Natural disasters push the near poor to below the poverty line & contribute to more persistent and severe poverty, creating poverty traps. Impacts on their livelihood pushes them further down the poverty line and as they own few assets it is very difficult for them to break this cycle.
Poor are caught up in and disaster-poverty vicious circle- are more likely to reside in hazardous locations and in substandard housing exposing them more to disasters. Poor households in disasters use harmful coping strategies, such as reducing expenditures on food, health, & education or increasing incomes by sending children to work.
Resilience is increasingly recognized as a key attribute of an effective urban system. Discussions on resilience often center around disasters caused by natural hazards. However, cities are increasingly exposed to multiple shocks and stresses beyond disasters. Cities in the Middle East and North Africa (MENA) are no different and are equally if not more vulnerable to a large set of shocks.
Disasters caused by natural hazards result in average annual welfare losses of over US$500 billion and push up to 26 million people into poverty each year. Some of these negative consequences are due to recovery that is not resilient, takes too long and is not equitable. According to the Building Back Better report, this can be mitigated by building back stronger, faster and more inclusively following a disaster.
Imagine a place where you've lived for decades. Not just you, but your parents’ parents, too. When they lived there, the place wasn't that big. There were just a few dozen families. Today the place is home to hundreds of – or maybe even a thousand – families.
This is a highly fertile, verdant place… You're at the foot of a volcano.
Machine learning algorithms are excellent at answering “yes” or “no” questions. For example, they can scan huge datasets and correctly tell us: Does this credit card transaction look fraudulent? Is there a cat in this photo?
But it’s not only the simple questions – they can also tackle nuanced and complex questions.
Today, machine learning algorithms can detect over 100 types of cancerous tumors more reliably than a trained human eye. Given this impressive accuracy, we started to wonder: what could machine learning tell us about where people live? In cities that are expanding at breathtaking rates and are at risk from natural disasters, could it warn us that a family’s wall might collapse during an earthquake or rooftop blow away during a hurricane?
When U.S. Commodore Matthew Perry arrived in Yokohama in 1854, it was a backwater village in Japan with a largely rural, relatively undeveloped economy. But it soon grew to an urban agglomeration with around 3.7 million people. Since then, Yokohama has managed to continuously reinvent itself – from a port city, to a large industrial area, and now to a modern, global service and lifestyle hub.
Within a century, Japan would become the world’s second largest economy. Its growth has been fueled by cities such as Tokyo, Yokohama, Osaka, and Kobe. Japanese cities can offer a myriad of lessons to their counterparts in developing countries.
Japanese cities are also at the forefront of dealing with some of the world’s most pressing challenges. For example, cities like Osaka and Toyama have developed a number of tools to address the social issues caused by rapid aging. Most developed and developing cities in the world will face similar challenges in the years to come. Providing a platform where these cities can learn from the experience of Japanese cities may lead to significant development impact.
When people cannot find a decent and safe place to live, or are discriminated against because of their race, religion or where they live, or lack the skills, education and transportation needed to find a job to support themselves, something needs to change.
To make cities safer, more inclusive, and more resilient to a range of shocks and stresses, mayors, planners, and other city leaders should support integrated approaches promoting social, economic, and spatial inclusion. City leaders need to carry out this work in close partnership with the communities themselves.
From April 23–27, 2018, representatives from 16 cities in 13 countries visited Japan for a Technical Deep Dive on Safe, Inclusive, and Resilient Cities to learn from one another about improving urban safety, inclusion and resilience. In this video, Jefferson Koije (Mayor of Monrovia, Liberia), Ellen Hamilton (World Bank Lead Urban Specialist), and Phil Karp (World Bank Lead Knowledge Management Officer) discuss how cities can address these crucial aspects of urban resilience. Watch the video to learn more.