The purpose of this study is to understand the differentiated impact of politics- and science-oriented education on pro-environmental behavior among university students. A questionnaire was designed and sent to more than 14,000 university students from 152 universities in China using the snowball sampling method. In the questionnaire, the environmental knowledge was divided innovatively into two parts: Science-oriented knowledge spread by traditional environmental education and politics-oriented knowledge spread through political education. The structural equation model was used to understand the conduction path of pro-environmental knowledge, attitude, and behavior. It shows that politics-oriented knowledge has a quicker and stronger effect on improving behavior than science-oriented knowledge. Moreover, there is a significant positive correlation between science- and politics-oriented knowledge. However, the attitude is positively influenced by science-oriented knowledge, instead of politics-oriented knowledge. It suggests that traditional environmental education and political education should be integrated to promote the pro-environmental behavior of university students indirectly and directly, which may provide an opportunity for pro-environmental political education in other countries. The study contributes important theoretical and practical implications for environmental education and sustainable development.

In recent years, the Chinese government has been paying more and more attention to agricultural development and ecological protection. Improving cultivated land green use efficiency (CLGUE) is becoming a crucial issue in promoting the sustainable development of agriculture. This study aims to study the current situation and influencing factors of agricultural production from the perspective of green utilization efficiency of cultivated land. It takes 39 cities in the upper, middle and lower reaches of the Yangtze River basin in China as an example. The CLGUE values in those 39 cities from 2011 to 2020 were specifically measured, using the Super-SBM model, kernel density estimation and geographic detector method. Their temporal and spatial heterogeneity was described, and the influencing factors were detected at both single and interactive levels. The results showed that (1) from 2011 to 2020, the green utilization efficiency value of cultivated land in the Yangtze River basin showed an upward trend on the whole; (2) there is clear spatial heterogeneity between CLGUE values in the Yangtze River basin cities, and the distribution is as follows: downstream region > midstream region > upstream region; (3) cultivated land resource endowment, socioeconomic development and agricultural production technology are important factors affecting the variability in CLGUE values. However, there are some differences in the degree and direction of influence of different influencing factors on different sample subgroups.

In recent years, the Chinese government pays more and more attention to agricultural development and ecological protection. While improving the cultivated land green use efficiency(CLGUE) is the key to promote the sustainable development of agriculture. This study aims to study the current situation and influencing factors of agricultural production from the perspective of green utilization efficiency of cultivated land. It takes 39 cities in the upper, middle and lower reaches of the Yangtze River basin in China as an example. The CLGUE values in those 39 cities from 2011 to 2020 were specifically measured, using the Super-SBM model, kernel density estimation and geographic detector method. Their temporal and spatial heterogeneity was described, and the influencing factors were detected at both single and interactive levels. The results showed that: （1）From 2011 to 2020, the green utilization efficiency value of cultivated land in the Yangtze River Basin showed an upward trend on the whole; （2）There is a clear spatial heterogeneity the CLGUE values in the Yangtze River Basin cities, as shown by: downstream region > midstream region > upstream region; (3) Cultivated land resource endowment, socioeconomic development, and agricultural production technology are important factors affecting the variability of CLGUE values. However, there are some differences in the degree and direction of influence of different influencing factors on different sample subgroups.

Introduction: The uncertainty associated with PM_2.5 pollution hinders the economic high-quality development and threatens public health. Industrial agglomeration stands as a critical factor in regional economic and environmental governance, and the current studies about its impact on PM_2.5 pollution are mostly limited to a specific industry or unidirectional influence.Methods: Our study constructed spatial econometric models to analyze the effect of three major industrial agglomerations on PM_2.5 pollution, based on evidence from 110 prefecture-level cities of the Yangtze River Economic Belt in 2005–2019.Results: The results show that: 1) The three major industrial agglomerations and PM_2.5 pollution present different spatiotemporal characteristics and show prominent positive spatial autocorrelation and agglomeration effect. 2) The primary industrial agglomeration contributes to a decrease in PM_2.5 pollution and exhibits negative spatial spillover effects. A nonlinear relationship is observed between the secondary industrial agglomeration and PM_2.5 pollution. The tertiary industrial agglomeration results in an increase in PM_2.5 pollution. 3) The effects of secondary industrial agglomeration on PM_2.5 pollution exhibit varying degrees of ‘inverted U-shape’ curves in the upstream, midstream, and downstream cities. The midstream cities are the first to reach the inflection point of agglomeration. 4) Industrial agglomeration affects PM_2.5 pollution through three mechanisms, including scale expansion effect, technological spillover effect, and population scale effect.Discussion: Based on the empirical findings, this study provides scientific support and decision-making reference to improve the positive impacts of industrial agglomerations on PM_2.5 pollution.

A fixed-odds contract on short-horizon price direction has a positive expected value only when its win probability exceeds the break-even rate implied by the payout ratio. A deployable predictor must also produce signals at a sufficiently stable rate. We formulate this setting as selective prediction with a coverage constraint and combine a five-seed gradient-boosting ensemble over a 90-dimensional causal feature panel with daily adaptive quantile thresholds, each estimated from the preceding 14 to 28 days of model scores, with parameters selected on training data alone. Configurations are frozen after three chronological pseudo-out-of-sample folds and evaluated on a held-out period from 1 January to 10 June 2026, and the whole procedure is then repeated on a quarterly re-freezing cadence over seven successive windows. Across BTC and ETH at 5- and 10-min horizons, with a payout of 0.8 and a 55.56% break-even rate, the models execute 10.4 to 11.0 trades per day, and all four selective win rates exceed break-even. Under a dependence-aware block bootstrap, three of four remain significant, and within a 32-test confirmatory family, two survive Holm–Bonferroni correction. Coverage stays inside the operational band in 26 of 28 re-frozen windows. Compared under one execution protocol, a fixed calibration slice drifts out of band while a trailing window does not, and adaptive conformal inference (ACI) matches the proposed rule on coverage when its step size is tuned but not otherwise, whereas an outcome-driven conformal controller reduces coverage by more than an order of magnitude. The expected value is insensitive to exchange fees, which consume under 5% of the measured edge, and sensitive to the payout term. Under matched feature sets, training pools, and coverage, most of the apparent cross-asset difference does not persist. This paper presents a proof of concept for the framework rather than making any claim about cryptocurrency predictability.

Genetic programming (GP) has been widely used in quantitative finance for discovering formulaic alpha factors that can predict asset returns. However, GP often produces overgrown expressions that are difficult to interpret and expensive to evaluate. This paper proposes a large language model (LLM)-assisted pruning framework that reviews expression trees generated by GP, with the LLM acting as a semantic reviewer that flags redundant or financially implausible branches based on structural complexity and contextual reasoning. The proposed method is formalized as a closed-loop Trigger–Evaluate–Decide–Execute (TEDE) process. We present mathematical formulations, algorithmic design, and examples showing how redundant nested functions can be simplified while monitoring predictive performance. Experiments with high-frequency cryptocurrency market data, using DeepSeek-V4-Flash as the semantic engine, show lower expression complexity and higher rubric-based interpretability scores for the pruned symbolic factors. Under the reported test setup, the LLM-pruned configuration has higher Information Ratio (IR) values than the listed baselines and more compact expression trees than the GP baselines.