Sustainability limits needed for CO2 removal.

The true climate mitigation challenge is revealed by considering sustainability impacts.

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'.

Built structures influence patterns of energy demand and CO2 emissions across countries.

Built structures, i.e. the patterns of settlements and transport infrastructures, are known to influence per-capita energy demand and CO2 emissions at the urban level. At the national level, the role of built structures is seldom considered due to poor data availability. Instead, other potential determinants of energy demand and CO2 emissions, primarily GDP, are more frequently assessed. We present a set of national-level indicators to characterize patterns of built structures. We quantify these indicators for 113 countries and statistically analyze the results along with final energy use and territorial CO2 emissions, as well as factors commonly included in national-level analyses of determinants of energy use and emissions. We find that these indicators are about equally important for predicting energy demand and CO2 emissions as GDP and other conventional factors. The area of built-up land per capita is the most important predictor, second only to the effect of GDP.

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. Here, we present EUBUCCO v0.1, a database of individual building footprints for ~202 million buildings across the 27 European Union countries and Switzerland. Three main attributes - building height, construction year and type - are included for respectively 73%, 24% and 46% of the buildings. We identify, collect and harmonize 50 open government datasets and OpenStreetMap, and perform extensive validation analyses to assess the quality, consistency and completeness of the data in every country. EUBUCCO v0.1 provides the basis for high-resolution urban sustainability studies across scales - continental, comparative or local studies - using a centralized source and is relevant for a variety of use cases, e.g., for energy system analysis or natural hazard risk assessments.

Harnessing human and machine intelligence for planetary-level climate action.

The ongoing global race for bigger and better artificial intelligence (AI) systems is expected to have a profound societal and environmental impact by altering job markets, disrupting business models, and enabling new governance and societal welfare structures that can affect global consensus for climate action pathways. However, the current AI systems are trained on biased datasets that could destabilize political agencies impacting climate change mitigation and adaptation decisions and compromise social stability, potentially leading to societal tipping events. Thus, the appropriate design of a less biased AI system that reflects both direct and indirect effects on societies and planetary challenges is a question of paramount importance. In this paper, we tackle the question of data-centric knowledge generation for climate action in ways that minimize biased AI. We argue for the need to co-align a less biased AI with an epistemic web on planetary health challenges for more trustworthy decision-making. A human-in-the-loop AI can be designed to align with three goals. First, it can contribute to a planetary epistemic web that supports climate action. Second, it can directly enable mitigation and adaptation interventions through knowledge of social tipping elements. Finally, it can reduce the data injustices associated with AI pretraining datasets.

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. However, a systematic record and synthesis of the findings of evaluations of the effect of such actions on human health and wellbeing is currently lacking. This, in turn, impedes the development of robust knowledge on what constitutes high-impact climate actions of benefit to human health and wellbeing, which can inform future action plans, their implementation and scale-up. The development of a systematic record of studies reporting climate and health actions in cities is made challenging by the broad landscape of relevant literature scattered across many disciplines and sectors, which is challenging to effectively consolidate using traditional literature review methods. This protocol reports an innovative approach for the systematic development of a database of studies of climate change mitigation and adaptation actions implemented in cities, and their benefits (or disbenefits) for human health and wellbeing, derived from peer-reviewed academic literature. Our approach draws on extensive tailored search strategies and machine learning methods for article classification and tagging to generate a database for subsequent systematic reviews addressing questions of importance to urban decision-makers on climate actions in cities for human health and wellbeing.

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

SUMMARY: 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. This includes the impact on physical activity, dietary patterns, alcohol consumption and the resulting consequences, for example increases in body weight. Other risk factors for increases in BP and cardiovascular risk such as smoking, emotional/psychologic stress, changes in sleep patterns and diurnal rhythms may also exhibit significant changes in addition to novel factors such as air pollution and environmental noise. We also highlight potential preventive measures to improve BP control because hypertension is the leading preventable risk factor for worldwide health during and beyond the COVID-19 pandemic.

