Democratic Republic of the Congo (v002)
WePlan - Forests is a spatially explicit, forest restoration planning tool that evaluates a range of alternative scenarios, reporting the benefits, costs and spatial distribution of restoration priorities for each one. It considers two objectives: (i) climate change mitigation benefit, estimated as the change in carbon sequestration that would arise from forest restoration, and (ii) biodiversity conservation benefit, estimated as the average reduction in local (national) extinction risk among all forest-associated species. The analysis also considers opportunity and implementation costs of forest restoration. Analyses occur at a 1 km2 resolution on a national basis, for countries containing tropical and subtropical forests within +/- 25 degrees latitude.
Four main types of analysis are presented: (i) optimal solutions that maximise cost-effectiveness (benefit / cost); (ii) optimal solutions that maximise benefit, ignoring costs; (iii) optimal reference solutions that minimise total costs, ignoring benefits; and (iv) reference solutions that randomise restoration. The first two analyses also involve evaluation of the trade-off between climate change mitigation and biodiversity conservation benefits. Planning solutions were developed for five area targets, representing 10, 20, 30, 40, and 50% of the area available for forest restoration. For a detailed description of the methods, please return to the previous page and download the PDF report associated with this analysis.
The points in the graph are colour-coded to represent the overall cost of the solution (opportunity cost + establishment cost). Areas selected for restoration are shown in red in the map, though the area of forest restored in each map is variable (maximum: 1 km2). The dark grey areas represent other areas in the country that were not selected, and the light grey areas correspond to terrestrial areas outside of the country. White areas are ocean. The table contains further details on the costs and benefits among the solutions and can be downloaded as a comma-delimited text file (.csv).
How to use this interface: Click on any point in the graph to show the corresponding map and highlight the record in the table. Alternatively, click on a row in the table to show the corresponding map and highlight the point on the graph (the large point on the graph is the selected one). The layout of the graph, map and table can be adjusted using the buttons in the upper right.
Biodiversity conservation benefit units: mean percent reduction in extinction risk among all species
Climate change mitigation benefit: carbon sequestered (Gt)
Area available for forest restoration
Area deemed available for forest restoration. Areas in purple indicate the 1 km2 planning units that were assessed to have potential for forest restoration given (see Methods for details). The shading of cells from light to dark purple is proportional to the area within each planning unit available for forest restoration.
There are a number of general patterns that can typically be observed in these analyses:
(i) The cost-effectiveness analysis usually achieves somewhat lower returns than the maximum-benefit analysis, but at much lower cost. The cost-effective scenarios therefore provide the greatest return-on-investment and are the primary focus for forest restoration planning support.
(ii) is usually some level of trade-off between climate change mitigation and biodiversity conservation benefits. Hence, it is usually not possible to achieve maximum benefits for both simultaneously and the trade-off curves WePlan - Forests describes help to identify solutions that are good compromises between the two objectives.
(iii) As the area target increases the returns on climate change mitigation and biodiversity conservation benefits increase, but often not linearly. This implies that the return-on-investment per unit area changes depending on how much area is restored.
(iv) The minimum-cost solution identifies the cheapest solution for restoring forest for a given area target but typically performs poorly with respect to climate change mitigation and biodiversity conservation benefits.
(v) The random solution often performs poorly with respect to both benefits and costs, usually providing the lowest returns-on-investment among all of the analyses for a given area target. The random scenario is likely to be a fair approximation of returns for any planning process that is based on other concerns and that does not consider these objectives explicitly.
The solutions generated by the WePlan - Forests analyses do not prescribe where restoration action should occur, but rather support and inform decisions about restoration planning. These analyses account for several key dimensions of restoration planning problems but they do not account for all relevant factors. Local-scale factors such as governance, land ownership and tenure, livelihoods, and local community objectives are also often important to consider in the decision-making process.
WePlan – Forests provides a quantitative, spatially-explicit, transparent and evidence-based framework for evaluating a range of restoration targets and scenarios to inform national-scale planning. The Weplan -- Forests team can work with you to develop bespoke analyses that reflect national policy and priorities. For more information please visit https://www.iis-au.org and refer to our educational WePlan – Forests webinar series, in particular the "Foundations of systematic spatial planning and spatial restoration optimisation" session https://www.iis-au.org/news/events-webinars/.
The designations employed and the presentation of the material in this document do not imply the expression of any opinion whatsoever on the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Every effort is made to ensure these data are free of errors but there is no warrant that these data, or the maps and graphs resulting from this analysis, are accurate or fit for any particular purpose.