Effects of asymmetric information and reference emission levels on the emissions from deforestation and degradation
Introduction
Deforestation is arguably one of the major causes of global warming. It is possibly responsible for about 15% of global anthropogenic greenhouse gas emissions (van der Werf et al., 2009). In tackling global warming, United Nations Framework Convention on Climate Change (UNFCCC) proposed a mechanism named “Reducing Emissions from Deforestation and Degradation (REDD)”. An extension of this mechanism, titled REDD+, includes plans for forest protection, sustainable management of forests and enhancement of forest carbon sinks (Forest Carbon Partnership Facility, 2011).
Kameyama et al. (2016) find that investment towards low-carbon development can realize in Asia, and estimate that the investment of US$ 125–149 billion per year are needed in the region by 2035. REDD + mechanism includes provision of financial assistance to those countries that are capable of reducing emissions caused by deforestation (Scholz and Schmidt, 2008). In order to effectively implement REDD + at the national level, developing countries are generally expected to successfully make policies, and implement before they are rewarded. These result-oriented actions are expected to allow the development of necessary technical preparation and institutional capacity building. REDD + readiness is essential in the implementation of a comprehensive REDD + project. This includes preparation for a national REDD + strategy as well as a functioning legal and institutional implementation framework, setting national Reference Emission Levels (REL) and establishing a national forest monitoring system that includes a monitoring, reporting and verification (MRV) system for greenhouse gases (Maniatis et al., 2013). It is particularly important that the incentives obtained via REDD + projects are directly tied to reducing emissions for the respective country. Therefore, it is key to ensure that a reliable MRV system is accurately measuring the emissions change from deforestation and forest degradation in the post Kyoto era (UNFCCC, 2011).
Although, there is a sizable literature on the improvement of monitoring, reporting and verification methods for REDD+, measurement error is unavoidable (Grassi et al., 2008, Bell et al., 2012, Knoke, 2013). This is due to numerous factors, such as data source, site assessment method, model setting, sampling density, hierarchical rule, update interval and quantified method for errors (Plugge et al., 2013). This means that the exact emissions impact from deforestation or forest degradation may not be accurately detected. Measuring forest degradation and related forest carbon stock changes is much more complex than measuring deforestation, since the former involves forest structural changes that are not due to Land-Use Change (LUC) alone. Consequently, it is not always easily detectable through remote sensing (Achard et al., 2014). The results of previous studies suggest that information on actual emissions from deforestation and degradation is not readily available (see Plugge et al., 2013, Knoke, 2013). Different from these previous studies, this paper attempts to analyze the effects of asymmetric information about actual emissions reductions on REDD+. The emissions from deforestation and degradation measured by policymakers and others stakeholders will likely be different since these parties have competing goals. On the one hand, policymakers/principals often hope to be able to effectively implement REDD+, and reduce carbon emissions from deforestation and degradation. On the other hand, landholders/agents desire to maximize their economic benefits. These benefits derive in part from logging and Land-Use Change, as well as from REDD + projects due to the reducing emissions from deforestation and degradation.
Policymakers merely hope to realize the goals of reducing emissions via forest protection, sustainable management of forests, and enhancement of forest carbon sinks. However, considering that the MRV system is unable to accurately monitor changes in carbon emissions from deforestation and degradation, landholders may possess more information on actual carbon emissions than the policymakers. As a result, emissions amount reported may be inaccurate, and there exists asymmetric information between principals (policymakers) and agents (landholders). Landholders with information superiority could obtain short-term gains by neglecting the environment and other residents, but resulting in environment loss in the long term due to selfish incentives, i.e. so-called “moral hazard” (Vedel et al., 2015). In order to address such social dilemmas, co-evolutionary rules could be employed to promote cooperation between principals and agents to conserve more forests (Perc and Szolnoki, 2009, Perc et al., 2013).
