- Open Access
Assessing data availability for the development of REDD-plus national reference levels
© Umemiya et al; licensee BioMed Central Ltd. 2010
- Received: 4 July 2010
- Accepted: 30 September 2010
- Published: 30 September 2010
Data availability in developing countries is known to be extremely varied and is one of the constraints for setting the national reference levels (RLs) for the REDD-plus (i.e. 'Policy approaches and positive incentives on issues relating to reducing emissions from deforestation and forest degradation in developing countries; and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries') under the UNFCCC. Taking Thailand as a case study country, this paper compares three types of RLs, which require different levels of datasets, including a simple historic RL, a projected forest-trend RL, and a business-as-usual (BAU) RL.
Other than the finding that different RLs yielded different estimations on future deforestation areas, the analysis also identified the characteristics of each RL. The historical RL demanded simple data, but can be varied in accordance with a reference year or period. The forest-trend RL can be more reliable than the historical RL, if the country's deforestation trend curve is formed smoothly. The complicated BAU RL is useful as it can demonstrate the additionality of REDD-plus activities and distinguish the country's unintentional efforts.
With the REDD-plus that involves widespread participation, there should be steps from which countries choose the appropriate RL; ranging from simpler to more complex measures, in accordance with data availability in each country. Once registered with REDD-plus, the countries with weak capacity and capability should be supported to enhance the data collection system in that country.
- Forest Area
- Wildlife Sanctuary
- Standard Scenario
- Forest Carbon Stock
- Conservation Scenario
Around 13 million hectares of global forest area is being lost every year largely in the tropics . It is estimated that, following the burning of fossil fuels, tropical deforestation is the second largest source of greenhouse gas emissions . Recognising the critical role of tropical forests in combating climate change, it is expected that an agreement under the UN Framework Convention on Climate Change (UNFCCC) will be made with regard to: 'Policy approaches and positive incentives on issues relating to reducing emissions from deforestation and forest degradation in developing countries; and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries' ('REDD-plus') [3, 4]. REDD-plus could contribute to the mitigation of climate change only if various methodological issues are resolved, including how to set country-specific reference levels (RLs). National RLs are used to determine the level below which the countries' reduced emissions could be measured and credited. Desirable RLs must ensure that rewarded emissions are additional; while they should also encourage widespread participation, as they are directly linked with REDD-plus incentives.
This paper presents three types of RLs, which require different data levels, by taking Thailand as a case study country. We then evaluate the applicability of each under REDD-plus. Thailand was suitable for this study for two major reasons: 1) it had sufficient types of data to develop three different RLs, including those relevant to national policy approaches to prevent deforestation, which were the commercial logging ban in natural forests in 1989 and the protected area system (i.e., national parks and wildlife sanctuaries) starting from 1961; and 2) those data were available over a long period of time (e.g., from 1975 to 2003).
Variables used in the analysis
Land use area (forest, farmland, unclassified land)
GDP at the country level
Sectoral GDP (in agriculture, manufacture, construction, sales, transportation, finance, public)
Production of major agricultural crops
Production of major meat
Production of wood products
Productivity for major agricultural crops*
Area of national parks and wildlife sanctuaries
Scenario design for a BAU RL
Modification on variables
All the variables adjusted at the changing rate equal to 2000-2003.
Annual increased area of national parks and wildlife sanctuaries adjusted to be 100,000 ha from 50,000 between 2000 and 2003; and other variables consistent with the Standard.
Annual growth rate of productivity for major agricultural crops and GDP in non-agriculture sector (i.e., manufacture, construction, sales, transportation, and finance) adjusted to be 10% from 7% and 5% in 2000-2003, respectively; and other variables consistent with the Standard.
Depending on the period for which the necessary data were available, the historical and forest-trend RLs were developed for 1975 to 2003, and the BAU baseline for 1981 to 2003. Then taking a year, 2013, as an example, which is right after the Kyoto Protocol's first commitment period, we compared the values of the forest area by the three RLs.
It was reported that there was discrepancy of the data on the forest area before and after 2000, since the data after 2000 lacks ground truth surveys and deviates significantly from earlier figures . Therefore, we estimated values on the forest area after 2000 by a liner extrapolation of the last values between 1995 and 1999. The differences between the original and calibrated values on the forest area were added to unclassified land. To reflect this adjustment on land use data, we used a dummy variable in the econometric model when developing a BAU RL.
