African power plants - Installed capacity (MW)

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L-Moment of Annual Precipitation Analysis in Africa calculated with R function lmom::samlmu with default arguments. See R documention of the function for details.

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This map shows the Terrestrial Biomes, the eight major terrestrial biomes on Earth are each distinguished by characteristic temperatures and amount of precipitation. Comparing the annual totals of precipitation and fluctuations in precipitation from one biome to another provides clues as to the importance of abiotic factors in the distribution of biomes. Temperature variation on a daily and seasonal basis is also important for predicting the geographic distribution of the biome and the vegetation type in the biome. The distribution of these biomes shows that the same biome can occur in geographically distinct areas with similar climates.

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Composite indicator that includes the following values: Literacy rate, adult total (% of people ages 15 and above) Access to electricity (% of population) Internet Users (per 100 people) Mobile celluar subscriptions (per 100 people)

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At any given place on Earth, complex human-environment interactions are at play, which include differing rates and magnitudes of drivers (e.g. overgrazing, climate change, agricultural practices) and consequences (e.g. soil erosion,changes in productivity, loss of biodiversity). Because these are tied to specific places on the ground with their own intertwined biophysical, social, economic and political environments, land degradation is not a phenomenon that can be modelled or mapped at a global scale. WAD3 builds on a systematic framework of providing a convergence of reliable,global evidence of human environment interactions to identify local or regional areas of concern where land degradation processes may be underway. Concerns can be validated or dismissed only by evaluating them within local biophysical, social, economic and political contexts. Local context provides an understanding of causes and consequences of degradation, but also offers guidance for efforts to control or reverse it.

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This map shows species richness in country assessed by the International Union for the Conservation of Nature (IUCN) and documented in the IUCN Red List of Threatened Species TM (RLTS). Country summary statistics are expert based as reported by the IUCN in their summary tables: https://www.iucnredlist.org/resources/summarystatistics

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Signatory cities and municipalities of Covenant of Mayors in Sub-Saharan Africa. Covenant of Mayors in Sub-Saharan Africa (CoM SSA) initiative supports Sub-Saharan cities in their fight against climate change and in their efforts in ensuring access to clean energy. Started in 2015, the initiative is shaped by local authorities for the local authorities to reflect the local context and specifics. In order to translate the political commitment into practical measures, CoM SSA signatories commit to produce and implement a Sustainable Energy Access and Climate Action Plan (SEACAP).

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Composite indicator that includes the following components: Multidimensional Poverty Index, Gender Inequality Index, Income Gini coefficient - Inequality in income or consumption. The risk score ranges from 0-10, where 10 is the highest risk.

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Energy Water Plants (Energy produced (MWh)): Compilation of multiple databases: PLATTS [1], Energydata.info 2012 [2], WRI 2018 [3], Harvard 2010 [4]. [1] S&P Global Platts. World electric power plants database; 2016. https://www.platts.com/products/world-electric-power-plants-database. [2] Energydata.info. Africa – Power stations; 2012. https://energydata.info/en/dataset/africa-power-stations-2012. [3] WRI. Global power plants database; 2018. https://www.wri.org/publication/ global-power-plant-database. [4] Harvard. Africa power plants; 2010. http://worldmap.harvard.edu/data/geonode:africa_power_plants_gd4.

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Energy Water Plants (Ratio water loss / Energy production (mcm/GWh)): Compilation of multiple databases: PLATTS [1], Energydata.info 2012 [2], WRI 2018 [3], Harvard 2010 [4]. [1] S&P Global Platts. World electric power plants database; 2016. https://www.platts.com/products/world-electric-power-plants-database. [2] Energydata.info. Africa – Power stations; 2012. https://energydata.info/en/dataset/africa-power-stations-2012. [3] WRI. Global power plants database; 2018. https://www.wri.org/publication/ global-power-plant-database. [4] Harvard. Africa power plants; 2010. http://worldmap.harvard.edu/data/geonode:africa_power_plants_gd4.

