City-level policies are increasingly recognized as key components of strategies to reduce transport greenhouse gas emissions. However, at a global scale, their total efficiencies, costs and practical feasibility remain unclear. Here we use a spatially explicit monocentric urban economic model, systematically calibrated on 120 cities worldwide, to analyse the impact of four representative policies aimed… Continue reading Policy portfolios can reduce GHG emissions in urban transport in 120 cities by 20% while improving welfare

Large language models (LLMs) that power generative AI technologies like ChatGPT and Bard can analyze vasts amounts of textual data to generate easy to understand insights in human prose. But these technologies have also been demonstrated to hallucinate, generating convincing but false answers; prone to bias, and difficult to unpack due to the “black box”… Continue reading ChatNetZero: Harnessing Large-Language Models for Synthesizing Evidence to Evaluate Non-State Actors’ Climate Actions

Synthesis reports, such as those by the IPCC, aim to faithfully provide a complete overview of the entire scientific landscape on climate change. However, the sheer amount of literature on climate-relevant topics is massive and, at the same time, is growing exponentially each year. Therefore, authors of such reports often rely on existing maps, reviews,… Continue reading Mapping the Climate Literature

Beyond designing climate policies, the communication strategies and storytelling approaches employed by policymakers largely shape public engagement in climate mitigation and adaptation activities. This is due to the fact that communication design impacts the decision-making processes of information recipients, a phenomenon known as choice architecture, thereby influencing individual attitudes, behaviors, and actions. In an effort… Continue reading Framing Political Communication on Climate Change: A Topic Modeling Analysis of German Governmental Institutions’ Press Releases

International climate finance is key to achieving the goals of the Paris Agreement. Here we develop a machine learning classifier to identify international climate finance from 2.7 million official development assistance projects between 2000 and 2019, resulting in a consistent and replicable inventory of 82,023 bilateral climate finance projects (US$80 billion). Our findings reinforce concerns… Continue reading Consistent and replicable estimation of bilateral climate finance

Increased availability of and access to satellite and remotely sensed data have paved the way for innovations in methods and tools for climate evaluation. These have allowed us to observe, measure, and evaluate the impact of climate change on humans and ecosystems at a relatively low cost. High-resolution and frequent observations from satellites have facilitated… Continue reading Innovations in Climate Evaluation and Synthesis: Leveraging Remote Sensing and Machine Learning for Climate Solutions

As 2030 approaches and current Nationally Determined Contributions (NDCs) are not sufficient to meet the Paris Agreement climate goals, setting more stringent national emission reduction targets in 2040 and 2050 become more and more relevant. The basis of these targets, however, can be manyfold. In this research, we present a comprehensive framework to quantify national… Continue reading Fairness and uncertainty in determining national emission reduction targets

Governments, organisations, and communities worldwide are formulating and executing strategies to adapt to climate change. Yet, how to know if they are going in the right direction? What does “success” in adaptation actually mean? Conversely, what does failure look like and how can we avoid it? To address these questions, we develop an evolved theoretical… Continue reading Evidence Synthesis of Adaptation Success: Insights from 50 Leading Experts and Implications for Research and Practice

Researchers have incentives to search for and selectively report findings that appear to be statistically significant and/or conform to prior beliefs. Such selective reporting practices, including p-hacking and publication bias, can lead to a distorted set of results being published, potentially undermining the process of knowledge accumulation and evidence-based decision making. We take stock of… Continue reading More Than Half of Statistically Significant Research Findings in the Environmental Sciences are Actually Not