The quadric model based on the historical trend of the forest area is as follows:
FOR = 12.355t2 - 49388t + 4.937*107
(adj. R2: 0.965; S.E.: 416.509)
Where: FOR, forest area; t, year
The econometric model for a BAU RL
(adj. R 2 : 0.999; S.E.: 31.956)
(adj. R 2 : 0.950; S.E.: 109.500)
(adj. R 2 : 0.972; S.E.: 131.306)
Comparison of the three RLs
Projected forest area for 2013 by three RLs
% ratio to forest area in 2003
With sample base yr/period of:
The characteristics of each of the three RLs, which required different levels of data, can be highlighted. First of all, not surprisingly, different RLs yielded different estimations for the future forest area. Our analysis showed that a RL with limited data (i.e., historical and forest-trend RLs) did produce a higher estimation of forest area than RLs with a large number of datasets (i.e., a BAU RL with the Standard and Conservation scenarios). This reminds us that the data availability of a country can influence not only the choice of a national RL, but also the REDD-plus incentives that it can receive. In this context, the quality of data used to develop a RL is also critical, thus should be reported and evaluated at the national registration with REDD-plus by, for instance, following the IPCC guidance and guidelines.
Secondly, it was found that the simple historical RL could be varied considerably, depending on the selection of a base year or period; while the forest-trend RL, which is relatively simple and likely to be more reliable than the historical RL, could be useful, only when the country's deforestation curve is formed smoothly. We recommend that these types of RLs based on a smaller number of datasets should be selected for countries which have less capacity and capability, including collection of data. We believe that a relaxation of requirements for a national RL, especially for the countries with limited data, is essential to realise widespread participation in REDD-plus. As an international treatment, REDD-plus has to be accessible to all developing parties. Besides, involving as many countries as possible is important for the carbon effectiveness of REDD-plus, because it can minimise a risk for the international leakage of forest emissions (i.e., reduced emissions occurring outside REDD-plus participating countries). Therefore, we suggest that the developing countries which have less capacity and capability should be able to adopt the RL that their best available data permits. Nevertheless, to avoid possible unfavorable 'hot air' (i.e., credited emissions without a country's additional efforts) caused by adopting a simpler type of RLs, the countries are recommended to develop RLs in a carbon conservative manner when registering with REDD-plus. Further, we propose that once registered with REDD-plus, the participating countries, if necessary, should be able to enhance their capacity and capability, which could include, as appropriate, an improved data collection system. With that improved data condition, they may be able to apply a more sophisticated RL, based on a large number of data types, such as a BAU RL, as introduced in this study.
Thirdly, other than the fact that it reflects the national circumstances related to deforestation, the BAU RL with a wide range of data types showed its strengths which can not be seen with the other simpler RLs (i.e., historical and forest-trend RLs). First, it could demonstrate the effects of the country's policy approaches to reduce deforestation. This can be useful for identifying the country's efforts that are additional because of REDD-plus. Such information should be valuable not only for national policy makers addressing deforestation, but also at the registration process of national REDD-plus action plans at the UNFCCC level. Second, the BAU RL could eliminate the unintended effects of a country's development on reduced deforestation. The analysis of this study showed that industrialisation in Thailand could help to reduce national deforestation. Nonetheless, industrialisation in many parts of the developing world (e.g., in China, India, and other fast growing countries), is likely to be promoted even without REDD-plus incentives. Therefore, it is necessary to prevent such unintentional effects from being counted as part of REDD-plus credits by using a detailed RL, such as the BAU RL of this study.
Given that data availability in developing countries is extremely varied, we suggest that countries participating in REDD-plus should be able to use the RL that fits into the situation which relates to their available datasets. To do this, REDD-plus must prepare the steps from which countries can choose the appropriate RL; ranging from simpler to more complex measures in accordance with their data availability. Support from developed countries to the once registered REDD-plus countries must be in place, so that countries with insufficient capacity and capability could strengthen the data collection system in that country, which can be used later to establish a more sophisticated RL. The REDD-plus that gains wide participation can contribute to not only the mitigation of climate change, but also the opportunity to manage tropical forests in a sustainable way, which could also benefit a number of other forest functions and services, including such as biodiversity, water catchment, prevention of soil runoff, and stabilisation of local climate.
This study is supported by the Environment Research and Technology Development Fund of the Ministry of Environment, Japan. Chisa Umemiya acknowledges the support from the Japan Society for the Promotion of Science. We also thank Suchat Kalyawongsa of Royal Forest Department and Ladawan Puangchit of Kasetsart University for their support during data collection in Thailand.
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