4.21, 10.07, 7.05, 6.10, 7.3…
## $ `2003` <dbl> 1.40, 3.20, 5.15, 14.96, 4.03, 10.05, 6.69, 6.07, 7.9…
## $ `2004` <dbl> 1.20, 3.10, 5.15, 14.15, 4.80, 9.66, 6.23, 5.86, 7.65…
## $ `2005` <dbl> 1.41, 3.05, 5.08, 13.61, 5.55, 9.48, 5.73, 5.79, 7.74…
## $ `2006` <dbl> 1.50, 3.05, 5.76, 12.99, 5.60, 9.05, 5.31, 5.17, 7.59…
## $ `2007` <dbl> 1.53, 2.98, 5.91, 11.30, 5.72, 8.83, 5.03, 4.65, 7.47…
## $ `2008` <dbl> 1.94, 2.94, 5.76, 11.12, 5.72, 8.45, 4.76, 4.53, 7.33…
## $ `2009` <dbl> 2.25, 2.94, 5.91, 10.69, 5.66, 8.03, 5.07, 4.80, 6.89…
## $ `2010` <dbl> 2.46, 2.93, 6.00, 10.11, 5.42, 7.93, 5.05, 4.68, 6.76…
## $ `2011` <dbl> 3.23, 2.85, 5.82, 9.47, 5.20, 7.77, 4.81, 4.71, 6.68,…
## $ `2012` <dbl> 2.61, 2.77, 5.61, 9.51, 5.15, 7.41, 4.68, 4.70, 6.25,…
## $ `2013` <dbl> 2.46, 2.73, 5.71, 9.41, 5.04, 7.42, 4.47, 4.67, 9.30,…
## $ `2014` <dbl> 2.25, 2.66, 5.54, 9.32, 4.91, 7.08, 4.50, 4.78, 9.55,…
## $ `2015` <dbl> 2.37, 2.69, 5.79, 8.70, 4.94, 7.38, 4.58, 4.77, 9.40,…
## $ `2016` <dbl> 2.24, 2.61, 6.22, 8.30, 4.76, 7.56, 4.74, 4.65, 9.12,…
## $ `2017` <dbl> 2.30, 2.50, 6.11, 8.06, 4.72, 7.62, 4.59, 4.53, 8.80,…
## $ `2018` <dbl> 2.44, 2.30, 6.05, 8.22, 4.60, 7.71, 4.61, 4.43, 8.56,…
## $ `2019` <dbl> 2.41, 2.36, 5.69, 7.91, 4.49, 7.62, 4.68, 4.33, 8.39,…
## $ `2020` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Rank countries by indicator performance
WB_sovereign_ESG_country_groups_EIL_LIDC$Rank<-rank(WB_sovereign_ESG_country_groups_EIL_LIDC$'2019')
WB_sovereign_ESG_country_groups_EIL_LIDC#Filter top 10
WB_sovereign_ESG_country_groups_EIL_LIDC_top10 <- WB_sovereign_ESG_country_groups_EIL_LIDC %>%
filter(`Rank` < 11)
#Chart top 10
WB_sovereign_ESG_country_groups_EIL_LIDC_top10 %>%
ggplot(aes(fct_reorder(iso3c, -`2019`),`2019`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Energy Intensity Level of Primary Energy - Top 10 LIDCs",
subtitle = "MJ/$2017 PPP GDP, 2019",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
#Filter bottom 10
WB_sovereign_ESG_country_groups_EIL_LIDC_bottom10 <- WB_sovereign_ESG_country_groups_EIL_LIDC %>%
filter(`Rank` > 46)
#Chart bottom 10
WB_sovereign_ESG_country_groups_EIL_LIDC_bottom10 %>%
ggplot(aes(fct_reorder(iso3c, -`2019`),`2019`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Energy Intensity Level of Primary Energy - Bottom 10 LIDCs",
subtitle = "MJ/$2017 PPP GDP, 2019",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
The top 10 performers on this indicator range from Djibouti, which only uses 1.89 millijoule (MJ) of energy per dollar of economic output, to Cote d’Ivoire, which uses 3.31 MJ of energy per dollar of economic output.
