Landscape matters: Insights from the impact of mega-droughts on Colombia's energy transition

Mega-droughts can cause disruption to the affected society sparking a transition. We explore the causes and effects of the 2015−2016 mega-drought in Colombia. Using the multi-level perspective as a framework, we found that the mega-drought sparked an energy transition in Colombia whose dynamics were impacted both by the institutionalization of niches as well as the ability to predict the next drought. We were able to trace, using the current understanding of anthropogenic forces, the cause of the mega-drought to socio-technical landscape development influenced by human-induced warming and land use change. We found that the regimes in Bolivia and Brazil were able to influence the landscape through deforestation, and hence contribute to the intensity of a mega-drought in Colombia. The knowledge that a regime can cause disruption in regimes in other geographies and sectors has implications for transition research as well as decision-making for coping with droughts and other disasters. © 202

Integration of Case Studies on Global Change by Means of Artificial Intelligence

We present a novel methodology to integrate qualitative knowledge from different case studies on Global Change related issues into a single framework. The method is based on the concept of qualitative differential equations (QDEs) which represents a mathematically well-defined approach to investigate classes of ordinary differential equations (ODEs) used in conventional modeling exercises. These classes are defined by common qualitative features, e.g. monotonicity, signs, etc. Using the QSIMAlgorithm it is possible to derive the set of possible solutions of all ODEs in the class. Using this one can formulate a common, qualitatively specified cause-effect scheme valid for all case studies. The scheme is validated by testing it against the actually observed histories in the study regions with respect to their reconstruction by the corresponding QDE. The method is outlined theoretically and exemplary applied to the problem of land-use changes due to smallholders agriculture in de..

The efficient, the intensive, and the productive: Insights from urban Kaya scaling

Urban areas play an unprecedented role in potentially mitigating climate change and supporting sustainable development. In light of the rapid urbanisation in many parts on the globe, it is crucial to understand the relationship between settlement size and CO2 emission efficiency of cities. Recent literature on urban scaling properties of emissions as a function of population size has led to contradictory results and more importantly, lacked an in-depth investigation of the essential factors and causes explaining such scaling properties. Therefore, in analogy to the well-established Kaya Identity, we develop a relation combining the involved exponents. We demonstrate that application of this Urban Kaya Relation will enable a comprehensive understanding about the intrinsic factors determining emission efficiencies in large cities by applying it to a global dataset of 61 cities. Contrary to traditional urban scaling studies which use Ordinary Least Squares (OLS) regression, we show that the Reduced Major Axis (RMA) is necessary when complex relations among scaling exponents are to be investigated. RMA is given by the geometric mean of the two OLS slopes obtained by interchanging the dependent and independent variable. We discuss the potential of the Urban Kaya Relation in mainstreaming local actions for climate change mitigation

A Gini approach to spatial CO2 emissions.

Combining global gridded population and fossil fuel based CO2 emission data at 1 km scale, we investigate the spatial origin of CO2 emissions in relation to the population distribution within countries. We depict the correlations between these two datasets by a quasi-Lorenz curve which enables us to discern the individual contributions of densely and sparsely populated regions to the national CO2 emissions. We observe pronounced country-specific characteristics and quantify them using an indicator resembling the Gini-index. As demonstrated by a robustness test, the Gini-index for each country arise from a compound distribution between the population and emissions which differs among countries. Relating these indices with the degree of socio-economic development measured by per capita Gross Domestic Product (GDP) at purchase power parity, we find a strong negative correlation between the two quantities with a Pearson correlation coefficient of -0.71. More specifically, this implies that in developing countries locations with large population tend to emit relatively more CO2, and in developed countries the opposite tends to be the case. Based on the relation to urban scaling, we discuss the implications for CO2 emissions from cities. Our results show that general statements with regard to the (in)efficiency of large cities should be avoided as it is subject to the socio-economic development of respective countries. Concerning the political relevance, our results suggest a differentiated spatial prioritization in deploying climate change mitigation measures in cities for developed and developing countries

Vulnerability of humans and the environment: challenges and opportunities

60 page(s