Assisting and Protecting Refugee Women: A Policy Analysis

The number of refugees and internally displaced persons (IDPs) has risen sharply over the last decade. This trend is the result of several causes such as the impact of climatic change, conflicts over diminishing resources, and religious and ethical disagreements. The largest and most vulnerable subgroup among refugees is women and their dependent children, and they are frequently subject to abuse and neglect. To address protection issues, the United Nations High Commissioner for Refugees (UNHCR) released the Policy on Refugee Women in 1990. The authors provide a comprehensive policy analysis, building on an exploration of the historical background and a presentation of policy goals. This exploration sets the stage for a discussion of the influence and viewpoints of major interest groups, such as donors, governments, and non-governmental organizations. The authors draw upon casestudies and a variety of literary resources to explore diversity issues, social justice concerns, and ethical interests. Furthermore, the authors assess the policy\u27s implementation success by using the categories of positive outcomes (institutional change, new programming tools, improvement in refugee situation) and unintended outcomes (cultural and religious opposition, one-sidedness, negative conception). Finally, the authors present a comparison of the applications and implications of the 1990 UNHCR Policy from a global perspective, focusing primarily on the United States, United Kingdom, and Canada as exemplary countries. The paper concludes with a set of recommendations for policymakers and project managers to further improve protection and assistance programs to meet the needs of refugee women and girls worldwide. © Common Ground, Barbara J. Kampa, Raphael Nawrotzki, All Rights Reserved

Affluence and Objective Environmental Conditions: Evidence of Differences in Environmental Concern in Metropolitan Brazil

In an age of climate change, researchers need to form a deepened understanding of the determinants of environmental concern, particularly in countries of emerging economies. This paper provides a region-specific investigation of the impact of socio-economic status (SES) and objective environmental conditions on environmental concern in urban Brazil. We make use of data collected from personal interviews of individuals living in the metropolitan areas of Baixada Santista and Campinas, in the larger São Paulo area. Results from multilevel regression models indicate that wealthier households are more environmentally concerned, as suggested by affluence and post-materialist hypotheses. However, we also observe that increasing environmental concern correlates with a decline in objective environmental conditions. Interactions between objective environmental conditions and SES reveal some intriguing relationships: Among poorer individuals, a decline in environmental conditions increases environmental concern as suggested by the objective problems hypothesis, while for the wealthy, a decline in environmental conditions is associated with lower levels of environmental concern

Amplification or suppression: Social networks and the climate change-migration association in rural Mexico

Increasing rates of climate migration may be of economic and national concern to sending and destination countries. It has been argued that social networks the ties connecting an origin and destination may operate as migration corridors with the potential to strongly facilitate climate change-related migration. This study investigates whether social networks at the household and community levels amplify or suppress the impact of climate change on international migration from rural Mexico. A novel set of 15 climate change indices was generated based on daily temperature and precipitation data for 214 weather stations across Mexico. Employing geostatistical interpolation techniques, the climate change values were linked to 68 rural municipalities for which sociodemographic data and detailed migration histories were available from the Mexican Migration Project. Multi-level discrete-time event-history models were used to investigate the effect of climate change on international migration between 1986 and 1999. At the household level, the effect of social networks was approximated by comparing the first to the last move, assuming that through the first move a household establishes internal social capital. At the community level, the impact of social capital was explored through interactions with a measure of the proportion of adults with migration experience. The results show that rather than amplifying, social capital may suppress the sensitivity of migration to climate triggers, suggesting that social networks could facilitate climate change adaptation in place. (C) 2015 Elsevier Ltd. All rights reserved

Climate change as migration driver from rural and urban Mexico

Studies investigating migration as a response to climate variability have largely focused on rural locations to the exclusion of urban areas. This lack of urban focus is unfortunate given the sheer numbers of urban residents and continuing high levels of urbanization. To begin filling this empirical gap, this study investigates climate change impacts on U.S.-bound migration from rural and urban Mexico, 1986–1999. We employ geostatistical interpolation methods to construct two climate change indices, capturing warm and wet spell duration, based on daily temperature and precipitation readings for 214 weather stations across Mexico. In combination with detailed migration histories obtained from the Mexican Migration Project, we model the influence of climate change on household-level migration from 68 rural and 49 urban municipalities. Results from multilevel event-history models reveal that a temperature warming and excessive precipitation significantly increased international migration during the study period. However, climate change impacts on international migration is only observed for rural areas. Interactions reveal a causal pathway in which temperature (but not precipitation) influences migration patterns through employment in the agricultural sector. As such, climate-related international migration may decline with continued urbanization and the resulting reductions in direct dependence of households on rural agriculture

