This dissertation thesis details an interdisciplinary research project, which combines the strengths of resilience theory, the sustainable livelihood framework, complex systems theory, and modeling. These approaches are integrated to develop a tool that can help policy-makers make decisions under conditions of uncertainty, with the goals of reducing poverty and increasing environmental sustainability.
Achievement of the Millennium Development Goals, including reducing poverty and hunger, and increasing environmental sustainability, has been hampered due to global resource degradation and fluctuations in natural, social, political and financial systems. Climate change further impedes these goals, especially in developing countries. The resilience approach has been proposed to help populations adapt to climate change, but this abstract concept has been difficult to operationalize.
The sustainable livelihood framework has been used as a tool by development agencies to evaluate and eradicate poverty by finding linkages between livelihood and environment. However, critiques highlight its inability to handle large and cross-scale issues, like global climate change and environmental degradation.
Combining the sustainable livelihood framework and resilience theory will enhance the ability to simultaneously tackle the challenges of poverty eradication and climate change. However, real-life systems are difficult to understand and measure. A complex-systems approach enables improved understanding of real-life systems by recognizing nonlinearity, emergence, and self-organization. Nonetheless, this approach needs a framework to incorporate multiple dimensions, and an analytical technique.
This research project attempts to transform the concept of resilience into a measurable and operationally useful tool. It integrates resilience theory with the sustainable livelihood framework by using systems modeling techniques. As a case-study, it explores the resilience of household livelihoods within a local village Panchayat in central India.
This method integrated the 4-step cross-scale resilience approach with the sustainable livelihood framework through the use of a system dynamics modeling technique. Qualitative and quantitative data on social, economic and ecological variables was collected to construct a four-year panel at the panchayat scale. Socio-economic data was collected through questionnaires, focus group discussions, participant observation, and literature review. Ecological data on forest regeneration, degradation and growth rates was collected through sample plots, literature review of the region's forest management plans, and expert opinions, in the absence of data.
Using these data, a conceptual, bottom-up model, sensitive to local variability, was created and parameterized. The resultant model (tool), called the Livelihood Management System, is the first of its kind to use the system dynamics technique to model livelihood resilience.
Model simulations suggest that the current extraction rates of forest resources (non-timber forest produce, fuelwood and timber) are unsustainable. If continued, these will lead to increased forest degradation and decline in household income. Forest fires and grazing also have severe impacts on local forests, principally by retarding regeneration. The model suggests that protection from grazing and forest fires alone may significantly improve forest quality. Examining the dynamics of government-sponsored labor, model simulation suggests that it will be difficult to achieve the Government of India's goal of providing 100 days' wage labor per household through the National Rural Employment Guarantee Scheme.
Based on vulnerability analysis under the sustainable livelihood framework, eight risks to livelihoods were identified based on which six scenarios were created. One scenario was simulated to understand the resilience of local livelihoods to external shocks. Through these simulations, it was found that while climate change is a threat to local livelihoods, government policy changes have comparatively much larger impacts on local communities. The simulation demonstrates that reduced access to natural resources has significant impacts on local livelihoods. The simulation also demonstrates that reduced access drives forced migration, which increases the vulnerability of already risk-prone populations.
Through the development and simulation of the livelihood model, the research has been able to demonstrate a new methodology to operationalize resilience, indicating many promising next steps. Future undertakings in resilience analysis can allow for finding leverage points, thresholds and tipping points to help shift complex systems to desirable pathways and outcomes. Modeling resilience can help in identifying and prioritizing areas of intervention, and providing ways to monitor implementation progress, thus furthering the goals of reducing extreme poverty and hunger, and environmental sustainability.
Many challenges, such as high costs of data collection and the introduction of uncertainties, make model development and simulation harder. However, such challenges should be embraced as an integral part of complex analysis. In the long run, such analysis should become cost- and time-effective, contributing to data-driven decision-making processes, thus helping policy-makers take informed decisions under complex and uncertain conditions.
|Advisor:||Bailis, Robert, Oliver, Chadwick Dearing|
|School Location:||United States -- Connecticut|
|Source:||DAI-B 76/11(E), Dissertation Abstracts International|
|Subjects:||Geography, Climate Change, Systems science|
|Keywords:||Climate change, Environment, India, Livelihood, Modelling, Resilience|
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