Wednesday, April 08, 2026

 

Distributed solar and carbon trading enable more affordable and equitable clean heating in rural Northern China





KeAi Communications Co., Ltd.
Figure 1: County-level heating expenditure burden in the 2+26 region under different policy scenarios. 

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Figure 1: County-level heating expenditure burden in the 2+26 region under different policy scenarios.

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Credit: Feng Wang, et al.





China's campaign to replace bulk coal with clean heating has delivered air-quality and health benefits across the Northern China. But as subsidies begin to shrink in some places, a new question has emerged: can rural households still afford to stay clean?

In a study published in Fundamental Research, researchers from Nanjing University of Information Science & Technology, Shandong University, and the Chinese Academy of Environmental Planning addressed that question across the "2 + 26" region — Beijing, Tianjin, and 26 surrounding cities that have been central to Northern China's rural clean heating policy.

Using a township-level dataset covering 25,187 villages or communities, the team estimated what rural households spend on clean heating, how large that burden is relative to local income, and what happens when operating subsidies are reduced or removed. They also tested possible ways to ease the pressure: revenues from China’s voluntary carbon market and distributed rooftop solar photovoltaics

"We found that current heating subsidies mask potential regional inequalities," shares first author Feng Wang from Nanjing University of Information Science & Technology. "Under the 2020 baseline with subsidies in place, about 0.67 million households in 15 counties across Hebei, Henan, and Shanxi Provinces were already facing excessive heating burdens."

Notably, if operating subsidies were fully removed, total household heating expenditure across the region would rise by 36.2%, or about 10.3 billion CNY in aggregate. On average, each household would pay about 523 CNY more per year.

"The pressure would not be shared equally. Low-income households would be hit the hardest, particularly in parts of Hebei, Henan and Shanxi where incomes are lower and electricity-based heating is more common", says co-corresponding author Wei Zhang of the Chinese Academy of Environmental Planning.

In some cities, most retrofitted households would struggle to stay within an affordable heating threshold if subsidies were cut back. The team then tested whether new income streams could ease the burden. Carbon credits from clean heating emission cuts could help, but only modestly at current prices. "Across the whole region, clean-heating carbon credits would generate about CNY1.91 billion in revenue — just enough to offset around 18.7% of the increased heating costs, or roughly CNY97 per household on average," adds Wang.

Rooftop solar performed better. In regions with stronger solar potential and supportive conditions, distributed photovoltaics could offset a much larger share of the added heating expense. The study estimates that in Hebei, Shanxi and Beijing, rooftop solar could compensate for an average of 32.2–64.5% of the extra costs.

"Under one larger-scale solar scenario, the added benefits would offset 93.8% of the increase caused by subsidy removal, and 25 of the 46 hardest-hit counties would return to an affordable range," says co-corresponding author Jiashuo Li of Shandong University. "A one-size-fits-all subsidy exit would create serious affordability risks — a more durable path is to phase subsidies out selectively, protect low-income households, and expand distributed solar where it can genuinely improve household incomes."

The authors say their results point to a more targeted next stage for rural clean-heating policy: faster subsidy reform in wealthier places, more precise support for poorer counties and households, and earlier solar-heating pilots in provinces such as Henan and Shanxi where affordability pressures are most acute.

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Contact the author: Feng Wang, School of Business, Institute of Climate Economy and Low-carbon Industry, Nanjing University of Information Science & Technology, Nanjing 210044, China, vanf1004@nuist.edu.cn; Wei Zhang, Chinese Academy of Environmental Planning, Beijing 100041, China, zhangwei@caep.org.cn; Jiashuo Li, Institute of Blue and Green Development, Shandong University, Weihai 264209, China, lijiashuo@sdu.edu.cn.

The publisher KeAi was established by Elsevier and China Science Publishing & Media Ltd to unfold quality research globally. In 2013, our focus shifted to open access publishing. We now proudly publish more than 200 world-class, open access, English language journals, spanning all scientific disciplines. Many of these are titles we publish in partnership with prestigious societies and academic institutions, such as the National Natural Science Foundation of China (NSFC).

BAN DEEP SEA MINING

New study unveils rich biodiversity in Japan's deepest ocean trenches, featuring record-breaking discoveries and an unidentified "mystery" species







Pensoft Publishers
The submersible 'Limiting factor' before the expedition launch. 

