Efficiency traps beyond the climate crisis: exploration-exploitation trade-offs and rebound effects.

Higher levels of economic activity are often accompanied by higher energy use and consumption of natural resources. As fossil fuels still account for 80% of the global energy mix, energy consumption remains closely linked to greenhouse gas (GHG) emissions and thus to climate change. Under the assumption of sufficiently elastic demand, this reality of global economic development based on permanent growth of economic activity, brings into play the Jevons Paradox, which hypothesises that increases in the efficiency of resource use leads to increases in resource consumption. Previous research on the rebound effects has limitations, including a lack of studies on the connection between reinforcement learning and environmental consequences. This paper develops a mathematical model and computer simulator to study the effects of micro-level exploration-exploitation strategies on efficiency, consumption and sustainability, considering different levels of direct and indirect rebound effects. Our model shows how optimal exploration-exploitation strategies for increasing efficiency can lead to unsustainable development patterns if they are not accompanied by demand reduction measures, which are essential for mitigating climate change. Moreover, our paper speaks to the broader issue of efficiency traps by highlighting how indirect rebound effects not only affect primary energy (PE) consumption and GHG emissions, but also resource consumption in other domains. By linking these issues together, our study sheds light on the complexities and interdependencies involved in achieving sustainable development goals. This article is part of the theme issue 'Climate change adaptation needs a science of culture'.

Learning from urban form to predict building heights.

Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change.

CO2 Emissions from Direct Energy Use of Urban Households in India.

India hosts the world's second largest population and offers the world's largest potential for urbanization. India's urbanization trajectory will have crucial implications on its future GHG emission levels. Using household microdata from India's 60 largest cities, this study maps GHG emissions patterns and its determinants. It also ranks the cities with respect to their household actual and "counter-factual" GHG emissions from direct energy use. We find that household GHG emissions from direct energy use correlate strongly with income and household size; population density, basic urban services (municipal water, electricity, and modern cooking-fuels access) and cultural, religious, and social factors explain more detailed emission patterns. We find that the "greenest" cities (on the basis of household GHG emissions) are Bareilly and Allahabad, while the "dirtiest" cities are Chennai and Delhi; however, when we control for socioeconomic variables, the ranking changes drastically. In the control case, we find that smaller lower-income cities emit more than expected, and larger high-income cities emit less than expected in terms of counter-factual emissions. Emissions from India's cities are similar in magnitude to China's cities but typically much lower than those of comparable U.S. cities. Our results indicate that reducing urban heat-island effects and the associated cooling degree days by greening, switching to modern nonsolid cooking fuels, and anticipatory transport infrastructure investments are key policies for the low-carbon and inclusive development of Indian cities.