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Seasons and the Anthropocene
No full textSeasons are changing in the Anthropocene. Seasons serve as temporal frameworks for communities and societies to organize their livelihoods and activities around the expectation of recurrent environmental, social, and cultural events. In this article, we make an original case that the scale and rapidity of changes to our planet's biogeochemical cycles profoundly impact the sociopolitically interpreted (re)definitions of seasonal rhythms. We propose a conceptually novel typology for collating how new and evolving interactions between human and more-than-human environmental cycles are reflected in the seemingly simple—yet widely relatable—concept of “seasons.” We define emergent, extinct, arrhythmic (changes to timing), and syncopated (changes to intensity) seasons through our typology, to bring together disparate literature on evolving human–nature interactions, environmental knowledge production and deployment, local realities of environmental risk and disaster management, and the uneven spatiality of socioenvironmental feedback loops. Seasonality in the Anthropocene is political as it reflects a diversity of temporal ontologies and unveils unjust manifestations of the hegemony of standardized time and timescales, while the discursive construction of “seasonality” may be deployed for political and economic gains. We set an agenda for a cross-geographical research agenda that explores seasonality from place-based, multiscalar perspectives to unravel the complexities of seasonality in the Anthropocene
Balancing interference and correlation in spatial experimental designs: a causal graph cut approach
No full textThis paper focuses on the design of spatial experiments to optimize the amount of information derived from the experimental data and enhance the accuracy of the resulting causal effect estimator. We propose a surrogate function for the mean squared error (MSE) of the estimator, which facilitates the use of classical graph cut algorithms to learn the optimal design. Our proposal offers three key advances: (1) it accommodates moderate to large spatial interference effects; (2) it adapts to different spatial covariance functions; (3) it is computationally efficient. Theoretical results and numerical experiments based on synthetic environments and a dispatch simulator that models a city-scale ridesharing market, further validate the effectiveness of our design. A python implementation of our method is available at https://github. com/Mamba413/CausalGraphCut
Making the case for innovation capacity in city governments
No full textEuropean city governments need to be able to innovate if they are to tackle existing and emerging policy challenges. Sudeep Bhargava outlines how best to understand a city’s ability to innovate, and what it will take to build capacity in city governments
Trump has the power to raise tariffs because Congress has given up its control over trade policy
No full textOn April 2nd, President Trump announced wide ranging “reciprocal” tariffs against many other countries, causing turmoil across global markets. Stephanie Rickard writes that while the US Constitution gives Congress power over tariff policy, it has increasingly delegated that power to the presidency. Now, despite the seemingly severe consequences of Trump’s radical action on tariffs, political considerations mean that Congressional legislators – especially Republicans – are reluctant to act to end the tariff chaos
The economic burden of obesity in children and adolescents in Austria
No full textIntroduction: In Europe, one in three school-aged children live with overweight or obesity and are at high risk of continuing to be affected by it throughout their lives. The objective of our studywas to quantify the economic burden of obesity among Austrian children and adolescents born between 2000 and 2019. Methods: We used obesity projections and the share of young adults assumed to have remained with obesity since childhood or adolescence to project the lifetime costs of birth cohorts from 2000 to 2019. We estimated lifetime costs per individual using populationattributable fractions, considering a discount rate of 3%, obesity-associated mortality, an obesity-associated "income penalty,"and future cost increases in the healthcare system. Results: For Austria, we estimated that around 95,000 of all children and adolescents in 2019 remain with obesity as adults, which leads to a present value of EUR 9.2 billion or an annuity of approximately EUR 285 million (0.07% of GDP in 2019). Approximately 15% of costs arise from direct costs and 85% from indirect costs. Conclusion: We highlighted the longterm economic burden of early-onset obesity in Austria and concluded that public health programs addressing children and adolescents with obesity could relieve high costs not only for individuals but also for society
Inconvenience and generalization in building a better psychology: commentary on Sherman (2024)
No full textIn this commentary, we supplement Sherman's (2025) defense of convenience sampling, reviewing the complementary role of broad generalization and diverse samples. Specifically, Sherman's commentary could be misinterpreted as downplaying or disavowing the importance of broad generalization, despite the latter being necessary if we are to capture more than a narrow sliver of human cognitive variation. Moreover, we argue that stating the generalizability of our findings explicitly is key to both accurate interpretation and effective translation into applied work-a principle which holds even when our studies are not aiming to produce generalizable conclusions. We close with a review of practical ways in which broad generalization may be achieved. These include developmental, comparative, or computational approaches, as well as theoretical frameworks and "inconvenient" samples that capture cross-cultural variation. (PsycInfo Database Record (c) 2025 APA, all rights reserved)
Deliberating sufficiency in transport: fair car use budgets for London
No full textThis paper investigates notions of fairness and the role of deliberative exercises as part of urban transport policy design. Its point of departure is the sufficiency principle informed by conditions of scarcity for private car use in cities. It focuses on questions of fairness in assigning hypothetical car use budgets for the case of London. Two different budgets are considered, one associated with carbon emission ceilings and another for space constraints. The study that underpins this paper is based on a mixed method approach including a dedicated representative survey for London, a deliberation simulation based on a citizens’ jury with nine participants and a pilot behavioural experiment alongside interviews with a total of 19 London car drivers. Three key findings are established: First, deliberative engagement can be a constructive and feasible approach adding to the general democratic legitimacy of decision making in transport policy. Second, while fairness deliberations, perceptions and sentiments are complex, coherent understandings do emerge for both differential treatments of social groups and priorities of fairness principles. Third, car use budgets may be a helpful tool that can be indirectly utilised for policy design and deliberative formats. While they are generally understood by participants as useful tool to consider implications of limits and distributional questions of driving, they require additional research and testing to refine their role and utility. Alongside, the pilot experiment revealed the utility and feasibility of several methodological approaches, some ready for scaling other requiring further refinement. The use of mobility tracking and the deliberative approach to car use budgeting were confirmed as scalable
AI can revolutionise education but technology is not enough: human development meets cultural evolution
No full textArtificial Intelligence could dramatically boost educational outcomes and close gaps – but only if policymakers take a human-centred, systems-level approach to AI integration. Cultural evolution, the science of how beliefs, values, norms, technologies and institutions evolve over time, offers a framework for understanding the promises and pitfalls of different approaches to AI in education policy. Using this perspective and drawing on comparative evidence from Estonia’s successful “Tiger Leap” initiative and the failed “One Laptop Per Child” (OLPC) programme, we identify three missteps that derail national strategies: (1) techno-fix thinking, (2) weak infrastructure and teacher support and (3) lack of local adaptation. Uruguay’s Plan Ceibal is a notable exception to OLPC’s general failures, revealing why technology alone is not enough. We map AI’s headline promises – personalised tutoring, higher teacher productivity, smaller equity gaps – onto the specific capabilities each can expand, and we highlight three systemic risks: digital exclusion, algorithmic bias and widening inequalities. Synthesising these lessons, we propose a practical roadmap. AI will revolutionise education and enhance human development only insofar as it is embedded in human centred systems that grow everyone’s capabilities and freedom to learn, create and participate in society
