Institutes of Science and Development

Gas-bearing potential in marine shales is governed by lithofacies-scale mineralogical heterogeneity and its coupling with organic-matter enrichment. We analyzed 40 core samples from the Lower Silurian Longmaxi Formation in the Zheng’an area, northern Guizhou (wells AD-2, AD-3, and AD-4), using whole-rock XRD, total organic carbon (w(TOC) %), and in situ gas content (cm3/g). A normalized quartz–clay–carbonate ternary diagram was applied to classify samples into siliceous shale (S), clay-rich shale (CM), calcareous shale (C), and mixed shale (M), and further into subfacies (e.g., S-1, S-2, and CM-1). Most samples plotted within the siliceous–clay transition field. Against this compositional background, w(TOC) mainly ranged from 4% to 6%, with the 4%–5% bin accounting for 57.5%; well AD-4 showed a relatively stable distribution, whereas wells AD-2 and AD-3 exhibited stronger vertical variability. In situ gas content varied systematically with lithofacies: CM displayed higher and more concentrated values (maximum 4.78 cm3/g), whereas S was more dispersed, with persistently low values in the continuous S-2 interval (minimum 0.15 cm3/g). Favorable intervals were associated with the continuous development of CM-1 and S-1, whereas S-2 required interval-specific assessment under an overall low-carbonate background.

Abstract Asian countries face the dual challenge of fulfilling ambitious climate commitments under the Paris Agreement while simultaneously advancing the United Nations Sustainable Development Goals (SDGs). This study develops an integrated analytical framework that links decarbonization pathways with SDG indicators across energy, air quality, health, and the food-water-land nexus, as well as policy cost-effectiveness, explicitly incorporating national circumstances. Our findings indicate that current climate pledges primarily deliver gains in SDG indicators related to energy and air quality. Achieving broader progress across other SDG dimensions requires combining more ambitious climate targets with diverse, sustainability-oriented mitigation measures tailored to domestic contexts. Middle- and low-income countries face greater pressure in balancing transition costs and target feasibility; therefore, strengthened international support and robust domestic policies are needed to advance climate ambition alongside sustainable development, including climate finance, technology transfer, and measures that can ensure a just transition for vulnerable industries.

ABSTRACT Frequent and severe emergencies have significantly hindered the region's sustainable development. Existing research based on statistical methods essentially measures a region's potential emergency capability (PEC), defined as the expected capability under static resources and institutions. However, emergency performance (EP), namely the dynamic performance exhibited during emergency management processes, ultimately determines outcomes. Measuring regional EP and understanding discrepancies between EP and PEC are essential for enhancing regional resilience. This study collected the five most severe emergencies and their corresponding emergency management processes in each Chinese province from 2016 to 2023 to construct an emergency case library (1240 cases in total). Using the preparation–response–rescue–recovery framework, the Delphi method was applied to assess the EP through case library analysis. Based on the same framework, PEC was evaluated by replicating the mainstream statistical methods employed in existing research. Subsequently, differences between EP and PEC were examined by constructing a continuous differential index with confidence intervals. The results show that: (1) China's EP presented a fluctuating upward trend but remained low overall, with significant regional differences (eastern > western > central > northeast). (2) PEC demonstrated an upward trend but exhibited weaknesses in preparation and response capacities. (3) EP in Hainan, Xinjiang, and Xizang was significantly higher than PEC, while EP in Hunan and Shandong was significantly lower than PEC. The magnitude of the EP–PEC discrepancy across dimensions followed the order: preparation > rescue > response > recovery. (4) The disparity in resource conversion efficiency constituted the fundamental source of differences between EP and PEC and was shaped by resource availability, organizational learning ability, environmental support ability, and resource integration ability. This study advocates assessing emergency level through actual emergency management processes, emphasizing the importance of dynamic and situational factors.

Iron-based Prussian blue analogues (Fe-PBAs) have garnered significant attention as cathode materials for sodium-ion batteries due to their high specific capacity (∼170 mAh g–1), environmental compatibility, and cost effectiveness. However, their performance is hindered by substantial crystalline water and structural defects, which result in the insufficient electrochemical activity of FeLS(C). The low contribution of FeLS(C) to the overall capacity, compared to FeHS(N), results in diminished battery performance and rapid cycling degradation. This study presents an innovative synthesis strategy for low-defect, high-sodium-content nanoporous Prussian blue using an oxalic acid-assisted single-iron-source method. Subsequent heat treatment effectively removes crystalline water and introduces a controlled number of defects, further modulating the nanoporous architecture and activating the FeLS(C) capacity. The resulting thermally treated nanoporous material (PBA-HT) exhibits a high stable discharge capacity of 120.2 mAh g–1, an initial Coulombic efficiency of 95.4%, and an outstanding cycling stability (70.3% capacity retention after 1000 cycles at 5 C). Density functional theory calculations reveal that heat treatment reduces the crystal field energy, thereby activating FeLS(C). In situ electrochemical impedance spectroscopy and galvanostatic intermittent titration technique analyses confirm a significant enhancement in diffusion kinetics, facilitated by the optimized nanoporous structure, following thermal treatment. Moreover, PBA-HT demonstrates stable operation at extreme temperatures (−20 and 50 °C), highlighting its practical potential and offering a synthesis strategy for high-performance nanoporous Prussian blue analogues.

