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Economic productivity depends on reliable access to electricity, but the extreme shortage events of variable wind-solar systems may be strongly affected by climate change. Here, hourly reanalysis climatological data are leveraged to examine historical trends in defined extreme shortage events worldwide. We find uptrends in extreme shortage events regardless of their frequency, duration, and intensity since 1980. For instance, duration of extreme low-reliability events worldwide has increased by 4.1 hours (0.392 hours per year on average) between 1980–2000 and 2001–2022. However, such ascending trends are unevenly distributed worldwide, with a greater variability in low- and middle-latitude developing countries. This uptrend in extreme shortage events is driven by extremely low wind speed and solar radiation, particularly compound wind and solar drought, which however are strongly disproportionated. Only average 12.5% change in compound extremely low wind speed and solar radiation events may give rise to over 30% variability in extreme shortage events, despite a mere average 1.0% change in average wind speed and solar radiation. Our findings underline that wind-solar systems will probably suffer from weakened power security if such uptrends persist in a warmer future. The reliability of variable wind-solar systems may be strongly affected by climate change. This study uncovers uptrends in extreme power shortages during 1980–2022 due to increasing very low wind speed and solar radiation.

A transition away from coal power always maintains a high level of complexity as there are several overlapping considerations such as technical feasibility, economic costs, and environmental and health impacts. Here, we explore the cost-effectiveness uncertainty brought by policy implementation disturbances of different coal power phaseout and new-built strategies (i.e., the disruption of phaseout priority) in China based on a developed unit-level uncertainty assessment framework. We reveal the opportunity and risk of coal transition decisions by employing preference analysis. We find that, the uncertainty of a policy implementation might lead to potential delays in yielding the initial positive annual net benefits. For example, a delay of six years might occur when implementing the prior phaseout practice. A certain level of risk remains in the implementation of the phaseout policy, as not all strategies can guarantee the cumulative positive net benefits from 2018–2060. Since the unit-level heterogeneities shape diverse orientation of the phaseout, the decision-making preferences would remarkably alter the selection of a coal power transition strategy. More strikingly, the cost-effectiveness uncertainty might lead to missed opportunities in identifying an optimal strategy. Our results highlight the importance of minimizing the policy implementation disturbance, which helps mitigate the risk of negative benefits and strengthen the practicality of phaseout decisions. China’s use of coal is complex to establish a clean and low-carbon transition for the country. With an uncertainty assessment framework, this study displays the risks of missing opportunities in obtaining cumulative positive net benefits and identifying an optimal transition strategy.

Vetiver grass (Vetiveria zizanioides L. Nash) has a great application potential to the phytoremediation of heavy metals pollution. However, few studies explored the bioavailability and distribution of different speciations of As and Sb in V. zizanioides. This study aimed to clarify the allocation and accumulation of two inorganic species arsenic (As(III) and As(V)) and antimony (Sb(III) and Sb(V)) in V. zizanioides, to understand the self-defense mechanisms of V. zizanioides to these metal(loids) elements. Thus, an experiment was conducted under greenhouse conditions to identify distribution of As and Sb in plant roots and shoots. Antioxidant enzymes (superoxide dismutase, SOD) and changes of subcellular structures were tested to evaluate metal(loids) tolerance capacities of V. zizanioides. This study demonstrated that V. zizanioides had higher capacity to accumulate Sb than As. For Sb absorption, Sb(III) content is significantly higher than Sb(V) in tissues of V. zizanioides under all concentration levels, despite the oxidation of Sb(III) on the nutrient solution surface. Additional Sb was mainly accumulated in plant roots due to Sb immobilization by transforming it into precipitates. As was more easily transferred to aerial tissues and had low accumulation rates, probably due to its restricted uptake rather than restricted transport. In many cases, two inorganic species of As and Sb showed almost same biotoxicity to V. zizanioides estimated from its biomass, SOD activity, and MDA content as well as functional groups. In summary, the results of this study provide new insights into understanding allocation, accumulation and phytotoxicity effects of arsenic and antimony in V. zizanioides.Graphic abstractSchematic diagram of distribution of and biochemical responses to As(III), As(V), Sb(III), and Sb(V) in tissue of V. zizanioides.