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Energy Water (Ratio water loss / Energy production (mcm/GWh)): Country boundaries: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/countries#countries16

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Energy Water Reservoirs (Reservoir area (sqkm)): Reservoir surfaces derived from Global Surface Water data, Yearly Classification (version 1.1) [1] Evaporation rates were modelled using LISVAP [6] based on ERA5 data. [2] [1] EC JRC/Google. JRC yearly water classification history, V.1.1; n.d. https:// developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_1_YearlyHistory [accessed October 5, 2019]. [2] Copernicus Climate Change Service. ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Clim Chang Serv Clim Data Store; 2017. https://cds.climate.copernicus.eu/cdsapp#!/home [accessed August 12, 2019].

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Energy Water Reservoirs (Yearly Mean Evaporation Rate (mm/year)): Reservoir surfaces derived from Global Surface Water data, Yearly Classification (version 1.1) [1] Evaporation rates were modelled using LISVAP [6] based on ERA5 data. [2] [1] EC JRC/Google. JRC yearly water classification history, V.1.1; n.d. https:// developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_1_YearlyHistory [accessed October 5, 2019]. [2] Copernicus Climate Change Service. ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Clim Chang Serv Clim Data Store; 2017. https://cds.climate.copernicus.eu/cdsapp#!/home [accessed August 12, 2019].

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Energy Water (Water loss through evaporation from all reservoirs in country (mcm)): Country boundaries: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/countries#countries16

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The INFORM Epidemic Risk Index is a prototype version of hazard dependent INFORM Risk Index created in 2018. It was developed under the technical lead of the JRC and in close collaboration with WHO for the epidemic components. Through extensive consultation, the WHO identified the underlying risk drivers of epidemic, which enabled the development of a conceptual framework for epidemic risk assessment in countries. JRC developed the INFORM Epidemic Risk Index as an adaptation of the INFORM Risk index, preserving the integrity of the original model. The risk score ranges from 0-10, where 10 is the highest risk.

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The dataset contains warnings about low or delayed vegetation performance at sub-national level for crops . The warning classification scheme is applied globally and is based on rainfall estimates (RFE) and NDVI anomalies. The results are a reliable warning of hydrological stress for agricultural production and the warning level ranges from 1 to 4. The historical frequency of ASAP warnings reports the percentage of dekads with a warning for crop out of the total number of active dekads in the period 2004-2019.

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This map provides the geolocation of all sites that have received funding in the frame of a project - or an activity within a project- within the last 20 years or so. It specifies if the site is protected or not. A project usually includes several sites. By clicking on a site, further information is provided on (1) the name, total budget, timeframe and status of the project and (2) the donor and implementing agency. Targeted actions can be education/awareness, land/water management, land/water protection, species management, law and policy, livelihood/economic and other incentives, external capacity building. By clicking on the project, the type of action is specified, and the funding.

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These data contain a multitemporal information layer on built-up presence as derived from Landsat image collections.

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These data contain a multitemporal information layer on built-up presence as derived from Landsat image collections.

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Composite indicator that includes the following components: Current health expenditure per capita, PPP (current international $) Proportion of the target population with access to three doses of diphtheria-tetanus-pertussis (DTP3) (%) Proportion of the target population with access to measles-containing-vaccine second dose (MCV2) (%) Proportion of the target population with access to pneumococcal conjugate third dose (PCV3) (%) Density of physicians (per 1,000 population) Ratio of maternal deaths per 100,000 live births The risk score ranges from 0-10, where 10 is the highest risk.

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The INFORM Epidemic Risk Index is a prototype version of hazard dependent INFORM Risk Index created in 2018. It was developed under the technical lead of the JRC and in close collaboration with WHO for the epidemic components. Through extensive consultation, the WHO identified the underlying risk drivers of epidemic, which enabled the development of a conceptual framework for epidemic risk assessment in countries. JRC developed the INFORM Epidemic Risk Index as an adaptation of the INFORM Risk index, preserving the integrity of the original model. The risk score ranges from 0-10, where 10 is the highest risk.