The bottom 10 performers range from Uzbekistan, which uses 8.37 MJ of energy per dollar of economic output, to Liberia, which uses14.33 MJ of energy per dollar of economic output.
Renewable Electricity Output (% of total electricity output)
Renewable electricity output as a share of total electricity output is a critical sustainability indicator because it indicates the extent to which a country relies on clean, emissions-free sources of energy. The WB ESG dataset’s latest available data on this indicator is from 2015. 2015 data is available for all 59 LIDCs. The top 10 and bottom 10 LIDCs for this indicator are as follows:
#Filter merged dataset for indicator and LIDCs
WB_sovereign_ESG_country_groups_REO_LIDC <- WB_sovereign_ESG_country_groups %>%
filter(`Indicator Code` == 'EG.ELC.RNEW.ZS') %>%
filter(country_group == 'Low-Income Developing Countries')
glimpse(WB_sovereign_ESG_country_groups_REO_LIDC)## Rows: 59
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Renewable electricity output (% of total electricity…
## $ `Indicator Code` <chr> "EG.ELC.RNEW.ZS", "EG.ELC.RNEW.ZS", "EG.ELC.RNEW.ZS",…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1972` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1973` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1974` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1975` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1976` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1977` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1978` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1979` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1980` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1981` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1982` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1983` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1984` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1985` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1986` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1987` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1988` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1989` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1990` <dbl> 67.730496, 11.433006, 0.000000, 99.552430, 4.891304, …
## $ `1991` <dbl> 67.980296, 10.133011, 0.000000, 99.744246, 6.701031, …
## $ `1992` <dbl> 67.994310, 8.949854, 0.000000, 99.936061, 9.950249, 9…
## $ `1993` <dbl> 68.345324, 6.604388, 0.000000, 99.940618, 21.860465, …
## $ `1994` <dbl> 68.704512, 8.656991, 0.000000, 99.940688, 33.796296, …
## $ `1995` <dbl> 69.037037, 3.442532, 0.000000, 100.000000, 38.699053,…
## $ `1996` <dbl> 70.370370, 6.440648, 0.000000, 100.000000, 26.983547,…
## $ `1997` <dbl> 72.3880597, 6.0634171, 3.3898305, 100.0000000, 20.084…
## $ `1998` <dbl> 74.4360902, 6.7147958, 2.5316456, 100.0000000, 23.158…
## $ `1999` <dbl> 73.7226277, 5.7647059, 2.7777778, 100.0000000, 34.648…
## $ `2000` <dbl> 74.9890941, 4.7492233, 2.3809524, 100.0000000, 25.179…
## $ `2001` <dbl> 72.8114600, 5.7095216, 3.0303030, 99.9781490, 14.7195…
## $ `2002` <dbl> 79.0639712, 4.0128583, 3.1746032, 99.9586535, 17.4938…
## $ `2003` <dbl> 70.2497286, 3.7997159, 2.5000000, 99.9169665, 21.5699…
## $ `2004` <dbl> 70.8908407, 3.0344772, 1.2345679, 99.9235604, 21.4451…
## $ `2005` <dbl> 74.0618101, 2.8320793, 0.9345794, 99.9433428, 19.4654…
## $ `2006` <dbl> 70.7557503, 2.5363178, 2.5974026, 99.9601840, 14.6221…
## $ `2007` <dbl> 72.0000000, 2.4157394, 1.3636364, 99.9695215, 18.1372…
## $ `2008` <dbl> 68.6548223, 2.7763166, 1.3100437, 99.9985635, 21.9405…
## $ `2009` <dbl> 87.1766029, 1.1216612, 0.7812500, 99.9985710, 18.9054…
## $ `2010` <dbl> 85.9865471, 1.7872027, 0.8695652, 99.9959063, 20.7854…
## $ `2011` <dbl> 82.4875622, 1.9749507, 0.0000000, 99.9957552, 15.4557…
## $ `2012` <dbl> 85.9099804, 1.6000165, 0.0000000, 99.9970703, 15.4560…