Domestic and International Climate Migration from Rural Mexico

Evidence is increasing that climate change and variability may influence human migration patterns. However, there is less agreement regarding the type of migration streams most strongly impacted. This study tests whether climate change more strongly impacted international compared to domestic migration from rural Mexico during 1986-99. We employ eight temperature and precipitation-based climate change indices linked to detailed migration histories obtained from the Mexican Migration Project. Results from multilevel discrete-time event-history models challenge the assumption that climate-related migration will be predominantly short distance and domestic, but instead show that climate change more strongly impacted international moves from rural Mexico. The stronger climate impact on international migration may be explained by the self-insurance function of international migration, the presence of strong migrant networks, and climate-related changes in wage difference. While a warming in temperature increased international outmigration, higher levels of precipitation declined the odds of an international move

Undocumented migration in response to climate change

In the face of climate change-induced economic uncertainties, households may em-ploy migration as an adaptation strategy to diversify their livelihood portfolio through remit-tances. However, it is unclear whether such climate-related migration will be documented or undocumented. In this study we combined detailed migration histories with daily temperature and precipitation information from 214 weather stations to investigate whether climate change more strongly impacted undocumented or documented migrations from 68 rural Mexican mu-nicipalities to the U.S. from 1986−1999. We employed two measures of climate change, the warm spell duration index (WSDI) and precipitation during extremely wet days (R99PTOT). Results from multi-level event-history models demonstrated that climate-related international migration from rural Mexico was predominantly undocumented. We conclude that programs to facilitate climate change adaptations in rural Mexico may be more effective in reducing undo-cumented border crossings than increasing border fortification

A Proof-of-Concept of Integrating Machine Learning, Remote Sensing, and Survey Data in Evaluations: The Measurement of Disaster Resilience in the Philippines

Disaster resilience is a topic of increasing importance for policy makers in the context of climate change. However, measuring disaster resilience remains a challenge as it requires information on both the physical environment and socio-economic dimensions. In this study we developed and tested a method to use remote sensing (RS) data to construct proxy indicators of socio-economic change. We employed machine-learning algorithms to generate land-cover and land-use classifications from very high-resolution satellite imagery to appraise disaster damage and recovery processes in the Philippines following the devastation of typhoon Haiyan in November 2013. We constructed RS-based proxy indicators for N=20 barangays (villages) in the region surrounding Tacloban City in the central east of the Philippines. We then combined the RS-based proxy indicators with detailed socio-economic information collected during a rigorous-impact evaluation by DEval in 2016. Results from a statistical analysis demonstrated that fastest post-disaster recovery occurred in urban barangays that received sufficient government support (subsidies), and which had no prior disaster experience. In general, socio-demographic factors had stronger effects on the early recovery phase (0-2 years) compared to the late recovery phase (2-3 years). German development support was related to recovery performance only to some extent. Rather than providing an in-depth statistical analysis, this study is intended as a proof-of-concept. We have been able to demonstrate that high-resolution RS data and machine-learning techniques can be used within a mixed-methods design as an effective tool to evaluate disaster impacts and recovery processes. While RS data have distinct limitations (e.g., cost, labour intensity), they offer unique opportunities to objectively measure physical, and by extension socio-economic, changes over large areas and long time-scales.Zunehmende Wetterextreme und Naturkatastrophen sind Folgen des Klimawandels. Aufgrund dieser steigenden Risiken rückt die Resilienz der Bevölkerung im Katastrophenfall als zentrales Thema in den Vordergrund und hat zunehmende Bedeutung für politische Entscheidungstragende. Dennoch bleibt die Messung des mehrdimensionalen Konzepts der Katastrophenresilienz eine Herausforderung, da sie Informationen sowohl über die physische Umgebung als auch sozioökonomische Faktoren erfordert. In dieser Studie wird eine Methode entwickelt, um aus Fernerkundungsdaten (RS-Daten) Indikatoren zu entwickeln, die Aspekte des sozioökonomischen Wandels approximieren und somit messbar machen (Proxy-Indikatoren). Zu diesem Zweck wurden Algorithmen des maschinellen Lernens eingesetzt. Mit Hilfe dieser Algorithmen wurden aus hochauflösenden Satellitenbildern Klassifizierungen für Landstruktur und Landnutzung konstruiert, um Katastrophenschäden und iederaufbauprozesse auf den Philippinen nach der Zerstörung durch den Taifun Haiyan im November 2013 zu messen. Aus den RS-Daten wurden die Indikatoren für N=20 Barangays (Dörfer) in der Region um die Stadt Tacloban im zentralen Osten der Philippinen berechnet. Diese auf RS-Daten basierenden Indikatoren wurden mit detaillierten sozioökonomischen Informationen kombiniert, die für eine DEval-Evaluierung im Jahr 2016 erhoben wurden. Die Ergebnisse der statistischen Analyse zeigen, dass der schnellste Wiederaufbau nach der Katastrophe in städtischen Barangays zu beobachten war, die ausreichend staatliche Unterstützung (Subventionen) erhielten und über keine Katastrophenerfahrung verfügten. Im Vergleich hatten soziodemografische Faktoren allgemein stärkere Auswirkungen auf die frühe (0-2 Jahre) als auf die spätere (2-3 Jahre) Wiederaufbauphase. Es konnte nur ein bedingter Bezug zwischen der deutschen Entwicklungszusammenarbeit und den Wiederaufbauerfolgen festgestellt werden. Diese Studie versteht sich als Nachweis der Machbarkeit, weniger als detaillierte statistische Analyse. Sie belegt, dass hochauflösende RS-Daten und Techniken des maschinellen Lernens innerhalb eines integrierten Methodendesigns als effektives Werkzeug zur Bewertung von Katastrophenauswirkungen und Wiederherstellungsprozessen eingesetzt werden können. Trotz spezifischer Einschränkungen (hohe Kosten, Arbeitsintensität etc.) bieten RS-Daten einzigartige Möglichkeiten sowohl Umweltbedingungen als auch sozioökonomische Veränderungen über große Gebiete und lange Zeiträume hinweg objektiv messen zu können