The critical role of the iron and steel industry in decarbonizing global energy systems calls for refined strategies of climate mitigation. Here, based on a newly developed database of individual iron and steel facilities worldwide, we explore the distinct differences in age-to-capacity ratio and emissions intensity of primary steelmaking plants. We customize regional cost-effective decarbonization strategies by targeting a certain proportion of plants. We find that the more effective indicator for targeted decarbonization in developing regions is emissions intensity, while for developed countries it is age-to-capacity ratio. Whichever indicator we use to target plants, the strategy of transformation towards secondary steelmaking is generally more cost-effective than efficiency improvement in most cases, although obvious regional priorities exist. Our results emphasize the region-specific priorities of mitigation indicators and strategies in targeting plants, which help with designing short-term, cost-effective strategies for reducing steel-related CO2 emissions.The decarbonization of the global iron and steel industry is important for energy systems mitigation. Using a facility-level database, this Article presents cost-effective, region-specific strategies targeting plants with a large age-to-capacity ratio and/or high emissions intensity.

CO 2 emission inventory provides fundamental data for climate research and emission mitigation. Currently, most global CO 2 emission inventories were developed with energy statistics from International Energy Agency (IEA) and were available at country level with limited source categories. Here, as the first step toward a high-resolution and dynamic updated global CO 2 emission database, we developed a data-driven approach to construct seamless and highly-resolved energy consumption data cubes for 208 countries/territories, 797 sub-country administrative divisions in 29 countries, 42 fuel types, and 52 sectors, with the fusion of activity data from 24 international statistics and 65 regional/local statistics. Global CO 2 emissions from fossil fuel combustion and cement production in 1970–2021 were then estimated with highly-resolved source category (1,484 of total) and sub-country information (797 of total). Specifically, 73% of global CO 2 emissions in 2021 were estimated with sub-country information, providing considerably improved spatial resolution for global CO 2 emission accounting. With the support of detailed information, the dynamics of global CO 2 emissions across sectors and fuel types were presented, representing the evolution of global economy and progress of climate mitigation. Remarkable differences of sectoral contribution were found across sub-country administrative divisions within a given country, revealing the uneven distribution of energy and economic structure among different regions. Our estimates were generally consistent with existing databases at aggregated level for global total or large emitters, while large discrepancies were observed for middle and small emitters. Our database, named the Multi-resolution Emission Inventory model for Climate and air pollution research (MEIC) is publicly available through http://meicmodel.org.cn with highly-resolved information and timely update, which provides an independent carbon emission accounting data source for climate research.

Non-methane volatile organic compounds (NMVOC) play a crucial role in tropospheric atmospheric chemistry. Developing accurate NMVOC emission inventories is essential for chemical transport modeling and pollution mitigation. While global NMVOC emissions have been estimated in several inventories, representing the impacts of technology evolution on NMVOC emission dynamics remains challenging. Here, we developed a technology-based global NMVOC emission inventory (MEIC-global-NMVOC) under the Multi-resolution Emission Inventory model for Climate and air pollution research (MEIC) framework to better represent NMVOC emission evolution and drivers. We compiled spatially and temporally full-coverage and consistent activity rates from multiple sources using data fusion and reconstruction approaches. Next, we estimated the evolution of technology distributions and emission control measures by data-driven and policy-driven technology turnover models. The dynamics of global anthropogenic NMVOC emissions during 1970–2020 were presented by sector, fuel type, and product, highlighting activity growth and technology development as key drivers. In developing regions, emission growth was driven by surges in activity rates but curbed by advanced combustion technologies, improved vehicle standards, and substitution of solvent-borne paints. In developed regions, besides the mitigation measures above, emission control technologies substantially reduced fugitive emissions from energy production and emissions from solvent use, driven by policies such as the U.S. New Source Performance Standards and European Union Directives. Despite large uncertainties, MEIC-global-NMVOC emission estimates were generally comparable with other emission inventories at aggregated level for global or regional totals, but remarkable sectoral differences remained. The MEIC-global-NMVOC emission inventory offers a new database for atmospheric chemistry and pollution mitigation research.