The cement and concrete industry is one of the primary sources of anthropogenic carbon dioxide (CO2) emissions globally, responsible for nearly 8% of total emissions, making the need for a low-carbon transition urgent. CO2 curing provides both strength enhancement and carbon sequestration, yet the compressive strength of such concrete remains challenging to predict due to limited and strongly coupled experimental factors. This study developed an explainable Automated Machine Learning (AutoML) framework with integrated uncertainty quantification to predict the 28-day compressive strength of CO2-cured concrete. The framework was built using 198 standardized experimental data and trained with four algorithms—Random Forest (RF), Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), and the transformer-based Tabular Prior-Data Fitted Network (TabPFN). To enhance model accuracy and efficiency, stratified cross-validation, hyperparameter optimization, and bootstrap-based uncertainty analysis were applied during training. The results show that TabPFN achieves the highest predictive accuracy (test R2 = 0.959) and maintains a stable 95% prediction interval. SHapley Additive exPlanations (SHAP) indicates that cement content, aggregate composition, water–binder (W/B) ratio, and CO2 curing time are the dominant factors, with an optimal W/B ratio near 0.40. Interaction analysis further reveals synergistic effects between cement content and W/B, and a strengthening coupling between curing time and CO2 concentration at longer durations. The framework enhances predictive reliability and explainability, supporting mixture design and curing optimization for low-carbon concrete development.

The low-latitude highland (LLH) of Southeast Asia is a hot spot of soil–atmosphere coupling, and variations in the soil temperature and moisture in this region have a significant effect on the regional climate. In this study, the characteristics of the soil–atmosphere coupling were analyzed, two decoupling numerical tests were designed, and the impacts of the soil temperature and moisture on the regional climate in the LLH were assessed. Finally, the key factors of the soil–atmosphere coupling were explored, and their contributions to air temperature and precipitation were evaluated. The primary conclusions are as follows. (1) The soil–atmosphere coupling in the LLH is primarily characterized by the soil moisture-limited regime in dry seasons (spring and winter) and the energy-limited regime in the wet seasons (summer and autumn), indicating that there is a seasonal difference in the soil–atmosphere coupling within the LLH. (2) The quantitative assessments conducted via numerical simulation revealed that the soil temperature–atmosphere coupling has the greatest impacts on the average air temperature and precipitation in winter and autumn, with changes of − 0.08 °C and 0.13 mm d−1 (35.5% and 1.4% variability), respectively. In contrast, the soil moisture–atmosphere coupling has the greatest impacts on the average air temperature and precipitation in summer, with changes of 0.20 °C and 0.13 mm d−1 (14.4% and − 3.0% variability), respectively. (3) Regarding the air temperature, the soil temperature mainly controls the net longwave radiation in the dry season and the net shortwave radiation in the wet season, with significant contributions exceeding 36.6%. The soil moisture regulates the sensible heat flux in both the dry and wet seasons, with significant contributions exceeding 32.8%. Regarding precipitation, both the soil temperature and moisture mainly affect evapotranspiration in the dry and wet seasons, with significant contributions exceeding 25.9%.

In the industrial internet environment, the operation and control of production equipment have become increasingly complex, and their performance directly affects the efficiency, benefits and sustainable development of manufacturing enterprises. From the three-dimensional perspective of “asset-application-maintenance”, this paper constructs a performance analysis framework for the operation and control of production equipment, systematically identifies the combination of core factors affecting performance, and fills the research gap in the current lack of empirical analysis from the configuration perspective in this field. On the basis of data from 82 manufacturing enterprises, the fsQCA method was used to identify three performance improvement paths: the high-load output mode, the lean management and control mode, and the low-failure operation mode. These paths clarify the equivalent approaches to achieve high performance in the operation and control of production equipment under the interaction of multiple factors. On this basis, the study demonstrates the operability and effectiveness of the proposed strategies in actual industrial scenarios through empirical verification in a manufacturing workshop of aero-engine transmission units. In contrast to existing studies, this study introduces the fsQCA method in the field of industrial equipment management and control for the first time to reveal the influencing paths; its originality and methodology have significant innovative significance. The research results provide new ideas and methodological guidance for enterprise managers to improve the performance of production equipment operations and controls in the industrial internet environment, which helps to enhance the sustainable development capability of manufacturing enterprises.