## $ `2013` <dbl> 78.6364081, 1.9465361, 0.0000000, 99.9934556, 14.4382…
## $ `2014` <dbl> 85.3235490, 1.3197242, 0.0000000, 99.9928611, 10.3971…
## $ `2015` <dbl> 86.0501113, 1.2268899, 5.5555556, 99.9935464, 9.35403…
## $ `2016` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2017` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2018` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2019` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2020` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Remove any NAs
WB_sovereign_ESG_country_groups_REO_LIDC <- WB_sovereign_ESG_country_groups_REO_LIDC[!(is.na(WB_sovereign_ESG_country_groups_REO_LIDC$"2015") | WB_sovereign_ESG_country_groups_REO_LIDC$"2015"==""), ]
glimpse(WB_sovereign_ESG_country_groups_REO_LIDC)## Rows: 59
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Renewable electricity output (% of total electricity…
## $ `Indicator Code` <chr> "EG.ELC.RNEW.ZS", "EG.ELC.RNEW.ZS", "EG.ELC.RNEW.ZS",…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1972` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1973` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1974` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1975` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1976` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1977` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1978` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1979` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1980` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1981` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1982` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1983` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1984` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1985` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1986` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1987` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1988` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1989` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1990` <dbl> 67.730496, 11.433006, 0.000000, 99.552430, 4.891304, …
## $ `1991` <dbl> 67.980296, 10.133011, 0.000000, 99.744246, 6.701031, …
## $ `1992` <dbl> 67.994310, 8.949854, 0.000000, 99.936061, 9.950249, 9…
## $ `1993` <dbl> 68.345324, 6.604388, 0.000000, 99.940618, 21.860465, …
## $ `1994` <dbl> 68.704512, 8.656991, 0.000000, 99.940688, 33.796296, …
## $ `1995` <dbl> 69.037037, 3.442532, 0.000000, 100.000000, 38.699053,…
## $ `1996` <dbl> 70.370370, 6.440648, 0.000000, 100.000000, 26.983547,…
## $ `1997` <dbl> 72.3880597, 6.0634171, 3.3898305, 100.0000000, 20.084…
## $ `1998` <dbl> 74.4360902, 6.7147958, 2.5316456, 100.0000000, 23.158…
## $ `1999` <dbl> 73.7226277, 5.7647059, 2.7777778, 100.0000000, 34.648…
## $ `2000` <dbl> 74.9890941, 4.7492233, 2.3809524, 100.0000000, 25.179…
## $ `2001` <dbl> 72.8114600, 5.7095216, 3.0303030, 99.9781490, 14.7195…
## $ `2002` <dbl> 79.0639712, 4.0128583, 3.1746032, 99.9586535, 17.4938…
## $ `2003` <dbl> 70.2497286, 3.7997159, 2.5000000, 99.9169665, 21.5699…
## $ `2004` <dbl> 70.8908407, 3.0344772, 1.2345679, 99.9235604, 21.4451…
## $ `2005` <dbl> 74.0618101, 2.8320793, 0.9345794, 99.9433428, 19.4654…
## $ `2006` <dbl> 70.7557503, 2.5363178, 2.5974026, 99.9601840, 14.6221…
## $ `2007` <dbl> 72.0000000, 2.4157394, 1.3636364, 99.9695215, 18.1372…
## $ `2008` <dbl> 68.6548223, 2.7763166, 1.3100437, 99.9985635, 21.9405…
## $ `2009` <dbl> 87.1766029, 1.1216612, 0.7812500, 99.9985710, 18.9054…
## $ `2010` <dbl> 85.9865471, 1.7872027, 0.8695652, 99.9959063, 20.7854…
## $ `2011` <dbl> 82.4875622, 1.9749507, 0.0000000, 99.9957552, 15.4557…
## $ `2012` <dbl> 85.9099804, 1.6000165, 0.0000000, 99.9970703, 15.4560…
## $ `2013` <dbl> 78.6364081, 1.9465361, 0.0000000, 99.9934556, 14.4382…
## $ `2014` <dbl> 85.3235490, 1.3197242, 0.0000000, 99.9928611, 10.3971…
## $ `2015` <dbl> 86.0501113, 1.2268899, 5.5555556, 99.9935464, 9.35403…
## $ `2016` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2017` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2018` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2019` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2020` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Rank countries by indicator performance
WB_sovereign_ESG_country_groups_REO_LIDC$Rank<-rank(WB_sovereign_ESG_country_groups_REO_LIDC$'2015')
