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[ subject:"Climate change." ]
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Essays on Climate Risk.
~
Hobbs, Andrew.
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Essays on Climate Risk.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Essays on Climate Risk./
作者:
Hobbs, Andrew.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:
93 p.
附註:
Source: Dissertations Abstracts International, Volume: 82-05, Section: A.
Contained By:
Dissertations Abstracts International82-05A.
標題:
Climate change. -
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28029355
ISBN:
9798691212987
Essays on Climate Risk.
Hobbs, Andrew.
Essays on Climate Risk.
- Ann Arbor : ProQuest Dissertations & Theses, 2020 - 93 p.
Source: Dissertations Abstracts International, Volume: 82-05, Section: A.
Thesis (Ph.D.)--University of California, Davis, 2020.
This item must not be sold to any third party vendors.
Climate change is leading to more frequent and more severe droughts, floods, cyclones, and extreme heat events (Solomon et al., 2007). As a result, agricultural yields are becoming more unpredictable for farmers, and pastoralists who keep livestock are facing new struggles in keeping their animals alive and healthy. This dissertation studies the consequences of this increased risk and studies ways of improving index insurance to mitigate it.Chapter 1 studies how the benefits of index insurance for pastoralists are distributed within the household. Based on that analysis, it develops a model to study investment under risk in settings where women are principally responsible for expenditures on household public goods. It predicts that when women increase their investment, men will share less of their income, which means that women's assets are the first to be liquidated in the event of a negative shock. Insurance linked to expenditures within women's traditional sphere within the household has the potential to insulate women's assets and consumption, increasing their expected returns to investment by increasing their share of household income in the event of a negative shock. To test this prediction, I conduct a lab-in-the-field experiment in Samburu County, Kenya using a tablet-based insurance game and find that women buy significantly more insurance when it is linked to household expenditure.Chapter 2 develops a simple model to study the economics of forests as a buffer stock for agricultural income. It uses geospatial data to measure the effect of rainfall shocks on forest loss. I find that there is a robust relationship between shocks and forest loss, but only near human population centers: rainfall 40\\% below average is associated with a 50\\% increase in the probability of forest loss near population centers. I show that as climate change increases the frequency and severity of shocks, this relationship has the potential to accelerate forest loss, creating a human-mediated climate feedback loop.Chapter 3 introduces the first dataset of satellite images labeled with forage quality by on-the-ground experts and provides proof of concept for applying computer vision methods to index-based drought insurance. We also present the results of a collaborative benchmark tool used to crowdsource an accurate machine learning model on the dataset. Our methods significantly outperform the existing technology for an insurance program in Northern Kenya, suggesting that a computer vision-based approach could substantially benefit pastoralists, whose exposure to droughts is severe and worsening with climate change.Chapter 4 develops a method that uses machine learning to estimate optimal insurance contracts directly from observed data such as satellite images, precipitation, or regional yields. It does so by modifying the loss function of a machine learning algorithm so that its objective is to identify the insurance payoff function that maximizes farmer welfare given available data. In the example in this paper, I use a simple neural network as the machine learning algorithm, but in principle this method is generalizable to any algorithm that can minimize an arbitrary loss function.
ISBN: 9798691212987Subjects--Topical Terms:
2079509
Climate change.
Subjects--Index Terms:
Climate risk
Essays on Climate Risk.
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Climate change is leading to more frequent and more severe droughts, floods, cyclones, and extreme heat events (Solomon et al., 2007). As a result, agricultural yields are becoming more unpredictable for farmers, and pastoralists who keep livestock are facing new struggles in keeping their animals alive and healthy. This dissertation studies the consequences of this increased risk and studies ways of improving index insurance to mitigate it.Chapter 1 studies how the benefits of index insurance for pastoralists are distributed within the household. Based on that analysis, it develops a model to study investment under risk in settings where women are principally responsible for expenditures on household public goods. It predicts that when women increase their investment, men will share less of their income, which means that women's assets are the first to be liquidated in the event of a negative shock. Insurance linked to expenditures within women's traditional sphere within the household has the potential to insulate women's assets and consumption, increasing their expected returns to investment by increasing their share of household income in the event of a negative shock. To test this prediction, I conduct a lab-in-the-field experiment in Samburu County, Kenya using a tablet-based insurance game and find that women buy significantly more insurance when it is linked to household expenditure.Chapter 2 develops a simple model to study the economics of forests as a buffer stock for agricultural income. It uses geospatial data to measure the effect of rainfall shocks on forest loss. I find that there is a robust relationship between shocks and forest loss, but only near human population centers: rainfall 40\\% below average is associated with a 50\\% increase in the probability of forest loss near population centers. I show that as climate change increases the frequency and severity of shocks, this relationship has the potential to accelerate forest loss, creating a human-mediated climate feedback loop.Chapter 3 introduces the first dataset of satellite images labeled with forage quality by on-the-ground experts and provides proof of concept for applying computer vision methods to index-based drought insurance. We also present the results of a collaborative benchmark tool used to crowdsource an accurate machine learning model on the dataset. Our methods significantly outperform the existing technology for an insurance program in Northern Kenya, suggesting that a computer vision-based approach could substantially benefit pastoralists, whose exposure to droughts is severe and worsening with climate change.Chapter 4 develops a method that uses machine learning to estimate optimal insurance contracts directly from observed data such as satellite images, precipitation, or regional yields. It does so by modifying the loss function of a machine learning algorithm so that its objective is to identify the insurance payoff function that maximizes farmer welfare given available data. In the example in this paper, I use a simple neural network as the machine learning algorithm, but in principle this method is generalizable to any algorithm that can minimize an arbitrary loss function.
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https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28029355
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