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The submersible 'Limiting factor' before the expedition launch.  

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Credit: Minderoo-UWA Deep-Sea Research Centre, Inkfish and Caladan Oceanic





new study, published in the Biodiversity Data Journal, provides a profound look at life up to nearly 10 kilometers below the ocean's surface in the Japan, Ryukyu, and Izu-Ogasawara trenches. The research catalogs at least 108 distinct organism groups (morphotaxa), including the deepest-ever observation of a fish and a baffling, unidentified animal that has left global taxonomic experts stumped.

Conducted during a two-month 2022 expedition aboard the vessel DSSV Pressure Drop, the mission was a collaboration involving scientists from the Minderoo-UWA Deep-Sea Research Centre and the Tokyo University of Marine Science and Technology and funded by Caladan Oceanic LLC and Inkfish.

Rather than rely on traditional trawls and physical sampling - which can damage fragile organisms and rarely capture behavior - the team used a dual approach: crewed submersible transects to study seafloor-associated animals and their habitats, and free-fall baited landers to target bait-attending fauna such as fishes and decapods.

This combination enabled us to build the most comprehensive visual baseline yet for abyssal and hadal megafauna in the Northwest Pacific to date.

-  the team noted.

An animal science cannot name

Perhaps the most enigmatic encounter was a unique, slow-gliding organism, currently designated as Animalia incerta sedis, filmed twice at depths down to 9,137 meters. Despite extensive consultations with global taxonomic experts, the animal cannot be confidently assigned to any known phylum. While it shares some visual traits with nudibranchs or sea cucumbers, its identification remains a mystery.

Crinoid meadows and carnivorous sponges

Further, the crewed submersible transects allowed researchers to observe dense aggregations of deep-sea life in their natural benthic habitats and record rare behaviors. At the base of the Boso triple junction at 9,137 meters, the team traversed stunning "crinoid meadows" consisting of over 1,500 stalked crinoids anchored to rock terraces.

Additionally, the submersibles recorded carnivorous sponges belonging to the Cladorhizidae family in the Izu-Ogasawara Trench at depths between 9,568 and 9,744 meters, representing the deepest in-situ observation of carnivorous sponges to date.

A new depth record for fish

Building on previously published findings from the expedition, baited landers captured footage of a snailfish (Pseudoliparis sp.) feeding at a record depth of 8,336 meters - the deepest in-situ observation of a fish ever recorded. These landers also revealed the presence of the massive "supergiant" scavenging amphipod, Alicella gigantea, across all three surveyed trenches, as well as several other bait-attending fishes and invertebrates at great depth.

Patterns and pressures

The study revealed that while many organism groups are shared across the region, local patterns differ significantly, with the Japan Trench hosting the highest number of observed morphotaxa. These differences highlight how geological processes, depth, and nutrient inputs from surface waters shape life in subduction zones. The researchers also made an observation regarding human impact: "While it’s easy to think of deep-sea trenches as untouched wilderness, our findings also showed evidence of human-derived debris, likely transported by downslope processes."

A foundation for future exploration

The team emphasized the necessity of a non-destructive visual approach. "Historically, our understanding of abyssal and hadal ecosystems, including those associated with subduction features, relied largely on trawls and physical samples," they explained. "While these methods provide essential information, they can damage fragile organisms and rarely capture behaviour or ecological context."

By producing a comprehensive, illustrated guide of these habitats, the research team hopes to support future imagery-based biodiversity surveys.

"This study was not simply about observing deep-sea organisms, but also aimed to establish a foundation for future research at these depths. More than anything, the hadal zone remains one of Earth’s least-explored and most intriguing frontiers.

- the team concluded.

Original source:

Jamieson AJ, Swanborn DJB, Bond T, Cundy MC, Fujiwara Y, Lindsay D, Stott MS, Kitazato H (2026) Faunal biodiversity of the lower abyssal and hadal zones of the Japan, Ryukyu and Izu-Ogasawara trenches (NW Pacific Ocean; 4534-9775 m). Biodiversity Data Journal 14: e182172. https://doi.org/10.3897/BDJ.14.e182172

 

 

Green recycling process for spent lithium-ion batteries with extremely low chemical consumption




KeAi Communications Co., Ltd.

