Person:
Rentschler, Jun
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Fields of Specialization
Economics of Development, Environment, and Climate
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Last updated
May 3, 2023
Biography
Jun Rentschler is a Senior Economist at the Office of the Chief Economist for Sustainable Development, working at the intersection of climate change and sustainable resilient development. Prior to joining The World Bank in 2012, he served as an Economic Adviser at the German Foreign Ministry. He also spent two years at the European Bank for Reconstruction and Development (EBRD) working on private sector investment projects in resource efficiency and climate change. Before that he worked on projects with Grameen Microfinance Bank in Bangladesh and the Partners for Financial Stability Program by USAID in Poland. He is a Visiting Fellow at the Payne Institute for Public Policy, following previous affiliations with the Oxford Institute for Energy Studies and the Graduate Institute for Policy Studies in Tokyo. Jun holds a PhD in Economics from University College London (UCL), specializing in development, climate, and energy.
Publication Search Results
1 - 2 of 2
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Publication
Rapid Urban Growth in Flood Zones: Global Evidence since 1985
(World Bank, Washington, DC, 2022-04)As countries rapidly urbanize, settlements are expanding into hazardous flood zones. This study provides a global analysis of spatial urbanization patterns and the evolution of flood exposure between 1985 and 2015. Using high-resolution annual data, it shows that settlements across the world grew by 85 percent to over 1.28 million square kilometers. In the same period, settlements exposed to the highest flood hazard level increased by 122 percent. In many regions, risky growth is outpacing safe growth, particularly in East Asia, where high-risk settlements have expanded 60 percent faster than safe ones. Developing countries are driving the recent growth of flood exposure: 36,500 square kilometers of settlements were built in the world’s highest-risk zones since 1985–82 percent of which are in low- and middle-income countries. In comparison, recent growth in high-income countries has been relatively slow and safe. These results document a divergence in countries’ exposure to flood hazards. Rather than adapting their exposure to climatic hazards, many countries are actively increasing their exposure. -
Publication
Where Are All the Jobs ?: A Machine Learning Approach for High Resolution Urban Employment Prediction in Developing Countries
(World Bank, Washington, DC, 2022-03)Globally, both people and economic activity are increasingly concentrated in urban areas. Yet, for the vast majority of developing country cities, little is known about the granular spatial organization of such activity despite its key importance to policy and urban planning. This paper adapts a machine learning based algorithm to predict the spatial distribution of employment using input data from open access sources such as Open Street Map and Google Earth Engine. The algorithm is trained on 14 test cities, ranging from Buenos Aires in Argentina to Dakar in Senegal. A spatial adaptation of the random forest algorithm is used to predict within-city cells in the 14 test cities with extremely high accuracy (R- squared greater than 95 percent), and cells in out-of-sample ”unseen” cities with high accuracy (mean R-squared of 63 percent). This approach uses open data to produce high resolution estimates of the distribution of urban employment for cities where such information does not exist, making evidence-based planning more accessible than ever before.