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AbstractDevelopment 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.

Durch den Klimawandel entstehen insbesondere in Entwicklungsländern als typische Folgen residualer Klimarisiken zunehmend hohe Schäden und Verluste. Residuale Klimarisiken sind solche Klimarisiken, die nach Risikoreduzierung durch Anpassung und Klimaschutz verbleiben. Um die Ziele für nachhaltige Entwicklung zu erreichen, ist ein effektiver Umgang mit residualen Klimarisiken erforderlich. Bisher gibt es nur vereinzelt Evidenz zur Wirksamkeit von Instrumenten zum Umgang mit residualen Klimarisiken. Vor diesem Hintergrund schließt dieses Evaluierungsmodul die Wissens- und Evaluierungslücke zur Relevanz und Wirksamkeit der bislang angewendeten Instrumente im Umgang mit residualen Klimarisiken. Dafür
wurde ein theoriebasierter Ansatz gewählt, der qualitative und quantitative Analysemethoden integriert. Die betrachteten Instrumente wurden vier Instrumentengruppen konzeptionell zugeordnet und analysiert: Drittfinanzierte Risikofinanzierung, Risikopooling, Risikovorsorge
und Transformatives Risikomanagement. Übergreifend zeigen die Ergebnisse, dass die Relevanz der Instrumente stark von ihrer Konzeption und Implementierung abhängt, der Anspruch eines umfassenden Umgangs mit residualen Klimarisiken teilweise erfüllt ist und die deutsche Entwicklungszusammenarbeit bereits vielfältige Erfahrungen mit der Implementierung von Instrumenten zum Umgang mit residualen Klimarisiken hat. Sie erweisen sich als effektiv, wenn Eingangshürden überwunden werden. Basierend auf den Ergebnissen spricht die Evaluierung Empfehlungen in Bezug auf den Instrumenteneinsatz, die Bedürfnisorientierung, umfassendes Risikomanagement, die Portfolioausweitung und die Wirkungsausrichtung aus.

Climate change is causing increasingly high losses and damages, particularly in developing countries. Typically, this is a consequence of residual climate risks. 'Residual climate risks' are those climate risks that remain after risks have been reduced through mitigation and
adaptation. To achieve the Sustainable Development Goals, residual climate risks need to be managed effectively. So far, only sporadic evidence is available on the effectiveness of instruments for managing these risks. Against this background, the present evaluation module report fills the knowledge and evaluation gap on the relevance and effectiveness of the instruments applied so far to manage residual climate risks.

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.

"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)

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.