Figure 1. Schematic diagram of reaction mechanism 

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Figure 1. Schematic diagram of reaction mechanism

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Credit: Zhi Sun






Lithium-ion batteries (LIBs), known for their superior electrochemical performance, are critical components in new energy vehicles and energy storage systems. With the development of the global new energy industry, LIBs, which have a service life of approximately 5 to 8 years, are entering their end-of-life phase. This is expected to generate a large number of spent batteries.

A typical LIB consists mainly of cathodes, anodes, separators, current collectors, and electrolyte. Commercial LIBs primarily use cathode materials like LiCoO2, NCM and LiFePO4, which contain valuable metals such as lithium, nickel, cobalt, and manganese. The electrolyte is usually composed of carbonate solvents and LiPF6. Therefore, achieving efficient and low-consumption recycling of this urban mine is important for ensuring resource supply, reducing environmental pollution, and mitigating safety risks.

Recycling spent LIBs, however, is a complex engineering process involving multiple steps, such as pre-treatment, metallurgical extraction, separation, purification, and material regeneration. Although metallurgical routes have matured into established technological pathways and achieve large-scale production, they continue to face challenges posed by the rapidly updating battery materials, increasingly complex compositions, and continuously rising industry standards.

In a recent study led by Prof Zhi Sun from Institute of Process Engineering, Chinese Academy of Sciences, a novel mechanical activation assisted strategy, which can achieve the selective extraction of Li+ from spent cathode materials with highly utilization efficiency of H+ (>97%), was developed obviating the need for auxiliary reagents, and substantially reduces secondary pollutant generation.

The main findings, published in Fundamental Research, outlined:

  1. Two leaching stages and one intermediate: The low reaction activity of the intermediate product from the initial leaching stage hinders the further leaching of Li+. The introduction of mechanical force has proven effective in significantly change these intermediates through activating spent cathode materials, thereby increasing the presence of defects and H+, which subsequently reduces the energy barrier for the second stage.
  2. A novel mechanical activation assisted selective recycling technology was developed, which achieves a Li+ leaching efficiency exceeding 90% with high Hutilization efficiency at 160 ºC. This technology is applicable not only to LiCoO2 but also to various NCM cathodes and LiMn2O4. Demonstrating broad cathode material compatibility.
  3. As cathode materials continue to develop, recycling technologies must accommodate a growing diversity of cathode types. Therefore, recycling technologies should possess broad applicability. While hydrothermal methods have already reached scalable production, mechanical ball milling still faces challenges in large-scale implementation.

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Contact the author: Zhi Sun, Institute of Process Engineering, Chinese Academy of Sciences, China, (sunzhi@ipe.ac.cn)

The publisher KeAi was established by Elsevier and China Science Publishing & Media Ltd to unfold quality research globally. In 2013, our focus shifted to open access publishing. We now proudly publish more than 200 world-class, open access, English language journals, spanning all scientific disciplines. Many of these are titles we publish in partnership with prestigious societies and academic institutions, such as the National Natural Science Foundation of China (NSFC).

 

Small quantum system outperforms large classical networks in real-world forecasting



University of Science and Technology of China





Can a handful of atoms outperform a much larger digital neural network on a real-world task? The answer may be yes. In a study published in Physical Review Letters, a team led by Prof. PENG Xinhua and Assoc. Prof. LI Zhaokai from the University of Science and Technology of China of the Chinese Academy of Sciences demonstrated that a quantum processor comprising just nine interacting spins outperformed classical networks with thousands of nodes in realistic weather forecasting tasks.

By exploiting unique quantum features such as superposition and entanglement, quantum devices offer new ways to represent and process information. Recent experiments have shown their advantages on specialized benchmark tasks, but extending these gains to real-world applications remains a challenge. In particular, many quantum approaches rely on complex circuits that are difficult to implement accurately on today’s noisy hardware.

In this study, the researchers realized that the natural dynamics of quantum systems could inherently provide rich computational power, bypassing the need for deep quantum circuits. This led to reservoir computing, a brain-inspired machine learning approach in which a dynamical system processes incoming signals and retains memory on its own, without precise control.