Climate Mobility and Development Cooperation

Development cooperation actors have been addressing climate change as a cross-cutting issue and investing in climate adaptation projects since the early 2000s. More recently, as concern has risen about the potential impacts of climate variability and change on human mobility, development cooperation actors have begun to design projects that intentionally address the drivers of migration, including climate impacts on livelihoods. However, to date, we know little about the development cooperation’s role and function in responding to climate related mobility and migration. As such, the main aim of this paper is to outline the policy frameworks and approaches shaping development cooperation actors’ engagement and to identify areas for further exploration and investment. First, we frame the concept of climate mobility and migration and discuss some applicable policy frameworks that govern the issue from various perspectives; secondly, we review the toolbox of approaches that development cooperation actors bring to climate mobility; and third, we discuss the implications of the current Covid-19 pandemic and identify avenues for the way forward. We conclude that ensuring safe and orderly mobility and the decent reception and long-term inclusion of migrants and displaced persons under conditions of more severe climate hazards, and in the context of rising nationalism and xenophobia, poses significant challenges. Integrated approaches across multiple policy sectors and levels of governance are needed. In addition to resources, development cooperation actors can bring data to help empower the most affected communities and regions and leverage their convening power to foster more coordinated approaches within and across countries

Grounding Evaluation Capacity Development in Systems Theory

While “systemic thinking” is popular in the context of capacity development and evaluation, there is currently a lack of understanding about the benefits to employing systems theory in evaluation capacity development. Systems theory provides a useful orientation to the work involved in complex systems (e.g. national evaluation systems). This article illustrates how evaluation capacity development practitioners can use systems theory as a conceptual tool to gain a better understanding of the functional aspects and interrelationships present within a given evaluation system. Specifically, the systems theory perspective can help elucidate the reasons for the success or failure of a given evaluation capacity development program or activity. With the goal of motivating evaluation capacity development practitioners to use systems theory in their work, this article presents a systems theory framework for evaluation capacity development and offers practical examples of how it can be adopted

Variation by Geographic Scale in the Migration-Environment Association: Evidence from Rural South Africa

"Scholarly understanding of human migration's environmental dimensions has greatly advanced in the past several years, motivated in large part by public and policy dialogue around 'climate migrants'. The research presented here advances current demographic scholarship both through its substantive interpretations and conclusions, as well as its methodological approach. We examine temporary rural South African outmigration as related to household-level availability of proximate natural resources. Such 'natural capital' is central to livelihoods in the region, both for sustenance and as materials for market-bound products. The results demonstrate that the association between local environmental resource availability and outmigration is, in general, positive: households with higher levels of proximate natural capital are more likely to engage in temporary migration. In this way, the general findings support the 'environmental surplus' hypothesis that resource security provides a foundation from which households can invest in migration as a livelihood strategy. Such insight stands in contrast to popular dialogue, which tends to view migration as a last resort undertaken only by the most vulnerable households. As another important insight, our findings demonstrate important spatial variation, complicating attempts to generalize migration-environment findings across spatial scales. In our rural South African study site, the positive association between migration and proximate resources is actually highly localized, varying from strongly positive in some villages to strongly negative in others. We explore the socio-demographic factors underlying this 'operational scale sensitivity'. The cross-scale methodologies applied here offer nuance unavailable within more commonly used global regression models, although also introducing complexity that complicates story-telling and inhibits generalizability." (author's abstract