WB_sovereign_ESG_country_groups_REO_LIDC#Filter top 10
WB_sovereign_ESG_country_groups_REO_LIDC_top10 <- WB_sovereign_ESG_country_groups_REO_LIDC %>%
filter(`Rank` > 49)
#Chart top 10
WB_sovereign_ESG_country_groups_REO_LIDC_top10 %>%
ggplot(aes(fct_reorder(iso3c, `2015`),`2015`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Renewable Electricity Output - Top 10 LIDCs",
subtitle = "% of Total Electricity Output, 2015",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
#Filter bottom 10
WB_sovereign_ESG_country_groups_REO_LIDC_bottom10 <- WB_sovereign_ESG_country_groups_REO_LIDC %>%
filter(`Rank` < 11)
#Chart bottom 10
WB_sovereign_ESG_country_groups_REO_LIDC_bottom10 %>%
ggplot(aes(fct_reorder(iso3c, `2015`),`2015`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Renewable Electricity Output - Bottom 10 LIDCs",
subtitle = "% of Total Electricity Output, 2015",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
The top 10 LIDCs on this indicator range from Nepal and Lesotho, which are tied for #1 with renewable electricity accounting for 100% of their electricity output, to Malawi, for which renewable electricity accounts for 91.3% of total electricity output.
The bottom 10 LIDCs range from Eritrea, for which renewable electricity only accounts for 0.49% of total electricity output, to the 9 other countries in the bottom 10 that are tied for last place with renewable electricity accounting for 0% of their total electricity output.
Renewable Energy Consumption (% of total final energy consumption)
Renewable energy consumption as a share of total final energy consumption is a critical sustainability indicator because it indicates the extent to which a country relies on clean, emissions-free sources of energy for energy consumption. The WB ESG dataset’s latest available data on this indicator is from 2019. 2019 data is available for all 59 LIDCs. The top 10 and bottom 10 LIDCs for this indicator are as follows:
#Filter merged dataset for indicator and LIDCs
WB_sovereign_ESG_country_groups_REC_LIDC <- WB_sovereign_ESG_country_groups %>%
filter(`Indicator Code` == 'EG.FEC.RNEW.ZS') %>%
filter(country_group == 'Low-Income Developing Countries')
glimpse(WB_sovereign_ESG_country_groups_REC_LIDC)## Rows: 59
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Renewable energy consumption (% of total final energ…
## $ `Indicator Code` <chr> "EG.FEC.RNEW.ZS", "EG.FEC.RNEW.ZS", "EG.FEC.RNEW.ZS",…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1972` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1973` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1974` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1975` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1976` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1977` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1978` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1979` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1980` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1981` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1982` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1983` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1984` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1985` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1986` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1987` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1988` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1989` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1990` <dbl> 15.924532, 71.664971, 93.703241, 95.898852, 93.158060…
## $ `1991` <dbl> 17.036444, 73.159667, 94.964848, 95.919938, 93.253823…
## $ `1992` <dbl> 26.52163, 71.66070, 94.85184, 95.28749, 93.29010, 94.