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.

Engage, don't preach: Active learning triggers climate action.

Traditional communication of research on climate change fails to encourage individual, corporate, and political leaders to take appropriate action. We argue that this problem is based on an overly simplistic unidirectional model of science communication. Conversely, theory shows that active learning processes are better suited to initiate and mobilize engagement among all stakeholders. Here, we integrate theoretical insights on active learning with empirical evidence from serious gaming: communication should be understood as an integral design feature that relates active learning on climate change to tangible action.

A global dataset of CO2 emissions and ancillary data related to emissions for 343 cities.

We present a global dataset of anthropogenic carbon dioxide (CO2) 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 CO2 emissions from those of other greenhouse gases. Further, a set of ancillary data that have a direct or potentially indirect impact on CO2 emissions were collected from other datasets (e.g. socio-economic and traffic indices) or calculated (climate indices, urban area expansion), then combined with the emission data. We applied several quality controls and validation comparisons with independent datasets. The dataset presented here is not intended to be comprehensive or a representative sample of cities in general, as the choice of cities is based on self-reporting not a designed sampling procedure.

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. The scope and methodological approaches in studies differ widely and only a small share of the studies sufficiently reports on context and/or baseline conditions, which makes it difficult to get a general understanding of the attribution of impacts. Nevertheless, we identified regional patterns of positive or negative impacts for all categories - environmental, economic, institutional, social and technological. In general, economic and technological impacts were more frequently reported as positive, while social and environmental impacts were more frequently reported as negative (with the exception of impacts on direct substitution of GHG emission from fossil fuel). More focused and transparent research is needed to validate these patterns and develop a strong science underpinning for establishing policies and governance agreements that prevent/mitigate negative and promote positive impacts from bioenergy production.

From Targets to Action: Rolling up our Sleeves after Paris.

At the United Nations Climate Change Conference in Paris in 2015 ambitious targets for responding to the threat of climate change have been set: limiting global temperature increase to "well below 2 °C […] and to pursue efforts to limit the temperature increase to 1.5 °C". However, calculating the CO2 budget for 1.5 °C, it becomes clear that there is nearly no room left for future emissions. Scenarios suggest that negative emission technologies will play an even more important role for 1.5 °C than they already play for 2 °C. Especially against this background the feasibility of the target(s) is hotly debated, but this debate does not initiate the next steps that are urgently needed. Already the negotiations have featured the move from targets to implementation which is needed in the coming decade. Most importantly, there is an urgent need to develop and implement instruments that incentivize the rapid decarbonization. Moreover, it needs to be worked out how to link the climate and development agenda and prevent a buildup of coal power causing lock-in effects. Short term entry points into climate policy should now be in the focus instead of the fruitless debate on the feasibility of targets.

Future urban land expansion and implications for global croplands.

Urban expansion often occurs on croplands. However, there is little scientific understanding of how global patterns of future urban expansion will affect the world's cultivated areas. Here, we combine spatially explicit projections of urban expansion with datasets on global croplands and crop yields. Our results show that urban expansion will result in a 1.8-2.4% loss of global croplands by 2030, with substantial regional disparities. About 80% of global cropland loss from urban expansion will take place in Asia and Africa. In both Asia and Africa, much of the cropland that will be lost is more than twice as productive as national averages. Asia will experience the highest absolute loss in cropland, whereas African countries will experience the highest percentage loss of cropland. Globally, the croplands that are likely to be lost were responsible for 3-4% of worldwide crop production in 2000. Urban expansion is expected to take place on cropland that is 1.77 times more productive than the global average. The loss of cropland is likely to be accompanied by other sustainability risks and threatens livelihoods, with diverging characteristics for different megaurban regions. Governance of urban area expansion thus emerges as a key area for securing livelihoods in the agrarian economies of the Global South.

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.