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Repurposing Data Analytics for Monitoring, Evaluation,  Learning & Adaptation

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A loss & damage Registry on Climate Adaptation

Climate change is transforming our lives; global temperatures are rising leading to increased frequency, duration and intensity of drought and flood events. Agriculture is the predominant emitter of green house gases that are responsible for global warming. While this fact has implications for food security, the focus of climate mitigation efforts has been on technologies  that support a transition away from coal-based manufacturing. Robust Agriculture Research for Development (AR4D) has the potential to enhance the accountability of climate adaptation efforts by identifying financing systems and monitoring regimes that compensate for "loss and damage" of infrastructure, cultural assets and harm to human health especially for historically marginalized communities who are disproportionately affected by climate change. 

  

AR4D has an important role to play in helping communities adapt to climate change. Besides contributing towards the design of crop insurance tools, ARD4 can enhance the comprehensiveness of climate action through improved data curation to support forecasting, response and preparedness to hazards like droughts and floods. Presently AR4D relies predominantly on results of Randomized Control Trials (RCTs) to predict the technologies and management systems that can most likely enhance the efficiency of crop production, livestock breeding and/or water management. Very little is known about the socio-economic and institutional conditions that will determine whether end users will actually adopt superior varieties of seeds, livestock breeds and water management strategies. Consequently, lower than expected adoption rates for technologies and management models can adversely effect the impact of AR4D on development outcomes such as food, energy and water security. 

The Climate Panel initiative is focused on supporting integrative analysis of bio-physical, socio-economic and institutional factors that mediate the adoption of improved technologies and management strategies with the potential to enhance the resilience of water, energy and food systems. Data interoperability makes it possible to leverage existing databases, harmonize datasets and transform the way evidence is used to inform decisions about loss and damage via AR4D. Through an initial focus on a dedicated site in Sub-Saharan Africa, a region that is vulnerable to climate-induced displacement of people, the Climate Panel will explore the applications of earth observations, machine learning and Internet of Things (IoTs) to identify opportunities for experiential learning about opportunities to lower the costs of monitoring and evaluating loss and damage through effective strategies for data reuse in AR4D. 

Discover Environmental Science Courses - What We Offer

We have developed a “model of models” to help us understand and act by connecting data to models to influence decision-making at the level of local governments and communities in Sub-Saharan Africa. The Climate Panel is an open-access platform that uses online learning and environmental science courses to help us understand and apply the model of models in 3 steps: (a) constructing typologies of trade-offs, (b) constructing composite indices and (c) identifying institutional pathways that address acute environmental trade-offs. The 3 step approach underlies a tutorial to thesis model (or the T2T model) that will guide participants from an initial introduction to climate change-induced disasters to develop a personally crafted thesis for which they may draw upon two examples: one of global wastewater reuse and another of watershed management in South Asia. The thesis will double up both as a personal testament of individual learning while at the same time generating a field guide that demonstrates innovative approaches to undertaking environmental planning by using the Nexus framework.

The T2T model will explore the modalities for co-design and co-curation by using data and models sourced from multiple sources and using different medium (public, private, SMS, remote sensed data) to nudge practitioners to consider the use of typologies of trade-offs and composite indices as tools that can guide environmental planning and management. The following are the key learning outcomes of the T2T model which our environmental science courses will explore through climate tutorials spread over 12 lectures covering a three- month period (mid September to mid December & mid March to mid June every year for the period 2021 to 2031):

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  • Data harmonization: ontologies, units and formats/semantics of data collection and use
  • Data transformation: indicator selection, weight allocation and composite indices
  • Data valorization: categorizing geographies based on planetary thresholds, trade-off intensity & institutional response to environmental disasters.
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