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1. Evaluating sustainable development policies in rural coastal economies

   https://doi.org/10.1073/pnas.2017835117  

   Lindsay, Amanda R. ; Sanchirico, James N. ; Gilliland, Ted E. ; Ambo-Rappe, Rohani ; Taylor, J. Edward ; Krueck, Nils C. ; Mumby, Peter J. ( December 2020 , Proceedings of the National Academy of Sciences)

   null (Ed.)

   Sustainable development (SD) policies targeting marine economic sectors, designed to alleviate poverty and conserve marine ecosystems, have proliferated in recent years. Many developing countries are providing poor fishing households with new fishing boats (fishing capital) that can be used further offshore as a means to improve incomes and relieve fishing pressure on nearshore fish stocks. These kinds of policies are a marine variant of traditional SD policies focused on agriculture. Here, we evaluate ex ante economic and environmental impacts of provisions of fishing and agricultural capital, with and without enforcement of fishing regulations that prohibit the use of larger vessels in nearshore habitats. Combining methods from development economics, natural resource economics, and marine ecology, we use a unique dataset and modeling framework to account for linkages between households, business sectors, markets, and local fish stocks. We show that the policies investing capital in local marine fisheries or agricultural sectors achieve income gains for targeted households, but knock-on effects lead to increased harvest of nearshore fish, making them unlikely to achieve conservation objectives in rural coastal economies. However, pairing an agriculture stimulus with increasing enforcement of existing fisheries’ regulations may lead to a win–win situation. While marine-based policies could be an important tool to achieve two of the United Nations Sustainable Development Goals (alleviate poverty and protect vulnerable marine resources), their success is by no means assured and requires consideration of land and marine socioeconomic linkages inherent in rural economies. 

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2. Machine learning and phone data can improve targeting of humanitarian aid

   https://doi.org/10.1038/s41586-022-04484-9  

   Aiken, Emily ; Bellue, Suzanne ; Karlan, Dean ; Udry, Chris ; Blumenstock, Joshua E. ( March 2022 , Nature)

   Abstract The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards 1 . In response to this crisis, governments and humanitarian organizations worldwide have distributed social assistance to more than 1.5 billion people 2 . Targeting is a central challenge in administering these programmes: it remains a difficult task to rapidly identify those with the greatest need given available data 3,4 . Here we show that data from mobile phone networks can improve the targeting of humanitarian assistance. Our approach uses traditional survey data to train machine-learning algorithms to recognize patterns of poverty in mobile phone data; the trained algorithms can then prioritize aid to the poorest mobile subscribers. We evaluate this approach by studying a flagship emergency cash transfer program in Togo, which used these algorithms to disburse millions of US dollars worth of COVID-19 relief aid. Our analysis compares outcomes—including exclusion errors, total social welfare and measures of fairness—under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo, the machine-learning approach reduces errors of exclusion by 4–21%. Relative to methods requiring a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine-learning approach increases exclusion errors by 9–35%. These results highlight the potential for new data sources to complement traditional methods for targeting humanitarian assistance, particularly in crisis settings in which traditional data are missing or out of date. 

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3. Economic impacts and nutritional outcomes of the 2017 floods in B angladeshi S hodagor fishing families

   https://doi.org/10.1002/ajhb.23826  

   Starkweather, Kathrine E. ; Keith, Monica H. ; Zohora, Fatema tuz ; Alam, Nurul ( November 2022 , American Journal of Human Biology)

   As climate change continues to increase the frequency and severity of flooding in Bangladesh and globally, it becomes increasingly critical to understand the pathways through which flooding influences health outcomes, particularly in lower‐income and subsistence‐based communities. We aim to assess economic pathways that link flooding to nutritional outcomes among Shodagor fishing families in Bangladesh.

   We examine longitudinal economic data on kilograms of fish caught, the income earned from those fish, and household food expenditures (as a proxy for dietary intake) from before, during, and after severe flooding in August–September of 2017 to enumerate the impacts of flooding on Shodagor economics and nutrition. We also analyze seasonally collected anthropometric data to model the effects of flooding and household food expenditures on child growth rates and changes to adult body size.

