Skip to main content

Transversal Component

Objectives: 

  1. Through the transversal components, the program will be coordinated, steering thematic support provided to the RCoEs with the aim of enhancing cross-thematic and cross-regional synergies, as well as fostering collaboration with major pan-African projects and initiatives.
  2. The Africa Knowledge Platform enables data and information sharing across the RCoEs and other stakeholders. The platform also develops and hosts decision-support tools to help policy makers with spatial prioritisation and land use planning, contributing to the strengthening of the science-policy interface. AKP stands at the core of the program serving as knowledge and communication hub while promoting stakeholders' active participation.
  3. The Innovation Ecosystem work package aims at developing and implementing a methodology for a systemic place-based approach to mobilise STI for green transition. The methodology targets the strengthening of RCoEs capacities to identify and assess the interlinkages between sustainability challenges, with the objective to help in anticipating and mitigating potential negative effects of investment decisions.
  4. Formulate and execute responses to environmental degradation and DRR.


Duration: Full project duration
Budget: €3.5 million  
Lead Organisation:  DG-JRC


Available Resources
Displaying 31 - 45 of 78
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
Protected areas in Africa play a critical role in biodiversity conservation, supporting livelihoods, and mitigating climate change. This tool offers a comprehensive analysis of the dynami...
The GHS Settlement Model layers (GHS-SMOD) GHS-SMOD_GLOBE_R2023A delineate and classify settlement typologies via a logic of cell clusters population size, population and built-up area densities as de...
The GHS Settlement Model layers (GHS-SMOD) GHS-SMOD_GLOBE_R2023A delineate and classify settlement typologies via a logic of cell clusters population size, population and built-up area densities as de...