REDD Early Movers (REM) Mato Grosso
Sector: Commercial • Location: Brazil
Source: KFW Bank aus Verantwortung
The results-based financing of the REDD Early Mover program will remunerate deforestation reductions achieved ex-post. The aim of the project is to achieve significant emission reductions (ER) in the Brazilian state of Mato Grosso. The funds amounting to EUR 17 million with EUR 30 million in stock audits and mandate funds from the United Kingdom (Department of Business, Energy and Industrial Strat
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Participants
Sponsoring Agency | Obfuscated Data |
Company | Obfuscated Data |
Status
Original status | active |
Taiyo status | Obfuscated Data |
Taiyo last update | 00-00-0000 |
Available timestamps | 00-00-0000 |
Available timestamp type | Obfuscated Data |
Contact
Contact name | Obfuscated Data |
Phone | 0000000000 |
ObfuscatedData@email.com | |
Address | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Description
Description | The results-based financing of the REDD Early Mover program will remunerate deforestation reductions achieved ex-post. The aim of the project is to achieve significant emission reductions (ER) in the Brazilian state of Mato Grosso. The funds amounting to EUR 17 million with EUR 30 million in stock audits and mandate funds from the United Kingdom (Department of Business, Energy and Industrial Strategy, BEIS) of up to GBP 23.9 million will be used to make results-based payments for emission reductions from avoided deforestation. It thus contributes to the overall goal of the REDD Early Movers (REM) program to promote climate protection through forest conservation as REDD interim financing in accordance with UNFCCC requirements. The new FC project will also support the building of bridges between subnational REDD+ approaches towards an integrated national system. Result-based payments are only made if annual deforestation is below the agreed REM performance trigger of 1788 km² (average gross deforestation in the period 2004-2015). This increases the incentive for further deforestation reduction. The module objective is to compensate emission reductions (ER) from avoided deforestation from 2016 to 2019 via the REM program on a result-based basis, with a target volume of approx. 3.74 million tCO2e (via German funds) or 8.94 million tCO2e (including mandate funds from BEIS). As a contribution of its own and as a risk provision, Mato Grosso sets aside at least one additional tonne of CO2e for every tonne of CO2e compensated that cannot be compensated for in any other way. The funds from the results-based financing (EBF) are reinvested within an agreed distribution system (benefit sharing) and are intended to contribute to the generation of diverse positive development impacts. This will contribute to the sustainable support of local population groups living in and from the forest, including indigenous groups, as well as small and medium-sized landowners, and thus to poverty reduction. In addition to supporting indigenous communities, incentives are to be created for sustainable and emission-reduced production in small and medium-sized agriculture. The sponsor is the state government of Mato Grosso, represented by the State Ministry of the Environment (SEMA). The Brazilian biodiversity fund FUNBIO is proposed as the recipient, which will manage the project financially and administratively. The funds will be implemented by state ministries and other governmental and non-governmental organizations in accordance with the agreed benefit sharing system. The GIZ project to promote REM instruments provides targeted support, in particular for REDD+ safeguards, participation and governance, with a particular focus on the participation of the indigenous population. The project is part of the development cooperation program Protection and Sustainable Use of Tropical Forests in Brazil. |
Original sub-sector | Obfuscated |
Original Currency | USD |
Original budget | 000000000000000 |
Procurement method | Obfuscated Data |
Budget | 000000000000000 |
Location
Region | Obfuscated |
Country | Obfuscated |
State | Obfuscated Data |
County | Obfuscated |
Location | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Source
Source reliability | High |
Data quality score | 100% |
Source | Obfuscated Data |
URL | obfuscated_data,obfuscateddata.com |
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