…
## $ `1993` <dbl> 30.585667, 70.557123, 94.988801, 95.858075, 93.479993…
## $ `1994` <dbl> 32.796251, 68.979907, 94.975035, 95.290964, 93.608094…
## $ `1995` <dbl> 35.075640, 63.802809, 94.771987, 94.371750, 93.140200…
## $ `1996` <dbl> 37.945748, 62.124077, 84.145968, 93.240021, 92.156594…
## $ `1997` <dbl> 41.432601, 59.972856, 80.626432, 91.350064, 91.390955…
## $ `1998` <dbl> 44.094337, 60.233971, 79.568432, 91.531471, 91.012549…
## $ `1999` <dbl> 52.185774, 60.534396, 76.905425, 91.565623, 86.854917…
## $ `2000` <dbl> 44.99, 59.06, 70.29, 91.40, 85.41, 93.23, 81.58, 84.5…
## $ `2001` <dbl> 45.60, 55.82, 66.86, 91.75, 83.68, 94.84, 80.51, 85.3…
## $ `2002` <dbl> 37.83, 54.33, 64.04, 91.20, 79.34, 94.65, 80.97, 85.2…
## $ `2003` <dbl> 36.66, 52.62, 61.73, 91.92, 79.08, 95.76, 79.92, 85.3…
## $ `2004` <dbl> 44.24, 52.05, 60.69, 93.46, 83.53, 96.04, 80.69, 85.5…
## $ `2005` <dbl> 33.88, 50.66, 59.20, 91.67, 86.54, 96.01, 79.24, 86.3…
## $ `2006` <dbl> 31.89, 48.69, 57.31, 91.92, 84.62, 95.31, 78.01, 85.5…
## $ `2007` <dbl> 28.78, 47.19, 54.49, 92.07, 82.43, 95.29, 74.79, 80.8…
## $ `2008` <dbl> 21.17, 45.20, 54.76, 91.81, 82.94, 95.15, 74.13, 80.8…
## $ `2009` <dbl> 16.53, 43.10, 52.83, 92.38, 83.60, 95.18, 68.04, 79.2…
## $ `2010` <dbl> 15.15, 40.28, 47.19, 90.80, 81.45, 92.57, 64.82, 78.7…
## $ `2011` <dbl> 12.61, 38.41, 48.70, 89.01, 80.57, 91.65, 63.99, 78.6…
## $ `2012` <dbl> 15.36, 37.31, 50.33, 87.32, 77.18, 91.45, 64.36, 78.5…
## $ `2013` <dbl> 16.86, 37.08, 51.97, 86.70, 75.43, 91.08, 64.97, 77.6…
## $ `2014` <dbl> 18.93, 35.32, 51.05, 86.54, 75.24, 91.28, 63.70, 76.7…
## $ `2015` <dbl> 17.53, 31.93, 49.94, 86.68, 72.71, 91.15, 60.63, 78.0…
## $ `2016` <dbl> 19.92, 30.49, 45.42, 85.02, 72.27, 89.52, 58.01, 78.7…
## $ `2017` <dbl> 19.21, 28.36, 45.38, 83.63, 69.08, 88.12, 56.25, 79.1…
## $ `2018` <dbl> 17.96, 26.88, 43.97, 82.22, 66.97, 85.58, 56.38, 79.2…
## $ `2019` <dbl> 18.51, 24.75, 46.47, 82.27, 64.85, 84.77, 53.36, 79.4…
## $ `2020` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Remove any NAs
WB_sovereign_ESG_country_groups_REC_LIDC <- WB_sovereign_ESG_country_groups_REC_LIDC[!(is.na(WB_sovereign_ESG_country_groups_REC_LIDC$"2019") | WB_sovereign_ESG_country_groups_REC_LIDC$"2019"==""), ]
glimpse(WB_sovereign_ESG_country_groups_REC_LIDC)## Rows: 59
## Columns: 70
## $ `Country Name` <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ iso3c <chr> "AFG", "BGD", "BEN", "BTN", "BFA", "BDI", "KHM", "CMR…
## $ `Indicator Name` <chr> "Renewable energy consumption (% of total final energ…
## $ `Indicator Code` <chr> "EG.FEC.RNEW.ZS", "EG.FEC.RNEW.ZS", "EG.FEC.RNEW.ZS",…
## $ `1960` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1961` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1962` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1963` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1964` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1965` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1966` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1967` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1968` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1969` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1970` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1971` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1972` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1973` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1974` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