Addressing carbon inequality: public perceptions and policy attitudes.

The problems of climate change and economic inequality are connected in multiple ways. Carbon inequality means that some individuals generate multiple times larger annual carbon emissions than others. Income and wealth disparities are main drivers of carbon inequality. Little is known how citizens perceive carbon inequality and related aspects, nor whether such perceptions are consequential for attitudes to (progressive) climate mitigation policies. Here we study such perceptions and policy attitudes through an online survey for Spain. Using quota sampling, we collected data from a sample of 3009 citizens from all regions of the country. Public perceptions of carbon inequality are measured in a qualitative and numerical way. When asked for a qualitative judgment, many people state that they perceive moderate to large differences in carbon footprints. When asked for a numerical judgment, the picture becomes more complex: comparing public perceptions with academic research data, it appears that many people actually tend to over- rather than underestimate the magnitude of footprint differences. Nevertheless, most people are aware that footprint differences are related to income differences. We also examine respondents’ attitudes to addressing carbon inequality in general in the context of climate policy, as well as to specific policy instruments with an explicit inequality dimension (wealth taxation, higher top income tax rate, frequent flyer tax, private jet ban), as well as several other instruments whose link to inequality is less obvious (e.g. carbon-border tariff). We find that a qualitatively stronger perception of the existence of carbon inequality is consistently and significantly associated with more support for all eight policy instruments. Left-wing political orientation, worry about climate change, and to a lesser extent household income are further significant predictors of favorable attitudes to several policies.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Co-dynamics of climate policy stringency and public support

Unidad de excelencia María de Maeztu CEX2019-000940-MAcord transformatiu CRUE-CSICPublic support for stringent climate policies is currently weak. We develop a model to study the dynamics of public support for climate policies. It comprises three interconnected modules: one calculates policy impacts; a second translates these into policy support mediated by social influence; and a third represents the regulator adapting policy stringency depending on public support. The model combines general-equilibrium and agent-based elements and is empirically grounded in a household survey, which allows quantifying policy support as a function of effectiveness, personal wellbeing and distributional effects. We apply our approach to compare two policy instruments, namely carbon taxation and performance standards, and identify intertemporal trajectories that meet the climate target and count on sufficient public support. Our results highlight the importance of social influence, opinion stability and income inequality for public support of climate policies. Our model predicts that carbon taxation consistently generates more public support than standards. Finally, we show that under moderate social influence and income inequality, an increasing carbon tax trajectory combined with progressive revenue redistribution receives the highest average public support over time

Intrinsic photoanode band engineering: enhanced solar water splitting efficiency mediated by surface segregation in Ti-doped hematite nanorods

Band engineering is thoroughly employed nowadays targeting technologically scalable photoanodes for solar water splitting applications. Most often complex and costly recipes are necessary, for average performances. Here we report very simple photoanode growth and thermal annealing, with effective band engineering results. Strongly enhanced photocurrent, of more than 200 %, is measured for Ti-doped hematite nanorods grown from aqueous solutions and annealed under Nitrogen atmosphere, compared to air annealed ones. Oxidized surface states and increased density of charge carriers are found responsible for the enhanced photoelectrochemical activity, as shown by electrochemical impedance spectroscopy and synchrotron X-rays spectromicroscopies. They are found related to oxygen vacancies, acting as n-dopants, and the formation of pseudo- brookite clusters by surface Ti segregation. Spectro-ptychography is used for the first time at Ti L3 absorption edge to isolate Ti chemical coordination arising from pseudo-brookite clusters contribution. Correlated with electron microscopy investigation and Density Functional Theory (DFT) calculations, our data unambiguously prove the origin of the enhanced photoelectrochemical activity of N2-annealed Ti-doped hematite nanorods. Finally, we present here a handy and cheap surface engineering method beyond the known oxygen vacancy doping, allowing a net gain in the photoelectrochemical activity for the hematite-based photoanodes.Comment: 2 parts: first main manuscript with 39 pages, second supplementary informations with 14 page

Securing Verified IO Programs Against Unverified Code in F*

We introduce SCIO*, a formally secure compilation framework for statically verified partial programs performing input-output (IO). The source language is an F* subset in which a verified program interacts with its IO-performing context via a higher-order interface that includes refinement types as well as pre- and post-conditions about past IO events. The target language is a smaller F* subset in which the compiled program is linked with an adversarial context that has an interface without refinement types, pre-conditions, or concrete post-conditions. To bridge this interface gap and make compilation and linking secure we propose a formally verified combination of higher-order contracts and reference monitoring for recording and controlling IO operations. Compilation uses contracts to convert the logical assumptions the program makes about the context into dynamic checks on each context-program boundary crossing. These boundary checks can depend on information about past IO events stored in the state of the monitor. But these checks cannot stop the adversarial target context before it performs dangerous IO operations. Therefore linking in SCIO* additionally forces the context to perform all IO actions via a secure IO library, which uses reference monitoring to dynamically enforce an access control policy before each IO operation. We prove in F* that SCIO* soundly enforces a global trace property for the compiled verified program linked with the untrusted context. Moreover, we prove in F* that SCIO* satisfies by construction Robust Relational Hyperproperty Preservation, a very strong secure compilation criterion. Finally, we illustrate SCIO* at work on a simple web server example.Comment: POPL'24 camera-ready versio

A review of agent-based modelling of climate-energy policy

Unidad de excelencia María de Maeztu CEX2019-000940-MAltres ajuts: Russian Science Foundation. Grant Number: 19-18-00262Agent-based models (ABMs) have recently seen much application to the field of climate mitigation policies. They offer a more realistic description of micro behaviour than traditional climate policy models by allowing for agent heterogeneity, bounded rationality and non-market interactions over social networks. This enables the analysis of a broader spectrum of policies. Here, we review 61 ABM studies addressing climate-energy policy aimed at emissions reduction, product and technology diffusion, and energy conservation. This covers a broad set of instruments of climate policy, ranging from carbon taxation and emissions trading through adoption subsidies to information provision tools such as smart meters and eco-labels. Our treatment pays specific attention to behavioural assumptions and the structure of social networks. We offer suggestions for future research with ABMs to answer neglected policy question

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

The virtues of virtual conferences

Unidad de excelencia María de Maeztu CEX2019-000940-MAltres ajuts: Acord transformatiu CRUE-CSICIn this comment, we share our experiences from organizing the ICTA2020 Virtual Conference on Low-Carbon Lifestyles and argue that virtual events have potential to become the new norm among academics. We present an overview of tools that can be used and support our arguments with results from a feedback survey that was filled out by the participants of our conference. Main challenges for virtual conferences are the facilitation of informal spaces for social interaction and the prevention of 'screen fatigue'. Advantages are that they can increase societal outreach, improve the academic quality of discussions, create new opportunities for networking, and provide an inclusive environment