Keywords

REDD+; Land system change; Regime Shift; System Dynamics

Location

Session H4: Modeling for Low Carbon Economies

Start Date

19-6-2014 9:00 AM

End Date

19-6-2014 10:20 AM

Abstract

Land system change has major consequences for climate change, biodiversity and ecosystem services, and is central to the debate of sustainable development. Land policies aimed at guiding land system towards sustainable pathways need to be informed by better understanding of land system change, and often rely on the forecasting of future land system change. For example, initiatives of Reducing Emission from Deforestation and Forest Degradation (REDD+) provide financial incentives to developing countries in tropical regions in exchange for a reduction of carbon emission from land system compared to business-as-usual. REDD+ schemes therefore rely on predictions of future land use without REDD+ intervention as the benchmark to calculate incentive payments. One key question for REDD+ schemes therefore is how future land use can be predicted. This is notoriously challenging due to the intrinsic non-linearity and complexity of land systems. One example for such non-linearity are rapid and persistent regime shifts of land systems to alternate states. In this paper, we present evidence of regime shifts in land systems in four case studies in Southeast Asia: Xishuangbanna Prefecture, China; Huaphan Province, Laos; Nghe An Province, Vietnam; Kutai Barat District, Indonesia. Land systems in all four sites were dominated by largely subsistence-based shifting cultivation in the early 1980s but land system change later embarked on distinctly different pathways with different agricultural production strategies and divergent outcomes in terms of livelihoods and ecosystem services. To further reveal the causes of these regime shifts, we simulated regime shifts of land-use systems with a stylized system dynamics model. Such models can help better understand how regime shifts in land system happen and thus can support proactive decision making to prevent (or foster) land systems tipping towards undesirable (or desirable) regimes.

 
Jun 19th, 9:00 AM Jun 19th, 10:20 AM

Understanding Regime Shift in Land Systems with System Dynamics

Session H4: Modeling for Low Carbon Economies

Land system change has major consequences for climate change, biodiversity and ecosystem services, and is central to the debate of sustainable development. Land policies aimed at guiding land system towards sustainable pathways need to be informed by better understanding of land system change, and often rely on the forecasting of future land system change. For example, initiatives of Reducing Emission from Deforestation and Forest Degradation (REDD+) provide financial incentives to developing countries in tropical regions in exchange for a reduction of carbon emission from land system compared to business-as-usual. REDD+ schemes therefore rely on predictions of future land use without REDD+ intervention as the benchmark to calculate incentive payments. One key question for REDD+ schemes therefore is how future land use can be predicted. This is notoriously challenging due to the intrinsic non-linearity and complexity of land systems. One example for such non-linearity are rapid and persistent regime shifts of land systems to alternate states. In this paper, we present evidence of regime shifts in land systems in four case studies in Southeast Asia: Xishuangbanna Prefecture, China; Huaphan Province, Laos; Nghe An Province, Vietnam; Kutai Barat District, Indonesia. Land systems in all four sites were dominated by largely subsistence-based shifting cultivation in the early 1980s but land system change later embarked on distinctly different pathways with different agricultural production strategies and divergent outcomes in terms of livelihoods and ecosystem services. To further reveal the causes of these regime shifts, we simulated regime shifts of land-use systems with a stylized system dynamics model. Such models can help better understand how regime shifts in land system happen and thus can support proactive decision making to prevent (or foster) land systems tipping towards undesirable (or desirable) regimes.