Estimating the costs of reducing CO2 emission via avoided deforestation with integrated assessment modelling

Estimates for deforestation and forest degradation were shown to account for about 17% of greenhouse gas emissions. The implementation of REDD is suggested to provide substantial emission reductions at low costs. Proper calculation of such a costs requires integrated modeling approach involving biophysical impact calculations and estimation economic effects of these.However, only few global modeling studies concerning this issue exist, and the actual implementation can take many forms. This study uses the approach of assuming that non Annex-I countries protect carbon rich areas from deforestation, and therefore loose the opportunity to use it as agricultural area. The opportunity costs of reducing deforestation within the framework of REDD are assessed with the global economic model LEITAP and the biophysical model IMAGE. A key methodological challenge is the representation of land use and the possibility to convert forestry land into agricultural land as REDD policies might prevent the use of forest for agriculture. We endogenize availability of agricultural land by introducing a flexible land supply curve and proxy the implementation of the REDD policies as a shift in the asymptote of this curve representing maximal agricultural land availability in various regions in the world. In a series of experiments, increasingly more carbon rich areas are protected from deforestation, the associated costs in terms of GDP reduction are calculated with the economic model. The associated reduction in CO2 emissions from land use change are calculated by the IMAGE model. From this series of experiments, abatement cost curves, relating CO2 emission reduction to costs of this reduction, are constructed. The results show that globally a maximum CO2 reduction of around 2.5 Gt could be achieved. However, regional differences are large, ranging from about 0 to 3.2 USD per ton CO2 in Africa, 2 to 9 USD in South and Central America, and 20 to 60 USD in Southeast Asi

Linking processes and pattern of land use change

Land use change results from the interaction between the human and the natural system and therefore various scientific disciplines have developed paradigms and methods to study land use change. However, these disciplinary approaches can only cover part of the complex system of land use change. The objective of this dissertation is to develop interdisciplinary methodologies to identify and integrate factors that are important in the land use system to describe and model the land use system in a comprehensive manner. The methodological challenges that are addressed in this study include bridging differences in spatial and temporal scales and organisational levels, identification of appropriate units of analysis, combining different disciplinary paradigms and developing new paradigms that unify the disciplines in one concept. The development of these methods is illustrated with a case study in a municipality in the Philippines, where in the past century large land use changes have taken place through commercial logging and expansion of agriculture. To make projections of future land use in the area models were constructed for the case study. In this dissertation it is the combination of approaches that have led to a greater understanding of the land use in the study area. Especially moving between empirical, inductive methods and theoretical, deductive methods has proven to be a useful approach to stimulate theory building.Centrum voor Milieuwetenschappen LeidenWOTRO-NWOConservation Biolog

Multi-actor modelling of settling decisions and behavior in the San Mariano Watershed, the Philippines: a first application with the MameLuke framework

Contains fulltext : 35618.pdf (publisher's version ) (Open Access)Land-use system dynamics and demographic dynamics are tightly coupled. In environmental science and studies of changes in land use and land cover, an unequivocal relationship is sometimes found between both systems, especially in coarse-scale studies. To obtain a better understanding of these intermingling dynamics, we formulated an agent-based model, the MameLuke settlement model, that used a deductive approach to investigate these relationships. The model was constructed based on ethnographic histories of farm households in San Mariano, the Philippines. The model was calibrated visually. Although this calibration approach proved to be very inefficient, the model itself still outperformed a random model. The model formulation process and the model outcomes were quite extensively discussed with stakeholders, and the conceptual modeling approach and framework proved to be clear and useful tools for local-scale studies dealing with interacting human and biophysical subsystems.33 p

Multilevel modelling of land use from field to village level in the Philippines

In land use research regression techniques are a widely used approach to explore datasets and to test hypotheses between land use variables and socio-economic, institutional and environmental variables. Within land use science researchers have argued the importance of scale and levels. Nevertheless, the incorporation of multiple scales and levels and their interactions in one analysis is often lacking. Ignoring the hierarchical data structure originating from scale effects and levels, may lead to erroneous conclusions due to invalid specification of the regression model. The objective of this paper is to apply a multilevel analysis to construct a predictive statistical model for the occurrence of land use. Multilevel modelling is a statistically sound methodology for the analysis of hierarchically structured data with regression models that explicitly takes variability at different levels into account. For a land use study in the Philippines multilevel models are presented for two land use types that incorporate the field, household and village level. The value of multilevel modelling for land use studies and the implications of multilevel modelling for data collection will be discussed. The results show that explanatory variables can account for group level variability, but in most cases a multilevel approach is necessary to construct a sound regression model. Although land use studies often show clear hierarchical structures, it is not always possible to use a multilevel approach due to the structure of most land use datasets and due to data quality. Potentially, multilevel models can address many important land use issues involving scales and levels. Therefore, it is important in land use change research to formulate hypotheses that explicitly take scale and levels into account and then collect the appropriate data to answer these questions with approaches such as multilevel analysis

Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model

Land use change is the result of interactions between processes operating at different scales. Simulation models at regional to global scales are often incapable of including locally determined processes of land use change. This paper introduces a modeling approach that integrates demand-driven changes in land area with locally determined conversion processes. The model is illustrated with an application for European land use. Interactions between changing demands for agricultural land and vegetation processes leading to the re-growth of (semi-) natural vegetation on abandoned farmland are explicitly addressed. Succession of natural vegetation is simulated based on the spatial variation in biophysical and management related conditions, while the dynamics of the agricultural area are determined by a global multi-sector model. The results allow an exploration of the future dynamics of European land use and landscapes. The model approach is similarly suitable for other regions and processes where large scale processes interact with local dynamic

Analysis of land use drivers at the watershed and household level: linking two paradigms at the Philippine forest fringe

Land use and land cover change (LUCC) is the result of the complex interactions between behavioural and structural factors (drivers) associated with the demand, technological capacity, social relations and the nature of the environment in question. Although no general theory of land use change exists, different disciplinary theories can help us to analyse aspects of LUCC in specific situations. However, paradigms and theories applied by the different disciplines are often difficult to integrate and their specific research results do not easily combine into an integrated understanding of LUCC. Geographical approaches often aim to identify the location of LUCC in a spatially explicit way, while socio-economic studies aim to understand the processes of LUCC, but often lack spatial context and interactions. The objective of this study is to integrate process information from a socio-economic study into a geographical approach. First, a logistic regression analysis is performed on household survey data from interviews. In this approach the occurrence of the land use types corn, wet rice and banana is explained by a set of variables that are hypothesised to be explanatory for those land use types, with fields as the unit of analysis. The independent variables consist of household characteristics, like ethnicity and age, and plot and field information, like tenure, slope and travel time. The results of these analyses are used to identify key variables explaining land use choice, which subsequently are also collected at watershed level, using maps, census data and remote sensing imagery. Logistic regression analysis of this spatial dataset, where a ten percent sample of a 50 by 50 m grid was analysed, shows that the key variables identified in the household analysis are also important at the watershed level. Important drivers in the study area are, among others, slope, ethnicity, accessibility and place of birth. The differences in the contribution of the variables to the models at household and watershed level can be attributed to differences in spatial extent and data representation. Comparing the model with a mainstream geographical approach indicates that the spatial model informed by the household analysis gives a better insight in the actual processes determining land use than the mainstream geographic approac

Understanding tropical land use change

Contains fulltext : 33439.pdf (publisher's version ) (Open Access

Comparison of a deductive and an inductive approach to specify land suitability in a spatially explicit land use model

In this paper, two research approaches to specify the relation between land use types and their explanatory factors are applied to the same modelling framework. The two approaches are used to construct land suitability maps, which are used as inputs in two model applications. The first is an inductive approach that uses regression analysis. The second applies a theoretical, actor decision framework to derive relations deductively using detailed field data. Broadly speaking, this classification coincides with the distinction between empirical and theoretical models and the distinction between deriving process from pattern and pattern from process. The two modelling approaches are illustrated by a scenario analysis for a case study in a municipality in the Philippines. Goodness-of-fit of the deductive approach in predicting current land use is slightly lower compared to the inductive approach. Resulting land use projections from the modelling exercise for the two approaches differ in 15 percent of the cells, which is caused by differences in the specification of the suitability maps. The paper discusses the assumptions underlying the two approaches as well as the implications for the applicability of the models in policy-oriented research. The deductive approach describes processes explicitly and can therefore better handle discontinuities in land use processes. This approach allows the user to evaluate a wide range of scenarios, which can also include new land use types. The inductive approach is easily reproducible by others but cannot guarantee causality. Therefore, the inductive approach is less suitable to handle discontinuities or additional land use types, but is well able to rapidly identify hotspots of land use change. It is concluded that both approaches have their advantages and drawbacks for different purposes. Generally speaking, the inductive approach is applicable in situations with relatively small land use changes, without introduction of new land use types, whereas the deductive approach is more flexible. The choice of modelling approach should therefore be based on the research and policy questions for which it is used