1975` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1976` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1977` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1978` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1979` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1980` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1981` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1982` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1983` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1984` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1985` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1986` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1987` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1988` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1989` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `1990` <dbl> 15.924532, 71.664971, 93.703241, 95.898852, 93.158060…
## $ `1991` <dbl> 17.036444, 73.159667, 94.964848, 95.919938, 93.253823…
## $ `1992` <dbl> 26.52163, 71.66070, 94.85184, 95.28749, 93.29010, 94.…
## $ `1993` <dbl> 30.585667, 70.557123, 94.988801, 95.858075, 93.479993…
## $ `1994` <dbl> 32.796251, 68.979907, 94.975035, 95.290964, 93.608094…
## $ `1995` <dbl> 35.075640, 63.802809, 94.771987, 94.371750, 93.140200…
## $ `1996` <dbl> 37.945748, 62.124077, 84.145968, 93.240021, 92.156594…
## $ `1997` <dbl> 41.432601, 59.972856, 80.626432, 91.350064, 91.390955…
## $ `1998` <dbl> 44.094337, 60.233971, 79.568432, 91.531471, 91.012549…
## $ `1999` <dbl> 52.185774, 60.534396, 76.905425, 91.565623, 86.854917…
## $ `2000` <dbl> 44.99, 59.06, 70.29, 91.40, 85.41, 93.23, 81.58, 84.5…
## $ `2001` <dbl> 45.60, 55.82, 66.86, 91.75, 83.68, 94.84, 80.51, 85.3…
## $ `2002` <dbl> 37.83, 54.33, 64.04, 91.20, 79.34, 94.65, 80.97, 85.2…
## $ `2003` <dbl> 36.66, 52.62, 61.73, 91.92, 79.08, 95.76, 79.92, 85.3…
## $ `2004` <dbl> 44.24, 52.05, 60.69, 93.46, 83.53, 96.04, 80.69, 85.5…
## $ `2005` <dbl> 33.88, 50.66, 59.20, 91.67, 86.54, 96.01, 79.24, 86.3…
## $ `2006` <dbl> 31.89, 48.69, 57.31, 91.92, 84.62, 95.31, 78.01, 85.5…
## $ `2007` <dbl> 28.78, 47.19, 54.49, 92.07, 82.43, 95.29, 74.79, 80.8…
## $ `2008` <dbl> 21.17, 45.20, 54.76, 91.81, 82.94, 95.15, 74.13, 80.8…
## $ `2009` <dbl> 16.53, 43.10, 52.83, 92.38, 83.60, 95.18, 68.04, 79.2…
## $ `2010` <dbl> 15.15, 40.28, 47.19, 90.80, 81.45, 92.57, 64.82, 78.7…
## $ `2011` <dbl> 12.61, 38.41, 48.70, 89.01, 80.57, 91.65, 63.99, 78.6…
## $ `2012` <dbl> 15.36, 37.31, 50.33, 87.32, 77.18, 91.45, 64.36, 78.5…
## $ `2013` <dbl> 16.86, 37.08, 51.97, 86.70, 75.43, 91.08, 64.97, 77.6…
## $ `2014` <dbl> 18.93, 35.32, 51.05, 86.54, 75.24, 91.28, 63.70, 76.7…
## $ `2015` <dbl> 17.53, 31.93, 49.94, 86.68, 72.71, 91.15, 60.63, 78.0…
## $ `2016` <dbl> 19.92, 30.49, 45.42, 85.02, 72.27, 89.52, 58.01, 78.7…
## $ `2017` <dbl> 19.21, 28.36, 45.38, 83.63, 69.08, 88.12, 56.25, 79.1…
## $ `2018` <dbl> 17.96, 26.88, 43.97, 82.22, 66.97, 85.58, 56.38, 79.2…
## $ `2019` <dbl> 18.51, 24.75, 46.47, 82.27, 64.85, 84.77, 53.36, 79.4…
## $ `2020` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2021` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ `2050` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ country_name <chr> "Afghanistan", "Bangladesh", "Benin", "Bhutan", "Burk…
## $ country_group <chr> "Low-Income Developing Countries", "Low-Income Develo…
## $ group_type <chr> "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF", "IMF…#Rank countries by indicator performance
WB_sovereign_ESG_country_groups_REC_LIDC$Rank<-rank(WB_sovereign_ESG_country_groups_REC_LIDC$'2019')
WB_sovereign_ESG_country_groups_REC_LIDC#Filter top 10
WB_sovereign_ESG_country_groups_REC_LIDC_top10 <- WB_sovereign_ESG_country_groups_REC_LIDC %>%
filter(`Rank` > 49)
#Chart top 10
WB_sovereign_ESG_country_groups_REC_LIDC_top10 %>%
