Enhanced rock weathering could become an important carbon dioxide removal option in European agriculture, but applying rock dust is not itself evidence of durable carbon removal. A new Perspective in Energy and Climate Management argues that the European Union should develop an ERW-specific certification methodology before large-scale carbon crediting begins. The proposed approach distinguishes potential from certifiable removal, combines near-field measurements with conservative modelling of downstream processes, deducts lifecycle emissions, treats time lags and uncertainty explicitly, and requires feedstock, site-suitability and no-harm safeguards. Agricultural and soil-policy instruments could support monitored deployment while tonne-for-tonne crediting is not yet justified.

Road networks are often monitored by only a limited number of sensors, leaving many segments unobserved. Researchers from the Technical University of Munich and Imperial College London developed RADGCN, an AI approach that learns traffic relationships from reliable observations and extends them across the network. Across three benchmark datasets and different sparse-sensor settings, RADGCN improved traffic-state estimates by 2.97%–11.82% compared with leading methods.

The Environmental Dynamic Stochastic General Equilibrium (E-DSGE) model developed in this paper is a macro-financial general equilibrium model for evaluating the macroeconomic and financial stability impacts of climate transition policies in China. It incorporates pollution externalities, financial frictions in the banking sector, carbon tax policies, and a differentiated capital adequacy ratio (CAR) requirement policy. The model is well-equipped for analyzing how carbon pricing generates transition risks, how financial frictions amplify these risks, and how structural macroprudential policy can mitigate them while achieving emission reduction targets.

To improve the reliability of autonomous vehicle platooning in real-world environments, researchers from Southeast University and Monash University developed a physics-informed learning framework that integrates multi-sensor fusion, model-based control, and data-driven optimization. The proposed method addresses uncertainties from sensors, vehicle dynamics, and actuators, enabling more stable and adaptive cooperative driving performance.

Evidence from 20 European countries links political uncertainty to weaker disclosed green investment, particularly among financially constrained firms. The findings highlight predictable national implementation of climate policy as important for mobilizing private capital for decarbonization.