FAQs

 

What is the INCAS?


The INCAS is a Tier 3 level greenhouse gas (GHG) accounting system being developed to support Indonesia’s Measurement, Reporting, and Verification (MRV) requirements for the land-based sectors.

The INCAS provides a systematic and nationally consistent approach to monitoring GHG emissions and removals for the land-based sectors. The INCAS generates detailed information on historic, present time, and future
projections of GHG emissions and removals. This information will allow Indonesia to better understand, manage, and ultimately reduce GHG emissions in a targeted and effective manner.

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What is the status of the INCAS?


The Minister of Environment and Forestry formally endorsed the INCAS framework as the basis for Indonesia’s national MRV system for the land-based sectors, including REDD+ activities, at a public seminar on 27 March
2015.

MoEF tested the INCAS at the subnational level in the REDD+ Pilot Province of Central Kalimantan in 2014. Methods and results were published in March 2015.

The INCAS was subsequently used to produce the first comprehensive GHG inventory for Indonesian forests and peatlands, nationwide. These results are presented in detail for entire country and also for all 34 provinces
individually. The results, methods, and planned next steps will be launched at the Global Landscapes Forum alongside the 21st UNFCCC Conference of the Parties (COP21) in Paris in December 2015.
At this stage INCAS results have been produced using a nationally consistent approach, with predominantly national datasets and by a national agency.

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What are the future plans for the INCAS?


The INCAS is ready to be operationalized as an ongoing component of Indonesia’s national MRV platform for REDD+ activities.

The INCAS Roadmap has been prepared to guide the future development of INCAS from 2016 to 2019. This includes continued improvement of the existing results, expanding to coverage of the system to include the other land-based sectors, eventually including the full Agriculture, Forestry and Other Land Use (AFOLU) sectors and seeking to establishing operational links with subnational GHG accounting activities.

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Who manages the INCAS?


The INCAS is managed by the Ministry of Environment and Forestry (MoEF). It utilises inputs from various other agencies such as the National Institute for Aeronautics and Space (LAPAN), the Ministry of Agriculture and others.

The development phase of the INCAS has been led by the Forestry Research, Development and Innovation Agency (FORDA) within MoEF. The system is now available to be expanded across the MoEF to support ongoing GHG data and reporting requirements.

The development phase of the INCAS has been supported by the Australian Government, through a partnership with the Center for International Forestry Research (CIFOR) and previously through the Indonesia-Australia Forest Carbon Partnership (IAFCP). This support includes funding and technical assistance, however system development and implementation has been entirely led by Indonesian officials and national experts.

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Why are assumptions used by the INCAS?


Many factors influence changes in carbon stocks, and GHG emissions from forests and peatlands. Assumptions are used to simplify these interactions to a manageable level to enable repeatable calculation of GHG emissions and removals.

Assumptions based on the best available data allow INCAS to divide input data into the main factors that influence GHG emissions and removals. This improves transparency and allows improvements to be readily included, improving emissions estimates.

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What is the purpose of uncertainty analysis?


The purpose of uncertainty analysis is to help improve the accuracy of inventories over time by guiding decisions on methodology and priorities for inventory improvement. These assessments are made through the review of the technical assessment process.

Uncertainty estimates are not used to question the validity of emissions estimates in the National Inventory.

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How does the INCAS reduce uncertainty?


Identifying the sources of uncertainty is the first step in estimating uncertainty. These sources are typically disaggregated into uncertainties related to emissions and removals (e.g. initial biomass, growth, turnover, and decomposition rates) and uncertainties related to activity data (i.e. the area from which the emissions have come).

The INCAS framework has been designed to use the best available data for each input. Every effort has been made to minimise uncertainty for each input variable and modelling step through quality control and quality assurance processes.

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How is continuous improvement achieved?


Quality assurance and quality control, combined with uncertainty analysis identify data gaps and areas for improvement. Opportunities for improvement in data or processes have been identified for each of INCAS’s Standard Methods.

The INCAS Roadmap has been prepared to guide the future development of INCAS for the period 2016 to 2019, setting out the plan for continuous improvement of the system.

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What is the practicality and cost-effectiveness of using the INCAS in Indonesia?


The INCAS provides a practical framework to compile existing data and to prioritise research to produce new data needed to accurately quantify GHG emissions and removals from Indonesia’s land-based sectors. Much of the existing data have already been collated and analyzed. These data can be made available to other agencies, particularly for land-use planning.

The flexibility of the INCAS framework allows for temporally and spatially consistent historical, current, and future estimates of the impact of land management policies and actions, including monitoring the impact of REDD+ activities on net GHG emissions. The cost of operating INCAS is very small compared to the potential benefits: better land management policy and potential returns from REDD+ investments.

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What definitions does the INCAS use for REDD+ activities?