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Composite indicator that includes the following values: Literacy rate, adult total (% of people ages 15 and above) Access to electricity (% of population) Internet Users (per 100 people) Mobile celluar subscriptions (per 100 people) Road density (km of road per 100 sq. km of land area) People using at least basic sanitation services (% of population) People using at least basic drinking water services (% of population) Current health expenditure per capita, PPP (current international $) Proportion of the target population with access to three doses of diphtheria-tetanus-pertussis (DTP3) (%) Proportion of the target population with access to measles-containing-vaccine second dose (MCV2) (%) Proportion of the target population with access to pneumococcal conjugate third dose (PCV3) (%) Density of physicians (per 1,000 population) Ratio of maternal deaths per 100,000 live births The risk score ranges from 0-10, where 10 is the highest risk.

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Composite indicator that includes the following values: Government effectiveness Corruption Perception Index CPI Hyogo Framework for Action scores The risk score ranges from 0-10, where 10 is the highest risk.

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For the coping capacity dimension, the question is which issues the government has addressed to increase the resilience of the society and how successful its implementation is. The coping capacity dimension measures the ability of a country to cope with disasters in terms of formal, organized activities and the effort of the country’s government as well as the existing infrastructure which contributes to the reduction of disaster risk. It is aggregated by a geometric mean of two categories: institutional and infrastructure. The difference between the categories is in the stages of the disaster management cycle that they are focusing on. If the institutional category covers the existence of DRR programs that address mostly mitigation and preparedness/early warning phase, then the infrastructure category measures the capacity for emergency response and recovery. The risk score ranges from 0-10, where 10 is the highest risk.

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For the coping capacity dimension, the question is which issues the government has addressed to increase the resilience of the society and how successful its implementation is. The coping capacity dimension measures the ability of a country to cope with disasters in terms of formal, organized activities and the effort of the country’s government as well as the existing infrastructure which contributes to the reduction of disaster risk. It is aggregated by a geometric mean of two categories: institutional and infrastructure. The difference between the categories is in the stages of the disaster management cycle that they are focusing on. If the institutional category covers the existence of DRR programs that address mostly mitigation and preparedness/early warning phase, then the infrastructure category measures the capacity for emergency response and recovery. The risk score ranges from 0-10, where 10 is the highest risk.

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The Natural Land Pattern Index (NLPI) assesses the spatial pattern of the natural/semi-natural land by reporting the area (in km2) covered by six spatial pattern classes (core, edge, perforation, islet, margin, core-opening) in which natural/semi-natural land has been classified as of 2015. The Natural Land Pattern Dynamics (NLPD) reports the trends in the area occupied by these pattern classes in the last 20 years (1995-2015). The six pattern classes are determined based on the spatial context and size of the patches of natural/semi-natural land cover, accounting for its proximity to non-natural (agricultural and urban) areas. See below (Use and Interpretation section) for a detailed description of these six classes.

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National retail diesel prices [US $ cents /liter] for year 2012

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Natural Areas is calculated using Copernicus Global 100m Land Cover map 2015.

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Industrial mature oil palm plantation in Country (v1) Smallholder mature oil palm plantation in Country (v2)

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PV versus diesel least-cost option

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Each pixel represents the area fraction of the specific cover (i.e. percentage of the pixel with crops/rangeland). Data are scaled between 1 and 200 (50 = 25%, 100 = 50%, 150 = 75%, 200 = 100%): image values V = 0-200, scaling 0.5 - > physical value 0-100%. These layers were generated for ASAP, combining existing data sets.

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Main non-food, non-energy raw material commodities imported by each country in 2017. The European Commission's (EC) Raw Materials Information System (RMIS) is developed by the Directorate-General (DG) Joint Research Centre (JRC) in cooperation with the DG for Internal Market, Industry, Entrepreneurship and SMEs (GROWTH). The RMIS is the Commission’s reference web-based knowledge platform on non-fuel, non-agricultural raw materials from primary and secondary sources. This section provides an overview of the European raw materials context, the policy mandate that underlies the development of the RMIS, its goal and scope.