ggplot(aes(fct_reorder(iso3c, `2019`),`2019`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Renewable Electricity Consumption - Top 10 LIDCs",
subtitle = "% of Total Final Electricity Consumption, 2019",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
#Filter bottom 10
WB_sovereign_ESG_country_groups_REC_LIDC_bottom10 <- WB_sovereign_ESG_country_groups_REC_LIDC %>%
filter(`Rank` < 11)
#Chart bottom 10
WB_sovereign_ESG_country_groups_REC_LIDC_bottom10 %>%
ggplot(aes(fct_reorder(iso3c, `2019`),`2019`)) +
geom_col() +
coord_flip() +
scale_y_continuous(labels = )+
scale_x_discrete (guide = guide_axis(n.dodge=1.75))+
labs(
x = "",
y = "",
title = "Renewable Electricity Consumption - Bottom 10 LIDCs",
subtitle = "% of Total Final Electricity Consumption, 2019",
caption = "Data source: World Bank Sovereign ESG Data"
)+
theme_pander()
The top 10 LIDCs on this indicator range from the Democratic Republic of the Congo, for which renewable energy consumption accounts for 96.2% of total final energy consumption, to Zambia, for which renewable energy consumption accounts for 84.5% of total final energy consumption.
The bottom 10 LIDCs range from Kyrgyz Republic, for which renewable energy consumption accounts for 27.9% of total final energy consumption, to Uzbekistan, for which renewable energy consumption accounts for only 1.57% of total final energy consumption.
Energy Use: Key Findings
Across the four energy use indicators examined in this sub-section, the Democratic Republic of the Congo, Ethiopia and Zambia are the top performing LIDCs as they each appear in three of the four top 10 lists. Ghana, Cote d’Ivoire, Uganda, Central African Republic, Malawi, Nepal and Tanzania are also strong performers as they appear in two of the four top 10 lists.
On the other hand, Uzbekistan is the worst performing LIDC as it appear in three of the four bottom 10 lists. Yemen, Liberia, Vietnam, Bangladesh, Timor-Leste, Kyrgyz Republic, Guinea-Bissau, Zimbabwe and Somalia are also poor performers as they appear in two of the four bottom 10 lists.
For sustainable development investors, the top performing LIDCs in this category are likely to present investment opportunities to accelerate economic growth while maintaining sustainable energy use. On the other hand, the worst performing LIDCs present investment opportunities to incentivize transitions away from emissions-intensive energy production.
Overall Findings
This factbook has identified the LIDCs that outperform or under-perform their peers on 10 sustainability indicators under three categories (Emissions & Pollution, Natural Resources Management and Energy Use). The top performers and under-performers were the following:
In terms of CO2, Methane and Nitrous Oxide emissions per capita, the top performers are Burundi, Rwanda, Malawi, Sao Tome and Principe, Kiribati, Liberia and Cote d’Ivoire. The under-performers are Uzbekistan, the Republic of the Congo, Sudan, Mauritania, Chad, South Sudan and the Central African Republic.
In terms of natural resources and net forest depletion, the top performers are Moldova, Solomon Islands, Honduras, Benin, Nicaragua, Central African Republic and Cambodia. The under-performers are the Republic of the Congo, the Democratic Republic of the Congo, Guinea-Bissau, Burundi and Somalia.
In terms of terrestrial and marine protected areas, the top performers are Bhutan, Zambia, Republic of the Congo, Cambodia and Tanzania. The under-performers are the Solomon Islands, Sao Tome and Principe, Lesotho, Yemen and Comoros.
In terms of the four energy use indicators, the top performers are the Democratic Republic of the Congo, Ethiopia, Zambia, Ghana, Cote d’Ivoire, Uganda, Central African Republic, Malawi, Nepal and Tanzania. The under-performers are Uzbekistan, Yemen, Liberia, Vietnam, Bangladesh, Timor-Leste, Kyrgyz Republic, Guinea-Bissau, Zimbabwe and Somalia.