As for the effects of REL on actual emissions reductions, there is little literature focused. Most literature are more interested in how to create REL for REDD+. Ryan et al. (2014) propose a simple and transparent method for creating REDD + baselines by using radar remote sensing and ground surveys with a simple spatial model. Romijn et al. (2013) analyze the impacts of different definitions on estimation of forest REL. In order to define a credible and accurate baseline, Sloan and Pelletier (2012) try to develop and validate a GEOMOD projection of forest-cover change. Köthke et al. (2013) propose to use uniform global deforestation patterns to establish REDD + baselines by an empirical analysis. However, REL may also impact actual emissions reductions. Higher REL may result in more emissions which can endanger the effects of REDD+. This paper attempts to validate the effects of REL on actual emissions reductions. Different from previous research, this paper develop a new principal-agent analysis framework to reflect the effects of asymmetric information and REL on actual emissions reductions in REDD + projects.
The current literature indicates that the asymmetric information or reference emission levels can affect the emissions from deforestation and degradation (e.g. Delacote et al., 2014, Skidmore et al., 2014). However, how do the reference emission levels and information of emissions affect the performance of REDD + project is still uncertain. Furthermore, it is also unclear whether these impacts are global or regional issues. In order to address these issues, a multi-task principal-agent model is employed to analyze the interaction of landholders' behaviors and policymakers' goals as well as distortion caused by asymmetric information. Our empirical study utilizes panel data that contains information from 75 countries. This paper can help us understand the inner mechanism of REDD + projects between policymakers and landholders, and design REDD + projects with high effectiveness in the future. The structure of the paper is as follows. Section 2 establishes a multi-task principal-agent model to explain the effects of asymmetric information on actual emissions from deforestation and degradation. Section 3 presents the data and the model specification to analyze how rate of carbon emissions from deforestation and degradation is influenced by underreported emissions caused by asymmetric information and REL by making use of panel data in 75 developing countries for the time period of 1990–2010. Results and discussion are provided in Section 4, followed by the conclusions in Section 5.
Section snippets
Landholders' behavior under asymmetric information
We use a multi-task principal-agent model to analyze the causes of deforestation behavior of various landholders and the change in carbon stocks. We assess the landholder behavior under different types of information. In this analysis, it is assumed that REL have been set in all countries through negotiations. The REL set can be considered to be equivalent to the Business-As-Usual (BAU) scenario without REDD+ (Cattaneo, 2011). Furthermore, we focus on the effects of asymmetric information of
Data and model specification
The landholders' decisions are affected by many factors within REDD+. In this section, the panel data model will be adopted to test the effects of asymmetric information, reference emission levels and other factors.
Results and discussion
According to the previous analysis, the rate of carbon emissions from deforestation and degradation may be affected by seven independent variables. The results will be shown and discussed in this section.
Results
Because independent variables include a lagged dependent variable, there is a correlation relationship between the independent variables and random disturbance, which will make some variables to be endogenous. Thus, inconsistent estimation for parameters will occur, if one uses the methods of standard random effects or fixed effects (Greene, 2011). Arellano and Bond (1991) propose that the problem of inconsistent estimation could be solved by the method of Generalized Method of Moments (GMM),
Discussion
From the global perspective, the effects of all independent variables on RCD are significant at the 5% level. These results are consistent with our expectations:
- (i)
The effects of lagged RCD and FCS on RCD are positive, which indicates that an increase of lagged RCD and FCS will result in a rise in REL. Consequently, actual emissions from deforestation and degradation will increase. The conclusion is consistent with the results of the theoretical model. Previous studies focused on setting up
Conclusion
Accurate monitoring and measurement for carbon emissions and establishing appropriate REL are essential in the implementation of REDD+, because they are directly related to a country's revenues obtained from REDD + projects. Therefore, setting up a reliable MRV system and a reasonable REL are critical in ensuring an effective implementation of REDD+ in the post-Kyoto era. However, due to measurement errors and imperfections in MRV systems, there exists asymmetric information on actual emissions
Acknowledgements
The authors are grateful to the financial support provided by the National Natural Science Foundation of China (Nos. 71303123, 71403132 and 71403120), National Basic Research Program of China (2015CB953603), National Social Science Foundation of China (No. 13CGL094), Humanities and Social Science Foundation of Ministry of Education of China (Nos. 13YJCZH148) and China Postdoctoral Science Foundation (2015M570209). This paper is also funded by the Fund of Flagship Major Development of Jiangsu
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