Reflecting Disaster Risk in Development Indicators

Evaluating Resilience-Centered Development Interventions with Remote Sensing

Natural disasters are projected to increase in number and severity, in part due to climate change. At the same time a growing number of disaster risk reduction (DRR) and climate change adaptation measures are being implemented by governmental and non-governmental organizations, and substantial post-disaster donations are frequently pledged. At the same time there has been increasing demand for transparency and accountability, and thus evidence of those measures having a positive effect. We hypothesized that resilience-enhancing interventions should result in less damage during a hazard event, or at least quicker recovery. In this study we assessed recovery over a 3 year period of seven municipalities in the central Philippines devastated by Typhoon Haiyan in 2013. We used very high resolution optical images (<1 m), and created detailed land cover and land use maps for four epochs before and after the event, using a machine learning approach with extreme gradient boosting. The spatially and temporally highly variable recovery maps were then statistically related to detailed questionnaire data acquired by DEval in 2012 and 2016, whose principal aim was to assess the impact of a 10 year land-planning intervention program by the German agency for technical cooperation (GIZ). The survey data allowed very detailed insights into DRR-related perspectives, motivations and drivers of the affected population. To some extent they also helped to overcome the principal limitation of remote sensing, which can effectively describe but not explain the reasons for differential recovery. However, while a number of causal links between intervention parameters and reconstruction was found, the common notion that a resilient community should recover better and more quickly could not be confirmed. The study also revealed a number of methodological limitations, such as the high cost for commercial image data not matching the spatially extensive but also detailed scale of field evaluations, the remote sensing analysis likely overestimating damage and thus providing incorrect recovery metrics, and image data catalogues especially for more remote communities often being incomplete. Nevertheless, the study provides a valuable proof of concept for the synergies resulting from an integration of socio-economic survey data and remote sensing imagery for recovery assessment.

Competitiveness and Its Impact on Sustainability, Business Environment, and Human Development of EU (28) Countries in terms of Global Multi-Criteria Indices

In this paper, the following research problem was addressed: Is there a significant economic impact of multidimensional specified competitiveness within the EU (28) countries on the competitive business environment, human development, and sustainable growth? Based on the mentioned research problem, we formulated the aim of paper: To detect the significant interrelations among the assessment of global competitiveness, business environment as well as human development in the EU (28) countries for the period of 2006–2017. To address these problems, the methodology of global multi-criteria indices, namely the global competitiveness index (GCI), doing business index (DBI), and human development index (HDI), as well as panel analysis and non-linear regression analyses with ANOVA, were applied. The panel analysis results suggest that there is a direct linear relationship between the GCI and HDI. Moreover, the impact of the DBI on the change in the GCI score was not confirmed. We identified the main areas of countries’ interest, and important economic and statistical significant relations of competitiveness by creating three models: The GD model (constructed by GCI and DBI scores), GH model (GCI and HDI scores), and GDH model (GCI, DBI and HDI scores). Based on the results, all interrelations were confirmed. However, the highest extent of variability for the explanation of the selected data was recorded in the case of the GDH model (87.12%). We detected the impact of the business environment and human resources as competitive advantages on global macroeconomic competitiveness. As the business sector in EU (28) countries is represented mainly by small and medium-sized enterprises (SMEs), enterprise activities play a key role in the process of sustainable competitive economic development. Moreover, human resources are considered to be another important driver of the internationalization of European SMEs.