   While Shodagor fishing income declined during the 2017 flooding, food expenditures simultaneously spiked with market inflation, and rice became the predominant expenditure only during and immediately following the flood. Our nutritional models show that children and adults lost more body mass in households that spent more money on rice during the flood. Shodagor children lost an average of 0.36 BMI‐for‐agez‐scores and adults lost an average of 0.32 BMI units during the flooded 2017 rainy season, and these metrics continued to decline across subsequent seasons and did not recover by the end of the study period in 2019.

   These results show major flood‐induced economic impacts that contributed to loss of child and adult body mass among Shodagor fishing families in Bangladesh. More frequent and severe flooding will exacerbate these nutritional insults, and more work is needed to effectively stabilize household nutrition throughout natural disasters and economic hardship.

    

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4. Coupled changes in biomass and distribution drive trends in availability of fish stocks to US West Coast ports

   https://doi.org/10.1093/icesjms/fsz211  

   Selden, Rebecca L. ; Thorson, James T. ; Samhouri, Jameal F. ; Bograd, Steven J. ; Brodie, Stephanie ; Carroll, Gemma ; Haltuch, Melissa A. ; Hazen, Elliott L. ; Holsman, Kirstin K. ; Pinsky, Malin L. ; et al ( November 2019 , ICES Journal of Marine Science)

   Fishing communities are increasingly required to adapt to environmentally driven changes in the availability of fish stocks. Here, we examined trends in the distribution and biomass of five commercial target species (dover sole, thornyheads, sablefish, lingcod, and petrale sole) on the US west coast to determine how their availability to fishing ports changed over 40 years. We show that the timing and magnitude of stock declines and recoveries are not experienced uniformly along the coast when they coincide with shifts in species distributions. For example, overall stock availability of sablefish was more stable in southern latitudes where a 40% regional decline in biomass was counterbalanced by a southward shift in distribution of >200 km since 2003. Greater vessel mobility and larger areal extent of fish habitat along the continental shelf buffered northerly ports from latitudinal changes in stock availability. Landings were not consistently related to stock availability, suggesting that social, economic, and regulatory factors likely constrain or facilitate the capacity for fishers to adapt to changes in fish availability. Coupled social–ecological analyses such as the one presented here are important for defining community vulnerability to current and future changes in the availability of important marine species.

    

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5. Flood Risk Management: Exploring the Impacts of the Community Rating System Program on Poverty and Income Inequality

   https://doi.org/10.1111/risa.12853  

   Noonan, Douglas S. ; Sadiq, Abdul‐Akeem A. ( June 2017 , Risk Analysis)

   Flooding remains a major problem for the United States, causing numerous deaths and damaging countless properties. To reduce the impact of flooding on communities, the U.S. government established the Community Rating System (CRS) in 1990 to reduce flood damages by incentivizing communities to engage in flood risk management initiatives that surpass those required by the National Flood Insurance Program. In return, communities enjoy discounted flood insurance premiums. Despite the fact that the CRS raises concerns about the potential for unevenly distributed impacts across different income groups, no study has examined the equity implications of the CRS. This study thus investigates the possibility of unintended consequences of the CRS by answering the question: What is the effect of the CRS on poverty and income inequality? Understanding the impacts of the CRS on poverty and income inequality is useful in fully assessing the unintended consequences of the CRS. The study estimates four fixed‐effects regression models using a panel data set of neighborhood‐level observations from 1970 to 2010. The results indicate that median incomes are lower in CRS communities, but rise in floodplains. Also, the CRS attracts poor residents, but relocates them away from floodplains. Additionally, the CRS attracts top earners, including in floodplains. Finally, the CRS encourages income inequality, but discourages income inequality in floodplains. A better understanding of these unintended consequences of the CRS on poverty and income inequality can help to improve the design and performance of the CRS and, ultimately, increase community resilience to flood disasters.

    

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