In the implementation, input signals were encoded into quantum states whose entangled evolution naturally processed information in ways that are difficult to simulate classically. Even dissipation, which was usually seen as harmful in quantum computing, was turned into a useful resource for regulating the system's memory. By harnessing these native dynamics rather than fighting against them, this approach was naturally better suited to near-term quantum devices.

Using nuclear magnetic resonance techniques, the researchers built a quantum reservoir computer based on nine interacting atomic spins. They first tested it on a widely used time-series prediction benchmark known as NARMA, which achieved the best performance reported among experimental quantum approaches, reducing prediction errors by one to two orders of magnitude compared with previous circuit-based implementations.

The researchers then tested it in weather forecasting which is vital but difficult. Experimental results showed that the quantum model accurately captured temperature trends over several days. Besides, the researchers compared it with a standard classical reservoir model known as the echo state network. Notably, the nine-spin quantum reservoir achieved higher accuracy than classical reservoir networks with thousands of nodes in multi-day forecasts.

The findings provide experimental evidence that a quantum machine-learning system may outperform much larger classical counterparts on realistic tasks. This work suggests that harnessing the native dynamics of current quantum devices rather than waiting for fully fault-tolerant quantum computers may promote useful applications.

 

FI-R model, a novel remote sensing method for fine-scale extraction of vegetation




KeAi Communications Co., Ltd.
Fig. 1 Comparison of rapeseed with other typical underlying surface features at different flowering levels. 

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Fig. 1 Comparison of rapeseed with other typical underlying surface features at different flowering levels.

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Credit: Sixian Yin, et al.





The fine-scale characterization of vegetation surface information serves as a basis for studying the spatial distribution of resources and the dynamic patterns of environmental responses. Accurately extracting the distributions of different crop species is important for improving agricultural production efficiency and ensuring food security. Traditional fine-scale vegetation extraction methods, however, have limited applicability across large areas due to the presence of spectrally similar features and the substantial influence of background interference. As a key phenological stage of angiosperms, flowering is characterized by distinctive flowering times, floral morphology, and canopy spectral signatures, so it is an effective pathway for fine-scale vegetation extraction using remote sensing.

In a new study published in the Journal of Integrative Agriculture, team of researchers from China developed the FI-R model, a novel flowering spectral index for the fine-scale extraction of angiosperms over large areas in complex multi-regional backgrounds, using rapeseed as an example. FI-R shows low sensitivity to background complexity and rapeseed varieties, and has good applicability to multiple multi-spectral sensor images.

"Using rapeseed as an example, we developed a spectral index model for precise flowering vegetation extraction (FI-R) based on Landsat OLI imagery," shares corresponding author Taixia Wu, a professor at Hohai University. "The model integrates a yellowness index (Blue, Green) and a peak index (Red, Nir and SWIR1) while leveraging the NDVI to mitigate background interference from spectrally similar objects."

Notably, the model successfully enables the rapid and accurate large-scale mapping of flowering vegetation under complex background conditions. "It was tested in five rapeseed cultivation regions worldwide with diverse backgrounds and validation datasets were generated using GF imagery and the U.S. CDL dataset," says Wu. "The FI-R model demonstrated superior capability in distinguishing flowering rapeseed from other vegetation, and achieved overall accuracies exceeding 94% in all study areas."

"Furthermore, FI-R is compatible with other multispectral sensors that have similar band configurations, so it is applicable to rapeseed extraction in broader contexts," adds co-corresponding author Hongzhao Tang, a Professor at Land Satellite Remote Sensing Application Center. "It also shows strong potential for the fine-scale extraction of other types of flowering angiosperm vegetation."

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Contact the author: Sixian Yin, E-mail: yinsx@hhu.edu.cn; Correspondence Taixia Wu, E-mail: wutx@hhu.edu.cn; Hongzhao Tang, E-mail: tanghz@pku.edu.cn

The publisher KeAi was established by Elsevier and China Science Publishing & Media Ltd to unfold quality research globally. In 2013, our focus shifted to open access publishing. We now proudly publish more than 200 world-class, open access, English language journals, spanning all scientific disciplines. Many of these are titles we publish in partnership with prestigious societies and academic institutions, such as the National Natural Science Foundation of China (NSFC).