Climate

High future discounting rates favor inaction on present expending while lower rates advise for a more immediate political action. A possible approach to this key issue in global economy is to take historical time series for nominal interest rates and inflation, and to construct then real interest rates and finally obtaining the resulting discount rate according to a specific stochastic model. Extended periods of negative real interest rates, in which inflation dominates over nominal rates, are commonly observed, occurring in many epochs and in all countries.

Complex social-ecological systems shift constantly in response to individuals, organizations, and government decisions. Policy makers need to anticipate system-wide consequences to both targeted and non-targeted system components, to avoid costly or irreversible mistakes. However, complex joint outcomes of individual and collective decisions are difficult to predict. Scenarios can help anticipate system-wide responses to specific interventions. We illustrate their use with a policy to set aside land for forestation in an economic region, to mitigate climate change.

The accurate prior information of the south asian monsoon helps the government and farmers to mitigate agricultural losses and proper planning of water resources. However, the forecasting of south asian monsoon is a challenging task due to the involvement of complex nonlinear dynamics and its variability over time. In this work, we developed a method to predict the mean seasonal, intraseasonal and meteorological region-wise south asian monsoon using evolving climate networks combined with machine learning.

Abstract Critical infrastructure (CI) functions and spatial boundaries are often implicit premises which can result in misalignments between CI resilience goals and their societal utility. Furthermore, the historical legacy and institutionalization of CI, powered by national security issues, has resulted in technocentric approaches of infrastructure systems that can be counterproductive to disaster risk reduction.