Recently, the remarkable success of ChatGPT has sparked a renewed wave of interest in artificial intelligence (AI), and the advancements in Vision–Language Models (VLMs) have pushed this enthusiasm to new heights. Differing from previous AI approaches that generally formulated different tasks as discriminative models, VLMs frame tasks as generative models and align language with visual information, enabling the handling of more challenging problems. The remote sensing (RS) field, a highly practical domain, has also embraced this new trend and introduced several VLM-based RS methods that have demonstrated promising performance and enormous potential. In this paper, we first review the fundamental theories related to VLM, then summarize the datasets constructed for VLMs in remote sensing and the various tasks they address. Finally, we categorize the improvement methods into three main parts according to the core components of VLMs and provide a detailed introduction and comparison of these methods.

To date, researchers have not systematically investigated the geological characteristics of deep shale gas reservoirs in the Western Chongqing Block. Furthermore, the single technique remains insufficient for characterizing the complexity of their multi-scale pore structures. Therefore, this study integrates scanning electron microscopy (SEM), argon ion polishing-field emission scanning electron microscopy (AIP-FESEM), high-pressure mercury intrusion (HPMI), and low-pressure gas adsorption (LPGA) to qualitatively and quantitatively investigate the microscopic pore structure of shale gas reservoirs in the Western Chongqing block. Meanwhile, the pore fractal characteristics were analyzed based on HPMI and LPGA experiments using the mercury saturation model, the Frenkel-Halsey-Hill (FHH) model, and the volume-surface area (V-S) model. The results show that, first, the pore types of the samples in the Western Chongqing block include organic pores, intergranular pores, intragranular pores, intercrystalline pores, and interlayer fractures; second, micropores are the main contributors to the total pore volume, mainly developed in the three ranges of 0.45 ~ 0.5 nm, 0.55 ~ 0.6 nm, and 0.8 ~ 0.85 nm, followed by mesopores and finally macropores; finally, the macropores of the samples exhibit stronger heterogeneity and more complex pore-throat structures compared to mesopores. The heterogeneity of the pore structure is stronger than that of the pore surface, indicating a more complex internal pore structure. Additionally, the microporous structures of the samples are also characterized by relatively complex. The experimental results provide important guidance for the economical and efficient development of shale gas.

To conquer the challenges of charge accumulation and surface flashover in epoxy resin under direct current (DC) electric fields, numerous efforts have been made to research dielectric barrier discharge (DBD) plasma treatments using CF4/Ar as the medium gas, which has proven effective in improving surface flashover voltage. However, despite being an efficient plasma etching medium, SF6/Ar has remained largely unexplored. In this work, we constructed a DBD plasma device with an SF6/Ar gas medium and explored the influence of processing times and gas flow rates on the morphology and surface flashover voltage of epoxy resin. The surface morphology observed by SEM indicates that the degree of plasma etching intensifies with processing time and gas flow rate, and the quantitative characterization of AFM indicates a maximum roughness of 144 nm after 3 min of treatment. Flashover test results show that at 2 min of processing time, the surface flashover voltage reached a maximum of 19.02 kV/mm, which is 25.49% higher than that of the untreated sample and previously reported works. In addition to the effect of surface roughness, charge trap distribution shows that fluorinated groups help to deepen the trap energy levels and density. The optimal modification was achieved at a gas flow rate of 3.5 slm coupled with 2 min of processing time. Furthermore, density functional theory (DFT) calculations reveal that fluorination introduces additional electron traps (0.29 eV) and hole traps (0.38 eV), enhancing the capture of charge carriers and suppressing surface flashover.