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The CDR field is simultaneously racing to prove its economics, preserve its hard-won knowledge, and confront a biomass crediting system that may be built on shaky foundations. That tension between building and salvaging defines today’s stories. The Knowledge Drain Is Real Here’s a quiet crisis that deserves more attention: CDR startups are failing, and when they do, their technical know-how vanishes with them. One startup is now explicitly trying to rescue that institutional knowledge before it disappears for good. This isn’t nostalgia. Carbon removal is a field where failed experiments carry enormous informational value. Every reactor design that didn’t scale, every sorbent chemistry that degraded too fast, every MRV (measurement, reporting, and verification) protocol that couldn’t survive contact with reality represents lessons that the next generation of companies will need. Losing that knowledge means paying to relearn the same mistakes. The fact that someone has to build a company specifically to prevent this loss tells you something uncomfortable about how thin the CDR talent and knowledge base really is. ...

A new startup called Ctrl-S is racing to buy up intellectual property and experimental data from failing direct air capture companies before that knowledge vanishes for good. Founded by Jason Hochman, who spent four years running the Direct Air Capture Coalition, the company aims to build a licensable library of DAC innovations that surviving companies, energy majors, and even AI-driven materials science firms can tap into, essentially creating a knowledge rescue operation for a sector that’s hemorrhaging capital and talent. ...

The Clean Air Task Force evaluated 25 biomass carbon removal credit protocols and found not a single one worthy of its highest rating. Seven scored “satisfactory,” twelve were “weak,” and six were “very weak.” Perhaps more revealing than the scores themselves: the carbon removal registries that developed these protocols pushed back so aggressively that CATF stripped the names from the results before publication, anonymizing which registry earned which grade. Why It Matters Biomass-based projects account for roughly 88% of all carbon removal credits sold to date, according to CDR.fyi. That’s a staggering concentration of market activity resting on accounting methods that an independent assessment just found to be mostly inadequate. Many of these sales are pre-purchases for projects that haven’t yet delivered certified credits, which means there’s still time to fix the plumbing before the water starts flowing. But the registries’ defensive reaction to the findings suggests the industry may not be eager to do the hard work of self-correction. ...

The year 2025 has landed among the top three warmest years in recorded history, with average surface temperatures reaching approximately 1.44°C above pre-industrial levels. This comes directly on the heels of 2024, which shattered records as the first calendar year to exceed 1.5°C of warming. Carbon Brief’s ongoing “State of the climate” series tracks these numbers in near real-time, and the picture it paints is one of relentless, compounding heat. ...

Exomad Green, a Mongolia-based biochar company, and China’s Beston Group have entered a new phase of their strategic partnership aimed at scaling up biochar production capacity. The deal deepens an existing collaboration between the two firms and represents one of the more notable cross-border partnerships in the biochar-based carbon removal sector, linking Chinese pyrolysis technology with Mongolian feedstock and deployment. Why It Matters Biochar is one of the more commercially mature forms of durable carbon removal, but the sector’s biggest bottleneck remains production capacity. Most biochar operations today are small-scale, often producing hundreds or low thousands of tonnes per year. Partnerships that pair technology manufacturers with deployment-focused companies are exactly the kind of supply chain integration the sector needs to move from niche to meaningful scale. The China-Mongolia corridor is particularly interesting because it combines low-cost manufacturing with vast, underutilized biomass resources. ...

Sugarcane bagasse biochar in Brazil can break even in roughly 7.5 years with an 18% internal rate of return, but only on large farms of 20,000 to 50,000 hectares, only when the biochar is applied to soil rather than sold, and only when carbon credit prices exceed $120 per ton of CO2 equivalent. Below that price threshold, the economics fall apart for nearly every farm size. That’s the central finding from Sebastian G. Nosenzo’s scenario-based economic modeling study, published in the Journal of Power and Energy Engineering and recently posted to arXiv. ...

The latest paper by Bernecker and Müsgens, “Direct Air Capture in Europe - Where to Integrate, Where to Store, and What Drives Cost?”, offers some compelling insights into optimizing DACCS deployment across the continent. They’ve taken a granular approach, dissecting DAC, transport, and storage, and integrating these elements into a long-term European energy system model aimed at a fully decarbonized 2050. This kind of holistic modeling is precisely what we need to move beyond back-of-the-envelope cost estimates. ...

N. van Dongen, A. J. F. van Hoof, S. Calero, J. M. Vicent-Luna. Direct air capture of carbon dioxide (CO$_2$) is one of the most promising strategies to mitigate rising atmospheric CO$_2$ levels. Among various techniques, adsorption using porous materials is a viable method for extracting CO$_2$ from air, even under humid conditions. However, identifying optimal adsorbent materials remains a significant challenge. Moreover, the performance of existing materials can be improved by doping with active species that boost gas capture, a relatively unexplored fiel ...

Jan Martinů, Petr Šimánek. This study investigates the application of deep-learning diffusion models for the super-resolution of weather data, a novel approach aimed at enhancing the spatial resolution and detail of meteorological variables. Leveraging the capabilities of diffusion models, specifically the SR3 and ResDiff architectures, we present a methodology for transforming low-resolution weather data into high-resolution outputs. Our experiments, conducted using the WeatherBench dataset, focus on the super-resolution Source: arXiv

Minjong Cheon, Yo-Hwan Choi, Seon-Yu Kang, Yumi Choi, Jeong-Gil Lee. Deep learning-based, data-driven models are gaining prevalence in climate research, particularly for global weather prediction. However, training the global weather data at high resolution requires massive computational resources. Therefore, we present a new model named KARINA to overcome the substantial computational demands typical of this field. This model achieves forecasting accuracy comparable to higher-resolution counterparts with significantly less computational resources, requiring only ...