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From its establishment in 1963, the Organisation of African Unity (OAU) identified the need for the economic integration of the continent as a prerequisite for economic development. The 1980 Lagos Plan of Action for the Development of Africa, followed by the 1991 treaty to establish the African Economic Community (also referred to as the Abuja Treaty), proposed the creation of regional economic communities (RECs) as the basis for African integration, with a timetable for regional and then continental integration to follow. The Treaty provides for the African Economic Community to be set up through a gradual process, in 6 stages over 34 years, i.e. by 2028.[1] Article 88 of the Abuja Treaty states that the foundation of the African Economic Community is the progressive integration of the activities of the RECs, with the establishment of full continental economic integration as the final objective towards which the activities of existing and future RECs must be geared. A Protocol on Relations between the AEC and the RECs entered into force on 25 February 1998. In 2000, the OAU/AEC Summit in Lomé adopted the Constitutive Act of the African Union, which formally replaced the OAU in 2002. The final OAU Summit in Lusaka from 9 to 11 July 2001 reaffirmed the status of the RECs within the African Union and the need for their close involvement in the formulation and implementation of all programmes of the Union. At the same time, it was recognised that the existing structure of the RECs was far from ideal, with many overlaps in membership. At the Maputo Summit in 2003 the AU Commission was requested to accelerate the preparation of a new draft Protocol on Relations between the African Union and the RECs. Rationalisation of the RECs formed the theme of the July 2006 Banjul summit of the AU.[2] At the July 2007 Accra summit the AU Assembly adopted a Protocol on Relations between the African Union and the Regional Economic Communities.[3] This protocol is intended to facilitate the harmonisation of policies and ensure compliance with the Abuja Treaty and Lagos Plan of Action time frames.

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Residential population estimates for target year 2015 provided by CIESIN GPWv4.10 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer.

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At the African Union and European Union Commission College meeting in Addis Abeba, Ethiopia (April 25-26, 2013) this map contained in the Soil Atlas of Africa was introduced by EU Commissioner Hedegaard (Climate Action) on behalf of the European Commission President José Manuel Barroso. The atlas is available for download at https://esdac.jrc.ec.europa.eu/content/soil-map-soil-atlas-africa

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The outputs of the calculation consist of annual average values of PhotoVoltaic (PV) solar energy production [kWh] at optimal angle

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The above-ground carbon (AGC) is expressed in Mg (megagrams or tonnes) of carbon per km2. It corresponds to the carbon fraction of the oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees, excluding stump and roots, as estimated by the GlobBiomass project (globbiomass.org) with 2010 as the reference year.

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The deforestation year is the year when the TMF has been deforested for the first time (followed or not by a regrowth).

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The transition map captures the sequential dynamics of changes by providing transition stages from the initial observation period to the end of the year 2019. It depicts five main land cover types with a few sub-types: (i) remaining undisturbed moist forests (including the mangroves), (ii) degraded forests with two sub-types corresponding mostly to either logged or burned forests, (iii) young forest regrowth, (iv) deforested land that includes three subcategories of converted land cover: (a) water bodies (new dams and river flow changes); (b) tree plantations; and (c) other land cover that includes infrastructure, agriculture and mining, and (v) non-TMF cover (including afforestation).

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The Water Occurrence dataset shows where surface water occurred between 1984 and 2018 and provides information concerning overall water dynamics. This product captures both the intra and inter-annual variability and changes. The occurrence is a measurement of the water presence frequency (expressed as a percentage of the available observations over time actually identified as water). The provided occurrence accommodates for variations in data acquisition over time (i.e. temporal deepness and frequency density of the satellite observations) in order to provide a consistent characterization of the water dynamic over time.

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Turbidity is an indicator of water clarity, quantifying the haziness of the water and acting as an indicator of underwater light availability. Light penetration may or may not be sufficient to support the growth of aquatic plants and adversely affect fish and shellfish populations. Mangroves are known to reduce the turbidity of waters. The data show the total percentage deviation, from a baseline, for turbidity and trophic state. A five year baseline (2006- 2010), per lake, has been produced for both parameters. This is used to measure change against recent years (2017-2019). The data represent the number of lakes impacted by a degradation of their environmental conditions (i.e. showing a deviation in turbidity and trophic state from the baseline) compared to the total number of lakes within a country. The values produced account for different sized lakes.

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Reservoir dynamics: Annual extent of reservoir surface water area.

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