A joint research agenda for climate action bridges behavioral sciences and urban planning.

We argue for systematically integrating behavioral sciences and urban planning to develop a joint agenda for research and planning practice. By viewing urban form as a critical choice architecture for making people’s choices more climate-friendly, this approach may unlock new pathways for higher liveability of cities.

Built structures influence patterns of energy demand and CO emissions across countries.

Built structures, i.e. the patterns of settlements and transport infrastructures, are known to influence per-capita energy demand and CO emissions at the urban level.

EUBUCCO v0.1: European building stock characteristics in a common and open database for 200+ million individual buildings.

Building stock management is becoming a global societal and political issue, inter alia because of growing sustainability concerns. Comprehensive and openly accessible building stock data can enable impactful research exploring the most effective policy options. In Europe, efforts from citizen and governments generated numerous relevant datasets but these are fragmented and heterogeneous, thus hindering their usability.

Climate action for health and wellbeing in cities: a protocol for the systematic development of a database of peer-reviewed studies using machine learning methods.

Cities produce more than 70% of global greenhouse gas emissions. Action by cities is therefore crucial for climate change mitigation as well as for safeguarding the health and wellbeing of their populations under climate change. Many city governments have made ambitious commitments to climate change mitigation and adaptation and implemented a range of actions to address them.

Lifestyle, psychological, socioeconomic and environmental factors and their impact on hypertension during the coronavirus disease 2019 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic considerably affects health, wellbeing, social, economic and other aspects of daily life. The impact of COVID-19 on blood pressure (BP) control and hypertension remains insufficiently explored. We therefore provide a comprehensive review of the potential changes in lifestyle factors and behaviours as well as environmental changes likely to influence BP control and cardiovascular risk during the pandemic.

A global dataset of CO emissions and ancillary data related to emissions for 343 cities.

We present a global dataset of anthropogenic carbon dioxide (CO) emissions for 343 cities. The dataset builds upon data from CDP (187 cities, few in developing countries), the Bonn Center for Local Climate Action and Reporting (73 cities, mainly in developing countries), and data collected by Peking University (83 cities in China). The CDP data being self-reported by cities, we applied quality control procedures, documented the type of emissions and reporting method used, and made a correction to separate CO emissions from those of other greenhouse gases.

Bioenergy production and sustainable development: science base for policymaking remains limited.

The possibility of using bioenergy as a climate change mitigation measure has sparked a discussion of whether and how bioenergy production contributes to sustainable development. We undertook a systematic review of the scientific literature to illuminate this relationship and found a limited scientific basis for policymaking. Our results indicate that knowledge on the sustainable development impacts of bioenergy production is concentrated in a few well-studied countries, focuses on environmental and economic impacts, and mostly relates to dedicated agricultural biomass plantations.

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.

Timescale-invariant pattern recognition by feedforward inhibition and parallel signal processing.

The timescale-invariant recognition of temporal stimulus sequences is vital for many species and poses a challenge for their sensory systems. Here we present a simple mechanistic model to address this computational task, based on recent observations in insects that use rhythmic acoustic communication signals for mate finding. In the model framework, feedforward inhibition leads to burst-like response patterns in one neuron of the circuit.

Past-future information bottleneck in dynamical systems.

Biological systems need to process information in real time and must trade off accuracy of presentation and coding costs. Here we operationalize this trade-off and develop an information-theoretic framework that selectively extracts information of the input past that is predictive about the output future, obtaining a generalized eigenvalue problem. Thereby, we unravel the input history in terms of structural phase transitions corresponding to additional dimensions of a state space.