INCAS is designed to calculate GHG emissions and removals based on any definitions, and can be used to evaluate the impact of policy decisions, including REDD+ activity definitions. The Government of Indonesia has not formally adopted definitions for each of the REDD+ activities.

The impact of one set of potential REDD+ activity definitions is illustrated in the first nationwide GHG inventory for forests and peatlands. The impact of alternative definitions can be estimated using INCAS.

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How are the INCAS results validated?


The quality of results are directly influenced by the quality of input data. A large proportion of the INCAS work programme is focussed on ensuring input data are the best available and are documented transparently.

Outputs undergo quality assurance to ensure these accurately reflect observed or predicted changes in land use.

As with all approaches to MRV, INCAS results represent average emissions and removals at the landscape level (the first nationwide GHG inventory operated at the provincial level). Site-specific measurements will always differ from the results of MRV systems, but these differences average out across the whole region.

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What activity data does the INCAS use?


Many sources of information are used to derive annual activity data as inputs to INCAS.

National, fully spatial, temporally consistent, annual forest cover change is analysed by LAPAN using remote-sensing data in consultation with national experts with on-ground experience. These observed changes in forest cover allow for consistent monitoring of changes over time.

Forest-cover change data is combined with other spatial datasets—e.g. location (province), forest type, soil type, forest function, subsequent land use, concession type, burnt area, etc.—to quantify the annual area of change by type of event. INCAS refers to this as the ‘spatial allocation of regimes’.

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What time period does the INCAS cover?


INCAS is designed to produce annual results in a temporally consistent manner for any period of time, depending on the data available.

The first nationwide GHG inventory for forests and peatlands produced annual results for 2001–2012. Annual forest-cover change analysis is in the process of being updated for 2013 and 2014. Analysis of annual forest-cover change for 1990–2000 is included in the INCAS Roadmap for future development.

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How is the INCAS activity data different to other globally produced products and other Indonesian data?


INCAS activity data uses Indonesia-specific definitions and data, while global products use generic definitions, resulting in different outputs. Consequently, activity data based on global products are less accurate than INCAS activity data.

INCAS uses more detailed, annual activity data, which results in greater detail than those used by other Indonesian-produced activity data. This enables more accurate representation of land-use changes, resulting in improved estimates of GHG emissions and removals.

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How does the INCAS relate to FREL, INDC, BUR?


The first phase of the INCAS development will be completed by the end 2015. The Minister for Environment and Forestry has endorsed the INCAS framework as the basis for Indonesia’s future national MRV system for the land-based sectors, including REDD+ activities.

The INCAS can be used to analyse scenario options for Indonesia’s forest reference level (FRL) and national commitments (INDC), and produce results that can form the basis for these policy decisions, as well as monitoring progress. INCAS is also designed to produce Indonesia’s national GHG inventory for the land-based sectors, which can be used for its national communication (NatCom) and biennial update report (BUR).

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How does the INCAS interact with other GOI agencies and data sets?


The INCAS is designed to interact with any data relevant to forest and land management. Known data is held by numerous GOI agencies. Additional data may be available through other agencies, as well as non-government institutions. The INCAS team will continue to engage with any agency that has relevant data that meets quality assurance requirements.

The INCAS brings together the best available data from multiple agencies and organisations to produce complete, representative outputs.

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How can the INCAS be linked to M-related activities at the project/site level?


The INCAS framework is designed to operate at any scale: national, provincial, regional, district or project.

The first national application of the INCAS was conducted at the provincial level using the best available, nationally consistent data. Finer-scale applications can be undertaken where there are more detailed data available that meets the INCAS’ data quality requirements. Site-specific/project data can be used to improve INCAS inputs, which will increase the accuracy of INCAS outputs at specific locations.

Subnational linking is planned in Phase 3 of the INCAS Roadmap, scheduled for completion in 2017.

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How can M-related activities at the project/site level contribute to the INCAS?


The INCAS framework is designed to operate at any scale: national, provincial, regional, district or project.

The first national application of the INCAS was conducted at the provincial level using the best available, nationally consistent data. Finer-scale applications can be undertaken where there are more detailed data available that meets the INCAS’ data quality requirements. Site-specific/project data can be used to improve INCAS inputs, which will increase the accuracy of INCAS outputs at specific locations.

Subnational linking is planned in Phase 3 of the INCAS Roadmap, scheduled for completion in 2017.

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How can the INCAS be used to account for emissions other than from the forest sector (e.g. agriculture)?


The first national GHG inventory using INCAS-covered activities in forests and emissions from previously forested peatlands. Expansion to include other land uses, including agriculture, is planned in Phase 2 of the INCAS Roadmap, scheduled to commence in 2016.

The INCAS framework is designed to account for GHG emissions from all land uses. Implementation for other parts of the land sector will require collation, analysis, and quality assurance of data, as well as calibration of models for these sectors.

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